From 0cdc270ceb9cabab11e2c9ccae9e28022c281e99 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 7 Sep 2026 23:29:27 -0500
Subject: [PATCH 01/95] feat: add trusted Embody adapter and official ICASSP
 paper build

---
 .gitignore                            |   1 +
 RELEASE_FILES.sha256                  |  17 +-
 STATUS.md                             |  38 +-
 docs/EMBODY_ADAPTER_INTEGRATION.md    | 103 ++++
 paper/README.md                       |  47 +-
 paper/main.pdf                        | Bin 709369 -> 497508 bytes
 paper/main.tex                        |  79 +--
 pyproject.toml                        |   3 +
 scripts/build_paper.py                |  95 ++++
 scripts/fetch_icassp2027_template.py  |  76 +++
 src/phaseset_core/embody_adapter.py   | 680 ++++++++++++++++++++++++++
 tests/test_phaseset_embody_adapter.py | 412 ++++++++++++++++
 12 files changed, 1463 insertions(+), 88 deletions(-)
 create mode 100644 docs/EMBODY_ADAPTER_INTEGRATION.md
 create mode 100644 scripts/build_paper.py
 create mode 100644 scripts/fetch_icassp2027_template.py
 create mode 100644 src/phaseset_core/embody_adapter.py
 create mode 100644 tests/test_phaseset_embody_adapter.py

diff --git a/.gitignore b/.gitignore
index 987f065..5d29fd8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -36,6 +36,7 @@ paper/*.bbl
 paper/*.blg
 paper/*.log
 paper/*.out
+paper/.venue/
 figures/*_preview.png
 
 recommend paper/
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 873a4d2..91dc694 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -2,7 +2,7 @@
 ca4262d34e1553bf562d8ef5d3ef15b666b8f12214d5ff9345a8573baf9dd0f3	327	.gitattributes
 2c5a644e7b812eb516fedeee66218e87a3ee9a0e6206d0e37a383b24eb1f1522	129	.github/CODEOWNERS
 6e6cf3cba1ad55fb25e1aff2a0037d9791d45f0ca757c33f4f2335d75ec061c7	3019	.github/workflows/ci.yml
-4bed921bd4f4cdd73c5533ccf30fd765c426da1835e397c7e7ec90ae6d5f9302	378	.gitignore
+e9938bfe4bbd1ee2357396d1a6a8788e511389e297c28d7247d9e16a9a8137fc	392	.gitignore
 4c20bbdbd002e6f52d43e0d4e5b2dd826a577cf45c817582ae3ed896e6d9f075	6957	CHANGELOG.md
 34ad6f564d314e436426109fb8e7e67a2473e8dedf200d45a766d27d030cd080	780	CITATION.cff
 a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTING.md
@@ -11,11 +11,12 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 d44f41127431cee3838a5fb9a6b7b903ccc06a050609c7f843a60da17cf90f97	9139	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-c3166a0ca82dc07053c9ef67788f610ea21d6fe72ae8df2306ffcc742b0e6014	7975	STATUS.md
+db0ecc48b52d4fa922d873af9e142e8bba529f40a744c232b5fdeaab4a5e2281	9481	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
 e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA_PROTOCOL.md
+2a18716b57d21362672faaaf76944821764302064b7d7d931f9ec6a0d3f4c598	5564	docs/EMBODY_ADAPTER_INTEGRATION.md
 3633e37c3eae3289d552a363e565bee027ebd83661f1c44ea136f9498de9e5af	19059	docs/EXECUTION_PROTOCOL.md
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
@@ -59,14 +60,16 @@ dd5fd26c085c8fc9143ffe8d116d23a816c4360eccb1747abeeb0803284738d8	9839	figures/ph
 a8d257fce7d54cfbce67ff2f5aeb17bf081e10f70d3c5e7c5b02f21278baeb96	476421	figures/phaseset_motivation.png
 248ce77fc05ebd370a907b2c470bfe0fc50d6fa050759f5694e5ae081c8513a1	489547	figures/phaseset_motivation.pptx
 a028dddf74489baaade772b5881004abb27de38f4ab631a529693cd9badce75a	363524	figures/phaseset_motivation.svg
-304330ae853b1119ca0dd140603ed205f25c50b64aa83eece9a32e77228d3fc9	3472	paper/README.md
-47cf3be3daafde0286b78e0bd61338e32f799ccba90e7a5cf92f710b70004a7a	709369	paper/main.pdf
-1da98a709968867742e385a891deee8b50193618c2219654641b1715c0df4b53	22151	paper/main.tex
+c1dc6c3081e80085dc950457c3324f4780b8237adb278494a9a2298e2c536129	3763	paper/README.md
+d1b340ef39baf6ec2c16fd29f079eb550d7f68e4ac389cd5aed4137cd13710f2	497508	paper/main.pdf
+17a201a532798a30805f74f3abcafb4e811e02371f5f018a8bd3894b0c80c7f9	21347	paper/main.tex
 7db5537b9c85545e3fff89b29e17438936f0bcfb3e7d1ee2cd8de24f4de1a279	8419	paper/references.bib
-2da20c2ffd05c4d6419531d802aaf6f276c2575ad05d99b9c753850ad938cd90	1283	pyproject.toml
+530dbfa412faec88a72753876d28770466a249cf550162bc0f5c6758a710d8f3	1315	pyproject.toml
 a54cab7df5f76e32387eba55757a4bd8fa18d9f2cdc8e5b0c6725ceb00af1e78	9580	scripts/audit_all_refs.py
 b4184b377fc1dbde1bf4a14ff002c5b13be09dbe6a545cee30eb878ee017fdd3	13507	scripts/audit_release_artifact.py
+f6447b2870b24c657e3ca6f18d7330834826b6e5ec284392d94a82d25f5b180e	4422	scripts/build_paper.py
 ffb5b9e798fac11d7ec0f9cb13f1fe9ffd71dc197361795dd1a2c043d37d8080	3168	scripts/check_local_links.py
+4a3396142a7e012143ccac8e2df4c45bee39778f1565a8f1a395e3b8e7fab6b7	3286	scripts/fetch_icassp2027_template.py
 58c33c281a02a928556d0a25d87bdb0c893e99667d40d2c1c0326d2a899b0137	38002	scripts/public_release_audit.py
 79e3e6160ef210fa03f1c5d8a3eb17c9298ba4b38e910d93e609ce41c1d27ec7	32001	scripts/release_asset_inventory.py
 678b7a73a29360f5b87f525f9445ff05758d4f3e423a0e5ee215cbeb98900363	2769	scripts/release_manifest.py
@@ -108,6 +111,7 @@ b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phase
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 9636324cff46e2a89bb93b71f8c3a4cc07544f8dd0e10e2da60d5a2ffbc7b356	25892	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
+e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
@@ -156,6 +160,7 @@ ac57f0ea3950b7ac6795f6f9973cf68ece3d370b686835dc4f8a2892c7233fdb	12603	tests/tes
 a53fe5c6b53adb5c80421842f806fc45e8305360348d7065aa0ddfb0b9316dcb	6546	tests/test_phaseset_contracts.py
 a74143036a41dd9d8779dc4a9b70fe20e5b38d013ef23778d13e73c11273dbd8	26066	tests/test_phaseset_controls.py
 a17a50e801c389b4e24b2840f2aafcb7b476cbef86e2e142ad0af6245a54718f	4515	tests/test_phaseset_data.py
+2f2fa0bf4d10771573896cf5d46eaaeffaaa6d9c52ca647a9ab9aa32a1766c87	14442	tests/test_phaseset_embody_adapter.py
 8bd6e95a35523d108153f668f8919e6f22858e8283e6308041751c6c92d0f904	18605	tests/test_phaseset_evaluation.py
 aea41df712a25be78841e655a6fbd91fc96217ce92e60d3ab842f185fc39a58c	41553	tests/test_phaseset_execution.py
 dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/test_phaseset_experiments.py
diff --git a/STATUS.md b/STATUS.md
index ca85f07..6d8bd37 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -1,8 +1,9 @@
 # PhaseSet project status
 
-**Evidence snapshot:** 2026-08-26 (Asia/Shanghai)
+**Evidence snapshot:** 2026-09-08 (UTC)
 **Research state:** `ACTIVE / AUTHORITY0 / NO_REAL_DATA_RESULT / NO_CLAIM`
-**Release target:** `v0.2.1 / PUBLIC_DATA_FREE_STABLE_CORE_PORTABLE_TEST_HOTFIX`
+**Published release:** `v0.2.1 / PUBLIC_DATA_FREE_STABLE_CORE_PORTABLE_TEST_HOTFIX`
+**Final target:** `v1.0.0 / REAL_EXPERIMENTS_AND_PAPER / NOT_COMPLETE`
 
 PhaseSet is the active multi-person successor to PhasePair. The in-place public
 migration and `v0.2.0-alpha.1` pre-release are complete; the immutable
@@ -10,6 +11,29 @@ annotated PhasePair `v0.1.0` tag remains the dyadic compatibility baseline.
 Implemented code, passing synthetic tests, and frozen plans are software
 evidence, not training results or scientific claims.
 
+## Server continuation
+
+- Strict known-host SSH login to the registered host now succeeds. The initial
+  read-only inventory is complete. Existing workloads were not interrupted.
+- A separate data-volume Python environment is being built; CUDA libraries
+  are still downloading. No GPU qualification or real training has passed.
+- The native Embody loader and concrete licensed SMPL-X body-22 evaluator are
+  implemented. Six adapter contract tests passed on Linux/Python 3.12.12/
+  NumPy 2.4.6; these use explicit fixtures, not licensed assets or main data.
+- Neither Embody approval nor a licensed neutral model has been observed.
+  Open-license Multi-TPC acquisition is underway only for the registered
+  supplementary conversation task, not as replacement confirmatory data.
+- An actual Linux probe found last-bit formula-replay drift in both Morlet
+  banks. The original frozen kernel digests remain unchanged; portability is
+  being repaired and must pass server-side numerical tests before training.
+- The current no-result manuscript now builds using the hash-pinned official
+  ICASSP 2027 template: five pages, technical content through page four,
+  references-only page five, all fonts embedded, no unresolved citations or
+  overfull boxes. All result cells and pending author metadata remain explicit.
+
+This continuation is not a completed scientific release. Private receipts,
+data, model assets, connection details, and download material are not public.
+
 ## Completed in this migration
 
 - Full encrypted snapshots of the public repository and private research
@@ -96,11 +120,11 @@ Embody 3D requires a real applicant to submit the official release form with
 true identity, institution, and email. No automation may invent those facts.
 Private download URLs and licensed assets stay outside Git.
 
-At `2026-08-26 03:17:33 +08:00`, the registered experiment server again
-actively refused the registered non-default SSH port before authentication.
-The next attempt may probe only that port, must verify the host key, and must
-begin with a read-only system/GPU inventory. No default-port scan or substitute
-host is authorized by the frozen contract.
+The earlier connection-refused state was superseded by successful strict
+known-host login on 2026-09-08 UTC. Only the registered port and host remain
+in scope. Resource availability, numerical qualification, and actual data
+access are separate observed requirements; SSH success alone is not a passed
+training environment.
 
 The GitHub repository was renamed in place to
 `appleweiping/phaseset-multiperson-motion-language`. Its numeric repository ID,
diff --git a/docs/EMBODY_ADAPTER_INTEGRATION.md b/docs/EMBODY_ADAPTER_INTEGRATION.md
new file mode 100644
index 0000000..dc05ded
--- /dev/null
+++ b/docs/EMBODY_ADAPTER_INTEGRATION.md
@@ -0,0 +1,103 @@
+# Embody SMPL-X adapter integration
+
+Status: implemented integration seam. No Embody asset was available or evaluated, and
+the contract fixtures in the unit tests are not empirical data or experimental
+results.
+
+## Native boundary
+
+For every synchronized actor, supply exactly these local `.npy` tracks and an
+expected SHA-256 for each file:
+
+- `smplx_mesh_body_pose`: float `[T,63]`
+- `smplx_mesh_global_orient`: float `[T,3]`
+- `smplx_mesh_transl`: float `[T,3]`
+- `smplx_mesh_betas`: float `[T,300]`
+- `smplx_mesh_left_hand_pose`: float `[T,45]`
+- `smplx_mesh_right_hand_pose`: float `[T,45]`
+- `missing`: binary `[T]` or `[T,1]`; upstream defines 0 as corrupted and 1 as
+  good tracking
+
+The adapter retains corrupted frames in place, marks all 22 body joints
+untracked, and uses exact positive zero for their coordinates. It rejects a
+missing track, incomplete file provenance, non-finite observed parameters,
+inconsistent actor lengths, duplicate actor commitments, and invalid evaluator
+output. It has no dataset discovery, download, metadata-ID, caption, or audio
+surface.
+
+`LicensedSMPLXEvaluator(model_path, device=..., expected_model_sha256=...)` is the concrete conversion
+path. It lazy-imports the caller-installed official `smplx` package and calls
+`smplx.create` with Embody's fixed configuration: neutral SMPL-X,
+`flat_hand_mean=True`, `num_betas=300`, `num_expression_coeffs=100`, and
+`use_pca=False`. It loads only a local `SMPLX_NEUTRAL.npz`, observes that exact
+file's SHA-256 before and after model construction, and derives its evaluator
+provenance from the observed digest and frozen configuration. It neither
+downloads the model nor represents that the caller has a license.
+
+The expected model digest is mandatory and must be obtained from the caller's
+verified licensed-asset manifest. A mismatch stops before optional imports or
+model construction. This is important because the upstream loader enables NPZ
+pickle support. Hashing an arbitrary local file and supplying that same hash
+does not establish trust, provenance, or a license; do not use that shortcut.
+
+Install the optional software dependency with `pip install '.[embody]'`;
+it pins `smplx==0.1.28`. Model assets are not included in this extra. The
+environment lock and the actual licensed-model witness must also be recorded
+before treating any conversion as qualified. Unit fixtures are not that witness.
+
+The evaluator disables constructor-owned pose, shape, translation, hand, jaw,
+eye, and expression parameters. Every call supplies the six Embody tensors and
+explicit zero jaw/eye/expression tensors at the actual chunk length. It also
+sets the owned model's `batch_size` under a lock for each chunk because the
+official regular `SMPLX.forward` uses that member when repeating landmark
+barycentric coordinates. This prevents a short final chunk from inheriting a
+fixed constructor length. It runs in evaluation mode under `torch.no_grad`,
+does not return vertices or shaped vertices, and exports the first 22 official
+joints (pelvis through right wrist) as finite float32 NumPy data.
+
+A custom evaluator remains possible for integration testing, but then the
+caller must explicitly supply its provenance SHA-256. For the exact concrete
+evaluator, `embody_to_private_capture` reads the observed model binding itself
+and rejects any conflicting supplied value. All float64 input and output is
+checked again after float32 conversion so overflow cannot enter the PhaseSet
+seam.
+
+Pass the resulting `PrivateCaptureArrays` to the existing
+`prepare_capture(...)`. That existing path, rather than this adapter, owns
+body-22 extraction defense in depth, 300-frame windows, 30-to-20 Hz resampling,
+missing-run rejection/interpolation, group canonicalization, augmentation, and
+dynamic collation.
+
+## Access and license gates not implemented in code
+
+- Embody data access is not established. The official repository says a user
+  must complete the release form before downloads work, that approved access
+  supplies 21 links, and that the dataset is governed by the XRCIA license.
+  This adapter neither requests nor proves that approval.
+- The official Meta release-form page did not render in the documentation
+  fetch used for this review, so its current identity fields and review timing
+  are not asserted here.
+- SMPL-X model assets are a separate prerequisite. The official SMPL-X site
+  offers independent account registration (email/password) and requires
+  acceptance of its privacy statement, Model license, and Body license for
+  noncommercial research. SMPL-X access does not grant Embody access.
+- No licensed dataset array, SMPL-X model asset, credential, signed link,
+  private path, participant identifier, or generated output may be committed
+  to the public repository.
+
+## Official sources checked 2026-09-07
+
+- Embody README (native folders, 30 fps, missing convention, access and XRCIA):
+  https://github.com/facebookresearch/embody-3d/blob/main/README.md
+- Embody feature names:
+  https://github.com/facebookresearch/embody-3d/blob/main/src/enums.py
+- Embody official SMPL-X invocation:
+  https://github.com/facebookresearch/embody-3d/blob/main/src/run.py
+- Embody local-array loading behavior:
+  https://github.com/facebookresearch/embody-3d/blob/main/src/dataloader.py
+- Official SMPL-X model API:
+  https://github.com/vchoutas/smplx/blob/main/smplx/body_models.py
+- Official SMPL-X joint ordering:
+  https://github.com/vchoutas/smplx/blob/main/smplx/joint_names.py
+- Official SMPL-X registration and licensing:
+  https://smpl-x.is.tue.mpg.de/register.php
diff --git a/paper/README.md b/paper/README.md
index ecd6d95..694d411 100644
--- a/paper/README.md
+++ b/paper/README.md
@@ -28,34 +28,39 @@ single sealed-test execution and frozen aggregate.
 
 ## Template status
 
-The [official ICASSP 2027 publishing page](https://2027.ieeeicassp.org/publishing-and-paper-presentation-options/)
-confirms four technical pages plus an optional references-only fifth page, but
-its current author section does not expose a downloadable 2027 paper kit in a
-form available to this repository. The source therefore deliberately retains
-the engineering `article` template and labels that fact in `main.tex`. Page
-count in this template is only a drafting constraint. Re-template and re-audit
-page limits, fonts, figures, bibliography, and anonymization against the
-official kit before any submission claim. The official full-paper deadline is
-[September 16, 2026](https://2027.ieeeicassp.org/call-for-papers/).
+The source uses the [official ICASSP 2027 paper kit](https://cmsworkshops.com/ICASSP2027/papers/paper_kit.php),
+unmodified `spconf.sty`, its documented 9 pt mode, and `IEEEbib.bst`. The
+hash-pinned fetch script obtains only those two style files into ignored
+`paper/.venue/`; no example paper or third-party illustration is republished.
+The limit is four technical pages plus an optional fifth page restricted to
+references, acknowledgments, and ethical-compliance statements. This draft's
+fifth page contains references only.
+
+ICASSP 2027 is non-blind. The explicit author/affiliation metadata-pending
+fields must be replaced by the real author list before a submission build.
+Formatting success is not submission readiness. The official full-paper
+deadline is [September 16, 2026](https://2027.ieeeicassp.org/call-for-papers/).
 
 ## Local build
 
-From this directory:
+From the repository root, with Python, `pdflatex`, and `bibtex` installed:
 
 ```text
-pdflatex -interaction=nonstopmode -halt-on-error main.tex
-bibtex main
-pdflatex -interaction=nonstopmode -halt-on-error main.tex
-pdflatex -interaction=nonstopmode -halt-on-error main.tex
+python scripts/build_paper.py
 ```
 
-Verified locally on 2026-08-25 with MiKTeX pdfTeX/BibTeX: the build succeeds as
-a five-page PDF, pages 1–4 contain manuscript content, and page 5 contains only
-references. The final LaTeX log has no undefined citation/reference, multiply
-defined label, overfull box, underfull box, or package warning. The command-line
-tools emit the environment-level advisory “MiKTeX updates have not been
-checked”; this is a toolchain-maintenance notice, not a manuscript warning.
-A generated PDF remains a `NO_RESULT / NOT_FOR_SUBMISSION` engineering artifact.
+For an offline build, add `--template-archive <official-kit.zip>`. Its exact
+SHA-256 is checked; a different archive is not silently accepted. Each build
+gets a new ignored directory, four pass logs, and a source/style/PDF digest
+receipt. The script needs no `latexmk` or Perl and disables TeX shell escape.
+
+Verified on 2026-09-08 with MiKTeX pdfTeX/BibTeX: five pages, technical content
+ends on page 4, page 5 contains only references, no undefined references or
+overfull boxes, and all 20 font entries embedded. Two underfull horizontal
+boxes, one underfull vertical box, and a nonfatal `balance` warning remain
+reported in the logs. All 57 `HOLD` result cells remain visible. Inspect page
+boundaries, fonts, figures, citations, and claims after every content change.
+A generated PDF remains a `NO_RESULT / NOT_FOR_SUBMISSION` artifact.
 
 ## Non-negotiable language
 
diff --git a/paper/main.pdf b/paper/main.pdf
index d518eee87e02798027778008c5cc9d3c8863c4a4..5604411cb1e70cc7e01604e0342634ad12595773 100644
GIT binary patch
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diff --git a/paper/main.tex b/paper/main.tex
index 125a5e5..01bc96d 100644
--- a/paper/main.tex
+++ b/paper/main.tex
@@ -1,44 +1,29 @@
 % DRAFT / AUTHORITY0 / NO_RESULT / NOT_FOR_SUBMISSION
-% Engineering article template retained because no usable official conference
-% paper kit is available in the repository. Re-template before submission.
+% ICASSP 2027 official-template migration. Author metadata remains pending.
 \pdfminorversion=7
-\documentclass[10pt,twocolumn]{article}
-\usepackage[letterpaper,margin=0.58in,columnsep=0.21in]{geometry}
-\usepackage{amsmath,amssymb,booktabs,array,balance,graphicx,microtype,xcolor}
+\documentclass{article}
+\usepackage{spconf,amsmath,amssymb,booktabs,array,balance,graphicx,microtype,xcolor}
 \usepackage[T1]{fontenc}
-\usepackage{lmodern}
 \usepackage[hidelinks]{hyperref}
 
 \definecolor{holdred}{RGB}{160,22,22}
 \newcommand{\DraftState}{\textcolor{holdred}{\textbf{DRAFT / AUTHORITY0 / NO\_RESULT / NOT\_FOR\_SUBMISSION}}}
 \newcommand{\ResultHold}{\textcolor{holdred}{\textsf{HOLD}}}
 \newcommand{\K}{K}
-\setlength{\parindent}{0.9em}
-\setlength{\parskip}{0pt}
-\setlength{\textfloatsep}{5pt plus 1pt minus 2pt}
-\setlength{\floatsep}{4pt plus 1pt minus 1pt}
-\setlength{\intextsep}{4pt plus 1pt minus 1pt}
-\setlength{\abovedisplayskip}{4pt plus 1pt minus 2pt}
-\setlength{\belowdisplayskip}{4pt plus 1pt minus 2pt}
-\setlength{\abovecaptionskip}{2pt}
-\setlength{\belowcaptionskip}{0pt}
-\renewcommand{\arraystretch}{0.92}
-\raggedbottom
-
-\title{\vspace{-1.5em}PhaseSet: Permutation-Invariant Periodic Relation Tokens\\
+
+\title{PhaseSet: Permutation-Invariant Periodic Relation Tokens\\
 for Multi-Person Motion--Language Retrieval}
-\author{Anonymous Authors\\[-1pt]\small\DraftState}
-\date{}
+\name{Author metadata pending --- NOT FOR SUBMISSION}
+\address{Affiliation metadata pending\\\DraftState}
 
 \begin{document}
+\ninept
 \maketitle
-\vspace{-1.4em}
 \begin{center}
-\fbox{\parbox{0.93\linewidth}{\centering\small
+\fbox{\parbox{0.93\linewidth}{\centering
 This manuscript registers a method and evaluation protocol only. No training run,
 test score, confidence interval, or submission authority is represented.}}
 \end{center}
-\vspace{-0.55em}
 
 \begin{abstract}
 Group motions can share actor marginals and the same multiset of pair relations
@@ -54,6 +39,10 @@
 is a \textbf{NO\_RESULT} technical draft: every empirical cell is held.
 \end{abstract}
 
+\begin{keywords}
+multi-person motion, text--motion retrieval, periodic relations, set representation
+\end{keywords}
+
 \section{Problem and claim boundary}
 Single-person retrieval already includes contrastive motion synthesis and
 fine-grained wavelet encoders \cite{tmr2023,wamo2026}. Dyadic motion work covers
@@ -265,18 +254,11 @@ \subsection{Execution and scaling gates}
 insufficient edge budget yields an explicit resource failure rather than a
 different graph.
 
-\clearpage
-
-\twocolumn[{%
-\begin{minipage}{\textwidth}
-\begin{minipage}[t]{0.475\textwidth}
+\begin{table*}[t]
 \centering
-\refstepcounter{table}
-\textbf{Table \thetable:} Frozen full-gallery matrix; every result is
-\ResultHold.\label{tab:hold}\\[2pt]
-\scriptsize
-\resizebox{\linewidth}{!}{%
-\begin{tabular}{@{}clcccc@{}}
+\caption{Frozen full-gallery matrix; every result is \ResultHold.}
+\label{tab:hold}
+\begin{tabular}{@{}c p{49mm} c c c c@{}}
 \toprule
 ID & System & 1729 & 2718 & 31415 & Aggregate \\
 \midrule
@@ -291,16 +273,13 @@ \subsection{Execution and scaling gates}
 08 & PhaseSet full              & \ResultHold & \ResultHold & \ResultHold & \ResultHold \\
 \bottomrule
 \end{tabular}
-}
-\end{minipage}\hfill
-\begin{minipage}[t]{0.505\textwidth}
+\end{table*}
+
+\begin{table*}[t]
 \centering
-\refstepcounter{table}
-\textbf{Table \thetable:} Frozen slice/scaling report; dash is out of
-scope.\label{tab:scaling}\\[2pt]
-\scriptsize
-\resizebox{\linewidth}{!}{%
-\begin{tabular}{@{}clcccc@{}}
+\caption{Frozen slice/scaling report; dash is out of scope.}
+\label{tab:scaling}
+\begin{tabular}{@{}c p{45mm} c c c c@{}}
 \toprule
 $K$ & Source / role & Full & Hard-$\leq32$ & Memory & Throughput \\
 \midrule
@@ -314,11 +293,7 @@ \subsection{Execution and scaling gates}
 256 & synthetic / resource limit  & -- & -- & \ResultHold & \ResultHold \\
 \bottomrule
 \end{tabular}
-}
-\end{minipage}
-\end{minipage}
-\vspace{4pt}
-}]
+\end{table*}
 
 \balance
 
@@ -401,10 +376,6 @@ \section{Conclusion and limits}
 publication review; this repository exposes no raw motion, captions,
 participant identities, reversible memberships, or private model artifacts.
 
-\clearpage
-\sloppy
-\small
-\raggedright
-\bibliographystyle{unsrt}
+\bibliographystyle{IEEEbib}
 \bibliography{references}
 \end{document}
diff --git a/pyproject.toml b/pyproject.toml
index 922d111..a04eba4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,6 +33,9 @@ clip = [
   "requests==2.34.2",
   "transformers==4.57.3",
 ]
+embody = [
+  "smplx==0.1.28",
+]
 test = [
   "pytest==9.0.3",
   "ruff==0.15.22",
diff --git a/scripts/build_paper.py b/scripts/build_paper.py
new file mode 100644
index 0000000..b2bcb65
--- /dev/null
+++ b/scripts/build_paper.py
@@ -0,0 +1,95 @@
+"""Build the current manuscript with the hash-pinned official ICASSP 2027 kit.
+
+This compiles existing content. It never creates scientific results or grants
+submission readiness. Logs and receipts stay in a separate ignored build tree.
+"""
+from __future__ import annotations
+
+import argparse
+from datetime import datetime, timezone
+import hashlib
+import json
+import os
+from pathlib import Path
+import shutil
+import subprocess
+import sys
+
+from fetch_icassp2027_template import main as fetch_template
+
+
+def file_sha256(path: Path) -> str:
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--template-archive", type=Path)
+    parser.add_argument("--output-directory", type=Path)
+    args = parser.parse_args(argv)
+    root = Path(__file__).resolve().parents[1]
+    paper = root / "paper"
+    venue = paper / ".venue"
+    template_args = ["--destination", str(venue)]
+    if args.template_archive is not None:
+        template_args.extend(["--archive", str(args.template_archive)])
+    fetch_template(template_args)
+    executables = {name: shutil.which(name) for name in ("pdflatex", "bibtex")}
+    if any(value is None for value in executables.values()):
+        raise RuntimeError("pdflatex and bibtex must be installed and on PATH")
+    stamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%S%fZ")
+    output = (args.output_directory or paper / "build" / stamp).resolve()
+    # Never mix this attempt with another build or overwrite tracked paper files.
+    if output.exists() or output.is_symlink():
+        raise FileExistsError("paper build output must be a new directory")
+    output.mkdir(parents=True)
+    env = dict(os.environ)
+    env["TEXINPUTS"] = str(venue) + os.pathsep
+    env["BSTINPUTS"] = str(venue) + os.pathsep
+    env["BIBINPUTS"] = str(paper) + os.pathsep
+    tex_command = [
+        executables["pdflatex"], "-no-shell-escape", "-interaction=nonstopmode",
+        "-halt-on-error", "-output-directory", str(output), "main.tex",
+    ]
+    commands = [tex_command, [executables["bibtex"], str(output / "main")],
+                tex_command, tex_command]
+    for index, command in enumerate(commands, start=1):
+        with (output / f"pass-{index}.log").open("xb") as log:
+            result = subprocess.run(command, cwd=paper, env=env, stdout=log,
+                                    stderr=subprocess.STDOUT, timeout=180, check=False)
+        if result.returncode:
+            raise RuntimeError(f"paper pass {index} failed; inspect its private build log")
+    pdf = output / "main.pdf"
+    if not pdf.is_file() or pdf.stat().st_size == 0:
+        raise RuntimeError("paper build produced no PDF")
+    log_text = (output / "main.log").read_text(encoding="utf-8", errors="replace")
+    for marker in ("There were undefined references", "There were multiply-defined labels"):
+        if marker in log_text:
+            raise RuntimeError("paper has unresolved references or duplicate labels")
+    receipt = {
+        "schema": "phaseset-paper-build-v1", "created_at_utc": stamp,
+        "scientific_results_created": False, "submission_ready_asserted": False,
+        "source_sha256": file_sha256(paper / "main.tex"),
+        "bibliography_sha256": file_sha256(paper / "references.bib"),
+        "spconf_sha256": file_sha256(venue / "spconf.sty"),
+        "ieeebib_sha256": file_sha256(venue / "IEEEbib.bst"),
+        "pdf_sha256": file_sha256(pdf), "pdf_bytes": pdf.stat().st_size,
+        "overfull_hbox_count": log_text.count("Overfull \\hbox"),
+        "overfull_vbox_count": log_text.count("Overfull \\vbox"),
+        "underfull_hbox_count": log_text.count("Underfull \\hbox"),
+        "underfull_vbox_count": log_text.count("Underfull \\vbox"),
+    }
+    with (output / "build-receipt.json").open("x", encoding="utf-8") as stream:
+        json.dump(receipt, stream, sort_keys=True, indent=2)
+        stream.write("\n")
+    print("PAPER_BUILD_COMPLETED " + receipt["pdf_sha256"])
+    print("Inspect page boundaries, embedded fonts, figures, and claims before publication.")
+    return 0
+
+
+if __name__ == "__main__":
+    try:
+        raise SystemExit(main())
+    except (OSError, RuntimeError, ValueError, subprocess.TimeoutExpired) as exc:
+        print(f"PAPER_BUILD_FAILED: {type(exc).__name__}", file=sys.stderr)
+        raise SystemExit(1) from exc
diff --git a/scripts/fetch_icassp2027_template.py b/scripts/fetch_icassp2027_template.py
new file mode 100644
index 0000000..2069d16
--- /dev/null
+++ b/scripts/fetch_icassp2027_template.py
@@ -0,0 +1,76 @@
+"""Fetch only the hash-pinned official conference style, never example papers."""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import io
+from pathlib import Path
+from urllib.parse import urlparse
+from urllib.request import Request, urlopen
+import zipfile
+
+
+TEMPLATE_URL = (
+    "https://cmsworkshops.com/ICASSP2027/papers/PaperFormat/"
+    "ICASSP2027_Paper_Templates.zip"
+)
+ARCHIVE_SHA256 = "fd1cc4102c4ad9a3e85eba96ee5ed261a45fab9c2630875eb2566812230c91a8"
+MAX_ARCHIVE_BYTES = 32 * 1024 * 1024
+STYLE_SHA256 = {
+    "spconf.sty": "dc5d632639040cb183f2ab62780f314845aa021be73048c8a9ec9c2072d64a86",
+    "IEEEbib.bst": "7e6ca0c8b72158d504a12bb091f817c07032a021ba41ca783cca4c2dd80d570b",
+}
+
+
+def install_template(raw: bytes, destination: Path) -> None:
+    if len(raw) > MAX_ARCHIVE_BYTES or hashlib.sha256(raw).hexdigest() != ARCHIVE_SHA256:
+        raise ValueError("official ICASSP 2027 template archive digest mismatch")
+    selected = {}
+    with zipfile.ZipFile(io.BytesIO(raw)) as archive:
+        for name, expected in STYLE_SHA256.items():
+            matches = [entry for entry in archive.infolist() if entry.filename == name]
+            if len(matches) != 1 or matches[0].file_size > 1024 * 1024:
+                raise ValueError("official style member is absent, repeated, or oversized")
+            content = archive.read(matches[0])
+            if hashlib.sha256(content).hexdigest() != expected:
+                raise ValueError("official style member digest mismatch")
+            selected[name] = content
+    destination.mkdir(parents=True, exist_ok=True)
+    # Validate the complete target set before writing; preserve differing local files.
+    for name, content in selected.items():
+        path = destination / name
+        if path.is_symlink() or (path.exists() and path.read_bytes() != content):
+            raise ValueError("refusing to replace an existing different style file")
+    for name, content in selected.items():
+        path = destination / name
+        if not path.exists():
+            with path.open("xb") as handle:
+                handle.write(content)
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--archive", type=Path, help="optional offline official template archive")
+    parser.add_argument(
+        "--destination", type=Path,
+        default=Path(__file__).resolve().parents[1] / "paper" / ".venue",
+    )
+    args = parser.parse_args(argv)
+    if args.archive:
+        if args.archive.stat().st_size > MAX_ARCHIVE_BYTES:
+            raise ValueError("template archive exceeds the byte bound")
+        raw = args.archive.read_bytes()
+    else:
+        request = Request(TEMPLATE_URL, headers={"User-Agent": "PhaseSet-paper-build/1"})
+        with urlopen(request, timeout=60) as response:
+            final = urlparse(response.geturl())
+            if final.scheme != "https" or final.hostname != "cmsworkshops.com":
+                raise ValueError("template download left the official HTTPS host")
+            raw = response.read(MAX_ARCHIVE_BYTES + 1)
+    install_template(raw, args.destination)
+    print("ICASSP2027_TEMPLATE_VERIFIED " + ARCHIVE_SHA256)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/phaseset_core/embody_adapter.py b/src/phaseset_core/embody_adapter.py
new file mode 100644
index 0000000..b461968
--- /dev/null
+++ b/src/phaseset_core/embody_adapter.py
@@ -0,0 +1,680 @@
+"""Bounded Embody SMPL-X conversion seam for caller-licensed local assets.
+
+This module contains no dataset locator, download credential, participant ID,
+or SMPL-X model asset.  It validates the seven native per-actor Embody tracks,
+calls a caller-supplied SMPL-X joint evaluator in bounded chunks, and returns
+the existing :class:`PrivateCaptureArrays` seam.  Windowing, 30-to-20 Hz
+resampling, body-22 selection, canonicalization, and batching remain owned by
+``phaseset_core.pipeline``.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+import hashlib
+from importlib import import_module
+import io
+from pathlib import Path
+import struct
+import threading
+from typing import Final, Protocol
+
+import numpy as np
+
+from phaseset_core.pipeline import PrivateCaptureArrays
+
+
+BODY_POSE: Final = "smplx_mesh_body_pose"
+GLOBAL_ORIENT: Final = "smplx_mesh_global_orient"
+TRANSL: Final = "smplx_mesh_transl"
+BETAS: Final = "smplx_mesh_betas"
+LEFT_HAND_POSE: Final = "smplx_mesh_left_hand_pose"
+RIGHT_HAND_POSE: Final = "smplx_mesh_right_hand_pose"
+MISSING: Final = "missing"
+EMBODY_FEATURE_KEYS: Final = (
+    BODY_POSE,
+    GLOBAL_ORIENT,
+    TRANSL,
+    BETAS,
+    LEFT_HAND_POSE,
+    RIGHT_HAND_POSE,
+    MISSING,
+)
+SMPLX_BODY22_NAMES: Final = (
+    "pelvis",
+    "left_hip",
+    "right_hip",
+    "spine1",
+    "left_knee",
+    "right_knee",
+    "spine2",
+    "left_ankle",
+    "right_ankle",
+    "spine3",
+    "left_foot",
+    "right_foot",
+    "neck",
+    "left_collar",
+    "right_collar",
+    "head",
+    "left_shoulder",
+    "right_shoulder",
+    "left_elbow",
+    "right_elbow",
+    "left_wrist",
+    "right_wrist",
+)
+DEFAULT_MAX_ACTOR_FILE_BYTES: Final = 512 * 1024 * 1024
+ABSOLUTE_MAX_ACTOR_FILE_BYTES: Final = 2 * 1024 * 1024 * 1024
+MAX_ACTOR_FRAMES: Final = 360_000
+DEFAULT_EVALUATOR_BATCH_FRAMES: Final = 600
+MAX_EVALUATOR_BATCH_FRAMES: Final = 4096
+MAX_SMPLX_MODEL_BYTES: Final = 2 * 1024 * 1024 * 1024
+LICENSED_SMPLX_CONFIG: Final = (
+    "model_type=smplx;gender=neutral;flat_hand_mean=true;num_betas=300;"
+    "num_expression_coeffs=100;use_pca=false;axis_angle=true"
+)
+
+
+class EmbodyAdapterError(ValueError):
+    """Native Embody data, provenance, or evaluator output is invalid."""
+
+
+class SMPLXBodyJointEvaluator(Protocol):
+    """Licensed-host implementation of the official SMPL-X forward seam.
+
+    Inputs are finite float32 axis-angle/shape/translation arrays for one
+    actor and one bounded contiguous frame chunk.  The result must use the
+    official SMPL-X joint order and contain at least its first 22 body joints.
+    """
+
+    def __call__(
+        self,
+        *,
+        body_pose: np.ndarray,
+        global_orient: np.ndarray,
+        transl: np.ndarray,
+        betas: np.ndarray,
+        left_hand_pose: np.ndarray,
+        right_hand_pose: np.ndarray,
+    ) -> np.ndarray: ...
+
+
+def _runtime_modules() -> tuple[object, object]:
+    """Import optional SMPL-X and the package's torch dependency lazily."""
+
+    try:
+        return import_module("smplx"), import_module("torch")
+    except ImportError as exc:
+        raise EmbodyAdapterError(
+            "LicensedSMPLXEvaluator requires the caller-installed official smplx package"
+        ) from exc
+
+
+def _sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        while chunk := stream.read(1024 * 1024):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def _resolve_neutral_model_file(model_path: str | Path) -> tuple[Path, Path]:
+    supplied = Path(model_path)
+    if supplied.is_symlink():
+        raise EmbodyAdapterError("SMPL-X model path must not be a symlink")
+    if supplied.is_dir():
+        model_file = supplied / "smplx" / "SMPLX_NEUTRAL.npz"
+        create_path = supplied
+    else:
+        model_file = supplied
+        create_path = supplied
+    if (
+        model_file.name != "SMPLX_NEUTRAL.npz"
+        or model_file.is_symlink()
+        or not model_file.is_file()
+    ):
+        raise EmbodyAdapterError("model_path must resolve to the neutral SMPL-X NPZ asset")
+    size = model_file.stat().st_size
+    if not 1 <= size <= MAX_SMPLX_MODEL_BYTES:
+        raise EmbodyAdapterError("SMPL-X model asset is empty or exceeds the byte bound")
+    return create_path, model_file
+
+
+class LicensedSMPLXEvaluator:
+    """Concrete official-SMPL-X evaluator for a caller-licensed local model.
+
+    Construction performs no download and makes no license assertion.  It
+    verifies a trusted expected NPZ digest before importing the optional loader
+    and rechecks after ``smplx.create``. The expected digest must come from the
+    caller's verified licensed-asset manifest, not an untrusted file itself.
+    Facial tracks absent from Embody are supplied
+    as exact zeros at the current chunk length instead of using model parameters
+    whose constructor batch size can differ from the final chunk.
+    """
+
+    __slots__ = (
+        "_device",
+        "_lock",
+        "_model",
+        "_model_file_sha256",
+        "_provenance_sha256",
+        "_torch",
+    )
+
+    def __init__(
+        self, model_path: str | Path, *, device: str, expected_model_sha256: str
+    ) -> None:
+        if type(device) is not str or not device or "\x00" in device:
+            raise EmbodyAdapterError("device must be a nonempty NUL-free string")
+        create_path, model_file = _resolve_neutral_model_file(model_path)
+        expected = _sha256_hex(expected_model_sha256, "expected_model_sha256")
+        digest_before = _sha256_file(model_file)
+        # Official SMPL-X loads NPZ with pickle enabled. Establish the trusted
+        # artifact binding BEFORE handing any bytes to that third-party loader.
+        if digest_before != expected:
+            raise EmbodyAdapterError("SMPL-X model asset differs from trusted expected digest")
+        smplx_module, torch_module = _runtime_modules()
+        try:
+            model = smplx_module.create(
+                str(create_path),
+                model_type="smplx",
+                gender="neutral",
+                flat_hand_mean=True,
+                num_betas=300,
+                num_expression_coeffs=100,
+                use_pca=False,
+                dtype=torch_module.float32,
+                batch_size=1,
+                create_betas=False,
+                create_global_orient=False,
+                create_body_pose=False,
+                create_transl=False,
+                create_left_hand_pose=False,
+                create_right_hand_pose=False,
+                create_expression=False,
+                create_jaw_pose=False,
+                create_leye_pose=False,
+                create_reye_pose=False,
+            )
+            model = model.to(device)
+            model.eval()
+        except Exception as exc:
+            raise EmbodyAdapterError("official SMPL-X model construction failed") from exc
+        digest_after = _sha256_file(model_file)
+        if digest_after != digest_before:
+            raise EmbodyAdapterError("SMPL-X model asset changed while it was loaded")
+        if (
+            getattr(model, "gender", None) != "neutral"
+            or getattr(model, "num_betas", None) != 300
+            or getattr(model, "num_expression_coeffs", None) != 100
+            or getattr(model, "use_pca", None) is not False
+        ):
+            raise EmbodyAdapterError("loaded SMPL-X model does not match the Embody contract")
+        provenance = hashlib.sha256(b"phaseset-licensed-smplx-evaluator-v1\x00")
+        provenance.update(LICENSED_SMPLX_CONFIG.encode("ascii"))
+        provenance.update(b"\x00" + digest_before.encode("ascii"))
+        self._device = device
+        self._lock = threading.Lock()
+        self._model = model
+        self._torch = torch_module
+        self._model_file_sha256 = digest_before
+        self._provenance_sha256 = provenance.hexdigest()
+
+    @property
+    def model_file_sha256(self) -> str:
+        return self._model_file_sha256
+
+    @property
+    def provenance_sha256(self) -> str:
+        return self._provenance_sha256
+
+    def __repr__(self) -> str:
+        return (
+            "LicensedSMPLXEvaluator("
+            f"model_file_sha256='{self.model_file_sha256}', device={self._device!r})"
+        )
+
+    def __call__(
+        self,
+        *,
+        body_pose: np.ndarray,
+        global_orient: np.ndarray,
+        transl: np.ndarray,
+        betas: np.ndarray,
+        left_hand_pose: np.ndarray,
+        right_hand_pose: np.ndarray,
+    ) -> np.ndarray:
+        tracks = {
+            "body_pose": (body_pose, 63),
+            "global_orient": (global_orient, 3),
+            "transl": (transl, 3),
+            "betas": (betas, 300),
+            "left_hand_pose": (left_hand_pose, 45),
+            "right_hand_pose": (right_hand_pose, 45),
+        }
+        batch_size = len(body_pose) if type(body_pose) is np.ndarray else -1
+        if not 1 <= batch_size <= MAX_EVALUATOR_BATCH_FRAMES:
+            raise EmbodyAdapterError("SMPL-X evaluator batch length is outside its bound")
+        tensors = {}
+        for label, (value, width) in tracks.items():
+            if (
+                type(value) is not np.ndarray
+                or value.dtype != np.dtype(np.float32)
+                or value.shape != (batch_size, width)
+                or not value.flags.c_contiguous
+                or not bool(np.isfinite(value).all())
+            ):
+                raise EmbodyAdapterError(
+                    f"{label} evaluator input must be finite C-contiguous float32 "
+                    f"[{batch_size},{width}]"
+                )
+            tensors[label] = self._torch.from_numpy(value).to(
+                device=self._device, dtype=self._torch.float32
+            )
+        zeros3 = self._torch.zeros(
+            (batch_size, 3), device=self._device, dtype=self._torch.float32
+        )
+        expression = self._torch.zeros(
+            (batch_size, 100), device=self._device, dtype=self._torch.float32
+        )
+        with self._lock, self._torch.no_grad():
+            # SMPLX.forward uses this member for landmark barycentric repeats.
+            # Updating it to the real chunk length prevents a short final chunk
+            # from inheriting the constructor's fixed batch dimension.
+            self._model.batch_size = batch_size
+            output = self._model(
+                **tensors,
+                expression=expression,
+                jaw_pose=zeros3,
+                leye_pose=zeros3,
+                reye_pose=zeros3,
+                return_verts=False,
+                return_full_pose=False,
+                return_shaped=False,
+                pose2rot=True,
+            )
+        joints = getattr(output, "joints", None)
+        if joints is None:
+            raise EmbodyAdapterError("official SMPL-X output has no joints")
+        try:
+            result = (
+                joints.detach()
+                .to(device="cpu", dtype=self._torch.float32)
+                .contiguous()
+                .numpy()
+            )
+        except (AttributeError, RuntimeError, TypeError) as exc:
+            raise EmbodyAdapterError("official SMPL-X joints could not be exported") from exc
+        result = np.array(result, dtype=np.float32, copy=True, order="C")
+        if (
+            result.ndim != 3
+            or result.shape[0] != batch_size
+            or result.shape[1] < 22
+            or result.shape[2] != 3
+            or not bool(np.isfinite(result).all())
+        ):
+            raise EmbodyAdapterError("official SMPL-X joints violate [B,J>=22,3]")
+        return np.ascontiguousarray(result[:, :22])
+
+
+def _sha256_hex(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise EmbodyAdapterError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _commitment(value: object) -> bytes:
+    if type(value) is not bytes or len(value) != 32:
+        raise EmbodyAdapterError("actor_commitment must be exact bytes32")
+    return value
+
+
+def _readonly(value: np.ndarray) -> np.ndarray:
+    snapshot = np.ascontiguousarray(np.array(value, copy=True, subok=False))
+    snapshot.setflags(write=False)
+    return snapshot
+
+
+def _float_track(value: object, label: str, width: int) -> np.ndarray:
+    if type(value) is not np.ndarray:
+        raise TypeError(f"{label} must be an exact numpy.ndarray")
+    if value.dtype not in (np.dtype(np.float32), np.dtype(np.float64)):
+        raise EmbodyAdapterError(f"{label} must be float32 or float64")
+    if value.ndim != 2 or value.shape[1] != width:
+        raise EmbodyAdapterError(f"{label} must have shape [T,{width}]")
+    return _readonly(value)
+
+
+def _missing_track(value: object) -> np.ndarray:
+    if type(value) is not np.ndarray:
+        raise TypeError("missing must be an exact numpy.ndarray")
+    flattened = value[:, 0] if value.ndim == 2 and value.shape[1] == 1 else value
+    if flattened.ndim != 1 or flattened.dtype.kind not in "bifu":
+        raise EmbodyAdapterError("missing must be a numeric binary [T] or [T,1] array")
+    if not bool(np.isfinite(flattened).all()) or not bool(
+        np.logical_or(flattened == 0, flattened == 1).all()
+    ):
+        raise EmbodyAdapterError("missing must contain only the official binary values 0 or 1")
+    return _readonly(np.asarray(flattened, dtype=np.bool_))
+
+
+def _canonical_feature_sha256s(value: object) -> tuple[tuple[str, str], ...]:
+    if type(value) is not tuple:
+        raise TypeError("feature_sha256s must be an exact tuple")
+    if any(type(item) is not tuple or len(item) != 2 for item in value):
+        raise EmbodyAdapterError(
+            "feature_sha256s entries must be exact (feature, sha256) tuples"
+        )
+    supplied = {item[0]: item[1] for item in value}
+    if len(supplied) != len(value) or set(supplied) != set(EMBODY_FEATURE_KEYS):
+        raise EmbodyAdapterError("feature provenance must cover exactly the seven Embody tracks")
+    return tuple(
+        (key, _sha256_hex(supplied[key], f"{key} provenance"))
+        for key in EMBODY_FEATURE_KEYS
+    )
+
+
+@dataclass(frozen=True, slots=True)
+class EmbodyActorArrays:
+    """One actor's complete native Embody SMPL-X tracks at 30 fps.
+
+    ``missing`` follows the upstream convention: 1 is usable tracking and 0
+    is a corrupted frame.  Missing frames are retained, never dropped.
+    ``feature_sha256s`` contains only digests, never private paths or IDs.
+    """
+
+    body_pose: np.ndarray = field(repr=False)
+    global_orient: np.ndarray = field(repr=False)
+    transl: np.ndarray = field(repr=False)
+    betas: np.ndarray = field(repr=False)
+    left_hand_pose: np.ndarray = field(repr=False)
+    right_hand_pose: np.ndarray = field(repr=False)
+    missing: np.ndarray = field(repr=False)
+    actor_commitment: bytes = field(repr=False)
+    feature_sha256s: tuple[tuple[str, str], ...]
+
+    def __post_init__(self) -> None:
+        tracks = {
+            "body_pose": _float_track(self.body_pose, BODY_POSE, 63),
+            "global_orient": _float_track(self.global_orient, GLOBAL_ORIENT, 3),
+            "transl": _float_track(self.transl, TRANSL, 3),
+            "betas": _float_track(self.betas, BETAS, 300),
+            "left_hand_pose": _float_track(self.left_hand_pose, LEFT_HAND_POSE, 45),
+            "right_hand_pose": _float_track(self.right_hand_pose, RIGHT_HAND_POSE, 45),
+        }
+        missing = _missing_track(self.missing)
+        frame_count = int(tracks["body_pose"].shape[0])
+        if not 1 <= frame_count <= MAX_ACTOR_FRAMES:
+            raise EmbodyAdapterError(f"actor frame count must be in [1,{MAX_ACTOR_FRAMES}]")
+        if any(track.shape[0] != frame_count for track in tracks.values()):
+            raise EmbodyAdapterError("all native Embody tracks must have the same frame count")
+        if missing.shape != (frame_count,):
+            raise EmbodyAdapterError("missing must have the same frame count as SMPL-X tracks")
+        observed = missing
+        if not bool(observed.any()):
+            raise EmbodyAdapterError("actor has no usable tracked frame")
+        for label, track in tracks.items():
+            if not bool(np.isfinite(track[observed]).all()):
+                raise EmbodyAdapterError(f"observed {label} contains non-finite data")
+            with np.errstate(over="ignore", invalid="ignore"):
+                observed_float32 = np.asarray(track[observed], dtype=np.float32)
+            if not bool(np.isfinite(observed_float32).all()):
+                raise EmbodyAdapterError(
+                    f"observed {label} is not representable as finite float32"
+                )
+            object.__setattr__(self, label, track)
+        object.__setattr__(self, "missing", missing)
+        object.__setattr__(self, "actor_commitment", _commitment(self.actor_commitment))
+        object.__setattr__(
+            self,
+            "feature_sha256s",
+            _canonical_feature_sha256s(self.feature_sha256s),
+        )
+
+    @property
+    def frame_count(self) -> int:
+        return int(self.body_pose.shape[0])
+
+    @property
+    def observed_mask(self) -> np.ndarray:
+        return self.missing
+
+
+def _file_bytes(path: Path, *, feature: str, maximum: int) -> bytes:
+    if path.suffix.lower() != ".npy" or path.is_symlink() or not path.is_file():
+        raise EmbodyAdapterError(f"{feature} must be a regular non-symlink .npy file")
+    size = path.stat().st_size
+    if size <= 0 or size > maximum:
+        raise EmbodyAdapterError(f"{feature} is empty or exceeds the byte bound")
+    raw = path.read_bytes()
+    if len(raw) != size:
+        raise EmbodyAdapterError(f"{feature} changed while it was read")
+    return raw
+
+
+def load_embody_actor_npy(
+    feature_paths: Mapping[str, str | Path],
+    *,
+    expected_sha256s: Mapping[str, str],
+    actor_commitment: bytes,
+    max_total_bytes: int = DEFAULT_MAX_ACTOR_FILE_BYTES,
+) -> EmbodyActorArrays:
+    """Load one caller-supplied licensed actor record without pickle support."""
+
+    if not isinstance(feature_paths, Mapping) or set(feature_paths) != set(
+        EMBODY_FEATURE_KEYS
+    ):
+        raise EmbodyAdapterError("feature_paths must cover exactly the seven Embody tracks")
+    if not isinstance(expected_sha256s, Mapping) or set(expected_sha256s) != set(
+        EMBODY_FEATURE_KEYS
+    ):
+        raise EmbodyAdapterError("expected_sha256s must cover exactly the seven Embody tracks")
+    if type(max_total_bytes) is not int or not (
+        1 <= max_total_bytes <= ABSOLUTE_MAX_ACTOR_FILE_BYTES
+    ):
+        raise EmbodyAdapterError("max_total_bytes is outside the public safety bound")
+    commitment = _commitment(actor_commitment)
+    arrays: dict[str, np.ndarray] = {}
+    digests: list[tuple[str, str]] = []
+    consumed = 0
+    for feature in EMBODY_FEATURE_KEYS:
+        expected = _sha256_hex(expected_sha256s[feature], f"{feature} expected digest")
+        remaining = max_total_bytes - consumed
+        if remaining <= 0:
+            raise EmbodyAdapterError("actor files exceed max_total_bytes")
+        raw = _file_bytes(Path(feature_paths[feature]), feature=feature, maximum=remaining)
+        consumed += len(raw)
+        actual = hashlib.sha256(raw).hexdigest()
+        if actual != expected:
+            raise EmbodyAdapterError(f"{feature} SHA-256 mismatch")
+        try:
+            loaded = np.load(io.BytesIO(raw), allow_pickle=False)
+        except (OSError, ValueError) as exc:
+            raise EmbodyAdapterError(f"{feature} is not a safe numeric NPY") from exc
+        if type(loaded) is not np.ndarray:
+            raise EmbodyAdapterError(f"{feature} must contain exactly one numeric array")
+        arrays[feature] = np.array(loaded, copy=True, subok=False)
+        digests.append((feature, actual))
+    return EmbodyActorArrays(
+        body_pose=arrays[BODY_POSE],
+        global_orient=arrays[GLOBAL_ORIENT],
+        transl=arrays[TRANSL],
+        betas=arrays[BETAS],
+        left_hand_pose=arrays[LEFT_HAND_POSE],
+        right_hand_pose=arrays[RIGHT_HAND_POSE],
+        missing=arrays[MISSING],
+        actor_commitment=commitment,
+        feature_sha256s=tuple(digests),
+    )
+
+
+def _array_digest(digest: object, label: str, value: np.ndarray) -> None:
+    contiguous = np.ascontiguousarray(value)
+    digest.update(label.encode("ascii"))
+    digest.update(b"\x00")
+    digest.update(contiguous.dtype.str.encode("ascii"))
+    digest.update(struct.pack(">I", contiguous.ndim))
+    for dimension in contiguous.shape:
+        digest.update(struct.pack(">Q", dimension))
+    digest.update(contiguous.tobytes(order="C"))
+
+
+def _actor_source_digest(actor: EmbodyActorArrays) -> bytes:
+    digest = hashlib.sha256(b"phaseset-embody-actor-source-v1\x00")
+    for feature, file_digest in actor.feature_sha256s:
+        digest.update(feature.encode("ascii") + b"\x00" + file_digest.encode("ascii"))
+    for label, value in (
+        (BODY_POSE, actor.body_pose),
+        (GLOBAL_ORIENT, actor.global_orient),
+        (TRANSL, actor.transl),
+        (BETAS, actor.betas),
+        (LEFT_HAND_POSE, actor.left_hand_pose),
+        (RIGHT_HAND_POSE, actor.right_hand_pose),
+        (MISSING, actor.missing),
+    ):
+        _array_digest(digest, label, value)
+    return digest.digest()
+
+
+def _safe_chunk(track: np.ndarray, observed: np.ndarray, start: int, stop: int) -> np.ndarray:
+    source = track[start:stop]
+    present = observed[start:stop]
+    chunk = np.zeros(source.shape, dtype=np.float32)
+    with np.errstate(over="ignore", invalid="ignore"):
+        chunk[present] = source[present]
+    if not bool(np.isfinite(chunk).all()):
+        raise EmbodyAdapterError("observed SMPL-X track is not finite after float32 conversion")
+    return np.ascontiguousarray(chunk)
+
+
+def embody_to_private_capture(
+    actors: tuple[EmbodyActorArrays, ...],
+    *,
+    evaluator: SMPLXBodyJointEvaluator,
+    evaluator_provenance_sha256: str | None = None,
+    max_batch_frames: int = DEFAULT_EVALUATOR_BATCH_FRAMES,
+) -> PrivateCaptureArrays:
+    """Convert a synchronized native Embody group to the PhaseSet numeric seam.
+
+    The evaluator is invoked only with finite arrays and never with more than
+    ``max_batch_frames`` frames.  Corrupted frames remain present with a false
+    joint mask and exact +0 coordinates.  No window is accepted or rejected
+    here; the existing pipeline applies that frozen policy later.
+    """
+
+    if type(actors) is not tuple or len(actors) < 2:
+        raise EmbodyAdapterError("actors must be an exact tuple containing at least two actors")
+    if any(type(actor) is not EmbodyActorArrays for actor in actors):
+        raise TypeError("every actor must be exactly EmbodyActorArrays")
+    if not callable(evaluator):
+        raise TypeError("evaluator must be callable")
+    observed_evaluator_digest = getattr(evaluator, "provenance_sha256", None)
+    if type(evaluator) is LicensedSMPLXEvaluator:
+        evaluator_digest = _sha256_hex(
+            observed_evaluator_digest, "LicensedSMPLXEvaluator provenance_sha256"
+        )
+        if (
+            evaluator_provenance_sha256 is not None
+            and evaluator_provenance_sha256 != evaluator_digest
+        ):
+            raise EmbodyAdapterError(
+                "supplied evaluator provenance differs from the observed model binding"
+            )
+    else:
+        evaluator_digest = _sha256_hex(
+            evaluator_provenance_sha256, "evaluator_provenance_sha256"
+        )
+    if type(max_batch_frames) is not int or not (
+        1 <= max_batch_frames <= MAX_EVALUATOR_BATCH_FRAMES
+    ):
+        raise EmbodyAdapterError("max_batch_frames is outside the public safety bound")
+    frame_count = actors[0].frame_count
+    if any(actor.frame_count != frame_count for actor in actors):
+        raise EmbodyAdapterError("all group actors must have the same synchronized frame count")
+    commitments = tuple(actor.actor_commitment for actor in actors)
+    if len(set(commitments)) != len(commitments):
+        raise EmbodyAdapterError("actor commitments must be distinct within a group")
+
+    joints = np.zeros((frame_count, len(actors), 22, 3), dtype=np.float32)
+    track_mask = np.zeros((frame_count, len(actors), 22), dtype=np.bool_)
+    for actor_index, actor in enumerate(actors):
+        observed = actor.observed_mask
+        track_mask[:, actor_index] = observed[:, None]
+        for start in range(0, frame_count, max_batch_frames):
+            stop = min(start + max_batch_frames, frame_count)
+            result = evaluator(
+                body_pose=_safe_chunk(actor.body_pose, observed, start, stop),
+                global_orient=_safe_chunk(actor.global_orient, observed, start, stop),
+                transl=_safe_chunk(actor.transl, observed, start, stop),
+                betas=_safe_chunk(actor.betas, observed, start, stop),
+                left_hand_pose=_safe_chunk(actor.left_hand_pose, observed, start, stop),
+                right_hand_pose=_safe_chunk(actor.right_hand_pose, observed, start, stop),
+            )
+            if type(result) is not np.ndarray:
+                raise EmbodyAdapterError("SMPL-X evaluator must return an exact numpy.ndarray")
+            if result.dtype not in (np.dtype(np.float32), np.dtype(np.float64)):
+                raise EmbodyAdapterError("SMPL-X evaluator output must be float32 or float64")
+            if (
+                result.ndim != 3
+                or result.shape[0] != stop - start
+                or result.shape[1] < 22
+                or result.shape[2] != 3
+            ):
+                raise EmbodyAdapterError(
+                    "SMPL-X evaluator output must have shape [chunk,J>=22,3]"
+                )
+            if not bool(np.isfinite(result).all()):
+                raise EmbodyAdapterError("SMPL-X evaluator output contains non-finite coordinates")
+            with np.errstate(over="ignore", invalid="ignore"):
+                body22 = np.array(result[:, :22], dtype=np.float32, copy=True, order="C")
+            if not bool(np.isfinite(body22).all()):
+                raise EmbodyAdapterError(
+                    "SMPL-X evaluator output is not representable as finite float32"
+                )
+            body22[~observed[start:stop]] = 0.0
+            joints[start:stop, actor_index] = body22
+
+    ordered = tuple(sorted(actors, key=lambda actor: actor.actor_commitment))
+    source = hashlib.sha256(b"phaseset-embody-group-source-v1\x00")
+    source.update(evaluator_digest.encode("ascii"))
+    for actor in ordered:
+        source.update(actor.actor_commitment)
+        source.update(_actor_source_digest(actor))
+    return PrivateCaptureArrays(
+        joints=np.ascontiguousarray(joints),
+        track_mask=np.ascontiguousarray(track_mask),
+        actor_commitments=commitments,
+        source_sha256=source.hexdigest(),
+    )
+
+
+__all__ = [
+    "ABSOLUTE_MAX_ACTOR_FILE_BYTES",
+    "BETAS",
+    "BODY_POSE",
+    "DEFAULT_EVALUATOR_BATCH_FRAMES",
+    "DEFAULT_MAX_ACTOR_FILE_BYTES",
+    "EMBODY_FEATURE_KEYS",
+    "EmbodyActorArrays",
+    "EmbodyAdapterError",
+    "GLOBAL_ORIENT",
+    "LEFT_HAND_POSE",
+    "LICENSED_SMPLX_CONFIG",
+    "LicensedSMPLXEvaluator",
+    "MAX_ACTOR_FRAMES",
+    "MAX_EVALUATOR_BATCH_FRAMES",
+    "MAX_SMPLX_MODEL_BYTES",
+    "MISSING",
+    "RIGHT_HAND_POSE",
+    "SMPLX_BODY22_NAMES",
+    "SMPLXBodyJointEvaluator",
+    "TRANSL",
+    "embody_to_private_capture",
+    "load_embody_actor_npy",
+]
diff --git a/tests/test_phaseset_embody_adapter.py b/tests/test_phaseset_embody_adapter.py
new file mode 100644
index 0000000..8fcbd62
--- /dev/null
+++ b/tests/test_phaseset_embody_adapter.py
@@ -0,0 +1,412 @@
+from __future__ import annotations
+
+import hashlib
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from phaseset_core import embody_adapter as adapter
+
+
+def _commitment(index: int) -> bytes:
+    return hashlib.sha256(f"contract-actor-{index}".encode("ascii")).digest()
+
+
+def _provenance() -> tuple[tuple[str, str], ...]:
+    return tuple(
+        (key, hashlib.sha256(f"contract-file-{key}".encode("ascii")).hexdigest())
+        for key in adapter.EMBODY_FEATURE_KEYS
+    )
+
+
+def _actor(index: int, *, frames: int = 3) -> adapter.EmbodyActorArrays:
+    body_pose = np.zeros((frames, 63), dtype=np.float32)
+    global_orient = np.zeros((frames, 3), dtype=np.float32)
+    transl = np.zeros((frames, 3), dtype=np.float32)
+    transl[:, 0] = np.arange(frames, dtype=np.float32) + index * 10
+    betas = np.zeros((frames, 300), dtype=np.float32)
+    left_hand = np.zeros((frames, 45), dtype=np.float32)
+    right_hand = np.zeros((frames, 45), dtype=np.float32)
+    missing = np.ones((frames,), dtype=np.uint8)
+    if index == 0:
+        missing[1] = 0
+        body_pose[1] = np.nan
+    return adapter.EmbodyActorArrays(
+        body_pose=body_pose,
+        global_orient=global_orient,
+        transl=transl,
+        betas=betas,
+        left_hand_pose=left_hand,
+        right_hand_pose=right_hand,
+        missing=missing,
+        actor_commitment=_commitment(index),
+        feature_sha256s=_provenance(),
+    )
+
+
+class _ContractEvaluator:
+    def __init__(self) -> None:
+        self.batch_sizes: list[int] = []
+        self.saw_only_finite = True
+
+    def __call__(self, **tracks: np.ndarray) -> np.ndarray:
+        assert set(tracks) == {
+            "body_pose",
+            "global_orient",
+            "transl",
+            "betas",
+            "left_hand_pose",
+            "right_hand_pose",
+        }
+        self.batch_sizes.append(len(tracks["body_pose"]))
+        self.saw_only_finite &= all(np.isfinite(value).all() for value in tracks.values())
+        output = np.zeros((len(tracks["body_pose"]), 55, 3), dtype=np.float32)
+        output[:] = tracks["transl"][:, None, :]
+        output[:, :, 1] = np.arange(55, dtype=np.float32)[None, :]
+        return output
+
+
+def test_native_tracks_convert_to_existing_private_capture_without_dropping_frames() -> None:
+    evaluator = _ContractEvaluator()
+    capture = adapter.embody_to_private_capture(
+        (_actor(0), _actor(1)),
+        evaluator=evaluator,
+        evaluator_provenance_sha256=hashlib.sha256(b"contract-evaluator").hexdigest(),
+        max_batch_frames=2,
+    )
+    assert capture.joints.shape == (3, 2, 22, 3)
+    assert capture.track_mask.shape == (3, 2, 22)
+    assert capture.track_mask[0, 0].all()
+    assert not capture.track_mask[1, 0].any()
+    assert capture.track_mask[:, 1].all()
+    assert np.count_nonzero(capture.joints[1, 0]) == 0
+    np.testing.assert_array_equal(capture.joints[0, 0, :, 1], np.arange(22))
+    assert evaluator.batch_sizes == [2, 1, 2, 1]
+    assert bool(evaluator.saw_only_finite)
+    assert len(capture.source_sha256 or "") == 64
+
+
+def test_actor_contract_rejects_missing_provenance_bad_shape_and_observed_nan() -> None:
+    actor = _actor(0)
+    with pytest.raises(adapter.EmbodyAdapterError, match="seven Embody tracks"):
+        adapter.EmbodyActorArrays(
+            actor.body_pose,
+            actor.global_orient,
+            actor.transl,
+            actor.betas,
+            actor.left_hand_pose,
+            actor.right_hand_pose,
+            actor.missing,
+            actor.actor_commitment,
+            actor.feature_sha256s[:-1],
+        )
+    with pytest.raises(adapter.EmbodyAdapterError, match=r"\[T,63\]"):
+        adapter.EmbodyActorArrays(
+            np.zeros((3, 66), dtype=np.float32),
+            actor.global_orient,
+            actor.transl,
+            actor.betas,
+            actor.left_hand_pose,
+            actor.right_hand_pose,
+            actor.missing,
+            actor.actor_commitment,
+            actor.feature_sha256s,
+        )
+    bad = np.array(actor.body_pose, copy=True)
+    bad[0, 0] = np.nan
+    with pytest.raises(adapter.EmbodyAdapterError, match="observed body_pose"):
+        adapter.EmbodyActorArrays(
+            bad,
+            actor.global_orient,
+            actor.transl,
+            actor.betas,
+            actor.left_hand_pose,
+            actor.right_hand_pose,
+            actor.missing,
+            actor.actor_commitment,
+            actor.feature_sha256s,
+        )
+
+
+def test_local_npy_loader_requires_exact_keys_hashes_and_disables_pickle(tmp_path) -> None:
+    actor = _actor(1)
+    arrays = {
+        adapter.BODY_POSE: actor.body_pose,
+        adapter.GLOBAL_ORIENT: actor.global_orient,
+        adapter.TRANSL: actor.transl,
+        adapter.BETAS: actor.betas,
+        adapter.LEFT_HAND_POSE: actor.left_hand_pose,
+        adapter.RIGHT_HAND_POSE: actor.right_hand_pose,
+        adapter.MISSING: actor.missing,
+    }
+    paths = {}
+    digests = {}
+    for key, value in arrays.items():
+        path = tmp_path / f"{key}.npy"
+        np.save(path, value)
+        paths[key] = path
+        digests[key] = hashlib.sha256(path.read_bytes()).hexdigest()
+    loaded = adapter.load_embody_actor_npy(
+        paths,
+        expected_sha256s=digests,
+        actor_commitment=actor.actor_commitment,
+    )
+    np.testing.assert_array_equal(loaded.body_pose, actor.body_pose)
+    assert loaded.feature_sha256s == tuple(
+        (key, digests[key]) for key in adapter.EMBODY_FEATURE_KEYS
+    )
+
+    with pytest.raises(adapter.EmbodyAdapterError, match="exactly the seven"):
+        adapter.load_embody_actor_npy(
+            {key: value for key, value in paths.items() if key != adapter.MISSING},
+            expected_sha256s=digests,
+            actor_commitment=actor.actor_commitment,
+        )
+    wrong = dict(digests)
+    wrong[adapter.BODY_POSE] = "0" * 64
+    with pytest.raises(adapter.EmbodyAdapterError, match="SHA-256 mismatch"):
+        adapter.load_embody_actor_npy(
+            paths,
+            expected_sha256s=wrong,
+            actor_commitment=actor.actor_commitment,
+        )
+
+    unsafe = tmp_path / "unsafe.npy"
+    np.save(unsafe, np.asarray([object()], dtype=object))
+    unsafe_paths = dict(paths)
+    unsafe_paths[adapter.BODY_POSE] = unsafe
+    unsafe_digests = dict(digests)
+    unsafe_digests[adapter.BODY_POSE] = hashlib.sha256(unsafe.read_bytes()).hexdigest()
+    with pytest.raises(adapter.EmbodyAdapterError, match="safe numeric NPY"):
+        adapter.load_embody_actor_npy(
+            unsafe_paths,
+            expected_sha256s=unsafe_digests,
+            actor_commitment=actor.actor_commitment,
+        )
+
+
+def test_group_and_evaluator_fail_closed_on_lineage_or_shape_drift() -> None:
+    actors = (_actor(0), _actor(1))
+    evaluator = _ContractEvaluator()
+    with pytest.raises(adapter.EmbodyAdapterError, match="evaluator_provenance"):
+        adapter.embody_to_private_capture(
+            actors,
+            evaluator=evaluator,
+            evaluator_provenance_sha256="",
+        )
+
+    class _WrongOrderEvaluator:
+        def __call__(self, **tracks: np.ndarray) -> np.ndarray:
+            return np.zeros((len(tracks["body_pose"]), 21, 3), dtype=np.float32)
+
+    with pytest.raises(adapter.EmbodyAdapterError, match="J>=22"):
+        adapter.embody_to_private_capture(
+            actors,
+            evaluator=_WrongOrderEvaluator(),
+            evaluator_provenance_sha256=hashlib.sha256(b"wrong-evaluator").hexdigest(),
+        )
+
+
+class _FakeTensor:
+    def __init__(self, value: np.ndarray) -> None:
+        self.value = np.asarray(value)
+
+    def to(self, *args: object, **kwargs: object) -> _FakeTensor:
+        return self
+
+    def detach(self) -> _FakeTensor:
+        return self
+
+    def contiguous(self) -> _FakeTensor:
+        return self
+
+    def numpy(self) -> np.ndarray:
+        return self.value
+
+
+class _NoGrad:
+    def __enter__(self) -> None:
+        return None
+
+    def __exit__(self, *args: object) -> None:
+        return None
+
+
+class _FakeTorch:
+    float32 = object()
+
+    @staticmethod
+    def from_numpy(value: np.ndarray) -> _FakeTensor:
+        return _FakeTensor(value)
+
+    @staticmethod
+    def zeros(shape: tuple[int, int], **kwargs: object) -> _FakeTensor:
+        return _FakeTensor(np.zeros(shape, dtype=np.float32))
+
+    @staticmethod
+    def no_grad() -> _NoGrad:
+        return _NoGrad()
+
+
+class _FakeModel:
+    gender = "neutral"
+    num_betas = 300
+    num_expression_coeffs = 100
+    use_pca = False
+
+    def __init__(self) -> None:
+        self.batch_size = 1
+        self.training = True
+        self.calls: list[dict[str, object]] = []
+        self.device = None
+
+    def to(self, device: str) -> _FakeModel:
+        self.device = device
+        return self
+
+    def eval(self) -> _FakeModel:
+        self.training = False
+        return self
+
+    def __call__(self, **kwargs: object) -> SimpleNamespace:
+        self.calls.append(kwargs)
+        body_pose = kwargs["body_pose"]
+        assert isinstance(body_pose, _FakeTensor)
+        joints = np.zeros((len(body_pose.value), 55, 3), dtype=np.float32)
+        return SimpleNamespace(joints=_FakeTensor(joints))
+
+
+class _FakeSMPLX:
+    def __init__(self, model: _FakeModel) -> None:
+        self.model = model
+        self.calls: list[tuple[str, dict[str, object]]] = []
+
+    def create(self, path: str, **kwargs: object) -> _FakeModel:
+        self.calls.append((path, kwargs))
+        return self.model
+
+
+def test_concrete_evaluator_binds_model_and_supplies_current_chunk_face_zeros(
+    tmp_path, monkeypatch
+) -> None:
+    model_root = tmp_path / "models"
+    model_file = model_root / "smplx" / "SMPLX_NEUTRAL.npz"
+    model_file.parent.mkdir(parents=True)
+    model_file.write_bytes(b"contract-only-model-file")
+    model = _FakeModel()
+    smplx_module = _FakeSMPLX(model)
+    monkeypatch.setattr(
+        adapter,
+        "_runtime_modules",
+        lambda: (smplx_module, _FakeTorch()),
+    )
+    evaluator = adapter.LicensedSMPLXEvaluator(
+        model_root, device="host-device",
+        expected_model_sha256=hashlib.sha256(model_file.read_bytes()).hexdigest(),
+    )
+    assert evaluator.model_file_sha256 == hashlib.sha256(model_file.read_bytes()).hexdigest()
+    assert len(evaluator.provenance_sha256) == 64
+    assert str(model_root) not in repr(evaluator)
+    assert model.device == "host-device"
+    assert model.training is False
+    create_path, create_kwargs = smplx_module.calls[0]
+    assert create_path == str(model_root)
+    assert create_kwargs["model_type"] == "smplx"
+    assert create_kwargs["gender"] == "neutral"
+    assert create_kwargs["flat_hand_mean"] is True
+    assert create_kwargs["num_betas"] == 300
+    assert create_kwargs["num_expression_coeffs"] == 100
+    assert create_kwargs["use_pca"] is False
+    assert create_kwargs["batch_size"] == 1
+    assert all(
+        create_kwargs[key] is False
+        for key in (
+            "create_betas",
+            "create_global_orient",
+            "create_body_pose",
+            "create_transl",
+            "create_left_hand_pose",
+            "create_right_hand_pose",
+            "create_expression",
+            "create_jaw_pose",
+            "create_leye_pose",
+            "create_reye_pose",
+        )
+    )
+
+    for batch_size in (2, 1):
+        evaluator(
+            body_pose=np.zeros((batch_size, 63), dtype=np.float32),
+            global_orient=np.zeros((batch_size, 3), dtype=np.float32),
+            transl=np.zeros((batch_size, 3), dtype=np.float32),
+            betas=np.zeros((batch_size, 300), dtype=np.float32),
+            left_hand_pose=np.zeros((batch_size, 45), dtype=np.float32),
+            right_hand_pose=np.zeros((batch_size, 45), dtype=np.float32),
+        )
+        assert model.batch_size == batch_size
+        call = model.calls[-1]
+        assert isinstance(call["expression"], _FakeTensor)
+        assert call["expression"].value.shape == (batch_size, 100)
+        for key in ("jaw_pose", "leye_pose", "reye_pose"):
+            assert isinstance(call[key], _FakeTensor)
+            assert call[key].value.shape == (batch_size, 3)
+        assert call["return_verts"] is False
+        assert call["return_shaped"] is False
+        assert call["pose2rot"] is True
+
+    capture = adapter.embody_to_private_capture(
+        (_actor(0), _actor(1)), evaluator=evaluator, max_batch_frames=2
+    )
+    assert capture.source_sha256 is not None
+
+
+def test_untrusted_model_digest_is_rejected_before_import_or_model_construction(
+    tmp_path, monkeypatch
+) -> None:
+    model_file = tmp_path / "SMPLX_NEUTRAL.npz"
+    model_file.write_bytes(b"untrusted-contract-only-content")
+
+    def forbidden_import():
+        pytest.fail("untrusted model reached the optional pickle-capable loader")
+
+    monkeypatch.setattr(adapter, "_runtime_modules", forbidden_import)
+    with pytest.raises(adapter.EmbodyAdapterError, match="trusted expected digest"):
+        adapter.LicensedSMPLXEvaluator(
+            model_file, device="cpu", expected_model_sha256="0" * 64
+        )
+    with pytest.raises(adapter.EmbodyAdapterError, match="expected_model_sha256"):
+        adapter.LicensedSMPLXEvaluator(
+            model_file, device="cpu", expected_model_sha256="invalid"
+        )
+
+
+def test_float64_overflow_is_rejected_before_or_after_evaluation() -> None:
+    actor = _actor(1)
+    overflowing = np.array(actor.body_pose, dtype=np.float64, copy=True)
+    overflowing[0, 0] = np.finfo(np.float64).max
+    with pytest.raises(adapter.EmbodyAdapterError, match="representable as finite float32"):
+        adapter.EmbodyActorArrays(
+            overflowing,
+            actor.global_orient,
+            actor.transl,
+            actor.betas,
+            actor.left_hand_pose,
+            actor.right_hand_pose,
+            actor.missing,
+            actor.actor_commitment,
+            actor.feature_sha256s,
+        )
+
+    class _OverflowingEvaluator:
+        def __call__(self, **tracks: np.ndarray) -> np.ndarray:
+            result = np.zeros((len(tracks["body_pose"]), 22, 3), dtype=np.float64)
+            result[0, 0, 0] = np.finfo(np.float64).max
+            return result
+
+    with pytest.raises(adapter.EmbodyAdapterError, match="representable as finite float32"):
+        adapter.embody_to_private_capture(
+            (_actor(0), _actor(1)),
+            evaluator=_OverflowingEvaluator(),
+            evaluator_provenance_sha256=hashlib.sha256(b"overflowing-output").hexdigest(),
+        )

From 7a56f4b4465989ed1557b88a045f2cd43f6edf20 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 7 Sep 2026 23:55:46 -0500
Subject: [PATCH 02/95] fix: pin POSIX checkpoint ownership and snapshot
 release blobs

---
 RELEASE_FILES.sha256                          |   8 +-
 scripts/release_manifest.py                   |  47 +++---
 scripts/release_tree.py                       | 153 +++++++++++++++---
 src/phasepair_core/checkpoint.py              |  17 +-
 ...st_phasepair_posix_checkpoint_ownership.py |  61 +++++++
 tests/test_release_tree_batch.py              | 148 +++++++++++++++++
 6 files changed, 387 insertions(+), 47 deletions(-)
 create mode 100644 tests/test_phasepair_posix_checkpoint_ownership.py
 create mode 100644 tests/test_release_tree_batch.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 91dc694..394950e 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -72,8 +72,8 @@ ffb5b9e798fac11d7ec0f9cb13f1fe9ffd71dc197361795dd1a2c043d37d8080	3168	scripts/ch
 4a3396142a7e012143ccac8e2df4c45bee39778f1565a8f1a395e3b8e7fab6b7	3286	scripts/fetch_icassp2027_template.py
 58c33c281a02a928556d0a25d87bdb0c893e99667d40d2c1c0326d2a899b0137	38002	scripts/public_release_audit.py
 79e3e6160ef210fa03f1c5d8a3eb17c9298ba4b38e910d93e609ce41c1d27ec7	32001	scripts/release_asset_inventory.py
-678b7a73a29360f5b87f525f9445ff05758d4f3e423a0e5ee215cbeb98900363	2769	scripts/release_manifest.py
-a006b46722e3e711793b657b6a7c7c452deae39fb806b5b05420227279f0c337	10202	scripts/release_tree.py
+8c99175b3f115ba9e8d63870988a6ed3d7cf8009d272b5c5daaefb85887ca495	2950	scripts/release_manifest.py
+013dd2d7e7bbb7c07d86f49b49a400ad7c92a818a82cd98cbafee6d5a91d112a	14705	scripts/release_tree.py
 4db71f21e11539c4a1493ba7ea3d8127a56f1faba522c2813c61927e40ea9239	24981	scripts/verify_wheel_against_index.py
 7d0eb6a71f0c0964a653c2b536ba232728885a9a526186835ba8896137c4ea11	30359	slides/PHASEPAIR_EDITABLE_PAPER_FIGURES_DATA_FREE_DRAFT.pptx
 5ebe1cd2d83702888f77c642b8537d3544580e05fc73bf2e6baab9253e1f4dc6	464	src/phasepair_core/__init__.py
@@ -82,7 +82,7 @@ a74cdf1587509da10f5e787027e441cc810d1ab2efd3d7e062f58ed86157b9c7	17459	src/phase
 3221b572ef4dc2edae832cf26e05f07c2b69eb2e1d4808bec5f61e0c3c3e69a6	19672	src/phasepair_core/batching.py
 13a002c6146c3307639c84c6aba439dd0b3fbf216caea5ef49b06918b221f210	22962	src/phasepair_core/bootstrap.py
 efaa503b3d37653dfc790f2309d935b130a0dd0eaeec08ecfee74459d3da4123	20690	src/phasepair_core/caption_processing.py
-40735f9c3bfe68a761552402ba779c53ca89da63667bf52b411647049424cb5c	33986	src/phasepair_core/checkpoint.py
+80a4460126886bd3c7452a433c647331907af76d6bbb26bb86317b2dcb548297	34594	src/phasepair_core/checkpoint.py
 a444199a52f3b914d7ca8843ac2c95c288ca2728cd340d4d51f9523b0cd411c0	96389	src/phasepair_core/clip_resolution.py
 c9d9e5d114beae556da2e0fc0a729562edb57cf63f666b05d609288ff05c5cd8	36371	src/phasepair_core/contracts.py
 74e55c77d3c70204fcf13b29ca167eb31dc81fc62d7b0e71a8e1be72b2c984a2	29847	src/phasepair_core/dropout.py
@@ -154,6 +154,7 @@ b801d46fb184d99c65825d7f9bf91cbced022418600a95d421c5d9244dbded13	19373	tests/tes
 4044d65faf31bf5762c725f6103f561d7f915361c324baf938b449aedc65ea88	45570	tests/test_phasepair_core_trainer.py
 59dde3593b46a2a84453a722adb6b8748cec8246c50699e49087193af47da182	8604	tests/test_phasepair_core_training_batch.py
 8f31d46a9ff2d4ca96fcd34dc6fd617933f2e604c0dd6e95e0d10ec9db487074	10255	tests/test_phasepair_core_validation_gallery.py
+4da1d6c1b05bc485b9afb78776264ae7757744f104c37b947cdce0280cdfb172	2347	tests/test_phasepair_posix_checkpoint_ownership.py
 fa34950fb26f63d5d1ba169499e6e4af49c9b691f33c5cb3aac6e961bdac9e55	18384	tests/test_phaseset_caption_fusion.py
 5dbbb81d6725f1185f4c12dc85653b6eac1e47ae2ffb7fb36f5b951eb8dea632	8848	tests/test_phaseset_cli.py
 ac57f0ea3950b7ac6795f6f9973cf68ece3d370b686835dc4f8a2892c7233fdb	12603	tests/test_phaseset_cli_production_bridge.py
@@ -181,4 +182,5 @@ ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
 45b42266bc1c42c36af7eb7d07be2fc85d8b1d0a539ffb3674f98b47e1327254	4973	tests/test_release_artifact_audit.py
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
+8727e87f1a0d44db6a39b1e5cfd8e22e3bed05544ddaed07b66b759adee66d84	5587	tests/test_release_tree_batch.py
 3aef8f056ef38ecbe5af51c283285b132db97f175c181018d8a4278d82131915	8150	tests/test_wheel_index_binding.py
diff --git a/scripts/release_manifest.py b/scripts/release_manifest.py
index 1ba3729..1fe01f7 100644
--- a/scripts/release_manifest.py
+++ b/scripts/release_manifest.py
@@ -13,14 +13,14 @@
 from release_tree import (  # noqa: E402
     MANIFEST_HEADER,
     MANIFEST_RELATIVE,
+    GitIndexEntry,
     ReleaseTreeError,
-    clean_git_environment,
-    git_tracked_paths,
+    git_index_blobs,
+    git_index_entries,
     is_exact_git_checkout,
     load_archive_manifest,
 )
 
-
 ROOT = Path(__file__).resolve().parents[1]
 
 
@@ -28,32 +28,29 @@ class ManifestError(RuntimeError):
     """Raised when the tracked tree cannot be represented or verified exactly."""
 
 
-def _tracked_paths() -> tuple[PurePosixPath, ...]:
-    return tuple(
-        path for path in git_tracked_paths(ROOT) if path != MANIFEST_RELATIVE
-    )
+def _row(path: PurePosixPath, raw: bytes) -> str:
+    return f"{hashlib.sha256(raw).hexdigest()}\t{len(raw)}\t{path.as_posix()}"
 
 
-def _index_blob(path: PurePosixPath) -> bytes:
-    completed = subprocess.run(
-        ["git", "cat-file", "blob", f":{path.as_posix()}"],
-        cwd=ROOT,
-        check=True,
-        env=clean_git_environment(),
-        stdout=subprocess.PIPE,
-    )
-    return completed.stdout
+def _render_manifest(
+    entries: tuple[GitIndexEntry, ...], content: dict[PurePosixPath, bytes]
+) -> bytes:
+    header = MANIFEST_HEADER.decode("ascii")
+    rows = "\n".join(_row(entry.path, content[entry.path]) for entry in entries)
+    return (header + rows + ("\n" if rows else "")).encode("utf-8")
 
 
-def _row(path: PurePosixPath) -> str:
-    raw = _index_blob(path)
-    return f"{hashlib.sha256(raw).hexdigest()}\t{len(raw)}\t{path.as_posix()}"
+def _index_snapshot() -> tuple[
+    tuple[GitIndexEntry, ...], dict[PurePosixPath, bytes]
+]:
+    entries = git_index_entries(ROOT)
+    return entries, git_index_blobs(ROOT, entries)
 
 
 def render_manifest() -> bytes:
-    header = MANIFEST_HEADER.decode("ascii")
-    rows = "\n".join(_row(path) for path in _tracked_paths())
-    return (header + rows + ("\n" if rows else "")).encode("utf-8")
+    entries, content = _index_snapshot()
+    tracked = tuple(entry for entry in entries if entry.path != MANIFEST_RELATIVE)
+    return _render_manifest(tracked, content)
 
 
 def build_manifest() -> None:
@@ -69,8 +66,10 @@ def verify_manifest() -> None:
     if not is_exact_git_checkout(ROOT):
         load_archive_manifest(ROOT)
         return
-    expected = render_manifest()
-    actual = _index_blob(MANIFEST_RELATIVE)
+    entries, content = _index_snapshot()
+    tracked = tuple(entry for entry in entries if entry.path != MANIFEST_RELATIVE)
+    expected = _render_manifest(tracked, content)
+    actual = content[MANIFEST_RELATIVE]
     if actual != expected:
         raise ManifestError("release manifest does not exactly match the tracked tree")
 
diff --git a/scripts/release_tree.py b/scripts/release_tree.py
index de83448..54407c6 100644
--- a/scripts/release_tree.py
+++ b/scripts/release_tree.py
@@ -6,10 +6,9 @@
 import os
 import re
 import subprocess
+from collections.abc import Mapping
 from dataclasses import dataclass
 from pathlib import Path, PurePosixPath
-from typing import Mapping
-
 
 MANIFEST_RELATIVE = PurePosixPath("RELEASE_FILES.sha256")
 MANIFEST_HEADER = b"# sha256\\tbytes\\tgit_index_path; self excluded\n"
@@ -29,6 +28,15 @@ class ManifestEntry:
     path: PurePosixPath
 
 
+@dataclass(frozen=True)
+class GitIndexEntry:
+    """One validated stage-zero regular file in the Git index."""
+
+    mode: str
+    object_id: str
+    path: PurePosixPath
+
+
 @dataclass(frozen=True)
 class ReleaseTree:
     """One resolved tree whose paths and bytes share the same authority."""
@@ -37,19 +45,17 @@ class ReleaseTree:
     paths: tuple[PurePosixPath, ...]
     authority: str
     archive_content: Mapping[PurePosixPath, bytes] | None = None
+    index_content: Mapping[PurePosixPath, bytes] | None = None
 
     def read_bytes(self, path: PurePosixPath) -> bytes:
         if path not in self.paths:
             raise ReleaseTreeError(f"path is outside the release tree: {path.as_posix()}")
         if self.authority == "git-index":
-            completed = subprocess.run(
-                ["git", "cat-file", "blob", f":{path.as_posix()}"],
-                cwd=self.root,
-                check=True,
-                env=clean_git_environment(),
-                stdout=subprocess.PIPE,
-            )
-            return completed.stdout
+            if self.index_content is None or path not in self.index_content:
+                raise ReleaseTreeError(
+                    f"Git-index snapshot lacks release path: {path.as_posix()}"
+                )
+            return self.index_content[path]
         if self.authority == "archive-manifest":
             if self.archive_content is None or path not in self.archive_content:
                 raise ReleaseTreeError(
@@ -106,8 +112,8 @@ def is_exact_git_checkout(root: Path) -> bool:
         return False
 
 
-def git_tracked_paths(root: Path) -> tuple[PurePosixPath, ...]:
-    """Return safe index paths, rejecting an empty or non-PhasePair index."""
+def git_index_entries(root: Path) -> tuple[GitIndexEntry, ...]:
+    """Return validated regular-file entries from the stage-zero Git index."""
 
     if not is_exact_git_checkout(root):
         raise ReleaseTreeError("root is not an exact Git checkout")
@@ -118,7 +124,7 @@ def git_tracked_paths(root: Path) -> tuple[PurePosixPath, ...]:
         env=clean_git_environment(),
         stdout=subprocess.PIPE,
     )
-    paths: list[PurePosixPath] = []
+    entries: list[GitIndexEntry] = []
     for raw in completed.stdout.split(b"\0"):
         if not raw:
             continue
@@ -133,15 +139,119 @@ def git_tracked_paths(root: Path) -> tuple[PurePosixPath, ...]:
             raise ReleaseTreeError("release index contains a non-regular or unmerged item")
         if re.fullmatch(rb"(?:[0-9a-f]{40}|[0-9a-f]{64})", raw_object) is None:
             raise ReleaseTreeError("release index contains an invalid object id")
-        paths.append(_safe_relative_path(raw_path.decode("utf-8", errors="strict")))
-    ordered = tuple(sorted(paths, key=lambda value: value.as_posix().encode("utf-8")))
-    if not ordered or MANIFEST_RELATIVE not in ordered:
+        entries.append(
+            GitIndexEntry(
+                raw_mode.decode("ascii"),
+                raw_object.decode("ascii"),
+                _safe_relative_path(raw_path.decode("utf-8", errors="strict")),
+            )
+        )
+    ordered = tuple(
+        sorted(entries, key=lambda value: value.path.as_posix().encode("utf-8"))
+    )
+    paths = tuple(entry.path for entry in ordered)
+    if not ordered or MANIFEST_RELATIVE not in paths:
         raise ReleaseTreeError("Git index is empty or lacks RELEASE_FILES.sha256")
-    if len(ordered) != len(set(ordered)):
+    if len(paths) != len(set(paths)):
         raise ReleaseTreeError("duplicate Git-index release path")
     return ordered
 
 
+def git_tracked_paths(root: Path) -> tuple[PurePosixPath, ...]:
+    """Return safe regular-file index paths in canonical byte order."""
+
+    return tuple(entry.path for entry in git_index_entries(root))
+
+
+def _parse_git_batch_output(
+    raw: bytes, entries: tuple[GitIndexEntry, ...]
+) -> dict[PurePosixPath, bytes]:
+    """Bind one strict ``git cat-file --batch`` response to index entries."""
+
+    content: dict[PurePosixPath, bytes] = {}
+    cursor = 0
+    for entry in entries:
+        header_end = raw.find(b"\n", cursor)
+        if header_end < 0:
+            raise ReleaseTreeError(
+                f"Git batch header is truncated: {entry.path.as_posix()}"
+            )
+        header = raw[cursor:header_end]
+        cursor = header_end + 1
+        expected_object = entry.object_id.encode("ascii")
+        if header == expected_object + b" missing":
+            raise ReleaseTreeError(
+                f"Git batch object is missing: {entry.path.as_posix()}"
+            )
+        fields = header.split(b" ")
+        if len(fields) != 3 or any(not field for field in fields):
+            raise ReleaseTreeError(
+                f"Git batch header is malformed: {entry.path.as_posix()}"
+            )
+        raw_object, raw_type, raw_size = fields
+        if raw_object != expected_object:
+            raise ReleaseTreeError(
+                f"Git batch object ID mismatch: {entry.path.as_posix()}"
+            )
+        if raw_type != b"blob":
+            raise ReleaseTreeError(
+                f"Git index object is not a blob: {entry.path.as_posix()}"
+            )
+        if re.fullmatch(rb"(?:0|[1-9][0-9]*)", raw_size) is None:
+            raise ReleaseTreeError(
+                f"Git batch byte count is malformed: {entry.path.as_posix()}"
+            )
+        size = int(raw_size)
+        payload_end = cursor + size
+        if payload_end > len(raw) or raw[payload_end : payload_end + 1] != b"\n":
+            raise ReleaseTreeError(
+                f"Git batch payload is truncated: {entry.path.as_posix()}"
+            )
+        content[entry.path] = raw[cursor:payload_end]
+        cursor = payload_end + 1
+    if cursor != len(raw):
+        raise ReleaseTreeError("Git batch output has unexpected trailing bytes")
+    return content
+
+
+def git_index_blobs(
+    root: Path, entries: tuple[GitIndexEntry, ...]
+) -> dict[PurePosixPath, bytes]:
+    """Read all validated index blobs in one strict Git batch transaction."""
+
+    paths = tuple(entry.path for entry in entries)
+    canonical = tuple(sorted(paths, key=lambda value: value.as_posix().encode("utf-8")))
+    if (
+        not entries
+        or paths != canonical
+        or len(paths) != len(set(paths))
+        or MANIFEST_RELATIVE not in paths
+    ):
+        raise ReleaseTreeError("Git index entries are incomplete or not canonical")
+    for entry in entries:
+        if entry.mode not in {"100644", "100755"}:
+            raise ReleaseTreeError("release index contains a non-regular item")
+        if re.fullmatch(r"(?:[0-9a-f]{40}|[0-9a-f]{64})", entry.object_id) is None:
+            raise ReleaseTreeError("release index contains an invalid object id")
+        if _safe_relative_path(entry.path.as_posix()) != entry.path:
+            raise ReleaseTreeError("release index contains an invalid path")
+    requests = b"".join(entry.object_id.encode("ascii") + b"\n" for entry in entries)
+    completed = subprocess.run(
+        ["git", "cat-file", "--batch"],
+        cwd=root,
+        check=False,
+        env=clean_git_environment(),
+        input=requests,
+        capture_output=True,
+    )
+    if completed.returncode != 0:
+        raise ReleaseTreeError("Git batch object stream failed")
+    content = _parse_git_batch_output(completed.stdout, entries)
+    if git_index_entries(root) != entries:
+        raise ReleaseTreeError("Git index changed while reading release objects")
+    return content
+
+
 def _filesystem_files(root: Path) -> dict[PurePosixPath, Path]:
     files: dict[PurePosixPath, Path] = {}
     for absolute in root.rglob("*"):
@@ -242,7 +352,14 @@ def resolve_release_tree(root: Path) -> ReleaseTree:
     """Resolve paths and content reads through one consistent authority."""
 
     if is_exact_git_checkout(root):
-        return ReleaseTree(root, git_tracked_paths(root), "git-index")
+        entries = git_index_entries(root)
+        content = git_index_blobs(root, entries)
+        return ReleaseTree(
+            root,
+            tuple(entry.path for entry in entries),
+            "git-index",
+            index_content=content,
+        )
     entries, content = _load_archive_snapshot(root)
     paths = tuple(entry.path for entry in entries) + (MANIFEST_RELATIVE,)
     ordered = tuple(sorted(paths, key=lambda value: value.as_posix().encode("utf-8")))
diff --git a/src/phasepair_core/checkpoint.py b/src/phasepair_core/checkpoint.py
index deed470..f8e26d3 100644
--- a/src/phasepair_core/checkpoint.py
+++ b/src/phasepair_core/checkpoint.py
@@ -589,6 +589,7 @@ def write_checkpoint_atomic(value: ValidatedCheckpoint, path: Path) -> bytes:
     if _path_entry_exists(target, "checkpoint target availability is unavailable"):
         raise CheckpointPathError("checkpoint target already exists")
     descriptor = -1
+    ownership_descriptor = -1
     temp_name: str | None = None
     committed = False
     succeeded = False
@@ -637,6 +638,12 @@ def remove_committed_target_if_owned() -> None:
                 raise CheckpointPathError("checkpoint temp short write")
             handle.flush()
             os.fsync(handle.fileno())
+            if os.name == "posix":
+                # Keep the original inode alive even if every pathname is
+                # concurrently removed. Otherwise POSIX may immediately reuse
+                # its (device,inode) for a replacement owned by another writer,
+                # and failure cleanup could mistake that replacement for ours.
+                ownership_descriptor = os.dup(handle.fileno())
         temp_path = Path(temp_name)
         retirement_path = Path(temp_name + ".committed-retirement")
         if _path_entry_exists(
@@ -709,8 +716,12 @@ def remove_committed_target_if_owned() -> None:
                 Path(temp_name).unlink(missing_ok=True)
             except OSError:
                 pass
-        if committed and not succeeded:
-            remove_committed_target_if_owned()
+        try:
+            if committed and not succeeded:
+                remove_committed_target_if_owned()
+        finally:
+            if ownership_descriptor >= 0:
+                os.close(ownership_descriptor)
 
 
 def _stable_read(path: Path) -> bytes:
@@ -839,11 +850,13 @@ def _install_sealed_api() -> None:
     )
     os_proxy = types.SimpleNamespace(
         close=os.close,
+        dup=os.dup,
         fdopen=os.fdopen,
         fspath=os.fspath,
         fstat=os.fstat,
         fsync=os.fsync,
         link=os.link,
+        name=os.name,
         rename=os.rename,
         path=types.SimpleNamespace(abspath=os.path.abspath),
     )
diff --git a/tests/test_phasepair_posix_checkpoint_ownership.py b/tests/test_phasepair_posix_checkpoint_ownership.py
new file mode 100644
index 0000000..05823ca
--- /dev/null
+++ b/tests/test_phasepair_posix_checkpoint_ownership.py
@@ -0,0 +1,61 @@
+"""POSIX ownership regression; no model or training data is involved."""
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import pytest
+
+from phasepair_core import checkpoint
+from test_phasepair_core_checkpoint import _bindings, _sections
+
+
+@pytest.mark.skipif(os.name != "posix", reason="POSIX inode lifetime semantics")
+@pytest.mark.parametrize("replace_target", [False, True])
+def test_original_inode_is_pinned_and_descriptor_always_closed(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch, replace_target: bool
+) -> None:
+    value = checkpoint.build_cpu_synthetic_checkpoint(_bindings(), _sections())
+    target = (tmp_path / "owned.ppckpt").absolute()
+    original_lstat = type(target).lstat
+    original_open = type(target).open
+    proxy = checkpoint.write_checkpoint_atomic.__globals__["os"]
+    original_dup = proxy.dup
+    pins: list[int] = []
+    target_lstats = 0
+    outsider = b"replacement owned by another writer"
+
+    def pin(descriptor: int) -> int:
+        duplicate = original_dup(descriptor)
+        pins.append(duplicate)
+        return duplicate
+
+    def replacing_lstat(self: Path, *args: object, **kwargs: object):
+        nonlocal target_lstats
+        if self == target and target.exists():
+            target_lstats += 1
+            if target_lstats == 2 and replace_target:
+                pinned = os.fstat(pins[0])
+                target.unlink()
+                with original_open(target, "wb") as handle:
+                    handle.write(outsider)
+                replacement = original_lstat(target)
+                assert (replacement.st_dev, replacement.st_ino) != (
+                    pinned.st_dev, pinned.st_ino
+                )
+        return original_lstat(self, *args, **kwargs)
+
+    monkeypatch.setattr(proxy, "dup", pin)
+    monkeypatch.setattr(type(target), "lstat", replacing_lstat)
+    if replace_target:
+        with pytest.raises(checkpoint.CheckpointContractError):
+            checkpoint.write_checkpoint_atomic(value, target)
+        assert target.read_bytes() == outsider
+    else:
+        checkpoint.write_checkpoint_atomic(value, target)
+        assert target.is_file()
+    assert len(pins) == 1
+    with pytest.raises(OSError):
+        os.fstat(pins[0])
+    assert not tuple(tmp_path.glob(".*.tmp"))
+    assert not tuple(tmp_path.glob("*.committed-retirement"))
diff --git a/tests/test_release_tree_batch.py b/tests/test_release_tree_batch.py
new file mode 100644
index 0000000..648ebb4
--- /dev/null
+++ b/tests/test_release_tree_batch.py
@@ -0,0 +1,148 @@
+from __future__ import annotations
+
+import hashlib
+import subprocess
+import sys
+from pathlib import Path, PurePosixPath
+
+import pytest
+
+STAGED_ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(STAGED_ROOT / "scripts"))
+
+import release_manifest  # noqa: E402
+import release_tree  # noqa: E402
+
+OBJECT_ID = "1" * 40
+
+
+def _entry(path: str = "payload.bin") -> release_tree.GitIndexEntry:
+    return release_tree.GitIndexEntry("100644", OBJECT_ID, PurePosixPath(path))
+
+
+def _batch(object_type: bytes, payload: bytes, *, size: bytes | None = None) -> bytes:
+    raw_size = str(len(payload)).encode("ascii") if size is None else size
+    return OBJECT_ID.encode("ascii") + b" " + object_type + b" " + raw_size + b"\n" + payload + b"\n"
+
+
+@pytest.mark.parametrize(
+    ("raw", "message"),
+    (
+        (OBJECT_ID.encode("ascii") + b" blob\n", "header is malformed"),
+        (b"2" * 40 + b" blob 0\n\n", "object ID mismatch"),
+        (_batch(b"blob", b"", size=b"00"), "byte count is malformed"),
+        (_batch(b"blob", b"") + b"unexpected", "unexpected trailing bytes"),
+    ),
+)
+def test_batch_parser_rejects_malformed_output(raw: bytes, message: str) -> None:
+    with pytest.raises(release_tree.ReleaseTreeError, match=message):
+        release_tree._parse_git_batch_output(raw, (_entry(),))
+
+
+@pytest.mark.parametrize(
+    "raw",
+    (
+        OBJECT_ID.encode("ascii") + b" blob 4\nab",
+        OBJECT_ID.encode("ascii") + b" blob 3\nabc",
+    ),
+)
+def test_batch_parser_rejects_truncated_payload_or_delimiter(raw: bytes) -> None:
+    with pytest.raises(release_tree.ReleaseTreeError, match="payload is truncated"):
+        release_tree._parse_git_batch_output(raw, (_entry(),))
+
+
+def test_batch_parser_rejects_missing_object() -> None:
+    raw = OBJECT_ID.encode("ascii") + b" missing\n"
+    with pytest.raises(release_tree.ReleaseTreeError, match="object is missing"):
+        release_tree._parse_git_batch_output(raw, (_entry(),))
+
+
+def test_batch_parser_rejects_wrong_object_type() -> None:
+    with pytest.raises(release_tree.ReleaseTreeError, match="object is not a blob"):
+        release_tree._parse_git_batch_output(_batch(b"tree", b"abc"), (_entry(),))
+
+
+def _git(root: Path, *arguments: str, binary: bool = False) -> bytes | str:
+    completed = subprocess.run(
+        ["git", *arguments],
+        cwd=root,
+        check=True,
+        capture_output=True,
+        text=not binary,
+    )
+    return completed.stdout
+
+
+def test_batch_snapshot_and_manifest_match_individual_index_blob_reads(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    (tmp_path / "nested").mkdir()
+    (tmp_path / "RELEASE_FILES.sha256").write_bytes(release_tree.MANIFEST_HEADER)
+    (tmp_path / "alpha.txt").write_bytes(b"alpha\n")
+    (tmp_path / "empty.bin").write_bytes(b"")
+    (tmp_path / "nested" / "binary.bin").write_bytes(b"\x00payload\n\xff")
+    _git(tmp_path, "init", "--quiet")
+    _git(tmp_path, "add", "--all")
+
+    entries = release_tree.git_index_entries(tmp_path)
+    expected = {
+        entry.path: _git(tmp_path, "cat-file", "blob", entry.object_id, binary=True)
+        for entry in entries
+    }
+    assert release_tree.git_index_blobs(tmp_path, entries) == expected
+
+    calls: list[tuple[str, ...]] = []
+    original_run = subprocess.run
+
+    def recording_run(*args: object, **kwargs: object) -> subprocess.CompletedProcess[bytes]:
+        command = tuple(args[0])  # type: ignore[arg-type]
+        calls.append(command)
+        return original_run(*args, **kwargs)  # type: ignore[call-overload]
+
+    monkeypatch.setattr(release_tree.subprocess, "run", recording_run)
+    monkeypatch.setattr(release_manifest, "ROOT", tmp_path)
+    rendered = release_manifest.render_manifest()
+    tracked = tuple(entry for entry in entries if entry.path != release_tree.MANIFEST_RELATIVE)
+    rows = "\n".join(
+        f"{hashlib.sha256(expected[entry.path]).hexdigest()}\t"
+        f"{len(expected[entry.path])}\t{entry.path.as_posix()}"
+        for entry in tracked
+    )
+    assert rendered == (
+        release_tree.MANIFEST_HEADER.decode("ascii") + rows + "\n"
+    ).encode("utf-8")
+    assert calls.count(("git", "cat-file", "--batch")) == 1
+
+    tree = release_tree.resolve_release_tree(tmp_path)
+    (tmp_path / "alpha.txt").write_bytes(b"unstaged replacement\n")
+    assert tree.read_bytes(PurePosixPath("alpha.txt")) == b"alpha\n"
+
+
+def test_batch_read_rejects_index_change_during_snapshot(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    (tmp_path / "RELEASE_FILES.sha256").write_bytes(release_tree.MANIFEST_HEADER)
+    payload = tmp_path / "payload.txt"
+    payload.write_bytes(b"first index state\n")
+    _git(tmp_path, "init", "--quiet")
+    _git(tmp_path, "add", "--all")
+    entries = release_tree.git_index_entries(tmp_path)
+
+    original_run = subprocess.run
+
+    def changing_run(*args: object, **kwargs: object) -> subprocess.CompletedProcess[bytes]:
+        command = tuple(args[0])  # type: ignore[arg-type]
+        completed = original_run(*args, **kwargs)  # type: ignore[call-overload]
+        if command == ("git", "cat-file", "--batch"):
+            payload.write_bytes(b"second index state\n")
+            original_run(
+                ["git", "add", "payload.txt"],
+                cwd=tmp_path,
+                check=True,
+                capture_output=True,
+            )
+        return completed
+
+    monkeypatch.setattr(release_tree.subprocess, "run", changing_run)
+    with pytest.raises(release_tree.ReleaseTreeError, match="index changed"):
+        release_tree.git_index_blobs(tmp_path, entries)

From 04ee07c5cd0a66951d0fb2497759331d2aeacc33 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 00:13:36 -0500
Subject: [PATCH 03/95] fix: preserve canonical Morlet kernels across verified
 Linux runtime

---
 README.md                                  |   3 +-
 RELEASE_FILES.sha256                       |  20 ++-
 STATUS.md                                  |  27 ++-
 docs/MORLET_PORTABILITY.md                 |  56 ++++++
 pyproject.toml                             |   6 +-
 scripts/export_morlet_oracles.py           | 123 ++++++++++++++
 scripts/probe_morlet_portability.py        | 118 +++++++++++++
 src/phasepair_core/_morlet_oracle_bytes.py | 138 +++++++++++++++
 src/phasepair_core/signal.py               | 184 ++++++++++++++++++--
 src/phaseset_core/_morlet_oracle_bytes.py  | 101 +++++++++++
 src/phaseset_core/morlet.py                | 180 ++++++++++++++++++--
 tests/test_morlet_portability.py           | 187 +++++++++++++++++++++
 tests/test_phasepair_core_signal.py        |  50 ++++--
 tests/test_phaseset_morlet.py              |  17 +-
 14 files changed, 1146 insertions(+), 64 deletions(-)
 create mode 100644 docs/MORLET_PORTABILITY.md
 create mode 100644 scripts/export_morlet_oracles.py
 create mode 100644 scripts/probe_morlet_portability.py
 create mode 100644 src/phasepair_core/_morlet_oracle_bytes.py
 create mode 100644 src/phaseset_core/_morlet_oracle_bytes.py
 create mode 100644 tests/test_morlet_portability.py

diff --git a/README.md b/README.md
index 011c8c0..1b85f00 100644
--- a/README.md
+++ b/README.md
@@ -37,7 +37,8 @@ multi-person skeleton sequence
   an independently hashed 20-Hz formula bank after resampling. This is a
   compatibility boundary, not a claim that the two kernel banks or full models
   are bitwise identical. See the
-  [20-Hz rate contract](docs/PHASESET_20HZ_MORLET_CONTRACT.md).
+  [20-Hz rate contract](docs/PHASESET_20HZ_MORLET_CONTRACT.md) and
+  [canonical-byte portability policy](docs/MORLET_PORTABILITY.md).
 
 ## Current milestone
 
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 394950e..d48934c 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -8,10 +8,10 @@ e9938bfe4bbd1ee2357396d1a6a8788e511389e297c28d7247d9e16a9a8137fc	392	.gitignore
 a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTING.md
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-d44f41127431cee3838a5fb9a6b7b903ccc06a050609c7f843a60da17cf90f97	9139	README.md
+fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-db0ecc48b52d4fa922d873af9e142e8bba529f40a744c232b5fdeaab4a5e2281	9481	STATUS.md
+428adbd5997dd33ca00cb160bcd550108bc8031a4fcf92616686e02395c26d42	10344	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
@@ -20,6 +20,7 @@ e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA
 3633e37c3eae3289d552a363e565bee027ebd83661f1c44ea136f9498de9e5af	19059	docs/EXECUTION_PROTOCOL.md
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
+0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
 69b5e67cbb59c9c658e5feb11d3f6c01afbc5e2d26a1acd2d6dba8f81f9e67ff	4296	docs/PHASEPAIR_B0_ANNOTATION_CAPTION_CLOSURE_SUCCESSOR_PUBLIC_V4_20260825_145013.json
@@ -64,12 +65,14 @@ c1dc6c3081e80085dc950457c3324f4780b8237adb278494a9a2298e2c536129	3763	paper/READ
 d1b340ef39baf6ec2c16fd29f079eb550d7f68e4ac389cd5aed4137cd13710f2	497508	paper/main.pdf
 17a201a532798a30805f74f3abcafb4e811e02371f5f018a8bd3894b0c80c7f9	21347	paper/main.tex
 7db5537b9c85545e3fff89b29e17438936f0bcfb3e7d1ee2cd8de24f4de1a279	8419	paper/references.bib
-530dbfa412faec88a72753876d28770466a249cf550162bc0f5c6758a710d8f3	1315	pyproject.toml
+a5b896aed4121191b56294a34d40fa25fcb70238f9d9596a6c12ad7d5ebc6b03	1317	pyproject.toml
 a54cab7df5f76e32387eba55757a4bd8fa18d9f2cdc8e5b0c6725ceb00af1e78	9580	scripts/audit_all_refs.py
 b4184b377fc1dbde1bf4a14ff002c5b13be09dbe6a545cee30eb878ee017fdd3	13507	scripts/audit_release_artifact.py
 f6447b2870b24c657e3ca6f18d7330834826b6e5ec284392d94a82d25f5b180e	4422	scripts/build_paper.py
 ffb5b9e798fac11d7ec0f9cb13f1fe9ffd71dc197361795dd1a2c043d37d8080	3168	scripts/check_local_links.py
+e8f61209a8be7bdae537b5d21e0108f6c6e6bbec0ab932b957703c3dee011ba3	4232	scripts/export_morlet_oracles.py
 4a3396142a7e012143ccac8e2df4c45bee39778f1565a8f1a395e3b8e7fab6b7	3286	scripts/fetch_icassp2027_template.py
+cc60aca15d425b1f684bf9555ad127bd37d0072d0967839f00e2efc41fb26ac2	4460	scripts/probe_morlet_portability.py
 58c33c281a02a928556d0a25d87bdb0c893e99667d40d2c1c0326d2a899b0137	38002	scripts/public_release_audit.py
 79e3e6160ef210fa03f1c5d8a3eb17c9298ba4b38e910d93e609ce41c1d27ec7	32001	scripts/release_asset_inventory.py
 8c99175b3f115ba9e8d63870988a6ed3d7cf8009d272b5c5daaefb85887ca495	2950	scripts/release_manifest.py
@@ -78,6 +81,7 @@ ffb5b9e798fac11d7ec0f9cb13f1fe9ffd71dc197361795dd1a2c043d37d8080	3168	scripts/ch
 7d0eb6a71f0c0964a653c2b536ba232728885a9a526186835ba8896137c4ea11	30359	slides/PHASEPAIR_EDITABLE_PAPER_FIGURES_DATA_FREE_DRAFT.pptx
 5ebe1cd2d83702888f77c642b8537d3544580e05fc73bf2e6baab9253e1f4dc6	464	src/phasepair_core/__init__.py
 c01ce63e8b55b4b58b14366cf971fe7683f627ea285a2dd22a140a3230b8bbf8	3025	src/phasepair_core/_identity_registry.py
+deeadb80176998bc0c40eae598a8e4d292b8fac57867a900b6281bf5302db2a1	12186	src/phasepair_core/_morlet_oracle_bytes.py
 a74cdf1587509da10f5e787027e441cc810d1ab2efd3d7e062f58ed86157b9c7	17459	src/phasepair_core/base_qualification.py
 3221b572ef4dc2edae832cf26e05f07c2b69eb2e1d4808bec5f61e0c3c3e69a6	19672	src/phasepair_core/batching.py
 13a002c6146c3307639c84c6aba439dd0b3fbf216caea5ef49b06918b221f210	22962	src/phasepair_core/bootstrap.py
@@ -98,7 +102,7 @@ cadc323c74c8f12467cdb64fbb2214f2012d23a8911eb803ee170c56ba088329	60050	src/phase
 c66b76b92d9b0b2a5747e0571a67f848e0dea9d7de99f16c70428e9df8b1818e	25036	src/phasepair_core/runtime_dropout.py
 2957ecffb82d8e347d1bc37b41d902cd64858071bedaf51c719efb949cf4e0cc	10549	src/phasepair_core/sampler.py
 9d4b27d08f04b9a4fa23b69c1594ff00945b11d6ab23c4efc8760a347db87806	17567	src/phasepair_core/sampler_manifest.py
-135fa34813d27f14ceda731bf1515acbc42c496284fb3bfce4fd1f1d07c3fdbd	40708	src/phasepair_core/signal.py
+1db8b593c0e5987c26c866a5d6f91a03b8574a0d298330460bf0dcf8c1ed1c82	46827	src/phasepair_core/signal.py
 6d638065aa167f693cab53b074f385d8568b2baf4c84e9e9c75f6a486f54b988	25113	src/phasepair_core/statistical_reporting.py
 2fbb5f4ece73f97037ac77dcfa75722f5ebf0f6d864412061218ebcf869a0585	26606	src/phasepair_core/text_runtime.py
 067161d15b8f247e555df601a2b2f7221fb10d03b91997e3a40e06d719f877a7	42561	src/phasepair_core/torch_models.py
@@ -106,6 +110,7 @@ c66b76b92d9b0b2a5747e0571a67f848e0dea9d7de99f16c70428e9df8b1818e	25036	src/phase
 fa68bcab33d09588a8653151ad8e5741d5e6530263bcb633d871d7744598b194	10364	src/phasepair_core/training_batch.py
 dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phasepair_core/validation_gallery.py
 2e5fb5280ed27a46c587d07a60ce663fce676cbdd30b059ec34882b4507d157f	604	src/phaseset_core/__init__.py
+9668a89535264a3ed6b58ffc7ac31b4904a4d8f68636d405e15cbf7368096716	8437	src/phaseset_core/_morlet_oracle_bytes.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
 0ddb4070d6af6a3221afda66aef84261255af3008ca8da0a2cc3b471d9b682fa	15837	src/phaseset_core/cli.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
@@ -116,7 +121,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
 47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
-98f6c1ecbd29398c201b9595cea7f792e5f2a370ade124d01b24f19506bb4261	7640	src/phaseset_core/morlet.py
+57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phaseset_core/pipeline.py
@@ -129,6 +134,7 @@ eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phase
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 e5de1fee169341f56721b198d0708fd21f56bd26d2771fad2ea88bb03bbb2d94	153786	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
+d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 af9b5adc42d82b9a0aa4ed744bb069e36b7dd041c1957fdc97d17eb814a0fc0c	17284	tests/test_phasepair_core_base_qualification.py
 ad630675cc642350a988d92de6176053d1bfab5f81cbc49548cb0b5dbeb679d9	23596	tests/test_phasepair_core_batching_readiness.py
 3b4b3654d38d9b99983e924d838b7236b6d2e8b27554298762be1e8c4fcd00c4	10736	tests/test_phasepair_core_caption_processing.py
@@ -147,7 +153,7 @@ d834b7bd9dbb40f42e5f1b977a120493f028b5c77309434282c997eae8ee5d6e	333	tests/test_
 b950ab7ab0f02c31e4c645a752c549312eb0ed2022f113feb87784aa8ccfffe4	24468	tests/test_phasepair_core_runtime_dropout.py
 fd7aae8ba8fafffc7809eb8a6c0873ae7c93f345127d5d32a8d2776cf5a17d98	7564	tests/test_phasepair_core_sampler.py
 09648239fc50127698cc007e4335de0dfcc257ac789339b773a7acf4d512a818	19014	tests/test_phasepair_core_sampler_manifest.py
-561cc6b8b79802d34a9f0c2f1fa3561080c8a3ff8e13d8b5b589fc86c11abad7	32382	tests/test_phasepair_core_signal.py
+5ba6dab4543d61a0beeddd55a799fe2ccbdc3f816214fd4e549cd89b75bfdd24	33280	tests/test_phasepair_core_signal.py
 afb529c3a4e333a9159f196e877deeb8075381502949964fa355fb5e17b957bb	16079	tests/test_phasepair_core_statistical_reporting.py
 08dc2ec64b422542deb5383eae67afbc0dbedb845a4ce44fe7a6e5953d153b54	24793	tests/test_phasepair_core_text_runtime.py
 b801d46fb184d99c65825d7f9bf91cbced022418600a95d421c5d9244dbded13	19373	tests/test_phasepair_core_torch_models.py
@@ -166,7 +172,7 @@ a17a50e801c389b4e24b2840f2aafcb7b476cbef86e2e142ad0af6245a54718f	4515	tests/test
 aea41df712a25be78841e655a6fbd91fc96217ce92e60d3ab842f185fc39a58c	41553	tests/test_phaseset_execution.py
 dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/test_phaseset_experiments.py
 a75f583fd0a233760b1a421ca51680afe4195c33f9ef4bce6e9a1737828f207e	14907	tests/test_phaseset_models.py
-ec4cdc33ea31a0d4a2d4b0f3a529f69e458f1a9b1af9c991c8e78511d49d7131	4925	tests/test_phaseset_morlet.py
+e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test_phaseset_morlet.py
 6d7005585882e25be9479542aac96a5b2aeb1bddc5619a0f37c57f61f2cbe6c3	6348	tests/test_phaseset_objectives.py
 52ccd9d4ea8351d5aaaa82eb99d84c6bd240c8b6147a506fd65dbca3d1394331	11547	tests/test_phaseset_periodic.py
 6ebbf2ece33ff2390036e5ce55dcd34a0459211b79922219971c27bee37844a0	10278	tests/test_phaseset_pipeline.py
diff --git a/STATUS.md b/STATUS.md
index 6d8bd37..cc60fab 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -15,17 +15,28 @@ evidence, not training results or scientific claims.
 
 - Strict known-host SSH login to the registered host now succeeds. The initial
   read-only inventory is complete. Existing workloads were not interrupted.
-- A separate data-volume Python environment is being built; CUDA libraries
-  are still downloading. No GPU qualification or real training has passed.
+- A separate data-volume Python 3.12.12 / NumPy 2.4.6 / Torch 2.12.0+cu126
+  environment is installed with a clean dependency check. The complete Linux
+  CPU regression passed: 900 tests, 1 skipped, and 39 subtests, in 311.13 s.
+  This includes synthetic model/lifecycle checks, not main-data training.
+- A separately bounded shared-GPU functionality probe passed FP64 forward and
+  FP32 forward/backward using 8x8 tensors. It did not stop existing workloads
+  and does not qualify BF16, model execution, performance, or the full runtime.
 - The native Embody loader and concrete licensed SMPL-X body-22 evaluator are
-  implemented. Six adapter contract tests passed on Linux/Python 3.12.12/
+  implemented. Seven adapter contract tests passed on Linux/Python 3.12.12/
   NumPy 2.4.6; these use explicit fixtures, not licensed assets or main data.
 - Neither Embody approval nor a licensed neutral model has been observed.
-  Open-license Multi-TPC acquisition is underway only for the registered
-  supplementary conversation task, not as replacement confirmatory data.
-- An actual Linux probe found last-bit formula-replay drift in both Morlet
-  banks. The original frozen kernel digests remain unchanged; portability is
-  being repaired and must pass server-side numerical tests before training.
+  Open-license Multi-TPC acquisition completed with its official byte count and
+  MD5 verified. Its archive inventory has 322 files; format conversion remains
+  under audit. It is supplementary conversation data, never replacement
+  confirmatory data. The pinned CLIP snapshot's eight files also match the
+  previously acquired official SHA-256 values; model qualification is separate.
+- Native Linux last-bit Morlet formula drift is repaired using strictly
+  verified canonical coefficient bytes, while preserving both original hashes.
+  Windows still requires its original exact formula digest; Linux requires its
+  measured exact native formula fingerprint plus a bounded tap comparison.
+  See [Morlet portability](docs/MORLET_PORTABILITY.md). POSIX checkpoint cleanup
+  also now pins its inode so a concurrently replaced file is not deleted.
 - The current no-result manuscript now builds using the hash-pinned official
   ICASSP 2027 template: five pages, technical content through page four,
   references-only page five, all fonts embedded, no unresolved citations or
diff --git a/docs/MORLET_PORTABILITY.md b/docs/MORLET_PORTABILITY.md
new file mode 100644
index 0000000..2e8dc5f
--- /dev/null
+++ b/docs/MORLET_PORTABILITY.md
@@ -0,0 +1,56 @@
+# Frozen Morlet bytes and native formula verification
+
+The physical 30 Hz PhasePair and 20 Hz PhaseSet kernels are immutable serialized
+coefficients, not learned weights. Their original SHA-256 values remain:
+
+- PhasePair: `4f75ab687d333af8b436feea371dbaee56c55256e91c42c5e1ce45935beed5d7`.
+- PhaseSet: `633d754bba12dab0555722f4b8963409a110c8681796cc907d163096e2e42655`.
+
+The generated Python byte modules contain the original big-endian float64
+serialization. The loader validates provenance, digest, support lengths,
+finiteness and trailing bytes, then returns read-only canonical kernels.
+The build-only exporter preserves the original CPython 3.14.5 / NumPy 2.4.6 /
+Windows provenance. It neither trains a model nor creates experimental results.
+
+## Runtime policy
+
+The package pins NumPy 2.4.6. Within the advertised CPython 3.12--3.14 range,
+x86-64 Windows and Linux select different strict native-formula checks:
+
+- Windows must reproduce the original exact serialized digest and original
+  residual checks. Even a one-ULP within-tolerance mutation is rejected.
+- Linux must reproduce the observed native-formula digest below and remain
+  within `2**-52` maximum complex-tap distance from the canonical bank.
+- Other interpreter families, platforms, architectures, or NumPy versions are
+  not qualified by this implementation. Unrecognized formula bytes fail closed.
+
+Matching the exact formula fingerprint is required; an arbitrary Python patch
+number is not a rejection reason. This does not declare every accepted machine
+fully qualified for training. Each actual run still needs its own frozen
+environment and numerical/model/CUDA qualification records.
+
+Measured verifier lanes so far are Windows Python 3.12.0 and 3.14.5, and the
+Linux Python 3.12.12 server, all with NumPy 2.4.6. Simulated identity tests only
+check dispatch and do not count as execution on other interpreters.
+
+Linux native-formula fingerprints:
+
+- PhasePair: `3b4cec820d8202d4576d829d37626acc2414f0aeecf430e17c751d9bd1731f6c`.
+- PhaseSet: `926c06aa57a27139d1ed1a9e0d9c3da191717b8a9495d6fff900bda44459d9a3`.
+
+The measured maximum complex-tap differences are respectively
+`6.206335383118183e-17` and `3.1031676915590914e-17`, both below `2**-52`.
+The native diagnostic's DC/energy rounding residuals need not equal the
+Windows construction receipt. Physical output is always the original canonical
+bank; its original DC and unit-energy guarantees are preserved and tested.
+
+## Regression boundaries
+
+The unchanged five-reduction construction spy, 13D relation descriptor,
+endpoint-swap algebra and known-delay checks remain. Separate tests reject
+artifact mutation, Windows one-ULP drift, unqualified hardware and the native
+left-to-right reduction mutant. The latter must fail the Linux formula digest
+gate even when its tap distance alone is within tolerance.
+
+These checks qualify kernel software behavior, not empirical retrieval quality,
+BF16 precision, GPU performance, licensed data, or a completed scientific study.
diff --git a/pyproject.toml b/pyproject.toml
index a04eba4..e4bcfbf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,7 @@ requires-python = ">=3.12,<3.15"
 license = "MIT"
 authors = [{ name = "PhaseSet contributors" }]
 dependencies = [
-  "numpy>=1.26,<3",
+  "numpy==2.4.6",
   "torch>=2.10,<2.13",
 ]
 
@@ -23,8 +23,8 @@ Repository = "https://github.com/appleweiping/phaseset-multiperson-motion-langua
 Documentation = "https://github.com/appleweiping/phaseset-multiperson-motion-language/tree/main/docs"
 
 [project.optional-dependencies]
-# The exact signal oracle is presently qualified on Windows x86-64 only.
-# Linux installs remain useful for audits/builds but are not an oracle PASS.
+# Canonical bytes are shared by the strict Windows and Linux formula paths.
+# A matching formula bank alone is not full model/CUDA runtime qualification.
 signal = [
   "numpy==2.4.6",
   "torch==2.12.0",
diff --git a/scripts/export_morlet_oracles.py b/scripts/export_morlet_oracles.py
new file mode 100644
index 0000000..a35873d
--- /dev/null
+++ b/scripts/export_morlet_oracles.py
@@ -0,0 +1,123 @@
+"""Export the two frozen Morlet formula banks from the registered Windows oracle.
+
+This build-only script imports the unchanged public baseline, replays its
+diagnostic formulas, verifies the already-frozen bank digests, and writes only
+Python modules containing the canonical serialized bytes and source-runtime
+provenance.  It consumes no model, dataset, or experiment input.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import importlib
+from pathlib import Path
+import platform
+import struct
+import sys
+from types import ModuleType
+
+import numpy as np
+
+
+EXPECTED_PYTHON = (3, 14, 5)
+EXPECTED_NUMPY = "2.4.6"
+EXPECTED_PLATFORM = "Windows"
+EXPECTED_DIGESTS = {
+    "phasepair_core.signal": "4f75ab687d333af8b436feea371dbaee56c55256e91c42c5e1ce45935beed5d7",
+    "phaseset_core.morlet": "633d754bba12dab0555722f4b8963409a110c8681796cc907d163096e2e42655",
+}
+OUTPUTS = {
+    "phasepair_core.signal": ("phasepair_core", "_morlet_oracle_bytes.py"),
+    "phaseset_core.morlet": ("phaseset_core", "_morlet_oracle_bytes.py"),
+}
+
+
+def _serialize(module: ModuleType) -> bytes:
+    bands = module.morlet_formula_bank_diagnostic()
+    output = bytearray()
+    for band in bands:
+        output.extend(struct.pack(">H", band.length))
+        for value in band.kernel:
+            output.extend(struct.pack(">dd", float(value.real), float(value.imag)))
+    return bytes(output)
+
+
+def _render(*, module_name: str, raw: bytes, digest: str) -> str:
+    wrapped = "\n".join(
+        f'    "{raw.hex()[offset:offset + 96]}"'
+        for offset in range(0, len(raw.hex()), 96)
+    )
+    return f'''"""Generated frozen Morlet bytes; do not hand-edit.
+
+Source formula module: {module_name}
+Exporter runtime: CPython 3.14.5 / NumPy 2.4.6 / Windows
+Canonical serialized SHA-256: {digest}
+"""
+
+from __future__ import annotations
+
+from typing import Final
+
+
+ORACLE_EXPORT_SCHEMA: Final = "portable-morlet-oracle-bytes-v1"
+ORACLE_EXPORT_RUNTIME: Final = "CPython 3.14.5 / NumPy 2.4.6 / Windows"
+ORACLE_EXPORT_FORMULA_MODULE: Final = "{module_name}"
+ORACLE_EXPORT_SHA256: Final = "{digest}"
+CANONICAL_BANK_BYTES: Final = bytes.fromhex(
+{wrapped}
+)
+
+
+__all__ = [
+    "CANONICAL_BANK_BYTES",
+    "ORACLE_EXPORT_FORMULA_MODULE",
+    "ORACLE_EXPORT_RUNTIME",
+    "ORACLE_EXPORT_SCHEMA",
+    "ORACLE_EXPORT_SHA256",
+]
+'''
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--source-root", type=Path, required=True)
+    parser.add_argument("--output-root", type=Path, required=True)
+    args = parser.parse_args()
+
+    if sys.version_info[:3] != EXPECTED_PYTHON:
+        raise SystemExit(f"wrong Python oracle: {sys.version}")
+    if np.__version__ != EXPECTED_NUMPY:
+        raise SystemExit(f"wrong NumPy oracle: {np.__version__}")
+    if platform.system() != EXPECTED_PLATFORM:
+        raise SystemExit(f"wrong platform oracle: {platform.system()}")
+    source_root = args.source_root.resolve(strict=True)
+    output_root = args.output_root.resolve(strict=True)
+    sys.path.insert(0, str(source_root / "src"))
+
+    for module_name, expected_digest in EXPECTED_DIGESTS.items():
+        module = importlib.import_module(module_name)
+        module_file = Path(module.__file__).resolve(strict=True)
+        if source_root not in module_file.parents:
+            raise SystemExit(f"module escaped baseline source root: {module_name}")
+        raw = _serialize(module)
+        digest = hashlib.sha256(raw).hexdigest()
+        if digest != expected_digest or module.morlet_bank_sha256(
+            module.morlet_formula_bank_diagnostic()
+        ) != expected_digest:
+            raise SystemExit(f"frozen formula mismatch: {module_name} {digest}")
+        package, filename = OUTPUTS[module_name]
+        destination = output_root / "src" / package / filename
+        if not destination.parent.is_dir():
+            raise SystemExit(f"missing staging package directory: {destination.parent}")
+        destination.write_text(
+            _render(module_name=module_name, raw=raw, digest=digest),
+            encoding="ascii",
+            newline="\n",
+        )
+        print(f"EXPORTED {module_name} bytes={len(raw)} sha256={digest}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/probe_morlet_portability.py b/scripts/probe_morlet_portability.py
new file mode 100644
index 0000000..842d4e8
--- /dev/null
+++ b/scripts/probe_morlet_portability.py
@@ -0,0 +1,118 @@
+"""Bounded, NumPy-only cross-runtime probe for the two Morlet banks."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import importlib
+import importlib.util
+import json
+from pathlib import Path
+import struct
+import sys
+
+import numpy as np
+
+
+MODULES = (
+    ("phasepair_core.signal", "phasepair_core/_morlet_oracle_bytes.py"),
+    ("phaseset_core.morlet", "phaseset_core/_morlet_oracle_bytes.py"),
+)
+
+
+def _load_artifact(path: Path) -> bytes:
+    spec = importlib.util.spec_from_file_location("_private_morlet_artifact", path)
+    if spec is None or spec.loader is None:
+        raise RuntimeError("artifact import specification failed")
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module.CANONICAL_BANK_BYTES
+
+
+def _parse(raw: bytes, lengths: tuple[int, ...]) -> tuple[np.ndarray, ...]:
+    cursor = 0
+    kernels = []
+    for expected_length in lengths:
+        (observed_length,) = struct.unpack_from(">H", raw, cursor)
+        cursor += 2
+        if observed_length != expected_length:
+            raise RuntimeError("artifact support mismatch")
+        values = np.frombuffer(raw, dtype=">f8", count=2 * expected_length, offset=cursor)
+        cursor += 16 * expected_length
+        pairs = values.reshape(expected_length, 2)
+        kernel = np.empty(expected_length, dtype=np.complex128)
+        kernel.real = pairs[:, 0]
+        kernel.imag = pairs[:, 1]
+        kernels.append(kernel)
+    if cursor != len(raw):
+        raise RuntimeError("artifact trailing bytes")
+    return tuple(kernels)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--source-root", type=Path, required=True)
+    parser.add_argument("--artifact-root", type=Path, required=True)
+    args = parser.parse_args()
+    source_root = args.source_root.resolve(strict=True)
+    artifact_root = args.artifact_root.resolve(strict=True)
+    sys.path.insert(0, str(source_root / "src"))
+    observations = []
+    for module_name, relative_artifact in MODULES:
+        module = importlib.import_module(module_name)
+        if module_name == "phasepair_core.signal":
+            formula_receipt = module._build_morlet_formula_bank()
+        else:
+            formula_receipt = module._build_formula_bank()
+        formula = formula_receipt.bands
+        raw = _load_artifact(artifact_root / relative_artifact)
+        canonical = _parse(raw, module.MORLET_LENGTHS)
+        formula_sha = module.morlet_bank_sha256(formula)
+        differences = [
+            np.abs(band.kernel - kernel) for band, kernel in zip(formula, canonical, strict=True)
+        ]
+        observations.append(
+            {
+                "module": module_name,
+                "python": sys.version.split()[0],
+                "numpy": np.__version__,
+                "artifact_sha256": hashlib.sha256(raw).hexdigest(),
+                "formula_sha256": formula_sha,
+                "max_complex_abs_error": max(float(np.max(value)) for value in differences),
+                "max_component_abs_error": max(
+                    max(
+                        float(np.max(np.abs(band.kernel.real - kernel.real))),
+                        float(np.max(np.abs(band.kernel.imag - kernel.imag))),
+                    )
+                    for band, kernel in zip(formula, canonical, strict=True)
+                ),
+                "formula_zero_dc_errors": [
+                    float(value) for value in formula_receipt.zero_dc_errors
+                ],
+                "formula_unit_energy_errors": [
+                    float(value) for value in formula_receipt.unit_energy_errors
+                ],
+                "canonical_zero_dc_errors": [
+                    float(np.abs(np.sum(kernel, dtype=np.complex128)))
+                    for kernel in canonical
+                ],
+                "canonical_unit_energy_errors": [
+                    float(
+                        np.abs(
+                            np.sum(
+                                np.square(np.abs(kernel), dtype=np.float64),
+                                dtype=np.float64,
+                            )
+                            - np.float64(1.0)
+                        )
+                    )
+                    for kernel in canonical
+                ],
+            }
+        )
+    print(json.dumps(observations, sort_keys=True, separators=(",", ":")))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/phasepair_core/_morlet_oracle_bytes.py b/src/phasepair_core/_morlet_oracle_bytes.py
new file mode 100644
index 0000000..7014809
--- /dev/null
+++ b/src/phasepair_core/_morlet_oracle_bytes.py
@@ -0,0 +1,138 @@
+"""Generated frozen Morlet bytes; do not hand-edit.
+
+Source formula module: phasepair_core.signal
+Exporter runtime: CPython 3.14.5 / NumPy 2.4.6 / Windows
+Canonical serialized SHA-256: 4f75ab687d333af8b436feea371dbaee56c55256e91c42c5e1ce45935beed5d7
+"""
+
+from __future__ import annotations
+
+from typing import Final
+
+
+ORACLE_EXPORT_SCHEMA: Final = "portable-morlet-oracle-bytes-v1"
+ORACLE_EXPORT_RUNTIME: Final = "CPython 3.14.5 / NumPy 2.4.6 / Windows"
+ORACLE_EXPORT_FORMULA_MODULE: Final = "phasepair_core.signal"
+ORACLE_EXPORT_SHA256: Final = "4f75ab687d333af8b436feea371dbaee56c55256e91c42c5e1ce45935beed5d7"
+CANONICAL_BANK_BYTES: Final = bytes.fromhex(
+    "0078bf6093072bf7e6d13f24b0c3aeb73183bf62bd59b2ea393a3f41d628c4f891c9bf6497cf9ec8d2c93f50e69d38a4"
+    "5f7ebf65eecff02123b23f5a9c17d13b8129bf6686f72dad07533f6305ccc67de421bf661e625b738fed3f698e423433"
+    "ecdbbf646eb65f791faa3f70660d1fe13d1fbf612ff2b29709e73f744946630afa91bf583828cf571c7a3f7850a973aa"
+    "c548bf43ce0858a5b63b3f7c51a1ba1d37363f41f756912c62873f800b745a0f7a0e3f605ad201d0dcae3f81b06a321c"
+    "1fa83f6eaf22f74860e93f82f3284f96f0603f77b4a1b4ffdbc33f83ab4cfceeb85c3f8095315944aa063f83ade4d009"
+    "83db3f85c55629cda09f3f82cf1ba9e52a163f8b48bf5681a9233f80e45536f5d1ab3f9078fb11c905c73f7b8d445e26"
+    "ac843f9344039bac68a33f72aaff77b1abf93f95e38dc776e8b43f5bce32ae06b4fa3f9830e27d57c880bf5ebad34c51"
+    "3e283f9a0185cd4fdd59bf7932f8f16d4cc03f9b28a037567788bf86b66f8b19239f3f9b78c6e9fa0e64bf9102118a06"
+    "a43b3f9ac61915e1e292bf9721a09c3ac5553f98e8a1c6d0a9c5bf9d8f55ae6347a73f95bee70987d724bfa20b00c7f8"
+    "f80b3f913088f3fa032fbfa53b5847dfe2403f86619bcb422a1fbfa835137d71e4f63f6e07ac1f043b6fbfaad1807619"
+    "68d9bf743757ed1f1e88bface837304547e6bf8e3230ae034607bfae50bd3d34d4f2bf9a2c4ae484339bbfaee4614d69"
+    "7ebabfa300798df4ba2abfae803c0d5ba02fbfa91fcfe65442c4bfad073f58160c54bfaf40ed1f3048d7bfaa64394bd4"
+    "ff46bfb2954304d22672bfa68bae8ce174a5bfb54f933c5353d3bfa17d6e4356ce96bfb7afbf212b2397bf968b86aa52"
+    "9279bfb9967fcdebbc03bf7ff14910425902bfbae659bb40734b3f80ca459afd5335bfbb85118536fb343f9a318e7c26"
+    "dac5bfbb5d18de42e81a3fa6752284d8011ebfba5ee0365ee7173faffe68099051a9bfb881f99e540da73fb4b1ef1f07"
+    "0830bfb5c5fa97749e5e3fb9281db3c0faa5bfb2330cd12fd4f83fbd367efe62eeb7bfabb4427cf4bdc63fc059829c15"
+    "36d5bfa1a97a905df12d3fc1bb58e6943aaebf8a22f7c11447173fc2afb3c521e2c43f85742bdeed2bd93fc328cd5ea1"
+    "07ff3fa1abcd62e823743fc31ce3f63ae76d3fadffb02900f3203fc286cded466a8a3fb4f87a5abec54c3fc1665d757f"
+    "e0313fba8c13fac5fe163fbf811a84ff08123fbf8938363789243fbb3ee488d4e1213fc1e170e5715ea83fb623e30092"
+    "d8cc3fc389332d60ea933fb056788dfbe3313fc4ac94904515163fa40860a751db973fc540fb43378bcf3f8affaea6de"
+    "f54e3fc540fb43378bcfbf8affaea6def54b3fc4ac9490451516bfa40860a751db953fc389332d60ea93bfb056788dfb"
+    "e3313fc1e170e5715ea8bfb623e30092d8cc3fbf893836378924bfbb3ee488d4e1213fba8c13fac5fe16bfbf811a84ff"
+    "08123fb4f87a5abec54cbfc1665d757fe0313fadffb02900f320bfc286cded466a8a3fa1abcd62e82374bfc31ce3f63a"
+    "e76d3f85742bdeed2bd9bfc328cd5ea107ffbf8a22f7c1144717bfc2afb3c521e2c4bfa1a97a905df12dbfc1bb58e694"
+    "3aaebfabb4427cf4bdc6bfc059829c1536d5bfb2330cd12fd4f8bfbd367efe62eeb7bfb5c5fa97749e5ebfb9281db3c0"
+    "faa5bfb881f99e540da7bfb4b1ef1f070830bfba5ee0365ee717bfaffe68099051a9bfbb5d18de42e81abfa6752284d8"
+    "011ebfbb85118536fb34bf9a318e7c26dac5bfbae659bb40734bbf80ca459afd5332bfb9967fcdebbc033f7ff1491042"
+    "5907bfb7afbf212b23973f968b86aa529279bfb54f933c5353d33fa17d6e4356ce96bfb2954304d226723fa68bae8ce1"
+    "74a5bfaf40ed1f3048d73faa64394bd4ff46bfa91fcfe65442c43fad073f58160c54bfa300798df4ba2a3fae803c0d5b"
+    "a02fbf9a2c4ae484339b3faee4614d697ebabf8e3230ae0346073fae50bd3d34d4f2bf743757ed1f1e883face8373045"
+    "47e63f6e07ac1f043b6f3faad180761968d93f86619bcb422a1f3fa835137d71e4f63f913088f3fa032f3fa53b5847df"
+    "e2403f95bee70987d7243fa20b00c7f8f80b3f98e8a1c6d0a9c53f9d8f55ae6347a73f9ac61915e1e2923f9721a09c3a"
+    "c5553f9b78c6e9fa0e643f9102118a06a43b3f9b28a0375677883f86b66f8b19239f3f9a0185cd4fdd593f7932f8f16d"
+    "4cc03f9830e27d57c8803f5ebad34c513e2b3f95e38dc776e8b4bf5bce32ae06b4f73f9344039bac68a3bf72aaff77b1"
+    "abf73f9078fb11c905c7bf7b8d445e26ac843f8b48bf5681a923bf80e45536f5d1ab3f85c55629cda09fbf82cf1ba9e5"
+    "2a163f8095315944aa06bf83ade4d00983db3f77b4a1b4ffdbc3bf83ab4cfceeb85c3f6eaf22f74860e9bf82f3284f96"
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+    "3d1fbf661e625b738fedbf698e423433ecdbbf6686f72dad0753bf6305ccc67de421bf65eecff02123b2bf5a9c17d13b"
+    "8129bf6497cf9ec8d2c9bf50e69d38a45f7ebf62bd59b2ea393abf41d628c4f891c8bf6093072bf7e6d1bf24b0c3aeb7"
+    "31800050bf64fc6e545b2b003f33b2d76b62ddebbf68b3c74dffad183f520f1fed772314bf6b2436a253885a3f61f2a5"
+    "4ae99cfbbf6b5915d1def0813f6d3608b5fa9e65bf6839207cdbef983f753fbf2ab3a469bf609d10f773c7ab3f7c8a4c"
+    "cac9f960bf3b8b088f19b7393f81f01839579af03f601d7eabab09633f8532b2a181df023f753780b050a8b23f877fbd"
+    "539d56543f82cdc2b78af4aa3f88311fb37d2cec3f8c5278e1ac0a013f8693be4e6f72b63f934e7f50a576383f81f90e"
+    "3bb8dcfd3f986cd97d5c0ead3f739b88a9d5382f3f9cf880d1845cd4bf525f35cc0b76c13fa024e5f1a5ff06bf8260da"
+    "f726750e3fa0d44375cc6c59bf930a036c6939133fa02e378a85210dbf9e45ba461b489c3f9bc2029d7b5266bfa519e9"
+    "054bad8e3f936206c6ad1cabbfaaf2357a171a993f7c258cfcb57464bfb00c9cb90d7c93bf8243442224efa6bfb1f5d5"
+    "7c2be471bf9c9005ba49ca9cbfb2e1b62a7a178ebfa921d07c5413d5bfb28452e86c99d5bfb23510d9c47d88bfb0a245"
+    "83fdc1d7bfb7a0711c2e0ec5bfaa33cbec9aebc5bfbc54c4e0abb9bdbf9facc88cf5f07cbfbfd027cdcc67c3bf73d3a1"
+    "a118d86cbfc0ccd69dfdde783f9a7fd5fd216e62bfc0a75f4a882be03fae6b4733f88835bfbeafc6019466633fb7ed3d"
+    "21c35e03bfb9aa937d37f6ab3fc00db41b5943fbbfb26105f1eb7a573fc387436af77948bfa255a4d67ab7163fc60faa"
+    "175a1fea3f76e3d4877ffc763fc760f285f304793fa96f358e5254233fc74a9122db5b373fb7e42a8235b2293fc5b76f"
+    "874e75003fc10f0b5f4f9bef3fc2b17b8140b6bb3fc54d37d8957e6e3fbcc462edb9fa043fc8563e3042283b3fb21fe0"
+    "5f0e90ba3fc9eb4a23f8de303f98c225e911c41b3fc9eb4a23f8de30bf98c225e911c41b3fc8563e3042283bbfb21fe0"
+    "5f0e90ba3fc54d37d8957e6ebfbcc462edb9fa043fc10f0b5f4f9befbfc2b17b8140b6bb3fb7e42a8235b229bfc5b76f"
+    "874e75003fa96f358e525423bfc74a9122db5b373f76e3d4877ffc76bfc760f285f30479bfa255a4d67ab716bfc60faa"
+    "175a1feabfb26105f1eb7a57bfc387436af77948bfb9aa937d37f6abbfc00db41b5943fbbfbeafc601946663bfb7ed3d"
+    "21c35e03bfc0a75f4a882be0bfae6b4733f88835bfc0ccd69dfdde78bf9a7fd5fd216e62bfbfd027cdcc67c33f73d3a1"
+    "a118d86cbfbc54c4e0abb9bd3f9facc88cf5f07cbfb7a0711c2e0ec53faa33cbec9aebc5bfb23510d9c47d883fb0a245"
+    "83fdc1d7bfa921d07c5413d53fb28452e86c99d5bf9c9005ba49ca9c3fb2e1b62a7a178ebf8243442224efa63fb1f5d5"
+    "7c2be4713f7c258cfcb574643fb00c9cb90d7c933f936206c6ad1cab3faaf2357a171a993f9bc2029d7b52663fa519e9"
+    "054bad8e3fa02e378a85210d3f9e45ba461b489c3fa0d44375cc6c593f930a036c6939133fa024e5f1a5ff063f8260da"
+    "f726750e3f9cf880d1845cd43f525f35cc0b76c13f986cd97d5c0eadbf739b88a9d5382f3f934e7f50a57638bf81f90e"
+    "3bb8dcfd3f8c5278e1ac0a01bf8693be4e6f72b63f82cdc2b78af4aabf88311fb37d2cec3f753780b050a8b2bf877fbd"
+    "539d56543f601d7eabab0963bf8532b2a181df02bf3b8b088f19b739bf81f01839579af0bf609d10f773c7abbf7c8a4c"
+    "cac9f960bf6839207cdbef98bf753fbf2ab3a469bf6b5915d1def081bf6d3608b5fa9e65bf6b2436a253885abf61f2a5"
+    "4ae99cfbbf68b3c74dffad18bf520f1fed772314bf64fc6e545b2b00bf33b2d76b62ddeb0036bf686d558ffed44f3f26"
+    "db7143b55609bf6f2d79c97a3d0a3f5b0730bca77706bf70df16e84f27253f70091b6a3a5381bf6be2b5b82175723f7c"
+    "349eb4db20c3bf51d8a83b3d1d483f84de8996a85c8d3f6b0d76e596f83a3f8ace327cf519eb3f845c5593c8e2773f8d"
+    "a5a53d5a24273f92e614fe1aa7cc3f8a9d5a49f96d773f9c64b1355285563f7de517e03488633fa257730ef6fadbbf6d"
+    "26143d5a478c3fa48b8230d7ca29bf941df2fbe6eaf03fa352fb6413597fbfa457ff2f67db073f9ac72513422e38bfaf"
+    "43107d5ecdcf3f7075e60a346b10bfb4553946b3958dbf9caedc9e58bda1bfb6fa1c3e724c67bfb11a3daf87d7abbfb6"
+    "48caaee04f02bfbb52eda6f774a7bfb151ba0834e93ebfc1f53bb2d100e3bf9f40d36f9a1ad5bfc468102ca02b113f96"
+    "6437a17ac7b4bfc41ff8323dc3d43fb54fd7c64f732dbfc09ceae83031523fc27678db243c2dbfb3dec97a2610cf3fc8"
+    "d094d12dedd1bf796e8c0125e1043fcc6210a09ab39a3fb31185feb6dac93fcc36e75417cf0d3fc36b21411bdb573fc7"
+    "fa3e7106fda03fcb2e3ceb717b293fc016350e0a97f73fcf72944f29afa53fa6a7de0eaf7f273fcf72944f29afa5bfa6"
+    "a7de0eaf7f273fcb2e3ceb717b29bfc016350e0a97f73fc36b21411bdb57bfc7fa3e7106fda03fb31185feb6dac9bfcc"
+    "36e75417cf0dbf796e8c0125e104bfcc6210a09ab39abfb3dec97a2610cfbfc8d094d12dedd1bfc09ceae8303152bfc2"
+    "7678db243c2dbfc41ff8323dc3d4bfb54fd7c64f732dbfc468102ca02b11bf966437a17ac7b4bfc1f53bb2d100e33f9f"
+    "40d36f9a1ad5bfbb52eda6f774a73fb151ba0834e93ebfb11a3daf87d7ab3fb648caaee04f02bf9caedc9e58bda13fb6"
+    "fa1c3e724c673f7075e60a346b103fb4553946b3958d3f9ac72513422e383faf43107d5ecdcf3fa352fb6413597f3fa4"
+    "57ff2f67db073fa48b8230d7ca293f941df2fbe6eaf03fa257730ef6fadb3f6d26143d5a478c3f9c64b135528556bf7d"
+    "e517e03488633f92e614fe1aa7ccbf8a9d5a49f96d773f845c5593c8e277bf8da5a53d5a24273f6b0d76e596f83abf8a"
+    "ce327cf519ebbf51d8a83b3d1d48bf84de8996a85c8dbf6be2b5b8217572bf7c349eb4db20c3bf70df16e84f2725bf70"
+    "091b6a3a5381bf6f2d79c97a3d0abf5b0730bca77706bf686d558ffed44fbf26db7143b556090024bf701cfbe82b3f16"
+    "3f42f6203cdd874abf74a4ad7e734a263f706ddaaec14849bf6e11484f7506903f835255762286733f63ff4852dd8b28"
+    "3f8f3eb6b7cc7d8b3f8de900e25068bb3f9218f71db1e4653f9fe5033b8dac543f87073742bf6be33fa76ead61b7a56c"
+    "bf81d734453aa4943fa8879cb9d3e5f8bfa59c093dd5733c3f9b1d886766ab2bbfb4badc2ffb21dbbf98146bb8064fb1"
+    "bfbba84103b58595bfb8204f01899d3ebfba4ae2190b2983bfc4d77989c2408abfa9f55faae4a569bfc940e48b26809e"
+    "3fa702068a87547abfc5cd1b6d6f8bb33fc45204fbcc754bbfb333bcb72ac8ae3fcfdc2d3c32f4103fb0f7b14722db7a"
+    "3fd18967204286203fca7a9979e1b5743fcb54654cac49a43fd2d6bdfd00823e3fb4a2638880961e3fd2d6bdfd00823e"
+    "bfb4a2638880961e3fca7a9979e1b574bfcb54654cac49a43fb0f7b14722db7abfd1896720428620bfb333bcb72ac8ae"
+    "bfcfdc2d3c32f410bfc5cd1b6d6f8bb3bfc45204fbcc754bbfc940e48b26809ebfa702068a87547abfc4d77989c2408a"
+    "3fa9f55faae4a569bfb8204f01899d3e3fba4ae2190b2983bf98146bb8064fb13fbba84103b585953f9b1d886766ab2b"
+    "3fb4badc2ffb21db3fa8879cb9d3e5f83fa59c093dd5733c3fa76ead61b7a56c3f81d734453aa4943f9fe5033b8dac54"
+    "bf87073742bf6be33f8de900e25068bbbf9218f71db1e4653f63ff4852dd8b28bf8f3eb6b7cc7d8bbf6e11484f750690"
+    "bf83525576228673bf74a4ad7e734a26bf706ddaaec14849bf701cfbe82b3f16bf42f6203cdd874a0018bf75b4249004"
+    "3d913f58bb3b002132a8bf75ed4b917c24183f83ec6e9fd469873f78a5b4a997c2db3f94899b85ed02bc3fa0d7f509e1"
+    "1dc63f91897e348166ad3fae100466d35555bf9409afe98ef03c3fa5a219e749f7dabfb672881dd8b82ebfa904e8786d"
+    "b087bfc1528e7c5fbe82bfc722904b76dcd4bfb5691fed769cc1bfcee3289a42dc303fb6ee86c5db8287bfc0629236fa"
+    "bf5e3fd21c6cf3d7c80d3fc03f32398967b13fd4edf595d343733fd5efd6038313863fc297e43c37e6403fd5efd60383"
+    "1386bfc297e43c37e6403fc03f32398967b1bfd4edf595d34373bfc0629236fabf5ebfd21c6cf3d7c80dbfcee3289a42"
+    "dc30bfb6ee86c5db8287bfc722904b76dcd43fb5691fed769cc1bfa904e8786db0873fc1528e7c5fbe823fa5a219e749"
+    "f7da3fb672881dd8b82e3fae100466d355553f9409afe98ef03c3fa0d7f509e11dc6bf91897e348166ad3f78a5b4a997"
+    "c2dbbf94899b85ed02bcbf75ed4b917c2418bf83ec6e9fd46987bf75b42490043d91bf58bb3b002132a80010bf7d7928"
+    "3573ab8d3f6e4db67ca7230d3f60a736214ee5013f9672f58dd7462c3fa98cb39eb301553f8d5818f461f7433fb08f69"
+    "99212b1cbfb5d6cc15d1b45fbfb9818f10f3c216bfc50f0bce04b08dbfd2ce36cddb45623fa801dbd731d6ccbfb6ac7c"
+    "325e21a63fd89ac77b52c9193fd7d8def92444433fd059faa509c17c3fd7d8def9244443bfd059faa509c17cbfb6ac7c"
+    "325e21a6bfd89ac77b52c919bfd2ce36cddb4562bfa801dbd731d6d0bfb9818f10f3c2163fc50f0bce04b08d3fb08f69"
+    "99212b1c3fb5d6cc15d1b45f3fa98cb39eb30155bf8d5818f461f7453f60a736214ee501bf9672f58dd7462cbf7d7928"
+    "3573ab8dbf6e4db67ca7230d"
+)
+
+
+__all__ = [
+    "CANONICAL_BANK_BYTES",
+    "ORACLE_EXPORT_FORMULA_MODULE",
+    "ORACLE_EXPORT_RUNTIME",
+    "ORACLE_EXPORT_SCHEMA",
+    "ORACLE_EXPORT_SHA256",
+]
diff --git a/src/phasepair_core/signal.py b/src/phasepair_core/signal.py
index 51ef6f3..fabdd09 100644
--- a/src/phasepair_core/signal.py
+++ b/src/phasepair_core/signal.py
@@ -15,11 +15,21 @@
 from dataclasses import dataclass
 import hashlib
 import math
+import platform
 import struct
+import sys
 from typing import Final, Protocol
 
 import numpy as np
 
+from phasepair_core._morlet_oracle_bytes import (
+    CANONICAL_BANK_BYTES as _MORLET_CANONICAL_BANK_BYTES,
+    ORACLE_EXPORT_FORMULA_MODULE as _MORLET_EXPORT_FORMULA_MODULE,
+    ORACLE_EXPORT_RUNTIME as MORLET_ORACLE_EXPORT_RUNTIME,
+    ORACLE_EXPORT_SCHEMA as MORLET_PORTABLE_SCHEMA,
+    ORACLE_EXPORT_SHA256 as _MORLET_EXPORT_SHA256,
+)
+
 
 SIGNAL_CONTRACT_FAMILY_ID: Final[str] = (
     "phasepair-scientific-contract-successor-v7/20260824_165840"
@@ -51,6 +61,12 @@
 MORLET_ORACLE_SHA256: Final[str] = (
     "4f75ab687d333af8b436feea371dbaee56c55256e91c42c5e1ce45935beed5d7"
 )
+MORLET_FORMULA_MAX_ABS_ERROR_BOUND: Final[float] = float.fromhex(
+    "0x1.0000000000000p-52"
+)
+MORLET_PORTABLE_LINUX_FORMULA_SHA256: Final[str] = (
+    "3b4cec820d8202d4576d829d37626acc2414f0aeecf430e17c751d9bd1731f6c"
+)
 MORLET_ZERO_DC_BOUND: Final[float] = 2.22e-16
 MORLET_UNIT_ENERGY_BOUND: Final[float] = 2.220446049250313e-16
 MORLET_EXPECTED_UNIT_ENERGY_ERRORS: Final[tuple[float, ...]] = (
@@ -473,19 +489,150 @@ def morlet_formula_bank_diagnostic() -> tuple[MorletBand, ...]:
     return _build_morlet_formula_bank().bands
 
 
+def _canonical_morlet_bank() -> tuple[MorletBand, ...]:
+    """Decode the Windows-oracle bytes without replaying platform libm calls."""
+
+    if (
+        MORLET_PORTABLE_SCHEMA != "portable-morlet-oracle-bytes-v1"
+        or MORLET_ORACLE_EXPORT_RUNTIME != "CPython 3.14.5 / NumPy 2.4.6 / Windows"
+        or _MORLET_EXPORT_FORMULA_MODULE != "phasepair_core.signal"
+        or _MORLET_EXPORT_SHA256 != MORLET_ORACLE_SHA256
+        or hashlib.sha256(_MORLET_CANONICAL_BANK_BYTES).hexdigest() != MORLET_ORACLE_SHA256
+    ):
+        raise SignalContractHold("HOLD_MORLET_PORTABLE_PROVENANCE_MISMATCH")
+    cursor = 0
+    bands: list[MorletBand] = []
+    for index, (frequency, length, sigma) in enumerate(
+        zip(MORLET_FREQUENCIES_HZ, MORLET_LENGTHS, MORLET_SIGMAS), start=1
+    ):
+        if cursor + 2 > len(_MORLET_CANONICAL_BANK_BYTES):
+            raise SignalContractHold("HOLD_MORLET_PORTABLE_BYTES_TRUNCATED")
+        (encoded_length,) = struct.unpack_from(">H", _MORLET_CANONICAL_BANK_BYTES, cursor)
+        cursor += 2
+        if encoded_length != length:
+            raise SignalContractHold("HOLD_MORLET_PORTABLE_SUPPORT_MISMATCH")
+        stop = cursor + 16 * length
+        if stop > len(_MORLET_CANONICAL_BANK_BYTES):
+            raise SignalContractHold("HOLD_MORLET_PORTABLE_BYTES_TRUNCATED")
+        values = np.frombuffer(
+            _MORLET_CANONICAL_BANK_BYTES,
+            dtype=">f8",
+            count=2 * length,
+            offset=cursor,
+        ).reshape(length, 2)
+        kernel = np.empty(length, dtype=np.complex128)
+        kernel.real = values[:, 0]
+        kernel.imag = values[:, 1]
+        kernel = np.ascontiguousarray(kernel, dtype=np.complex128)
+        if not bool(np.isfinite(kernel).all()):
+            raise SignalContractHold("HOLD_MORLET_PORTABLE_NONFINITE")
+        kernel.setflags(write=False)
+        bands.append(MorletBand(index, frequency, length, sigma, kernel))
+        cursor = stop
+    if cursor != len(_MORLET_CANONICAL_BANK_BYTES):
+        raise SignalContractHold("HOLD_MORLET_PORTABLE_TRAILING_BYTES")
+    result = tuple(bands)
+    if morlet_bank_sha256(result) != MORLET_ORACLE_SHA256:
+        raise SignalContractHold("HOLD_MORLET_ORACLE_DIGEST_MISMATCH")
+    return result
+
+
+def _formula_max_abs_error(
+    formula: tuple[MorletBand, ...], canonical: tuple[MorletBand, ...]
+) -> float:
+    if len(formula) != len(canonical):
+        raise SignalContractHold("HOLD_MORLET_FORMULA_EQUIVALENCE_SHAPE")
+    maximum = 0.0
+    for expected_index, (observed, frozen) in enumerate(
+        zip(formula, canonical), start=1
+    ):
+        if (
+            observed.index != expected_index
+            or frozen.index != expected_index
+            or observed.frequency_hz != frozen.frequency_hz
+            or observed.length != frozen.length
+            or observed.sigma != frozen.sigma
+            or observed.kernel.shape != frozen.kernel.shape
+        ):
+            raise SignalContractHold("HOLD_MORLET_FORMULA_EQUIVALENCE_SCHEMA")
+        difference = np.abs(observed.kernel - frozen.kernel)
+        if not bool(np.isfinite(difference).all()):
+            raise SignalContractHold("HOLD_MORLET_FORMULA_EQUIVALENCE_NONFINITE")
+        maximum = max(maximum, float(np.max(difference)))
+    return maximum
+
+
+def morlet_formula_max_abs_error() -> float:
+    """Return the native-formula distance from the immutable canonical bytes."""
+
+    return _formula_max_abs_error(
+        morlet_formula_bank_diagnostic(),
+        _canonical_morlet_bank(),
+    )
+
+
+def _morlet_runtime_identity() -> tuple[str, tuple[int, int, int], str, str, str]:
+    """Return only the fields used to qualify a native formula replay."""
+
+    machine = platform.machine().lower()
+    if machine == "amd64":
+        machine = "x86_64"
+    return (
+        platform.python_implementation(),
+        sys.version_info[:3],
+        np.__version__,
+        platform.system(),
+        machine,
+    )
+
+
+def _validate_morlet_formula_receipt(
+    receipt: _MorletBuildReceipt,
+    canonical: tuple[MorletBand, ...],
+) -> None:
+    """Accept only a registered exact replay or the qualified Linux replay."""
+
+    identity = _morlet_runtime_identity()
+    implementation, python_version, numpy_version, system, machine = identity
+    supported_family = (
+        implementation == "CPython"
+        and python_version[:2] in {(3, 12), (3, 13), (3, 14)}
+        and numpy_version == "2.4.6"
+        and machine == "x86_64"
+    )
+    formula_digest = morlet_bank_sha256(receipt.bands)
+    if _formula_max_abs_error(
+        receipt.bands, canonical
+    ) > MORLET_FORMULA_MAX_ABS_ERROR_BOUND:
+        raise SignalContractHold("HOLD_MORLET_FORMULA_EQUIVALENCE_FAIL")
+    if supported_family and system == "Windows":
+        # Runtime identity, rather than an accidentally matching digest, selects
+        # the original exact-oracle branch. Any one-ULP receipt drift must HOLD.
+        if formula_digest != MORLET_ORACLE_SHA256:
+            raise SignalContractHold("HOLD_MORLET_ORACLE_DIGEST_MISMATCH")
+        if receipt.unit_energy_errors != MORLET_EXPECTED_UNIT_ENERGY_ERRORS:
+            raise SignalNumericError("MORLET_UNIT_ENERGY_ORACLE_FAIL")
+        if any(error > MORLET_ZERO_DC_BOUND for error in receipt.zero_dc_errors):
+            raise SignalNumericError("MORLET_ZERO_DC_FAIL")
+        if any(error > MORLET_UNIT_ENERGY_BOUND for error in receipt.unit_energy_errors):
+            raise SignalNumericError("MORLET_UNIT_ENERGY_FAIL")
+        return
+    if supported_family and system == "Linux":
+        if formula_digest != MORLET_PORTABLE_LINUX_FORMULA_SHA256:
+            raise SignalContractHold("HOLD_MORLET_PORTABLE_FORMULA_DIGEST_MISMATCH")
+        return
+    raise SignalContractHold("HOLD_MORLET_PORTABLE_RUNTIME_UNQUALIFIED")
+
+
 def morlet_kernel_bank() -> tuple[MorletBand, ...]:
-    """Construct the canonical bank and enforce the complete KERNEL01 oracle."""
+    """Return portable oracle bytes after replaying the unchanged formula."""
 
     receipt = _build_morlet_formula_bank()
-    if morlet_bank_sha256(receipt.bands) != MORLET_ORACLE_SHA256:
-        raise SignalContractHold("HOLD_MORLET_ORACLE_DIGEST_MISMATCH")
-    if receipt.unit_energy_errors != MORLET_EXPECTED_UNIT_ENERGY_ERRORS:
-        raise SignalNumericError("MORLET_UNIT_ENERGY_ORACLE_FAIL")
-    if any(error > MORLET_ZERO_DC_BOUND for error in receipt.zero_dc_errors):
-        raise SignalNumericError("MORLET_ZERO_DC_FAIL")
-    if any(error > MORLET_UNIT_ENERGY_BOUND for error in receipt.unit_energy_errors):
-        raise SignalNumericError("MORLET_UNIT_ENERGY_FAIL")
-    return receipt.bands
+    canonical = _canonical_morlet_bank()
+    _validate_morlet_formula_receipt(receipt, canonical)
+    # The returned physical bank is canonical, not the native diagnostic. Its
+    # verified original digest binds the original zero-DC and unit-energy bytes.
+    return canonical
 
 
 def morlet_bank_sha256(bands: tuple[MorletBand, ...] | None = None) -> str:
@@ -758,21 +905,19 @@ def pooled_relation_descriptor_diagnostic(
     *,
     energy_floors: np.ndarray | None = None,
 ) -> RelationDescriptor:
-    """Compute a synthetic formula replay explicitly marked ``NO_RESULT``.
+    """Compute a synthetic portable-oracle replay marked ``NO_RESULT``.
 
     This diagnostic is not a canonical-bank PASS and cannot grant data, cache,
-    training, or scientific eligibility.  It constructs the inherited formula
-    bank internally and checks the pinned byte digest; callers cannot supply a
-    substitute bank.
+    training, or scientific eligibility. It replays and bounds the inherited
+    native formula internally, then computes with the canonical frozen bytes;
+    callers cannot supply a substitute bank.
     """
 
     checked = validate_paired_activity(
         pair.actor_a, pair.actor_b, pair.valid_mask, pair.valid_length
     )
     floors = _validate_energy_floors(energy_floors)
-    bands = morlet_formula_bank_diagnostic()
-    if morlet_bank_sha256(bands) != MORLET_ORACLE_SHA256:
-        raise SignalNumericError("MORLET_DIAGNOSTIC_ORACLE_DIGEST_FAIL")
+    bands = morlet_kernel_bank()
     return _pooled_relation_descriptor_from_bank(checked, floors, bands)
 
 
@@ -1034,7 +1179,11 @@ def swap_relation_descriptor(descriptor: RelationDescriptor) -> RelationDescript
     "MORLET_FREQUENCIES_HZ",
     "MORLET_LENGTHS",
     "MORLET_EXPECTED_UNIT_ENERGY_ERRORS",
+    "MORLET_FORMULA_MAX_ABS_ERROR_BOUND",
     "MORLET_ORACLE_SHA256",
+    "MORLET_ORACLE_EXPORT_RUNTIME",
+    "MORLET_PORTABLE_LINUX_FORMULA_SHA256",
+    "MORLET_PORTABLE_SCHEMA",
     "MORLET_SIGMAS",
     "MORLET_UNIT_ENERGY_BOUND",
     "MORLET_ZERO_DC_BOUND",
@@ -1055,6 +1204,7 @@ def swap_relation_descriptor(descriptor: RelationDescriptor) -> RelationDescript
     "interedit_descriptor_from_source",
     "morlet_bank_sha256",
     "morlet_formula_bank_diagnostic",
+    "morlet_formula_max_abs_error",
     "morlet_kernel_bank",
     "morlet_length_mask",
     "morlet_unit_energy_errors",
diff --git a/src/phaseset_core/_morlet_oracle_bytes.py b/src/phaseset_core/_morlet_oracle_bytes.py
new file mode 100644
index 0000000..51e6bf4
--- /dev/null
+++ b/src/phaseset_core/_morlet_oracle_bytes.py
@@ -0,0 +1,101 @@
+"""Generated frozen Morlet bytes; do not hand-edit.
+
+Source formula module: phaseset_core.morlet
+Exporter runtime: CPython 3.14.5 / NumPy 2.4.6 / Windows
+Canonical serialized SHA-256: 633d754bba12dab0555722f4b8963409a110c8681796cc907d163096e2e42655
+"""
+
+from __future__ import annotations
+
+from typing import Final
+
+
+ORACLE_EXPORT_SCHEMA: Final = "portable-morlet-oracle-bytes-v1"
+ORACLE_EXPORT_RUNTIME: Final = "CPython 3.14.5 / NumPy 2.4.6 / Windows"
+ORACLE_EXPORT_FORMULA_MODULE: Final = "phaseset_core.morlet"
+ORACLE_EXPORT_SHA256: Final = "633d754bba12dab0555722f4b8963409a110c8681796cc907d163096e2e42655"
+CANONICAL_BANK_BYTES: Final = bytes.fromhex(
+    "0050bf64fc6e545b2b013f33b2d76b62ddecbf68b3c74dffad1e3f520f1fed7722fcbf6b2436a253885b3f61f2a54ae9"
+    "9cfcbf6b5915d1def0823f6d3608b5fa9e66bf6839207cdbefae3f753fbf2ab3a463bf609d10f773c7ac3f7c8a4ccac9"
+    "f961bf3b8b088f19b73c3f81f01839579af13f601d7eabab09643f8532b2a181df033f753780b050a8a63f877fbd539d"
+    "56583f82cdc2b78af4ab3f88311fb37d2ced3f8c5278e1ac09f73f8693be4e6f72c53f934e7f50a576393f81f90e3bb8"
+    "dcfe3f986cd97d5c0eb03f739b88a9d538173f9cf880d1845cd5bf525f35cc0b764d3fa024e5f1a5ff06bf8260daf726"
+    "750f3fa0d44375cc6c5cbf930a036c69390c3fa02e378a852111bf9e45ba461b48943f9bc2029d7b5267bfa519e9054b"
+    "ad8e3f936206c6ad1cacbfaaf2357a171a9a3f7c258cfcb574a4bfb00c9cb90d7c93bf8243442224efa7bfb1f5d57c2b"
+    "e472bf9c9005ba49ca8bbfb2e1b62a7a178fbfa921d07c5413d6bfb28452e86c99d6bfb23510d9c47d87bfb0a24583fd"
+    "c1dabfb7a0711c2e0ec6bfaa33cbec9aebc6bfbc54c4e0abb9bebf9facc88cf5f07dbfbfd027cdcc67c5bf73d3a1a118"
+    "d86cbfc0ccd69dfdde783f9a7fd5fd216e52bfc0a75f4a882be13fae6b4733f8882cbfbeafc6019466653fb7ed3d21c3"
+    "5e04bfb9aa937d37f6ac3fc00db41b5943fcbfb26105f1eb7a583fc387436af77949bfa255a4d67ab71b3fc60faa175a"
+    "1feb3f76e3d4877ffc763fc760f285f304793fa96f358e52541e3fc74a9122db5b373fb7e42a8235b22a3fc5b76f874e"
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+    "08696ffebfbabed75017eaa6bfd155244ee7eda5bfc00e884a66bef3bfbbaac8ab267df43fde21e15942d9973fe20425"
+    "a070fb213c71cfa5b8022987bfbbaac8ab267df4bfde21e15942d997bfd155244ee7eda53fc00e884a66bef43fb44ee3"
+    "08696ffe3fbabed75017eaa63f9b8379076c4304bfa09f5000cf8c62bf8207ef2a11c5b4bf728e4a7c3556d8"
+)
+
+
+__all__ = [
+    "CANONICAL_BANK_BYTES",
+    "ORACLE_EXPORT_FORMULA_MODULE",
+    "ORACLE_EXPORT_RUNTIME",
+    "ORACLE_EXPORT_SCHEMA",
+    "ORACLE_EXPORT_SHA256",
+]
diff --git a/src/phaseset_core/morlet.py b/src/phaseset_core/morlet.py
index de5bbdd..83a15a6 100644
--- a/src/phaseset_core/morlet.py
+++ b/src/phaseset_core/morlet.py
@@ -12,11 +12,21 @@
 from dataclasses import dataclass
 import hashlib
 import math
+import platform
 import struct
+import sys
 from typing import Final
 
 import numpy as np
 
+from phaseset_core._morlet_oracle_bytes import (
+    CANONICAL_BANK_BYTES as _MORLET_CANONICAL_BANK_BYTES,
+    ORACLE_EXPORT_FORMULA_MODULE as _MORLET_EXPORT_FORMULA_MODULE,
+    ORACLE_EXPORT_RUNTIME as MORLET_ORACLE_EXPORT_RUNTIME,
+    ORACLE_EXPORT_SCHEMA as MORLET_PORTABLE_SCHEMA,
+    ORACLE_EXPORT_SHA256 as _MORLET_EXPORT_SHA256,
+)
+
 
 STATUS: Final = "DATA_FREE_PHASESET_20HZ_MORLET_ORACLE_AUTHORITY0"
 PHASESET_MORLET_SCHEMA: Final = "phaseset-morlet-bank-v1/fs20/three-cycle"
@@ -41,6 +51,12 @@
 MORLET_ORACLE_SHA256: Final = (
     "633d754bba12dab0555722f4b8963409a110c8681796cc907d163096e2e42655"
 )
+MORLET_FORMULA_MAX_ABS_ERROR_BOUND: Final = float.fromhex(
+    "0x1.0000000000000p-52"
+)
+MORLET_PORTABLE_LINUX_FORMULA_SHA256: Final = (
+    "926c06aa57a27139d1ed1a9e0d9c3da191717b8a9495d6fff900bda44459d9a3"
+)
 MORLET_ZERO_DC_BOUND: Final = 2.220446049250313e-16
 MORLET_UNIT_ENERGY_BOUND: Final = 2.220446049250313e-16
 MORLET_EXPECTED_ZERO_DC_ERRORS: Final[tuple[float, ...]] = (
@@ -175,21 +191,156 @@ def morlet_formula_bank_diagnostic() -> tuple[PhaseSetMorletBand, ...]:
     return _build_formula_bank().bands
 
 
+def _canonical_morlet_bank() -> tuple[PhaseSetMorletBand, ...]:
+    """Decode the Windows-oracle bytes without replaying platform libm calls."""
+
+    if (
+        MORLET_PORTABLE_SCHEMA != "portable-morlet-oracle-bytes-v1"
+        or MORLET_ORACLE_EXPORT_RUNTIME != "CPython 3.14.5 / NumPy 2.4.6 / Windows"
+        or _MORLET_EXPORT_FORMULA_MODULE != "phaseset_core.morlet"
+        or _MORLET_EXPORT_SHA256 != MORLET_ORACLE_SHA256
+        or hashlib.sha256(_MORLET_CANONICAL_BANK_BYTES).hexdigest() != MORLET_ORACLE_SHA256
+    ):
+        raise PhaseSetMorletOracleError("PHASESET_MORLET_PORTABLE_PROVENANCE_MISMATCH")
+    cursor = 0
+    bands: list[PhaseSetMorletBand] = []
+    for index, (frequency, length, sigma) in enumerate(
+        zip(MORLET_FREQUENCIES_HZ, MORLET_LENGTHS, MORLET_SIGMAS, strict=True),
+        start=1,
+    ):
+        if cursor + 2 > len(_MORLET_CANONICAL_BANK_BYTES):
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_PORTABLE_BYTES_TRUNCATED")
+        (encoded_length,) = struct.unpack_from(">H", _MORLET_CANONICAL_BANK_BYTES, cursor)
+        cursor += 2
+        if encoded_length != length:
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_PORTABLE_SUPPORT_MISMATCH")
+        stop = cursor + 16 * length
+        if stop > len(_MORLET_CANONICAL_BANK_BYTES):
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_PORTABLE_BYTES_TRUNCATED")
+        values = np.frombuffer(
+            _MORLET_CANONICAL_BANK_BYTES,
+            dtype=">f8",
+            count=2 * length,
+            offset=cursor,
+        ).reshape(length, 2)
+        kernel = np.empty(length, dtype=np.complex128)
+        kernel.real = values[:, 0]
+        kernel.imag = values[:, 1]
+        kernel = np.ascontiguousarray(kernel, dtype=np.complex128)
+        if not bool(np.isfinite(kernel).all()):
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_PORTABLE_NONFINITE")
+        kernel.setflags(write=False)
+        bands.append(PhaseSetMorletBand(index, frequency, length, sigma, kernel))
+        cursor = stop
+    if cursor != len(_MORLET_CANONICAL_BANK_BYTES):
+        raise PhaseSetMorletOracleError("PHASESET_MORLET_PORTABLE_TRAILING_BYTES")
+    result = tuple(bands)
+    if morlet_bank_sha256(result) != MORLET_ORACLE_SHA256:
+        raise PhaseSetMorletOracleError("PHASESET_MORLET_DIGEST_MISMATCH")
+    return result
+
+
+def _formula_max_abs_error(
+    formula: tuple[PhaseSetMorletBand, ...],
+    canonical: tuple[PhaseSetMorletBand, ...],
+) -> float:
+    if len(formula) != len(canonical):
+        raise PhaseSetMorletOracleError("PHASESET_MORLET_FORMULA_EQUIVALENCE_SHAPE")
+    maximum = 0.0
+    for expected_index, (observed, frozen) in enumerate(
+        zip(formula, canonical, strict=True), start=1
+    ):
+        if (
+            observed.index != expected_index
+            or frozen.index != expected_index
+            or observed.frequency_hz != frozen.frequency_hz
+            or observed.length != frozen.length
+            or observed.sigma != frozen.sigma
+            or observed.kernel.shape != frozen.kernel.shape
+        ):
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_FORMULA_EQUIVALENCE_SCHEMA")
+        difference = np.abs(observed.kernel - frozen.kernel)
+        if not bool(np.isfinite(difference).all()):
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_FORMULA_EQUIVALENCE_NONFINITE")
+        maximum = max(maximum, float(np.max(difference)))
+    return maximum
+
+
+def morlet_formula_max_abs_error() -> float:
+    """Return the native-formula distance from the immutable canonical bytes."""
+
+    return _formula_max_abs_error(
+        morlet_formula_bank_diagnostic(),
+        _canonical_morlet_bank(),
+    )
+
+
+def _morlet_runtime_identity() -> tuple[str, tuple[int, int, int], str, str, str]:
+    """Return only the fields used to qualify a native formula replay."""
+
+    machine = platform.machine().lower()
+    if machine == "amd64":
+        machine = "x86_64"
+    return (
+        platform.python_implementation(),
+        sys.version_info[:3],
+        np.__version__,
+        platform.system(),
+        machine,
+    )
+
+
+def _validate_morlet_formula_receipt(
+    receipt: _BuildReceipt,
+    canonical: tuple[PhaseSetMorletBand, ...],
+) -> None:
+    """Accept only a registered exact replay or the qualified Linux replay."""
+
+    identity = _morlet_runtime_identity()
+    implementation, python_version, numpy_version, system, machine = identity
+    supported_family = (
+        implementation == "CPython"
+        and python_version[:2] in {(3, 12), (3, 13), (3, 14)}
+        and numpy_version == "2.4.6"
+        and machine == "x86_64"
+    )
+    formula_digest = morlet_bank_sha256(receipt.bands)
+    if _formula_max_abs_error(
+        receipt.bands, canonical
+    ) > MORLET_FORMULA_MAX_ABS_ERROR_BOUND:
+        raise PhaseSetMorletOracleError("PHASESET_MORLET_FORMULA_EQUIVALENCE_FAIL")
+    if supported_family and system == "Windows":
+        # Runtime identity, rather than an accidentally matching digest, selects
+        # the original exact-oracle branch. Any one-ULP receipt drift must HOLD.
+        if formula_digest != MORLET_ORACLE_SHA256:
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_DIGEST_MISMATCH")
+        if receipt.zero_dc_errors != MORLET_EXPECTED_ZERO_DC_ERRORS:
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_ZERO_DC_ORACLE_FAIL")
+        if receipt.unit_energy_errors != MORLET_EXPECTED_UNIT_ENERGY_ERRORS:
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_UNIT_ENERGY_ORACLE_FAIL")
+        if any(error > MORLET_ZERO_DC_BOUND for error in receipt.zero_dc_errors):
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_ZERO_DC_BOUND_FAIL")
+        if any(error > MORLET_UNIT_ENERGY_BOUND for error in receipt.unit_energy_errors):
+            raise PhaseSetMorletOracleError("PHASESET_MORLET_UNIT_ENERGY_BOUND_FAIL")
+        return
+    if supported_family and system == "Linux":
+        if formula_digest != MORLET_PORTABLE_LINUX_FORMULA_SHA256:
+            raise PhaseSetMorletOracleError(
+                "PHASESET_MORLET_PORTABLE_FORMULA_DIGEST_MISMATCH"
+            )
+        return
+    raise PhaseSetMorletOracleError("PHASESET_MORLET_PORTABLE_RUNTIME_UNQUALIFIED")
+
+
 def morlet_kernel_bank() -> tuple[PhaseSetMorletBand, ...]:
-    """Rebuild and enforce the complete independent PhaseSet 20-Hz oracle."""
+    """Return portable oracle bytes after replaying the unchanged formula."""
 
     receipt = _build_formula_bank()
-    if morlet_bank_sha256(receipt.bands) != MORLET_ORACLE_SHA256:
-        raise PhaseSetMorletOracleError("PHASESET_MORLET_DIGEST_MISMATCH")
-    if receipt.zero_dc_errors != MORLET_EXPECTED_ZERO_DC_ERRORS:
-        raise PhaseSetMorletOracleError("PHASESET_MORLET_ZERO_DC_ORACLE_FAIL")
-    if receipt.unit_energy_errors != MORLET_EXPECTED_UNIT_ENERGY_ERRORS:
-        raise PhaseSetMorletOracleError("PHASESET_MORLET_UNIT_ENERGY_ORACLE_FAIL")
-    if any(error > MORLET_ZERO_DC_BOUND for error in receipt.zero_dc_errors):
-        raise PhaseSetMorletOracleError("PHASESET_MORLET_ZERO_DC_BOUND_FAIL")
-    if any(error > MORLET_UNIT_ENERGY_BOUND for error in receipt.unit_energy_errors):
-        raise PhaseSetMorletOracleError("PHASESET_MORLET_UNIT_ENERGY_BOUND_FAIL")
-    return receipt.bands
+    canonical = _canonical_morlet_bank()
+    _validate_morlet_formula_receipt(receipt, canonical)
+    # The returned physical bank is canonical, not the native diagnostic. Its
+    # verified original digest binds the original zero-DC and unit-energy bytes.
+    return canonical
 
 
 def morlet_length_mask(valid_length: object) -> np.ndarray:
@@ -207,8 +358,12 @@ def morlet_length_mask(valid_length: object) -> np.ndarray:
     "MORLET_EXPECTED_UNIT_ENERGY_ERRORS",
     "MORLET_EXPECTED_ZERO_DC_ERRORS",
     "MORLET_FREQUENCIES_HZ",
+    "MORLET_FORMULA_MAX_ABS_ERROR_BOUND",
     "MORLET_LENGTHS",
     "MORLET_ORACLE_SHA256",
+    "MORLET_ORACLE_EXPORT_RUNTIME",
+    "MORLET_PORTABLE_LINUX_FORMULA_SHA256",
+    "MORLET_PORTABLE_SCHEMA",
     "MORLET_SIGMAS",
     "MORLET_UNIT_ENERGY_BOUND",
     "MORLET_ZERO_DC_BOUND",
@@ -220,6 +375,7 @@ def morlet_length_mask(valid_length: object) -> np.ndarray:
     "STATUS",
     "morlet_bank_sha256",
     "morlet_formula_bank_diagnostic",
+    "morlet_formula_max_abs_error",
     "morlet_kernel_bank",
     "morlet_length_mask",
 ]
diff --git a/tests/test_morlet_portability.py b/tests/test_morlet_portability.py
new file mode 100644
index 0000000..ed8d1b5
--- /dev/null
+++ b/tests/test_morlet_portability.py
@@ -0,0 +1,187 @@
+"""Portable canonical-byte checks kept separate from frozen Morlet tests."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+
+import numpy as np
+import pytest
+
+from phasepair_core import signal as legacy
+from phaseset_core import morlet as phaseset
+
+
+ORACLE_RUNTIME = "CPython 3.14.5 / NumPy 2.4.6 / Windows"
+
+
+def _is_registered_oracle_runtime() -> bool:
+    return legacy._morlet_runtime_identity() in {
+        ("CPython", (3, 12, 0), "2.4.6", "Windows", "x86_64"),
+        ("CPython", (3, 14, 5), "2.4.6", "Windows", "x86_64"),
+    }
+
+
+@pytest.mark.parametrize("module", (legacy, phaseset))
+def test_canonical_bytes_keep_the_original_digest_and_formula_is_bounded(module) -> None:
+    canonical = module.morlet_kernel_bank()
+    formula = module.morlet_formula_bank_diagnostic()
+    assert module.MORLET_PORTABLE_SCHEMA == "portable-morlet-oracle-bytes-v1"
+    assert module.MORLET_ORACLE_EXPORT_RUNTIME == ORACLE_RUNTIME
+    assert module.morlet_bank_sha256(canonical) == module.MORLET_ORACLE_SHA256
+    assert module.morlet_formula_max_abs_error() <= module.MORLET_FORMULA_MAX_ABS_ERROR_BOUND
+    zero_dc_errors = tuple(
+        float(np.abs(np.sum(band.kernel, dtype=np.complex128))) for band in canonical
+    )
+    unit_energy_errors = tuple(
+        float(
+            np.abs(
+                np.sum(
+                    np.square(np.abs(band.kernel), dtype=np.float64),
+                    dtype=np.float64,
+                )
+                - np.float64(1.0)
+            )
+        )
+        for band in canonical
+    )
+    assert all(error <= module.MORLET_ZERO_DC_BOUND for error in zero_dc_errors)
+    assert all(error <= module.MORLET_UNIT_ENERGY_BOUND for error in unit_energy_errors)
+    if _is_registered_oracle_runtime():
+        assert module.morlet_bank_sha256(formula) == module.MORLET_ORACLE_SHA256
+        assert module.morlet_formula_max_abs_error() == 0.0
+
+
+@pytest.mark.parametrize(
+    ("module", "builder_name", "exception", "message", "python_version"),
+    (
+        (
+            legacy,
+            "_build_morlet_formula_bank",
+            legacy.SignalContractHold,
+            "HOLD_MORLET_ORACLE_DIGEST_MISMATCH",
+            (3, 12, 0),
+        ),
+        (
+            legacy,
+            "_build_morlet_formula_bank",
+            legacy.SignalContractHold,
+            "HOLD_MORLET_ORACLE_DIGEST_MISMATCH",
+            (3, 14, 5),
+        ),
+        (
+            phaseset,
+            "_build_formula_bank",
+            phaseset.PhaseSetMorletOracleError,
+            "PHASESET_MORLET_DIGEST_MISMATCH",
+            (3, 12, 0),
+        ),
+        (
+            phaseset,
+            "_build_formula_bank",
+            phaseset.PhaseSetMorletOracleError,
+            "PHASESET_MORLET_DIGEST_MISMATCH",
+            (3, 14, 5),
+        ),
+    ),
+)
+def test_registered_windows_identity_rejects_within_bound_receipt_drift(
+    monkeypatch: pytest.MonkeyPatch,
+    module,
+    builder_name: str,
+    exception: type[Exception],
+    message: str,
+    python_version: tuple[int, int, int],
+) -> None:
+    canonical = module._canonical_morlet_bank()
+    changed_kernel = np.array(canonical[0].kernel, copy=True, order="C")
+    changed_kernel.real[0] = np.nextafter(changed_kernel.real[0], np.inf)
+    changed_kernel.setflags(write=False)
+    changed_band = replace(canonical[0], kernel=changed_kernel)
+    changed_bands = (changed_band, *canonical[1:])
+    difference = module._formula_max_abs_error(changed_bands, canonical)
+    assert 0.0 < difference <= module.MORLET_FORMULA_MAX_ABS_ERROR_BOUND
+
+    original_receipt = getattr(module, builder_name)()
+    changed_receipt = replace(original_receipt, bands=changed_bands)
+    monkeypatch.setattr(
+        module,
+        "_morlet_runtime_identity",
+        lambda: ("CPython", python_version, "2.4.6", "Windows", "x86_64"),
+    )
+    monkeypatch.setattr(module, builder_name, lambda: changed_receipt)
+    with pytest.raises(exception, match=message):
+        module.morlet_kernel_bank()
+
+
+@pytest.mark.parametrize(
+    ("module", "exception"),
+    (
+        (legacy, legacy.SignalContractHold),
+        (phaseset, phaseset.PhaseSetMorletOracleError),
+    ),
+)
+def test_unqualified_hardware_identity_fails_closed(
+    monkeypatch: pytest.MonkeyPatch,
+    module,
+    exception: type[Exception],
+) -> None:
+    monkeypatch.setattr(
+        module,
+        "_morlet_runtime_identity",
+        lambda: ("CPython", (3, 12, 12), "2.4.6", "Linux", "aarch64"),
+    )
+    with pytest.raises(exception, match="PORTABLE_RUNTIME_UNQUALIFIED"):
+        module.morlet_kernel_bank()
+
+
+def test_legacy_canonical_bytes_reject_a_single_byte_mutation(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    changed = bytearray(legacy._MORLET_CANONICAL_BANK_BYTES)
+    changed[-1] ^= 1
+    monkeypatch.setattr(legacy, "_MORLET_CANONICAL_BANK_BYTES", bytes(changed))
+    with pytest.raises(legacy.SignalContractHold, match="PORTABLE_PROVENANCE"):
+        legacy.morlet_kernel_bank()
+
+
+def test_phaseset_canonical_bytes_reject_a_single_byte_mutation(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    changed = bytearray(phaseset._MORLET_CANONICAL_BANK_BYTES)
+    changed[-1] ^= 1
+    monkeypatch.setattr(phaseset, "_MORLET_CANONICAL_BANK_BYTES", bytes(changed))
+    with pytest.raises(phaseset.PhaseSetMorletOracleError, match="PORTABLE_PROVENANCE"):
+        phaseset.morlet_kernel_bank()
+
+
+@pytest.mark.parametrize("module", (legacy, phaseset))
+@pytest.mark.parametrize("python_version", ((3, 12, 1), (3, 13, 2), (3, 14, 6)))
+def test_supported_python_patch_has_no_arbitrary_identity_gate(
+    monkeypatch: pytest.MonkeyPatch, module, python_version: tuple[int, int, int]
+) -> None:
+    """Receipt routing test, not qualification of the simulated interpreter."""
+    canonical = module._canonical_morlet_bank()
+    builder = (
+        module._build_morlet_formula_bank
+        if module is legacy else module._build_formula_bank
+    )
+    receipt = builder()
+    windows_receipt = replace(
+        receipt, bands=canonical,
+        zero_dc_errors=tuple(
+            float(np.abs(np.sum(band.kernel, dtype=np.complex128)))
+            for band in canonical
+        ),
+        unit_energy_errors=module.MORLET_EXPECTED_UNIT_ENERGY_ERRORS,
+    )
+    monkeypatch.setattr(
+        module, "_morlet_runtime_identity",
+        lambda: ("CPython", python_version, "2.4.6", "Windows", "x86_64"),
+    )
+    module._validate_morlet_formula_receipt(windows_receipt, canonical)
+    if module.morlet_bank_sha256(receipt.bands) == module.MORLET_PORTABLE_LINUX_FORMULA_SHA256:
+        monkeypatch.setattr(
+            module, "_morlet_runtime_identity",
+            lambda: ("CPython", python_version, "2.4.6", "Linux", "x86_64"),
+        )
+        module._validate_morlet_formula_receipt(receipt, canonical)
diff --git a/tests/test_phasepair_core_signal.py b/tests/test_phasepair_core_signal.py
index 3962225..3099699 100644
--- a/tests/test_phasepair_core_signal.py
+++ b/tests/test_phasepair_core_signal.py
@@ -430,24 +430,48 @@ def sqrt_spy(value: object, *args: object, **kwargs: object) -> np.generic:
         assert kwargs == {}
 
 
-def test_kernel01_kills_explicit_left_to_right_reduction_mutant() -> None:
+def test_kernel01_kills_explicit_left_to_right_reduction_mutant(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
     bands, zero_dc_errors, unit_energy_errors = _left_to_right_morlet_mutant()
     observed_sha = _serialize_morlet_bands(bands)
     one_ulp = MORLET_UNIT_ENERGY_BOUND
 
-    assert observed_sha == LEFT_TO_RIGHT_MUTANT_SHA256
-    assert morlet_bank_sha256(bands) == LEFT_TO_RIGHT_MUTANT_SHA256
-    assert tuple(error / one_ulp for error in unit_energy_errors) == (
-        2.5,
-        1.5,
-        0.5,
-        1.0,
-        1.0,
-        0.0,
-    )
-    assert zero_dc_errors[1] == 3.0493334034159067e-16
-    assert zero_dc_errors[1] > MORLET_ZERO_DC_BOUND
+    assert morlet_bank_sha256(bands) == observed_sha
     assert observed_sha != MORLET_ORACLE_SHA256
+    assert any(
+        not np.array_equal(mutant.kernel, canonical.kernel)
+        for mutant, canonical in zip(bands, morlet_kernel_bank(), strict=True)
+    )
+    if signal_module._morlet_runtime_identity() in {
+        ("CPython", (3, 12, 0), "2.4.6", "Windows", "x86_64"),
+        ("CPython", (3, 14, 5), "2.4.6", "Windows", "x86_64"),
+    }:
+        assert observed_sha == LEFT_TO_RIGHT_MUTANT_SHA256
+        assert tuple(error / one_ulp for error in unit_energy_errors) == (
+            2.5,
+            1.5,
+            0.5,
+            1.0,
+            1.0,
+            0.0,
+        )
+        assert zero_dc_errors[1] == 3.0493334034159067e-16
+        assert zero_dc_errors[1] > MORLET_ZERO_DC_BOUND
+
+    receipt = signal_module._MorletBuildReceipt(
+        bands,
+        zero_dc_errors,
+        unit_energy_errors,
+    )
+    monkeypatch.setattr(
+        signal_module,
+        "_morlet_runtime_identity",
+        lambda: ("CPython", (3, 12, 12), "2.4.6", "Linux", "x86_64"),
+    )
+    monkeypatch.setattr(signal_module, "_build_morlet_formula_bank", lambda: receipt)
+    with pytest.raises(SignalContractHold, match="PORTABLE_FORMULA_DIGEST_MISMATCH"):
+        morlet_kernel_bank()
 
 
 def test_kernel01_kills_decimal_rounded_energy_bound_mutant() -> None:
diff --git a/tests/test_phaseset_morlet.py b/tests/test_phaseset_morlet.py
index 1eaa231..9b75e75 100644
--- a/tests/test_phaseset_morlet.py
+++ b/tests/test_phaseset_morlet.py
@@ -53,20 +53,28 @@ def test_phaseset_20hz_bank_has_independent_schema_digest_and_legacy_is_unchange
     assert morlet.morlet_bank_sha256(bands) == morlet.MORLET_ORACLE_SHA256
     assert morlet.MORLET_ORACLE_SHA256 != legacy_signal.MORLET_ORACLE_SHA256
     assert legacy_signal.morlet_bank_sha256(
-        legacy_signal.morlet_formula_bank_diagnostic()
+        legacy_signal.morlet_kernel_bank()
     ) == "4f75ab687d333af8b436feea371dbaee56c55256e91c42c5e1ce45935beed5d7"
     assert legacy_signal.MORLET_LENGTHS == (120, 80, 54, 36, 24, 16)
 
 
 def test_each_20hz_kernel_is_zero_dc_unit_energy_and_peaks_at_registered_hz() -> None:
+    zero_dc_errors = []
+    unit_energy_errors = []
     frequency_grid = np.linspace(0.05, 9.95, 9901, dtype=np.float64)
     for band in morlet.morlet_kernel_bank():
-        assert abs(np.sum(band.kernel, dtype=np.complex128)) <= morlet.MORLET_ZERO_DC_BOUND
+        zero_dc_error = float(
+            np.abs(np.sum(band.kernel, dtype=np.complex128))
+        )
+        zero_dc_errors.append(zero_dc_error)
+        assert zero_dc_error <= morlet.MORLET_ZERO_DC_BOUND
         energy = np.sum(
             np.square(np.abs(band.kernel), dtype=np.float64),
             dtype=np.float64,
         )
-        assert abs(float(energy) - 1.0) <= morlet.MORLET_UNIT_ENERGY_BOUND
+        unit_energy_error = float(np.abs(energy - np.float64(1.0)))
+        unit_energy_errors.append(unit_energy_error)
+        assert unit_energy_error <= morlet.MORLET_UNIT_ENERGY_BOUND
         samples = np.arange(band.length, dtype=np.float64)
         responses = np.abs(
             np.exp(
@@ -106,6 +114,9 @@ def test_each_20hz_kernel_is_zero_dc_unit_energy_and_peaks_at_registered_hz() ->
         )
         assert registered > 1.7 * silently_shifted
 
+    assert tuple(zero_dc_errors) == morlet.MORLET_EXPECTED_ZERO_DC_ERRORS
+    assert tuple(unit_energy_errors) == morlet.MORLET_EXPECTED_UNIT_ENERGY_ERRORS
+
 
 def test_known_frequency_and_one_sample_delay_recover_band_phase_and_seconds() -> None:
     for band_index, frequency in enumerate(morlet.MORLET_FREQUENCIES_HZ):

From e982d6909ada59c903cfb4a7e7c42e558a523164 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 01:15:52 -0500
Subject: [PATCH 04/95] feat: connect private-host training lifecycle and
 checkpoint observers

---
 RELEASE_FILES.sha256                          |   10 +-
 docs/HOST_RUNTIME_INTEGRATION.md              |  184 ++
 src/phaseset_core/cli.py                      |    3 +-
 src/phaseset_core/host.py                     | 1696 +++++++++++++++++
 src/phaseset_core/training.py                 |   10 +
 tests/test_checkpoint_observer_integration.py |  207 ++
 tests/test_phaseset_cli_production_bridge.py  |   29 +
 tests/test_phaseset_host.py                   |  532 ++++++
 8 files changed, 2667 insertions(+), 4 deletions(-)
 create mode 100644 docs/HOST_RUNTIME_INTEGRATION.md
 create mode 100644 src/phaseset_core/host.py
 create mode 100644 tests/test_checkpoint_observer_integration.py
 create mode 100644 tests/test_phaseset_host.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index d48934c..357e4f2 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -20,6 +20,7 @@ e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA
 3633e37c3eae3289d552a363e565bee027ebd83661f1c44ea136f9498de9e5af	19059	docs/EXECUTION_PROTOCOL.md
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
+3861d213e951e059a3fa044c9f4c87eaa93054aef84323315db67521865838f2	9883	docs/HOST_RUNTIME_INTEGRATION.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
@@ -112,7 +113,7 @@ dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phase
 2e5fb5280ed27a46c587d07a60ce663fce676cbdd30b059ec34882b4507d157f	604	src/phaseset_core/__init__.py
 9668a89535264a3ed6b58ffc7ac31b4904a4d8f68636d405e15cbf7368096716	8437	src/phaseset_core/_morlet_oracle_bytes.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
-0ddb4070d6af6a3221afda66aef84261255af3008ca8da0a2cc3b471d9b682fa	15837	src/phaseset_core/cli.py
+707ad75839dd8e2a6fb3ed82536d66ccebb6aa049bcab12f198a9d6311623cb2	15896	src/phaseset_core/cli.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 9636324cff46e2a89bb93b71f8c3a4cc07544f8dd0e10e2da60d5a2ffbc7b356	25892	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
@@ -120,6 +121,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
+22f9855b0f689c3f34de78ec47a53f15b1c9c8fbedaf57130d5ced1e5da561f4	67811	src/phaseset_core/host.py
 47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
@@ -132,8 +134,9 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 0c8f52f8822f4e3822e5124ce794d482bb09d0545c73f5c6b341c1bbd7bc86f2	35557	src/phaseset_core/streaming_autograd.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
-e5de1fee169341f56721b198d0708fd21f56bd26d2771fad2ea88bb03bbb2d94	153786	src/phaseset_core/training.py
+f75b9a59dfc2ccfc04ed5f418fb318f3bff6bc11cfe61021f5ff552d71a2ae7f	154285	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
+c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 af9b5adc42d82b9a0aa4ed744bb069e36b7dd041c1957fdc97d17eb814a0fc0c	17284	tests/test_phasepair_core_base_qualification.py
 ad630675cc642350a988d92de6176053d1bfab5f81cbc49548cb0b5dbeb679d9	23596	tests/test_phasepair_core_batching_readiness.py
@@ -163,7 +166,7 @@ b801d46fb184d99c65825d7f9bf91cbced022418600a95d421c5d9244dbded13	19373	tests/tes
 4da1d6c1b05bc485b9afb78776264ae7757744f104c37b947cdce0280cdfb172	2347	tests/test_phasepair_posix_checkpoint_ownership.py
 fa34950fb26f63d5d1ba169499e6e4af49c9b691f33c5cb3aac6e961bdac9e55	18384	tests/test_phaseset_caption_fusion.py
 5dbbb81d6725f1185f4c12dc85653b6eac1e47ae2ffb7fb36f5b951eb8dea632	8848	tests/test_phaseset_cli.py
-ac57f0ea3950b7ac6795f6f9973cf68ece3d370b686835dc4f8a2892c7233fdb	12603	tests/test_phaseset_cli_production_bridge.py
+c3060c5567214d195e024152f2d427ff3a63f3cc5fef1ded01e73d6887a3431e	13653	tests/test_phaseset_cli_production_bridge.py
 a53fe5c6b53adb5c80421842f806fc45e8305360348d7065aa0ddfb0b9316dcb	6546	tests/test_phaseset_contracts.py
 a74143036a41dd9d8779dc4a9b70fe20e5b38d013ef23778d13e73c11273dbd8	26066	tests/test_phaseset_controls.py
 a17a50e801c389b4e24b2840f2aafcb7b476cbef86e2e142ad0af6245a54718f	4515	tests/test_phaseset_data.py
@@ -171,6 +174,7 @@ a17a50e801c389b4e24b2840f2aafcb7b476cbef86e2e142ad0af6245a54718f	4515	tests/test
 8bd6e95a35523d108153f668f8919e6f22858e8283e6308041751c6c92d0f904	18605	tests/test_phaseset_evaluation.py
 aea41df712a25be78841e655a6fbd91fc96217ce92e60d3ab842f185fc39a58c	41553	tests/test_phaseset_execution.py
 dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/test_phaseset_experiments.py
+4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
 a75f583fd0a233760b1a421ca51680afe4195c33f9ef4bce6e9a1737828f207e	14907	tests/test_phaseset_models.py
 e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test_phaseset_morlet.py
 6d7005585882e25be9479542aac96a5b2aeb1bddc5619a0f37c57f61f2cbe6c3	6348	tests/test_phaseset_objectives.py
diff --git a/docs/HOST_RUNTIME_INTEGRATION.md b/docs/HOST_RUNTIME_INTEGRATION.md
new file mode 100644
index 0000000..2e4041f
--- /dev/null
+++ b/docs/HOST_RUNTIME_INTEGRATION.md
@@ -0,0 +1,184 @@
+# PhaseSet private-host runtime integration
+
+The composition root is `src/phaseset_core/host.py`. It contains no server endpoint,
+credential, dataset root, or receipt payload.
+
+## Implemented production chain
+
+`host.RuntimeAdapterFactory(private_host_config)` is passed directly to
+`phaseset_core.cli.main(..., runtime_adapter_factory=factory)`.  The factory:
+
+1. loads an existing private receipt record;
+2. hashes every receipt artifact named by that record, including the rights,
+   runtime, execution, source/prepared-data, split, caption, scoring-census,
+   and hard-gallery records required by `PrivateReceiptAssertions`;
+3. reads every prepared NPZ once into a bounded buffer, authenticates that exact
+   buffer, and parses the same bytes without loading pickle data;
+4. constructs the repository's `ProductionRuntimeAdapter`.
+
+It never creates authority, rights, runtime, or execution receipts.  If an
+artifact is absent or changed, the factory raises the existing
+`ExecutionHold` codes and the public CLI remains authority zero.
+
+`PrivatePreparedDataSource` maps one immutable split manifest to the public
+`TrainingDataSource` protocol.  Each NPZ contains exactly:
+
+- `skeletons`: float32 `[B,K,200,22,3]`
+- `actor_mask`: bool `[B,K]`
+- `frame_mask`: bool `[B,200]`
+- `track_mask`: bool `[B,K,200,22]`
+- `actor_commitments`: uint8 `[B,K,32]` (ignored where actor_mask is false)
+- `group_commitments`: uint8 `[B,32]`
+- `text_embeddings`: float32 `[Q,D]`
+- `motion_positive_ids`: uint8 `[B,32]`
+- `text_positive_ids`: uint8 `[Q,32]`
+- `text_commitments`: uint8 `[Q,32]`
+
+`run-base` uses the formal seed-bound B0--B2 constructor and
+`PhaseSetTrainingRuntime`.  Its checkpoint directory must be
+`<attempt-root>/<attempt-id>/model-checkpoints`, allowing the harness to build
+the public write-once attempt/checkpoint/terminal ledger around the runtime
+checkpoint.  A controlled interruption is terminalized as
+`CONTROLLED_INTERRUPTION`, retaining a resumable checkpoint.
+
+The optional `checkpoint_observer` adds a post-atomic-write
+observer to the public runtime without changing model, optimizer, sampler, or
+numerical behavior. The host uses it to write live checkpoint receipts and a
+30-second heartbeat chain while `fit()` is active. The final update checkpoint
+of an epoch is deliberately deferred because the runtime immediately writes a
+validation checkpoint at the same global step, while `AttemptStore` requires
+strictly increasing receipt steps. Python failures and controlled interrupts
+terminalize against the latest live receipt. A report checkpoint restored from
+the predecessor is explicitly receipted into the new attempt even when `fit()`
+makes no progress and emits no observer event.
+
+Each active attempt holds an OS-released exclusive lock at
+`<attempt>/.lifetime.lock` from attempt creation through successful or failed
+terminal persistence. Heartbeats are immutable files under
+`<attempt>/heartbeats/`; checkpoint receipts are immutable files under
+`<attempt>/checkpoints/`; runtime payloads remain under
+`<attempt>/model-checkpoints/`. If a process is lost before it writes a
+terminal, `resume` may close the orphan only after nonblocking acquisition of
+the lifetime lock and a second complete attempt-chain verification while that
+lock remains held. It never infers death from PID identity or heartbeat age.
+
+An ordinary runtime or checkpoint-observer exception writes `failure.json` and
+a `HOST_EXECUTION_FAILED` terminal, then returns a `FAILED BackendExecution`
+whose artifacts are those actual files. `BaseException` paths attempt the same
+terminalization but are re-raised. If the failure or terminal cannot be
+persisted (for example, disk failure), the host propagates the error and does
+not manufacture a backend artifact.
+
+`resume` currently accepts only a base attempt.  The predecessor checkpoint
+must be under the same attempt root; the harness verifies its terminal and
+checkpoint receipt, creates a distinct resume-linked attempt with
+`AttemptStore.create_resumed`, and passes that exact `ResumeRecord` to the
+training runtime.
+
+## Executable invocation
+
+Install the package first. The
+private-host module accepts its private host configuration before `--` and
+passes everything after `--` through the public parser:
+
+```bash
+python -m phaseset_core.host --host-config private-run/host.json -- \
+  preflight --config configs/phaseset/training.json
+
+python -m phaseset_core.host --host-config private-run/host.json -- \
+  run-base \
+  --run-id phaseset-run-v1/BASE_QUALIFICATION/1729/B0 \
+  --checkpoint-dir private-run/attempts/base-1729-b0-a/model-checkpoints \
+  --config configs/phaseset/training.json
+
+python -m phaseset_core.host --host-config private-run/host.json -- \
+  run-residual \
+  --run-id phaseset-run-v1/RESIDUAL_TRAIN/1729/08 \
+  --base-checkpoint private-run/base/1729/selected-validation.pt \
+  --periodic-cache private-run/cache/1729 \
+  --checkpoint-dir private-run/attempts/residual-1729-08-a/model-checkpoints \
+  --config configs/phaseset/training.json
+```
+
+A trusted Python composition root can equivalently call:
+
+```python
+from phaseset_core.host import main
+
+raise SystemExit(main(PUBLIC_CLI_ARGUMENTS, config_path=PRIVATE_HOST_CONFIG))
+```
+
+The private host config schema is `phaseset-private-host-v1`.  It points to a
+prepared index, an existing receipt record plus the exact artifact file for
+each receipt digest, the installed source-tree digest, and runtime facts
+(device, BF16 qualification decision, edge budget, checkpoint cadence, and
+resume metadata).  Paths may be absolute or relative to the private config.
+They are never serialized by the public CLI.
+
+For residual runs, the optional `residual` object points to the one canonical
+base-qualification artifact and one canonical capacity-audit artifact per
+registered seed. The host process derives and verifies their SHA-256 values;
+the operator does not transcribe individual hashes. `--periodic-cache` names a
+seed directory containing canonical `record.json` (`PeriodicCacheRecord`) and
+the digest-bound `cache-content.npz`. The payload must expose float64
+`energy_floors[6]`. The host reads the payload once into a bounded buffer and
+hashes and parses those same bytes. It verifies seed, winning-base run, base
+terminal, qualification, and cache-content bindings before the public residual
+factory is called.
+
+The execution controller is responsible for creating the single private
+receipt record from the user's already granted execution scope and the actual
+rights/runtime/data measurements. This harness consumes and verifies that
+record; it does not ask the user to sign each digest and does not create a
+rights assertion when Embody approval is unknown.
+
+## Deliberate gaps (no placeholder completion)
+
+- Embody rights are not known. Until a real rights artifact participates in
+  the receipt record, preflight/run-base/run-residual/resume remain held.
+- The Embody-format conversion is outside this harness.  It must produce the
+  exact prepared index/split/NPZ seam above and bind it to the authenticated
+  prepared-data receipt.
+- `run-residual` is wired through the strict qualified-base loader, canonical
+  periodic-cache record, energy floors, canonical all-system capacity audit,
+  closed residual constructor, and the same attempt ledger. Residual resume is
+  still rejected: the host has no residual resume dispatch or reconstruction
+  path and does not claim otherwise.
+- `prepare-data`, `audit-split`, `qualify-base`, `build-periodic-cache`,
+  `evaluate`, `bootstrap`, and `render-paper` are not
+  implemented here.  Calling one raises before a `BackendExecution` exists.
+- The public `resume` parser has no stop-after-step option. It accepts B0/B1/B2
+  and registered final system IDs; the host currently implements base resume
+  only and recovers identity from the verified attempt ledger.
+- The current public training runtime does not accept a precomputed periodic
+  descriptor cache. The host authenticates the complete cache payload and
+  consumes its energy floors, but periodic descriptors are recomputed by the
+  model path. Wiring cached descriptors into training is a lower-level public
+  runtime gap and must not be represented as cache consumption in reports.
+- A hard power loss before the first atomic runtime checkpoint has no resumable
+  payload. The host refuses such a resume instead of manufacturing a checkpoint
+  receipt. SIGKILL/power loss cannot write a terminal synchronously; terminal
+  recovery occurs on the authorized resume operation.
+- Runtime checkpoint fields do not expose separate sampler, dataloader, and
+  dropout blobs.  The host receipt records the exact cursor/manifests and RNG
+  payload under those ledger identities; a future public checkpoint-to-ledger
+  helper would remove this local encoding convention.
+
+`score_execution_census_sha256` is treated as the preregistered census of score
+executions required by admission. It is never interpreted as a post-training
+score table and therefore does not make base training depend on future scores.
+
+## Observed tests and limits
+
+On the Linux server, 12 host contract tests passed, including a genuine child
+process lifetime-lock test and injected persistence failures. Four separate
+tiny CPU fits passed checkpoint-observer integration: durable update/validation
+callbacks, propagation of observer failure with a resumable checkpoint,
+unchanged model/checkpoint/loss/validation values with an attached observer,
+and a real checkpoint payload receipted by the attempt ledger. These use
+`synthetic_contract=True` and do not assert production admission or results.
+
+Report/failure JSON is file-fsynced and, on POSIX, parent-directory-fsynced
+before a terminal is persisted. A failed fsync cannot create a success terminal.
+Actual Embody rights, full model/runtime qualification, all registered runs,
+and sealed test remain separate requirements.
diff --git a/src/phaseset_core/cli.py b/src/phaseset_core/cli.py
index 98b307c..cb5680f 100644
--- a/src/phaseset_core/cli.py
+++ b/src/phaseset_core/cli.py
@@ -34,6 +34,7 @@
 _BASE_SYSTEM_IDS = tuple(system_id for system_id, _ in BASE_SYSTEMS)
 _FINAL_SYSTEM_IDS = tuple(system_id for system_id, _ in FINAL_SYSTEMS)
 _RESIDUAL_SYSTEM_IDS = tuple(system_id for system_id in _FINAL_SYSTEM_IDS if system_id != "00")
+_RESUME_SYSTEM_IDS = _BASE_SYSTEM_IDS + _FINAL_SYSTEM_IDS
 
 
 @dataclass(frozen=True, slots=True)
@@ -287,7 +288,7 @@ def build_parser() -> argparse.ArgumentParser:
     _add_config(resume)
     resume.add_argument("--attempt-dir", type=Path)
     resume.add_argument("--checkpoint", dest="resume_checkpoint", type=Path)
-    resume.add_argument("--system-id", choices=_FINAL_SYSTEM_IDS)
+    resume.add_argument("--system-id", choices=_RESUME_SYSTEM_IDS)
 
     return parser
 
diff --git a/src/phaseset_core/host.py b/src/phaseset_core/host.py
new file mode 100644
index 0000000..f86b65e
--- /dev/null
+++ b/src/phaseset_core/host.py
@@ -0,0 +1,1696 @@
+"""Private-host harness for the public PhaseSet runtime.
+
+This module is deliberately an in-process composition root.  It does not read
+an endpoint, key, or adapter import path from the public PhaseSet CLI.  A host
+configuration names existing private receipt artifacts and prepared numeric
+microbatches; missing or mismatched artifacts stop at the repository's
+existing ``ExecutionHold`` boundary.
+
+The implemented command surface is intentionally small: ``preflight``,
+``run-base``, ``run-residual``, and base ``resume``. Other commands raise
+before a ``BackendExecution`` can be manufactured.
+"""
+
+from __future__ import annotations
+
+import argparse
+import errno
+import hashlib
+import io
+import json
+import os
+import sys
+import threading
+from collections.abc import Iterable, Mapping, Sequence
+from contextlib import suppress
+from dataclasses import asdict, dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import BinaryIO
+
+import numpy as np
+import torch
+from phaseset_core import cli
+from phaseset_core.contracts import PreparedGroupBatch
+from phaseset_core.execution import (
+    AttemptRecord,
+    AttemptStore,
+    CheckpointRecord,
+    CommandIntent,
+    ExecutionHold,
+    HeartbeatRecord,
+    ResumeRecord,
+    RuntimeAdmissionRequest,
+    TerminalRecord,
+    artifact_sha256,
+    canonical_attempt_bytes,
+    parse_attempt_bytes,
+    parse_checkpoint_bytes,
+    parse_periodic_cache_bytes,
+    parse_terminal_bytes,
+)
+from phaseset_core.experiments import (
+    SEEDS,
+    BaseQualification,
+    BaseScore,
+    canonical_base_qualification_bytes,
+    parse_run_id,
+)
+from phaseset_core.production import (
+    BackendExecution,
+    PrivateReceiptAssertions,
+    ProductionRuntimeAdapter,
+)
+from phaseset_core.training import (
+    CheckpointArtifact,
+    PhaseSetTrainingRuntime,
+    ResidualCapacityAudit,
+    RetrievalTrainingBatch,
+    TrainingConfig,
+    TrainingReport,
+    construct_registered_base_seed_bound_system,
+    construct_registered_residual_seed_bound_system,
+    load_qualified_frozen_base,
+)
+
+HOST_CONFIG_SCHEMA = "phaseset-private-host-v1"
+PREPARED_INDEX_SCHEMA = "phaseset-prepared-index-v1"
+PREPARED_SPLIT_SCHEMA = "phaseset-prepared-training-split-v1"
+PERIODIC_CACHE_MAX_BYTES = 1024 * 1024
+PREPARED_BATCH_KEYS = frozenset(
+    {
+        "skeletons",
+        "actor_mask",
+        "frame_mask",
+        "track_mask",
+        "actor_commitments",
+        "group_commitments",
+        "text_embeddings",
+        "motion_positive_ids",
+        "text_positive_ids",
+        "text_commitments",
+    }
+)
+RECEIPT_DIGEST_FIELDS = (
+    "data_manifest_sha256",
+    "prepared_data_manifest_sha256",
+    "split_audit_sha256",
+    "rights_assertion_sha256",
+    "caption_manifest_sha256",
+    "score_execution_census_sha256",
+    "validation_evaluation_census_sha256",
+    "hard_gallery_freeze_binding_sha256",
+    "validation_hard_gallery_collection_sha256",
+    "runtime_assertion_sha256",
+    "execution_assertion_sha256",
+)
+
+
+class HostConfigurationError(ValueError):
+    """The private host configuration or prepared seam is not closed."""
+
+
+class _AttemptLease:
+    """Process-released exclusive lease held for one attempt's full lifetime."""
+
+    FILE_NAME = ".lifetime.lock"
+
+    def __init__(self, stream: BinaryIO) -> None:
+        self._stream = stream
+        self._released = False
+
+    @classmethod
+    def acquire(cls, attempt_directory: Path) -> _AttemptLease:
+        _reject_symlink_components(attempt_directory, "attempt directory")
+        if not attempt_directory.is_dir():
+            raise HostConfigurationError("attempt directory is absent")
+        path = attempt_directory / cls.FILE_NAME
+        flags = os.O_RDWR | os.O_CREAT
+        if hasattr(os, "O_NOFOLLOW"):
+            flags |= os.O_NOFOLLOW
+        try:
+            descriptor = os.open(path, flags, 0o600)
+        except OSError as error:
+            raise HostConfigurationError("attempt lifetime lease cannot be opened") from error
+        stream = os.fdopen(descriptor, "r+b", buffering=0)
+        try:
+            if os.name == "posix":
+                import fcntl
+
+                fcntl.flock(stream.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+            elif os.name == "nt":
+                import msvcrt
+
+                stream.seek(0, os.SEEK_END)
+                if stream.tell() == 0:
+                    stream.write(b"\x00")
+                stream.seek(0)
+                msvcrt.locking(stream.fileno(), msvcrt.LK_NBLCK, 1)
+            else:
+                raise HostConfigurationError(
+                    "attempt lifetime leases support POSIX and Windows hosts only"
+                )
+        except OSError as error:
+            stream.close()
+            contention_errors = {errno.EACCES, errno.EAGAIN, errno.EDEADLK}
+            if error.errno in contention_errors:
+                raise HostConfigurationError(
+                    "attempt lifetime lease is held by a live process"
+                ) from error
+            raise HostConfigurationError(
+                "attempt lifetime lease cannot be acquired"
+            ) from error
+        except BaseException:
+            stream.close()
+            raise
+        return cls(stream)
+
+    def release(self) -> None:
+        if self._released:
+            return
+        try:
+            if os.name == "posix":
+                import fcntl
+
+                fcntl.flock(self._stream.fileno(), fcntl.LOCK_UN)
+            elif os.name == "nt":
+                import msvcrt
+
+                self._stream.seek(0)
+                msvcrt.locking(self._stream.fileno(), msvcrt.LK_UNLCK, 1)
+        finally:
+            self._released = True
+            self._stream.close()
+
+    def __enter__(self) -> _AttemptLease:
+        return self
+
+    def __exit__(self, *_: object) -> None:
+        self.release()
+
+
+@dataclass(frozen=True, slots=True)
+class ResidualArtifacts:
+    qualification: Path
+    capacity_audits: Mapping[int, Path]
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _sha256_bytes(value: bytes) -> str:
+    return hashlib.sha256(value).hexdigest()
+
+
+def _sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        while chunk := stream.read(1024 * 1024):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def _read_bounded_bytes(path: Path, *, max_bytes: int, label: str) -> bytes:
+    if type(max_bytes) is not int or max_bytes < 1:
+        raise HostConfigurationError(f"{label} byte limit must be positive")
+    _reject_symlink_components(path, label)
+    if not path.is_file():
+        raise HostConfigurationError(f"{label} must be a regular existing file")
+    with path.open("rb") as stream:
+        raw = stream.read(max_bytes + 1)
+    if not raw or len(raw) > max_bytes:
+        raise HostConfigurationError(f"{label} size is outside the host limit")
+    return raw
+
+
+def _utc_now() -> str:
+    return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+
+def _load_json_object(path: Path, label: str) -> tuple[bytes, dict[str, object]]:
+    if path.is_symlink() or not path.is_file():
+        raise HostConfigurationError(f"{label} must be a regular non-symlink file")
+    raw = path.read_bytes()
+    try:
+        value = json.loads(raw)
+    except (UnicodeDecodeError, json.JSONDecodeError) as error:
+        raise HostConfigurationError(f"{label} is not valid JSON") from error
+    if type(value) is not dict:
+        raise HostConfigurationError(f"{label} must contain one JSON object")
+    return raw, value
+
+
+def _closed_keys(value: Mapping[str, object], expected: set[str], label: str) -> None:
+    if set(value) != expected:
+        raise HostConfigurationError(f"{label} keys are not closed")
+
+
+def _load_base_qualification(path: Path) -> tuple[BaseQualification, str]:
+    raw, value = _load_json_object(path, "base qualification")
+    expected_keys = {
+        "authority",
+        "completion_sha256s",
+        "evaluator_sha256",
+        "production",
+        "query_census_sha256",
+        "result_claimed",
+        "schema",
+        "score_artifact_sha256s",
+        "score_rows",
+        "score_rows_sha256",
+        "selected_checkpoint_sha256s",
+        "status",
+        "system_mean_rows",
+        "system_resource_rows",
+        "validation_manifest_sha256",
+        "winner_checkpoint_sha256s",
+        "winner_frozen_runtime_latency_ns",
+        "winner_parameter_count",
+        "winner_system_id",
+        "winner_system_name",
+        "winner_terminal_sha256s",
+    }
+    _closed_keys(value, expected_keys, "base qualification")
+    score_values = value["score_rows"]
+    mean_values = value["system_mean_rows"]
+    resource_values = value["system_resource_rows"]
+    if not all(
+        type(rows) is list for rows in (score_values, mean_values, resource_values)
+    ):
+        raise HostConfigurationError(
+            "base qualification row collections must be arrays"
+        )
+    try:
+        score_rows = tuple(BaseScore(**row) for row in score_values)
+        mean_rows = tuple(
+            (row["system_id"], row["mean_numerator"], row["mean_denominator"])
+            for row in mean_values
+        )
+        resource_rows = tuple(
+            (
+                row["system_id"],
+                row["parameter_count"],
+                row["frozen_runtime_latency_ns"],
+            )
+            for row in resource_values
+        )
+        qualification = BaseQualification(
+            score_rows=score_rows,
+            winner_system_id=value["winner_system_id"],
+            winner_system_name=value["winner_system_name"],
+            winner_parameter_count=value["winner_parameter_count"],
+            winner_frozen_runtime_latency_ns=value["winner_frozen_runtime_latency_ns"],
+            system_mean_rows=mean_rows,
+            system_resource_rows=resource_rows,
+            completion_sha256s=tuple(value["completion_sha256s"]),
+            selected_checkpoint_sha256s=tuple(value["selected_checkpoint_sha256s"]),
+            winner_terminal_sha256s=tuple(value["winner_terminal_sha256s"]),
+            winner_checkpoint_sha256s=tuple(value["winner_checkpoint_sha256s"]),
+            validation_manifest_sha256=value["validation_manifest_sha256"],
+            query_census_sha256=value["query_census_sha256"],
+            evaluator_sha256=value["evaluator_sha256"],
+            score_artifact_sha256s=tuple(value["score_artifact_sha256s"]),
+            score_rows_sha256=value["score_rows_sha256"],
+            schema=value["schema"],
+            status=value["status"],
+            authority=value["authority"],
+            production=value["production"],
+            result_claimed=value["result_claimed"],
+        )
+    except (KeyError, TypeError) as error:
+        raise HostConfigurationError("base qualification rows are malformed") from error
+    digest = _sha256_bytes(raw)
+    if canonical_base_qualification_bytes(qualification, expected_sha256=digest) != raw:
+        raise HostConfigurationError("base qualification bytes are not canonical")
+    return qualification, digest
+
+
+def _load_capacity_audit(path: Path) -> tuple[ResidualCapacityAudit, str]:
+    raw, value = _load_json_object(path, "residual capacity audit")
+    _closed_keys(
+        value,
+        {
+            "code_artifact_sha256",
+            "edge_budget",
+            "energy_floors_sha256",
+            "environment_sha256",
+            "frozen_base_checkpoint_sha256",
+            "frozen_base_state_sha256",
+            "full_system_id",
+            "qualified_base_selection_sha256",
+            "rows",
+            "schema",
+            "seed",
+            "tolerance_decimal",
+        },
+        "residual capacity audit",
+    )
+    if value["full_system_id"] != "08" or value["tolerance_decimal"] != "0.01":
+        raise HostConfigurationError("residual capacity audit constants changed")
+    rows = value["rows"]
+    if type(rows) is not list:
+        raise HostConfigurationError("residual capacity rows must be an array")
+    try:
+        audit = ResidualCapacityAudit(
+            rows=tuple(
+                (
+                    row["system_id"],
+                    row["trainable_parameters"],
+                    row["behavior_sha256"],
+                )
+                for row in rows
+            ),
+            seed=value["seed"],
+            edge_budget=value["edge_budget"],
+            energy_floors_sha256=value["energy_floors_sha256"],
+            frozen_base_checkpoint_sha256=value["frozen_base_checkpoint_sha256"],
+            frozen_base_state_sha256=value["frozen_base_state_sha256"],
+            qualified_base_selection_sha256=value["qualified_base_selection_sha256"],
+            code_artifact_sha256=value["code_artifact_sha256"],
+            environment_sha256=value["environment_sha256"],
+            schema=value["schema"],
+        )
+    except (KeyError, TypeError) as error:
+        raise HostConfigurationError("residual capacity rows are malformed") from error
+    if audit.canonical_bytes() != raw:
+        raise HostConfigurationError("residual capacity audit bytes are not canonical")
+    return audit, _sha256_bytes(raw)
+
+
+def _load_periodic_cache(
+    root: Path,
+    *,
+    seed: int,
+    qualification: BaseQualification,
+    qualification_sha256: str,
+) -> np.ndarray:
+    _reject_symlink_components(root, "periodic cache")
+    directory = root.resolve()
+    if not directory.is_dir():
+        raise HostConfigurationError("periodic cache must be a non-symlink directory")
+    record_path = directory / "record.json"
+    payload_path = directory / "cache-content.npz"
+    if any(
+        path.is_symlink() or not path.is_file() for path in (record_path, payload_path)
+    ):
+        raise HostConfigurationError("periodic cache record or payload is absent")
+    record = parse_periodic_cache_bytes(record_path.read_bytes())
+    payload_raw = _read_bounded_bytes(
+        payload_path,
+        max_bytes=PERIODIC_CACHE_MAX_BYTES,
+        label="periodic cache payload",
+    )
+    expected_run_id = (
+        f"phaseset-run-v1/BASE_QUALIFICATION/{seed}/{qualification.winner_system_id}"
+    )
+    expected_terminal = qualification.winner_terminal_sha256s[SEEDS.index(seed)]
+    if (
+        record.seed != seed
+        or record.qualified_base_system_id != qualification.winner_system_id
+        or record.source_run_id != expected_run_id
+        or record.base_terminal_sha256 != expected_terminal
+        or record.qualification_sha256 != qualification_sha256
+        or record.cache_content_sha256 != _sha256_bytes(payload_raw)
+    ):
+        raise HostConfigurationError(
+            "periodic cache is not bound to the selected seed/base/qualification"
+        )
+    try:
+        with np.load(io.BytesIO(payload_raw), allow_pickle=False) as archive:
+            if "energy_floors" not in archive.files:
+                raise HostConfigurationError("periodic cache lacks energy_floors")
+            floors = np.array(archive["energy_floors"], copy=True)
+    except (OSError, ValueError) as error:
+        raise HostConfigurationError(
+            "periodic cache payload is not safe numeric NPZ"
+        ) from error
+    if floors.dtype != np.float64 or floors.shape != (6,):
+        raise HostConfigurationError("periodic cache energy_floors must be float64 [6]")
+    return np.ascontiguousarray(floors)
+
+
+def _reject_symlink_components(path: Path, label: str) -> None:
+    for component in (path, *path.parents):
+        if component.is_symlink():
+            raise HostConfigurationError(f"{label} cannot traverse a symlink")
+
+
+def _resolve_existing_file(base: Path, raw: object, label: str) -> Path:
+    if type(raw) is not str or not raw or "\x00" in raw:
+        raise HostConfigurationError(f"{label} must be a nonempty path string")
+    unresolved = base / raw if not Path(raw).is_absolute() else Path(raw)
+    _reject_symlink_components(unresolved, label)
+    candidate = unresolved.resolve()
+    if not candidate.is_file():
+        raise HostConfigurationError(f"{label} must be a regular existing file")
+    return candidate
+
+
+def _resolve_config_path(base: Path, raw: object, label: str) -> Path:
+    if type(raw) is not str or not raw or "\x00" in raw:
+        raise HostConfigurationError(f"{label} must be a nonempty path string")
+    unresolved = base / raw if not Path(raw).is_absolute() else Path(raw)
+    _reject_symlink_components(unresolved, label)
+    return unresolved.resolve()
+
+
+def _resolve_under(root: Path, raw: object, label: str) -> Path:
+    if type(raw) is not str or not raw or "\x00" in raw or Path(raw).is_absolute():
+        raise HostConfigurationError(f"{label} must be a relative path")
+    unresolved = root / raw
+    _reject_symlink_components(unresolved, label)
+    candidate = unresolved.resolve()
+    try:
+        candidate.relative_to(root.resolve())
+    except ValueError as error:
+        raise HostConfigurationError(
+            f"{label} escapes its prepared-data root"
+        ) from error
+    if not candidate.is_file():
+        raise HostConfigurationError(f"{label} must be a regular existing file")
+    return candidate
+
+
+def _bytes32_rows(value: np.ndarray, label: str) -> tuple[bytes, ...]:
+    if value.dtype != np.uint8 or value.ndim != 2 or value.shape[1] != 32:
+        raise HostConfigurationError(f"{label} must be uint8 [N,32]")
+    return tuple(bytes(row) for row in np.ascontiguousarray(value))
+
+
+def _actor_commitment_rows(
+    values: np.ndarray, actor_mask: np.ndarray
+) -> tuple[tuple[bytes | None, ...], ...]:
+    if (
+        values.dtype != np.uint8
+        or values.ndim != 3
+        or values.shape[:2] != actor_mask.shape
+        or values.shape[2] != 32
+    ):
+        raise HostConfigurationError("actor_commitments must be uint8 [B,K,32]")
+    rows: list[tuple[bytes | None, ...]] = []
+    for batch_index in range(values.shape[0]):
+        row: list[bytes | None] = []
+        for actor_index in range(values.shape[1]):
+            if bool(actor_mask[batch_index, actor_index]):
+                row.append(bytes(values[batch_index, actor_index]))
+            else:
+                row.append(None)
+        rows.append(tuple(row))
+    return tuple(rows)
+
+
+@dataclass(frozen=True, slots=True)
+class _BatchEntry:
+    path: Path
+    sha256: str
+
+
+class PrivatePreparedDataSource:
+    """Strict ``allow_pickle=False`` prepared-microbatch source.
+
+    A split manifest lists immutable NPZ files.  Epoch ordering is a stable
+    digest sort over the frozen split-manifest identity, seed, epoch, and row;
+    no global RNG state or filesystem enumeration enters sampling.
+    """
+
+    def __init__(self, manifest: str | Path, *, max_batch_bytes: int = 2**31) -> None:
+        path = Path(manifest).resolve()
+        raw, value = _load_json_object(path, "prepared split manifest")
+        _closed_keys(value, {"batches", "schema", "split"}, "prepared split manifest")
+        if value["schema"] != PREPARED_SPLIT_SCHEMA:
+            raise HostConfigurationError("prepared split schema is not registered")
+        split = value["split"]
+        if split not in {"train", "val"}:
+            raise HostConfigurationError("prepared split must be train or val")
+        rows = value["batches"]
+        if type(rows) is not list or not rows:
+            raise HostConfigurationError("prepared split must list at least one batch")
+        if type(max_batch_bytes) is not int or max_batch_bytes < 1:
+            raise HostConfigurationError("max_batch_bytes must be positive")
+        entries: list[_BatchEntry] = []
+        seen_paths: set[Path] = set()
+        for index, item in enumerate(rows):
+            if type(item) is not dict:
+                raise HostConfigurationError(f"batch row {index} must be an object")
+            _closed_keys(item, {"path", "sha256"}, f"batch row {index}")
+            batch_path = _resolve_under(
+                path.parent, item["path"], f"batch row {index} path"
+            )
+            digest = item["sha256"]
+            if type(digest) is not str or len(digest) != 64 or digest.lower() != digest:
+                raise HostConfigurationError(f"batch row {index} sha256 is malformed")
+            if batch_path in seen_paths:
+                raise HostConfigurationError("prepared split repeats a batch path")
+            batch_raw = _read_bounded_bytes(
+                batch_path,
+                max_bytes=max_batch_bytes,
+                label=f"batch row {index}",
+            )
+            if _sha256_bytes(batch_raw) != digest:
+                raise HostConfigurationError("prepared batch digest mismatch")
+            seen_paths.add(batch_path)
+            entries.append(_BatchEntry(batch_path, digest))
+        self.split = split
+        self.manifest_sha256 = _sha256_bytes(raw)
+        self._entries = tuple(entries)
+        self._max_batch_bytes = max_batch_bytes
+
+    def _load_batch(self, entry: _BatchEntry) -> RetrievalTrainingBatch:
+        raw = _read_bounded_bytes(
+            entry.path,
+            max_bytes=self._max_batch_bytes,
+            label="prepared batch",
+        )
+        if _sha256_bytes(raw) != entry.sha256:
+            raise HostConfigurationError(
+                "prepared batch changed after source construction"
+            )
+        try:
+            with np.load(io.BytesIO(raw), allow_pickle=False) as archive:
+                if set(archive.files) != PREPARED_BATCH_KEYS:
+                    raise HostConfigurationError(
+                        "prepared batch NPZ keys are not closed"
+                    )
+                arrays = {
+                    name: np.array(archive[name], copy=True) for name in archive.files
+                }
+        except (OSError, ValueError) as error:
+            raise HostConfigurationError(
+                "prepared batch is not a safe numeric NPZ"
+            ) from error
+        exact_dtypes = {
+            "skeletons": np.dtype(np.float32),
+            "actor_mask": np.dtype(np.bool_),
+            "frame_mask": np.dtype(np.bool_),
+            "track_mask": np.dtype(np.bool_),
+            "actor_commitments": np.dtype(np.uint8),
+            "group_commitments": np.dtype(np.uint8),
+            "text_embeddings": np.dtype(np.float32),
+            "motion_positive_ids": np.dtype(np.uint8),
+            "text_positive_ids": np.dtype(np.uint8),
+            "text_commitments": np.dtype(np.uint8),
+        }
+        for name, dtype in exact_dtypes.items():
+            if arrays[name].dtype != dtype:
+                raise HostConfigurationError(
+                    f"prepared batch {name} has the wrong dtype"
+                )
+        actor_mask = np.ascontiguousarray(arrays["actor_mask"])
+        groups = PreparedGroupBatch(
+            skeletons=np.ascontiguousarray(arrays["skeletons"]),
+            actor_mask=actor_mask,
+            frame_mask=np.ascontiguousarray(arrays["frame_mask"]),
+            track_mask=np.ascontiguousarray(arrays["track_mask"]),
+            actor_commitments=_actor_commitment_rows(
+                arrays["actor_commitments"], actor_mask
+            ),
+            group_commitments=_bytes32_rows(
+                arrays["group_commitments"], "group_commitments"
+            ),
+        )
+        text = np.ascontiguousarray(arrays["text_embeddings"])
+        return RetrievalTrainingBatch(
+            groups=groups,
+            text_embeddings=torch.from_numpy(text),
+            motion_positive_ids=_bytes32_rows(
+                arrays["motion_positive_ids"], "motion_positive_ids"
+            ),
+            text_positive_ids=_bytes32_rows(
+                arrays["text_positive_ids"], "text_positive_ids"
+            ),
+            text_commitments=_bytes32_rows(
+                arrays["text_commitments"], "text_commitments"
+            ),
+            split=self.split,
+        )
+
+    def iter_epoch(self, *, epoch: int, seed: int) -> Iterable[RetrievalTrainingBatch]:
+        if (
+            type(epoch) is not int
+            or epoch < 0
+            or epoch >= 2**64
+            or type(seed) is not int
+            or not -(2**127) <= seed < 2**127
+        ):
+            raise HostConfigurationError(
+                "epoch and seed must be exact nonnegative/int values"
+            )
+        prefix = bytes.fromhex(self.manifest_sha256) + seed.to_bytes(
+            16, "big", signed=True
+        )
+        ordered = sorted(
+            enumerate(self._entries),
+            key=lambda row: hashlib.sha256(
+                prefix + epoch.to_bytes(8, "big") + row[0].to_bytes(8, "big")
+            ).digest(),
+        )
+        for _, entry in ordered:
+            yield self._load_batch(entry)
+
+
+@dataclass(frozen=True, slots=True)
+class HostConfig:
+    path: Path
+    prepared_index: Path
+    receipt_record: Path
+    receipt_artifacts: Mapping[str, Path]
+    source_tree_sha256: str
+    device: str
+    request_bf16: bool
+    bf16_runtime_qualified: bool
+    edge_budget: int
+    checkpoint_every_updates: int
+    max_batch_bytes: int
+    resume_retry_class: str
+    corrective_change_sha256: str
+    residual_artifacts: ResidualArtifacts | None
+
+    @classmethod
+    def load(cls, path: str | Path) -> HostConfig:
+        config_path = Path(path).resolve()
+        _, value = _load_json_object(config_path, "host configuration")
+        required_keys = {
+            "prepared_index",
+            "receipts",
+            "runtime",
+            "schema",
+            "source_tree_sha256",
+        }
+        if frozenset(value) not in {
+            frozenset(required_keys),
+            frozenset(required_keys | {"residual"}),
+        }:
+            raise HostConfigurationError("host configuration keys are not closed")
+        if value["schema"] != HOST_CONFIG_SCHEMA:
+            raise HostConfigurationError("host configuration schema is not registered")
+        receipts = value["receipts"]
+        runtime = value["runtime"]
+        if type(receipts) is not dict or type(runtime) is not dict:
+            raise HostConfigurationError("receipts and runtime must be objects")
+        _closed_keys(receipts, {"artifacts", "record"}, "receipt configuration")
+        _closed_keys(
+            runtime,
+            {
+                "bf16_runtime_qualified",
+                "checkpoint_every_updates",
+                "corrective_change_sha256",
+                "device",
+                "edge_budget",
+                "max_batch_bytes",
+                "request_bf16",
+                "resume_retry_class",
+            },
+            "runtime configuration",
+        )
+        artifacts = receipts["artifacts"]
+        if type(artifacts) is not dict or set(artifacts) != set(RECEIPT_DIGEST_FIELDS):
+            raise HostConfigurationError("receipt artifact keys are not closed")
+        source_tree = value["source_tree_sha256"]
+        corrective = runtime["corrective_change_sha256"]
+        for digest, label in (
+            (source_tree, "source tree"),
+            (corrective, "corrective change"),
+        ):
+            if type(digest) is not str or len(digest) != 64 or digest.lower() != digest:
+                raise HostConfigurationError(f"{label} sha256 is malformed")
+        residual_value = value.get("residual")
+        residual_artifacts: ResidualArtifacts | None = None
+        if residual_value is not None:
+            if type(residual_value) is not dict:
+                raise HostConfigurationError("residual configuration must be an object")
+            _closed_keys(
+                residual_value,
+                {"capacity_audits", "qualification"},
+                "residual configuration",
+            )
+            capacity_values = residual_value["capacity_audits"]
+            if type(capacity_values) is not dict or set(capacity_values) != {
+                str(seed) for seed in SEEDS
+            }:
+                raise HostConfigurationError(
+                    "residual capacity audits must cover the three registered seeds"
+                )
+            residual_artifacts = ResidualArtifacts(
+                qualification=_resolve_config_path(
+                    config_path.parent,
+                    residual_value["qualification"],
+                    "base qualification artifact",
+                ),
+                capacity_audits={
+                    seed: _resolve_config_path(
+                        config_path.parent,
+                        capacity_values[str(seed)],
+                        f"capacity audit seed {seed}",
+                    )
+                    for seed in SEEDS
+                },
+            )
+        return cls(
+            path=config_path,
+            prepared_index=_resolve_config_path(
+                config_path.parent, value["prepared_index"], "prepared index"
+            ),
+            receipt_record=_resolve_config_path(
+                config_path.parent, receipts["record"], "receipt record"
+            ),
+            receipt_artifacts={
+                name: _resolve_config_path(
+                    config_path.parent, artifacts[name], f"receipt artifact {name}"
+                )
+                for name in RECEIPT_DIGEST_FIELDS
+            },
+            source_tree_sha256=source_tree,
+            device=runtime["device"],
+            request_bf16=runtime["request_bf16"],
+            bf16_runtime_qualified=runtime["bf16_runtime_qualified"],
+            edge_budget=runtime["edge_budget"],
+            checkpoint_every_updates=runtime["checkpoint_every_updates"],
+            max_batch_bytes=runtime["max_batch_bytes"],
+            resume_retry_class=runtime["resume_retry_class"],
+            corrective_change_sha256=corrective,
+            residual_artifacts=residual_artifacts,
+        )
+
+    def load_receipts(self) -> PrivateReceiptAssertions:
+        _, value = _load_json_object(self.receipt_record, "private receipt record")
+        expected = {"admitted_at_utc", "authority", *RECEIPT_DIGEST_FIELDS}
+        _closed_keys(value, expected, "private receipt record")
+        for name in RECEIPT_DIGEST_FIELDS:
+            if _sha256_file(self.receipt_artifacts[name]) != value[name]:
+                raise HostConfigurationError(
+                    f"private receipt artifact mismatch: {name}"
+                )
+        return PrivateReceiptAssertions(**value)
+
+    def validate_runtime(self) -> None:
+        """Validate host runtime knobs against the formal public contract."""
+
+        TrainingConfig(
+            stage="base",
+            seed=1729,
+            device=self.device,
+            request_bf16=self.request_bf16,
+            bf16_runtime_qualified=self.bf16_runtime_qualified,
+            edge_budget=self.edge_budget,
+            checkpoint_every_updates=self.checkpoint_every_updates,
+            synthetic_contract=False,
+        )
+        if type(self.max_batch_bytes) is not int or self.max_batch_bytes < 1:
+            raise HostConfigurationError("runtime max_batch_bytes must be positive")
+        if self.resume_retry_class not in {
+            "INFRA_TRANSIENT",
+            "RESOURCE",
+            "IMPLEMENTATION",
+            "DATA",
+        }:
+            raise HostConfigurationError("runtime resume_retry_class is not registered")
+
+    def load_sources(
+        self,
+    ) -> tuple[PrivatePreparedDataSource, PrivatePreparedDataSource]:
+        raw, value = _load_json_object(self.prepared_index, "prepared index")
+        _closed_keys(value, {"schema", "train", "val"}, "prepared index")
+        if value["schema"] != PREPARED_INDEX_SCHEMA:
+            raise HostConfigurationError("prepared index schema is not registered")
+        receipts = self.load_receipts()
+        if _sha256_bytes(raw) != receipts.prepared_data_manifest_sha256:
+            raise HostConfigurationError(
+                "prepared index differs from authenticated receipt"
+            )
+        sources: list[PrivatePreparedDataSource] = []
+        for split in ("train", "val"):
+            item = value[split]
+            if type(item) is not dict:
+                raise HostConfigurationError(
+                    f"prepared index {split} row must be an object"
+                )
+            _closed_keys(item, {"path", "sha256"}, f"prepared index {split} row")
+            path = _resolve_under(
+                self.prepared_index.parent, item["path"], f"{split} manifest"
+            )
+            if _sha256_file(path) != item["sha256"]:
+                raise HostConfigurationError(f"{split} manifest digest mismatch")
+            source = PrivatePreparedDataSource(
+                path, max_batch_bytes=self.max_batch_bytes
+            )
+            if source.split != split:
+                raise HostConfigurationError(
+                    f"{split} manifest declares the wrong split"
+                )
+            sources.append(source)
+        return sources[0], sources[1]
+
+
+def _jsonable(value: object) -> object:
+    if isinstance(value, Path):
+        return str(value)
+    if isinstance(value, dict):
+        return {str(key): _jsonable(item) for key, item in value.items()}
+    if isinstance(value, (list, tuple)):
+        return [_jsonable(item) for item in value]
+    return value
+
+
+def _write_once_json(path: Path, value: object) -> Path:
+    raw = _canonical_json_bytes(_jsonable(value))
+    with path.open("xb") as stream:
+        stream.write(raw)
+        stream.flush()
+        os.fsync(stream.fileno())
+    if os.name == "posix":
+        flags = os.O_RDONLY
+        if hasattr(os, "O_DIRECTORY"):
+            flags |= os.O_DIRECTORY
+        directory_descriptor = os.open(path.parent, flags)
+        try:
+            os.fsync(directory_descriptor)
+        finally:
+            os.close(directory_descriptor)
+    return path
+
+
+def _torch_value_sha256(value: object) -> str:
+    stream = io.BytesIO()
+    torch.save(value, stream)
+    return _sha256_bytes(stream.getvalue())
+
+
+def _load_runtime_checkpoint(path: Path) -> dict[str, object]:
+    try:
+        payload = torch.load(path, map_location="cpu", weights_only=False)
+    except TypeError:  # torch 2.0 does not expose weights_only.
+        payload = torch.load(path, map_location="cpu")
+    if type(payload) is not dict:
+        raise HostConfigurationError("training checkpoint payload is not an object")
+    return payload
+
+
+def _checkpoint_record(
+    checkpoint_path: Path,
+    checkpoint_sha256: str,
+    *,
+    attempt_id: str,
+    run_id: str,
+    written_at_utc: str,
+    parent_checkpoint_receipt_sha256: str | None,
+) -> CheckpointRecord:
+    payload = _load_runtime_checkpoint(checkpoint_path)
+    required = {
+        "model",
+        "optimizer",
+        "rng",
+        "epoch",
+        "update_index",
+        "global_step",
+        "train_manifest_sha256",
+        "val_manifest_sha256",
+        "best_validation_metric",
+        "best_checkpoint_name",
+        "best_checkpoint_sha256",
+    }
+    if not required.issubset(payload):
+        raise HostConfigurationError("training checkpoint lacks attempt-ledger state")
+    cursor = {
+        "epoch": payload["epoch"],
+        "global_step": payload["global_step"],
+        "update_index": payload["update_index"],
+    }
+    data_state = {
+        "train_manifest_sha256": payload["train_manifest_sha256"],
+        "val_manifest_sha256": payload["val_manifest_sha256"],
+    }
+    validation = {
+        "best_validation_metric": payload["best_validation_metric"],
+        "best_checkpoint_name": payload["best_checkpoint_name"],
+        "best_checkpoint_sha256": payload["best_checkpoint_sha256"],
+    }
+    rng_digest = _torch_value_sha256(payload["rng"])
+    return CheckpointRecord(
+        attempt_id=attempt_id,
+        run_id=run_id,
+        epoch_index=int(payload["epoch"]),
+        global_step=int(payload["global_step"]),
+        written_at_utc=written_at_utc,
+        model_state_sha256=_torch_value_sha256(payload["model"]),
+        optimizer_state_sha256=_torch_value_sha256(payload["optimizer"]),
+        rng_state_sha256=rng_digest,
+        sampler_state_sha256=_sha256_bytes(_canonical_json_bytes(cursor)),
+        dataloader_state_sha256=_sha256_bytes(_canonical_json_bytes(data_state)),
+        dropout_state_sha256=rng_digest,
+        validation_state_sha256=_sha256_bytes(_canonical_json_bytes(validation)),
+        checkpoint_payload_sha256=checkpoint_sha256,
+        parent_checkpoint_receipt_sha256=parent_checkpoint_receipt_sha256,
+    )
+
+
+class _LiveAttemptLedger:
+    """Write live heartbeats and checkpoint receipts during a blocking fit."""
+
+    HEARTBEAT_SECONDS = 30.0
+
+    def __init__(self, store: AttemptStore, attempt: AttemptRecord) -> None:
+        self.store = store
+        self.attempt = attempt
+        self.latest_heartbeat_sha256: str | None = None
+        self.latest_checkpoint_receipt_sha256: str | None = None
+        self.latest_checkpoint_payload_sha256: str | None = None
+        self.latest_global_step = 0
+        self._heartbeat_sequence = 0
+        self._error: BaseException | None = None
+        self._started = False
+        self._lock = threading.Lock()
+        self._stop = threading.Event()
+        self._thread = threading.Thread(
+            target=self._heartbeat_loop,
+            name=f"phaseset-heartbeat-{attempt.attempt_id}",
+            daemon=True,
+        )
+
+    def start(self) -> None:
+        with self._lock:
+            self._write_heartbeat("STARTING")
+        self._thread.start()
+        self._started = True
+
+    def _write_heartbeat(self, phase: str) -> None:
+        self.latest_heartbeat_sha256 = self.store.write_heartbeat(
+            HeartbeatRecord(
+                attempt_id=self.attempt.attempt_id,
+                run_id=self.attempt.run_id,
+                sequence=self._heartbeat_sequence,
+                global_step=self.latest_global_step,
+                observed_at_utc=_utc_now(),
+                phase=phase,
+                previous_heartbeat_sha256=self.latest_heartbeat_sha256,
+            )
+        )
+        self._heartbeat_sequence += 1
+
+    def _heartbeat_loop(self) -> None:
+        while not self._stop.wait(self.HEARTBEAT_SECONDS):
+            try:
+                with self._lock:
+                    self._write_heartbeat("RUNNING")
+            except BaseException as error:
+                self._error = error
+                self._stop.set()
+                return
+
+    def observe_checkpoint(self, artifact: CheckpointArtifact, reason: str) -> None:
+        """Training callback invoked only after the atomic checkpoint commit."""
+
+        if type(artifact) is not CheckpointArtifact or reason not in {
+            "update",
+            "validation",
+        }:
+            raise HostConfigurationError(
+                "live checkpoint callback payload is malformed"
+            )
+        payload = self._validated_checkpoint_payload(artifact)
+        # The runtime writes an update checkpoint at the last update and then a
+        # validation checkpoint at the same global step. AttemptStore requires
+        # strictly increasing checkpoint steps, so wait for the validation file.
+        if reason == "update" and payload.get("update_index") == payload.get(
+            "updates_per_epoch"
+        ):
+            return
+        self._record_checkpoint(artifact)
+
+    def ensure_checkpoint(self, artifact: CheckpointArtifact) -> None:
+        """Receipt the report checkpoint even when a restored fit made no progress."""
+
+        self._validated_checkpoint_payload(artifact)
+        with self._lock:
+            if (
+                self.latest_checkpoint_payload_sha256 == artifact.sha256
+                and self.latest_global_step == artifact.global_step
+            ):
+                return
+        self._record_checkpoint(artifact)
+
+    @staticmethod
+    def _validated_checkpoint_payload(
+        artifact: CheckpointArtifact,
+    ) -> Mapping[str, object]:
+        if type(artifact) is not CheckpointArtifact:
+            raise HostConfigurationError("runtime checkpoint artifact is malformed")
+        payload = _load_runtime_checkpoint(artifact.path)
+        if _sha256_file(artifact.path) != artifact.sha256:
+            raise HostConfigurationError("live checkpoint changed after atomic write")
+        return payload
+
+    def _record_checkpoint(self, artifact: CheckpointArtifact) -> None:
+        with self._lock:
+            if artifact.global_step <= self.latest_global_step and (
+                self.latest_checkpoint_receipt_sha256 is not None
+            ):
+                raise HostConfigurationError(
+                    "live checkpoint callback did not advance global_step"
+                )
+            receipt = _checkpoint_record(
+                artifact.path,
+                artifact.sha256,
+                attempt_id=self.attempt.attempt_id,
+                run_id=self.attempt.run_id,
+                written_at_utc=_utc_now(),
+                parent_checkpoint_receipt_sha256=(
+                    self.latest_checkpoint_receipt_sha256
+                ),
+            )
+            self.latest_checkpoint_receipt_sha256 = self.store.write_checkpoint(receipt)
+            self.latest_checkpoint_payload_sha256 = artifact.sha256
+            self.latest_global_step = artifact.global_step
+            self._write_heartbeat("CHECKPOINTING")
+
+    def stop(self) -> None:
+        self._stop.set()
+        if self._started:
+            self._thread.join(timeout=self.HEARTBEAT_SECONDS + 5.0)
+        if self._thread.is_alive():
+            raise HostConfigurationError("heartbeat thread did not stop")
+        if self._error is not None:
+            raise HostConfigurationError(
+                "live heartbeat writer failed"
+            ) from self._error
+
+
+class PhaseSetHostBackend:
+    """Request-bound backend for real base/residual training and base resume."""
+
+    def __init__(self, config: HostConfig, request: cli.CLICommandRequest) -> None:
+        self._config = config
+        self._request = request
+        self.backend_sha256 = _sha256_bytes(Path(__file__).read_bytes())
+
+    def authenticate(
+        self,
+        request: RuntimeAdmissionRequest,
+        receipts: PrivateReceiptAssertions,
+        adapter_sha256: str,
+    ) -> bool:
+        del adapter_sha256
+        if receipts != self._config.load_receipts():
+            return False
+        # Receipt fields are checked against their exact artifacts above.  The
+        # public ProductionRuntimeAdapter binds plan/matrix/config/handler hashes.
+        return request.command == self._request.command
+
+    def execute(self, intent: CommandIntent) -> BackendExecution:
+        if intent.command == "run-base":
+            return self._run_base(intent)
+        if intent.command == "run-residual":
+            return self._run_residual(intent)
+        if intent.command == "resume":
+            return self._resume_base(intent)
+        raise HostConfigurationError(
+            f"host harness does not implement command {intent.command}; no result was produced"
+        )
+
+    def _training_config(self, *, stage: str, seed: int) -> TrainingConfig:
+        return TrainingConfig(
+            stage=stage,
+            seed=seed,
+            device=self._config.device,
+            request_bf16=self._config.request_bf16,
+            bf16_runtime_qualified=self._config.bf16_runtime_qualified,
+            edge_budget=self._config.edge_budget,
+            checkpoint_every_updates=self._config.checkpoint_every_updates,
+            synthetic_contract=False,
+        )
+
+    def _execute_base_runtime(
+        self,
+        *,
+        run_id: str,
+        system_id: str,
+        seed: int,
+        checkpoint_directory: Path,
+        live_ledger: _LiveAttemptLedger,
+        resume_checkpoint: Path | None = None,
+        resume_attempt_root: Path | None = None,
+        resume_record: ResumeRecord | None = None,
+        stop_after_global_step: int | None = None,
+    ) -> TrainingReport:
+        train, val = self._config.load_sources()
+        config = self._training_config(stage="base", seed=seed)
+        system, binding = construct_registered_base_seed_bound_system(system_id, config)
+        runtime = PhaseSetTrainingRuntime(
+            system,
+            config,
+            checkpoint_directory,
+            initialization_binding=binding,
+            checkpoint_observer=live_ledger.observe_checkpoint,
+        )
+        return runtime.fit(
+            train,
+            val,
+            resume_checkpoint=resume_checkpoint,
+            resume_attempt_root=resume_attempt_root,
+            resume_record=resume_record,
+            stop_after_global_step=stop_after_global_step,
+        )
+
+    def _execute_residual_runtime(
+        self,
+        *,
+        system_id: str,
+        seed: int,
+        checkpoint_directory: Path,
+        live_ledger: _LiveAttemptLedger,
+        base_checkpoint: Path,
+        periodic_cache: Path,
+        stop_after_global_step: int | None,
+    ) -> TrainingReport:
+        artifacts = self._config.residual_artifacts
+        if artifacts is None:
+            raise HostConfigurationError(
+                "run-residual requires qualification and seed capacity-audit artifacts"
+            )
+        if (
+            artifacts.qualification.is_symlink()
+            or not artifacts.qualification.is_file()
+        ):
+            raise HostConfigurationError("base qualification artifact is absent")
+        audit_path = artifacts.capacity_audits[seed]
+        if audit_path.is_symlink() or not audit_path.is_file():
+            raise HostConfigurationError(
+                f"residual capacity-audit artifact is absent for seed {seed}"
+            )
+        qualification, qualification_sha256 = _load_base_qualification(
+            artifacts.qualification
+        )
+        qualified_base = load_qualified_frozen_base(
+            base_checkpoint,
+            qualification=qualification,
+            seed=seed,
+            expected_qualification_sha256=qualification_sha256,
+        )
+        energy_floors = _load_periodic_cache(
+            periodic_cache,
+            seed=seed,
+            qualification=qualification,
+            qualification_sha256=qualification_sha256,
+        )
+        capacity_audit, capacity_sha256 = _load_capacity_audit(audit_path)
+        train, val = self._config.load_sources()
+        config = self._training_config(stage="residual", seed=seed)
+        system, binding = construct_registered_residual_seed_bound_system(
+            system_id,
+            qualified_base,
+            config,
+            energy_floors=energy_floors,
+            residual_capacity_audit=capacity_audit,
+            expected_qualification_sha256=qualification_sha256,
+            expected_capacity_audit_sha256=capacity_sha256,
+        )
+        runtime = PhaseSetTrainingRuntime(
+            system,
+            config,
+            checkpoint_directory,
+            initialization_binding=binding,
+            checkpoint_observer=live_ledger.observe_checkpoint,
+        )
+        return runtime.fit(
+            train,
+            val,
+            stop_after_global_step=stop_after_global_step,
+        )
+
+    def _run_base(self, intent: CommandIntent) -> BackendExecution:
+        request = self._request
+        if request.run_id is None or request.checkpoint_directory is None:
+            raise HostConfigurationError(
+                "run-base requires run_id and --checkpoint-dir"
+            )
+        role, seed, system_id = parse_run_id(request.run_id)
+        if role != "BASE_QUALIFICATION" or intent.run_id != request.run_id:
+            raise HostConfigurationError("run-base identity mismatch")
+        _reject_symlink_components(request.checkpoint_directory, "checkpoint directory")
+        checkpoint_directory = request.checkpoint_directory.resolve()
+        if checkpoint_directory.name != "model-checkpoints":
+            raise HostConfigurationError(
+                "checkpoint directory must end in model-checkpoints"
+            )
+        attempt_directory = checkpoint_directory.parent
+        if attempt_directory.exists() or attempt_directory.is_symlink():
+            raise HostConfigurationError("new attempt directory already exists")
+        created_at = _utc_now()
+        attempt = AttemptRecord(
+            attempt_id=attempt_directory.name,
+            run_id=request.run_id,
+            created_at_utc=created_at,
+            plan_sha256=intent.plan_sha256,
+            matrix_sha256=intent.matrix_sha256,
+            training_config_sha256=intent.training_config_sha256,
+            source_tree_sha256=self._config.source_tree_sha256,
+        )
+        store = AttemptStore.create(attempt_directory.parent, attempt)
+        lease = _AttemptLease.acquire(store.path)
+        try:
+            live_ledger = _LiveAttemptLedger(store, attempt)
+            try:
+                live_ledger.start()
+                report = self._execute_base_runtime(
+                    run_id=request.run_id,
+                    system_id=system_id,
+                    seed=seed,
+                    checkpoint_directory=checkpoint_directory,
+                    live_ledger=live_ledger,
+                    stop_after_global_step=request.stop_after_global_step,
+                )
+                live_ledger.ensure_checkpoint(report.latest_checkpoint)
+                live_ledger.stop()
+                return self._close_attempt(store, attempt, report, live_ledger)
+            except Exception as error:
+                with suppress(Exception):
+                    live_ledger.stop()
+                return self._persist_failed_execution(
+                    store, attempt, live_ledger, error
+                )
+            except BaseException as error:
+                with suppress(Exception):
+                    live_ledger.stop()
+                with suppress(Exception):
+                    self._persist_failed_execution(
+                        store, attempt, live_ledger, error
+                    )
+                raise
+        finally:
+            lease.release()
+
+    def _run_residual(self, intent: CommandIntent) -> BackendExecution:
+        request = self._request
+        if (
+            request.run_id is None
+            or request.checkpoint_directory is None
+            or request.base_checkpoint is None
+            or request.periodic_cache is None
+        ):
+            raise HostConfigurationError(
+                "run-residual requires run_id, checkpoint-dir, base-checkpoint, "
+                "and periodic-cache"
+            )
+        role, seed, system_id = parse_run_id(request.run_id)
+        if role != "RESIDUAL_TRAIN" or intent.run_id != request.run_id:
+            raise HostConfigurationError("run-residual identity mismatch")
+        _reject_symlink_components(request.checkpoint_directory, "checkpoint directory")
+        checkpoint_directory = request.checkpoint_directory.resolve()
+        if checkpoint_directory.name != "model-checkpoints":
+            raise HostConfigurationError(
+                "checkpoint directory must end in model-checkpoints"
+            )
+        attempt_directory = checkpoint_directory.parent
+        if attempt_directory.exists() or attempt_directory.is_symlink():
+            raise HostConfigurationError("new attempt directory already exists")
+        _reject_symlink_components(request.base_checkpoint, "qualified base checkpoint")
+        base_checkpoint = request.base_checkpoint.resolve()
+        if not base_checkpoint.is_file():
+            raise HostConfigurationError("qualified base checkpoint is absent")
+        _reject_symlink_components(request.periodic_cache, "periodic cache")
+        periodic_cache = request.periodic_cache.resolve()
+        created_at = _utc_now()
+        attempt = AttemptRecord(
+            attempt_id=attempt_directory.name,
+            run_id=request.run_id,
+            created_at_utc=created_at,
+            plan_sha256=intent.plan_sha256,
+            matrix_sha256=intent.matrix_sha256,
+            training_config_sha256=intent.training_config_sha256,
+            source_tree_sha256=self._config.source_tree_sha256,
+        )
+        store = AttemptStore.create(attempt_directory.parent, attempt)
+        lease = _AttemptLease.acquire(store.path)
+        try:
+            live_ledger = _LiveAttemptLedger(store, attempt)
+            try:
+                live_ledger.start()
+                report = self._execute_residual_runtime(
+                    system_id=system_id,
+                    seed=seed,
+                    checkpoint_directory=checkpoint_directory,
+                    live_ledger=live_ledger,
+                    base_checkpoint=base_checkpoint,
+                    periodic_cache=periodic_cache,
+                    stop_after_global_step=request.stop_after_global_step,
+                )
+                live_ledger.ensure_checkpoint(report.latest_checkpoint)
+                live_ledger.stop()
+                return self._close_attempt(store, attempt, report, live_ledger)
+            except Exception as error:
+                with suppress(Exception):
+                    live_ledger.stop()
+                return self._persist_failed_execution(
+                    store, attempt, live_ledger, error
+                )
+            except BaseException as error:
+                with suppress(Exception):
+                    live_ledger.stop()
+                with suppress(Exception):
+                    self._persist_failed_execution(store, attempt, live_ledger, error)
+                raise
+        finally:
+            lease.release()
+
+    def _resume_base(self, intent: CommandIntent) -> BackendExecution:
+        request = self._request
+        if request.attempt_directory is None or request.resume_checkpoint is None:
+            raise HostConfigurationError(
+                "resume requires --attempt-dir and --checkpoint"
+            )
+        _reject_symlink_components(
+            request.attempt_directory, "resume attempt directory"
+        )
+        _reject_symlink_components(request.resume_checkpoint, "resume checkpoint")
+        new_directory = request.attempt_directory.resolve()
+        checkpoint = request.resume_checkpoint.resolve()
+        if not checkpoint.is_file():
+            raise HostConfigurationError(
+                "resume checkpoint must be a regular existing file"
+            )
+        if checkpoint.parent.name != "model-checkpoints":
+            raise HostConfigurationError(
+                "resume checkpoint is outside model-checkpoints"
+            )
+        predecessor_directory = checkpoint.parent.parent
+        if predecessor_directory.parent != new_directory.parent:
+            raise HostConfigurationError("resume attempts must share one attempt root")
+        self._recover_crashed_attempt(predecessor_directory)
+        predecessor_attempt = parse_attempt_bytes(
+            (predecessor_directory / "attempt.json").read_bytes()
+        )
+        predecessor_terminal_raw = (
+            predecessor_directory / "terminal.json"
+        ).read_bytes()
+        predecessor_terminal = parse_terminal_bytes(predecessor_terminal_raw)
+        checkpoint_receipt_path = sorted(
+            (predecessor_directory / "checkpoints").glob("checkpoint-*.json")
+        )[-1]
+        checkpoint_receipt_raw = checkpoint_receipt_path.read_bytes()
+        checkpoint_receipt = parse_checkpoint_bytes(checkpoint_receipt_raw)
+        if checkpoint_receipt.checkpoint_payload_sha256 != _sha256_file(checkpoint):
+            raise HostConfigurationError(
+                "resume checkpoint differs from predecessor receipt"
+            )
+        role, seed, system_id = parse_run_id(predecessor_attempt.run_id)
+        if role != "BASE_QUALIFICATION":
+            raise HostConfigurationError(
+                "initial host resume supports base attempts only"
+            )
+        if request.system_id is not None and request.system_id != system_id:
+            raise HostConfigurationError("resume system differs from predecessor run")
+        created_at = _utc_now()
+        resumed_attempt = AttemptRecord(
+            attempt_id=new_directory.name,
+            run_id=predecessor_attempt.run_id,
+            created_at_utc=created_at,
+            plan_sha256=predecessor_attempt.plan_sha256,
+            matrix_sha256=predecessor_attempt.matrix_sha256,
+            training_config_sha256=predecessor_attempt.training_config_sha256,
+            source_tree_sha256=predecessor_attempt.source_tree_sha256,
+        )
+        resume = ResumeRecord(
+            new_attempt_id=resumed_attempt.attempt_id,
+            predecessor_attempt_id=predecessor_attempt.attempt_id,
+            run_id=predecessor_attempt.run_id,
+            created_at_utc=created_at,
+            predecessor_terminal_sha256=artifact_sha256(predecessor_terminal_raw),
+            checkpoint_receipt_sha256=artifact_sha256(checkpoint_receipt_raw),
+            corrective_change_sha256=self._config.corrective_change_sha256,
+            retry_class=self._config.resume_retry_class,
+        )
+        if (
+            predecessor_terminal.latest_checkpoint_receipt_sha256
+            != resume.checkpoint_receipt_sha256
+        ):
+            raise HostConfigurationError(
+                "resume does not select the predecessor terminal checkpoint"
+            )
+        store = AttemptStore.create_resumed(
+            new_directory.parent, resumed_attempt, resume
+        )
+        lease = _AttemptLease.acquire(store.path)
+        try:
+            live_ledger = _LiveAttemptLedger(store, resumed_attempt)
+            try:
+                live_ledger.start()
+                report = self._execute_base_runtime(
+                    run_id=predecessor_attempt.run_id,
+                    system_id=system_id,
+                    seed=seed,
+                    checkpoint_directory=new_directory / "model-checkpoints",
+                    live_ledger=live_ledger,
+                    resume_checkpoint=checkpoint,
+                    resume_attempt_root=new_directory.parent,
+                    resume_record=resume,
+                    stop_after_global_step=request.stop_after_global_step,
+                )
+                live_ledger.ensure_checkpoint(report.latest_checkpoint)
+                live_ledger.stop()
+                return self._close_attempt(
+                    store, resumed_attempt, report, live_ledger
+                )
+            except Exception as error:
+                with suppress(Exception):
+                    live_ledger.stop()
+                return self._persist_failed_execution(
+                    store, resumed_attempt, live_ledger, error
+                )
+            except BaseException as error:
+                with suppress(Exception):
+                    live_ledger.stop()
+                with suppress(Exception):
+                    self._persist_failed_execution(
+                        store, resumed_attempt, live_ledger, error
+                    )
+                raise
+        finally:
+            lease.release()
+
+    @staticmethod
+    def _recover_crashed_attempt(attempt_directory: Path) -> None:
+        """Close an orphaned attempt at its latest already-receipted checkpoint."""
+
+        terminal_path = attempt_directory / "terminal.json"
+        if terminal_path.exists() or terminal_path.is_symlink():
+            return
+        with _AttemptLease.acquire(attempt_directory):
+            # A predecessor may have terminalized between the first observation
+            # and lease acquisition. Re-read everything only while holding the
+            # same process-released lease used by active runs.
+            if terminal_path.exists() or terminal_path.is_symlink():
+                return
+            attempt_path = attempt_directory / "attempt.json"
+            if attempt_path.is_symlink() or not attempt_path.is_file():
+                raise HostConfigurationError(
+                    "crashed predecessor attempt record is absent"
+                )
+            attempt = parse_attempt_bytes(attempt_path.read_bytes())
+            store = AttemptStore.open(attempt_directory.parent, attempt.attempt_id)
+            checkpoint_paths = sorted(
+                (attempt_directory / "checkpoints").glob("checkpoint-*.json")
+            )
+            if not checkpoint_paths:
+                raise HostConfigurationError(
+                    "crashed predecessor has no live checkpoint receipt to resume"
+                )
+            checkpoint_raw = checkpoint_paths[-1].read_bytes()
+            parse_checkpoint_bytes(checkpoint_raw)
+            heartbeat_paths = sorted(
+                (attempt_directory / "heartbeats").glob("heartbeat-*.json")
+            )
+            heartbeat_sha256 = (
+                artifact_sha256(heartbeat_paths[-1].read_bytes())
+                if heartbeat_paths
+                else None
+            )
+            store.write_terminal(
+                TerminalRecord(
+                    attempt_id=attempt.attempt_id,
+                    run_id=attempt.run_id,
+                    completed_at_utc=_utc_now(),
+                    outcome="FAILED",
+                    attempt_receipt_sha256=artifact_sha256(
+                        canonical_attempt_bytes(attempt)
+                    ),
+                    latest_heartbeat_sha256=heartbeat_sha256,
+                    latest_checkpoint_receipt_sha256=artifact_sha256(checkpoint_raw),
+                    failure_code="HOST_PROCESS_LOST",
+                )
+            )
+
+    @staticmethod
+    def _persist_failed_execution(
+        store: AttemptStore,
+        attempt: AttemptRecord,
+        live_ledger: _LiveAttemptLedger,
+        error: BaseException,
+    ) -> BackendExecution:
+        """Persist a real failure artifact and terminal before returning FAILED."""
+
+        completed_at = _utc_now()
+        failure_path = _write_once_json(
+            store.path / "failure.json",
+            {
+                "attempt_id": attempt.attempt_id,
+                "authority": 0,
+                "completed_at_utc": completed_at,
+                "error_type": type(error).__qualname__,
+                "failure_code": "HOST_EXECUTION_FAILED",
+                "latest_checkpoint_receipt_sha256": (
+                    live_ledger.latest_checkpoint_receipt_sha256
+                ),
+                "latest_heartbeat_sha256": live_ledger.latest_heartbeat_sha256,
+                "production": False,
+                "result_claimed": False,
+                "run_id": attempt.run_id,
+                "schema": "phaseset-host-failure-v1",
+            },
+        )
+        terminal = TerminalRecord(
+            attempt_id=attempt.attempt_id,
+            run_id=attempt.run_id,
+            completed_at_utc=completed_at,
+            outcome="FAILED",
+            attempt_receipt_sha256=artifact_sha256(
+                canonical_attempt_bytes(attempt)
+            ),
+            latest_heartbeat_sha256=live_ledger.latest_heartbeat_sha256,
+            latest_checkpoint_receipt_sha256=(
+                live_ledger.latest_checkpoint_receipt_sha256
+            ),
+            failure_code="HOST_EXECUTION_FAILED",
+        )
+        store.write_terminal(terminal)
+        return BackendExecution(
+            outcome="FAILED",
+            completed_at_utc=completed_at,
+            artifacts=(failure_path, store.path / "terminal.json"),
+        )
+
+    def _close_attempt(
+        self,
+        store: AttemptStore,
+        attempt: AttemptRecord,
+        report: TrainingReport,
+        live_ledger: _LiveAttemptLedger,
+    ) -> BackendExecution:
+        if (
+            live_ledger.latest_checkpoint_receipt_sha256 is None
+            or live_ledger.latest_checkpoint_payload_sha256
+            != report.latest_checkpoint.sha256
+            or live_ledger.latest_global_step != report.global_step
+        ):
+            raise HostConfigurationError(
+                "terminal training checkpoint lacks a live attempt-ledger receipt"
+            )
+        succeeded = report.status == "COMPLETED"
+        terminal = TerminalRecord(
+            attempt_id=attempt.attempt_id,
+            run_id=attempt.run_id,
+            completed_at_utc=_utc_now(),
+            outcome="SUCCEEDED" if succeeded else "FAILED",
+            attempt_receipt_sha256=artifact_sha256(canonical_attempt_bytes(attempt)),
+            latest_heartbeat_sha256=live_ledger.latest_heartbeat_sha256,
+            latest_checkpoint_receipt_sha256=(
+                live_ledger.latest_checkpoint_receipt_sha256
+            ),
+            failure_code=None if succeeded else "CONTROLLED_INTERRUPTION",
+        )
+        report_path = _write_once_json(
+            store.path / "training-report.json", asdict(report)
+        )
+        store.write_terminal(terminal)
+        terminal_path = store.path / "terminal.json"
+        return BackendExecution(
+            outcome="COMPLETED" if succeeded else "FAILED",
+            completed_at_utc=terminal.completed_at_utc,
+            artifacts=(report_path, terminal_path),
+        )
+
+
+class RuntimeAdapterFactory:
+    """Callable passed directly to ``phaseset_core.cli.main``."""
+
+    def __init__(self, config_path: str | Path) -> None:
+        self._config_path = Path(config_path)
+
+    def __call__(self, request: cli.CLICommandRequest) -> ProductionRuntimeAdapter:
+        try:
+            config = HostConfig.load(self._config_path)
+            receipts = config.load_receipts()
+            config.validate_runtime()
+            # Preflight must prove that the authenticated prepared index and all
+            # immutable batch digests are present, without materializing tensors.
+            config.load_sources()
+        except (HostConfigurationError, OSError, TypeError, ValueError) as error:
+            text = str(error).lower()
+            if "rights" in text:
+                holds = (
+                    "HOLD_RIGHTS_GRANT_ABSENT",
+                    "HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",
+                )
+            elif "runtime" in text:
+                holds = (
+                    "HOLD_RUNTIME_RECEIPT_ABSENT",
+                    "HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",
+                )
+            elif "execution" in text:
+                holds = (
+                    "HOLD_EXECUTION_GRANT_ABSENT",
+                    "HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",
+                )
+            elif "prepared" in text or "batch" in text or "manifest" in text:
+                holds = (
+                    "HOLD_PREPARED_DATA_MANIFEST_ABSENT",
+                    "HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",
+                )
+            else:
+                holds = ("HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",)
+            raise ExecutionHold(request.command, holds) from error
+        backend = PhaseSetHostBackend(config, request)
+        return ProductionRuntimeAdapter(backend, receipts)
+
+
+def main(argv: Sequence[str] | None = None, *, config_path: str | Path) -> int:
+    """Run the public CLI with one request-aware private factory."""
+
+    return cli.main(argv, runtime_adapter_factory=RuntimeAdapterFactory(config_path))
+
+
+def entrypoint(argv: Sequence[str] | None = None) -> int:
+    """Private executable wrapper; arguments after ``--`` go to PhaseSet."""
+
+    parser = argparse.ArgumentParser(prog="phaseset-private-host")
+    parser.add_argument("--host-config", type=Path, required=True)
+    parser.add_argument("phaseset_arguments", nargs=argparse.REMAINDER)
+    namespace = parser.parse_args(argv)
+    arguments = list(namespace.phaseset_arguments)
+    if arguments[:1] == ["--"]:
+        arguments = arguments[1:]
+    if not arguments:
+        parser.error("a PhaseSet command is required after --")
+    return main(arguments, config_path=namespace.host_config)
+
+
+if __name__ == "__main__":
+    raise SystemExit(entrypoint(sys.argv[1:]))
+
+
+__all__ = [
+    "HostConfig",
+    "HostConfigurationError",
+    "PrivatePreparedDataSource",
+    "RuntimeAdapterFactory",
+    "entrypoint",
+    "main",
+]
diff --git a/src/phaseset_core/training.py b/src/phaseset_core/training.py
index e186beb..3896af8 100644
--- a/src/phaseset_core/training.py
+++ b/src/phaseset_core/training.py
@@ -1234,6 +1234,9 @@ class CheckpointArtifact:
     global_step: int
 
 
+CheckpointObserver = Callable[[CheckpointArtifact, Literal["update", "validation"]], None]
+
+
 @dataclass(frozen=True, slots=True)
 class TrainingReport:
     """Local runtime report; it intentionally cannot attest external execution."""
@@ -2816,6 +2819,7 @@ def __init__(
         checkpoint_directory: str | Path,
         *,
         initialization_binding: TrainingInitializationBinding | None = None,
+        checkpoint_observer: CheckpointObserver | None = None,
     ) -> None:
         if type(config) is not TrainingConfig:
             raise TypeError("config must be exact TrainingConfig")
@@ -2827,6 +2831,8 @@ def __init__(
             TrainingInitializationBinding
         ):
             raise TypeError("initialization_binding must be exact TrainingInitializationBinding")
+        if checkpoint_observer is not None and not callable(checkpoint_observer):
+            raise TypeError("checkpoint_observer must be callable or None")
         if not config.synthetic_contract and initialization_binding is None:
             raise TrainingRuntimeError(
                 "formal training requires a seed-bound initialization binding"
@@ -2882,6 +2888,7 @@ def __init__(
         self.system.to(self.device)
         self.precision = resolve_precision(config)
         self.checkpoint_directory = root.resolve()
+        self.checkpoint_observer = checkpoint_observer
         self._optimizer: torch.optim.AdamW | None = None
         self._scheduler: torch.optim.lr_scheduler.LambdaLR | None = None
         self._state = _CursorState()
@@ -3056,6 +3063,8 @@ def _write_checkpoint(self, reason: Literal["update", "validation"]) -> Checkpoi
         self._latest = artifact
         if self._state.best_checkpoint_name == name:
             self._state.best_checkpoint_sha256 = artifact.sha256
+        if self.checkpoint_observer is not None:
+            self.checkpoint_observer(artifact, reason)
         return artifact
 
     def _restore_checkpoint(
@@ -3600,6 +3609,7 @@ def fit(
     "BaseRetrievalSystem",
     "CHECKPOINT_SCHEMA",
     "CheckpointArtifact",
+    "CheckpointObserver",
     "EFFECTIVE_GLOBAL_BATCH",
     "GRADIENT_CLIP_NORM",
     "OFFICIAL_SEEDS",
diff --git a/tests/test_checkpoint_observer_integration.py b/tests/test_checkpoint_observer_integration.py
new file mode 100644
index 0000000..450cbe6
--- /dev/null
+++ b/tests/test_checkpoint_observer_integration.py
@@ -0,0 +1,207 @@
+"""NO_RESULT CPU integration tests for the checkpoint-observer seam."""
+
+from __future__ import annotations
+
+import hashlib
+from pathlib import Path
+
+import pytest
+import torch
+from phaseset_core import cli, host
+from phaseset_core.execution import AttemptRecord, AttemptStore, parse_checkpoint_bytes
+from phaseset_core.training import PhaseSetTrainingRuntime, TrainingConfig
+from test_phaseset_training import (
+    _assert_state_equal,
+    _base_system,
+    _source,
+    _state_snapshot,
+    _training_batch,
+)
+
+
+PUBLIC_ROOT = Path(cli.__file__).resolve().parents[2]
+
+
+def _sha256(path: Path) -> str:
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def _config() -> TrainingConfig:
+    return TrainingConfig(
+        stage="base",
+        seed=1729,
+        effective_global_batch=1,
+        edge_budget=1,
+        checkpoint_every_updates=1,
+        synthetic_contract=True,
+    )
+
+
+def _sources(label: str):
+    return (
+        _source(_training_batch(split="train"), f"{label}-train"),
+        _source(_training_batch(split="val"), f"{label}-val"),
+    )
+
+
+def test_real_fit_observer_sees_durable_digest_correct_update_and_validation(
+    tmp_path: Path,
+) -> None:
+    observed: list[tuple[str, str, str, str]] = []
+
+    def observer(artifact, reason: str) -> None:
+        assert artifact.path.is_file()
+        observed.append(
+            (reason, artifact.sha256, _sha256(artifact.path), artifact.path.name)
+        )
+
+    train, val = _sources("events")
+    report = PhaseSetTrainingRuntime(
+        _base_system(initialization_seed=91, dropout=0.2),
+        _config(),
+        tmp_path / "events",
+        checkpoint_observer=observer,
+    ).fit(train, val, stop_after_global_step=1)
+
+    assert [row[0] for row in observed] == ["update", "validation"]
+    assert all(declared == actual for _, declared, actual, _ in observed)
+    assert observed[0][3].endswith("-update.pt")
+    assert observed[1][3].endswith("-validation.pt")
+    assert report.latest_checkpoint.sha256 == observed[-1][1]
+    assert report.authority == 0
+    assert report.production is False
+    assert report.result_claimed is False
+
+
+def test_observer_error_propagates_after_digest_valid_resumable_checkpoint(
+    tmp_path: Path,
+) -> None:
+    observed = []
+
+    class ObserverFailure(RuntimeError):
+        pass
+
+    def observer(artifact, reason: str) -> None:
+        observed.append((artifact, reason))
+        raise ObserverFailure("injected observer failure")
+
+    train, val = _sources("observer-error")
+    with pytest.raises(ObserverFailure, match="injected observer failure"):
+        PhaseSetTrainingRuntime(
+            _base_system(initialization_seed=101, dropout=0.2),
+            _config(),
+            tmp_path / "observer-error",
+            checkpoint_observer=observer,
+        ).fit(train, val, stop_after_global_step=1)
+
+    assert len(observed) == 1
+    artifact, reason = observed[0]
+    assert reason == "update"
+    assert artifact.path.is_file()
+    assert _sha256(artifact.path) == artifact.sha256
+
+    resume_train, resume_val = _sources("observer-error")
+    resumed = PhaseSetTrainingRuntime(
+        _base_system(initialization_seed=999, dropout=0.2),
+        _config(),
+        tmp_path / "observer-error-resume",
+    ).fit(
+        resume_train,
+        resume_val,
+        resume_checkpoint=artifact.path,
+        stop_after_global_step=artifact.global_step,
+    )
+    assert resumed.latest_checkpoint.sha256 == artifact.sha256
+    assert resumed.authority == 0
+    assert resumed.result_claimed is False
+
+
+def test_attached_observer_preserves_baseline_model_and_checkpoint_state(
+    tmp_path: Path,
+) -> None:
+    baseline_system = _base_system(initialization_seed=777, dropout=0.25)
+    baseline_train, baseline_val = _sources("equivalence")
+    baseline_report = PhaseSetTrainingRuntime(
+        baseline_system,
+        _config(),
+        tmp_path / "baseline",
+    ).fit(baseline_train, baseline_val, stop_after_global_step=1)
+
+    observed_system = _base_system(initialization_seed=777, dropout=0.25)
+    observed_events: list[tuple[str, str]] = []
+
+    def observer(artifact, reason: str) -> None:
+        observed_events.append((reason, _sha256(artifact.path)))
+
+    observed_train, observed_val = _sources("equivalence")
+    observed_report = PhaseSetTrainingRuntime(
+        observed_system,
+        _config(),
+        tmp_path / "observed",
+        checkpoint_observer=observer,
+    ).fit(observed_train, observed_val, stop_after_global_step=1)
+
+    _assert_state_equal(
+        _state_snapshot(baseline_system), _state_snapshot(observed_system)
+    )
+    assert baseline_report.latest_checkpoint.sha256 == (
+        observed_report.latest_checkpoint.sha256
+    )
+    assert baseline_report.last_train_loss == observed_report.last_train_loss
+    assert baseline_report.best_validation_metric == (
+        observed_report.best_validation_metric
+    )
+    baseline_payload = torch.load(
+        baseline_report.latest_checkpoint.path,
+        map_location="cpu",
+        weights_only=False,
+    )
+    observed_payload = torch.load(
+        observed_report.latest_checkpoint.path,
+        map_location="cpu",
+        weights_only=False,
+    )
+    assert baseline_payload["state_digest"] == observed_payload["state_digest"]
+    assert observed_events == [
+        ("update", observed_events[0][1]),
+        ("validation", observed_report.latest_checkpoint.sha256),
+    ]
+
+
+def test_live_attempt_ledger_receipts_actual_runtime_observer_payload(
+    tmp_path: Path,
+) -> None:
+    root = tmp_path / "attempts"
+    root.mkdir()
+    attempt = AttemptRecord(
+        attempt_id="observer-ledger",
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B0",
+        created_at_utc="2026-09-08T00:00:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    store = AttemptStore.create(root, attempt)
+    ledger = host._LiveAttemptLedger(store, attempt)
+    ledger.start()
+    try:
+        train, val = _sources("live-ledger")
+        report = PhaseSetTrainingRuntime(
+            _base_system(initialization_seed=123, dropout=0.2),
+            _config(),
+            store.path / "model-checkpoints",
+            checkpoint_observer=ledger.observe_checkpoint,
+        ).fit(train, val, stop_after_global_step=1)
+        ledger.ensure_checkpoint(report.latest_checkpoint)
+    finally:
+        ledger.stop()
+
+    receipt_paths = sorted((store.path / "checkpoints").glob("checkpoint-*.json"))
+    assert len(receipt_paths) == 1
+    receipt = parse_checkpoint_bytes(receipt_paths[0].read_bytes())
+    assert receipt.checkpoint_payload_sha256 == report.latest_checkpoint.sha256
+    assert receipt.global_step == report.global_step == 1
+    assert ledger.latest_checkpoint_payload_sha256 == report.latest_checkpoint.sha256
+    assert report.authority == 0
+    assert report.result_claimed is False
diff --git a/tests/test_phaseset_cli_production_bridge.py b/tests/test_phaseset_cli_production_bridge.py
index 9d1a0dd..bf8196f 100644
--- a/tests/test_phaseset_cli_production_bridge.py
+++ b/tests/test_phaseset_cli_production_bridge.py
@@ -260,6 +260,35 @@ def factory(request: cli.CLICommandRequest) -> execution.RuntimeAdapter:
     assert str(checkpoint) not in stdout
 
 
+def test_base_resume_system_id_reaches_request_aware_factory(tmp_path: Path) -> None:
+    captured: list[cli.CLICommandRequest] = []
+
+    def factory(request: cli.CLICommandRequest) -> execution.RuntimeAdapter:
+        captured.append(request)
+        return _SyntheticAdapter()
+
+    attempt = tmp_path / "base-resume-attempt"
+    checkpoint = tmp_path / "base-checkpoint.pt"
+    code, stdout, stderr = _invoke(
+        [
+            "resume", "--attempt-dir", str(attempt), "--checkpoint", str(checkpoint),
+            "--system-id", "B1", "--config", str(CONFIG),
+        ],
+        runtime_adapter_factory=factory,
+    )
+    payload = json.loads(stdout)
+    assert code == cli.EXIT_OK
+    assert stderr == ""
+    assert payload["production"] is False
+    assert payload["authority"] == 0
+    assert len(captured) == 1
+    assert captured[0].attempt_directory == attempt
+    assert captured[0].resume_checkpoint == checkpoint
+    assert captured[0].system_id == "B1"
+    assert str(attempt) not in stdout
+    assert str(checkpoint) not in stdout
+
+
 def test_private_parameters_cannot_be_silently_dropped_by_prebuilt_adapter(
     tmp_path: Path,
 ) -> None:
diff --git a/tests/test_phaseset_host.py b/tests/test_phaseset_host.py
new file mode 100644
index 0000000..b57fe21
--- /dev/null
+++ b/tests/test_phaseset_host.py
@@ -0,0 +1,532 @@
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import stat
+import subprocess
+import sys
+import time
+from io import StringIO
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+from phaseset_core import cli, host
+from phaseset_core.contracts import group_commitment
+from phaseset_core.execution import AttemptRecord, AttemptStore, parse_terminal_bytes
+from phaseset_core.training import CheckpointArtifact
+
+PUBLIC_ROOT = Path(cli.__file__).resolve().parents[2]
+PUBLIC_CONFIG = PUBLIC_ROOT / "configs" / "phaseset" / "training.json"
+
+
+def _sha(path: Path) -> str:
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def _commitment(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _write_json(path: Path, value: object) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(
+        json.dumps(value, sort_keys=True, separators=(",", ":")) + "\n",
+        encoding="ascii",
+    )
+
+
+def _write_batch(path: Path, label: str) -> None:
+    actor_ids = (_commitment(f"{label}-actor-0"), _commitment(f"{label}-actor-1"))
+    family = _commitment(f"{label}-family")
+    text_id = _commitment(f"{label}-text")
+    np.savez(
+        path,
+        skeletons=np.zeros((1, 2, 200, 22, 3), dtype=np.float32),
+        actor_mask=np.ones((1, 2), dtype=np.bool_),
+        frame_mask=np.ones((1, 200), dtype=np.bool_),
+        track_mask=np.ones((1, 2, 200, 22), dtype=np.bool_),
+        actor_commitments=np.asarray(
+            [[list(actor_ids[0]), list(actor_ids[1])]], dtype=np.uint8
+        ),
+        group_commitments=np.asarray(
+            [list(group_commitment(actor_ids))], dtype=np.uint8
+        ),
+        text_embeddings=np.asarray([[1.0, 0.0]], dtype=np.float32),
+        motion_positive_ids=np.asarray([list(family)], dtype=np.uint8),
+        text_positive_ids=np.asarray([list(family)], dtype=np.uint8),
+        text_commitments=np.asarray([list(text_id)], dtype=np.uint8),
+    )
+
+
+def _write_split(root: Path, split: str, labels: tuple[str, ...]) -> Path:
+    batches = []
+    for label in labels:
+        path = root / f"{split}-{label}.npz"
+        _write_batch(path, f"{split}-{label}")
+        batches.append({"path": path.name, "sha256": _sha(path)})
+    manifest = root / f"{split}.json"
+    _write_json(
+        manifest,
+        {"batches": batches, "schema": host.PREPARED_SPLIT_SCHEMA, "split": split},
+    )
+    return manifest
+
+
+def _prepared_tree(root: Path) -> tuple[Path, Path, Path]:
+    train = _write_split(root, "train", ("a", "b"))
+    val = _write_split(root, "val", ("a",))
+    index = root / "prepared-index.json"
+    _write_json(
+        index,
+        {
+            "schema": host.PREPARED_INDEX_SCHEMA,
+            "train": {"path": train.name, "sha256": _sha(train)},
+            "val": {"path": val.name, "sha256": _sha(val)},
+        },
+    )
+    return train, val, index
+
+
+def _host_config_with_missing_rights(root: Path) -> Path:
+    _, _, index = _prepared_tree(root)
+    artifacts: dict[str, str] = {}
+    digests: dict[str, str] = {}
+    for field in host.RECEIPT_DIGEST_FIELDS:
+        path = (
+            index
+            if field == "prepared_data_manifest_sha256"
+            else root / f"{field}.json"
+        )
+        if (
+            field != "prepared_data_manifest_sha256"
+            and field != "rights_assertion_sha256"
+        ):
+            path.write_text(f"{field}\n", encoding="ascii")
+        artifacts[field] = path.name
+        digests[field] = _sha(path) if path.exists() else "0" * 64
+    receipt = root / "receipts.json"
+    _write_json(
+        receipt,
+        {
+            "admitted_at_utc": "2026-09-07T12:00:00Z",
+            # This deliberately incomplete fixture must never mint authority.
+            "authority": 0,
+            **digests,
+        },
+    )
+    config = root / "host.json"
+    _write_json(
+        config,
+        {
+            "prepared_index": index.name,
+            "receipts": {"artifacts": artifacts, "record": receipt.name},
+            "runtime": {
+                "bf16_runtime_qualified": False,
+                "checkpoint_every_updates": 1,
+                "corrective_change_sha256": hashlib.sha256(b"none").hexdigest(),
+                "device": "cpu",
+                "edge_budget": 32768,
+                "max_batch_bytes": 10_000_000,
+                "request_bf16": False,
+                "resume_retry_class": "INFRA_TRANSIENT",
+            },
+            "schema": host.HOST_CONFIG_SCHEMA,
+            "source_tree_sha256": hashlib.sha256(b"source-tree").hexdigest(),
+        },
+    )
+    return config
+
+
+def _invoke(arguments: list[str], config: Path) -> tuple[int, dict[str, object], str]:
+    stdout = StringIO()
+    stderr = StringIO()
+    code = cli.main(
+        arguments,
+        stdout=stdout,
+        stderr=stderr,
+        runtime_adapter_factory=host.RuntimeAdapterFactory(config),
+    )
+    raw = stdout.getvalue() or stderr.getvalue()
+    return code, json.loads(raw), stderr.getvalue()
+
+
+def test_prepared_source_loads_strict_batches_and_orders_deterministically(
+    tmp_path: Path,
+) -> None:
+    train, _, _ = _prepared_tree(tmp_path)
+    source = host.PrivatePreparedDataSource(train, max_batch_bytes=10_000_000)
+    first = tuple(source.iter_epoch(epoch=3, seed=1729))
+    second = tuple(source.iter_epoch(epoch=3, seed=1729))
+    assert source.split == "train"
+    assert len(first) == 2
+    assert [row.groups.group_commitments for row in first] == [
+        row.groups.group_commitments for row in second
+    ]
+    assert all(row.split == "train" for row in first)
+    assert all(row.text_embeddings.dtype.is_floating_point for row in first)
+
+
+def test_prepared_source_rejects_mutation_after_construction(tmp_path: Path) -> None:
+    train, _, _ = _prepared_tree(tmp_path)
+    source = host.PrivatePreparedDataSource(train, max_batch_bytes=10_000_000)
+    batch = tmp_path / "train-a.npz"
+    batch.write_bytes(batch.read_bytes() + b"changed")
+    try:
+        tuple(source.iter_epoch(epoch=0, seed=1729))
+    except host.HostConfigurationError as error:
+        assert "changed" in str(error)
+    else:
+        raise AssertionError("mutated prepared batch was accepted")
+
+
+def test_prepared_source_parses_the_exact_buffer_it_hashed(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    train = _write_split(tmp_path, "train", ("original",))
+    source = host.PrivatePreparedDataSource(train, max_batch_bytes=10_000_000)
+    batch = tmp_path / "train-original.npz"
+    replacement = tmp_path / "replacement.npz"
+    _write_batch(replacement, "train-replacement")
+    replacement_raw = replacement.read_bytes()
+    original_load = host.np.load
+
+    def swapping_load(value: object, *args: object, **kwargs: object) -> object:
+        batch.write_bytes(replacement_raw)
+        assert isinstance(value, host.io.BytesIO)
+        return original_load(value, *args, **kwargs)
+
+    monkeypatch.setattr(host.np, "load", swapping_load)
+    loaded = tuple(source.iter_epoch(epoch=0, seed=1729))
+    expected_actor_ids = (
+        _commitment("train-original-actor-0"),
+        _commitment("train-original-actor-1"),
+    )
+    assert loaded[0].groups.group_commitments == (
+        group_commitment(expected_actor_ids),
+    )
+
+
+def test_preflight_holds_when_real_rights_artifact_is_absent(tmp_path: Path) -> None:
+    config = _host_config_with_missing_rights(tmp_path)
+    code, payload, stderr = _invoke(
+        ["preflight", "--config", str(PUBLIC_CONFIG)], config
+    )
+    assert code == cli.EXIT_HOLD
+    assert stderr == ""
+    assert payload["authority"] == 0
+    assert payload["production"] is False
+    assert payload["execution_authorized"] is False
+    assert payload["hold_codes"] == [
+        "HOLD_RIGHTS_GRANT_ABSENT",
+        "HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",
+    ]
+
+
+def test_run_base_and_resume_do_not_reach_backend_without_rights(
+    tmp_path: Path,
+) -> None:
+    config = _host_config_with_missing_rights(tmp_path)
+    attempt = tmp_path / "attempts" / "base-a"
+    run_id = "phaseset-run-v1/BASE_QUALIFICATION/1729/B0"
+    run_code, run_payload, _ = _invoke(
+        [
+            "run-base",
+            "--run-id",
+            run_id,
+            "--checkpoint-dir",
+            str(attempt / "model-checkpoints"),
+            "--config",
+            str(PUBLIC_CONFIG),
+        ],
+        config,
+    )
+    resume_code, resume_payload, _ = _invoke(
+        [
+            "resume",
+            "--attempt-dir",
+            str(tmp_path / "attempts" / "base-b"),
+            "--checkpoint",
+            str(attempt / "model-checkpoints" / "checkpoint.pt"),
+            "--config",
+            str(PUBLIC_CONFIG),
+        ],
+        config,
+    )
+    assert run_code == resume_code == cli.EXIT_HOLD
+    assert run_payload["authority"] == resume_payload["authority"] == 0
+    assert not attempt.exists()
+
+
+def test_live_ledger_defers_last_update_and_recovers_crash(tmp_path: Path) -> None:
+    run_id = "phaseset-run-v1/BASE_QUALIFICATION/1729/B0"
+    attempt = AttemptRecord(
+        attempt_id="live-a",
+        run_id=run_id,
+        created_at_utc="2026-09-07T12:00:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    store = AttemptStore.create(tmp_path, attempt)
+    ledger = host._LiveAttemptLedger(store, attempt)
+    ledger.start()
+    checkpoint = store.path / "model-checkpoints" / "checkpoint-validation.pt"
+    checkpoint.parent.mkdir()
+    torch.save(
+        {
+            "model": {},
+            "optimizer": {},
+            "rng": {},
+            "epoch": 1,
+            "update_index": 1,
+            "updates_per_epoch": 1,
+            "global_step": 1,
+            "train_manifest_sha256": "5" * 64,
+            "val_manifest_sha256": "6" * 64,
+            "best_validation_metric": 0.5,
+            "best_checkpoint_name": checkpoint.name,
+            "best_checkpoint_sha256": None,
+        },
+        checkpoint,
+    )
+    artifact = CheckpointArtifact(checkpoint, _sha(checkpoint), 1)
+    ledger.observe_checkpoint(artifact, "update")
+    assert ledger.latest_checkpoint_receipt_sha256 is None
+    ledger.observe_checkpoint(artifact, "validation")
+    ledger.stop()
+    assert ledger.latest_checkpoint_payload_sha256 == artifact.sha256
+    assert ledger.latest_checkpoint_receipt_sha256 is not None
+    assert ledger.latest_heartbeat_sha256 is not None
+
+    host.PhaseSetHostBackend._recover_crashed_attempt(store.path)
+    terminal = parse_terminal_bytes((store.path / "terminal.json").read_bytes())
+    assert terminal.outcome == "FAILED"
+    assert terminal.failure_code == "HOST_PROCESS_LOST"
+    assert (
+        terminal.latest_checkpoint_receipt_sha256
+        == ledger.latest_checkpoint_receipt_sha256
+    )
+
+
+def test_live_attempt_lease_refuses_false_crash_terminal(tmp_path: Path) -> None:
+    attempt = AttemptRecord(
+        attempt_id="still-live",
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B0",
+        created_at_utc="2026-09-07T12:00:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    store = AttemptStore.create(tmp_path, attempt)
+    lease = host._AttemptLease.acquire(store.path)
+    try:
+        with pytest.raises(host.HostConfigurationError, match="live process"):
+            host.PhaseSetHostBackend._recover_crashed_attempt(store.path)
+        assert not (store.path / "terminal.json").exists()
+    finally:
+        lease.release()
+
+
+def test_attempt_lease_releases_only_after_owned_child_exits(tmp_path: Path) -> None:
+    attempt = AttemptRecord(
+        attempt_id="child-live",
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B0",
+        created_at_utc="2026-09-07T12:00:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    store = AttemptStore.create(tmp_path, attempt)
+    ready = tmp_path / "child-ready"
+    child_code = """
+import sys
+import time
+from pathlib import Path
+
+from phaseset_core import host
+
+lease = host._AttemptLease.acquire(Path(sys.argv[1]))
+Path(sys.argv[2]).write_text("ready\\n", encoding="ascii")
+try:
+    time.sleep(30.0)
+finally:
+    lease.release()
+"""
+    child = subprocess.Popen(
+        [sys.executable, "-c", child_code, str(store.path), str(ready)],
+        stdin=subprocess.DEVNULL,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        text=True,
+    )
+    try:
+        deadline = time.monotonic() + 10.0
+        while not ready.is_file() and time.monotonic() < deadline:
+            if child.poll() is not None:
+                _, stderr = child.communicate(timeout=1.0)
+                pytest.fail(f"lease child exited before ready: {stderr}")
+            time.sleep(0.02)
+        assert ready.is_file(), "lease child did not signal ready before timeout"
+        with pytest.raises(host.HostConfigurationError, match="live process"):
+            host._AttemptLease.acquire(store.path)
+        assert not (store.path / "terminal.json").exists()
+
+        child.kill()
+        child.wait(timeout=10.0)
+        lease = host._AttemptLease.acquire(store.path)
+        lease.release()
+        assert not (store.path / "terminal.json").exists()
+    finally:
+        if child.poll() is None:
+            child.kill()
+            child.wait(timeout=10.0)
+
+
+def test_ensure_checkpoint_receipts_no_progress_report_checkpoint(
+    tmp_path: Path,
+) -> None:
+    attempt = AttemptRecord(
+        attempt_id="no-progress",
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B0",
+        created_at_utc="2026-09-07T12:00:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    store = AttemptStore.create(tmp_path, attempt)
+    checkpoint = store.path / "model-checkpoints" / "restored.pt"
+    checkpoint.parent.mkdir()
+    torch.save(
+        {
+            "model": {},
+            "optimizer": {},
+            "rng": {},
+            "epoch": 1,
+            "update_index": 1,
+            "updates_per_epoch": 1,
+            "global_step": 1,
+            "train_manifest_sha256": "5" * 64,
+            "val_manifest_sha256": "6" * 64,
+            "best_validation_metric": 0.5,
+            "best_checkpoint_name": checkpoint.name,
+            "best_checkpoint_sha256": None,
+        },
+        checkpoint,
+    )
+    ledger = host._LiveAttemptLedger(store, attempt)
+    ledger.start()
+    artifact = CheckpointArtifact(checkpoint, _sha(checkpoint), 1)
+    ledger.ensure_checkpoint(artifact)
+    first_receipt = ledger.latest_checkpoint_receipt_sha256
+    ledger.ensure_checkpoint(artifact)
+    ledger.stop()
+    assert first_receipt is not None
+    assert ledger.latest_checkpoint_receipt_sha256 == first_receipt
+
+
+def test_failed_execution_returns_only_after_real_artifacts_persist(
+    tmp_path: Path,
+) -> None:
+    attempt = AttemptRecord(
+        attempt_id="failed",
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B0",
+        created_at_utc="2026-09-07T12:00:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    store = AttemptStore.create(tmp_path, attempt)
+    ledger = host._LiveAttemptLedger(store, attempt)
+    ledger.start()
+    ledger.stop()
+    execution = host.PhaseSetHostBackend._persist_failed_execution(
+        store, attempt, ledger, RuntimeError("not serialized")
+    )
+    assert execution.outcome == "FAILED"
+    assert all(path.is_file() for path in execution.artifacts)
+    failure = json.loads((store.path / "failure.json").read_text(encoding="ascii"))
+    terminal = parse_terminal_bytes((store.path / "terminal.json").read_bytes())
+    assert failure["error_type"] == "RuntimeError"
+    assert "not serialized" not in (store.path / "failure.json").read_text(
+        encoding="ascii"
+    )
+    assert terminal.failure_code == "HOST_EXECUTION_FAILED"
+
+
+def test_failed_terminal_follows_durable_failure_artifact(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    attempt = AttemptRecord(
+        attempt_id="durable-failure",
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B0",
+        created_at_utc="2026-09-07T12:00:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    store = AttemptStore.create(tmp_path, attempt)
+    ledger = host._LiveAttemptLedger(store, attempt)
+    ledger.start()
+    ledger.stop()
+    events: list[str] = []
+    real_fsync = host.os.fsync
+    real_write_terminal = AttemptStore.write_terminal
+
+    def tracking_fsync(descriptor: int) -> None:
+        mode = os.fstat(descriptor).st_mode
+        events.append("directory-fsync" if stat.S_ISDIR(mode) else "file-fsync")
+        real_fsync(descriptor)
+
+    def tracking_terminal(self: AttemptStore, record: object) -> str:
+        events.append("terminal")
+        return real_write_terminal(self, record)
+
+    monkeypatch.setattr(host.os, "fsync", tracking_fsync)
+    monkeypatch.setattr(AttemptStore, "write_terminal", tracking_terminal)
+    host.PhaseSetHostBackend._persist_failed_execution(
+        store, attempt, ledger, RuntimeError("failure")
+    )
+    assert events[0] == "file-fsync"
+    if os.name == "posix":
+        assert events[1] == "directory-fsync"
+        assert events[2] == "terminal"
+    else:
+        assert events[1] == "terminal"
+
+
+def test_fsync_failure_prevents_failed_terminal(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    attempt = AttemptRecord(
+        attempt_id="nondurable-failure",
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B0",
+        created_at_utc="2026-09-07T12:00:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    store = AttemptStore.create(tmp_path, attempt)
+    ledger = host._LiveAttemptLedger(store, attempt)
+    ledger.start()
+    ledger.stop()
+
+    def fail_fsync(_: int) -> None:
+        raise OSError("injected durability failure")
+
+    monkeypatch.setattr(host.os, "fsync", fail_fsync)
+    with pytest.raises(OSError, match="injected durability failure"):
+        host.PhaseSetHostBackend._persist_failed_execution(
+            store, attempt, ledger, RuntimeError("failure")
+        )
+    assert (store.path / "failure.json").exists()
+    assert not (store.path / "terminal.json").exists()

From 7e7f4e8709ed91eb88d9d0049f970dfa47173d93 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 02:02:12 -0500
Subject: [PATCH 05/95] docs: publish native editable figures and verified
 ICASSP draft

---
 RELEASE_FILES.sha256                  |  35 +-
 STATUS.md                             |  33 +-
 figures/EDITABLE_FIGURES.json         |  81 +++
 figures/README.md                     |  77 ++-
 figures/phaseset_architecture.drawio  |  94 +--
 figures/phaseset_architecture.pdf     | Bin 76877 -> 82774 bytes
 figures/phaseset_architecture.png     | Bin 743516 -> 385686 bytes
 figures/phaseset_architecture.pptx    | Bin 742318 -> 147344 bytes
 figures/phaseset_architecture.svg     |   2 +-
 figures/phaseset_motivation.drawio    |  60 +-
 figures/phaseset_motivation.pdf       | Bin 64923 -> 58231 bytes
 figures/phaseset_motivation.png       | Bin 476421 -> 209581 bytes
 figures/phaseset_motivation.pptx      | Bin 489547 -> 118539 bytes
 figures/phaseset_motivation.svg       |   2 +-
 paper/BUILD_RECEIPT.json              |  16 +
 paper/README.md                       |   8 +-
 paper/main.pdf                        | Bin 497508 -> 514458 bytes
 paper/main.tex                        |  20 +-
 scripts/render_editable_figures.py    | 823 ++++++++++++++++++++++++++
 tests/test_editable_figures.py        |  85 +++
 tests/test_render_editable_figures.py | 157 +++++
 21 files changed, 1380 insertions(+), 113 deletions(-)
 create mode 100644 figures/EDITABLE_FIGURES.json
 create mode 100644 paper/BUILD_RECEIPT.json
 create mode 100644 scripts/render_editable_figures.py
 create mode 100644 tests/test_editable_figures.py
 create mode 100644 tests/test_render_editable_figures.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 357e4f2..5332f5f 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,7 +11,7 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-428adbd5997dd33ca00cb160bcd550108bc8031a4fcf92616686e02395c26d42	10344	STATUS.md
+d7cb9c77abdcdd0061ce83f630541586111cea3d146d9fcf905bd84a45cbca71	12240	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
@@ -51,20 +51,22 @@ f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHAS
 ecb85faa3b442450f6ecc8d6f2fff77baf19e833254fa8a715127d9127742f63	13825	docs/PHASESET_RESEARCH_CONTRACT.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 8d689f49b2704ff7c5988eff8a7e3e062f209b85d18425ccdba1e1099581079b	10658	docs/phasepair_research_contract_20260824_165840.md
-d55d8e47040e9cf99d360f9e58df0e83bf2e24eddd8c165e40e09e1084dbd119	1057	figures/README.md
-5a715d557ddb0a87236960e5af0142c6448e801d17e2a161d614948e78c3e203	13879	figures/phaseset_architecture.drawio
-e544b64654355f01055eaf0806a66b6ab547a34354f62b97b24cbfc20104d652	76877	figures/phaseset_architecture.pdf
-429a9052468f3ecc5038a2f1d5bc5bbe072537e14a83d29085c91f22fcbcab81	743516	figures/phaseset_architecture.png
-4f5208d7e123c29079e86239e1248ec050b06cffe47f5b33901bc7436842e1d5	742318	figures/phaseset_architecture.pptx
-3074af3aaf26f5782eebd3f64dbba3a6c183d5071af32315234a3a8ab08953f7	786654	figures/phaseset_architecture.svg
-dd5fd26c085c8fc9143ffe8d116d23a816c4360eccb1747abeeb0803284738d8	9839	figures/phaseset_motivation.drawio
-61bf4a503c8126b9df1be29303ef25955babd6b6bbdaa881414c487396013827	64923	figures/phaseset_motivation.pdf
-a8d257fce7d54cfbce67ff2f5aeb17bf081e10f70d3c5e7c5b02f21278baeb96	476421	figures/phaseset_motivation.png
-248ce77fc05ebd370a907b2c470bfe0fc50d6fa050759f5694e5ae081c8513a1	489547	figures/phaseset_motivation.pptx
-a028dddf74489baaade772b5881004abb27de38f4ab631a529693cd9badce75a	363524	figures/phaseset_motivation.svg
-c1dc6c3081e80085dc950457c3324f4780b8237adb278494a9a2298e2c536129	3763	paper/README.md
-d1b340ef39baf6ec2c16fd29f079eb550d7f68e4ac389cd5aed4137cd13710f2	497508	paper/main.pdf
-17a201a532798a30805f74f3abcafb4e811e02371f5f018a8bd3894b0c80c7f9	21347	paper/main.tex
+8d4b03326029b2a06478295b7328d61181d022604b1465bccd3a6cfcc8ce9704	2155	figures/EDITABLE_FIGURES.json
+efb48515925d5412bc840a5ae07415cda4645591deaa1ab6aca90e1e4f528591	4208	figures/README.md
+1a61a503c935a72cbfbe1ce175ab54bf975eb3e0a2164018aeda80448f472a1a	16465	figures/phaseset_architecture.drawio
+7efb6fd4964cce91163a419fc5a312c2844e1e686c044b29502ff381c793c35b	82774	figures/phaseset_architecture.pdf
+2dea358d457f9f12483af22e1f66c022b923cbc97d8c5ec12fd3b98be7027faf	385686	figures/phaseset_architecture.png
+9793fcafb8dccbd1d19a75ee7cfe23a654ec03bf193d84574a9dcbc38260b046	147344	figures/phaseset_architecture.pptx
+83b9c2c7464cf53e79e1a79dffbd15965506dd6cab1f1f019b967ada752492ab	902946	figures/phaseset_architecture.svg
+a0a25fbc8c55575d50c6ffa8d6add01ce780cc8842328bb3f6bba20c6509d18a	10601	figures/phaseset_motivation.drawio
+2c55a3103950187740f0200d1d4497d393c0c93c16d0f0722f7410065141d4ba	58231	figures/phaseset_motivation.pdf
+a43fbeb379f7357af12deddc8b837f81218716ea30428ca320bfd5467968ca6e	209581	figures/phaseset_motivation.png
+4c7ef9150eddd60f9ee75adbd4d17f2755c8144aad796251a0310a6dd545b048	118539	figures/phaseset_motivation.pptx
+5ff14d17ee8dc393047131558e147f76b7037c903bed835d763a7a1363ccd7d9	369531	figures/phaseset_motivation.svg
+6c3079e72f159eb737e336f64a776c6b19753dd25ff6a86649b782f6ce06b3a1	741	paper/BUILD_RECEIPT.json
+492a2f33f90446fcc53c855bb1d91170740306612fe4f2fe64ef41d2bfa02213	4162	paper/README.md
+e6589c0e35da06b3b7b8f72c3fbacc99eaa44adf6be9c20465b4cb03fe3961a8	514458	paper/main.pdf
+21f99fb638bce1d6451bf58fa75ce01a7f0d081c8573c4d82aaf1ec502c33f50	21449	paper/main.tex
 7db5537b9c85545e3fff89b29e17438936f0bcfb3e7d1ee2cd8de24f4de1a279	8419	paper/references.bib
 a5b896aed4121191b56294a34d40fa25fcb70238f9d9596a6c12ad7d5ebc6b03	1317	pyproject.toml
 a54cab7df5f76e32387eba55757a4bd8fa18d9f2cdc8e5b0c6725ceb00af1e78	9580	scripts/audit_all_refs.py
@@ -78,6 +80,7 @@ cc60aca15d425b1f684bf9555ad127bd37d0072d0967839f00e2efc41fb26ac2	4460	scripts/pr
 79e3e6160ef210fa03f1c5d8a3eb17c9298ba4b38e910d93e609ce41c1d27ec7	32001	scripts/release_asset_inventory.py
 8c99175b3f115ba9e8d63870988a6ed3d7cf8009d272b5c5daaefb85887ca495	2950	scripts/release_manifest.py
 013dd2d7e7bbb7c07d86f49b49a400ad7c92a818a82cd98cbafee6d5a91d112a	14705	scripts/release_tree.py
+18babb8ff4042dc091bb426e6679186f7427b3b01695d82e56d4468de2740222	33666	scripts/render_editable_figures.py
 4db71f21e11539c4a1493ba7ea3d8127a56f1faba522c2813c61927e40ea9239	24981	scripts/verify_wheel_against_index.py
 7d0eb6a71f0c0964a653c2b536ba232728885a9a526186835ba8896137c4ea11	30359	slides/PHASEPAIR_EDITABLE_PAPER_FIGURES_DATA_FREE_DRAFT.pptx
 5ebe1cd2d83702888f77c642b8537d3544580e05fc73bf2e6baab9253e1f4dc6	464	src/phasepair_core/__init__.py
@@ -137,6 +140,7 @@ eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phase
 f75b9a59dfc2ccfc04ed5f418fb318f3bff6bc11cfe61021f5ff552d71a2ae7f	154285	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
+af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 af9b5adc42d82b9a0aa4ed744bb069e36b7dd041c1957fdc97d17eb814a0fc0c	17284	tests/test_phasepair_core_base_qualification.py
 ad630675cc642350a988d92de6176053d1bfab5f81cbc49548cb0b5dbeb679d9	23596	tests/test_phasepair_core_batching_readiness.py
@@ -193,4 +197,5 @@ ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test
 45b42266bc1c42c36af7eb7d07be2fc85d8b1d0a539ffb3674f98b47e1327254	4973	tests/test_release_artifact_audit.py
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
 8727e87f1a0d44db6a39b1e5cfd8e22e3bed05544ddaed07b66b759adee66d84	5587	tests/test_release_tree_batch.py
+eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
 3aef8f056ef38ecbe5af51c283285b132db97f175c181018d8a4278d82131915	8150	tests/test_wheel_index_binding.py
diff --git a/STATUS.md b/STATUS.md
index cc60fab..ecc0098 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -17,20 +17,37 @@ evidence, not training results or scientific claims.
   read-only inventory is complete. Existing workloads were not interrupted.
 - A separate data-volume Python 3.12.12 / NumPy 2.4.6 / Torch 2.12.0+cu126
   environment is installed with a clean dependency check. The complete Linux
-  CPU regression passed: 900 tests, 1 skipped, and 39 subtests, in 311.13 s.
+  CPU regression including the private-host lifecycle integration passed:
+  917 tests, 1 skipped, and 39 subtests, in 345.97 s. Source bytes were unchanged
+  before and after the full suite. Its separate 25 focused tests also passed.
   This includes synthetic model/lifecycle checks, not main-data training.
 - A separately bounded shared-GPU functionality probe passed FP64 forward and
   FP32 forward/backward using 8x8 tensors. It did not stop existing workloads
   and does not qualify BF16, model execution, performance, or the full runtime.
+- A subsequent actual-width PhaseSet periodic-core CUDA observation passed
+  with 512-dimensional tokens and a 256-dimensional hidden layer: K2/K3
+  forward/backward, actor permutations, K2 topology output/gradients as exact
+  positive zero, and K13 (78 edges) bitwise equality at chunks 64/128/256.
+  Maximum observed CPU/CUDA token difference was 1.431e-6 within the frozen
+  FP32 tolerance. The engineering fixture peaked at 80,722,944 allocated bytes
+  under a 2 GiB allocator cap; this is not a performance benchmark. Its first
+  attempt stopped before model construction because physical FB total and
+  CUDA context-usable total were incorrectly equated; that failed receipt is
+  retained. The corrected memory instrumentation did not relax a model gate.
+  This does not qualify BF16, the learned group base, or the full training runtime.
 - The native Embody loader and concrete licensed SMPL-X body-22 evaluator are
   implemented. Seven adapter contract tests passed on Linux/Python 3.12.12/
   NumPy 2.4.6; these use explicit fixtures, not licensed assets or main data.
 - Neither Embody approval nor a licensed neutral model has been observed.
   Open-license Multi-TPC acquisition completed with its official byte count and
-  MD5 verified. Its archive inventory has 322 files; format conversion remains
-  under audit. It is supplementary conversation data, never replacement
+  MD5 verified. Its archive inventory has 322 files. The bounded audit confirms
+  19 Euler rotation triplets plus three translation anchors, not body22 joint
+  positions, and unresolved modality-alignment/group-identity details. It is
+  supplementary conversation data, never replacement
   confirmatory data. The pinned CLIP snapshot's eight files also match the
-  previously acquired official SHA-256 values; model qualification is separate.
+  previously acquired official SHA-256 values. Actual frozen CLIP text CPU
+  loading and two public-caption forwards passed, with bitwise repeat outputs
+  and unchanged RNG. This does not qualify GPU execution or real data training.
 - Native Linux last-bit Morlet formula drift is repaired using strictly
   verified canonical coefficient bytes, while preserving both original hashes.
   Windows still requires its original exact formula digest; Linux requires its
@@ -41,6 +58,14 @@ evidence, not training results or scientific claims.
   ICASSP 2027 template: five pages, technical content through page four,
   references-only page five, all fonts embedded, no unresolved citations or
   overfull boxes. All result cells and pending author metadata remain explicit.
+- The concrete private-host bridge now connects base/residual training to
+  live checkpoint receipts, a periodic heartbeat, and a whole-attempt OS lease.
+  Base resume verifies the predecessor checkpoint chain, including process-loss
+  recovery; residual resume and the remaining host commands are not yet wired.
+  Four actual tiny server fits verified observer behavior without changing
+  checkpoint/model/loss/validation outputs. These are engineering fixtures,
+  not formal training attempts. GitHub CI passed both Windows runtimes and the
+  Ubuntu public-tree/wheel audit for the integration commit.
 
 This continuation is not a completed scientific release. Private receipts,
 data, model assets, connection details, and download material are not public.
diff --git a/figures/EDITABLE_FIGURES.json b/figures/EDITABLE_FIGURES.json
new file mode 100644
index 0000000..364d80e
--- /dev/null
+++ b/figures/EDITABLE_FIGURES.json
@@ -0,0 +1,81 @@
+{
+  "figures": [
+    {
+      "empirical_result": false,
+      "external_relationships": 0,
+      "native_connector_segments": 18,
+      "native_container_headers": 2,
+      "native_edge_labels": 0,
+      "native_node_shapes": 16,
+      "native_shapes": 18,
+      "package_parts": 36,
+      "pptx": "phaseset_motivation.pptx",
+      "pptx_sha256": "4c7ef9150eddd60f9ee75adbd4d17f2755c8144aad796251a0310a6dd545b048",
+      "raster_media": 0,
+      "source": "phaseset_motivation.drawio",
+      "source_edges": 14,
+      "source_sha256": "a0a25fbc8c55575d50c6ffa8d6add01ce780cc8842328bb3f6bba20c6509d18a"
+    },
+    {
+      "empirical_result": false,
+      "external_relationships": 0,
+      "native_connector_segments": 41,
+      "native_container_headers": 2,
+      "native_edge_labels": 3,
+      "native_node_shapes": 26,
+      "native_shapes": 31,
+      "package_parts": 36,
+      "pptx": "phaseset_architecture.pptx",
+      "pptx_sha256": "9793fcafb8dccbd1d19a75ee7cfe23a654ec03bf193d84574a9dcbc38260b046",
+      "raster_media": 0,
+      "source": "phaseset_architecture.drawio",
+      "source_edges": 19,
+      "source_sha256": "1a61a503c935a72cbfbe1ce175ab54bf975eb3e0a2164018aeda80448f472a1a"
+    }
+  ],
+  "package_policy": {
+    "compression": "ZIP_STORED",
+    "create_system": 0,
+    "external_attr": 25165824,
+    "retained_part_count": 36,
+    "timestamp": [
+      1980,
+      1,
+      1,
+      0,
+      0,
+      0
+    ]
+  },
+  "renderer": {
+    "bytes": 33666,
+    "name": "render_editable_figures.py",
+    "sha256": "18babb8ff4042dc091bb426e6679186f7427b3b01695d82e56d4468de2740222"
+  },
+  "runtime": {
+    "libxml2": [
+      2,
+      11,
+      9
+    ],
+    "libxslt": [
+      1,
+      1,
+      45
+    ],
+    "lxml": "6.1.1.0",
+    "machine": "AMD64",
+    "python": "3.12.14",
+    "python_implementation": "CPython",
+    "python_pptx": "1.0.2",
+    "system": "Windows",
+    "zlib_build": "1.3.2",
+    "zlib_runtime": "1.3.2"
+  },
+  "schema": "phaseset-editable-figure-census-v2",
+  "template": {
+    "bytes": 34030,
+    "name": "python-pptx-default.pptx",
+    "sha256": "e10cc9e120961f6bd4074a373c9c80d2a06c497157e8f4972977b7bea83a8f34"
+  }
+}
diff --git a/figures/README.md b/figures/README.md
index a74756f..ecc281c 100644
--- a/figures/README.md
+++ b/figures/README.md
@@ -1,19 +1,74 @@
 # PhaseSet paper figures
 
-The figure sources are data-free and contain no empirical result.
+These are data-free conceptual/method figures, not empirical results.
 
-| Figure | Editable source | Publication exports | Slide version |
+| Figure | Editable source | Publication exports | Native editable slide |
 |---|---|---|---|
 | Pair-bag incidence counterexample | `phaseset_motivation.drawio` | PDF, SVG, PNG | `phaseset_motivation.pptx` |
 | PhaseSet architecture | `phaseset_architecture.drawio` | PDF, SVG, PNG | `phaseset_architecture.pptx` |
 
-The `.drawio` files are the semantic/editable authority. PPTX files contain a
-lossless full-slide rendering for convenient placement; edit the matching
-draw.io source and regenerate all exports rather than manually changing one
-format. Paper captions and numerical annotations must be rendered from the
-frozen aggregate once real results exist.
+The draw.io files are the shared semantic/layout source. The PPTX files now
+contain native PowerPoint shapes, text runs, and connector segments, with **no
+slide-wide raster image**. Individual labels and nodes are editable. Regenerate
+all formats from draw.io for a consistent publication update; a PowerPoint-only
+edit does not update the source or other exports automatically.
 
-The motivation figure deliberately constructs two four-actor complete graphs
-with the same actor marginals and the same multiset of six pair tokens (two of
-each relation type) but different actor–edge incidence. Pair-bag averaging is
-therefore identical, while incident moments can distinguish the structures.
+The motivation figure contains 18 native shapes/text boxes and 18 connector
+segments; the architecture contains 31 and 41. `EDITABLE_FIGURES.json` binds each
+source and PPTX by SHA-256 and records the native-object census. The renderer
+uses an allowlisted, uncompressed ZIP package with fixed metadata and records
+the renderer, document runtime, and default template hashes. Repeat rendering
+in that recorded runtime produced byte-identical PPTX files. Its fixed creation date is
+not an assertion about when the research or figure was created.
+
+## Rebuild
+
+Canonical native-PPTX rebuilding requires the recorded Windows AMD64 CPython
+3.12.14 document runtime, python-pptx 1.0.2, lxml 6.1.1.0 / libxml2 2.11.9 /
+libxslt 1.1.45, and zlib 1.3.2. The renderer rejects other tuples and altered
+source filenames/bytes rather than issuing a canonical-build receipt. Opening
+or editing the provided PPTX does not require that Python runtime. The portable
+artifact tests verify the delivered hashes and native objects without it.
+
+Within the registered document runtime, run the closed project renderer:
+
+```bash
+python -m pip install 'python-pptx==1.0.2'
+python scripts/render_editable_figures.py --figure-dir figures --output-dir figure-build
+```
+
+This renderer supports only the limited shapes/styles used in these two pages;
+it is not a general draw.io importer. Draw.io remains the native publication
+exporter. With draw.io 31.1.8, export PDF with `--export --format pdf --embed-diagram
+--crop`, and SVG/PNG with `--export --format svg` or `png` plus `--embed-diagram`.
+Use explicit output files in a new build directory. Check the PNG IEND trailer
+after export; the known truncated-IEND exporter defect must be repaired before
+strict PNG validation. Embedded diagram metadata is scanned with the rendered
+content before public release.
+
+On 2026-09-08 both final native slide files were opened in Microsoft PowerPoint
+without a repair prompt and visually inspected; their hashes were unchanged
+after inspection. Further renderer gate hardening reproduced the same PPTX
+bytes. Both packages pass exact object-identity, relationship, XML, and ZIP
+checks and reopen in python-pptx. Unused thumbnail/printer parts were removed.
+This is not a claim about arbitrary future input.
+PDF exports use embedded Arial TrueType fonts;
+Unicode math subscripts that caused Type 3 fallback have been replaced by
+native subscript text runs. The `*_preview.png` files are display conveniences,
+not independent figure sources.
+
+## Scientific correspondence
+
+The motivation figure constructs two four-actor complete graphs with identical
+actor marginals and the same six-token pair multiset, but different actor-edge
+incidence. Pair-bag averaging is identical; incident moments can distinguish
+the constructed structures. It is not an observed motion sample.
+
+The architecture depicts the current implementation: fixed mask-aware body
+activity feeds the physical Morlet branch, in parallel with the qualified
+learned B0/B1/B2 group base. The learned base embedding does not feed Morlet.
+For residual training the selected seed-specific base is frozen. Both its
+global text score and the six-band text alignment contribute to retrieval.
+This drawing correction does not change the model or claim encoder-to-Morlet
+end-to-end gradients. Empirical annotations and paper values must come from
+the same frozen aggregate when actual results exist.
diff --git a/figures/phaseset_architecture.drawio b/figures/phaseset_architecture.drawio
index a3fd630..6eb5b96 100644
--- a/figures/phaseset_architecture.drawio
+++ b/figures/phaseset_architecture.drawio
@@ -5,91 +5,103 @@
       <root>
         <mxCell id="0"/>
         <mxCell id="1" parent="0"/>
-        <mxCell id="2" value="PhaseSet: streamed permutation-invariant group periodic tokens" style="text;html=1;align=center;verticalAlign=middle;fontSize=28;fontStyle=1;fontColor=#17233C;" vertex="1" parent="1">
+        <mxCell id="2" value="PhaseSet: streamed permutation-invariant group periodic tokens" style="fontFamily=Arial;text;html=1;align=center;verticalAlign=middle;fontSize=28;fontStyle=1;fontColor=#17233C;" vertex="1" parent="1">
           <mxGeometry x="500" y="20" width="1500" height="40" as="geometry"/>
         </mxCell>
-        <mxCell id="3" value="Motion path" style="swimlane;startSize=36;rounded=1;html=1;fontSize=18;fontStyle=1;fillColor=#F8FAFD;strokeColor=#7792B8;strokeWidth=2;container=1;pointerEvents=0;" vertex="1" parent="1">
+        <mxCell id="3" value="Motion path" style="fontFamily=Arial;swimlane;startSize=36;rounded=1;html=1;fontSize=18;fontStyle=1;fillColor=#F8FAFD;strokeColor=#7792B8;strokeWidth=2;container=1;pointerEvents=0;" vertex="1" parent="1">
           <mxGeometry x="40" y="80" width="2420" height="510" as="geometry"/>
         </mxCell>
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diff --git a/figures/phaseset_architecture.pdf b/figures/phaseset_architecture.pdf
index 883a603db3e6529c99e95f363187519e0caadd32..a48e202b6a730e6abf29dd1c71979ae109fb08ab 100644
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diff --git a/figures/phaseset_architecture.pptx b/figures/phaseset_architecture.pptx
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diff --git a/figures/phaseset_architecture.svg b/figures/phaseset_architecture.svg
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coverage, missing</div></div></div></foreignObject><image x="1462" y="312.5" width="228" height="39.75" 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9q39TIi2QDkySCwJkjEAHyzF9wqndyCGBAMfRLCm5XgHrZEtoxGgymVfSxMH6PqH9WastZZAg6T1GUvjNHWOwmZztth5ycCaiV7aShCvybk6GXAwi5mPzLIct8nqPI2HJGDaGfyuho4IjnT4349b3m6t+nHXu2q9SdfRsL7/s5UzQn2PTpz+XZudQldZnrj9rWLy5IyEKMBZwFpKMkNAtafduZ5SXlGCYuXmt8spBjQWcYb+nvU8JZH2en6pZ+PoXMOn9nsFvHI6RGA8U3jC2IEAhdHkVQFBYbBFYjkAjzCESAnMcoFEHgKhH4k8rMJFjOrMHMz02sU4lgLufORxG6MLxTaRyDP9UmAmurLI+uwL8ru8bYhbRzuSZOR+simpZKlZ1j766jn3MJ362yOOfqgpm5dKbCtbml5UGn/FNpnaM/7DGxW9SNgLlUWN81P5eXvNhM5J+o8KXjTJHeMXafXbZ0x2PkobX7PkJ+x+vUcE4/v/PqDvrw15W6HcJyZo1deYuXdrLNjdqry6EiUM5CF4IgsA6BIxYg11UkiQeqeQAAEABJREFU1EHgTBCw62D34dDVIXC0zkfK34123GO2zlSqS6uMdnVb4WNhBAYMyFjYnN/c7Zh2cNYItJggTNRUvnZB7PxOhc/5r9kNnUvrHMMJzYSbLermve9zvnZJGaitaxMt2qU7fcM0LCjNnfN08/Mu7fHUcE4/H7aOw/zWLnzSZZfU3UTsZti+QPnQSojwbxG1HmOCQBDYBYEIkLugljjzCITi2BGwc+cbgK1yEqJa4ccQZrdljlneRUDC6O8qQLfOlcLMbZXOyy211Ihbwr4zh1QHpR27PQLawlZCn7Sonm9fyiek6LyxTx88wefuJ8LwPn3b7vzdKd79y7hifLnbd/4XbE4J5/Tz+Xe6FYUjBT+4QWIEyveudGjXmBeMxS1tkSKNCQJBYAyBCJBjqMQvCFwfAodmLruaUfWZ3CXoiG6wS7g7lQsVMOtuKtz1dV1Vm9u1fOcUj/qvHZVTrtNcezGuGF+us47ngPMQv5vczy0uvGMBQugrZxPj6INPJdmNf9VKMTuSBUJMEFiKQATIpUiFLghcDQJbTpBXU+LkcmwIrC1P2txaxEIfBE4PgVPv51T/fbLjhzeGniApzferdCNEFggxQWAJAhEgl6AUmiAQBILA8SJg9/Ecv714vIgftGRJPAiMIpB+fnHxp4WMz3a4KOd363lL8ymV2BuUjQkCQWABAhEgF4AUkiBwhghQQ6MW1KravVqBRxJ2LvU4EjhTjDNAwEU3h1S/nlNPNa7ol2cA5Q5VSJRDI6Btf3Nl8jxl3ST+ReVucXYWP/zRlZZzkuXEBIEg0EJAh2mFJywIBIHzRMA5LOeEWrW7dyvwSMIOVY8ll/NMQTD3ncSvrohUpba0D6s0Y4IABHz2pXVr7747Wc8nk4b1WZhdPhHSSPIog9LPr/e1OKf+a1WEdynrEjGXqT2knn3XtpydzAtWrPuVjQkCQWAGgQiQMwAlOAicKQI+FeKTIa3q+b5gK/wYwg5VD6vQrZssW3Wf+2yIT48QHltpJCwI7IrA31bE3y/bMvdpBTbCtN37NsIFYer1S8/nbNPPj+ftEia9D2qoL1TFetKyXALlz9XzP5Vdal59KWHojg6BFOgKEYgAeYVgJ6sgcEQImHDnVmoJkGuEKLt2P1Z19ImAR5T7xWXfrexrlLWb6XbGetzUHKIeCoj5UB/Pa+2jK4JylTNqfIPMpzdGA+MZBPZEgAqpNthKxmUku3y+wCdCXqKVcIX9UNmbYGCcfn6cb5oKtfmNQPnSVUQC5cuVu+RTIE9TdFngKxBigkALge0FyFZuCQsCQeCYELAy22KACJDPuaLAhEQ7G89YcUzarl3/vHr+gbK+Zfhn5b542a3NXD1euDKcU7srkjvGLsvr3/m1/uH3Koq6ljNq1uI6mkg8g0ADgTkh7lUq7ouWXWvEe7ZGJFoNPovQIDmboPTzq3mVPl9iTnnlyu4tyn522e8s6zu9f1Xuy5edM85N/nQRvW5Z81E5k8Y3d598MjQBQSAI3EIgAuQtGPKvQyDujUIAo/ebjRo7K/V6Fb50NfbVitZZlHJGDdU2TNdo4B6ev15xMRPljBqrz+9QIQTDcmbNyxTFA8vuapTl4Y3IcH2rCl+Cq13bHy1a59p+sdwvK/tBZb0Xl0hktbzAiLmEwM+Xj88elDNq9IkPrZA1u5DUuj+44rQMxp4qYYvmXMLSzw//Jp+/srAoQavFOPi19fs9yxIEn6VcGjJrVE6dzZVORZ00+o1d/EmCBASBIHBxEQEyrSAInA8Ca2til+ybZiK9R4W/ZNk5g7l0mUGL7nsrsHW5RwXvZDAX3zMT8+0qHNNRTtM8ZYV+fFkMdjk7GbdgfutMTLuzLzVDI9jq+ivVw9OVfZGyb1/2k8t+R9nfKvsXZd+9bEwQ6COgTxDm+n7D59cuD8z4E5U7Zwian1BEbr0sZ9T8Q/l+RdmWVkMFn41JPz/8q3SW13GIVk7vVIHPXXaJ0dbntFH+aElCoQkCNx2BCJA3vQWk/jcdgW8pAAgh5YwaZ/UIQy0hkrrPp1fsFnPpmnXpFNnmBsP6dZXq48pOGWPdl1Zga7Xa7qnr4V+h6PY1P1IJOINTzqixC0notdt5m+CSgymiroXpuRR428PqPMH89s+TcNyc22pzKoHJa9UbTew0AvrE51awG1HLmTQWIz6qQgmI5Ywa/cLOj138UYLbnl9S7i+UvUkm/Xz6bW/Rz6XxNdNZ3Aqh3voN9fTsZeeM7zy+dYNIv5nboWxET1AQuDkIYKpuTm1T0yAQBIYIUCv9wqHn4DdVISqZn1H+z1X2icsy1CtNyL9UP96ybMt8VQW2BKoK3ss8smLPMRqEYZco+IyG3bzue3bUQDHHVrtd+FNJ7W2onH7STCpPW+HO5XxBuX1cu/JQQ6SmWsGT5nMqRLnLORlj52bulk5M3ptWjbQ1Z6Betp4/razzo+UcoTm+Iv1OFcnCTjlN8xEV+htl37astlfOBeH9meqBurT2pZ/Xz0kjvveDAZ8kOsOA9PPpl7pVP7fQ9uPT2dwKcbGTucxiiDGzG9sFGveNH99ePyxitvhe5+nNJUUaEwSCQAuBVkdqxUtYEAgC54EAhg/j98Mz1TEhv2/RYEoxBuL5XMC3lR/hp5xJ89sV8pllxSnnIEaZPq5SXnLG0vlD5wntWCqT3TC7k/eq+H3jgob+77XPsMH8zMWj+tvHdao8w3QwOso99D+F3xYdWuU0N319EXivzi1ZwHjv+m03rJyYhQjYwbYzOEf+rEVA/VTb0yd89sDuth3KYb8o0rsM7YK3KZ+TVv2r8u9q0s+nkduinzuPaCFDO5vO6eLCLvpHFgHV/m5s15YdmzB+zF2Mps1T0/6bSiMmCASBGQRM0jMkCQ4CQeDMETBh2oEj6G1dVdepv38l6mKCcg5qXN7xgZWDPMvZyxCo32evFC4uCD7OkB4CV8yUtP9yzzJeV3Qr/YTDNfk/SRG7NKOcmIUI6Av6xPcvpF9Lph2+cUXyPsu5kSb9fPq1axdb9HPpPLiyIeSVc8ls4fEhlcjDysYEgSCwAIEIkAtACkkQuAEIEPBcMrNkB28pHJhXaohXOSlTU5KnvJeWc0hH1ZZAjTkehq39DdfXrEhbCpHK9VqVJqaqnJM0bgCmoru28NTT1sa56fQWiN6oQPjKslsan+ah8j33yZAt8zzWtNLPx9/Mlv3c2G5cPoQQSdX70Foy4wjF9+LiIiCcIgIRIE/xraXMQeAwCPgcxstV0m74LGcv87sVW1om/Xq8UiPP+1eO1PHKWWXEfcWKgSEsZxNDPfV+lZILi8rZy1Bb9Y1NZyf3SuiaIxNq3qvKQBguZ7FxHnIxcQjvIODMqRt8fWpH37wTsOODtuw7ktrjjkmcXbT088uvdMt+Th3VIoixdIs2rLQWGqnGP6R+eC4nJggEgSUI3BEglxCHJggEgbNH4E+rhm9Y1qcjdmEOH1NxCQb3KXeX+BVtE+N2RGcz3US5JEHlfvMifFDZfc8+VhKXjDTfpHz3wVX53NrqjE8ldfLGDqrvWa5hBp/35Gt9fRXAgOsXvq33ZlWMNbgX+S1DFdYFVNqyNn3LM//uIAAT2KSf34HkYut+bl4xvzh3u0sbVjLC4ufVw7OVdU748eXGBIEgsAKBCJArwDpi0hTtahBwoYTbCaesz1m01PJcBvDKVdSp+PzlUSSLjUlUvCkrfHFitwkxmj9Rz75T6HINl7w4Ezj2SQCC188WrRVcN2S6Ut3NoM4Flfdio5xTdeAvfHFitwkfW67v3Lnt1HlG5bQTU963jMsVXIDxOvXrmcu6Cr7PSFC9lfeUFV7RFps+rs9QsZzp+a5yXVYyVMtSNuX9mArvcB2Wr4JmjXeGSZqqA/819di6DbtdkUDjHXgX6t2vlN8u4qBeRniGWT98i2f1h8OUhR8cl+Sl/06lw1//h+GStLR5caas8CXpDGkwz99Ynj4T02+Hwzpqk9qm9+LiKf3oARXv0WW15XJWmZuCM2y68bOPLyxh2gdN+04/v7iAw5p+bn5xmzaV9ucoQM1RxlJq1dp3ed1ltO0OZ5+kMlc7Q87/LsL8CAJBYBkCESCX4RSqIHBABI46aRPyF1UJX72sa/2HzOzTlz+Vok8plwpsXwArr6MwLpr5rCqJcvr0SFcHN3o6G4axHWM6KsrBzJ9Vyl9W1g7cvcv1uYquXFxlU163Ch4rrlXsTQzsvQPvQr3Vv7N+U5V8v8rpEWXRlhOzEQL9djjs39qktum9+A6kfrRRtjcmmT6+sIRp17a52nf6+cUFHHbp54R1l6eZo4ylFjyftFoXbPtW2+5wflSFZxwpEGKCwD4IRIDcB73EDQJBIAgEgSAQBIJAEAgCQSAI3CAEIkDeoJedqgaBIBAEgsDdCORXEAgCQSAIBIEgsA6BCJDr8Ap1EAgCQSAIBIEgcBwIpBRBIAgEgSBwDQhEgLwG0JNlEAgCQSAIBIEgEARuNgKpfRAIAqeKQATIU31zKXcQCAJBIAgEgSAQBIJAELgOBJLnjUYgAuSNfv2pfBAIAkEgCASBIBAEgkAQCAI3CYF96xoBcl8EEz8IBIEgEASCQBAIAkEgCASBIHBDEIgAea0vOpkHgSAQBIJAEAgCQSAIBIEgEAROB4EIkKfzrlLSY0Mg5QkCQSAIBIEgEASCQBAIAjcMgQiQN+yFp7pBIAjcg0D+B4EgEASCQBAIAkEgCKxHIALkeswSIwgEgSAQBK4XgeQeBIJAEAgCQSAIXBMCESCvCfhkGwSCQBAIAkHgZiKQWgeBIBAEgsApIxAB8pTfXsoeBIJAEAgCQSAIBIGrRCB5BYEgcOMRiAB545tAAAgCQSAIBIEgEASCQBC4CQikjkFgCwQiQG6BYtIIAkEgCASBIBAEgkAQCAJBIAgcDoGjSTkC5NG8ihQkCASBIBAEgkAQCAJBIAgEgSBw3AhEgNzl/SROEAgCQSAIBIEgEASCQBAIAkHgBiIQAfIGvvSbXuXUPwgEgSAQBIJAEAgCQSAIBIHdEIgAuRtuiRUEgsD1IJBcg0AQCAJBIAgEgSAQBK4RgQiQ1wh+sg4CQSAI3CwEUtsgEASCQBAIAkHg1BGIAHnqbzDlDwJBIAgEgSBwFQgkjyAQBIJAEAgChUAEyAIhJggEgSAQBIJAEAgC54xA6hYEgkAQ2AqBCJBbIZl0gkAQCAJBIAgEgSAQBILA9ggkxSBwVAhEgDyq15HCBIEgEASCQBAIAkEgCASBIHA+CJxfTSJAnt87TY2CQBAIAkEgCASBIBAEgkAQCAIHQeBGCZAHQTCJBoEgEASCQBAIAkEgCASBIBAEbggCESBvyIs+g2qmCkEgCASBIBAEgkAQCAJBIAhcMwLHJkD++8Lj18v+84x90wqPCQJBYH8EHlhJzPW3YfiPVZynKLvC3CF96np6ZNlhmnO/lbOi7WSepWK9c9nvLPtHZR9ftp/fX56apUUAABAASURBVNXvR5T96LIvXvaJy8YEgSAQBIJAEFiKgDmqP68Mn/+gEnrGsjHzCOAv8BlDDPu/HzKfTCgOicCxCZAvXZV9vrJz5m2L4MnKxgSBIHD1CNynsiSUlbPaPGfFeIGyhzaEwNetTCxI/Y9yv7Cs389c7nDcI9Qaez6iwh5VlkD5nuVmjCkQbpn8CwJBIAgEgSAQBILAbQSGjNRt72txnqRyfbOyS8yrFNGLlo0JAkHg6hH4d5XlfcvuYiwQ/ZtdIq6I8x+K9mfK2nGUXz2uMvcq6s8u+8tlX6lsTBA4aQRS+CAQBIJAEAgCWyJwTAIkRu/+Cyv3pEX3FmWfqGxMEAgCV4/Aq1eWa/sfevEq6sHMa1XK/63sS5bd1zxXJfCDZd+4rLKXExMEgkAQuFIEklkQCAJB4OgQOCYB8o0Knacqu9S8XhE+e9mYIBAErh6Bl6osn6bsGoNevDVx1tBK+2srwpY7nP+y0vvGsm9QNiYIBIEgEASCwAoEQhoEzhOBYxEgnUGyc7AGZWeZnFtaEye0QSAIbIOAxZtnW5kULQPqpSujLSK/d1F9Vdk1i1BFvsgYJz+lKNW5nJggEASCQBAIAkHg7BFIBScRwBhNBl5hAEHQ7Ydrs8xlOmsRC30Q2AYBu3wvsTKpFyx6Z53L2dw8uFIkoJZzEOPynw+olKPKWiDEBIEgEASCQBAIAjcXgWMQIDGUb1OvYIoxq6BJk8t0JqFJQBA4OAL/sXLQf8uZNejQzxLuQODzP2+yIJ7bWN+u6J62rPGG9Uz4fGz5zRmXfB1SSJ3LP+FBIAgEgSBwnAg8rIplTpmyNHb+pGhi5hH4uyJ55bJTWPL/5AqPuUYEjkGAfJ6q/2uU3cXkMp1dULvSOMnsjBGwA0l4W1JFdOiX0K6locEwJ9j9aCX6YmW/suxflu2M5y+rH89b9ifLtozbZ1+xRZCwIBAEgkAQCAJBIAicOwLHIEC+WoGMMStnJ5PLdHaCLZGCwAIE7iH5+3ucS/+dQ36OS77jHs9f3s9adsxMpT9GO+Y3d7OrVd93qoh/VXbK/GkFvG/ZfyjbMi/bCkxYEAgCQSAIBIEgEATOHYHrFiCfsgB+y7It820V+M9lpwwm1g7EVHj8g0AQ2A+BH56ITi315SbCht7OOFM7Gfr7TfWHu4v9txXJzmI5k+YHKuR3y86ZXyqC/1K2ZaghPUWL4JjCUpYgEASCQBAIAkEgCGyNwHULkC9TFXL1fjmj5o/L99PL/mbZlrmOy3Qwwy9Qhfqssr9TlpDbWTsd31p+DyzrMwDlNI3PG7x3UTyi7OPKdul4/sX6/fFl3V4pz3rcxCjX/Sol5f/tch9ftsuX+z/rtzoQ8JWvfm5qnrhSsyv10eX+UFn5yXdoYYn5/7qiURaqkPV4JUYZLU58YeX2B2X7ZVMu5X6H8j8EPpXs0RjfVfzbidJQ6fzXE2Gdt3B03e++K925/t2nHz4T5qZ2Njta78m7635Puf+vAh5Z9pyNMcRtsu9flYTLn5cLm77VF4V9UIVtPe5UkneMvqz/fGf5yLNfBs/8lOOdK1yZlb0ez9ZYUPXNUeOu9wKDzpoLfqNqbrx+1XKN3+VciXnGysU8/NflduUx531c/X6WsktN976/qSIY09WpS69z+Tmr7L1rf+YI43BF2cwos3nH3Cq/ft7mYH0DzWYZLkiow0a9zS1dmbjOgykrPsBC3NZ4LCjetZM8eZXA/A8feMCFxbc8usK8zyXvDHa0SBxlwF9Ko7P6nL6nD8qvkr12Y8wz9mmT6v5HVSJ17srcucqujXxGhdPIMefW42amG5vwYfpu/x10ZeAnDI13pU1vVoArSkj7uA6ez3hOXtD+vMsOU652qr066ofuiqCYz+Y6BUgdw8UX3KmS/mAFGNC/vdyW2fcyHUzoj1UGXtaYFYamSG4ZguPP1tOvln2vss9Vtm98luQNy+O7y/5FWZdvaJj1eJfx6YGvKB80Dy1Xw+03EM8vUv4fWhaT/dPlYujK2dk8WcV8z7Ly/Jlylf+5yx22BUyDOnxNhaG1M6Pe9XMv0+VPrfDXKqWPKEuNWX71eMnA0g2Yb14hykLV8OfqmUDSajtFcpfROcfebecnvIsgXUwazLU/DOxQSFEu5f7SigSfzyn3XmXP0fx+VQoW5Vwy9y2fZyrbMsLRjdFIV/pjYUv8YP9WRWjSf0i5X1L2m8v+Xllt7P+US/gv50Yb48+DCgGLRfD+tHrWfp+u3KHRF4W5pMD7EUdcaQxp1/6Whk826cP6sv7zupWIPMu5y/BTDgs4yry2HC9UqfWFnq6v9933KJpdzUdVxH5aw+fvr3DjXTlN080D/6uotF3j7vC9mAuc0zVe0wj4m6L1fnYZc+CqTwzL2/32OZxK/pbxrgiL71e/+p/IMed9WPkR9pR5ahHNWGqs7r9vbcmYrk6VxF2GH0HAe1c/c4R+bM5aguVdiQ1+mL/Mecps3jG3yq8j82wO1jfQqJd304UbXzqMhi41fMJdR3txcbHoUZn+a1F2fUG9zS3ldccQZpQVH/Co8t0Kj0rqWoy5dohf/7fwrmDeuYUE/dj8Dx94dOH4lvvUD+/TO8Mr9t9ZBd0yxh3tzrzw8PJxcaN5qR7vGH1O3/PeH1O+hDZtoh4XGeXu12P4rM/pe0sS68r734vY2KdNqjuNO3Uu77uMsmsjjmHgm7VHZ/vHsLgr4syPrn12YxM+TN/tv4MuCX7C0HhX2rR+r/8bBzq6JS5+G989xLD/W39spTX3PoR38ZXvOng+7fsDqxDGc/KC9uddltcdo51qrzSpCJN9eUJ70q76uPSf+2P5nQS3ehhriFulPZeOVZXWrYxAsFLJ/Z5KzEphOaPmqi7T0antFP5yleIlyy4xJvivL8LPLdsNRhrra9dvAqjd03pcZOwY2g0yqS+KMCCymvsL5ffZZZWrnMXm/kWpvAZqjb5+rjb9/IedZE1isP/ximBVfNeyVPRRYxD88grBpGGS6nGRwYhafbOrvijCiRDZlTPp/spEeZ+h/DHq5UwajC+6MQLpSl8+Y+Fzfv+3CH6irJU732p01tHClHdn4LUS+1MVvpVRTmPSVuldRToWnTDOxlO4rM1THHFdMmTcXhu/o8eMKIfxXB/u/Je6XTmkIa25eHbsCHEtOivlVtdbNGNh4hgTx8I6P6vG/9j9GHHNA29d/tr/mnmgolwY9zDWxhzzqLT4b2nfoBL7jrI+2VPOpLG4pl8MCbqx1Fi9y/vu0jNXmLP0813aX3/eNod26c65FqXMtxjLOdq14XiBD69I5lTvrx4Xmw4Pc/kLL451eoTetXduIQFeS2rw+kXknfUvbHPHxjeUvzFMW63HWfNkFxcXhDa8m74+G2FDAvU21q4p7zB7vP3bl6exxRizdnwwvnxqxd+lfVa0O0a/1//xVMaDOwFH9KBcynfVPJ/3rH3jW+C9BBJ9X5u08KrcS+IcjEYjO1jiMwkTgqymTJFZ+TYQCLc17xZFz1P20Jfp/KvKmBBop3AX3N6l4lsdMpm9ez1/V9m1QlxFuTCZf209tFR/K/iSgbfVy7nbKi9FHHhQFbHKt3ZQNaDrLPvm3y8OPKl0LZ1c+nHHntWJILKWmevS8kmI76sfc2fyiuRkjIUbF8vYiZ0qtFX7qTD+L+/fhJWu9OUzQXJl3ibZufZpRdjq7pUVas+MqDPRljCR75nUhcURffhFVyYE13erOMbxLcohDWnNMUaEGiuwLYFfX10ijFbx7zKwaI3BhNfWnGXMwqApn+e7El/xw5hjB8sOIZxXRG2S0kox3y0pGyHdCno/wX3H0n5a3bM5RF2dQ+785lzlV49d52342tGxMzCX19Jw2GACP3ZphAk6Y5U5naA/QXKy3t614wTctZXwzuCrndiBs3NjMWBtOujfqP7pp9pRPR7cKLM2bnzZIjPlNsbY5Vo6PoiDr/Ld4y3KIA08FZ5X2/f74HZhBspzHTyfdr1r+1a1o8BzF0FI4fe1XpqV31Y6hJQ/u01gFdeq9e2fow5hdI6RHY24wBNOhBVC4ALySZJPrJAvKKtzSrMedzJUiZw/sc2/JAFMJKGT8LmEfo7GyrvLjbzHOVrhT1//rMYb2OtxU/OOlZpzVOXsZWCqTuq2T0LS+ZhKYOmKUpGehLHarR+OFdZFOlNtkb/wsXjSk+5Y2HX4aadzOxQtoeA6ytzKU7//liLQJsvZxFAvpOKF0VmSIKYF8/J5RYwxKWcTI60ljJHdSmpUU5nSXqESNBU+5q9Odrm5Y+H8rGY7y+J5aJWd+icVuWHYrr/tlsC5VaalaVvZ/swiph5VTtNY+MGg94mUwULjvmNpP83u2We/CIRLxlfl2GLeNldb8X+FrhB7uLBVfoLJHsnciaot6eNbCrh3Er+mB2qQBMB9+AXthBolbDDr+1QFf/GAfRJYGBc/Re1U2RdGWUyG91yy0ND1GXzV4sQXElLBvUphfK5Y5sXr4PnMnfu2b3WDp4WxJWMh+s2tgXHzRO9JsPnfiq+V3ykik9JQYPzeIqb/W86kIZUfAkw7KCb8yYwXBsBbx+QujDJJhjlUrkmC2wG2yb+onnWWcjYzGi8mwYDTSlS48ysv2CLaM8x7N/juk4zzTOq0TxpdXLu9r9T9OBPXOQx2rDrOoBAsxsL4Cx8Lkx47FnYdftSfrOpP5e1yi2MSeKfKyf9Q/V7aGJyljACmBfMi3iEs9bbW7pudMTtkrbypEK658AG24kylaf5ykcTUzidMHAWYir+rP5zNC7vG7+IpnyMW3e+WS0vIOcU+jXnpXfseGz+/RqW3ZBfcGSeYFPnexvxpXN8nIXOhM6xzi+dr88BPOCd8yDl2bZn2obd4YYzZJw1xCfxzC4Lo5ix8aVAcgrfs522DYisepJ+uZ3XAr1kk9XvKuqvgI6cCN/AnjL/yBulskcR18HwW1N3TsEX7hsF/qn/UWsu5eqNRXXWuBtG3qEyt/JYzaqwaO2fYD/zD+kEvvJxJs+9lOlMJK3M/zGHttysPK2TC1MUBempV5b3KOMNi9dDnCKRlonKOi1pfKyG0c6vgVifpV1vRa6VlK91BZwOkdJ/q4uLiwRXhsWVbBpOAWWjRWCwwMLZo6NlTQe4wUAaYOrtFcJ/DQh6YulYec2FDjLwXOxP9d4xpoaI6l5byv+Yc0YmFO0T/6xNldsbkvhNhzr0KHwuWnnTHwq7aT/uxg9PKl2qRi1xaNMcQtrTfD8cx8wFNjg+pSsz1OTsoc8yORUI7N9KtJCeNccaFCPJGq//od8ZYZZyMeDvAWTJqrbd/XnKoKLXamUWDNdorBBhlvZTRbQ+71FNzAdVQDLI63iYfdQgEaH0qBya+t8pvlPi2Jzo3dU5daHObbNaZK1s/AcI5Ib3zM4c4m2n87vyGrraljcFQmeXHJcS76MR8MIzT/y3tORU/GGgX0u3HHT47Q+19wBctq1zao3Z4AhAHAAAQAElEQVQ5pN/nt7kS09pKQ3kw8MqgLN6/c44u2GjF01/E0/dbdKcQpj30y2kn0dzrvQtTVxcK/VOfaOGzPtT1qw7bJXM6YRSfsTCb1WTqRCW/FZEqtZ1QtNoGLFi/8Sp4llZ8Cwz6V4vGZS0tTTXjMX7OLlr3PpRBv6dpNNdOlZvmBfpWOa4i7Dp4Pnxua/Gxq7f5Ay2cOnzx6NpAR3Ptrpd51YXAqAGmla8J35XAfZol51k0aMIpwPtxt3ymWosxopL5l7cTNujbmbAaMDf53Y5yy6FW6uYsaXb1NRlboSAMt5geCRgIrWh4HrMYPIzeWFjnZ1VKxyec/+/bnspAlcLlJ/xve19y4I1Z0MgvBd72cPbAAHf75yWHqpf86cd3GCCCKWb9E+qHSwwwHfU4agieSw/HjyYw8MRQ2DX7xvLvv2PfCdR2vZ8KahqTfuvdNCPvG3iA+PqfW4CnkrbzMdbvWquN0pPuVJpX5W/3mqAznFD6+WNWHLQ/hvL2yzX2DHOLUmNhnZ8xZziO2THz6YxPKiLjkv5Xj6PGu36fCpnq+/ypclsQK7JJQ5XHLrWFLnkrA2L9zhhLuJMOvykrDyqc3uMYjfOIBJ2xsM4PA4ah7H5Pueplp3oqnL9yU8/23LcdZi3VUGM+BsPiHIawu7Lf2Vt+wtD00+0/E6SNuX2/Qz3baR2qr2L0CdhTeSq7uU0b679v753KL5VD80Fr3pG29smdshhtWEyF83fBiHTsYMNXGVjl0h7n5j9pLLXajcVQc+ZUnK482rsyKIv3bzHdvGNBxTg0FV+f1/enwk/N35zvhmZCjbkXT6AOxgY7y+6S8HuJ1e70HX2o61d9bOfmdEIVoWlJXrvQGOcsmE/FJTDjL41j6q9tdLR+41W0ZTxl5z/m2v2eGifd/ttamNEn8TU02giS3fuQD97RfL6kner/xgnxjsVeBc9nUYsGwlyd8eTO1+OJ4YqeC39tYG4cQH8l9joESJOL1bWpCuroru8dC6cu43KdsbDOj+qN2xe731u6dursDlJlG0vXBGiFayxs6KcxuLlz6kIOefk+2jBe/zehaUpIMWFh8DAt/Tj9Z4yOFev+QNAPdw2zVTETW9+//+x9Tg0Gytaa0HyCwa22BNZ+msPnny8PnxEpZ9JstTpoIrH6PMYAyhxW/7keMKXlTBor2gTbSYITDHCWbEqAchMrRr5frdaEJB3p9emv49mKPXVMiy2t/J2VmBqXWvF2CdsnjltnqVu1xnaLNm7omxrH5O/TDRi0FsPaYgTstsztwutr1M9bOz36oV0bfVK5pqwdAkLgWLi25rxkn+ka0hmnjKdD/+FvY516D/27365UN7Z3v/uuHQALnH2//rPyWd33vbe+f/9ZGBq0ff/+M0ynmMQ+3ZJn452dLbcom0vMK91KuHF5qL4qrCUk2Xmc6/fmA0xqq3yYefPLGI0yYpTHwjo/fIb2Qgui8xu65j+fCGotpAzjTP12pMFcORVOWPL5g6nyeN/asF3VqTT0ef16yULIVBrH5O/zWXa01H2sXG4InpuHxRNfn9F3/B5abVx78w6GYf3fW6kd9tPsno0NU+/NGKjs6tHRj7l4SYKmsXssnB9NOYvjnodWH28tohJe9c1hvP5vZSTE+jxd37//bHzAp/f9rvPZPGR+gfNYObSPLXg+C1pz53GVpcWTw9c4YBwdK+uV+hlwrjJDA/vc6i3mZmpQcKmOq41bZTaxYCZaNLuG+Uir1Z5WfDceTjXELp5GYGet1RnRGDS6OGMuBn1Kp92V9y21LBOoz2DoHGNpd35WZq3OdL+HbmswsKPoIgU7DFZOMKR2cawm+q6TdzlkQIbp+40BJFB7PqR1xtbK5hwm2qEdnFZZMDdDgapFfwphzivCaKysBPjh5OM3/zF66UhvLOyq/AiPLndxLrmVp4UOq4Jz7aKVxlWF0UogDE3lRyB0brA19nRxff6kpTIz1fcxQhjZ1vxCUwMzvARTY6HvrA7PxXfl7NyW4ORYREtwwTzNCbzywfiot+cx+z0XFxd2j8bCvJfWmEBFGsM8FrfvhwZt36//3GIS+3Rzz9oKIcuOmDEPfX8l/HXKY9iO7ARTRTX3EOK8N3O6bxd655j+ijZr8ABzDP1UIsacOR7ARTZL5hQLAh9cGWmD5exkCN7aDXcqAQx3q32KpwzmT9j4PWa1sSULIWNxj8kPH/WwmQJZsKf5NUN2oa/oMy06i+RzvMghBcjWuIBv1p9a5e/CHPVy/hp/ZbPlqyuAcKSv0v5zXs64Xt6XjF1WC5CXAm57WBxSlts/Jx38Gg02C5TKYyfNIq2xhPBqfDjEGfDJAjUC8CFXwfPp+3NHzoyRNB/m5kTjwOdXnXwepZzrM60J/hClmlu9lSf1VQ3Q85ilMkN1Ziys88NILGnoHf0SV0Ob6nj9+ATM4aTaD/esY9tN9dyyOmAr3CdBMGtjNNSAWoOS1aTWRNRP0xkBq7F9v/6zlVXCbN+ve8Zw/FH9MIhr9A5RawfOxdmB1RkqeG+Dadg3EcwygXlJOlbfW3QGYgNyi+bUwrRtTOBYudV1uLpGLYf/GL10pDcWdhV+hEfflpsTHpXFTY7K6/nY7Vy/xzTPMapdHS2E+WxHnxkhTFCRxAhQTScEdvSd671bOOp+j7lukcYAjoWN+SmLybU19lP7sco7Ft+YbGV8LKzzw+S35g2MjwtmOvqhq2yYt7ExzXhArXIYp/+bRoh69v3GntE4FzYWxs+cMJcXujnrxvCphTJ1tLAyTINaoPeqjcGCyhb1dlpBtBQ6QXQYb+3vloaH9jc17sjH4qnFS3Xw+257+RcBWH0uhyzzobrW2rVutZthDvCbeidoLYS01BDRnIIl8OmzrbJ6f0vmawtP+kwrLe9gLj9zRiuNQ4ZZiNEfLcbP5UNjA3+lH1D/NW4Sxi2GaPtz8afCbQIQWl62CPCe5UwaC3bavcUMaq2OOlGz/ZWKYXwwTtTjtZur4vnw4sa/VoX1a+rVLZouzObMUm3HLs7m7lUKkCRw6jut1VtCmpWnVkUdLnVJQYvGYE1IadGsDaMWsKTzGagMRq30MaNLmGcdbZdVWFhbLWqVgTpeS1DvxzVpKXPfr/9sp8nE1ffb4tmA7RY1q7P7fjNrSXl8k9CktISW2t1S2iXpnQKNxYCHNwrqjAnmFYk2iHH0PGalI72xsEP7ERJMxtSa5vKiUoLZnKM7hnCYz/V7zPAco9SvC02JPjNCndEiH0bAGDbWB+aEWAtXc2N4vwzds0U3jEn3e+iqv7F/6N/9nlsIE7c1b9gFwJR16Q1ddTI/Df39Jmw7o+R5zFospJkxFjbmB/+/HQu47acehNbbP1c75jAC4Nj7XZ3Ywgjen10SangEiJYQ2ErSe2qFO6tlPm/R9MP0F/2m77fmmbDLTsVRljVqsnML2XP1nyrHMfk797mkPObhu+gGP/A4U32yT0r9E//Z97vK57ndT2UxXzl7qJy0SCyY7dPHpdm3NCfGFoX6NBbozN34XAKhm44Jin2aU3q+Kp4Pf4xPnsLGOOvIGneKZuhvUau1sTOk3/z3VQqQVC3nzhkBcG5FScOdU4MhpBJWTUhbgWb1htS/RXo6qoFti7TG0rDa0TqQLY4ycJfYucEVg2niX5JWi4bap0GRuqCdEkwMhtGOMmGyFXeLMIPz0nTsymiLS+nPhc6gNTXI2YG0S6OuJhXv0vPQii+dof9V/PYdti+ujOyEl9M0mEY3s86plDQTucJA/ceKbyvLJYxKK/5cmDF3ToildmZxbC6tYbjxF/My9O//xjx7x32/7ln/tnDW/R665g2r7EN/vy2MUMHyPGVbO4jGx65vjMVXtyULlF1c+BmDut9Dl6C7zznIR1eCBP1yDmaM6dQCnXUz9/+fyolauzNAdizr52qDoXZ8oxWRQLF28WpOaGvlZy62aDVF85gKWFMe7761sEwbZ6oPVFZHb9RtDX/SqhAB00JXi+YYwiwiWERaUhZ9w46ezRY8iMUkfcZ8q/0vSWOMxngirbGwoZ++SyXTQg+h0yaD8W9fgXKYz6F/mxOW5gEfeC+l79O592VKSw8d/KhRe15qxWlt7CxNZ2e6qxQgre6a1KYKi6mkasCdoun8qUNahel+j7nUkXS0sbBd/KxC7hJvLM5c2cfirPEzebRu+ttlgKZ22yqDzzW0wodhGE1MFebBWU8MlBV1gyL9eMLIMM4hf8PExHzIPE4xbYwNbLqyG+QMXN3vvmuQ7Fba9XWMUz+8exZfOt1v6cun+30oF0NtknM2ay4PKt5uNdyy38/luW/4nABpbIX9vvm04s+VQVzCibJ4XmvnVMcJJPeaSNSinQsIWnlTz7VbOExCe26p5RrTjV3DeN1vC6iE0O730CU4Gf+G/lO/jZct5sdZJ6veU/Hn/H+rCNYItEU+a/Q/DKY+CC8LhPKhiuW2TUzpbCIzBDA2/7XIWrhNxTM3GKemwlv+7mVoha9dnNaHMbNTaRqD9cOp8GP3NxfsivWwbvrJ1u14mMcWv+1A42t3ScsFPHbtjT8EHEIgvsoi7pr04N5SjW+lZXyjLtsXKB9aEfAAeL16PDqjjenXV1EwC0gtHCweWOxYU5a5jZ01ae1Ee1mA3CmZ2UjAs4vUInT72KNaBL0wB3OnbrrryAzacwfpO9olrglvCd0SmkMPaFa6W6sdVINgjZFaaq1wtepGGGyFd2F0+F3eo8NgmjAPdqbnJv0uftyrRWA4ARtwpxYTMG/UF5XQpOa356G1aoYJ6vz1B/l0vw/hYqadA5n7rI28qWi+aT2ckvBYxb2gedBiHDEXLcbzYoM/+R9yF9TYrx5TRZU/HKbCaTS0dr8thI0tXrmcpJWuHbSW9gzBdqpM/N3SSYBcOh5jNtx0K+6YhQMhcixsiZ+6KMsS2haNcd3cbzdTP8dgYjS3XNzt5+/mawJU32/4vMvulvFq177jcyDDMvR/u+kc1kutPmCxrp9G/1k7pRXU9zulZ3OBtrJFmaV1ChokFrecVdyi3uZefJWdQbfX08poCS99nG3iOBfY99vlmUDphn28AuFYGycH7JLWOcShFdCqx1othC4t+HbPV+5elQDpPIYdyFYFff9Jg2/RdGE629xKMloT1zE22l0mMPWZtddIYHW5lb1dAQfBrf767pZJrkW/NmzfjuRQt3a1Nt+bRm9CtoM0VW/qi5hX5yGnaKyUWj2bCt/a/96VIOFxyYKS69xNdqcmPFYVLywM7aPCJI1jt24GxWhPlVP94TAV7r22LtPBaFHN4nZpGNtau9Z20q76vGBXtilX+feZ+wioU2kv8XfJBpVfc50z7HNC1JI0+zTmEWNR3++Qz+YH9pB5bJX2XB/YKp9TSGdXxvw66uaM94MrY2NcOZsYWhPO+fkMlXlwLlF9ysVyhL452qXhVMrx6861vmpFMjaVc+1Gfz51nk8drg3IqxIgrlUknQAAEABJREFUnStxvmSqoiZgt6tOhY/520GzazkW1vkRWgmv3e+414OAVX2Do1tXD1WCfTuS1WW7a4cq35J0T4WmdY7svlWJFys7totT3rcMAfLWwxX8o4lAHbVVnq4YvvX0nvWjtcNVwTEnjsD3VvlbGiUWP/oaFW6c9WmMijZqWpfnjEY4Ac99GEi7jtRUv77qaeGwnM2N8b61kLB5hkkwCFwBAgQ948/SzZSlRfL5PDzYknmQSrWFYHcALE1/CR1BUpo2EI5BiAzPt+StNWiuQoCkTmlFt1GMC2pFVidaNMMwHcyu5dC//5vQuvVlOv308/wEBDDqdp6e4HPPE3/fAWrdQHUP5eX/BjKfWnAtOeb+MkV8rgMBCzdUkMfypiqqz02pMYq3D3M6lueUn7ZHWKDSM0XDn4oTtTpq2p757WAT5UQQoJ5J5XSquNQDqX0Jx+hQZ55Sx0bj3NAxLjrMqU0p+5TdtR84x/iZlWhrx7aCRw2tBAz0gyrUDc7OKNVjzEoE7MAfSkV4ZVFCvgMCP1JxCFtUWrccV5zttqhjXqwsmsbtnjZ+8O5LPy3RTLAXqF6tzyH1SPN4zAhchQBpF9AFBC0cnDmydd6iGQujljY3yWx9mc5YOeI3jgCmy0eY587/dLGdjfuw+oF5o35lF4AePRWMXRmaSi5mYwRMLv1LcIbJv1N5YLzLuWTcAuq7oJcCNvYgyGLs54RHZ05es/L2weVT39EwFroIoaozaiyo6VejgRt5Ur86pFqQOWuqbamC+sPB85T1nl1Bf3c576Z2qYvxy1keZ7TvDn3CLzuZPgvxBJ+b/fSAqv6SG46L7ILGB5Vx/Q9zawHSGWULw/olmrUWH0G9tRVv6XzUT4NApox9vzwHgUMhQIX8IZU49fnXKdcFO8bWetzLaPvvWym0xtAKvmXs8n9zPYljHtVXd+2XlcwdYwx3076x9Y7nmT/MLZq7G4TmxloY9lkkXJvXJXov8pLnhh4mYBNCK0k7Era1WzRTYZjRq75MZ6osx+SPgcJITZVJOFUGg8hW1iUTJu9+nhYOrN73/YbPBMMPKU9nIn1o1XfnXHLRKn+Rx1wjAlQ/Wt+s06amikeNxmUhU+Fb+Jt0nXObO/NICPZRZLcAb5Hvdadhch/2wX6ZjMety7X6tLs+20VyyUcr/j6Tnom2JQTDwJzSyl8YjZfWja7ajryor7bO79nJtKMpzZZVrla4D3S7AEbf2cr6Vlwrz63DvJd3q0Tn+Aqf4cKUWuTxbTsXfez9qYXKl8Fk+xyI5ylrYXIqbMofs2tnbyq85e/cbSvc4tVW77xLx+J6K8+EnQYC+CPv0m6gs620sQhyFl92rcHrVUQLIuUsMhbc8Nr6qsuZXFJFuLXgvyiBESIC6f1G/M/Va24nGf8L2zX1J3DSllkTZ1PauYF+38wIEK3VW+lbWZmTztGNWS/FZDQW1vc71st0+mXc8hkD1WJYTDKHfvfq81r1T8coZ9QQZO1Qf1KFzk2yRRJzRAg497VLcQ59/pEK3adWwebGHWeo3XxJHbfIz8IQHud2X/YR3paApAxUz1u0L1yBxqByVpvWWUSJue1zyViCxiKDOGPWxEx4tPo/VVZn95denjP3Xlw6M5XPWPmO0U/bIni3yuYogk+l+O4dxrRFu0uYBQzMbiuucmK+WjTDsNaNt0Pa4e+5BQYLO6f+7od1Xvs79PMIECZpYxHkHA2z4OS8pGM+c2NuP3XHS9xL0fdb+qzPas/UUC3402rhEigt/FvAWZoWNfWltKdOBzN8+VQ9CI9rF7a0AYL4VJoH9z+0EGH3sSVAqKDzcS1VIjQtSwClRtSiIaTcpMt0MEcYqSlMrBT7Ps9U+Bb+JsWXnEnI7XxL1b+sWM8kl+ArRED7sgCwJks7j75RtSbOGlpMGPUct8i14hEeXSpwFaq0rXJsHUZ4mxNU9El9c2nebu7D7MPKTu3nV0QqijQOfGvWqnh53WXm+rSLluzo3BVpwQ8qjt1nYqbIf6IClmovOB/bmjvspmHQKslRYxHlF0dDLnvOMRDPXVH2+exGRb92Y8G4tUtnzPiSKiXVuHKaRvtopdWKPLdIZRHCLkorjX4YjYa5Bak+/fDZrdUY76F/99tcPMcndbRxg0CHgPHemOyYD+HD+Ph2FTg3B5gnt7rcilD7qxcXFwRKR48IlMboJZ8CeZoqq7KUc/Zm7tgPAGy4rMHjpSoSLY5yrsccUoAkHTuA26qZVZM5FdRWfGEm5rnGqlG72GPNy5H2qVoM1BxjY/XnkHhgLOfOjFB1a02sHf7Swqx2v+NePwL6rr63piQ+tTI3ua1Jb0j7wPL4xLItQ9vBwhaBqEV3imH6EgGqVXaq68bmFk0/jFDgAiw7cpjod63ALy1LePKJBkKBsPK6YwjorcUFaVrUuxNh4YPbfalwTZHbEZyrfz+u9ksFte/Xf6Y2pqx9v/4z7RdaMH2/qec5BgIjYCV/Kv4p+FtsaJXTtxTtELZoujC7JN3zWvcXKkJrtZ+whjdZOv8ZVzDHlexOhqq8uk9FtiOqj02Fx//8EdAmXW6jrVm4+uKqst1GYyx3ydEDfcvtxwSRlgZaJX0xtiDf8VkWB/HLdjaNj+Zs/WnJLrzFIQuIzpDPfVnBvLFWE0DZT9E69uOy0FbZXSzk8qQWTRdG+Habbfd7P3fH2IcUIE32rbMjivw99U8HKWdng2n6porNLWfS3LTLdJwrae3swsPENQlYL4Aal3MlBhIDioHFAGOgIdgZeHrkix+pbS0htjrvrNoS2tBcDQLOLM0tUgxLYvdxbmIbxln6G/PqzFdrTKNe47wt4XdpuqdG9/AqcAtjTMpS4Q2D7bwMt5IdNYTFIXNsoeCRo9RP8PzwerTLVM4iQ2vig4rSYmA5o4ZQu6ZNmjOooBI8RxNseNq5pP3SILkrCAMxR2/nXD3vijjyw/ugvYGpI8D7xppLKSyMEELhunRsHUn+YF7KpOxzGcCAhsAc3VS4hYG59u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AI7EFg7UC7p+zkOQYCKcNgyvhBDwmWERlM6dFcdxormGsYFIYt3qAah5HCbHgnGZhirOSXKr7nSHQtXJcWuL0ypKlXyP96/WON2nlIE+bQnn9R8Vudskz67kc5mKHHVSHUZCvoupevVIafKtjlxvekAHW+cl0wGCZNv3rZ+t3aIamkpnNelCESZSiLquh7FvVvle85C4Q1Knd/uQohLYdZXTad/qGfDPhqi36kP+lXzYwPE96twiXtiCLpOn1kak0YPrAdYw0vz6BbWCXC6G9WeFOOMI0V0aX6v78IGJCpYNbBxbuJMZolGEXaJYLP0J+8JzQYGJwZkc1eEgawFWAsmCWoyL9T3vtcwazTj5TBzsHQBn1JO9gi0BdnM44inXFliG8UdeOXMHEf44bAFLbuTZr3xaflYDCmm7umiWP8mktbG6fONbQt9eRz6lfuw/1g/O1y+93y8G2NVYRFrXzijVE9AcraNjhLSs12PC75LV49LW8uQtdKF08wR4Dhes4bj9+4EszVQ9/Tbu+b8Y5RsUpuOnMBTQT0LSKMsCL10QAAEABJREFU7ZJ6uP6vPgbl1M8r2xzCnobn1CpfvPnrxVzEB4FzR0DHPvc2pn33GwGLFGfiZlF4GMlS7C8/vD6HwIKfBeal9+u9q7F29Bi0+J91zbH+aTfa+U87Lwz2iG95kxrmCk4tmrXxmD2Tn509k/7w+RvtebMqxPmiClY5hmxYrXSP8rsvDK8zWiTRziT2CoLhIQzuuGwMu8//2BEZDIZRMfU5DowYDYAxfe8a8wMj6oPKQPvT9U+8XeMlJvcvFa1FRQWzzgIGZhi9WYKHkfqF/qGfDPhK0o/0J/1KOeJaXh2sPdv9aNFsiXfvb1UZqOPCdow1vNY898p+4VkJr9t7Nvrlmp0oTOnc7trQZrvArBMPv+dCDDIVemqr8Bn6k/fkuyqDc4DeIe9S/Ww6DJod/zkCDFHP2I2y1WG8Ym17aIOytMPRD5oQytBe8XNef3PP0zRWcS3q9Xne50d6qt3S0KAde31n6E/TOtb81nb3AFPYujf5vC922anZO7coruUxJs7Ew7RFc6x4/cF4PC0PxufQr4Z2MVqHcRt+z4WsI+tjrbGKkObfzWUcxZl/WgIU+TFnI/Irl9R2tYFNhqEf6et+i5d+JdMowvESc9Io6tElZtGY9yhicuFZGlvscmN0h76HTBuc9SV8JZg0hoqf85494cxcmji7yM/iYsabf2kQ6f/q8z4MZNLMITRxWFPX3iFtGnonMfPX8Q5M687vILAJgaUF+KbCQhwEToCABb+FxQmKPlmRFpsm3V4FpNkY097OM2kqIxMf3yuo0kxaFoB1eclt+WEX/HUqg923Cq44k6CdnPHEeIXoYYRFqk8e2OV6GHUp8PkB1h0vRU5+eO52RifRm39+buWAdQtnn09gbbPIFh3G0fcUxwuUcSaWO3uLA7Qk5dTuXc95O0R23ObShjj9Qb/QP4a4aeh+9S/3Pk0b/7bz0tsdGdMuXTs77bkSjMzReu4Y4KU+hCFaEmrNlb8ljmVdC1YCIgs2DCvGZw1za3etxwzpuyxu9+ZXGGBs9RnvVqvt310JDMphPuty1ll0Uh10P1MCQgxnE6fxw2/W51ngHX63QgbE7Ei30sWzCnyOC19MAyGAe9DOOe9TKgRUS+8vgQRmfq6MrXHGWu+7+U1fMTZ4X/UNwripAOWc+tVwryx72wUffk9D/Va/+JFpwug37H3OpidohDkGb5TtwaW+j8HWzx9EzPzzTDHArKPPJF+Il45uLl2c+dx76HrqzVO9cYOwz9g3zTf+bQzAZPt81zh+fK2f2E0ex42vfeJn/Ht8jYElXB7HzV3bTXeW2Bw+ly7OWmOrdWr54oPAtSJgUL3WClNZENiIAOnoXjP3G6s6CrndMMxHrzDGOJx7Nan16KRhpqiS2knxe857jy00DllckmT7jMxc+UOchbDPCA2/W6FJmiXLVrp45ZhMXbc85qOVtiYe0273FoYz9A+iPANGgB786Pyz6PS5oV5ZdgaWFhF2XVsMrufnOXqeraboB/pDrx1DXvemn+lvQ9xcqL/qt3Npa+NYe7VDoc5env9ciXZJK2g6nwp5hmbqugRqdLQ6tGfOYx7svP9gFUdw8fgK1zqY0hpp0dtlealW4sN4woCld+Qh6YX3zg4WRm2Im4atBTjNjp6wwK7PHGM8Ld9vBuRodmDwPUM4OJ9uYW2RT1ACV7Tn5LXT+LbUJkwIVW3jRosWI0VNs5W+Nh5+dpSNF95lfRQTYpcTo8ty87Ssc+pX2mZHdUkY9yFFqP9W0HVrBI0YZWPkuCB9t3cMAK60Ezzbcb7ptXR06Kdpw++5+qVRTe6NV8PxA7Q9792hTYDht3tPyMG4lPfq+SojvH2jti5nXW8Mpw3luMFsxkkkQZ+62Rhwzcii8YdAzvxlXqb5McmWn0HgvBDoLaTOq6VpTRC4HQhQVepZGrR4MpHaYVt7Rz9ThEu7JxbUJp4i3eXsdJpge5m1GVPQo7FAwFAJe3Sk5j0psbwW58K9HgOBkV7KD9+le8fAWdwvlUXlsUdjgdYad+1S2CHq5X9iJWpvBaucZ2bRrN+1MuivzrldTt/26wuL3CKxgq6D87d3KS4uMGSHPvuFKnYn2+VhWbtVgB0lmhBP1yKoeH2Jgbi6XO18MoWF2F6G16vE3kK7kq84fQ6TRUVyCXNMvbO8Pk9DIGCX+jOqRO+ZcQGjVj/PyhFM0TxZGo+GRlOnXVKXxbBsxXkoX6hNNBla74tx0fiIdvDn2K8wXVRahzZOQ4IQGgrT+Lnfng9Bo/vGPBGmDMwdAQrmziebjB/j/Ett0DeXBLdDed7Lbxt+zISOpuzZucQYfl2V5+gFzZG6bDptZVBLPd5J574/q6jNY8b9lnZMkXSd/uOcLwZZ+T1iGNPAIjwgVGHg6mMqA62VH6+QILeCuCBw3gi0Flrn3eq0LgicLwIk2iaTVgstntYY25nml68nCXf2xm7MNN+a3xZUvbKHMkx8FiDD77nQQteu2FzaOE45pNTjuGNfWyjZxVsqFxPYYwDlt/BB57rnqeT10ntpFlB2iFo0dj/1g1Z6K953ky2cWun665z6X4t+Gm9xCp9pfOv3khCgle+m4zEmDOo8YEwajbF4XMKSKmKvjLmiMY/yzaUNcd5/OzXDb6H+jzF13fLPVQkEFM4DYkjsyipHv6ikW+2cN/yhDXfgHYdFL4udMJpFPZpeGpVV73KPZpp2bv1K+wjGen2E+vuWMd4uO+GW/oiZHZg7TGqLuVtqg7xr37VfqJuyY1nBrMNkz6ki23nvqVcrjACRIEn/wrAyQumZSjuWtwveK4sAi60IbdX/WBin+SG+ly9pQeBWIhAG91Y+tnvV6DWTB5XPcxikWWdkAbf3gHy70iTXo5lLwzjaXZhLG+KWFtYD3TQ04cF5Gr/nN/Wq3tnQPWXuzbM04W8pl3GQLfRbaS0U7Qz18tn5tNDr0cylkbhbXM2lDXF2SbxHw+8tofKps63Nc6y+tra+Y9BZqHu/LJh75TFWxLdovPt2ilrpvXi7OD21fmrC0x01z8Y53l65Q5r1AE0QllbtvmGOP68S9zC8le0sHGEixn1LY7xnnlMrjzOnBACt9KV4Gi6EhUt043R9ih/Hja+19zr7lfEKczRuw/T6Bypi631WltWONgyhTi+DftxLn6b1BJTumbGoaR6MsXdzGj/32zoF82431bxrXndE4hgMLyEjYeNcvdM44wR7Etrt/dB3CBQ8U22c0ud3ELh1CJjQbl2j0+AgMEGAKs0hKmOT4nb/dAamtwhR8Pf4t8NbKFh49bJSHyQB79HMpVkE2yGaS9sah9ExYW7Ndwp6O27HKvfUTJnnRi2t116MytoFzLQci59p3Pg39fa9Z90t8PYw3uP6z/najp6dIszSUjudUWMMqEXn/aDm10rvxcu7tBs5twCnDr2koTBXL4GHz6OMGV7n6wlDllQt58q7ibg9Qi6WnwnqWu115nIvg4sR/cGLi1bRzfhz61f6hrPCzQZXwqmFgt4zu6pVVdNtff49BlclrEYLx9585x0Zx629tnZh5G7M8H5YZfbJLgx1Xa52jJYtCeDmCsMHGN/evxIHhvff1/WrlfecK4gLArcPAR379rU6Lb5PCGC+liZKzEFPtfO68DLZ9lTXqAIfwihRK+rdy17jPBbOJule2WvTqB5jxtfSn5LuEKzH7cJUWpiO4459rQ8vMbhbF2vjNtqB7N2D+s/hHRq3+aavLRgZq8Lk9XZOx+0kKBj/nl5TF7YzOo1f83vNWDjHdLgPhmLW1NGjsdhlkIzqKSbQjs9eoUivnmOm7dEmcc6RP2Y7hrKoy+5R0T+3fkWgbL4Z7msu3D/+zpV2Nc6YtTRmYhyN32v90jn3Z6tmuPcKLjm78iyxX4rc8QPD+5aVz+43ZtsXEjDyFbXo9C32PXrG6BYLKQJ8geMyzkF7fz614uz4VhAXBG4PAjry7WltWnofEbAop8bTu3dM5dzCrpfnNqYt7bKa8G9avYja1bGY5XN5RvqgXcpzac+edlj0WOS18lq02ZlqpffiCTSU36M51zTtpsZIAMByKMM2PvNF3deuhiMFPdyu8760A9a9OmmRTNPl8R1PlnunaXt/Y27s+MCNldfbsqO75n4JIAgierRLDGcrr51040kr/Sbi9/Qr84z55ibae5N1EvJQjZ62gfDJ96IxpdO0vb+dRcZc0vryiaJHO7qdAlnfZqG9Q7IpCY+AySZcZ4383AVam24uxHcbAZ33bt9h7u62I2BxtqSaa+CfU83be+++G0lF559UAcc2BFFFxgWBO4UAJsfO/TndFKulzsQbG3oeY8YqsJ2gwWopleQlodo53euattjdeaUidN6vgqM5C94nV2kfVR7TU8G9cHvvlTYSZuhegHTPbpKhRnYUjilIAiFGV5lvUz+MZRU0HWEFIR215yWBeLOQRoJvBTPa5jvlDZKziE4jgsADBMLgPoAh/84cAVZgl5rIBP+cZHUp31w6wxVUdHwOxI4kC7QWh3a55ugTFwSCQBA4dwQIIXxnmdo1teVjtveNq7C3Lr+0AC+SO+H2nqOm+owJuRMg3MObYGOD4Kx1644y+CwQQ1JrPivXKmcu3ufeaJfMpY3j9K9PqwgWnx1NoKVSP4/iGKHyrd45TZGjVJBCTo3A/Sk/DO79eda3+U6px1Aj7N0DhtQuTI9mTRom1jfgxrQvUj++qLwF4mdXiJm281OXl5zdZguYS5FH/LG0a2Bn4NhS2yM2P0V1ELAI0X86JAclGet7zAe1cmfED6okmS9O+f55fktCPDu1vcdg8Uvtmvq18ZIl1WOdlXz7qnjOCE9F3yqHgcHI9BrtbGIv/dhp59avtMd807tPu/u99LuaZg3g/Co1dgzhx9WNHsPgoTHcZ36c060iFx0hjG/Yeg6+kb30FYbFAh8SvGSFjiVUEBcEzhcBL8zm1iVDELhmBKhAPnWhTmfmWP1bIFtM9pmfxzeovC+vXmmMSbxAhVPHkmzvrKYzjnutb6rruf3reIZfzu18V6e5SRohQHiin4+irlwewjwsWWG1AOtZjr3SmETMIrB0Bg/jhIGazbwQ6cwjprRH9iO9xEma/vZuFcd+gfHzDeuaFVbClrrc7Hw+5+U35zpthj3vDOEl32oZIcF1j7Pn1q8IxBgnbGEknuq/8FR+DZP9ilU5wdCxPPVc82wVu+j0E4YBaTc8S1F799+xwu8rv9dhmAnct+Q3t/iGtm/xMlj1spWZoF58Xe5yr1m5jEcVxAWB80TAgv08W3Z7W5WWHx8BO1uMLZgweqWzPuhTOT2aXpodUmp2T9cjqjTqfawc1uUlt8Y4yfNdyrH+h50bqtO9HCbTQyatXtlJOy0CdkOc4erV4py5hVqPppX2Qq2Eh/G0JJZ2/x6SJuggYDe0txPOONPehb+8vfHN+OhTUp3mNZOcOaZ66BNAxj/1WJg7+4eRaGacJDiDSAtmEuUqUnEAABAASURBVH1jP/e8MyzlYkhajfauLjF3rbx748+tX5lnlrSq9ggXtuBDyLDEbJrTt5R5KlrvJuETNePnrUq8Y0IM71ahUksAX8UuOoKJryyqgUEllCIEsL4ioK+kVc46htB0FXGIgsBNIBAG9yZQv9d17r5552BNBL0CLEycm917PsRA75MgvTqkfV79s8ip4JITt7TAtAAkRb2UccUPnyB64QW6r11IT/J5I8CAR6+FBBxr1dPG5ZC0U+Efx02vqa1a/Ezj83sbAoQUFrKtXN59TGQrvRdvQWyntUXDgNH3txI3xFOxdH6QaqXF9NNXXjtHa771SR3yXJiKavYFTZuthgJ9g9Rzumj82fleYu4aWXdHn1u/wrAt7URicH3hYO1N02x4ShFj5n2K6l/W9ZuWf6nyhELGsbp85DDZhLqPImYulgR7M1muJYrQCH4YXuMBhtcYTTtsqQH6815B57Rsn6xiednnv6wxMLyY7iUVfO/H9HlMy87vIHCjCITBvVH4U/kGBOwumQxMrL1szoew6Mmcf49ummYh9wkVufRO/FDR+JxIBbNuicl8icpl0VXBjGtHyUc9qkVhweVzAq30xJ8/As5I9Xb/qKjrB1vvhNr9X+9k8ukSDG6HJEkrEbBgXBLEvW6VtVUIh2mUr7I2HSGgndgxgXwWrs7c2rUxhlJPxMASyL3OmLhxbczFSLDM+tENmiGaCvU5LXxZxF8SDA5tF9KU+bsuOp4QgUp/h+ToSefWr9ygfq5vuJ7zGFyGjubS5uIwsXYGCaoxfT67o799VRETKujbU4b1Wyqt5zCNp+6PNBa0+cWrId6nj6iQzQ4CJ8c+Hle/lxyhEgEnY5but0fvSMF0fYPhxfg69ztYg6eR8Z1VkJ1ZZ4L17frZdfqZMYKRrN6zxeD2hG3dSpIYBK4DgaXF/HW0IXUEgbUIkDTaPV2itxD0mR8T7BKtgdo3Ik0qz7REXOmYYJNtXc66b6tY0vYKZh1J7btUinorWOXs2mljj9iE2ts56uW9NWl3vKHU3qm/927TmUn9oUczTtPP3qEi9LsKZt2/q1gLoQriDkTAotBnhnrFWLxvNdJi8dzTLlEvNUPHOYa67cZ4riy7YgQ+sxIYgmJHAONhkezM7JqFb2W9UIdx0nXL/7dKcFSjgrNwFv7GzrVMzvNXq51RrKDp7LKNcW4SHjEB9ufSr4bbIlA1Zg2/pyGbE5gtz2CaNvebcLqnGk7I8sOTjIQ6+vck+tHPv1VXPeFeJT9ymGeq54wzEVR/TKUw0tTaQa7kCwIUwmW7zsZRZ13fvBIwqlR47WATnlfUKkftWjk9YusLwqkxzYfXD198+MEK9ZP3r5DFdP3Z0QaGMbWnole57yqq3jqHINY9F1lcEDhPBMLgnudzSavmEaDWY7E+neTmqO2SUgEyWTA+hSkYJlq7GiSdGE2TxftVAWveBcwHBrfIm86gj9lsElSCRaVJcGhPRTUdBuV9K5WKYAWzzmTzyZViEVRB3C1FwKLlsxbarh88sWj04QouuekPhnLeqiL1twpmnT5DFdDCapYgkZsRoF5p7OllNObYVe3RDGnOw9rJ6o1RduAJ9YY8QgteZ2hdt7zPjqxlAJSxJDRU57mpuvssElXXpfHWrjrGAGPmXue8HTlYz6WdOu5c+tVwn3ZUaQMMv+fCf1qRjBtV0HXmZ2e+e0RfXolTI452Sak1V9Ks8868e6V4thU0nb7xBpVKKGSHFLP9JvXbfE+og9njHROo6EeOQMdzeRQxc/FGFecdrmDRaQdNnR4hBnya/iUVYSyvYNa5Jwyv8mcJJpGMYcJiEv3oJxXm6bN4lJiLIHAOCHj5z6EdaUMQWIuAXUq7EJjdNXnsfFDPMRmzDmoSoJJJ0ukTGaSba8qhkmaypsLTo1c+Fekl4xcWUu9ZBWFgK5h1pNkkwqTIswQPIz++wm8vH3f7EfjcugWClAqajurep1Sq844VzDr96gMrhXCkgqazaPyKZuqdSri2m6ES+KSF2miLWDi/dNH1Fp2Yg39bNARyFcw645qxjIBkSmDX1ng3jR9+29n/jPqhngq6DkNuN7RHtLS7aVFscdwqA3NpMd5K3xtPoPAWlZnQp4Irzm62cRtjcyVxFGF3bMmi8Yj8qJfn1K+GGzNeEWoMv6ehfk7rqte/YP/BlZHwroJZZ/5VzjTRTvonVaR3oIJZZxfXGsB8Okfg/aPVgKGdSx/iGGdijG/4LSTMWTqbTn3Ze/inZFjwBE6v36GxviC0n5KYM6iMT+PHvwn0e+/AQEsYYH3iXRzipqH1BmH+ND6/g8DZIBAG92weRRqyAYEvKFoqfr1JrUiO5tQzfEJjTaHO6Zqwl2jfowhMmAw8OD9TPy9MtqSndqpJh0144lte/g+qRBNfBXG3HAECFDu0+lzvVnyr2Y6CfqK/6Dfo9SP9Sb9gEVxcy1s06oPZvW0htD+eFgdmr1eCxb9FM0bXItxZPvR256kWflr9sGhdUi20Az+36K3sFxa+Swtw5X/zxcUFo1J2qIZ2yE+1V5w06qDaLH7OU9X86rmEUZy+1mO4kRrP/ryL8vozDZz3rmuMUAW7nLXOh1VO9zDG2v0ZY6lkOtpSJE3nfbGTjqlqEp044Vz61XCb1IYZdhx+z4X61zdUwoeUf67yg5BhC/bU71taEXZwP7/K7TlqxuZTwmJ9Cq12aA9VZJoz+oj4OU9LSr+ce/ZfVhm+rnzPOQcOK0Jtwirv+EDvnaJCbF2Die+1w3jgnR7yDuEaGyXK9Q7Q9HiZyqjeCh447XF+/s3qFwHOkqDHmIK5L/K4IHCeCOjw59mytCoI9BEwobF6bOLpUx6WitHw+SGTz5aSGJqws7qUx6RCvZgUHJOqPgtFEtTeDp1yLbgYfplTWZIefzsRcNb8nVc0Xf/QT/QX/Ub/0Y/0J/2qVwR6Qpvv6BElbTcCdlPttFssLhXyhCKg9or58wwxgM7Ovl7FLzkqyHZV5xbe8op/n7pYaoe1AFVK5yqHdmiL87TipKGpopruQysFE1FB09m9XWoLBoCARv36s90352LtNjcLXplgJ3GMtfszl2B0ukVUol1gz6Uub8ydS78aAPCMMH764RA3F2KgCNwIf/VJ+dZir7/oW/LMlU2b670qQV+poOmMl1SO0SlLO7RnSTVagZhzu5aup94zIWg0H0/Txr9p1fyziqA95h3XBp5WAwHAknEzYzaNHMxsFXPFmTfc35WEScSL1G9aO+pVP689jmvRYljSaiO4w9RXMXFB4HwRWJqwzrflaVkQuLigXslC7KkWHRZjjEWQ2psEtmBu0qVKbYdmS761tCZTOxskumvzhO52IKCvWdAt7YzsvRsLJTsZW4U2e+u7r/ksGF+7bt6CuoKju6dWiXYdWwveSn7gtAOdMeNBxAn+EeYR6i0V7dMuDF4t0U3TndEc7yxP01u/Gf1rpW2Jd3/eSe/mlnynoPU8z6FfDfem/xlPMKJD3LFC8+jbVmHuuYKms7uLUTW2NYl2JmDeMfG9Z28eJjA8Rf1Dswk9MbHD72kIK2sO7Z2mHeu3Z0xwh6k/VpkpJwicBIEwuCeBNYVeIwKkodR7SFDtPByrameLnAkiqexNbL36TPyvWgTOS1ZwNEc1lcoVS49HKzQFnRUCFivOSzGQcsyG0XiwU0Dlb2+/PmZ77npZPv3k29d2JI95r4QTLNT2LMiO67MAJxA7BZNLrdLCWp8d19m6NqbaMWqlz8WzRovJnUvrxdl1XXNcpFcGIeWW++uVday0c+lXw/1gQAmD1xiAHPIshfqTT1v1mLpxGd4JjP8xmUyq/87nmsvHdc1dqx+jP1P/HPmmOH14jYBFOwmzCL82VbCC2LoDFp71CvKQBIGbRSAM7s3in9qPgwDGlkEd32V7xyqSqf8KNjsTKmaUFUMD+TFUf+1YsF7oTItvT25u1CQDxpuF6LlzOBPS/LzlCOiPzuMStBzjeTt/qKwli5u3HLaza77vpr5gtcrY5JnW5W4nvyMTxicWfbcURCBG40U/2JKvR2tni1EcC+se3TiN1dmtghufMtqjooyRhjtr4nsYD/MBIeWW+xvf6ymvz6VfDfeoPb47yxDXELc3NFcqC9O4tgwCO1adqeDKvzZfi+4jK+EVyq99z9Svvxyr/qr6Ynjf9WHX4pY8oRfjdd7NJdq16cYMgrrTaMutbUXogsAGBMLgbgArpGePAJXiD6hWPmv55yxPZemLKyR5nE4OFjvOLlLnMXkwpMKIiU8F9L7tV8VtdiY+llB9M49xrD2Tr10Ehmdeo2pfO+EWadwdQIBxEp9yISTZw+jKIy/jOqyQ3wFIbt0tEMIZm1hUFfq95SbQyyc/VeDf3JJ5ROv56wcvX3H6RQWbnbHTuVWCQDub2ralEOOh3ShaN9NxuVWO84sEmK30XjysfCf0+Ypo7T0bo70zhJOElJX1LB3sh34h9HtLQ9HLd2i/GurEXPles08zrcV6yCsknKa5suVZyTf26pVfOcobp625JoAhCJR/z7Mf6mcfQz9aU+eUxnvhaNRzVMKe991z9W56Rxmr885WUZsd1W+aIsYMY8fmApLhcARSwj4EwuDuwy25zhsBCyiDMcufBmcGd0j/WZodPAuKz1638fjyJngDuUmlfp7MKZ+1Rt/Es1hzZgcDPv2kkMkI820RydCM79ExsvLd1TL3VsEmxxDRcN9zoU8prT1TY9KeK2OIk76mcepT75BvLtTuVlkwM/nP5Rvi1qq2OadHuDHkm4bS0LTaMo5X5zT/+Lc2a/s4z5pri3RCEp9qYf3SDt6TK6N+rr/U5SOnfIIbAh59Xx55lfGIaMOF5zC+h+m15+h5ri3yUIzUpc5pO8a/paNb26bromMlm0Dt91eFvinLOrAzqQy+VNQjZ2FN0GZxi9Hy6Q755H9EtPNCP2C/QL8wDhlj9CXqpeqdFotJYGUYU8oglvfBrqb2TWnX/jYW0rrBWNldhYG4cf6hH79dRRJYUm2uy91uEBTZGaSiP/3MiW+qmjPswLHi7J3ZOt4e+11Ze7P6hf5xk/1qaCvMGPLSv4w/xiHjkec50AyhvuXZa7v3QX+wc4pBG2j2hPIrR3nKHd4z9U3L0y7zsPnY+0CYqK9M6bb8Vj/GksE0fRcG6pgTtitXG+CgnQyhecd8llC89L3eO2pOVp531zvsXZ7DwbtvDDAWGBNgQfCv3caMvW1IviBwSgSaZYfBbUKThCBwUgR+ukr/xPIY8OEzL8MCfWC+LSI/vWgsXiqICwIPEKAuSVX9deqXxZP+MvQdof5EcGOxbkFVZHFnhoAFI5VOVlUxVL7R6dkN3idU7L5QR8ZoTZm/Y92OccgYoy+xJKzeoQ1D6OyrYxFvU5VScz7m50GMbXZXYTAVQg792PlDghyMUzXhIAf3b6wSfErL93aHexQSPmFEMBroiuzWOe0+h341AGf8MQ4ZjzxwM+I4AAAQAElEQVRPOI+9vuXZEzJrt/YPeY8RKk+5w3umvnH9rrXLPGw+9j4co96hDH1W34WBOjD8034+tAEO2ul89bHfd++sd9c77F2ew8G7bwwwFhgTjo3FgEnCIHAtCITBBXN8EAgCQSAIBIEgEASCQBAIAkEgCNx6BMLg3vpHePobSA1BIAgEgSAQBIJAEAgCQSAIBIHbgEAY3NvwlNLGc0YgbQsCQSAIBIEgEASCQBAIAkHgTBAIg3smDyLNCAJ3E4HcVRAIAkEgCASBIBAEgkAQuD4EwuBeH9apKQgEgSBwGYH8CgJBIAgEgSAQBIJAEDgqAmFwjwpnCgsCQSAIBIFjIZBygkAQCAJBIAgEgSCwFYEwuFsRC30QCAJBIAgEgZtHIC0IAkEgCASBIBAEZhAIgzsDSqKCQBAIAkEgCASB24xA2h4EgkAQCAL3FYEwuPf1yee+g0AQCAJBIAgEgfuJQO46CASBIHCHEQiDe4cfbm4tCASBIBAEgkAQCAJBYBsCoQ4CQeB2IxAG93Y/v7Q+CASBIBAEgsB9ROAn66afo/xv6/gvrbS4IBAEjotASgsCZ49AGNyzf0RpYBAIAkEgCASBIBAEgkAQCALnj0BaeA4IhME9h6eQNgSBIBAEgkAQCAJBIAgEgSAQBO4yAtd0b2FwrwnoVBMEgkAQCAJBIAgEgSAQBIJAEAgCp0XgtjK4p0UlpQeBIBAEgkAQCAJBIAgEgSAQBILArUMgDO6te2RrGhyaIBAEgkAQCAJBIAgEgSAQBILA/UMgDO79e+a54yAQBIJAEAgCQSAIBIEgEASCwJ1EIAzunXysuakgsB+B5AwCQSAIBIEgEASCQBAIArcVgTC4t/XJpd1BIAjcBAKpMwgEgSAQBIJAEAgCQeCMEQiDe8YPJ00LAkEgCNwuBNLaIBAEgkAQCAJBIAjcLAJhcG8W/9QeBIJAEAgC9wWB3GcQCAJBIAgEgSBwcgTC4J4c4lQQBIJAEAgCQSAILCGQ9CAQBIJAEAgCx0AgDO4xUEwZQSAIBIEgEASCQBA4HQIpOQgEgSAQBFYiEAZ3JVAhCwJBIAgEgSAQBIJAEDhHBNKmIBAEgsBjCITBfQyLXAWBIBAEgkAQCAJBIAgEgbuFQO4mCNwzBMLg3rMHntsNAkEgCASBIBAEgkAQCAJB4GkI5P/dQyAM7t17prmjIBAEgkAQCAJBIAgEgSAQBILAoQjcyvxhcG/lY0ujzwSBF6p2/Gr5/9fw0tBUclwQuFcIfGrdbeu9EC+9SM7GvXG1RLvG/rcq7lXKr3HvWUTjvNPrT67031b+GO73VSFPKT+t43sr7o+UjwsCQeAqAq9QUdN3Zvz7v1f6s5WPCwJB4A4gcH0M7h0AK7cQBILAnUbgGevunlj++cvH3R8Enqdu9X3KT923VsS/Kb/GfV4R/WL5lntCJfyp8sdwv1KFfGz5qXMf71KRx2Kkq6g7735H3eFrlX+j8nFBIAgEgSBwRxAIg3smDzLNCAJB4MYQGBa5P1YteOvyv7N83P1A4HfXbT6p/B8qP3WfXhH/q/wa91+L6FvKt9wfr4SXK38s9++qoP9cfurevCIeXz5uGYG/WCTfVP7J5Z+5fFwQCAJBIAjcEQTC4N6RB3nHbyO3FwROhcB4kWsH91T1pNzzROAVq1lzjCfm8XMrba37P0X4KeV77uUrEUNdwcHup6uEufrM6f+80v5g+bh5BLznH1lJ31f+r5SPCwJBIAgEgTuGgMnwjt1SbicI3DcEcr87EMgidwdodyyL86rvXvc0p9L72RWPiaxgtbOD+xMd6peotBcofyz3OVXQXH2Ytn9QaXGXERhravzTy0n5FQSCQBAIAncJgTC4d+lp5l6CQBC4isDlmCxyL+Nxn3+9Qd28c6sVXHLO0jpTeylyxY8fKZovKt9yT1cJr1N+jqGu6M2uV9/bVml/pnzc0xCIpsbTcMj/IBAEgsC9QCAM7r14zLnJIBAEHiJgB+0T6toObgVx9xQBzN9bNu6dYSkqyo3kZjSLrBhcYYvoJSvBznEFBzv1fFmVIqzgkmMN9u9XzLGY6Srq1jpWpz+mWm9nu4K4IBAEgkAQuOsIhMG9608493dKBL6tCv+95S0i57w0NEUSFwSCwJkg4F3F/GEC55pk9/Y3HiZsDf5DZWB9uYJZ99wVS1W5gqM4RpJa9f2jquG5yscFgSAQBIJAELhXCITBvVePOzcbBE6CAEbBNwTtJM153x88ScUpNAjsQMDneqgnz2W1c8tC8VzamjhWl79ygdD52GMZm1LfFzbq816+biPtSNEpJggEgSAQBILA+SEQBvf8nklaFASCQBAIAqdD4NWraJ/tqeCKo578M1dit0XYAXaOt5Xrr1XCMXdWv7zKa9X3hpX2nOXjbgKB1BkEgkAQCAI3gkAY3BuBPZUGgSAQBILADSDwrFWnHdQKrjjaB0tnaK9kmon4oYr7xvIt90yV8Krlj+V63+DFyM99BulYdaecILAbgWQMAkEgCJwKgTC4p0I25Tp/Sj3umwuKXylv8ci7FsfAyym+1WgB6+yZz3xY+P3fUd3q58X9aMV/bfl3KP8XyrOuW8FZO2qNr18tpEbpPvifr9+fVf5vlL8N91DNvPPOc9Cn3qvuVB/7HxV6VlPv2emjn1Hp3hV9ty6vxf2uquWlyus7dizHbfPbLuSrVbr3uIJrce7fuGB8ME4MbfrNqv2/lP/w8qzhOkNbl7uc86/Owc5l/v6K/M7yh7pfqwL+Vfmee41KdL8VHOx8g5exqVZB+tYfaCUm/ugI6J/U4Fmy9v57n4a+LNSff7xq9Y55NsfqB1XkameMMmdM339tO9a7ttQY922uJlSaGyPFwe+fVEHwhGtdnrV7hmrdy5T/uPLmaWsNz3zwxnxjjG9Vv3DRGIcrmHWHRv6JKsAcpL5xO1wbY/VPNEUWFwTuHgJhcO/eMz3FHb1jFToM0HPh+IwlJgzT+AuVxyKPOt54kexa3IdV+s+V/+Tyz17+EGfie9EqgLGVn6qQlVxqiH+6rucmEHEGdhZN379o/lP5nyz/5uW1v4JV7oWK6lfLz2EiThqaImm6T60UtHPeuVbn6IrkAkZfXxfo/3yFg3vmurBY/oYKf6D8Xy0f9xgC8IPjgO9/rKTfU77lpKEZ6IfwKZWBNdYKmk7f0Yf0JX3qPYpSH9OGurziPDt99LUrxbui7+rD+rI+XdGrXO/9nPZBC9vXqlK9e19Vob7zhyscO7//XkX4zqr32Pvs3irqJE7fVpf7Ny4YH4wTQ2XmKVaP36Iivq/8t5f/m+W3ut9ZGV6pfMs5e/s/W4lHjvcO/+UjlsnY1P9ulKcegoFG8p2MNk4O7+4v1x2+WPmee1IlDvRDOH13iqTrvCMEkBjE/1aUH1Te++99qstHTn+2s+4dO+S9Hwo0x2jr0O5pCIuB1rjyt+uHucKcMX3/tW36rm0dj6r4pjP+0CgwznnfzdXeybkxUhz8PrZKgydczevKqKizct5nOHsOX1Ete6Py4qw16vKRM+Y/f/16l/IM0jlDbw1yTKv+3nXjASG+OUh943a4Nsbqn2iMvcbgatIDt2U+kcE3pad9bvz7t4oI01/BZuezauOyRtcXrs1Pz7u51GS4FwgYzO7FjeYmrwUBUlZMmAHbILqmUuqCFq0vu4Z4hsZC+JMq/uvKH/IZCIuQj6gytN991OXZuOeoljBcQ+Jbl02HaT/Hyb/Z4DuUYMcW46UP6Ut7b00f1pc/uAqwYK7gaO6PVkmY2idXuHZB5T32PluMWfhW1qM5i20MwQ9XiXaLK1jlfOrp3xclxntLf/+zlceudQWz7qsr1qKpgoOc52Zc6xXi3llyxnT36NamwZBQZY7e93cJNebSEnc4Ap4lpvF7qihMzp7z1cN7//lVxpjZqJ9Hc/qlccU587Vt9K4ZjwgAjAWHNGZgvGgbuN+tZWkzzSzMm7K25j8FvXH0I6tgu7XGsq1ras/EOPZjVYZ3dMt4VlkuOXlpwOiHL3Ippf/D2PsdRaIPV7DZsQHwE51c3o+Xr3RhBaudsfHvLlB/S6XTgqog7hIC+XGx9WUMZEGghYCJEHO4xITN5TdJmPQsCrcMgtTuqHnJN1funjjtt9DFVO7Jf+w8FqcfWIU+T/klZ5JqLXKX8iZ9PwL6jL7fUn3dU/JbVyYquYcuKquYB05/pu5nR+RBxMZ/+t+XVp5jLb6fvsr6Z+UxBHvvEeNNzW7tmGEn6lmqzjlnN+l75xJ2xBkLqUIvZbWrcaw+YyfDwr9V51+vBLtHFcQdEQF9F/O3hWnsVW9Bbxzfy2y0yjZXflolGlcq2OwwYQTJBMpbM3s/37QyUZXdw9hW1ktOGcrCUCr7UuI1/vCeE2rawTy0Wmsggkc72nsw1g8/qhpBA2bPuv4PVd6vKU+roIJN7keKWt4Kms68s/X733+ySlvS1KEB4YhGkcYFgcsI9F6Ey5T5FQTaCFCzpOZiB7FN1U/RFz+xSF6l/BpnYnO+xCJxDf0WGjs9JgvS1S35TkH7j6tQEtYKFp1dXipPi4QhOBoCJu1PqdIsECo4qvPsnVE7tFBWdC1AMKmHlIUZe7sqwLtXwUGOqiEG96BCKvM7l7fQrKDr7AY8oUNBZdNZyQ7JqiTYUKsjmFrKcGxjU3YzWnXC6NDn3yr7vsZjKj66bh7zV8HRnLHE+7qH2ZhrBGESIemhhs2cF/7QqsB9V7DKeR/evijhtCVfZek6ZRGOKVsdXeITJDoK5EiDXeVjFk9Y/8VVIIFEBauc+ye4eONV1G0iazAM9t9qk8ym0Hqxsy6cJahI6tqOStTlakcoR+uolcGucc+YXytf4u8JAjr0PbnVY99myhshwGACpnAUtetSf6Tus0YV8nFVw5uUP5WjymhReKry15RLgumszhpahiOon66hDc1xELCwcOb2lIyDBc+Wxc70zjBaFpfHeD+VTa32GPcLO+Ud6jGJhABL5TFoY6e9Vd93VwLDVhUc5Ag87FasLcQZSG1bS9+jswPtvOkcDQZ/aTdkLl/i5hHQ3zAVhFDzFIfFmgs/s4pYK/At0qZThs9FNQk2JLhffXZtFnW/31riHXS0ON6m8nkeFVyLow3z6VWTsaeCozvjB+Nfa8d9NlCOhbF7ckZ6600Rrn1bJ5Pno98IO2SPkoxXtBkeRcxcPLXiqHZXEBcEriJgEL0am5ggsA2BqUrNj15cXLxBFUESbUCzyH6B+v0l5ZccwxLvW0QktBXMOjurpObKnSWoSAZX7O4w5qGfa4fQYtLukXO/RdZ0yiZBbBJcU4J2r6mKWlvUk9cgdTwa58CWpOb62StXlb+/vGfJ61t/rn6/a3n9tIKmU8chZ8ItFDBd4wqcp3XmfWiT94n6LrW/Md3ctQXQsRklRkj+ZVXGWAhsYKRtdrDWnK+C7xJGdrGNBVXNrLNAm03YGEk12U732mxo5VlL36OjkAG8WwAAEABJREFUZv2zHQKqnfpDhyRJKxFYy1Qw4jOeC81B+iFDPr+0UJc5kPozhmqBtJusHO/UQPTrdWEnliBZfxjaRKNCWiV33QdUqrwVdJ0dOzuCyu8RwgEecEGrrdYOcINfL6+0d6t/+nYFJ3cMDX581bJGYEh91rpnGNOGcdbufBXRdTTTWI+GRY/QlyjcP9x6dJ6rZ24cRMvDG+7w7+Vdk+aIhLO4PVrj3B/rEYzSaAMunSMmAPqNUZ5cBoFLCOjklyLyIwgciADJpkU5tc3BIqnB9buqXAtRzKXf9bPpWFXs7baYNOywtgr4xUowmFoc+NTAoDojpIb4uZVukU4CWJdNx/pgM/GQhBPkjXryPKgsGlsgWijw+lWPqZSGBu3Yv3gVP93hozpuIVZJs865PP2Mytk4r/7PIihBjrN26pwtoCIxenby6/Jgh5F8qyqF4EZ/GdrkszbeBfeozUXSdT4v0iXYkMg4EsbWYo4wADaya9sX1AVNjSXm06JpSVWQSqHFfBV5xVkksbR9JWFjhAWsHfeN2S7kkffiwD+YsTbbKgbDYUHcSj9mPPVR423LSz9mfdOyaBoM7693iKGkKc349zvVj4F+CAlu53alCIy8u0vrp/euMgkwxnMhPMxJmESLeOcui6zpMFLvWamY1AoOdt4385pdT8IjnwUa2qS95m7vYa8iQmjqyj0a/Vl5BGI9OvcPB3jARVvQWzvADX7KEdfy6mAReO2OZ6ucNfHuu3fUQRkDxs4IW/cMY9owzr50EWFgrVPqsukIHIxbTYJKIKhcYu6H9njmzsvCmIc33KkPG/+ruIMceyi9ezIPs/i9phJWnjHgLVpGvZbmhVbexN8TBJYG6HsCQ27zSAhYHDO4wFT+XJEGVczl61Wi6wpmHYmnc2wWGnMEdpvQzKWJs3PrMwSuW95ZVd+qa6WLNyCT2Lo+B28H/C9VQyzUeVJ0Utnhky+VdDRnIvIcPaclb6LsMWHavVTGkE6ifLSbOGFB+gWGsFWFZ8KipX7WohFvAe2zEq5b3qKzlbYl3nvByrNF7Vw+bcUA9xYp8h3rvVAPy6Hfr9CGtytpga9/NEgujBNLDK5FfSu/Z6UPt9LXxhO8EaytpR/o5JF3+L039L5awLbyO892LCNhrTruQzy12yU1feOYftszgGPnjHDDjmAPN4xSb6zp5R2nrXnffqgysHiLKarLpqNuShuqRUAw9XdaiQ/j3bf7h8PDqCsB/OAIzyuJowjnRo0lo6ijXxJsWN/0Cia4JLR05KFFZyxznAij65m06Hyyzu4sYcEcjXgM91zaEKd8uCyNsdZk2j7k2xPqO0tnYqkdG6975Vvb2OTo0Vhr2qzo0STtniMQBveed4Aj3r5F4poFvSrtzJD2uW55k6ydmbl00lqTKymfAd65XQOe3TrS5y+cyzQTRwrY2z2byXJjURYDjIT4BAAGhSeBJ5Ul5fft1htr3D2s2G4ZKbzdBwuVNysMWBklsaeuxejGGpVxu4dUhiv7WreLjrSbQMfiqleAd8K71KPxGaRn6BGsTPuMoltz7/p2b4FWxVzYxblo/BFGYMobyRc/Xwm9RXYlLzqLNkK5luDt/6sSPOsKrjh5LEKvJOyI6C1SLZhbY+qOqu5lFruEmLLezfsigDlp6V1Tht09TIx5y+85b532DyvBwr+C3Y6Aa0nwq3BCEruHrlvezqr5dy5dO7VXu+fSxblf9+3+/e55OMITrj06z8Xz6dEckoZx7Qk2aMiYB9Zqg3gWnkmvTTTVWsIvgk+MfS8/Y5lrxlhtpsUA6155vTQ71NSyezR2v5eOk5hTe/eljV9UlQgriAsC8wj0BqD5HIkNAvMIrF3Qy21Swwz0Big7gr4tin7qMXekdyYIi+S3KILHl7d4o+7403V9DOe8CvW2Y5R1SBmYjnevAuBWwRVHSttKu0KciKMhYEL/8SrNZ6U+pkLGjixGfIqGpL/Xv4t8tbOQWU3cICT0sUPbSH4UjQnz6YtHETMXGMZDVSbVw5DKGozg3DtbqomPYeTXZe8d9i5fjn3sl7LV8VjM9itjD6FcK6fxkTpmK93Cj9Culb42vsfgKqOHk/T4PgLU+3tqo4z9UV+289gv6bFUc5njNI/FXL3C6BBkXk1ZF2MOMQaso764cE5Unh69OXcuHfNL+DeXNsQ9sS7cdwWrHDyp08K3lcFzWVLXbeVdirdbioHu0fmSBMvKPZppGo22HgPaE37BmdBqWubw27rAuLNmjJWHYNOayvVeT9UZs9zKT+24JRgZ8jxfXaCrYNbReuJnExMZBAYEwuAOSCQ8BAED6FaJ2tKujB0RarKHtGsur3JJEFknpjrbmyDm8t9EnEnqWEz7TbQ/dV5FAIPIiMYnV9L7lD+l835uWRA4/3bK9iib8ZglZgwdb7e8t2hC0/N2dzHlLRqfm6De20pfE2/HobdL/PlVSE8V3WKVqnKRHeSWBF1/9qDSk5narTmkhQQGZ42xtml++Xp9nNDsBaeZNvzGsG6ZQ9DK06vCcZm594rdAe9cKy/G2f220lvxDCl+Uyux4j2XY7xDVdQVZ83QY56Nsc6VE9xdydyJMNYaGzokF4QqzzxDsMQoUhemNjyTdTaKABSTO5u4MtJRj6WddurHdvnnivQMqTHPpQ1xjFnRkhp+JwwCswiEwZ2FJZEbETAZLp3ZmRZpYF9aCBxjtwEjQfLNiA0mnKogQyw+bWTXZdquc/ttwvzac2tU2rMZAQtBOwy+3UxibyfCYs2ugD66ucANGTCIdinXZrFIWUu7l87nHQ5lKtfWTYjVU6n2jlmgri1vSre0u4NxIWCgJo6ZnuYffusLyhp+7wk9u96xC+VbRO4p+z7k6d0jTQCGuno0X1+Jdhsr2OTMoYS+vUyHMG/6Xq/subSnzEWO4mhFwGQU9eA8PBsZ47jpNQ2RLbu3Q37j2DcMPxohpo9xsEby7mjaZIQMrQK8dz7T1UrvxbsnY1CLhhEoGm3jdOPZkt0B66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LQ9XbY9dMIlhAU/mn8lRTC2o8teEBYxUcqavkhCrYUIxBj+/aLYkFeYlzqi56UC9oEeSbv+VlUD4orccXYiSJPfDl9PTEEoLV+kbCUi7NnebnWlfsliQxBa0s90OcQPkFUh0fAHEtAAsdMgEnHMZfPsklAAhKQwP4JfHeygKCIZw4TMCYQj4wdEws+EoHwIz/PFX6YNCJQOLfsuWClGwIOVu+tx7HqnbOKmBS2TNQRnpHX1fCbXDMBZnULX5VsiYeVEQgWhvxSXiZXQ+7YI6i5by4od4wLCu7fGNvviS4p0kF4WvIzxo36YrVZaQUhK/jgxSS0FjfbI2t+du0OUwRqpVV6CBMQPNWEGeQdAd+QsOK14oGVRjEGFcI0BFil+4PACIFpE1yX9BBztkuzongoLPX5E3Gk7cUoqufH9d7Rr4weUtxXbBkuuX9MHFklGGNQIYQt1Rf1iPCGc0IHI4lDi8Ax3jZSu2N8VTZrjPHFqk6EdlwPaV5aINBn9V6pHf5ZIuC+oK3kclCV7gmEw6xyHAx82YH7lBcYrOC7xWBaVAAAEABJREFUbHXBIK/kma3H9F0XPKxYIFRDuLZitfElL6hYRdqtKmUlNmXjg1T0oax+X03kOvnB9twYg4oy8WygXFz3eaQeeF7Wylyqh754tZOABCQggR0QYLK1g2RMQgISkIAETpAAEzsEOgjr/jLlZ0LBBJ8J/P3zmzPeEM7l8gqFEOHDrrC5+IO4maANrXRbD8FEnVVrf7TusPYboRZCtDXryT/vnJCtX0VEiPCd8Q+nGBcUEz22A15wWLFAKPJF+Y3AJsagYvUU29iYzA16isNdohEkxbhaTbz6goRrqS9W9j0lfmtqaQe907YQ0MK2lne2xCMQoL5Kfm8UR7Yyx7igWGWGcIWtde8TVyblbHFlJRor5BBeINxryU+CnxGmJlyZypy2y4dISKdF/148cT/8Q0zaA9vnEcTRL7ACEqEnrOPcq+hbOB+t1/Gy5d/GfEh0jQ/3EmmX2CAM415JdHtTu2ZMQVnlRp/J9ZD+/jjQJmNU1TPjA+FTjEFF/bNCts8Dq8pqL4ZoV7xEqNV7Fz8rX+m7ut9DJsI1hGxD7mPsyeNNEoCXPdznuTyjHf5oLt4lmrRY9ZzLc8UK2hue/+q/QMhOnP2uV9vST7Pi9mqbi1elvumib20kIAEJSGAnBBTw7QSziUjg5AhYYAlAoDaxY4UZgjf8rmrO0mML5qrd+vVPxqI2EYyXKxRbgb80NkMCtDhdUgglWS1z6ccG/zivjFVRLc9aVnMh8GESN5Qkq2XYdjjkjj1n9yEc4bqm2S77QxVPbJUe2pZZCXqFMwKtn73CZvgHE1q2Eg/7uMoFQfBVV8v4z0pV2lhrbqgn6qvkH8ERE/0hPwgp2FrHhJy6ZIsrQj+2pbNltG8b31Bc27SnHI9LAqyqaxES0i98cPwjqEPAyb3BVsonxI57ha3PpfuYrbU1YQeCDlZJJcqqQshY62/Y+j7mA0PVREd62DVjVlojcC1l87/jyCpJzFxWFf6eGF8vj6Z/QjCI0BrhNccwsA0Wxr8Z93WF0Lcm1EWgzrmspX52PV5+87Xcp3JR0AgdEbQXvDQ5IUhGoDyUR16Scc+vR8b9QhtYt+9+c78gKKfP6OxK5o/FEaF/jF5FWqVVtL2BtJSABCQgge0SaJl0bCkHRisBCUhAAkdMgInamIldh4LVYrWVMGMnKl3cmAhUWCXC9ZBGqFU7yH0obGePcIUtwS1CKLZCMYFFCNaF7zNZMVETfFI+hCN94fvsfjmW1FWMXsXqsJqgpDfgmiXCrL5J6Zq38581FnhsERThbxcaYTWrbsakRT2xRb0WhvPxmEzX/E11Z9URK2oRwNVWPw2lwWoizvMbcsf+DfIPQQkCXNJidSv3SaxnV2yZrTF7clKlL4lRVdwjCJ5KHrlPStuUS2Fb3JbGmC2j9EmlvP9JHNlSGqNZIajmBQssEVYjtMaO+mJ7Oxz6IkMQXMsP/SN9UV/4kh1psq235Ae32rMLPzXNxzMQKNf8rbq3nM/I/cmZe6vhStcIWDkjt+QHgebrljzoJoErCfhLAhLYNgEFfNsmbPwSkIAETpMAX6Vky+LY0l8rAfjYRYxBNXaishrRP+dHbVUTkyW2QcXrJIXg6RsSsraaJF7Oxmw1Lq3kIi6EFTXhJf5WNVuna4I3tn6thplyzXlRCElaw7K9tNXvEvw9L5lA+BBjlHpWfCPoizGo2AI55ySaewzhGgJ46p5zD1l9uIlQ+9+S+2dHtyjGnnxJlxVxCLfJA3khT+StJY6SH1aWIaQv+UEgy0rAkp91N4RVpbqi39iE4Xp667+XxJi80S5ZUcf1kOY5wGrLIfeL9tNt6B9rQl0EfKU6LKXOvUq7KfnhDEBWGJb81NwQ+o/pK4mP7e+cA8j1kOZ5PKYuyANlHooPe1Ytblpe4lFLQAISkMBMBBhkzRSV0UhAAhKQgATOCSDwQJh2btF4wXZQtuWVvLMCA6FAyU/JreV8Nz4EUopjyI0JJl9xbPkCMHGw/YytglyXNPGyRbfkh4nrWOZsheMsslK8rEwqube4zSawa0lsD35emDQRwMQYpRAKcs5cKRAffUF4VPIz5Iaw+b3i+HnRrMZhi+I/5hrhGueV1e61eG1WbMFGYN0c4LJH8kBeyBN5Y7UUZ43dMe6s6I0xSnEWHasSS4FIA+Fiyc+6G8KRoW2Tnd+WFbud3ynmUhiTd4RKrLTjekjzAoEXD0Puc9nTP7LStRQfAquWrf9DcXCv8lwbcseerfGbCLzYnjt2xSPpIhivrYbl3mL1LP5bdW0lNWWt3WutaelPAhKQgARmIKCAbwaIRiEBCSyKwCllhpVsCMSY3GyqOS+uJugZw7Y2ERqKiy2hTNCH3LF/cf4xWYsxSbH1CGFYKTATtZL7kNvt4jDmoxpfFv8tE2BWcLFiJt5nVZzHhJCvFCltozaRL4XHbWx7oH5auBD3EjST/yn5QNDEfTwl7FAY2gpbvvnSJgI9zIfGM9vpEPjlcisKwTlnY24aOfnnIwJsVaRt/lwi5GMO14jZomirbO9s8TvGD20SnqUwU18MlOJcdVsKY/JEP81zh+shvSvBPm2m1j+SF4S0Q3mt2bfcq2xDR+hVi2vIHSEcz7ch9yH7lufmUNiSPcxK7mzpZwxS8qObBCQgAQnskMCJCvh2SNikJCABCZwmgakCDyZINWKlg79rYXFnlVFNeMRkkUkj/ls1grDviOeWZyvbA/lwQG1FUKJrVky2WN1I2Vo1q85uXUmBlTpTVlJVoi06sxIRgUrR04IcaxPhoaxSTxyaP+SOPR8PaJlEXy+eaX/k5bG5ZuVejJ0phMV8nZS2PVei3Et8PICVV89NpLeNrgmV4qWqEAAiSIF/q8Y/4UqRU1fbvFeWxLhFmDn1OVBi3OeG4LrWXyOgG7tqczUt2kntxRJ9MCtSV8ONueYjPWNX2bXG/3HxSBnGaFbVJlhRtfRNxQh0lMBuCJiKBE6DAAOn0yippZSABCQggV0SQMgwJb2pK+fGpMVEnZUSpTCc4zVGkMD2qLk/qrGaP7ZoslVz1W5X16yIYgK9q/RMZzyBD0oQtvZ9asx9ju3Y0vehyQNblmPMqjjviw+Z8CXQkjAHgQOCtlkTb4yMfNF3NHqf5G0JjFszPvU50Bp/5w+h6iaCtaviqf9vWYm8Sf2zYhUhbj0nV/poEbZeGWK+X3xsZb7YjEkCEpCABDYisM9B4EYZN7AEJCABCSyaQG2lw1DmN1lhMRTntu0Rfm3joxrbzrfxjySwUO93T76eGN2y+jXetq6enxT4MMvDY25D3TuR/kQ0QvUYJ6kOhTF94y4qCKEYK/R2kdY20+C5yQq7baZh3BKQgAQkcMQEFPAdceVaNAlIYOcETPAqAhwUzlajq36N+8/qunEhxvtuWeHDSgomWy2xs9W29aMarPJr2fa0ni7btqYyXY/L3/MT2KZwjVVQbFnuyzWCtO+Nw9TxHG3qMQl/l2jOj+TezeXGijw/ILGwpfWbY8693ZoPcLBasW+VLaxIP8ketaKM+2LcCnbbHx3p8kH/WPtYTed3E7O2Ug7h3CZtneMnePaMzeOutkKPzZf+JSABCUhgxwSmDgh3nM1jSs6ySEACEpBAgUCLUI3z8QpRVJ149vUJBlYDsoWXSeOqXd/1TWM55qManFHGapMEG6UIM2XiNyoRPU8msM2t5XzYoU9owFZAvo7bukWOsyefkRI+OJoPbvCRBL6AyYo4vs66jdWzfNDmQUmPLebvG/MR0XOl82mJ662j1xWsYLZuf6y/98EYli3bVfG3C41grfbs4KiB1nulL888M7jn+tw6O9oeAubu965MhL27Sst0JCCB0QQMIIHdEWCSs7vUTEkCEpCABCRQJsCKotoqIlbg1SZapVTeKo4IN2IMqhcMulztwEc1fjA/WyaNfHiAlX5TP6rB5LUm4ONrrLBhIjqnppxzfmE5yI5OIXTm/LWxBSMM7bEUjnuiT9jMmXSsZCuFxe3P8+/DojmnDMHe1+caQV9fnHHaiuJDIr+TmD85+k2i4XW/mJyph/A6l6MVcdy+JxTx1bZrPjXhEDrOeZ8Q120Sby3teNmK2iVjCkCfhFnStRVvpbBj3OgbWf1WCsMq22uVPFTcEBCyIrXkDeHeS0oeBt02c0CwiJCzFMvnx5E2Ord+aOJVSUACEpDAQggo4FtIRZgNCUhAAhK4RIAtuqyeu/Rj4B9fB2Xl0YBz1ZpJeM0THywo+eH8L7bbtmxBY8XSxyYyDsePMUkhjKkJ2a6ZmGuCy3hRbYHA2yXOFkFvvF2hrptffKU4xqBCwPefPa4It2pp/lrCscr0CTERfMXYu0IQwX3O/cO24NdMjhDW3SvmD0dzv8RoUjfv8YWwo3avIFjd1flwPVncutW2GVMA6vDfuCho6hXWBS+zOdVWFPJS6BYbpMYq3ZrA8i8S/9SXOAk6WbE9uSZYRsA5OQEDSkACEpDAYRBQwHcY9WQuJSABCdQIHIs7KyCeWykMqyjeteJnyPlN4/AB0SX10jhyiH2MXsUKiK+ISy2eeDljWyQrlVgxxe9N9O9WAiP0fNuKH523Q2Bqm3yXZIeVZDEGFSvwEPCueqAN3mTVoueaFVZfFXvac4yqYkxIvFWPM3vohFGshv2YxM0KvxvHfE50TbG6lJVVq/5YzVUT9nCfIFxdDXfM13MzhhUr5tgezPWQfuc40C/FaFZvHJ9Pj0aASBtAEMxW9LvGjhcq1455jeh19cxYUM4Yg+r94zL1JcitEpa2GWNQPTsuNaFnvMyuWuri3ZMqq3hjqCQgAQlI4FgJMJg71rJZrgsEtJCABCSweAIIMn69kkuEEPeNnykrcFgxVFuFgSDtLxP/kLpbHDjcPkZVfUF8/FT0HOqPEglCmxi9Ci5MYHsdBywfEntWfiBEelyuvy0aIQurw1ixsi48ibOqhwDs7xF7VgnFaFL4vVODT7a2rntjVRSro9btV38jIKmtRF31/w75MVX4geDgegnPakE4IADn4x2kzwpEVhK2tiWENH+QuLjHSu09XgZVH7NVzwhq3nPVonJNXfHlXoSllImysdX5IxOOFcGUHQb5uTVF/KSzFMastCy9CAEEQlTyy3WrRvjKCxzKiqCXvp5toI9PBH8SzRZY+irqJD/PFXnhmIJzi54L+kdWgPc4Fa1YsX2foo+zM9rtz1f8bMu5pS5o7xzh0JoH6oGPd9CPIHB9ZAIiaOX+5sXEkKA13lQSkMByCJiTUyOggO/UatzySkACElg+AQR8tUPy755i8OXPGM2KCckXN/hmIsk2vz6vTFZbv1r6PYngW6KZ+MXYWCFYYIJbigiBQ9+HB/rCsLIGYSXCIiZzHxFPCFVYSfWLuWZ1Doe3IxTNT1WFAFtMb1bxs+qMX/iv2q1fI1B61rpl428EarUtvF1UCMs/tPsx0qTtsDURQQCCtR9P+C+N/rjoG0azBZmttAht8rNZITSqjwoAABAASURBVHQmzlIABIB99xfMYFcKe/84IriJUVWcd8iKXVaXUSbKxgdzfiQhEV6ST1ZR3Si/t6GWyBj29JW18nLuIm2x5q9zv3MuXie6pH4pjqQf41wh+Hva+a/+C44x+LI4tdZ7vF5SH5X/nF8ZY1AhYPy9QdftOsACJqVUELbeMx54GRFjRfVffnCseYmAoJWyf2J+I2jl/v7DXMP7Z2IieI6hkoAEJCCBJRBQwLeEWjAPEpCABCSwSoBtWUyaV+3Wr3l+IWhD4Lbu1vcbIdY3xYEtYzEGFUI0zivr88B2QIRfLUITPh6AAGDOc884m5DJVV/eOjsEAV+YHwhsYhQVH15AcFHyxLY3dMmPblcRQHjAdkLayVU2w/8RMCBoIMywr7MzVs4gPCv5GXJjlRpCqSH3zp4J/2flx4dH96qKJaumfqvih3KyXZhyV7yeO9M2ayuOWGnLltzzQJcv+KjNky9fDxm3jANCPsqfy0GF+/3iWrvv6bNIN15nV0tkTCF/O/8QbMYYVLeOy6dGwzFGUXGW5ScVfZydsVqtr70h5GJlZZ/A92zlj3viG/K7pY+MtzNecHwtFxX9XXGnj46xF0V7r9UFK/DeuyF3vAxDMFvzypmZrLqv+dNdAhKQgAR2RIAJ0o6SMhkJSEACEjhSAnMXi9VzbAeqTdSYcP9cEv/A6NLkkZULTET40EW8FtXD4srKhBhXKLYuPjw2nAEVo6iY5H9CfGzjsHW2CTLBTfSDipUW8EOoOeSJFVUtk1YmzJyLOBSP9lcSoH1QR6VVlLTHn00wBA0xiuqxce2bQCPUqk3mE/QMoRp1zXWfpl1/SRxYmRNjkkKwQplrgSkv91CpXXZxwIhtvuvbMDv3zkSo1l2vmuSJtlvrQyj3FyXgkLCHfqVb2Rpvg4p0eOFAuoOeNnAg3qUxpjgIWIdeiODeafqaB+ZH39l5sb6kqPNH5YoVYzEG1ZPiwouYGBfUb8aGFeAxioo+kvoqfXiCumdF9E8nJp41MQYVxyf82KDrbhxa6oJy8My8bSFL9AlfE/eWl2G/HH8qCUhAAhJYEAEFfAuqjOPOiqWTgAQkMIrAL8Q3E5EYRcVHM1i5wJZSVuR0W8GYSN4gIb88+oXRLdt5WXXHlrt4v0Ix0WPrast5aQREyMNki0n/JhoBBfGtaia2P7lqMXB979iz8uvTYrIKijKguWb1ylNiz2QvxqD6s7gwuY2hGkGAM77YSv3NCUMbpC3m8uxa+Yfg9wUxaasxioqzIIe23dGuWlZWUsfUNVtmqXvaAJpr2sZfJQcI0mI0qaHzK7lf2Z5biwQhO+2SVabcJ6uCNe5dzvb61kTCPVtjhICTFWTx3qtahT0IQTnzD2EOdURk1BlnEiJw+tFY1MbLCJVIL163ppbImHbYsnINftQrW1jZCk5dA6pri6wsa7kv+GgRdYLAk/DrmvNEOVcUf+tu67/ZZs0xBKTNeaPUOX7I2x1zQdvieVDbLkxarMblrMkE25uiLr4/qf9HdEnxzORZx9mwCP+7LbaYvCyj3PRTpThwe3T+9b0Mi7VKAhKQAATU+yDAA3cf6ZqmBCQgAQlIoESAVXyfHw+tW56YmDDJ5guGTHSYACKgYuLFioREVVSsikPo0LfqjpUlCEOKEezIkXJxrl8LF84+Y8UUghAmoWiuPyd5XRWs5GevYkszE+BeRy2LBODLtlfaIHVGm6TOaquGukjx/3X58U/RQ+qpcahN5uPljLwgxKPuaQNormkbtBH8tGpW3/WtquO+YdUPcdfiIk2EMAhBOWuTsqK5dznb6zMSQcs9izADQXq896oxwh62AyPMoY7IC3WGwAlBeW/kK5aUmfKQ3or17JdLZEwh+WIxLw24rmm+5MqKP+oazrCjLbKSsrSarov3B3KBwDrGoGJV2bcPul7pQJqkTT9HnZMn8oYw9X2v9Dr4i235nEU36GGHDpw9ydmvLUlyRANbnXnWUm5MXpZRR7XwrFjk/qv5q7vrQwISkIAEZiWggG9WnEYmAQlIQAIzEmASwTlATAJnjPZCVMTPGVvPuOCyTAu4fHqyxqQsxlYUZ/0xcd1K5EcWKUI2PggxZ7EQBLKNtxQnq85aVrmW4hjrxvmCrz8QiPy2ClYGorhoPWDDPUBatXsAYU/Lh3UGkmmyRrBJOk2eN/S0RMbUwSNSLlaFxdia4ugDVmTXzjUlP7zY2XZ+KChpzH3WKvFO1V3Zf2NqBA3hEMp/Svzte8VisqCSgAQkIIF1Agr41on4WwISkIAElkSAbURsnRua1G2aV+Ll4wIt2143TWvO8JzHxdlhc8bZxYXwZEmT1i5fSzVZ9XWvZA4BRIyNVavQgLbL2WasPt040URAOah3VjLlZ69iiy4fculzRLjwBXHYtmCY8t436bQIGMgTW0O3lSfOg/vG5IV0Ymxdkc7SGFNoVhey/XpbgiXqnPjZUk56NU1+ODuRFWk1v1Pdqfu7JzBpxViMIj/cH3P1R+sFY6X7trejr6fpbwlIQAISaCSggK8RlN4OmYB5l4AEDpgAE1pWk90iZWAbVYzZFAINzlpCiDhbpDuK6FVJB8ECH0jI5WyKM7Jg0jqRni3hA4+IraJsE0c4uklREJAgyGCS3hLPs+OJ1aesQs3lZMW9Rf75qAdfbB2KiPPIOGNwyJ1tfpxXyYquIT+b2HPPks8xq23JE9t+OTNsk7TXwyJk5+VDa12th5/6m/IsjTFl+bv8u2v03EI1hHu8hBlT58nG2Uvzj/xwpEEuZ1W0pXskxl3XfZJsUhwNwFmHnNnaFKDBE30M7Y6zRXkuNwTRiwQkIIF9ETjddBXwnW7dW3IJSEACh0SADw5wAD/b8phobJr3xyeCd43+1ehDVazg4uyvO6cAL4veVDFpvU0ieVG0ajwBhKK3SjAE0jFGK/h/UEIhKInRrBBQ3ye+aQ8xRivOD7tpQj0nmjP/aoIUzu4qnZGHAIotfAi/5miXydYlxT37HrlCCB1jlOKrwwhCEfRN5bSaIIL1j47FvgQ8S2QcHOdCtQfnxxycn5Z4qPOpW6Dh9KDEwRec5xB20Z5p16yQo00l6sWq5ydnvBib2h8l+LnqBOuc23k8wr3z4nkhAQlI4HgIKOA7nrq0JBKQgASOnQCTqwemkHzxkFUETN7yc5RiW9WNE4KtVWMFKQm2OMVk64nJFV9F/cyY/xA9Vj09AThQ/hAmrcnqohWrhu6ZHCJQaD2XD3/4n8qfNsDHBzgcn7pM8k2KdFnhdLf47u4FtufSnmI1qN4/LqQVY1CRp8fFdZN2meCXFKsaaZ/cs5sIn1n1+m2J8XrRXx9d7T/iZ10hZOSDHF8dhzkEWIlmsloiYwoDV/iylftRsZjyQoYXOndK2FtGszo2xmQFJ17k8IKIOIl7bGT0q/SvtGfaNXGOjWMf/rv+iGcez76xeaAuEWbfIAFhGEMlAQlIQAJLJqCAb8m1Y94kIIFTJsBh6q8RACXNgfd/Gz+7VHwpklVepXzxpc1nbjFTlJlVGRz2z5lgX5m0fieaSViMc8UE/IX59Zjou0S/UTQrpPg4wZgJGunBer3M2/798clvq2I1ycPiGeEFTFhBw1lu5D3WVyjscGNF03XjcrNo+I1hkiDninzWWNCezwM0XNB+aEeleEm3IaqNvZD3Uj5oGzDtEoIjk+EbxgLBFNtVX5zrTiHwYAsdwibc8Yd/wnV+ppjPS6CbR79NNPfE78ekXcS4pPrS/em4IPiKca5+KFel8r5m3FlZFaOqSJ92eZ34JF98NIc02RLM/RnrKxT3MPn+zth29yyrIjdpn4nqCsUqRe6PN4zte0fDivhJOz/PFbz+Jr84n5Pz3Og/EIa+IHZLUktkDB+EsZ+QC+5j+t1H5hp26/XOb/rpH447Z1nSJ7GilA/IrLfNeJmsiIs4iZu6pE5Jk7TJw2rEtAXaBO2E/pR+lXYM61V/Y65r/Qj33Db6NPoVnnnUAeXmvurqYr08/KaO6Jt4LnCPIMzm5dqYsupXAhKQgAT2REAB357Am+ypELCcEpDAFgkwYWMbEl9MRFBy7aTFJKnT18xvVh7cOyaTq31tp0vyO1MdE1bQ3C6psuKk49GZ2OHGhwdeEj+q7RCgLhASINR6iyTR8b9Grt8+mu2iuOMvP2dRTOZZ8cQ9wSq710usu0g3yRRVly+EnawavH58c392eetM7mHy/Wlx3/Y9i1CH7b6w6us/qCdWC3PWGtscl95/LJFxqvHsFfnH6rH7x2T143q985t++mPi/oPRu+iTqEvqlDRJmzx0bRCTdkiboB/lGTPnPZoi7k1Rbu6rri5W+wfKzW/qiL6J1cDcI3vLrAlLQAISOEwC+821Ar798jd1CUhAAhKQgAQkIAEJSEACEjgVApZTAhKQwJYIKODbElijlYAEJCABCUhAAhKQwBQChpGABCQgAQlIQAJjCSjgG0tM/xKQgAQkIIH9EzAHEpCABCQgAQlIQAISkIAEzgko4DtH4YUEjo2A5ZGABCQgAQlIQAISkIAEJCABCUjg+AmcnSngO4VatowSkIAEJCABCUhAAhKQgAQkcNoELL0EJHDUBBTwHXX1WjgJSEACEpCABCQgAQm0E9CnBCQgAQlIQAKHSUAB32HWm7mWgAQkIAEJ7IuA6UpAAhKQgAQkIAEJSEACCyOggG9hFWJ2JHAcBCyFBCQgAQlIQAISkIAEJCABCUhAArsisD8B365KaDoSkIAEJCABCUhAAhKQgAQkIAEJ7I+AKUtAAlsnoIBv64hNQAISkIAEJCABCUhAAhKoEdBdAhKQgAQkIIHpBBTwTWdnSAlIQAISkIAEdkvA1CQgAQlIQAISkIAEJCCBHgIK+HqgaCUBCRwyAfMuAQlIQAISkIAEJCABCUhAAhI4LQKnKeA7rTq2tBKQgAQkIAEJSEACEpCABCQggdMkYKklcCIEFPCdSEVbTAlIQAISkIAEJCABCUign4C2EpCABCQggUMnoIDv0GvQ/EtAAhKQgAQksAsCpiEBCUhAAhKQgAQkIIHFElDAt9iqMWMSkMDhETDHEpCABCQgAQlIQAISkIAEJCCB3RNQwLdr5qYnAQlIQAISkIAEJCABCUhAAhKQwPETsIQS2CEBBXw7hG1SEpCABCQgAQlIQAISkIAEVgl4LQEJSEACEpiDgAK+OSgahwQkIAEJSEACEtgeAWOWgAQkIAEJSEACEpBAkYACviIeHSUgAQkcCgHzKQEJSEACEpCABCQgAQlIQAKnSkAB3ynVvGWVgAQkIAEJSEACEpCABCQgAQlI4PgJWMKTI6CA7+Sq3AJLQAISkIAEJCABCUhAAhI4O5OBBCQgAQkcDwEFfMdTl5ZEAhKQgAQkIAEJzE3A+CQgAQlIQAISkIAEDoCAAr4DqCSzKAEJSGDZBMydBCQgAQlIQAISkIAEJCABCeyTgAK+fdI/pbQtqwQkIAEJSEACEpBqOkqtAAAQAElEQVSABCQgAQlIQALHT8AS7oWAAr69YDdRCUhAAhKQgAQkIAEJSEACp0vAkktAAhKQwLwEFPDNy9PYJCABCUhAAhKQgATmIWAsEpCABCQgAQlIQAKNBBTwNYLSmwQkIAEJLJGAeZKABCQgAQlIQAISkIAEJCABBXy2geMncFolvHOK++qKfkzcVRJYMoHrJXN/FV1qy7jjL95OWnnPn3T1W/gdE3hw0iv1S7jhJ95UJ0bg9VLep0TTBob0y+N+k2iVBIYIMK5hfDPUhrCnndHehuLQXgISOGECCvhOuPItugQkIAEJSEACEpCABCQggVMjYHklIAEJHCMBBXzHWKuWSQISkIAEJCABCUhgEwKGlYAEJCABCUhAAgdFQAHfQVWXmZWABCQggeUQMCcSkIAEJCABCUhAAhKQgASWQUAB3zLqwVwcKwHLJQEJSEACEpCABCQgAQlIQAISkMDxE9hzCRXw7bkCTF4CEpCABCQgAQlIQAISkIAEToOApZSABCSwLQIK+LZF1nglIAEJSEACEpCABCQwnoAhJCABCUhAAhKQwGgCCvhGIzPACRG4Scr68uhXD+inxP71olUSkIAEdkzA5CQgAQlIQAISkIAEJCABCVxNQAHf1Sy8ksBxEbA0EpCABCQgAQlIQAISkMChEPjbZPT/RL9GQd8mbv8erZKABCRwJYH8UsAXCCoJSEACEpCABCQgAQlIQAISkMAxE7BsEpDAcRNQwHfc9WvpJCABCUhAAhKQgAQk0EpAfxKQgAQkIAEJHCgBBXwHWnFmWwIzE3jdxPex0b8c/dLo1XMH/yq/Hx19++j/Hb1tdZ0kQF5+IuaLol8VvZqfv8/vp0d/RfSNoq8RreonQL1+eJxgCbd1jk+KG+74y+Xs6lqJ8ROiS+3q5nHfRx2SJu3nIUn/OdFsd1nlw32wq3bGVpx3SB66vLwy111euCZ/tPe3iv0C1KxZWC37HyTm1fudOqEOPiP2tKUYVUW9vk98fXc0fVfHEZM6pS3Sv7xh3Leh6L+6Nr9+z1GXf5xEHxF9x+jXit6Woq9+r0ROu4Hhi3MNg3UN4/8btydEf1409wQMc7lTRdt+UFIkr+Spyyft4S9iT31Sr/vIW5LfWNF+acd95fvTxP6w6G2Wj/Z+56RB26MNci90jFdN2izu3xe/3Ce051zuVNF2GW8w7ui7h2HIPQbTbWSM5yHPxaHnJvawJJ/bSH9XcZJ/+ojuudPXJug36DN31TfQ3mh3MN73+K/jU+tDYcQz+ptTcbeL3ma/nui3rij3Pu8/Ckg74B6n7dEnrfZRu3yOkhe1BA6OgAK+g6syMyyBWQm8QWL7huh/jn5s9AdEv3H0qrp+fnx89C9G/2P0A6JfO3pYjXdh0vYhCfaM6L+LJi93j/kW0ev91JvFjonQl8Z8XjTnlTD4pCz5OUldL6HWJxKrAwqux35UhQ+wEIawQ5qPuPAxlyRfVI+J61Ac2OMeL+cKFvChXn8strCEWy7PFb/vkF+4vyzmt0UTLsbG6i0Tw/dH016+N2apXf1W3KnDj4pJO4ixVUUapEWatJ8vTGo3jmZSF+NccR+st7P7xnXOto9w61aJ81nRL4ju8sIAOz8vKa7JH/WJIOZpsX2n6CUq2iHtcUgzKV7NN+X47Vh0ZX/XXK/e79QJdfCtsact0Uaxy88LCpa3jS1xIQC4f67pu2KcK+qUtkj/wmSWeIfiOw/UeEFZqBv6r67Nc4+tBqcubxiLT4r+hWju/++ISf8TYxaF0OOhielfo383mnYDw6E0KD/CtQ+NX8JxT3BvTOnnSaPWj663AXj8dNKmbX9jTPJKnnJ5SdEe3jpX1Cf1St64f7mPY714tdoX0t76yvd2KcUDoynfC2N+RPQc5eOeeLfERVv7l5g/E03bgzn3Qn5eULRZ3OnruE9ozzyX6aeI70KAgsWD4zbUF2DP85HnZLxdUvStpMu9zniDcUffPQxD7jH88fyC8aUINvzH868bDxHv0HMTe1hyj31u0iTfMQ5GUc4vTm7JP31E99zpaxPc0/SZq30DdTRnmWnr+xz/BcUVivaEgPsVsYVPrQ+FEc/oz4r/X4r+z2juOZ4JuRytiK/Wj67fO32JHNr9RxlgtoTnKHlRS+BgCTBw2kvmTVQCEtg7gfdODhAsfE5MJp4xqoqBIZNsJuWsOKoGaPBw6/hhUv7EmOQpxijFhITB518n1CFN/JLdrSgYUq+8dW6tV54FTOifmxwRPsYkxQTw4xKSSeq9Y7Yq6vCH45lJ1ZvE3JaizTJ4JC3SHJMO/hn0w5ZJ85iwfX4RYjwqDk+NfvfoVvW+8fiH0awGYmKUy4NTtEsmmH+UnFOeGE2KNsrEhgnYaoCO5a/E8gbRLYo2D8PfiWcELDEmKdKmTxxbFhIjD5+aCyZzTJo3qU/uvY9MXAjKeOGxyQSctk6ZaGeb9AfJzqCirAi1SOMug74uOpA37t9t9xUXUx5nQ33cKUFoF2P6QgRaj0u4n4q+bvRURbt8eAL/fjSrRWNMVrQB+inyta3+GSEu9y99LOOM1syy0o7xA20I5q3h1v3xcuDPYzlmPMQ99vUJ8+vR5D/GohX3HGMkxkpflZyS/xijFPcfdcR9S7sYFbjH85LGf/ChT2L8Qn9M/9yT5SYr7jnufQTGUzg3JTKjJ9rvTu6/gTzTX/HMgdmYMQHRUU9zPUeJTy2BgyfATXHwhbAAEpDAaAK8LWWi3DoZXk8AgQQrr9jese7W+rub5G+Sj9W0mBQcwsRvNc9zX98tEVIvU+uVlTxPThw3jR6rqM8vSyBWcXGdy9GK/G9rskSbf3ZytOmk5B0Tx+9FM7GcOqFk4s4b/jET/yR5rnh2sxqICfw1z20P44KBPIJNJphTckyfw9ZyvjJI+E1ZvnMi+fnoLr5cNisEjfRfCB6bA/V45H5h0sxqvimTQdrhZyfeH4l+nei5FP0I/QHbzuaKk3goL22XbalcYzdW01cg5GPr6diw2/ZPfXxaEmFlIs+lXI5WCKzYnjalXcKELY5MekcnXAhAn8dWbu65grfRTtzTrJS62dnZ2ejACUCbf3xM2kSMUYq6oh9m5dWbjgp5tecu/5s+W66Ocf4r+hX6F8ZIU9vkaq7oGxhr0CZguOrWcs19z0se+k/iaglT8kOZKBt9whQhNPnhRfEmfVJf/hAYPzIOPPdiLFJ17Xcf9x9AlvIcJS9qCRwFASYJR1EQCyEBCTQT+KD4ZBDEoDiXkxWDYQbFU4RBDDa/KylPneQn6KBikL+NSchgggtxYNvQjycvDFRjTFZvlJBMvtnql8smxQCf7SkI+JoCFDwhcGFiy6Cv4G2UE22CCeCmbb5LFMY/mh/EG2OUYvL9gwlxy+hN1ScnAiYPMQ5CwZ8JFGcsTczwpWC0kS/KFatJ5mD59onry6Op1xhNCsELgkYmR00BGjyxDRU+Y/JBtLRDVhJxPbemP/iBRLrJKscEP1cIpBE00HbPLSdesHWQCTT9z8QothLsUxIr9bjpGJt2zjOWtpYomxRth1VDrCBqCjDSE/0WK21IZ2TQXu9st6WMjCd6PTRawvrb43dsO+XeQbhO+ASfrMg/wvCxq48mJzgiIMIlnhP0LyOCVb3SBqY8B5c2/qP/YPzCGaDVQk/wwPPuWxIOXjEWpfZ9/9G3LeU5uqiKMTMS2ITApg+0TdI2rAQksB8CTIqZaM+ROpM/VuNce0RkDHKY/HCA7ohgjV6v8sYkhIk/wpSrbI77P9tcOJNorj6dlQi8mW+lxiTpa1s9N/ijfc5VFgTQTOBodw1JN3shfzAn/tZA5IHJN4KJ1jA1f1NWK9Ti3Jb71yTiue574uGogLlYfnTy1jo5p1+hTSGASbBZ1ScmNiabTDpzWVUINBBs0B6rnid64Eyoz0zY1jzF66D6grhQxhizKASFnNs0S2QzRcI227nqA+EzL1wQirRk757xNCffRHdB3SM2c9x3HJnAqivGEYlyY0U7/fTE0tpO6bvpw+eqq/+VtF8zekmKPHG2JUKmbeQLdvQ/9EMt8fMMXNr4j3Ywx8vJUvl5XvFyveRn1277vv+W9BzdNXvTk8BWCdAxbzWBJUZuniQggVkJMMlli1rroJqBzrYnIBSQCQjCFAaU/D5mzVvQuSZJHSfO81o9AL2zXzdZaffVsVzi84RViEyO52aT4l5SxDtGwM0An/Z/KfAJ/nvbGctMe2udVLYky8oyzl2q9WO4s2qM/qUl3il+EJa3bIslLwg0EGxMSWdMmPePZ+6nGBuplo8KjUmAlVOcdTcmzKH5pXys0K7lmxdtn1/zNIM77e6DZ4iHdkueZ4jqPIq75urNo2uKZxvPLfrwmt9DdufjOXxYZZtloB55edMy1uL5t6TxHwJQXl7wUnGbjHhesSV5jj50rnxSb/u6/+hDdv4cnQuc8Uhg6QTocJaeR/MnAQlslwCHst8nSTBR4qFLv8DXa78kdnwNLEZVta6iQBjYui2XL7vyFTDOhWMQRt5ePzlBSMLXBnNZVQwkGVBWPR6hB76AyFmJCC46dqxS4quILcXli6awr/m9XzxwLl2Mqvrj+GCiSj2SJ+oVIQ3bV/4rbnMr8sZqxFq83T3AZI98wez9EohzoWIUFW265awr3lbztULur2KElx3ZUrxaf6zg4UuWbGe77OXgjY571/fQHviACV+onFI42hArMei/4NzFxxl7LfGxkqM2AXvPRMR5dzGKirzQrt8mvsgLmuvvzu+awi/9JG2m5JeD0VlNVfKDGzxvkQvaEO0bTRvnxQB9N/UQ56IiLfwXPU1w7Pr5rs4oO9fflLj+J7pFcV+8VovHPfl5TtJF8LTe731n7FvLiOCuNhnnQxH0R4l2UNEuvzKu9LvcH7QFNHmjz2c1Fn7ipai4TxGSFT1NcKR/oz67top5+8TD4fsxqorVkzeq+jo7Q2j6gWv+hn7+Qxx4lnT9VNdGGZ/QfuO8SEV74WNb5LeWQZ51jK1oB7QHwnAf8gKhpYwIEmsroGmb9Gu1vOBOmvBlDNK1U/JGHucc/7FClvZFmkOa+4H7gvvjjeMJPmgYXSe/PyKaezxGUTEWaW1zxYi26Lir+29Jz9Et4jRqCeyHAJ3TflI2VQnsngBvq/ha4auTdIvmIP3SWz22Rf5bY1ykx8cH4n1RigkowplHJ1f/FI0iry/OBW+33yHmb0TXFAPf2pZOBmlsPcNvLT7OfGJgx7lSL4rnV0Wj/j3/OK/j5jEZUP5HzJriXBXiqvk7Fne+kIeggq/Z8gVFBqeUDXYcwv8e+dFSp2z9ZBIY74OKwS1bwgY9rDgw0WBSyNchyQtO1CtfLkRgQlvjnsN+Do0whZWltbjID0y4B/71smeYcYA4h04jlLtsPWgw+SO9QQ9xIC7qJZdF9S9xZRLAtufV+ntF7Km3O8RsbfvxuljFFnq2VsK963toD3ydEaEV9mMyT9uhwY076AAAEABJREFUDSG8oP+iH+viQ7jyPQ2R0U8wqR3wekYfxqqD0nOBsNyDCNRo138ZC/KC5prz2XCjnuM0qDgbicnsoIc48OGYUn7j5YxJMuVnOzNtCDs0bRzB3iPyg/uStpXLQUWZW1ZGDUbQ48C9x31DP9/VGZy4hjNtvcaJaN8l/5h4x1iUQnjH6iDOaeSDG+v9Hh/ioO95YUOuEY4gwBvyiiCMj0UMuWMPS15cIASn3+X+wB5N3p6biwdGc8RF7dlKvSFwifdZFOnRr1HntMWurWIidOB+oM+oJYbgpbZKtIVVlw5pUla25Hf9VNdGabe40Y47/0syETzRbkp56toozyfGVrQD/Hdl5LxMyvhrWBY0Lwx40UUf2ecN+yWO/xAGM9bpyzN2f5t/9I/cF9wf3EOxuqRg9Pe54uxjnu2McfKzqO4V1yW+jNjl/UdboH/nmRIcg2pXz9HBDOgggUMloIDvUGvOfEtgcwJMeJn8lVbpIVxjQPKnDckh4CsJ726cOD4quqbIFwIT3uAO+WVgxaoUBCGrA64+/wipdrOKry/13drBAuEIb+OHUmYF3+fGkQFdjKLi7XbJA5MuBMQlP7jRzhj8IlTgd59G+EHeW9paX/h1uxbhBxNJtmW+dD3w5d9MgDm/iDZ52arXQMhSWgXApBNBKGZvBJctqT8muHxk5LLVBaNr+whwmZxd8HAAFr+SPLIy5OUx+xTthBVc8OhzX7djEkbfQhtad+M38fFhgFp8TPxLQgvaeu3jBdxX9IWlexBhG31crf7oe4cmg9jXBIBMPHmJQ5uBw5BGsE07H3Lv7Gv9QeevxaQNlO494sAPQhSuSxrhHquVSn724cZ5g7Q7+pGh9P8gDrTdWtuMt7NSe2CFJSuE8DekeeHxjCHHFXv88CGuFasLl3My5z6gP+OZPtRW6SvYVdAiDK21U14sIMS8UKg1C/p92ihprzmd/+TZgR/a6rnlAi5Y/dty7l5LG+3KWGPPM3CI/VLHf0P57aoQ4TI7D7rfQybPmIfEkT43xqBi5drcL0oGE2t02PX9t6TnaCMivUngsAgo4NtxfZmcBBZCgO0unAfCoKSWJVY9IiB7ZcUjb0LZUjjk7ePjUHtjxyAZ4VNLvhLdGRNlBqhclzTCFVablfwcgxssmJzVysLKsKfUPMW9dKYOwqoPi5+aQojGKqGhidtqeNoaq1oYcK7aj71GUMMWrFI42jNfYkW4UfJHW2Rb0Z+VPMXtLtEIXWJcUAzoOcPsgsOaBcKMkmBo1TurRtg2tGp3CNdwZ6Je405bYMVES5k4/6lWPy3xsbIAwcVQmreJA9u4Ywyqb45Lyz3Iiq7vj9+SYssbq2f6/LCyiZV5140jgnYm87QH4v2b2PGCBOEe7Tc/q4owCCdLHkv9QSncuts/xuIzomttgD7jCfGHgCHGoLpGXKi7GItRT0xOqA/KkMuior0gfCt6imOpPTw/7myhZhUqLwnoRxFQ/U7s2WL6tJg/G92iyHNNqDEnczjRn9XyhgD/52qe4l5rp5xvWRuLIMyiX2m5f2jHtGfadZJfhGI7aW0l41OTU7aJU9+5LCrYc+xHyRMr4RBg9fk51PEf40bGE31lWrejrXAmLwJhVp/RF9O26Zt5/jMOoD+nba2H3edv8rjL+29Jz9GdcTchCeySgAK+XdI2LQkshwDnQLG1oDVHbH+rbdFA4DM0oGRlX+1Qeib+rPJisNyaL/yxhYaBKtdDmjeGTIKH3I/BHgFG6wQO4QBbUGvlLr3dRtDB1rhSHEwcvjUextTpb8Y/b81jTFYtq1loz7TrlkQYrP9QxSMTGwbwfd4Y1LPKr8+ts0NARVuGWWdXMvHHFyBbVv6U4tm1G4IGtuHW0mWrGFtIa/5Ykdpyxh7HKWwysUJ4W1sxR108LhmmbmIU1X/HlW1/9Hu57FWlCTMBmFC+JBcIiGifbCND6MeHb1jBA+s4L04h/HpeY65YRV7zi7BmSSv4qH+2NpZWx68Xnz6vJlSjPZT6ZO4ZeD05kSO44eMKCAVhw7EWY/rhRLEThVAZwRHMWhJseTFFX/u6A5EhCK6tdCToI/MPoVaMJkUb5d5v8tzjaW4r+irKWooXATBtpuRn1Y2V5f8cC56HvIz9rlyzEpmXWzxzeTlCPxTrK9Qhj/8QllMmnuFXFGrgB9ucOZuSsyDpi+mTCU+7ZaU5Y6+BoHux3vX9t8Tn6F7Am6gEtklAAd826Rq3BJZJgNUQDM7G5I6JCof+18JwVgmCvnV/rOxjhd+6/ervMQKX1XAMUBmortr1XTMI7bM/FjuEVUz2W8uDQLDmt7QSgpVDta+iviAJtAgS4+1c0dYQAJxbTLigrTEZLgWlvZFWyc+qG5MbhDKrdqvX8LjhqsXKNecg1SZbvxj/TM5jNCvO0WKFTnOABXikPXDP1rLChL/EuwvPauTWdj+Wb5cGJpNUtplxPaRZGTtGKIBAhy2aQ/Fhz8qPs7MzLreiWaFy/8SMcAJBWS63rriXqN+WhLhHW+q3dn+1pDWXH1bT0R+PiY8yIuSrham9VKmFL7mzKo++k5cynNNX8juXG4KxMYJ3XkzW+gXaQt84hDzzYooycj2kEdS3rBRcDU975rmFuWq/j2uEKIy5SmkzDnxWyUOPGy/feK4ivGIVJGfu8ZKCF4uMJ+DWE+yMvNSY8zwee8+c5Y9nySbjv5YjQdipQBvlJRxn8lIW7pUkf/Bq1/ffsT5HD74hWIDjIqCA77jq09JIoIUAD/QpE10Gg0y2SmkMvTlnZd/QgLuLjwFeLf7O77pJ3hiwrtuv/mZyzhvmVbvjuT47Q5hWm/islpctfKu/x15T1zWeCF+6g8nHxM8X6caUZT1u2tu63epvJmHPXLVouEZwwyS85JUD8/vcEfD12a/asdqKfK3a1a65X8aWoxbntt3pf1rTaFlxRL2wJaolzk3aPCtUEIaV0kFYx4Sz5GfVjQnxn6xa9FwjNC6dC9gTpGh1rbjeOZqPmNCeWQHJiu4bxG4XivsaYUBrWvhvrd/WOLftj/5rSr/HKp9a3mgPNT+t7ggpqHcEvAhd6U+4P9lu2rotsTWtIX+0BVbXDrmv25PH0qrXdf/rv98sFryMiTGoEOSwZX3Qw4AD9zL31IDzzqxZvcuuhVKC1POUcWApziE3nsdLHf8hjK+NHbtyca9wzh7saIO8XPu8OPJ8L70MjZfFql3ff8fyHF1shZoxCUBAAR8UTkRbTAlcJsCbyDED6svBzhgMcpZP97vPZBvD+mSUgR0r+/r8d3ZM4ljZ0/0ea5I3Bl2lcGxbqwmkSuGX7raJ8GJK2d4ggajbGIMKwSt1O+hhwAGhzdSJEnliEDkQ9SVrJolsNbr0o/EfQga22JS8902+yQ9ClVI48sPksORnyO3QBHxj2mnLJLtlBcYQuzH2bHNkZVApTEt+V8Mj0KWtr9qtX7Nahi1f6/Ytv5l08gVXJqFPTwAmpZwTxscMOBOLMsV6p4o80N53muiOE0PQS92OTZbJNlvmSuE4Z29qe0Dww9bNhyUBhFj0zZgIeDlCg/YSp50q+lXysatEYVcTXtIXI3wfmyfGR/AcG25u/6yyY6ViKV6er7u4D3n+LXn8R//LWc4lVn1uzJ/5cu5D48hZsfRrmPS1jD8od5wWr3Z9//HMObTn6OIrsZZB3U+PAB3U6ZXaEkvgtAkgDJtCgJUpLWdircfNWTis9lq3X/3NpH/Kiocujpa8IZA6ZgFfTcDZsepMBvdTJqFd+NJZUJ0fJqzd9RiTc2paVm/1xdnS3vrC1ew474zBcMlf3+S7JT+UdWr7597ZpB5L5ZnbDeHFi2eOdMpEfEoWWtr7lHipv1I4tjQNne3YF472xqHufKyFSScmk1DOIN2HAGc9jwgYx7YB7o/1eJb8e+ozFqHL2BcPNQ58dOOr4wnhM/cK51VyLhirkWK9dzX2uUUfwvNhasZZvVcT8CH0mdKn8oyo3c9T8z0mHFvt2aZbCsOzrBOslvxt6kZ/tOTxH+MgPm5FX7lpWVnJR1/7F4mIPgBhX+3lXrzuVe36/juU5+heK8XEJbApAQV8mxI0/CERYPUNE3DerLVoVj4wmBwqIx92YLVaS1z4YcXEUFy7tJ86AGXA+6pKRvsmo0wqGeSVgiKgY/JR8lNyI2+1wSqDXt4eluI5JTcmkgxut1nmqW2NCRyrIbaVN9oC5/3Qblo1q15vXckQ279qE6u+KJhcTK2LXdRjX56XYjdWWLTNfH9dIm9tT50/wiTYoEIY0fJi4nqJgQ87cM/x0QKeX7GaQ+09DoRTe8/EiAxQByO8n3vl+Yo+t+i56Fslv+6N8QYCXbb981Lui+JhjJA43herGCcwXthmBqfWH88tBNjbzFtL3NQ1z7iSXwRQJfe53A5h/MfZgl8yV4Evx0MdIOzjzMhvix0vmGMcvNrF/cczsXs+tpqEKcFtfY6W4tBNAgdDQAHfwVSVGZXAwRJA4LELwVrLm8ja1oCDhbzXjC8rcYTuTIL3kSu2fzGh2UfaprldAu+03egHY0dYw+Rk0EMc2HbJBzs49N5xXYCcsKL/YbLLluz3PWEOFr1MYKoQsxzrRddDGP8hRPqWZP0roudW9McPSKR8EOu2MU9dLfk5eup1Y/mPiAAdzxEVx6IslYD5OmkCbFvZ9hv3kwZs4SUggZMlcPeUnK93HssKkRRHNZEAwj1WcbI1cGIUBjsRAnxgZRdFPZTxH/lEwMdZlAjj5mbD7pYnJdK7RaskcDIELOh+CCjg2w93U5XAPglscyLIW2G2DK6Wj20r29xu2aVVezPIW9qp2yC7NDTHEWDCOS7E5r5pb2yL2Tym3cXAZAu9uxRNaSyBXW1pG5Mvvtr8vQkwdSzHffKYhL9L9O2iS0dSxFnVSGCbz1jqjD6uLyucrfeJfQ6Ndnz1/Gvi972j596ymCgPRp1CX9z3QahtVBBttTT+myvNOcZ/jBF/NRm6UTR9IueX5nI2xXjoGxIbH+GIcZJqic/Rk6wIC33cBKYOCo+biqWTwHET4Guy2yph31liDJpq5+OxtbH21bdSntnGVtt+i3BvXfhYilO3MgHeeJd9nJ1NPVCZgXDt3MahtMkXB4gPue/avqX9c8badSZmjO2b15wY1mDtBHh50e57+z7p71ip1dpv/k+y9IzoB0dzPhvthjZ37/z+2WjOVoqxNHWQ+dnmM5a+jT5uHQzHEnzWumXhN/H8ZNyp/7eLSXt6l5ic18f5pH1pxPngVW0sQgGnCr9gOPW5Rbpzac4lXYqwvuX5t7TxH22fPvGmqRBW3t0vJvdEjI3V2yYG7rkYJ6mW9hw9yUqw0MdPQAHf8dexJZTAOgG+aDZlEEoYvsi3Ht/q777VBUwkaoeks+Jhk6+NMUC8/mpGeq4R7vGVwh6nrVhx9gwfXdhK5AuI9E8b8kBba/B2wQttjY8GXHC4YHHRggkcbe6iy0zSAAQAABAASURBVNU2HDzPIdgIhufUfMSHj/lcndLZGXmptX+ELQj5VsO1XnO+JRPLVv/6m0agRQDGqo8521MXF5PN9Vy/YyzuGF1TbDf7sHiiP0KwxxcjEfSxsibWqi0QmPpig3u51g/wEYe+uvuQlKOlv/2Z+ONrn5xVeo9cs4KTNlL7uEe8HoVijFITfiHU4X4ZW2D6cYTmY8PN7Z9nYK0+eVYxbpo77fX4Wp5/Sx7//VMK9H3RrGqlfm+Raz6a8Vcxp6oPT0DaWIyTU0t7jp5cBVjg0yCggO806vmkS2nhLxDgbX3rqo/VwNfNj5rAisEzK+Xi9QpV+wAGAgoGTlcEGvGDFRO1LRp/kfj+NXqqQriJ4Kk1PKvQdjGAbs3P3P7+LhH21XWsz9UNcjVlosQWlqkTJSa/60K2ZOMKxYo3ButXWG7xxwsqcSPIgVXFW6/zTXpttZybAELhWpxMVGt+5nK/fSKq9eO/Fj+sQnlCTFalxFDtgADP2Cl9Py8dELyVssjqLAQ4q354Lt111WLg+gtjzxlgPI9ZWZWfJ6d4ycfLvlLBef5MqT/GR9R9Ke5duPFsrm2LHTue2STftLdS+EMY/5F/xha/nYvPiKaNMIbgpclX5veYFX6ERcCaYCenlvYcPbkKOKUCn3JZFfCdcu1b9lMlwEq3d51QeLbv1CYfrARgELQe/TNjUZtQvH/8MGCKMVrdKiHeJLqknh3Hf4ueqhiEjukzmawd81tahGgvrcBkpciUlZkIe+FdiX7QuXZ2DsJDtsoMRjCzA184rUXJFy8R9NX8rbozCYXVqp3X2yHwl4m21t6Z7MXb1hXtpCbYRQj0VclJLc/xcknRtxHvpR/+24jAjROaSXyMUer9GnxzTt66N1b9saJz3X71N8+/R8aitrIrXi6pYz2Hjpd8vOy7VMiBf9TdFAEMYyRehA5EuzNrylh75jAOHLvSlGfyj6UUvMglflZ/8mEKVoJSdp6rvNiMlyvUUsd/V2Rywg/GuqyG/rKEZYUfz2OE6ByHEKtB9TpxYXwY4+TUkp6jJwffAp8OAQZ0p1NaSyoBCUCASRwDMgZr/G7R+L1Tg8ffGfDz/NjX3twh4Huv+Bur3jAB7hNdUggXf77kocGNwVttxcxqNEy4GMit2h3TNVvFfr9SICZJt6z4WXemPtlSuG4/5jeTYAQcQ2G4B2hvQ+599g+JJV+DRoj9uFyzTedjYrKSihWktI/87FW0/5qg5QMTkolljGaFoP5mzb71uAkBVsW8sBIBdYGwpeLt3BmBDmcScfD4L8cWAcynxmQ1FpNvhOO01VhdoVixVduOyUovJuFXBCz8eIe4TX3BkqDHqCaXiecEW2bHREC/9wGVAPRp9G3r3hCs1NrdHyYQ2w1jVBXP+/es+jpMD7zkQ9hZyj3P7TuXPPS4weyjeuz3YYXg6TmVhMkvH9apeLvCmXbGimBWKnJO4cfF9Uujfzya9kUfyQq39bbI82+J4z/6VvpYxinU9yekHN8ZTV9Mfv//XHPsQowmxXZkPqLx+Cbfp+mJNrKU5+hp1oClPgkCCvhOopotpAQuELhXbJiMxmhS+GVrT8kzAoxnDXhgW8zTBtw6a7ZN8iaUiU5n12IyqK6tnGGAWdpCwRtXJk+l9Bi0MhAs+encmCjDuPt9jCaTiF9vKNhnxw+D6BhNioF2rT5rESHY+JOKp4+Me6tAjYkN7R/BCiv/PiJhH3B2dvaDMX8x+q+jEdQMTZhwZwIUb4MKgc1948qkI0ZVsVKCQ/W5b6qe9bAxAbb11fowVtUh8G1JjHqmDbIqmrpHuPOJCfgd0UwQab8I0R+W31MUAmcETS1haUsf2uJRP80E2MY3ZrtmS79Hm6Bva87EikdWk7cel0C/ePOVsMd2iQCnVqZPiocx9dcyRkqUO1O/kJRqYxpeULFVN16bFC/ramMg+kj6ytUIlzr+++pkkj6WVWWcTfm9+f0p0fTFcOHZyvgSYWismxTMazsIiAh/mKemaRu0kVK5l/QcLeVTNwksloACvsVWjRk7BgILLgMDFw4Org3WKAICNwRvhOH3kH56HIa2vjCYYTsHK+nibVAxsOINKBPOQU8rDghUvnbl99Dld8WBgVyMXsWbV4QwvY6XLZmQI4Cp5Q1/D0iYO0Qfu0LA98pKIdm68jnx07Lli9UB3x6/myrqmlUFpXgQ1LGdplafxMGKQlZkcj2k2YaE7nNn5d9T+hzW7D4vv1tWFtLGEO5xWHeCqHZE4EeSTqm9Uy9si23pVzkz9J6Jr6ToN9kSV/Iz5MaRBfSnQ+6dPXm2LXU05jP5SNB3JzqenzGKivbCSqiipzj+SjR9W4zRCoFdi8CK/JJv8j86kQMJwMuW2oePKP/XpTy8rItRVNQf46naGKkYycyOlHHoedQlRXv4kvxoeQbyko6XdfE+qBjf9e2UoB9b4vivRQjKy7wxLz9gyXEbg5DiwCpSVljn8iTVIT1HT7KCLPTmBPYdgwK+fdeA6UtgfwTYAvYTSZ639TF6FefJ8AXHlo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font-size: 28px; font-family: Arial; color: light-dark(#17233C, #c6d0e5); line-height: 1.2; pointer-events: all; font-weight: bold; white-space: nowrap; ">PhaseSet: streamed permutation-invariant group periodic tokens</div></div></div></foreignObject><image x="778.5" y="3.5" width="865" height="41" 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coverage, missing</div></div></div></foreignObject><image x="1452" y="302.5" width="228" height="39.75" 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bTT935dNd3m195cERRmN6vHfRd+1cfV77PC4GU5sQR6JmCEy9Osh8EzhoBx7/c/cHcLNH/q1D5tdLuNbjvsqnfYwRchC/vQ+WonUc1ekd3WP5jT5zx/f/Kz/eX7pWX1vw+UE+/rm8vGNoRuq7418WLwbM70rv7PSrH4Hr6pm/5F673g2nvdz0w8/3ugjZFQHHsro9j2zch27HX3p+01x3D6v2235h4ddLS1tm1oe9Y42jBwe4ZJneNlxWye4Z2kVaI9eFYWxmL1Q9WCDs2sC3rLOWYmTtbfRhC4roXNh1/HD1GYjdUvcwt/5RB/gmWI6HgXcpT356KtFURjrzKudXjfQ/K73VYixX3SZeG3zG7/OgsBGpHBXshUt26t6lsXZCNn3YcCWm90Kz/6EcbAw8clcsimP46cB6S+B31B+MkgXUYKMQgEAROF4FNjNrtlyo5CAJB4CYRcO9j3VGaKR9/XBbMBUaHwIcBtCP3+KLvqkaPE9j1G90d2TWNTeEw+Nd1fHFTutzcGSJ4s0/ab5A5foqRm2hzTQKBI6aTPIwj9QAAEABJREFUQG5XzY7nbzQRYOYdM2tI96yOtm7aibrnac0/eXUvx+/E9V4cVcM09/RN3x52mLvLIR7M94iR9yrmkoUDixkjAZUgLZ2l2h1Gu2JLw9k5HQnodl16wUPc8mc3hb3VjipqCy1trp1QMBL43Slzj3RuPPzpY+6gaie+52rji6OGvf9NY4O7nO7a9WHsYs7ZserD+SY0+w079lbrR/pTS9tmt+Pr9dtt/np3YxQcW7oHWJb2rTZ87EEgCBwpAhHQjrRikq3rRyApXCLgXszb1pc7G1aLy7pWYZofV64eqHCR3TPe7vosWRmv4EelPPyxKxO7T0HsEL7aIAKMWCtQDbysJRGQ3JdyRFT9OCJG8GqPt9n9wND3kSwVivrwMLTj0dO9eLfkGBYhye+x9fFs+iacWTzo/ThCO6L3/qZvDHCL1UTfxSQ0enlzl7CEOvXRh12HpV3t/tjxkyvw6D5bkWcr+VeOPoCFhZ626fuXynEUT5E3KrtdS+vDz4YYk9qID4GFfql9tPHqR/pTS9tmJ3xb1Nrmr3c39jom2tPzHQSCwBkiEAHtDCs1RQoCMxH4nfLn3pgnlt1NW7q6XcF3Vhh2D1tYVR8d3dk54h0CuoiPId4h6NogcxysvBOmer92fXpGsPezz7cHFrxk2MZBwHF8tqUttWs/Hu3owzmC5YXAnr7uG/Nqx2Kd+4juKOPopbrR7sso/HXQfroiXVqOCnKp7LTA9JJQFj+HYAerrCvKAyArhPp4UunR4ypFnq3kXzn6AF4ctMvd09d9E95H9bPO/0QXZrSjObmPzNHunp04O9Mj/3NpXsHsd/v1I4tUc+Pgrz8qiTZHS9vDQXP8xk8QCAInjkAEtBOvwGQ/CMxEwCMeP1t+HblyDw2T5xnnTS+Ilfe9lGNI0vDqmocF3MXxOqRVYIyG43R2efjbK6E9A9tBtFK/ZzSLgxNc3CHpA859oKMPN/fbqn/vF/O66/GvNi7MNGGvpTnCaRGgpW2yE5jtom3yM8eNkEvYm+P3Ovy4y9YLWEvSsRva559A8FwXFyvREJQ8ArRCrA9HOwk4Zd1Zyb9y9BHYoZWXnr7uW39fspO5Lp5tdLvSI4FJXj0a4tXDXbXn8e1i9XlwpLKnbfomaG1yj1sQCAJB4CICWhpBEDgdBDC+nrh3B2WpxiC/VBXVU99+dwmTgPkq0kEUxsh9DEKYo0yEMBrj75jUoysVv+9jpf+2BbLKyoq6KeZxJdH6wDT2ryuqY7sW5XxtinDeR26nZelRsj4O39oVPNlbrf213zdh1/5GDPVNpC2NfXfv1u0K9m1G3xsJ+l7klI999agchMIlD6ccQvifUw73sUavv3r10R0yY9+uWnjx9PlYIqDd9qJBn/fxd6hBIAjcOgIR0G69CpKBIHDSCDi65nl6zL27aYQwTMyxCWHHCLIXMTGUN503Ryv7NDGOhxTY+/iXMPN2jrSnPo58P4BALxDoawSmB1yf8v86d4UJhXZGn5LaZptd6s0+DuNKOHMM9DCxzYsF/vN8Xlzc9qLB3HzGXxC4NgQS8TwEIqDNwym+gkAQWEUAU/LwIrnb4TeWdhlL7BDYcfOcd0UVdYsIHJNQdN3C4k3AbCfUbuJNpLUujUPtoK2L/xjpxqHbWPQ4RiySpyAQBE4YAYPZCWf/trKedIPAnUaAcObJai8Ess8B48/Kkzsxfi9tugPnpbX3KPqul+YraNSBEHDs0e7DgaK789F4UbE/ingoUObujPU7bYdKP/EEgSAQBILANSMQAe2aAU70QWAxAscdwN23D6gsvnvpdcpDEZ9bjm9Z2hPU7lr5vR4M40OL5g6I3YXrPFJXyRy9cj/qNjAYvVhp10Hd7guaeqb7eO7abg48HWHtcTjEd48lgW30GMjcxZNteRrF4/ESP0+xLexNu8Nh9BMVH1oZ0b6vQz+k4o4KAkEgCBwUgQhoB4UzkQWBs0fAC4CONo4K+rVF9BgEoez9yu53nAhrmKb6XKtGDOBaz2fkgMklpLVFcq/n2VrCoe0Vn+OMZawo93YOsYP2nBWrndEyVlRfzhXHM/14xj3LpS89axeH45/aTUv2OqKfzGhp7BZEmPvq0U9B/J+KlGBYxlEpCx6j33I8FBZHVdhkJggEgfNFIALa+dZtShYErgOBt65Ie6axSBcfVf/eqbTn/MtYpO4q82QXcfTiod8pWwRg5/kD69sP8/pJAw+4fEh9v2rpaWdr9CqfJ/89+FLe9lIvVKH7o33uY133y5SV7NGp0e9xLcmkp/MJ7G0Y7UW7aWkWQCyEtDT2pb9VJkyv7QT64eeevu7Vz97fTX9fJxY3XZakFwSCwB1GIALaHa78FD0ILETADsurDMJ4Vv9RRR+tXBd5o3Lk6CU3+jhfRy/b9cfVlPbV6h9cylishHvZCuV4nZ808IDLp9Q3wXqqH0fA+t8ZIwj4CYfyurOS9usMQvuphZt6Zn2Q/K2RtOvR64pzM+T3A3u/v1iEkbDrNw7LaUW9RH3ZGS1jZ+V3DEftwn1SwtDOEV9jQD/03kevL9jZ7+lLvl+8PFuA0pYfX3bHuN+hzBcpHXUjCCSRIHB3EIiAdnfqOiUNAvsigNn0A7V9PD9dhBHTWOStynG+0Qr91oBn4MFT8j8+KMerF21XZtKxuFeu8L1q0/m1chztor1d0dVxGTspeX7dQcifKdrvlr5r6pWqwISkMhYrgtEIy++vmEaC0Y8W3RPuZVwqR03f9PJrN4udVzt5fWi/bdjTjuX7Jysjdm3LuFR+5sEPVF8SdrAIb6fZgzp+tPphFYffRhvhU05RQSAInBQCR5bZCGhHViHJThA4YgSMF4489Vl0L8bdj54+5/stytPojkuR74Ty23HwawuLAfTSZUuba3/N8tgzjHawMPDldE95JOS77tlW/9m5e+NV0uwvu2d26+S9D/RNRejLWKSzV15yfNcq5ajPFHmjesdy7fsFAey7iz5SBG6CcO/mdwkJzj19zvezlCfHZctYUe4wjnapVjzd4ofdvScN0oeFY58Dp60k4YTvPdrRHOHe+8t3EAgCQWARAhiuRQHi+agRSOaCwHUi4FGA0eo9gWCXnZdXqMx+Uum7rNwT++EBAF6dg8/AaS1JPXz0wNWui2ONrZOXNN1nammErE8swi53Ar3Y+eEVtleOvx7zbkuf30N/v0tF+Gallyj1/pGDAI8tmt2hMq4oQveXX6FeXDx/0R5Zur8XWKSNysM9+ubLD3x9W9HswpZxlGodFh6w+eLK8TOVXqJg4R6n8H24ry/CMb5mWdmKCgJB4JQRiIB2yrWXvAeBG0Xg4vcvLi5+oXSvdtl5wfjZWXH0qI/PN4FvnRv3c9EEXndZ+h1IZfcq5tz7LZhOD4L0wpVdly8ssPodLDsumNVyWlEYertrc9Ml1DlG9xUVy2g++Zyi32UGFiaPLgxGd/OKfEW9VFFG/UI9fl65WSQpY6gI3U8cuHgG3u8PukM6cL5CIsy5tzj6KQ1C/RdViL69Fumo1DosHE38ssqp459lbFWEs48oXyMs7CQ+ptxOVenrfgLlVPOffAeBs0bA5HHWBUzhgkAQOBgCmHxH8voIMemYHow6e+/efjs2hdH8iSJuesDgacvdwxVlnL369iohJr6MFYWBsmPimBymecXx/ge8MfU/Vt+YzzJWlHppjzdOjhhs9TD6kXDpenTCLt4mpl5dEi7t7Izq6nsrsa8qPU+dry/CtqOJH1ZFXIen+rVLs65fEIp+qMJvUu75ESb+bODJkUl16l7b9Jpn701b8rDJvy4Hv3VYxhX1yUUZtZkiH5WCxQdVjgi2Zawou73K8LZFJYCVMVTu2xKWP2boenGhf/zqGrdjI1tc6/Pkrqp7p+vaQ+8/30EgCNwgAhHQbhDsJBUEzgABwgTmpi8KBh2j7sGQ9ypHL545EkQ7soXmqJ0V+Pcp91b5TaX+Uj9m8a6MT3ZFPq4AIYyVsaLgSnhzbMvRMjh6rABzaXfKi3LuwPT3lUTyhPqHiSRYl/WK8rDLOxdVnZSxojCujrh5afL7yoXwIF36Y+vbTo1wfV2W0z2ljdh1sEN4j3DH/2nLjo/CUz+Z6pHw/dWFDYHCrhXc63NFqUdCAqF6xWHw4XVBdTVwuniBIn5PafUmTWmrTy8RfknRtSVCnN3t+ryi7LgSyK84HCmBQEtIuyewdnn02Icdag/19P3qM8uvo7m/UqZFpzKuKK/WOt54xeFICb9c+erbjzFWvRtbHIGmf7D8ebipjKggEARuEwGTxm2mn7SDQBA4LQQw9e9fWSZUlHFF2c35gqK6pO/3mmgvCKJ5nbCcVpTdHc94M1sHOwqbdthav+dgxxx7MGUkpCkfxt1dJjg6voW5fN9y8GpjGVcUAQkDjvm64tgQpGdXxe9aNeRLqznCwyOEB+nS7rmps0tPnUWcb1C00W9zFfnOqO+skhKuyrhU8MT0T/VI+H77ctXey7ii3N/zYMy2epwCYsIJGF4YHAkm/NnRk6a01addTg9grGtLwnxG/dPe1vX7cj46BQs4v1/lbB0Wo35l9/CFK8w65SixOE8JCwKasXhUJru6xlrai7rMkb/Q1iMQlyBwcARMFgePNBEGgSBw1ghYpSdM9LteSwqNuXlEBXAs7+fKdCeqjBU1+h2oFQ9n9kFIg4f7XPsUDdPtXuBcAclDJXY5v3WfRO+H/fQypa0sZb3Tyq4Y4cqu1S5AwNKrmk9eGJhg8vkV5rVKY8zL2FnZ8XtQhX54aX22jJNSh8RC+e1Ovmch8EelT0l51GXO7qcd+02C+imVOXkNAieNwFMEtJMuRjIfBILADSNgd+ClK013n8qYrTA2n1q+HX1k+q7PC7tszFbb2fF7UC3t3O0YYscOX7EKuhRbx7LevMK5V7KUqfcU/4MrrF1OAltZZyu7E+66ubuGkZ/qdHYEZ+xRPbxJlQ9jj8Ev61bl7t6Lla99sXSXzO+w2fnSrirK2ep3yqedcsz6d5SdoFPGyaoWC2VbWhCLHtr3p1XAufVYXo9Gqb/PqtwYc8vYqF50o2scg0AQuBEEIqDdCMxJJAicJQK/VKV6pdLPW/qDSxOyfq/MVrnj4beCrN7y67VBO2d2F1p/flfJq2gtzb0qQlpLuwt2zBQs4QXb96hCOyrXM5YYRXeG3G3CVLn3537Tn5b/XZR0f6ACErw3pUsgsyLvBbs3LP+OSDlOeSoPJlSWb1SpJ4z9M1eq71Ra3aKV9Z7SR9D0Ibi/XlFHO8pFXqwIyx6D8WqhY6naijYjzTYy3+jctSULKO44orf+Ttk+YaFsyujeJ9z7fjW176+pwtoBdSSUeeq7wtqcsde46u6ZO2dVxCsKNleIIQSBIHCzCERAu1m8k1oQmIuAOz8ucbcaI/xTcyO4QX/uL7mjYtcHI9jm2XP5GAJ3NuwIYRJGWcPcP3c5tGHZ3Y8p8lB5PpyfVqMNPd8ncidMJqcAABAASURBVG/9s6Pddz46A7aYKc+0u7wvv5N+msqtO39e7vPzB7sKZhXNFbUpXa++eeHOTp8n+f/4Suj5hFE736U+fruS9DtwEzbMXfuL9IVvNVolsZci7GjP+om6m+LXR9D0IbjvlciawNqGsUNb0WakOaXP9I3Ofd+2BCtxtlo9r8naVvKh44OFMnp4Be59v5rat8dT7JzNvf/XF+SQbVLch4rPgplFn76/TPUFb+lFB4EgcIsIREC7RfCTdBAIAkEgCBw7AslfEAgCQSAIBIGbRSAC2s3indSCQBAIAkEgCASBIPAAAvkfBIJAEBggEAFtAEpIQSAIBIEgEASCQBAIAkHglBFI3k8XgQhop1t3yXkQCAJBIAgEgSAQBIJAEAgCZ4bACQhoZ4Z4ihMEgkAQCAJBIAgEgSAQBIJAEFiDQAS0NcCEfEcQSDGDQBAIAkEgCASBIBAEgsARIRAB7YgqI1kJAkHgvBBIaYJAEAgCQSAIBIEgsBSBCGhLEYv/IBAEgkAQCAK3j0ByEASCQBAIAmeKQAS0M63YFCsIBIEgEASCQBAIArshkFBBIAjcJgIR0G4T/aQdBIJAEAgCQSAIBIEgEATuEgIp61YEIqBthSgegkAQCAJBIAgEgSAQBIJAEAgCN4NABLTdcU7IIBAEgkAQCAIQ+JT691Sdfkh9RwWBIBAEgkAQWIxABLTFkCVAELgJBJJGEAgCQSAIBIEgEASCwF1EIALaXaz1lDkIBIG7jUBKHwSCQBAIAkEgCBwtAhHQjrZqkrEgEASCQBAIAqeHQHIcBIJAEAgC+yEQAW0//BI6CASBIBAEgkAQCAJB4GYQSCpB4E4gEAHtTlRzChkEgkAQCAJBIAgEgSAQBILAegSOxyUC2vHURXISBIJAEAgCQSAIBIEgEASCwB1HIALaGTaAFCkIBIEgEASCQBAIAkEgCASB00QgAtpp1ltyHQRuC4GkGwSCQBAIAkEgCASBIHCNCERAu0ZwE3UQCAJBIAgsQSB+g0AQCAJBIAgEgQhoaQNBIAgEgSAQBILA+SOQEgaBIBAETgSBCGgnUlHJZhAIAkEgCASBIBAEgsBxIpBcBYFDIhAB7ZBoJq4gEASCQBAIAkEgCASBIBAEgsAeCHQC2h4xJWgQCAJBIAgEgSAQBIJAEAgCQSAI7IVABLS94EvgRQjEcxAIAkEgCASBIBAEgkAQCAIbEYiAthGeOAaBIHAqCCSfQSAIBIEgEASCQBA4BwQioJ1DLaYMQSAIBIEgcJ0IJO4gEASCQBAIAjeGQAS0G4M6CQWBO4fAc1aJf730n3f6QfUdNUbgMUXu8UIrclQQ2IhA+ttGeBY5Pn35/v7SfV+8prGrUooKAkEgCDQIREBrwIg1CASBIBAEgkAQCAJBIAicDQIpyEkiEAHtJKstmQ4CQSAIBIEgEASCQBAIAkHgHBE4FQHtHLFPmYJAEAgCQSAIBIEgEASCQBAIAisIREBbgSMfdxOBlDoIBIEgEASCQBAIAkEgCBwHAhHQjqMekosgEATOFYGUKwgEgSAQBIJAEAgCCxCIgLYArHgNAkFgEQK/Xb7/Vumn6vS313dUEAgCB0AgUQSBIBAEgsD5IRAB7fzqNCUKAkEgCASBIBAEgsC+CCR8EAgCt4RABLRbAj7JBoEgEASCQBAIAkEgCASBu4lASr0JgQhom9CJWxAIAkEgCASBIBAEgkAQCAJB4AYRiIC2J9gJHgSCQBAIAkEgCASBIBAEgkAQOBQCEdAOhWTiCQKHRyAxBoEgEASCQBAIAkEgCNwxBCKg3a0Kf9oq7huU/vLSv1r6T0v/eaP/c9m/rfS7ln6W0odQf7EieZHSH1Ja3L9VZp+uPPy3ov9Y6Y8t/bKlhStjkRLuf1cI8U36EfXdq2cqwjuWfnzpJ5ee/DLl45uK9taln670odWzV4TwhQW8pTlpuKiXry4/0pfPsu6klHuKlwkX+LSReV3xbxcB5v+mzD8pzS8tL/+hvj+n9N8vvUt9PGeF+/XS4mv1g4q2q4IJbNTRNvwOVX9/qTL7cqXhpM306U5lQ+eurb9Q+YdvGceqVvI1p71oA9rCF1fIvl71o+8qurqZg7vxRT/Q5/+gwk0YTu3uU4p2SAzl/abGocr6FTWNvV9SLv++dNvXlB1+6Prb65Q7/2XcuNK/vrFSlade/3LRYVjGbKUPGGM+qELoG8bXNl44TOXWttRTeT2o0h61S2NGnz7c5ct8wN9BE94xssdUuBYjfc1YWuRLZUx6vfr6utJ9mXyjc+evvGxUz1Ouxqx+DlA3aNz4KW8HU9qZOjHXmfP0+7bMxlL1xc911Yu5WL3/syqVNtiOQ1Ne0Ljpt+atXfKi7tThFCdTHVeyK0qff7OiKLc65G/SUzs1Zho7y1vUuSMQAe3ca/iB8j1fGQYhTPrjyv6Q0ibNvv4NJAaIR5f7fy/9DaWfu/Qu6hkr0EeWFs+/KxPDJe6/WfY+3SJd/I36Z4L+6DJ/srQn2h9W5l8pfSglT59bkRnsvrLM1y79zKVbJR8PLoKy/88y+ReurDsrTIqy/WjF8F9KwxcW8K7PSwUX9fL2RZH+75f5LaVfuPSh1YtWhD9S2uQI879X9nYyl5cXLNr7lsZEq4+3K/t1MFAV7UYFv5csH9ouTGCjjrbht2/9if/zK1395ollwkmbQa/PKwqdu7b+i+VKwP2HZd4GZpXsQdUrV2z6pbbw0LI/b+lW6UevXwR1g7l6m7KrtzJWlP6M4dOe9AP9omV6pnbHDwy/uULvOgZV0At99zbHIW3iCyoj2pD2+0/Krj+3fa1IF/BD19++twj8G7P3KXtFs0ipB+39rQahtGX1+wsDtxHpqS4uLl6rHIz9xphPL7u+YXwt66WCw1RubUu7ONS4r+6N38YB7dKY0acPd/kyH/DHv3CXGTwyiz6lHP+x8vXdpfWzvky+0bmrr1cvfyOlnJ9XDr9R2pjVzwHqBo0bP/wKU953VtqzupjGcXOdOU+/byPVb5STX/VyqL5gLDb3/lwlZi5W7/+47Nqg9l/WFYXGTb/9F+VyyLxUdPeUMfHhZcOXWLhVbnVYpEs1tVNjJp4KLrC89BDL+SHQd4rzK+HdLpGOj6n8lYLBILS0vq1eCYtpMLBVNFuVCcTk8Jvl8+NL/7XSuygDlMnypyvw0lXbCnJFvVRRDMom/7k48Me/cMJXFIvVc1QITCbm4xXLvlS9RQWwgvdpZarPMvZS6ud9KoYnlV6SH/XxNRXGyqzVz7LeiMIQ2PH92UrN7m8Zs9VUf9rim1QoZS9jq8KYmDCter53+fZdxmL1AhXi60tjlLSDsp6cUnYCzg9Vzuf2AXWmnXxMhRG+jHsKI/YDZcPwtfQirVXa/8+Uqx3MMmYrdX3b49AbVm713fcqU1ssY7bi35i9dPydnUDnESNqZ9SOQud08fNFeN3ShIIytipt3Zj3hPL5d0svUcaZady3KLMkbOuXcGjHz/gNy9ZtnZ0//o33r7DO0y3S1dGXVfp2WPoFkiIPlTHo+8rlnUvrE2XcUy9f/5XTXFDWWYpfi3p+23JWgMaT/m5M1Z7xFY3TVqt6mfoC8x4vsjXUVQ925C2SEoJe7KrzLEqbl39UIVpM63OxImQZEz+1Qi6Z32FoAeuNK1zUmSKgsZ1p0e58sTDRdl8cy9oHDAOrYzcfVZGwl7FWcceUYc4waWs9LnAwwT+2/O8yKVSwe8pk+6/KtusxDeGsai8d1DG0P1HpYjLL2Et9cIX+9tKYnzJ2UiYTk6SV0F37vtX1r6rUl0wm5X0npc4xBCbCnSK4H0hb1IbsHMPgPnloTEyQCVN7HnpaSLRCDzN9cmHQW/X+NJX6J5W20FLGYmUsICQJqC6/pyxLBa0KcvGs9Y+Q/mxlzlHqTdq3OQ5ZBf+Oyqy2V8bOSlmMv44Ibmu7uyaiza8TzuyaWhixWDEn/kONecZ9ab/lnEQbPzAijBivtZvGabbVeG9RZckC1uzId/Ro7NAHlG1pFMZ6R/Re5X5A86G+qJz3SbMNc6Bd1iXjv7xb3Nt3TNUX7KRJn312psujY8M/XibBtIy9lfT/ecWytH1WkEtlTCQ87zImiuSv1j+7ehZPyhp1QASOIiod9ygykkwcFAEDohVME+u6iE1+H1eOVmJod1D+dX2vUxieidla54c7fyP3PysiYcVOkKNy0qTfrehWBTcxAH+n/HxAaZNvGYuUFUTMcb+TJz92kaT9qIoRA+9YUVmHymSvbAbmoYeOaNDFJKybBKVPaIQ7HGj1oV66qC4/92X01Q+G+zLC+xblttoNB1o9yd995yuG3SgM6BWHAxJMXvDDEIyilb8Rfu5MjPwb61omZeRH+/rQchjtIhT54v/WPzipJ/U1abtsjpy4N1BehkrdafdDxyMlvkfli2BQxopyhFF5tRXmpnJ/RIW0co2x0o/r81Jpd/qdeL6iqJvGAO3ATlR526q0c3115FG70b6vcxyyU4gZ1eb6PGhDyty2Ie1Cfjb1fULyxGD3ce7zjdEmAI7a/A9WxO7duDdc1q1q25g3lV1/0Xemcq/rs8Za9+GWjDUYZm1thL0C2HX4xLJIn1YPo/Sfuvz85dLHoOwYfUJlxOJYGSvKLqG+ow9pV/rUiof7HxZb9EWC72g+1If1ZfEwfd8PesUw/jsmeMVhQMCLWCgZ5X3yrt2rB/VBmxMJHvrq5Kc1jUuODWsfLX2d3VF9GPU8wOTfuIMPwItIn57aprFiXT60MQvgcxeOpvSY7pFpp/2YyK2t0211IQ/ahviEjT4jBFTuGRUnRSkEMJkPvbi4wBDW5xVlAH6uolpJwsQ4LkFjKF6j6AQRg1lZryh+DHZXHIqAzr2sV5QjTc5Qu2/ibokBW5q0QcrFV0zN81fIby09UgZ4fkZum2jvXo7tIOgsvcHXhVxHaKTtfPmbl79nKO3M/WjCLqcLkxImhH2TNmBb7YRl7w+T8mFFtLLuGA7M4ECrD/UinFXC8nZFqVfM3NzJaYrAaptduLbPE4CUV7mtMMKBVk/weacK/IelR8pxF5PvyG1fmrJhJtp6m+I0WT6yPrSnEX4vXW7aEUGqrCsKk/IZRVk3mWGAtc/ysqLUGaZSeeGkntTXpL+wfBMK7G7C0321Il1RVr/FccXhCAnaS79zaZfF0Sr3SJVXW2Eqt3bpKGlfFAyhY0XcJzf4wEm70+/EY3dT/1Z3mLPJb2tinPSNltbbb3scMv6+f2VKOcq4VNoQBtBulTK3bch4qN3p+xZ0RuXXdgmoBIfLSPe0EM7sphsD+6gsfsinezq92+h705gnDu1EeuLUX/Sdqdz6rHKPxn7jlXZHQB+l29K0H+OpMC2d3ZguDu3R2CJ9Wj1IX32NcBf2trX+RvCc8qEtybe+oL3rO/oQbI0v71AeR+O2R0MIQ+24CnNl92CGOhIPU5/WR+FW0V1R5gZzxBWHhqAvEGBGC8UbFEC/AAAQAElEQVTGcWOx/Gr3yqM+aHVorn36iouwprxlXVHm9VG7XfFUH+YSArk7bfW5ohwt1B60G3HhRaRPT23TXGiuNmeP8qFNmYdWIp7x4WhiOya622bcUOa2TtWFulFH69on/LKLNgP0U/MyGshOrQzJ7yoCHn9wjG2VenFv9d+DBS74u4jdu0/fv1sW57w/vMxeGeQw5gbe3s0KLPeejrE1uBmAerf+22VyTNgn9w71bZJ6iTL3UY5ZiMPgOxpsvZj0bysBgqrVyLKuKEyS1cMVYvcBG/cYDNyd04Xz9yY95Vu30imMOnDvzyQ1mmgxeu2ELcwSbXIUhzsyyqvcfXj4WGl91XK40l6K5kVIWJb14Ar+Jsw+YpejCVEm/U3tSTuy8m/C7eMwmWljPR3jiwFWx62bl8wcUcVU/lHrMLDDEZ4u5dvB7r3Ay+Tb04/9WxvUFuEzEsKU21EduPLbl+evNwRMmQdH4CRc43TPqu4IhiMB204chuqexzX/bnscIvz3zJJyYmi1R/1qTdbvke1WvW3ZRq+8YbBb5rq87awIS5uEMwtiHi2Yk4Axz3wxGvMcpUO3E+ClvnXxKbedsncpD8anMi4VQQQDj9m+JHYWecDgYmQ7pwvjmHHDgyW92/St3SmzsWWiHaOp/+k/dpzME30eYWyeM4drd607jCwQoMHYPAVzZUdrtbD6KKHPAzGtG/vL1D8LvWWsVRazpNF7mMZxC4abxnHjLWHNnGnu7ONxv37beGpB1djVh/3OIrxRaTuqZWxU5mpzNkHTfNB79ngObHv63G871fqInWxpjcKpI+3zs0eOReNmDitr1LkgEAHtXGrygXIYJN6zrCa0Mi6VwRaD4LgI+6XDGotB3qtb/PdeDEZWS1u69DBnLY1deDt2c9Lkn5b2l5blP5Xu1T6PhciLHYxNE8KU3v8oi8ljJJhYUZsmufJ2RRGQHcHoHUxyVtq8qtW7jb5hZsUMs2oybf2oZ0fPMIMtfa7diitmEdbbwnikxep+789k4C5DT9/3G+OobP3YhPEnHNuNmZOGyd3qq92a3j+mQzotHeOLAW5p7BiEf8myQEubINJP5naltjE1C5K5Ma8WdbRFbXJToj9VjvyVMVR2lS3WbBNS9L+Pqhh6/LQ5jEw5DdUxjEOO5lqRbzMIF4JKS9tk136Mv166a/0RdPcZA6e4CDqYwdEiiJ9KsCAxVzgTpzoxprC3mpBtUXBuXNoXwfT92kju2wkSFm7uf14xCO9ORvQO8mBRcR3j2/rXLh0XM2e19GOx6w92x0ZjWp9H9eh4Xk+fvj3845ggzCfayHT8jyDUu027Oj19+ja+jsZxbdru0dxxXHzmTAuSBDvfk7YgrG7NhxOtN7WJftHNvE6Yn9Mm2vjsuLnX39LYn6/+beIJynmtUpf6mwWKtZ7uO2if/7TswpSxouwC78oPrESUj+NBoGeCjidnyckuCGD+MLF9WFv2o4Gl99d+Gww+qwgmhTIulSdn7QRcEspiVdsEWdZLJZzw4rkkzrQYQL3a13u3i9bT5nybFKw4LsmLc+AuivfxG4wdQ+jp0zdBFaM4fTNNghhOk53vJVq9OaLRh7ET1K/U935G38o02t0Z+Z1ojjuN8k4YnfwcyhSnXbs+vs8swiaGo5yvKCvMwvUOsOuZaCvCGODWr0UCdxPUX0ufYyeQj16900fnhD8WP0vay/+rTHsYo4wrSh8ktM7tg14/tILfR7TuHgl/xzAOqV+CuPxM+o/LApsyZivldwT5dyoEIcNul91BzGqRdlaEM4y5I2J9JOYJu3cE5N5t07fV+75e1LeTHEvj0tcsHvXtCK9iV4iQPsqLsbBfODQHGXe7PIyCX9K0T8KLlysviUdiITh6TXVOdpRZuxn51baWjGvGXcdU27gIRYSwltbaPRbzmi3hvp2AIb77n7MN/IC67ANoe/1YPvlxDP6Vpo/GtAA8GpsbL0OrPvz9Axcvj2477jkIdqGtE37NUyP3EY3f0fwtD46ljsKEdqIIGPRONOvJ9gABx86s4LZOBhXPFpt4Wvocu0c0bL8TmBz5I+Q4LuDZ6za87Xe7U5gIkwL//JgIWn9z7RiafrVMWOfVmUu1PC3Ni8HTD5j2aWEQ1vUb+XMMog9j9XzTzkLvv/2WD5MpYaGls6sL+WGfq/3uixX6uf75+6/1z92FMlYUZm+FcIAPx2J6Bhdz4MUsWCxNQvt1jOXXKqBjVnbVrKq6i1ikS2Wn0LHPLyqK174wxl6QdKSoSIuVNiyOPmDPyPbux/btjsqS9qKf2e3sywHLJUzR/6oIli4KHMM49HuVb2NuGZfKTjOG9ZIwwyIOu0+EDn3CkWc/6ruPgKa/rhPOHAM0hmPsZ2Tv0ovFKItSl4T7Fnldd3/pvpe1hvZGGOk9WLhxV62nY44d1+7pXr0d7Tb0/vpvD2S499bTb/Pb2Od5eObcfPhpl5Ffgr8xfeQ2omnTox0ei2kj/2jG2H7nytignXHfRavPfkywaOtUyyg+YzABCN9i7HdMUllG8/oo/IjmgaSebsFW33qAPv+/nXVzzfwQD/j84TKMD2VcKg/JLOUFLgPHcpwIrGM0jzO3ydU2BEzkvR+T5K6rgX9QkYnTqrBjYe4BGCSt4pTTpSI8mJAxEZN/TxQbDC893aLFhNQPaHOyM+c4ZBuPlbzRpLWUyW3jZCdcmJzZW43xW3KswVEjgkgbxxw77JYeB5kTb+8Ho/VqPbG+TUi9QFXkWUrbtLNi59OFa0cWCcx9eTC+Vu7ds9J2McYYZGWfldAZeiIkrWPy1hWXcIYx6t0dDxrRe3/TN9z7Oprc1pnq+rbHIQxcv7iEUZUvR5ztPKzL/3XSMZDrhDPt3uMQS/GWX32rPz1BiDBeMfnZRfuNLgszbVjMuPRaGrv5yQ44e6uNu9pRS5trt6hnvJzr/7r9WXywYLokHYLuqA7Mh0visbi7pG2s27mS7hLBsM+jsKOFQjxH79e38j+uLPgWY78XnZ2SmLsLWUGvVTnmuQuPpFx2h681c4n89hEYCWi3n6vkYBcEnIEeCQeOBuwyCOySh33CWAEygLrvYtXT3at94pvCmqAcV5y+l5iYLQzn3DBeuPMyXetf+NGk0vrZZlcGjzD0/hxp8EOXPX3dN4ZntKuzzn9L33TBvvW3j90O5IgBs/uyK6O1T36WhsUEu4/jwQR17rGQpXEck3/jht2EJXlytM3CTh/mJtpPn+Yu3/uOQ8aM0SKInR9Mv6Ov7jlpJ9rLLnlcGoaA6BTF6Fijn1b5wIoQ01fGYqUc/co9RtpuxeLImgDanZ3vhnTP6q7NPUvzz8Mg7kQ1pAvjrh2blrbEbsfomNqsnfwlQpKy6r99vcJFG+V+Xdq8pE76+HfZLWrjMAfgDVoau2sX/byLfihtEdSjUx4XGx0v3DWdXRfO1V+/CLRrHhLuiBGIgHbElbMwa7bZ7RL0wY5pkrm4uLiQT8cwDXgen/j6yrBjCwZfR6AcLfHyUpEPokxQJqqDRLYlkpGATCgy2W8JutUZRibX1qOjgFaPW9omO+FsyS7Gpriuw83E7ix9H7fdrZ52m9+OKXpUxK6yV9/sFmAorWpa+ceAYxpuM4+HSNuqvV20fePSt2+qD87N63WNQ/qXl9b6h32mfFmEIsBrJ/xiWj2mgE44nPwd0vSTHaOHi6Th2KD7W+y7aAJaH85OuCfE3Q/aVTsqPuJPRv0Kdv3OJAYWI9vnbe63dr+vkDk3rTn+jP+jhY85YVs/ToVcd1+002kXrU2X3c/a7NoepnB2wMTVaguj+nNLW2q3WELI9xCNx3P8ZqbTR8Z0bckVBbtw/C2Ne53/fdrnujhDPyMERgPgGRXvThXFDhqGvS+0FduedlPfBjyDHQbWYGQnyMTnyJ4Bz++jOUZmgL2uPN3EhDTl3cQ02Sdzl5XPKWxrws9E0dLYlwhowmMKhTtGbRW0v3hOKCX8zMvvYX1h+hxbxUC7tyAf2jAG55cqKUeo3HFwF3AkWJaXqEIAk2OhpKy3om56HPKojruM2wpr/nVM2YuNFqdg5PU9d7rsJm8LP9fdwsc6v/LwSeVo0ayMxWqUTwsYHmLwcu4+mvDYZ8gYa65r6dJrv9kdid5XoBnt4In7lPVN9EVjuLGzx8mC7D7tQVhx9PESBh1J7+nrvi0gWAAghNllhQltMVXffXQFdOTX8/6HFMgq2kt1jItWl5mL5TgQMDgfR06Si30RMEj5QcV949k3vIHZPSIvNRnwDHYYWE/i7hv3LuENvBifXcIuDTNiVgzEmPqlcc31P2JO1oV1REZ+1rnfNl0b7o9L3Uae7GRYOLC4YRcJA21H4K4JYe50aTO3UQf7pnkt49DMTOljH1B+R6/OFXmtwgz6GRMPGjgqinn0cEcvkKyNYEcHu8GPrLDSL2O2ki8C0+wAB/BofFC3bVTy336z+wmRfcddcYjrGPQpCYuj+rhODI3X2sW2NFwHcN/SmOZuGiHMjuzSdr8tnTnuN8mXzMlP/BwhAhHQjrBSTjhLVmqd0XavwVPmuxTFROQ3yPpnlneJ6xjCnDKTewz43XQePHjg8rajt45O7ZK++2ceG2HuEv5YwhA09mVyb6MsxzAOeVjBk+KOAC59aGXCDPPo9VJHk/3eE0Z0ctvVtGDmZdM+vN8x81tTPX3TN0FpDmO8KY5DuN0Gg32IfPdxbPo+JmFxUz65HVt9yI+ffHBk1U817ML3WjCx4/ZhChgdBG4CgV0a6k3kK2mcHgKOyHipzY8uzs29lSx3MDyD6zdTHHHzSITfSzqmF7TmlmfkzwqzleaRW2jHhYDjZtrjksUFxx09/uD5fs89O94jvJ8z0L6Pq4Tnn5tjG4fcAfZbTIRGC08WoJbWgjblJ0wcm8RsLg0/+Xdf0sKBH4LGcE50JmHL7y060uv7lDRh+JTym7zeHAL6i9dLvd7LPidl90cdOXZM10kKc7i7b+5x7vqwx5x0r9lPoj81BCKgnVqNrc+vuzq3db/I0T5HB9YdbTDgOfKIiSWIYVa8LOZiryfNPYPrByD3vTOwHp2bcRn9hpAVZszPvjmwek738TiG19NO9dtR1NvascHY+42edUdGMYEEsfcucAli7jWpWxO3Z54xANr4bfXBytadV8c8DnnZ0MKTBSht7E2rtuzSLhHivcLo6OQu44mHST6+0tSOPX7g3ll9rigv7xmL5zKy8u6EwEok9fEVpeXxOrRHHPp5YjQGGiulX1mJumEERvXhTqD70urk0NoCqN8U64spHf1Fv+ndpm+Pr3jh1O6xxQmLxNoOXsaL0u69uf99W/PSlM+YdxCBkxLQ7mD9LCmyFdF+4hLeQMO8Tv12Fbk7OmWsKMwAhpYghqnFxBLEMCvcVjw3HwZWzG9DOgnriFnxvLYJZN8CuMNHGOjjIdT0tFP9thvVl8fDN0sugO9Sdu3NRD7qKx5WeXBFaheDIPaF+dxeDQAAEABJREFUZSeIeTBk07EjcZroy3vUDSFwKuOQhRy/J/m2hYux8VnLJLA9pkzjeBlrld+axEiu9TBwIJx9ctGn9orZdFxr36OO4nEMtqJeUXYcDjHmrUS64WP0Sq6Hp2C7IdhWJ+XY6ikeriAwmtvdL77ucbzPiFeVHW3s6b6/tv4RGPUlO8oefCKsjXio8nqp5i5eXAaIJQjsikAEtF2RO75wXkf0oEGfMwNQT1vy/Ybl2UuILqxjIByTcZH9LxedsmrtIjt7q+3o2S3D0PZMd+tvZDe53/TkOMrHUprjTH0Yu4UuJ/f0pd/uRhES2nAwJii0tFO2W6kkEPVlwGz1tCXffufJkVk7B3Z6vQTmhbhJgNJHPOPcx+nOwj8o4reUnpjbss5SmMN98z0roXi6h8Apj0O/WyUgsBlHLcL4uRQ/N1LkK8q4+HpXqJsJnvQnTLW+CImYV2NIS7ewsOSo42jMe8GK0C5hGTeiMNa9oOhBn33GXS/97XoH9UYKfcSJGMf7dmVnyimFm8y211AtfvRperznnYr426WXqtEi3tI44j8IzEIgAtosmE7Ck5Uf56b7zPphT4NjT5/7PYV3Yd2PR3tW3CquiVwcmFsrVeytdrndDwy3tLl2k7tJfq7/Y/HnGAdBuc2PHaB9f9cN1nZv2njZvZI5Wj3mdoraQsDoeIxXQWGwS5mEe9kKaAXXs8kuift5B5P3JHQ5dobxLW8r6rPqa5SfIm9VmENM4n2PMa4ZgXMZhwhSfobkPQsviwaOh5d1RbnjuELY8cNO8GcOwi456uhomTy30ViYeImWsIPdEWKCJWbfIysW+vxki5MYfXTwciqjpVvMGvlt/WyyP0s5euCljKiFCBCY1UkfzMKC8binz/0W1m/6WWybFowttllEVt9tPBZ5X6Ul3LcL96iyT2N/WWcr6fstt9kB4jEI7INABLR90Du+sH7Do8+Vhw9evCfO/LYLYFDtvduJmO7aOCYwEgC/rwL1E3eRZin5Fe8sz0fkyaQ0WlF29AqWu2YV8zn6MdmfqQitvpdxFsqdFo909IV59SLAoIzFSjt65UGoNp0XGbjb9W39DLxsJGEOe6ZhY4A47oWAer7tccjxJ0w9QcIRQuMkZtUCwS6F++4KNDqGKJ1yatRuVuPzZ1fQJ5buld28t+mJg+9fKJqFqTIuFUbWq5D7HFN3+gKWjnbrSwRWL1COhFMLVYS4ywzct7xjmXZWy1is7LDf9I7P4kweaQBzEuG/z96bF2HXcbyCXggrDott04KxxTZHFC+6P/OtneiOfPHTRdj11Ikjmi9T4aOCwI0gEAHtRmC+sUQ8D26yahP0GAeGYZfJ0u6ZF8ja+Bwl8RsiE21dvKNz6FOYTSam1r01k/wmf8fo5tiQH5rt82YH6I174sxvONj1Ge3wfFPFoT7KOBulbfVlUnavae1SSIxez2jpI/rKFN+I4cW8jnYvpjCbTBO5F782+YnbYRE4hnFIvX9nFYsg4TeW7NjaTerH0PIyS1kEGx35nRV4pifM9EeU31Fbd2d420kGO1dtX6qo7imPLuxabrtXjiXfi6j5536eO8wN6Z7VeGEsvPfR/LM46QeJG9IsK+Y+/beBaqHV2GlxoQ9mHDeXmdN6t23fwggrjt6vn+SxoNbS8bajMUFbkb/W71y7F6qdtpjrP/6CwF4IaMR7RZDAR4WAlUwMQp+pdynCm5Veoqw8etGLgNeGc6TlSQ3BwDga8EbHHptgQ6tB+GHl8ialT1V59alnqpTLvY5dzq9jdFzy7/FwVGO0Y9r7O7Vvuw4/PMj0hxYNw1XGbEUwcyS3D4DJ01cmOsZvsk+mo6kjZmByX2cS9qbfv1rnJ/TDI3AM4xBhZfTbdxacHHldWmoPDHnltg/3sz1hz+/HV3jP+JexouxeGbe06RWH5gPD+2X13Qt45g33PfXBcp6tjJUWFEc7ZRa/jHujyJzY6J9AF5eXK5fkQRg/RWDXfpROaPMQGNWHkOYycxr7Eq09+C3APoyHuUY8jwVi1z56/9oCAbynb/s294xePt0WLu7bEYiPNQhEQFsDzImSTZZ+K6e/oKuereqO7jGNimpC/rRyGL3M6Ay4naJyvqc8pIAxuffR/LOC7EhCQ9po9WCDu22bBkEXfl3e3hjRLTs64ji64O9eBwZjdJxulGWMgpfdPFmt/no/n1OEEe5FPmllUvXscS/0u5fo5a25+FlgwCD2QvGfFDrus+grZb2nMHbt9z1i/fO7VeIp6yxl99eRm03POu8i9M1K/I57OoZxSBsa7eRYdTeuuRczt5qMwX4Ut28vFhO+Z24kM/3pawQx/aAP4q7mtqOOFjy+uQ9Y3/oePObOA8Y8RyNhVcFXlH5rXIDxisP9D8fWvFR5//PSkAcM/LadQAE2pc89ej4C6+rDXGZOM7fBe06Mxnxjvzmg94+vGT2Opp84ftv73+U0C+FQOx6lL34C3zo37tFBYCcEdJadAiZQg8BxWZ2x9tJinysDiGMHdsWesXdsvp3bdsxsxGR+b/n7ttKtmi5xtzR2AondpG2Ts0HaxVvHZNyH2NQmMTijYwvSOxaN2bEaPWJ2YGL1226QsqzLsyM+mJHHlgc7OWWsKPXgN7tWiGf04UU7E29fJPjZobDCThjq3X1rTy9VFndSRgsMVvu1tfJyqUzkBOtLwn2Leyj8e1nvPmloWFxwR9DOsuf6h57uE5cIfPeDxJiBwLGMQ/rm6E6X+1A/VOVwn0obLetaZbcNQzgagwlC63aR1kY4wwFDbXej3wkTdNtRR7sVwo5exfNAkrFQv9g25n1BJeZxqdEcIA/6dHlZq2DjuFvvwV02Jz/cBdZXe3ffxtxN6fMTvQwBv/OHB+hDmdPMbV6Fxm/07tO3RQr9xphv7J/ok6mfmWun79YkyONjWhq7vmdMnyMgahPid5/ObrbwI23RWJlGbqEFgZ0RGA2EO0eWgEeBAAHBBXUTfJ8h9f3IIvq9KUcQvIDkpTD6E4pu4r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IR/fQbLGa7Tag/G+9t39xrr73Chg0biuU1cIx0ikixZ5OxhsEbLGJY7ruLMnNpS8Vj7xBh1BflIvJaPMxAGHy3+6Qlhj1BWPnx0w8WFCYF2k+7mchfeOGF2ivc7W53C+zJ4SCSlZF8Y50knP1cfQkrPkaq0EAoP5FQUX4viIEBwdfzmHQAMIMrGrOITPqk/yNMYcTHPOYxfZHYK5JavuqLmPFBpyyzsnBiglkcjVCWDZ0cixSzAR0O86Ac6U6lw6Ob3ffvfve7i/0H0Y83CgCKDe7cB1rBiU2PVWkw1dN+Nk4ToW7Tpr7R3tmMCb4xDkuCCFXoiH5l7xQ2ZXFz/BDiKJ4I75z4p556amD5RcdFuaW9oE37WzftztIMliZ4XYezbKBnzDpsnNzwMJugRdL9U0QCcTDfa9qxMDBZ0X7DcNMn7Bo9yjFCl5l5XRnEIS5pYlwGRZYu6MvRL+cNL6B0YKFJxWeZ+vGPf3xpMMo1lh1rgY6RRSSgPCMjox9vFCgsQLhzH3iQmTZbAUTS7QlfE89OIJm8oiiUlddFavkzsAAAEABJREFUm6AUIv81PQzKKKcpPIkrIoHlDSzSfDd5uiizCX1VcenLWKCrsGGSzUQaOanzwroNz2m/6GZiB+bxmzdLmPALm6/5Tj1MPOiLjFupONp/ZAoNwsDuNdAFWzeVsx0QKw6d1cZNfdPRaBSsOjoOgw6DpvZr48ZEjulZp2VJjQGJ46naX7tZxmHGpP2e8pSnBDvb1+HWjbKEYNN1Y1ZEY9u4Vd/sUK+iVadFSIKp9sP0jLlb+83UjdBh5qzzYZnCKqc6XLvBBuFDW2j/Nm7WeFGOc9JikrWWFDoqsxFoyslDZGpAYolLx2cA4JSF9hs3NwM/FiiR9OAXaRaRYl8Nm6ajH2+Ohw6jb5JXfOgT9I34zRu5kDu5Ij5xrUJPH6YvE57zoJRzxLWOFxhAWOKxeSJDmfDUKdeEW+UCxYvlIptn1Tenm+jzVXFiGHVikkYdox8TEvoD7+gX37PdJv/4xz/Ce97znlh88QZPNnmDV+FR8UdEAryNnK6I1hfURZl9BMzgg9NfWK5E6vsy1ku2quji2DuIdUr7RTfXjtixHcuklQUxvn0zXmHsQOG2YfZ7BArNVBFocGy2mvqq/gsjsM9Gx2KPCkJepB5gnY5NzbZTck4fDVLHa+pmwNVr2qSng6CI1ZnEOP2irTMiUzNWBBn55D5ssly2bFlfdMqPJsO+gJIPGMMKvpJoPS/2T2EW7HlMOlhLpb0mnUP7/+EPf7hvvR9cwVokv+2xODGznClRHKUXySsXnuKory6T/UcsBWi/OjcC9gUveEFxF4qOyyAwbOVR5z9TNyZhTNG5+bBsaNtoGH1Tl09foE9oPwRnmcKg45S5kSNaoacP05fL4pb5ceCCayvKwqwfy0/WD+sOtFv/sm87g4VvwKIsbpkfiglLW/BiWXiZH3LPTjpQ+pj06PjQMdttAg1f+9rXNBmBrQ+5eJIQ+YxlUSRPHnRRJnQO46F/oKTm5oUCpOOm+I2xAvmu49IGlKf96tzcCZayYuq0I1No2B/DwKQLS7nLGIHjoEuWLEklSfpz8oi9FToCYDPj1X5N3CgzzNz1rA9tkf0PzOSq8uIcv913Qr047laVriyMfSMIOR3GgMqGNu2XcqMQNRmAEG6YGFP5DcOfWST7YHRe7OOZmJjQXrVuEQl2Xb82kYmAAINvjXfyE+GtZ6PwHid/RPIEoM6YTeooU9qPI46a53RY126UXUtvDk0sIYJTjDvTvhnziW/6An0ifvNmfxrKFO4mDxsgbT+lL9Onc/KBj8tObJSlRaHR1lficD+Rxgq/1JMbL5Ue5YuBJhVe5k9/sQoNM3GWGHT8LtoEHuAggaYDa0tTnOiX8IHOJ+VuVWZJZk3KLEne2EtEAoaAJgmrllB1PkyA2Qej/bbccsvi8lbtV+cu47WyNCNTaBg8RfIEOwoNFdcENunMOh1uZit6ZoUfyxq8mz4cM2QTlB1YMGHnLIdhiuO0gC4XhYYbGLVfrpu66bgoBAgM7ZdysxyCUpQKt/4oM7lWNps295u7QdhLoeMjXNvgw2yYgVbn1cQNz4BRThoGYgSYjsug2VQRi+mpL/WO37xZk2aAwD1uDxZYrKhN6QIfcNLp2vZNnUd00xfoE/GbN9Y73k2fMv6nL9Onc/KyfTUnjY7TRMFgA6dViHRedW4wylW+dF4MhCLTcp5+YSePXbQJJ1g1nQyITSYrMS0TQMay+F317qLMKnpyw5A9TfsychZM68pgbyxL8zoeCo3INM/osCo3E5M6hXRkCg0bHquI02GYJOkI2g8tVX83cS9evLj4GQGdBoXElqHDy9ys7bOOihDT4WzUwzwrUt8oZcoat8KyGQrNv+kDPZoW3Ow85133NBWwIhLqGKiuzLpw1l3BWcdrq0Qxa0Ap0Xk1ccOzPDlpMKVaJZxBHt7LSV8Wx9a7zT6IsnxH4cdS5CabbNI4a5QEqzRyZBg8G2dWkqCsL7AZuWk/i/HZmK6Loc3p09ov5W7a32w+WEitX8Pv7OgoNNmRVUSUIDuwIWtVlNI7cUbdJlZmIxcs32kaU24mgLl787ooM0V3E3/aEIW4SRpWKETqxz8ONthxF2tkk7JiXJRLZHz8LnuPTKEpKyzllzvjSaW3/gharCDaH0HURGgymLCBzB79ZMmH2yNnImw40snsu82DRUPXa667MQuDta5HW+GK8Gk609DlNnHThrlLD7n52nojCFD2c9PPZry2/E86lBpNKzhSV+03TDcYtulrpIG2trRQ17Zp50o6BkM7yJQplbY+o24T+qcuk4kKj/bLddt+mUrXRZkpWsbF3yp5WBHbKjRMFrEmVdVtLBQaLjaqInK2w2BMNjxyFFuXzYwLvyabp5jNxftfdF7DdOcIkGGW53mFgHLM7GOhYkFfCGNY+VH3BfoyfXoMq94JSVhn2FBcVbi3SRU6HjZMBMZCock9JptbcTZq8lsaufF1PNJy1wyXm2l/tEOOmHJSSPvXuZnBjHrZpomCVUfvOISjUI4DHVU0YA1qaqatyo+wuVBv6OQZtlWVPIfxDFuWWJpEZOBWchtnIX2zvGQvVLNtYL+HjY+It8mwMR1Vfoyv3Ok0qvzHQqGxJsuZVpYOxkY0nQ/7E6ypW4dHd9ldMygzHDusO9EU89DvshkMl6bRsMN6+OVYXeZccpcpfNeaKRtXA6vJbC3JsZyQw09NKmHrDe+w+a5JHrlxWebjGHJufBuvrVWVJRyWcnR+uX1Tp0m5rSzBxM3PaQyrryGMOamVKn+h+WOxot/pets2sN+z0Sb0T00TvA7Pa79cd67y3kWZuXXoKp5VZmkDthm0oadMebb5jIVCUzaocceHJTb3m6UA1mh1fAYHkepNTNyCy/0nCK2Yls1PHM/mqvro1+RN3WyHtgNXk/zmW9yyjV5t8UHw8Dsgs4ERFhq7WZDTWnS6tuXbemPZqzPnUxZCwvI7/lUP8cGrKk5VWNM9aTEv2sdixMknkeq+GdPXva0AZcBFHtSlW+jhlvdy8QBbMNbx7XJkF21iaYBf2ywVoghzUkfXL+XuoswULePiz4lGkf6+3ZbXOEBSJ7PGQqFhk5AdHLgUqe1GQX4Uzx7d5FbTvkY2H9w1s+OOOwYrbHOPZ5vsep8oNLZDcy6fAaUXaQE72MRrrRAoBljZmsLCTxDYI4JN88iNj7Kx2Wab9UWn7Fzh15dw8gN+gC8mnb3/3MiJ9aLnkXAgdFFqEsGl3mUDUWnEhCdHcxEwieCkN8IMpUZHyN10qdOk3CwJ0zY6nN/M0t/uHkQAvsV6NhhS7WOxxfpCG+hUfM92m+QcJ9c0ptyMB/xOYCpc+3dRpi5/HN3IL8ZATRvGCmSW9stxI3PqdIKxUWi4d0ZXiqPN9oImHZ5yU2F7myedqeougdRdM02OZ6foYbmK+1F0OPcV8ANg2i/XzW/UsNOb22i5dZWr0RnIc9OPWzwsNHYpj4EdAduUVnvRXdP0TeNzqZjI9OwD3uNHNtt0VvgBvtA0cLQf/tF+KXfT/Td2IErlm/JnSRfBlAov8wcXrkHXYRyntX1fhzd1c7oRIarTcVLRXg2gw1NulER+Z4m9UkuXLi2uwme5GP9Umrnqz+WWtGkT+sEUbHUalFOO9Gu/LtoE+chEWdPBL4U3bbuyfqnz1O4uytTlj9LdNm8mZfYKlksuuSQwoWqSJ7KV38mqSzMWCg3me/vbEOyFOO200/quxK+rDOEXX3xxYB8M7vhwoVLqkio6JXe7MHOM8XkP43g2+YhIsNc8s57LxuOmgxB7D/jNImZSKDH8QBy/V2JNvpQ7Vx4RCShmItIjmVlR6jdhepGMA+V37dq1xne0n3RUa6WhPa644opGBcMH8AN8oRNy1TfKuPbDjaBmJow7PvyEQHTXvbEk2T5Sl8aGo5zwA5zQbsNS35x2oV11OJdsMeBpv5m4wQZFU+fB7b4omtovx83PsZx33nkBWYSSTX3Xr1+fk3TOxcFqZq+or6sE2GDZ1vG4rdvOyLtoEwZS+zMvTHjYT6XprXLD46effnqw/TKVposyU7SMiz+T7x122KGPHJTEHOVEJ+LyzZw+PBYKDYTzQ5Z2ZsWvejYRvHRKftDSMiBLSXZJizJRZtgzY2cZrIU2PZ5NfqmHGw7tkhe/08NDp0ml0/4MHEcccURgOUb78xsXzAy031xzc4W+tdIcd9xxwQrLVL3AhqXB2b5Vl/VhlDFNF5fE7b333gHe0v4pN+0PH/DoOPQFBgftF90MGHZfFsotCm+Mk3pz+u/4448PCJVUnFx/aM7tn+DBVQjsZYj5i0hh9WBCE/1m+haR4kcw7d4jfiTSTlqqysJqy49T0j463sqVK+ftKSesT7l9Dnz4VXqND9ZE+7MzYCdS1ybEqn8oM7dNmAjsu+++fb+Pxrjw0pe+tFBQ60sLxcT4xBNPzIlaxOmizKLgMf9TNr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index 035ba27..4913d6d 100644
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diff --git a/figures/phaseset_motivation.pdf b/figures/phaseset_motivation.pdf
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diff --git a/figures/phaseset_motivation.png b/figures/phaseset_motivation.png
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diff --git a/figures/phaseset_motivation.pptx b/figures/phaseset_motivation.pptx
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style=&quot;rounded=1;whiteSpace=wrap;html=1;align=center;verticalAlign=middle;fontSize=18;fontStyle=1;fillColor=#D5E8D4;strokeColor=#82B366;strokeWidth=2;&quot; value=&quot;PhaseSet incidence moments&amp;#xa;distinguishable&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;100&quot; width=&quot;420&quot; x=&quot;1090&quot; y=&quot;590&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;53&quot; edge=&quot;1&quot; parent=&quot;1&quot; source=&quot;50&quot; style=&quot;edgeStyle=orthogonalEdgeStyle;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#6B7280;strokeWidth=2;endArrow=block;endFill=1;exitX=1;exitY=0.35;exitDx=0;exitDy=0;entryX=0;entryY=0.35;entryDx=0;entryDy=0;&quot; target=&quot;51&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;54&quot; edge=&quot;1&quot; parent=&quot;1&quot; source=&quot;50&quot; style=&quot;edgeStyle=orthogonalEdgeStyle;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#6B7280;strokeWidth=2;endArrow=block;endFill=1;exitX=0.9;exitY=1;exitDx=0;exitDy=0;entryX=0;entryY=0.75;entryDx=0;entryDy=0;&quot; target=&quot;52&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot;&gt;&#10;            &lt;Array as=&quot;points&quot;&gt;&#10;              &lt;mxPoint x=&quot;700&quot; y=&quot;720&quot; /&gt;&#10;              &lt;mxPoint x=&quot;1060&quot; y=&quot;720&quot; /&gt;&#10;            &lt;/Array&gt;&#10;          &lt;/mxGeometry&gt;&#10;        &lt;/mxCell&gt;&#10;      &lt;/root&gt;&#10;    &lt;/mxGraphModel&gt;&#10;  &lt;/diagram&gt;&#10;&lt;/mxfile&gt;&#10;"><defs/><g><g data-cell-id="0"><g data-cell-id="1"><g data-cell-id="2"><g><rect x="251" y="10" width="1000" height="40" fill="none" stroke="none" pointer-events="all"/></g><g><g><switch><foreignObject style="overflow: visible; text-align: left;" pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 30px; margin-left: 751px;"><div style="box-sizing: border-box; font-size: 0; text-align: center; color: #17233C; "><div style="display: inline-block; font-size: 26px; font-family: Helvetica; color: light-dark(#17233C, #c6d0e5); line-height: 1.2; pointer-events: all; font-weight: bold; white-space: nowrap; ">Why pair-bagging is not enough</div></div></div></foreignObject><image x="552.5" y="15" width="397" height="37.5" 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48tzZ41I+4PGHPaEqEn3HkTLAjIbmeSL4fzPUMV0lmOmPGmeB/VJQ2BXc+w+Uultx9M84s2p+LEi0bu56fSnfd1MiHDujE0E80KG/S2vVUYjpKj6cbyyuWts9W5pf6vn93Kr33sZaNnZ9cdVJX63SHLscahlemJir86Rk/0HFLuvOX69ZtLuuTPSecsnmthm9QbaAwTXdXifC5MNe0e/f0cAY87lmwMtaOykRhFihPNWi47b07L2fiL0oit1PD8KA2AN6ReuqFs/OfOq28sYpMO1XvVLtjJJEbIfLuEKHP673yerVP0xCdqNbioT9FzXF69sSCW6HX272j8tcvS+Qe0bia/WJq45anlLMDo/nvG+pX6HyrMguntUasmGlNd25S2SfUBvSz0Ll7wuRm6zu+/dwnaUPws1o+Pcwi39Lrz1ZrTPXnsGkV4Zfa9dx4437194bUyZ3n6lVWuyTNmvSWE/X+/mTqQNOjPvPNyudftEw3JquLAGm1a+ZGPLnR8rnmmV80p7ecV858WSQcxU9NlSkbu36ldc5YTnIPSodHIwAAEABJREFUiNAPiPhtRGT5we7B2XlCLx02dn+am6my5l2aMX5kekxfyvS2d72wTQchqAo+azZ0nKod4//WtHtChO+0MkODZfG0+tfqYf172BxXKFuY/5+m4TVTaVcq/9u9lkp3xnKPHg5JDAdWj2tZ+1dqd9i9rHE70iYhmp3GxkKZ8Av5SP2RbUl3XWLlxkLBaUl3hi4TTWYhnfNdc2Q9UXa2d/75fHoXOm9h1fSd0BGwXWUyC+m0a5Y3Ve8X9b55zNqAE3mTX6jXNB3I0kMPDxu776buuan6vZA+Vq8edrWIB/WY3xYDp9Y1Lm1Jy5/3x9F/mBPnyTpD8/QvNa+OWFnPRD9i4ncRFfKr1QfF6kKa/FhZcyIxnStCf0PCXyFt+1n5qvn/Ti2f3llLA+yTcQr0Y+17jWOPWkufwNbyQAkP2Ool1ft9rdsOHW6XTNThrXNktQ/KRKeStputbcdew5DK9reku16bqpL+wfQwW5k2MfHVebPlE2tLTZSP9HJ1Z+Wg5Sk9nGNaNY7Wl3iTEH+dxevXPKuTwV0/18mNC0nz8hyJGE5YeinPOenrjeYeU/V/rDa0EaK/KqZzzjnHei10gMooYHnQ8qKWBTsKzJiuYStftMxQb+fmZz2pppDWzPS3Wq78IJHj3crGJrq+2NLedbpeZ7VlNy1lav/UW/tgZkJclJgsv7T90/mwpZ2lIVk/ZCLNi7WB9DLZWMaJevBCEf4XEbre8oum++5Ue9d7ra9GFfy4pL2G1coJp/GA+aJ2pJ4p9P9Nf2BrcZhPb1zn0TYMQ7I6xpJnd0Ao+7urn9AJghvDRGWa22OIJT3tf+BDL+dtUMdWIOhPCCN0rU/UqxI5taGMx+T0Ho316ztXktApoTybdMwsvx0+qWmR35+hBbMO4Gij6R5h+Zmm99mTwQv70yRM/5BIyp5Uuutzx+vgY1gFU+7FH7teOw87pv6H+dVG1LnjyzgdRias26Tv2QTWSWHlzL1PcuWDD96gg+H2r16t5qmNna9KHWjarfeGDkKJa55ao/ng0/TEwUdadGDKKpXFIGaNy5Z05y9sUE1bmP+m9tQyhMOe0ngzsf+7VPveO1N6TxLhqacpzrYCuIEatC4qDFzPqaum3BX5fSb59OtUunO7pWOR63V7ygaHWtJd/5knf5eVzRpRp4leLVOfy+T3FfJlesufqB69BfS7guZ4rU+0s3iplimPFsoUlgvV+zD5QJ2XNB6ZXpGrdBDCOpbvN39LSpXBgeXVVHvn1XmPdzDxa9ULGxDWn3DG0q7W8r+V86l019vGlyWGmOgbal3L29U2iNQgDUN6H7zL9IajB9fFCEwMVHT92PKmXn+zWqdyReUK6aP16rBNVB+v97ieq7ixfFGz+a3itGrHQ8tP0+sMLQufo6F3KkdVbj5zhpZP/1YYYNfyunlDl+vT+vPpr7fzkxMZXTdavaSRswF+T39dTFrL9//W/sG+Kinf2dK/ZbKvQIWJL3qGS8RmySgf+VMW71fW/rE8bRMms9zg7yIQsDLG6i7Ng3+wvKhlgWtbZSr0hX6giNyVau+8O1WD/cB6ax9MJYz92sRVamPnO3S8bO9k+WXtH0szuxzFriGRj1pfTdsiN6UK/Sz0/6MAjUMWbcMwFHXcT8srvTeqYixZK825gdcZxFvnng12Rmeeu4O5nOkqQdyhZ5Jqw5iDCeYdDckGGwi/N4yguRXhJ6/ZQ6ELpkMJWk9EbpWYzzdTT0/oyRf1LxZj22yk0kM/0oL0KlUY4ySA/J0OIjygBf+fqV6t4/U7hBneeeOw+HRdCJHpTlcysVO+m65kgWMWoRcvcH2hS0Ps8TULOaj1ay1t3W2ap7YR03c0LgutetPLgU2DNhYv1QGuO1o2nuc6OVLwLMzX8TrAoo2Lz1njUjvDzw0jG8mt0Gl2TxpH4xlJVx0I22SG7/s/I+PiHp9nkvg2GO+uoXYkWTtZr9DJ5R1637wltmAX+PvXpdKdP1zd1t0Sm94FFWkjSSdHrT5hokvUqevgqYqGMubPh81f13oslG+Tjq0B3ZLu+oSVOZrfL5g8HcfPM1XnHar7knKt8owjkFa+WzlPJJ9VfU9SG4dp0vvgsvHGxPZVGzudFp/EEYha12F5s3lj179MTGTIC2KMj/Y9+G16r2WaN3RYntdbPUbtC6hCflsATs1e0joj3fl2zU/7NSNVrs4QuoA9uV3ri+/rvXJ8zeIrU8CtPd2S7vw6+fRrLd/PjtGbifJ9eeI3NqEQo97AqiYXIPzK0r/MfYUmy9M6Cby3ZREXeQUGU7cOL0oo/9dqGTNEtiqf5jyRSJE/1t4WuUr7gTdY/oqsr8wKtMxrqvb2gSsCi1sq3fU2nbjao2Mbn1Y9cY1tqKrZxhaAWj9rqCbSfXbo6+U/2obBU7Iax5K1UzE3AolE4vd6Az8890qQM3y6zWgGcTnlpq2te7kwb6Hwn7sbxmlgz709D2l4K/cejYmnUI4JH1zScXFynixy8G+GiM0A5yT3W21Yxtkxne7HkzQdftrS3vkBhwEUZSPfU2WOkz38Z1YBqXzs5oRTNq9h4mc5Ku7Jrk3ucpSterGWjV3Pl0TuLs1Tm8sU2LSw92vtML6j3E8v2CD6xBMB8ndliksAtbLZeKbaO+we5QACdeekdcN5J+lkxo81YvaYvv44mwM6qNnjLF0jglbupTZ2fJKIrfx8EpXn82IvkbvJGnzlUT+h1doOzek9X9F6xCZHyxWXCc/m/36S+v9TY2ps53cW/YpNXupg/lbNp/8YXVtRDZ7qvrTlQNP3NC5VNuCmg5AbO99h5buGPMbFFaptyjCdlWD67VIuT6dQBPw97GwqbzLLBw+fjP9gFXt8dXO68x9UNasto0F+KyPcRVNt5VpLeu8PNACfURv30xiqMoBhukjL8dvKXT8GCEnVODEWOvh7pw72/1XZAqUDwDqhcEvzxo6XlM2PWYqtjZJKd3xWRO6KeQHCLJ/m/D2yyKu9y2lLqDkacSIQgdVt3S2p9N5faVvqv1XAFr7oTzmNnK35q6oXo9Rf++BIerbo/aXl+R06rhHnIpsjHsx7VEj3u6x8sXJmXme4EDMBtA3DAE2lt/xJNY4lF53QaBj1h7SitpeDh4njlNvTkgfH5tsrcsrNjN+RhtwaLTiePuNkoD9yw65dvY+oUy37Zav+hjVJdniPhsrY9kNh/TL3e5Je0p4mseNKWm5Jd+ggrW9PCqwqt8c6M/GBlgMr/p/6E6qDmqOmOzTfOU34aKO5bNtOefnEUzQu9lSO/sw2Jf6zfHcxn8gpEboolzVPdb1WWH6iSlaoLafxdIDx09ppvdRhoixQuFo3djxDB9G3av47LZBAeR2tIOEr9Z59q3oT6h5S9zVtdHCiyU94/6GROFltJKPgbmwYlUwkJVUubC/gGmtMfJPsHQrlD+uJOvjcV67BA4sLP3HwCp7Ybqn8sSnlgzI1thauUk5drq8++byzdPLS2i1xbFGxYBC0fnyJdtB+ZatqFnRYqYubNjUUynOmTxOVYaWjKp1mlmx5Oo1BqMPm9vNsYv1mFSp73lQ/iIn+rSXd9S491kP9jtsgv8VNtCr0WXlm5ZqVb1UQIKsfe1o2dj2/CsKymEHglvbOi6ytoIE4UW25TQMzX968seul5fbIFj3RwYO30MQKfVqkT1oHPHpS6c73l6s/tEjxqkpvrZ1mi3k0cPYeM/2Zbsp6XFiM0vxI03fK1QZ1DX3dtQ+mQGg7we4ru7/0VHkW2aji0obfZuVMobwp7RguohDQNEdfJDBAHfer3rFkr1g0CpMEQrcXuxbgXGrMH28L4O6IkzzZStzUkRPBjnTg/PDq2wSxNjLoYDDJI67Cvkejra17OZF3Fjl9+PaGHO13EnUVEuHmdOc/CPHnVIWntiJGSD6mDcxQq2YeGbj2UWb5tmMAy7XtlAaJnLbRIqYdks/d6BifqhaztNU0/qoGsoJ5it6ZOtD0+bgb8a32RACzrQgv+2Sf8gpqtDHLnzHOQQVq3p2WVbllybeRFO63GKIjP6vnd9cI0cplfOh7zPSyGGAFVVGewQNtVDbyoU9onCq3zVuAGBtbC1fcZU7Lhq5zPN+7XoNQiQEf9aZgnjEuue/YasPCv8X4Mj81ra0c17R+p/2tkPWE+DM6aH6x+sdqYeYhkNrYeT6L9796uaL1obYnPty6oaNT/Y3XIL/Fy7NKtFk5ZuWZBqcik27qTxCzQrQPY+V7EMd16EYHPLouFiF7WrSST8t42lH73Or05vZyMbWJKt/3byGhalj0ZNH8cDUOdlvA6sU2p7ectwjttBn4JtugV65bt/n4GRcW6U/dtQ8mOdqkkd1P+vfDahffaDlj5Y2VO4sfmDoNAdqGwRNWqn8sed7BSO1cWGc7eGSPuExqAXzGkb8BjlhsO59wnUymhwtbY02qt62n9PButaGMplGo92gc5JFjicSxQePfNjjYcyhMACO6ZZ/5jQr2soh6XMS1gUkfX5PeEmrARkhskuqAi4dE8W871bzh3Gb2yGllhvh0nb0bhOrs4x3Kn6udh69otOYtP/RaucwbWxye/pkvMNaI8T3PJmYiPxEwnx8Rzus9JJfZo70RdNSMqHh8rt7/74kpwHW/3ZSW658WosWYALB8+ZUYB2148p5+Y0xpH7eav04dWB5qcn6hANjTYOLJz9VN6Pd3qUwko3mmy0vkvu0LV9zvQsC1A6FpbY/xL0Za66SGfLV5Y1dsaVmIUx19Fe5ppss1SouRPxp9z/vk2tPOOUH9j8cgv8XDsdq0aLpyYvxjTNRVbUHT8KzU8v3brRvOO0mPl5SxslXbcNaeXoy+QasniX+Ne/GBJiCn0h2v0ImqSjyNrt4dMaWOdKzlZY186IfWjynlFtfDEbC6kMlflHZakZA+a3xZ4geLnc7GhOqpfTAJ2iaL9D660u6nyVPV8mMT5D/RcrXsT59VS4QrFg5tQ6AvEph2TYwlz9voSDDb1kihn3iYxPNM/dW2pn6XMKtP7z6aiDso7EfojlyD98CU2C7besrtqZJTxnPjgScohJM28Ll+yt8wvwqkN4z7GNzaXo9vUj3zprNeK6OR9jz55r9GPZg32RMbd+hg+XXBXM90pYN7sW87xdzwFBI6daZPgf6JRvoqdanB0u86MRqZFm3QfFGjsxgDHuqtTic6PP1TEJz7xdqIsb3rnSas5qory5mNksj9vWrW7KTf9WuadALwUo1eXPnqdn9Z46Dqq18jtJgrtmIbtGnd0NGpgyBxrIoqV1rrvcf/nEqf1xzVAxus9dn7guqp6Op39W+6eTaxvF9PaLz0u4Km+dEVr9C0fksFvZztlU3GfU5rEdtGcva1Jf1ftJ0jXuEJ2bjKYAeecvb4WMNFDoJFRZDfimKp+ZPNjyx/A1fL1oTFaZ5sW2fqQJn1wYq7qLOzwtSlfbfFWug0QZPppc0PL++e+BPPtw4mvrtN/dUAABAASURBVISI7QnuRepHU6nPs7Ufg0mNUpRCXG/VycjFrwvnBNjS+RNlmLCb41GxE/XYPrB42iSRTRbpsS2s1p+qM9Zm/YGWQ5jUiDFp0DYMBdPaMTaWu0h1YKCxZJo3cJxI3q/RdRoQ0obmmevXdwbqFCXH/RN1gNShcyl9++7ueVzDeNhoR9nlqZKkzsoGfqJk0m3ysKfBD4bE43uCO68bl69Lnbol+BZkPT05Ee8Kx9jHv+0Uyws0LKw2rLl1WY6c3gcS1qNKulcQOrkj7ZX0s4hfVsF/ZXX7lj8qci3wqdTJneeoY3sZqf5UtXnRCadsXlPVIYweONZ64KjoaiY0sNAvZtcPE1fwHSOByIM21pnIe/xRDVOj2mo2ZxCzLdRwD2N3dzI/vuwjOph+truS2CRt0McaqbEpLKXIymsW0cmEUi7Lfr1ViCPVHWUP4WJ4wHSuemsLdvRn8YxWBK+1ciFqCJDfohKsTnl76pvZq+R2dW4ghP5sbHmyigeh3KK1gJS9OyRQv38BHVEveezx66m726WPPsdvW5G+6JM0c0JV9MSiDnYXDVGNnrS6x6/ep/bf2Hyg6e2LgrbO2gcFhps2Nehk4Cf0+Nlqq9kUxjysPKrmQNZK2NA2rJWUmhHOkmPJ805o7FlDB7Qi/z25fJg2ji/zWoKI+pI/mRxWmjLRnKcdEonEb4jp4SD+TnfDzFv0v6rU7xKGhV0HI+7xG5LDJdTX4+W1nMtbQzdw3HKJ3DZ1bBNq+hPWxLft1JpTulcRsVNFx0w/32VPDRE+ZSKw0iP/Q84r4LQhQ3myTnG5X2geR/RPash7T41D0RLRcVDvv2tjjysUziUQcdCmwRv9c614q3HbkLlx9dkmt+eeD3im+f7xZwmJrXIJKFE/zqyctvJaY7RSLQwIzE+A6ZxGOuTazi7oRX4rYKjLL1/8v9BJ4cVeVBOIrU7g/lMcT/YF8gyOJggwXdi62488MWvpJp58TZXWSp0V63a8Gu+laFgHuKv6qX1tL7/fBmSXYuIU4hxD+6CgR7/sST/9WYztT9Xb0Ca2p+JD+1xHAs5twzpiUKNRKTmWPO+EBvX05Hzi7U4R1wkK8fNtgWSFzg/kbqajok875GwLKqE7ZjoN8E/oLB28Lrlv76pTO47RgbJTA2gs4kR+V30rhvlmYf7LhMhJ2ZUjjdlML5sdzSeP84meJ0KuT0rMiL+Q97y2tu7lFPDz0L037FG/fxbQ+QxnQhTbtlP5XO4sVf5ktWHNmE/+r8IK1ZH7DBO/2xfvlKZ8ssnylNmpfKXx/IFaX200E2EwtfnRZc/Qyc8XRgjAiMp+hn0+1+Jl8TtiW5NEfkrtU4j5fZon71G3kYzq6IykYGkJ35xblozMvMaR7dPw/7Plwen3oL8seYzlWb32GbWWh/UnmnEdtLGVcOzL30XxnYl+SUwvyOd4TTbTmjxyD/ay3Zee77dZHUfCV6s/0cocpqetX995nOoJbWxrTfb4fSo4f5tLLwY0FUvbgOEp6Wys0XseCYVa2DCf0qk017yd0vT21Gq7pTWZ5OSJhbQmvnk+WZyPRqAoe207Wl1PzJa//xDNh4I008STsYU/Ll/Iby7U4pUphzYrf4XpZRF02/v5vqTtqZdY3WB1xET5MdH3sWM7p2VLoe2m/mTURjFniPBLoiioI1mfib6p9cAFxthYm7X2SYE3FZ7eG48cXx1/yPt5W6QYRZUG1XuvKjhDbVRj7awvzY63tVes3aJjCi9XD2JZgCMkH27d0IG+ggJ1MXE9tS86dqKNzecVz+ek7XKyNpxLEE0m2mI+01AmqzdNoT2u9/ORtlkVtg8s+i3tHWcwczxb3QrdpvfeW3wd85g+lmb9LSL/KVrf2Htso6S5BdnsyT57n1isbccsALVu0TYsRwpyVYwlL9i5Fl9u0qjn1IY2wl7JR/qtcUrEmyn0h3/TNJ7cM1tsYsJA+mafD/B/g58b21DKXcLn1VowtZdyV+y6CNnLrqk6PnyzFuRnZDPbzhnu3/btPQN9u+jWWw83JA8M9hzYl+n95fBA70XmThtit0ULtzx9pCG3JoQOxSXfU/cueS++badYbE9FraM1JOHMDdLYGH5iLZwf1ej6AS0zLtSG+qlDmW2X7RvYet/gYM+hqYBO5SvtxLxCB59O0vNXqo1kWAdEbWA0pBIm33u1yrhtcyPyqYbR/Akaj78f2rntJouX6ppmLs9nM9uH1d6Z7d/2r8OZ3ifrAI2Vc4ff+TPN8dThgr/MdCrF9Bj9gh7VwUVe2ttNHRSdpM6uHFmr+fMjlgen34NWR1qe1Wt/b3mYRN6jSa59L/12N2cwsXXKQ2lI8qHNxHROKKEjjm/hfHLDUKb3edn+3p/uv3/bXqLL80cuE9l9uXfn9vuHtY7LDmx7jnYsWpnoR9PdhDxeN7qMnCY0vNH8n6lff6w2ihlnnSjWdDtJ02/BtKWjVqyeTNvD9XoUj6PIWvms4b5EdbDaCGai3TKV5lktY1WZNsn0W9N+d3/Pg4W0zmw7V3x+GmFig2L8XKl19olDk/dbdjp7bTtaXW91nebNFs13n4ruL5+eeuqFTlsQIr9Fp1+tGkaSYgPMttAobBD3i8hLtW26SsvOt2ib7EqrG6yOmK3Izmn+LrTd1O3JRJ49pe3ediOOZQu12eGsrf/8H+M8ftxQpvfi7EDvtcZ4KvzWPslmtt+ZzfS93coPjlZHF9SyOO+kUJCfnBT4m8If969xLQvfY3VxNtP7ltnxtvaKtVuG+nsv1+sXWPmqXkXtEzX6nvfJtaedU3KB5tBA76XqL8+2E4Ow9P9pWEIbTbtvztZX9L+22UIrL7PAunWbm3Ss4/3qTZSn9gv15LCOnezTMZTi+bz3I5rPTyrkDW2Uqn/hjdDzR5clorYnw/s7v0Qh3kM10D4oRGHTpgZtI35Aj1epjWD4ZtXzNL23nz6c6fvSPh3zmD6WZv2trJZtw5ned2vd06r56y/Us2KLPvR0QMN0kb3/IaBrOJtGAG3DaTBiOZzok2Uz26piLHnBCQ0/6e/UOO9WG9roiOHTSw3AHUrQelUc+okHFtlqjSCVnWuYbpx7suSZJp/56SVd5WmjukmpDWeYHk4kEm7bd4XzqaRr1gGRbKZl81D/trtLOlYH5m6Mltt+29fpX1eTogl2geVz1HSHFv6O76CIvu3U+sJKXL4wcICnOWSSK6wim3ZqKRxe5+eT5wzv7LvGGuqlImyDT9lM60Waxn+vbn21bobp3EYefV4YYXuni04QvCiMzJTbwv0z0PfOBx+8wVZdTZ0u9SvZ/r5fJ/J5y0868FrKeZHrwqlVe8ebilzBqZkElu52U0w7PM97xrAO4E9vVM/Ec+Sf5eHsQN/HhX1b0bf/yJXwRyL8GmssBpbUyTltfFys7lltWHPdmCw/f2iwJ9Q7vrRjMZwczb+GWX4Y1sNJ98dy3i85UDDp9vCPdZS1Tnjt4RNuBwOatk8b0oliS7dSKrK/u/oJS1sWv0PdOg/GqWxkE3HiquC/ltcfzK482GHtkcKJhb9keOe23zWM5v5YBw1iGFxf2LM6v3pQJ+NfqINSL7U6u1RcLW9qvnsnazuzlNsS109LHhxbYPJwfmnkt/nZ1PoVLQfO0Dgk1YYxB9jn5w8P9P0oSNt0lmLJZrb+nw42n6fnb1Eb3jBtStLIU8IL1oXEA1oHnZPNbHvbw/03lhzI0/Lj4aGVI6/UmP+XWmej+eRU21HBRYHV1zLxNKXbgifzdKItpgOdfR+3uthOlbJWvlo5a+Wtuj2o1tHI2fmxxr92FF6yYmMNXqf2R//UFYDVedantXQspUPz+YjWkx8X8my7VXtqrJTI7OucIPp7y6uzL1T4/0HLr5ZvQ8R7UdsHxmf1gaZnE0d60s/U/LO1Sa2tqX8mF9bo0bzm8nx2oPe7DaP5DRxx0paFPmDvkprXK1woSgBtw6JYnE6ytvGzVTaWrGMK88flmLFlDxHxXeTw0QGOJ6/ZQwvvPclyJhEdozaMES055h/oZr5TlQ2pDWU4yHs0PLbVpBxKsTkWGmgY9UOHyUTjtKwZUAdEPhG2Uf/IwLWP+pR/i4bFbTCWtMpjSVOIj/nJRE6rVTR/RN52KsJKsANC0hMiqpVxWl5fbIDxpfsGe0Lm8UIF/1kd7HuHBs9X62RU/rWhGnY5OVc9Wqs2nBG6fGjlQRsgk3CCE673DN6wQxsittXPxIkQ3+rhejrkHR1CpJ6cjhPJ53Qg+szsyqmt8VqTOhDfzlqmaUQzaqfMUt1u6j4v7z93731bQ0+cD/dvv8EGfBSgS8dKxdQwnVNoLOphEJPa65+o7qwzpz+hzF5f6yKrH0JJTTq2jiQLf0z/HlQb1jSJF+zdYNMVjy3ns/T+taf9pp8Oc3yfxvk5Lmk7NLD9Zt/z7d0fizOpEW3iaoIRy3uH+ls/HGSSbkJg4tvSOnvcoXcz0ScnzuA7JIEDJPTCbH/fT1ROs7B+BzNSqCe1vgzmvKir43zJhV88hPxWFGa9nNR7+YywcdEO4/8M7dwWaRs6G6zjfPIi9XtAbViT9JhCLboJ60GVurdJ+OdYHRQqfLfeOp5sGHsPRXjCTgsr5/by6LLEH6v8heT4Udltfi75LJf6Wr0UK2+FvAv02Lk9JkzvWJ3e7LSbhPq79IzWG2wvkydacExsPjCs/RCXsZXhzNbt2vZ+juoNndaaz55lbUuVLW3K46L22gfKwcYKbDJID1mti/GZ+HXZTO9Hw7ZJzTNtlz6cHM2/RttWX7X/blba8+S/SWVZLUwQAnqP683tuohuwgf0RQoc2LG8s3679mXLNpas6VsIX9GvQdsuhsllCyfTV2obJ2VCLu/P2JVL5ucdtJnYiop/YwEIZYUWfo+G3gxMfumnOIp5KnT7rl29jxS7VLFz2rksdDJ1ZNDFz32ZG/qZ6csusibjCS88uWWOZlkdsPy5ngpd0avMSibu1l9nk/DY8mbYlWDqH29vyjf268FSMfd5vv8GK6gcIyxDmb7Pq+y/qnUy2rALM4HFQr41IMP6dYDY/5BLA2a6RxHy9HGeJ83TdS2NY/5JkpMbs5m+tw/19911hP/leR2Iz2gn4rJspvVU2+pMeWQ0cX+xBJ+OshWor7EtNJSBk9EBn5tE2Fb1+U4KiHS+if6MiJiCfPJ5W7EaelKRSf7V6qIgXsznJjGav1sDuW2+6wud17Lm5IWuF7vGwsalsdi1AOfGPF/eECXO++7bfpvqsC32xgL4F6uT1O6cPYXrXhdruyV77KF/I5q5nVjgQOrg2NDKkfdo5/HywDJEBLfaUGR+W3ag121vd+Uunv8p5eia51YIha/vkN+UeB0bJj42bPS0k/uAymjRrd8RjD0RKMwfcFPBF0489e0mXYNSVm+91nFQn3bvuOkhjfO/q3U1Tu3ldes2N0Uc7Lwv4ft/GX5x18xoDutAd8T2WGuCE6/1R706AAAQAElEQVSZqRX/5iOQemDsNL32XLXhjbZRhiIsdJtoexe2fg3rdyNNbJ3MYQXjcK9lYc21DyzeUScsmfg92u/8hupyrlN0UmNkjJa/U3VE2fnkdbbbhOqACUAAbcMAkII4iVjeWV+2XGPJ2tZbOAYs/m8XdjHv1Saf+JT5rtpLuLVgOGe+6/Od11GTWx5asyw73/VB90mYBSdg7GkTbWA8eT5/FzovJNcvdL0C1w56TJceGQx08zHvi3Vui73XorRCptNLO5rpYmJigLfPPBvsn5D3vLa27uXk8LGtU8Qnp8aNiP+dQcuDDv7WoIivA8lvjTKQOhlnSTaMfZrcV2SFmcCS4ZWH3pRL5Nfp5MJztDz5EJH8mIj2qp3XiND/ZDPbnZ5Wm6502bi3W/+7THglEh45TLBRLX+uG5Nlf2WrIxeOhE5u7Oy7pmE0/5Rcg3xhYbf1d1Vb1R+xTlHUmA2vT/xYB37/21WPNlKeZfV6EPlsf99P9Z5fxT6fq3JvZaJvajzuUdlxtcUN0w7fz0UemLbOhJDM/5Rncd8LZz2fGgoHAb8mBrHYebWnenPZ3p19vfobyUzqsBcTRtITVljyslllWtS6mL0+598btd1i8gU9Jcp4lwDWrQzTzxoP5X4YJX6Nh+Q2va+d277CHLq+Q36LkmL1KStMz6RNm0KV2/OSyI9do1P2O+a9PnFB25Ly40LbkukFnu+3NeWTXbsWe1HbRNgq9f2fk3WOs3/ij10fgPV8+p3ay2PLOcrTlAfJo7+MoT9UiNPwwLYfsQ6eFv44fAnRK5o3nBv+KbfwftW8BIv3fI1E6EWXKhPPQrflDd9WXaHfW8IePVvTuPIL3Wq0fWDb4Ougp/Mqfe03fD3K5JWm8WHzyMC1j2rd8AY9cZ9aF7OWc3nLty6yS04GbcNYkryqx5L13l44kuKxDTKE3EpmQqcOVNjWLhN/Zn2P58ZtRvyUWadL/vWFrqGentyCDn25Sa9rfa7fwU2Tv8B7NHLjOVtNemJwdYddHkwwl2oAH3ZcpoOf7D0xeXtU3ZL3bGuXPVH1BJUfnJgY+FZQ9zPcsTxztMHfMONcwD8N3sHTtSH9RwGdT3e2mxsSThMw05XU0PEPhk9quD6O8EZdkRVqAuvWW8cfuveG3cP9268e6u/7QDbT96JspneNWh7NJ49jHWQlFlut/mXNB3bfDAnJ9zSeYcsUFZlpRo5O2oDtEzPPBvrHOllWsrwOpKk2HO31Q24tZAPV++/pe6w2ohdbKO9i8t3KyNlBsHqVfZ1YpAPk9jllsl4PIi12z9tEzFB/7xeGMr0XD2d6T9N7sFEHf5p88U5h5ouE5BOqzFYx7SOhK4Z33jjvYgZ1F9iwcMn9vIsp0/ZQotj5+c6NN5C9d+v0mdcD/9tNSe9r6jpyuVPQMaHLJlT1b0WMjseQPeno5BkzfXlf5gaXyd85/pke0zfnAk4UIyDazv68lafFLgY9Z/LCvDWo+9nu2A+9CAb5bTZE/DcCL0w90vS3esBqI5lC/aP1kCrZp1brJfmciLyGyH+KtR21/mK12pbse9GQtS37e39qA9yDE30ZFVkSRtsP/lc0ppHqLWU9rDp+o9bFrPCZ1oQU1KaU92qVaVTrYv49e1+vjT24yBaTkUTD6FeJ2G27NKHT2GuMstUlLYXP6tO7jxYmpy3hROJZ6DbxVDl/MTRvoVOZG+xp59CiEQRqtX1AkZ7EIdor5H/UFshEYDdD1OoGYf7QjJMh/kjYdxeG0F1nTlnHctAXiZ6oVT2WXHKAzG9IWqPCJjUcUPDpqadeeFQxQe1cnqHng63AUoeTZsQTKdnAmdySatekTOAfXug9Gm7v+zC/BzmRvN8OFs2yfLfkJFCQwC33H2ei0FxNtbZs186XF+z6vDbJN+q18IMwQsfn/fwWlQ1tPN8zOZdG7bZsq/dAaA9rUyDHJF+OJV9Nxj/aiix52niSVk2qcv45MNhzwAZZs/19X9WO6Zuz/b1n6W/rvoHoq6SdAzUh6LR//4Ro7X1zjIOZtRf74CFmoW9lM9utjg4utIDL7ImNO5hFB2oWcDT/paSmm9Xr87sIcGVwsOfQvoGt9w31b7tiONN3id5/56ttVvvPAcTL6kRnFU8N40FexBZurAgjM+VWhK7K3rN1cOp/1F/VtZNI7FH5qKoCya9f37mSic8M5HiuowMi/hVzT7ufEaZvqrTT4hyVW0rm1qZxsnZXDHGWO2JQEkgF8lsgTEvRkRZD9OlUunN7c7qrI+rTGlYPqbX6SOulvrcPD/TZ07t3WttxKcKdHWdrP1g7YvZ5h/+ik5q/IwdBFxFb6c4ePdtFVmXiXHyg6iaMLfrQI+ett5j9l9mqdNUBMw+B5Lh/og52ukwKiLYpfqZqdXhDvyOaXCJn26CGHSvSsk0qPWlVk+0DSx6FZdufujyJQ9q3iW2BjYVlyo77y35K4tjeYtqUpBGXvDvl/ZL4RdswpmSu8rHkkhMaEzPH8jtHHKclD44dN0e2u9teknbBnPOlT+z0ko3aKV/YoW1JxSy3LOyqyFWZ/z0aWpjN+7RJEU1HTjHdtufeHtsP9Mi5yh4NaUl8ZxxeJpYd5WvNPeqoK+nnxjisrA7CDIoO7ISVM/dM3gvaQm47FWW1BsV1s1MNfIRuHZWmkpOLYWIybKuvRX4URmaa29SYP9427T8Oa5dA7IOZtYtiwZAf0B6/vWdohqNIf3p6cr5PV7rqYOGzXWXrUc653UAkWllepUy0ytXvmIxW4L9QVQs/4aoO4jDjy7wWYtropiv+d1Fl1yZ3abvQ9Z1wbtGoRakY3/nGvj+kCEbUlt0gv5Ud8aJ7IMyOi+sKQX8mk/SmDjQ9kUp3fW91uvO5bSH7B4RPSQI+8fa4Fjr5JPeV9DAmB4WV7kKhFixMeW19VOurTv2P8zfKQi8R7pjcOz7OINWVrnw+/2QSOt4hUrv8RD62CXvncSvyzmqrZDlWo+0DYyTkOT2JQ0Rl65Pa1lPaLv22+uFikp7j00UuntWqzFJqG5YxjYaIuarHkktOaBgcEeoht8+anJ+zFYozpE/YM5oS4WfMOBngj5DcFGhywAZlhK4JoHK2k6Lv0Zh4soCdto0Qka3qSayDEqovjLln8imbMDJF3WZ/d/UTOsgS/mmJotoCnxT1021gx2HbqeRozhq04QflmHZIPhfTqsbAbBbPIct1VhHHHgBh28LK5X6JZXV47PGZUrhpU0Nz+3kXJkZz9g6AP546jd9iBOIfzCzmS62f03LxznySw67oKhntRCLxG2J6uKTDYg6Y2m1SuNilKjjHqzZ2npJKd35aK5UPlDs8UdoNGrZdk0+a6mF8pinH9h6g2+LTOL8m8fNtjgMFxNpuGhzsOTS/docr1i6MMFnn4GNNigiL2xYnRWLL7Pl62qU+V7FwBvktHK9adM3u73ScHt0GInmFdn5/MZLIjTSnO3ek0h0fbdnQdU5bJQcGqS4/EudTFezxeMUosdhKd21WhfZRVMitjxrAq4gLvVrYF+vTBvBpaTphpk2OMe9PLj/mgKPsXDFtnwh5v5l7odQZiWV3glK+TF2XGm0fjDTk1mi5//SpeIT7LW+fVDz+Xw3PkFoHw+dO9DUcRJeICNqGsSR01Y8la5uudER1gOP35DbAweLRU2f7kMwlnqzn1qsNZ4SuVYFAnSMRso572JWITX6R92hMPmUyZ2JGw1LK5CbDUcpd2a5rQ+v+iadsyuZF2RWP0fKbSSj83qRCobed0vSyffbCbxEick2h4Vl2GtXhgXJyeqluqdAnvaS9b8bpPS2a1yNvd1MqfCGu86pTO9ak2jvekGrvvDF1oOkQi/crLbycXjYfwt8yOy2/eiH/J4NxD2aWP9gV90HvwXvK8c6QhlF/iIQGHCO0QZ7IH+MoG7uYTa7YStxUuut7qY2djyWYbIXvO9SjJrVlNfLYo0erB07vcSLiu44ZWxb7k527dvUe0LbcvVSBj7alrJ3n4pNmbSpL/eKRWNwr8sSAS8SrQUY7BU7bihYL+3iOrS7fX+xa3OeQ3+ImWn36JJmw9mGsi6q03agDvvxe8eTGkURuJJXuHFT7Ga0vzl/Z1r2y+ihUdYge18J7X1wh9ISs/DgYl7759EwMCPK5810vcf73iWSyvIsE3Bd6kbDn8j7IElGuj8tthQlM7yxy+Gi5sdsWeTqILiDitEWjjnOMtS6gNNZLnuO248UCUcn2weTEXkuxcJQ6J2XukzaNJ7WcY4fJrELIi++EU7gU91dt6vOZ0ReJmHRa3t1f7WPJWjaVjmWUAQ6FYLPf+nPEH/0z59yRq/MePaY1c+DHXRqSDdbwtc7rvAqLXeAi79GY3MomVcx9iXO7WHIVe2S2aFiE7i56voZO2pMA7NEvXYLMIbadWrduc5MO9tgqHQr5yflCPwgpU8vO7V7MlCMCY410gIicXgTLTGuouzup8hU3bdowTqXPO7Ml3XWJdoavVTuayPFuLbO+QkLnEFGgslbdLXWz6JPAtZIAca6Umh7nXdEGvY/zPGmerq9yxxcl1rZ3r2tu73qNTiL+j96DWW8095jeeL8gkldo2X5U5cJClEguO179dBwQ8zOD5ZnU0/Gm8kwWzGbLIm2zzwX6z/SIl/BiHbSc8teXvA3W7536j985BA4ycWzv5JmjvYwnqjK/Ib/FmuKT76izveZj1TtL2Un6/+1adl+zLJF7RCc2Hm9p77jC6pXmDee69ANV3ZIxj/h+7ZUf+UdHnqQpdLLa0IZZfr9nDVm/hcr2SRQWmGRd9DP5T6dF6he5hLeSMr6fa9K2oVN7VaJtf1c0mr4vj+iF8AtxiU9UuUqYmm0fkPCcxdUBgZW9TzrR1vddJ0Wx3XaJhETbsASgIJdrYCxZ+/qlY6IDHI+Q0O2lXRZxIXTK+vWdhzv1bTrwpxWBvXSZQn7uWZ4n64wGErOtqYTEZVX/WWtO6T5huic6UGorv0MPlGpD57fDJzXFvspyethKHfskD5ZyE+a6Mo1VX1C/ZeIFoeEbjSG2nRpfxmkhCr9KR+jWHDXFtpdmUCaL6K5snRabAWYip210RHjV6mFaXm4ubVqGFSYv2jv/NpXuuCqV7nzYVvYReXfo/XEpET1bbYNamPAEso1ew2B4sWASq0/vPlrTq0etVMg+0ZrutAn8YAEM4Yq5sPo5hERgp1oMsj3hGFhgmsNEwqPQdSWF/kxMXrS0d16k6fgltf2p9N5cTnIPaOP1W9pe+QtV6dRRVbl4jO+v0HA4lUfaRirbPcBEFVlkwcTHOoEUOpDPjT7sJFtCSJqanlAnD6iFKU7gD75HNqhS/GoVn0V+q+LEiStoPT05z5cvqLoxtZUxTEdp2/KlVq+w1zCkdc3u5nTnpa0nb3ky0UWJygSiZnwZXiFyqGZCOxnQhqSs0cNVakMbsW2CNF+GFgwhkPdkH5PbQi8STq3aO64D9yE8XCJO8YGN2wAAEABJREFU841JW+Ti9HQD+5KPG1OS2Z4eDV22ecROk3EO4S9r+8AhPMFFmE4P7niGy7L2Sad8Eh1HmjoO+ZvUsmFx+zohA1xp54y+SGTktTCW7AWNpfNqUKaNhReyTHrkvo+d3FCYWJnUE+BHxxbJtqgK4HSGk9nv0dBxI3aZgCHy+ea4Xo42I4Qh/rDHfwjhvKRTlnj1lfRw0kHDqGSYKPw7KiT4tlNM3K3erVQbyrBHv7SnSEIJ1bBjtkdOl/uPlysKOphn28K4qF/dkIt/QqPNJjA2dp6vHdnPqB08PHkh9HkifiERHacWJh4CexNjORt0jEdb/Wo5OLkdQ1liKOK7TsSv8O1JqdhDdVHCBpAKT0DZFm7pvWM2eaGdAHsy7k3qXVptVZlJDuG3L9RYaBlbtkkHrsCLmm3iUGfFbKWzxia0eUAmJh5CC5YS0AnzJ4ipLE9/lPK7DNfLoXIsP1YYVIlF9+TgZtmf+EB+iyW5akLJ3pMabmDiry1iYNcw0SW+79+tk+h7tU34kTXpLZVaIb2I0Q7k9RMjRydje+/F5ORqrH3YYrGIUleL79tuEMXUxnZu/z19jwsHX9A53WOth9fTIe/o6edwPEHAl5w9ceXUf9P0+Lje+7EujPKJbtGQhW8zug/Wq3ehzJJrH2hZv3M0Mf5oKEoOjj0SW2hjE1qhpb3KTWiFDttiC6BtGE8KcA2MJXtBo+qJ2P5u4W82HVD2JX949pgnXlAVfh87pvCTE8K2RdVjQeM46a7Jn/YejfX2dInQKZPXwvzoWEtltnYIE6hadfvggzeMCPHXXcLPh7edonk/bTpoLeQ9j8J/DrLQT8OL1a6EEOU50aA/5YmDlhGuK1+OHhvNN8QRqrWnnXNCc7rjzc3pzh2FCQyma1Tv29W6DtKpKEwAAo+OrlwReoVSAL1wEoKAzuLvCeG8PE43bWpobj/vwlS66+c6cDRmA0hCcikJYQu3CMSZPV/Fy1Z+q+5qNqL1da6aA1hPYZsc3HyinuIUMi7IbyGBlXTe05NjP/9xdVe2SV/VHdSsVofvy5O/K9XeeXXzhi7b1oT1HEwMBCYnV8veHhRfnPsNHlP4cZHwbIR8R3+Yjm5o5NAL9cIHsdol5oYvJ2JPrjTOvVJbZ4R4VZuOYVCNfSrVPpD8ODOR09N0QjSaWHaUtZnLStcXz8o5p7apNjLWljVwUF4OApps5JTe5QhMPegMPqGRbNypETarPyGN0OFtfLRw6AwpTcT0sMeJ0I3XyS2qQq/25mnv0Sg8XcK0kcJ/9iS9ZNlXbpQI1ghPrMYs4axGLifZntAIv7oywLZTow3+BlJ3YUkw0bbEaL7m31MSNt7ldB/3o23Bw3pRojm95byWdOcvcuON+5n4i0x0anD58rpkkbqv/JR3GV60V950gfZ4Cdhq15aNnZ9MHWh6lMX7FZH8qfoQuK2ibstmRCi21acLBLKsT994nLS9uEtvK7RAAMt5ScuAsr58Tsqw93U5eVRY904+KhF2EVCFgxivd8hv8fKshLa9O7ffzz6/Rv0Kvw2tCpXFCF3AntyuExu/amnvct3epCxBq5TSct9L5YqH577CuWLvFFC2ocdACryElpNtf1n4g6/pBDyP7emM5PRztXksRz2xohJbvVJttg8OeUfr2ON6l7TV+64ifdLJdw85tcvZdUslFyBLSIaJ0BeZSO+aGEv2JsJa+tteesUsvy/tspgL76w2nT22R3+IuIPCfoTuyDV49jhWKMmJLarkhlBC5ljo8Hs0xM+3kdDxdrqUnXmdb2/I0f6Z5yr+T/vuhdWYFfe4HB5m79k6qANKV4XWremX9/MLbhtWuK7uQusm+Zk9PRJeDhLzEYj70bb5/Jl+3jqgqfTQDUx+nzZ8njv9WpUcV6zjtJjx1ft7fDH9h98TBHy3lxNOCDt+H99+7pNS6a7P5cnfJUz/oGps9Zz+VI/RBq7boEL1RIFGx/N5DY5Z/Vl6hn1ZsnFfeqm9+DFGfitPGgzt3HaTJ3KBal/sfpYGYZrRiQ0Rua0l3fUu2rTJeeX/NI04rAECCCIILCKBo5oez6GsWcQEgNcgEJZADbUNhbn6x5IDT2jYuyB84u1hE2zCvZx2kEeOTY77JxLTUybOhfmWvn1397jt2c8UfqsqosPv0RD2/ihMSI+49W8bHOw5dOQ/jmIgIML0M9WjY876HcLwQttOdXcnPSbrGFGoD9PD7CWuCyUDx9VFQDucqXTn+7UDeheRnF1dgVt6oamHAeN6SLWk48sJQ8R9htNUesufNEhDRu/Bv5txAX9AAARAAARAoAiBvQN9t3A+qe02Dr9wrYi+GE81CMllqUeWfy311AuPilEvVIEACIDAbALNB5mXzz6J/yAAAiBQhQTKEqTgExrqvfhyk/64bHvS6nFifT6ff7LD0w7aLuTr1V8no/7ep5MoD4cULrxHo62te7mGN/wTJUTqJfUSPrETaBon23bq1tCKWZ5Z2FaqiGBqd269zpSETmcmuX7vWu++IipxqgYIWEfTOpwa1A+rhQEBEKg8AW5Jd72WyLd31KyqvPdV6SOL+KHaZlUZC8dACZNtAaFzm44KIAYCIQggv4WAVYVOhwZ7BrMrD/4xE79Og1f2F0irH8EN82voiYOfiv6kRnAv4RIEQAAEQAAEqplAQ45sAs7eQVW1wUTbsGqTpmjAQnWaG72GQdVi+y/rTyjT5BOfwkzdFP6TpQQNhBebkChsVSV0x8S/4N/MvOVxyR9HJKcFlzrsckg8Dv3ujsPSOJiXgG0jxkw/n9fBfBeEjs/Ps+2U5GWzirWoDWVEvCuop8dlgi+UP0vNsYisKXucN21qKHQ0rcNZds/gQcUJwMNIBISkWRWsUFtW07yx6yXq11fVk1BtEXVfz6ZJPC90fRQUSEOyUL5W7+SR0NrVp3eXb1Uz05Lc3z5o/lhy7pDfaj/Jb711fCiz7esNo/kWIbpYI5RRWy3mjc0Hmt5eLYFBOOYS8EmwMG0uFpwBgfogsNx/nIl21Udk6iMWY6P5Bo3J0WrjN3FpRNswLpIV0RNqEEFn1PYT8e3k8GHm5xOxDRxTuA//pmk8uSeczBHXE1tVSd+RMwGPhM5KJujp6rpVbVhzj9+QHA4rBPfBCIj4V6jL0C8D5GLbTtl2Ux69mMJ/dlOS7WmR8JK1L7FaywKbXS9LTDzhlWVRPE3pZAfzjdNOlfPQ3gtxnQ7cvoXEe6p2uLeV07Na1i0e3V3L4a9g2BtzImV7x4Qw2yr5skZndfuWP2KWr6gnntpKGNtW7l89kbPVs39WCwMCIAACS55AvQCw99kNZ3q/mc30nqx9zjM0Xl9SO6J2UQ0T/VNLe4eFZ1HDAc+LE2CPrY1e/OLCZ1dou94WfyzsKoar2m842VHNH3yPHnGUhRgI1DwBTjSI3j9O705TubW2m0O5IXheYRHZcS7+aBn0oIscZECgngiEGkgYHLR3Qvi3OQJ4pco9Q20owyJbJ/wNJTbTsbBtWaXl0szTJf5tIKY/UzcOg0byu4mJFJWGiZ1Aw6hktIMQfjKhyLZTqb3+iSL8zLCBFJJr7CXlYeXqxP2x7BceFyxLdMTjhItizRO7aLlf8l071rFUt//k4sekzEI/4yR0o+aPTzDJnyYbxlZp57pR7fnDmb4v+cu9neq3v5ACXAOBAASSnsdOjd8Aukl8sdUzQZzOdnPQE9oz++Ts/+vWbW7yyP+Qni/X5GVG6++v6T34WmG/PZtpTeo9eGY20/e+wr7rQq4DGBrkYIZ9f0hdOg2mafl6usqWxfhMa1RxWZ++0fbPE8p/t/rjYjbIE/ljXARLyaw+vftobQieVModrtcWAeS32kqvCoRWhvq33Z3N9L5Fy/5jPM87Xe/7y9TfknWTuimHWaX9DFtAo82/cqiHzigEfF9swN/paXtfyGGMIGRou7uT7IlrW2ksP8ZO7ZCQoaw555Nt1YM1F3AEOBSBfc10iFn2hxKadKwF9rL86BPTx0onr8T7IyzW5nUqS1j4D/GGpn60oW1YP2lZKiahb1K9uV3fDaGipYIz53ouTxL+6YpZapJecoeeCtuQbRKRv1S50EaEegifshGwVVi+yLdCeyA0d9upnJyretaqDWM0ifn7KiBql6I5zpdcqlwR10nMU110a2KMJpYdtfBkgXYMxOf/p/pXqY3D3K9KPquTGBeM5pPHaQe6MTvQ+0ydvLhkKNP3i907bnpIr8OAQOwEyjno7RG7rgbM530qOTAw1ug9T++Z58cCRegJ7az8UJj/MsnJE3UAyyYv2rP9vW/Qe/Abw/3bM0SXO63OihI+X7wxlS/JQt3MMVqWufKfo2v2iXLqnuaXkE+ugyjHTa5Wm6YunkMeGbGtrE6MRxu0BCNQEVfIbxXBXIueXJ7fe9/W3w9net+t7bO14o+3WF2hHdJfaWzG1VbGMD2necO5FVnNX5kI1Y8vOiD4mMbGqb5iz3PZllq9C25W7R1vImHXPtdOPiph8Qvu4RJxOdlWdWsbMr1AyxOuEnvS/vu37V0iyRY+mj09Oe33h97VwzzS9vKGZfmGY+24nFbrI6sbnJ6Mlxrc2WCyjV+2RXnT0gptw2kw6vnQCxs5mXg3hK08DCvq4n63n/R3ughOl2nIkc7Mcvitspis8ztdVeljpocTicTvSzuEiygEuCGxXeVDrwD1mC4gHdRWWaLCr7ywcBzmi+ke9sd/G0YklNvqd7wiL1KWVa5RVtBqg2B39ndXP7EQvtQDY6cR00sXchPg2l3WIR6T5Su1Mdum9h06iXHtgcEepwZTAP/gBATmENCGdjUOej/i+7zgdovr7OkM5jdphPSW1W83YyuSPsr55Aa9944Z6u972XD/tm/v7u95cDEmL4pFYZKDrfwsdnnhc0xtZXrMnRX6poU9j+eq+uO6L/kx2tmxp0jiCcg0LR4n1utfl21EVQymmgkgv1Vz6lRP2IZ33pi1umIo0/tcbbstszpESN6iIbxObfkmOIROZW54ivoBU3UExm1MY59LsJj8p0/0JV2kg8kkfF6t7b32YK5nutJy8f59d/eUfHJ9ptTS+BepjVYuRNBbFgLiPuifGvPH28oSqGlKtWHu2i4P9FT8NK9cD2PdajzhUZKInHbjULlQhonQFwlFrDYdh57QmHw3xD2ViC6z3PLQmmXZqH4NDvYcYpGtUfUEkhcaaBgtbDURyDkcuRGw7Z6E5Jqw0iLckdqds0ENOmHPqK14Cf9eF5EfWacorN/15D5C5bsghsmtRjYs6GieizrJULJcYvFsVbjro9sHSejV2Uzr06xD/MjAtY/OExScBoGyE9B78NRVp3bYY8qx+lUYSNcBdUelJVcD5pclbPuPLkf9pPH+z3EeP1EHo94/NNgzqHq0r6/fVWYSxzbZpItrQ/q05MGx4+KO0ppTuk/QMuysuPUW1cfyu6LnA5wU9v4ogLOCkzBfPrO9F+gockQAABAASURBVM3psf4w/sDtIhBAflsE6DXvpVgdMpzp+5LWJ+erbbRtQonpBRqzL6u1J3D1JxbD5PE5hE/VEeBjjrUBf6fFk9qnfPKaPeTap6BAnzxtVHfWX9WfcMZnCr+YM5wXtet6uf+4DnbucokA+1S2bUFdwgOZhQl4Iq4LjZPa5yjr+4/a2rqXE3mu7fKhvD/+IJX/c/TYaN51K+I5odPxO3sipaxb3x72FG3Dwyjq+cALG7mJmX73jmoY/3yha6inx2nLhtn+TG5dFYuu2bpn/Be6fdeuXrdVmTMUle1PvSgWIadtn1okL4VJjKTv2Yx42CcNxoTlZ/UC0T0e3GFPU7jLF5fkhJ/WK06rc8X3bWs5FS9u2rTRoJMeW8jtM8bCL88O9H6nWlaAu0VjaUtZ/aWDFt1qK/Wo+FF7M723loO6MJ3e4CfWxa575KB1nJ0a8ExUcjWg1sX2Li23hqzIp4aOHXnHw/032mRB7FGPU2HhaTEhm3Ahh8+aMcmd6SC3oEg+l7NO05MXdBTTRd9n2+bTaasLFtliT/LEFJSCmjYt/5k8G6gkfOqPAPJb/aXpYsTItgnN9vf+VNsIb1bb1pRPNnm+WLvxBxqehbc0VQcLGS3XnLZTXUgnrkUnUKirSe521PTkyXrVUTyAGIvVW9q8CuB2ppOc+HLTtFM4nEZg/z19j2s72mlCQ4jK9oT0tCDiMCYC7CUGielhF3Ws7cY2bT9SmT6jDf4GYnmmi3pm+e3wSU01t7W1ju8e7xJfFxm0DV2o1Z5M6AkNi6KOJN9ov2W2Izqj+pu4/GDJ3ae6nCoulQtsdNbx+sCO4TASgaZxsnwYerDQ8+jFtGlTg+bjF4cOgNBvx/0Vrg3f0N5VscDZydFc7J2zBHGHxtkeRdSfUOYxEs4sJPG45I8jEtdBwv8cGtj2vwvpD3Kt6fGcrXA4KohbuAGBBQkIHS9+/tkLunG5KIV7pMVF1A+wGtAjPs9Ft8rcpY3+j9Ott47rcSSjk9JPCqYgmqsI7QHtp9Cfqe8ugxgqVtSwdsJfpFfi1KnqipvlebL2Vsmn5opJazjPHV/GNrld7LLTuZHEWLuW/655z8lPCFWOAPJb5VgvJZ8GB3sO7d3Zt00nN16R5ORJWjZtc42/yq4tPAHpqgBy5SPAdK2j8rLWq80bzk0R8wWOYbu3Idmw4EIvR731IqbDABR6DKEQeaanrV/fqX3Kwj98VTmBXIP3AAnd4RZMOW+i/egmXUqq0I/T/lwpd8Wu68TANXEt/C6mf9q5WN9tx1y5J5zQNpyWinV86DnFTfhOlSu+8k4vxGR2eslGp0dAi/lvM5g6QlDu9x4cTDCj8VAsAcpwzp6E0ULx52FVi3BH68PLn8nEzwory0RXYquhArUVPlGsg2P2xIcwPY/cPg8kG5ILvlOlISn25McqB/U5X+QKldP+qH5HMPnGpE1mYA/3CAwhOp0A/9m6dZvj3EKHRSj8RO9EkEquBizc40Rhn4qb0E7y42xm+4Lv55h0WOqHibnse+JaICbbAwftOKxlppesTm+ObVA/lT7vDNX56rDhcHWv9fMBIbG2oouKlUTeK1SQ1cZhNF97tk++6o1DHXRUGwHkt2pLkcqFZ2Vb98rVG7ec3JzuelFLe8cHU+mOq1Lpzgeb0507Tjhl89q4QmLvaBLKv171ub6AN+nnxuIq0zQYVWZqOTjMVlfZuzRCx0Lr1RelTt1SljYFe43PIqHTQgdKBbQtd/2ee3tqbvW2Br1iZvIJlpyDh2eNJMXpSWYHvyASkYA9nU8kfY5qVjJ7L3OUXVDsuI3nHyvCr1nQ0fwXD3DIOImQ64KwpMd+4/xBCX6lrS3SFlsU9oO2YVhiteneaUIjymxXUEzaEb4p1oq4p0cHJSn0OxeChnfS3SAnknHuuTqpFj/zEWChn+q1sANGLZKgd6hc4V0a+hvUHBD2Q0+gBFVeg+7esDrGAbfkyPgfKYPCdmD6G8oEKS/UTbMqddnqZizJPKKykU3Oz1nHxCZWIuuCgtonEDUGOsN24Vij5zoJOMd7HQw6nYntyYA51wKc2O0n/QUXIfDIiE3onRhA1xwnLBzLNlOVfI/EIVneT86rwqjVo8TfKQhWG83YE4nE71QllRzQ1/lp71fqp6t5i+VHV+HpcqmTO8/RQafXTT+H47ojgPxWd0l6OEK89rRzTkilzzuzeWPHq1Ppjs+m0p3Xqt2jVpYlco/oYMu9OrhzpQj/CxG/kIjWasF5ipdPPEWPYzP7MjdkiAlP4sdGtDoUNY0n9xCx664Qaynn20SXZjmK7WN5XpX9g1oXo7cC2fbI2kx0EV8aMpNPsNzrENskE79S5VhtZJPSsk3LsgfVZtR+X9s+H0y1d/5564bzTlp9evfRkT2AAp3PYCu3ne4HEXrT6vTm9rgxNnijf671ybkuejXj3dgwKpkwsipzXxj309w2+cROfbdpOgqHE0+7SCWflq7KtmEBBr5iI+A0oRFxtitY4IXs8U+ngmc+DxLEt+i1sIPfKhLQMN0W6yRMQG+XsrPEaP5uLaBDPwIuwi9Vbiqq3wGNOg5deQRUXavOWhOUfCd1d7tsETUzzjrg5jO/TU86rQBgKgycLVheCLNrOFnEdyorafpnglNsDeDpqnG8ZAmwfj6knSGbrIsKQfM5v1GVuDzFpGJ0w0NrlmXtYD7r+412Hzndh+JxYj69Yc7ncuNb1H1F3iNhT/OxR79U/1zN37Ru6Oh0FZ6Sa350xSuY+LVT/yv2m2TbFnLBJ+cWCMsq8fkDpHXDAm5KXlqrA6Hk8+fUoctktorB1AwB5LeaSaowAW1Od7wpN964n8i7Q+u7bxMV2oq23WKpp13ZE3p91DKEZn6EhVxWdJMOXO3ed3fPE4RP1REYHOw5pIH6llpX8w82ce4qXESOc7mGN+ko7NlFrpU+JXRTTpbfUNrh0nZhYzYibhOUzPS61nTn0+MgyBNPANjTZBtV38vFJmaFfuJ73qA3mnssle7Mq9UJj46rbLKjOd31InsqDZMdSiugGaPlN5PeFwGdz3bWyuS9L866pKWtu007XR8kx48Qf/3BB28ItdhSRzJcn9Agze/djkE9ImbjIOL9vZ6o5OIqIrQNFXl9G88xeqJyv1ZbHsP0cCKR+E3cyhvGaUB1lu39ByKyVfUbG/2BqQQBK8x9kisr4Zf5Y/5Vwq9a8UNI3tS8K2+r4SIFOXVg+UuIyfWRzt00UVlFCsMCwk3EfO4C1wNdar5//FmkDeBAjmNxBCVLhMAZOtDzkagN7eYNHecrL5tU1B8Xwz+mnh63gZ4A3mnDf8u6iNtr2cQPM39IvWO1FTEi/hXq0QG1LqbR9/irrRvOc9ymi6h5Y8dLlN3XXDyPKpO9Z+ugCF1Frh+mi1oOrPh/Ku6UXvY4f368UQdAxW1QSD2GqR0CyG+1k1ZhQsoT22q4laFML21+eHn0gZjJALe1dS8XYrdJfxHb/kcIn+okMNGPcJ2AX0E+fStKXT0dSvPGrpeQ8EemnwtzrH2zr9uCijAyS9StCNPPNO4u96VtvXyptTNU3tmsTm9uFyGdvFpQhY3X6YQHv1B0skPLxCvtqTRvLHd/68lbKrJAZ8HQ1cBFux84wgIjJn5tlPbodESWZySR+4qeswks/Qlt7iLK/9/8UsWvMJHrExpEzBcU3ulD7p/CeNEijIOgbeieZrUiaQWkU1g9EZtwCDUzGNgjoYGGUd9pL8uF/CjzkyU5EdICZqEQ4Fo5CHAycbXqdW2EqmggM8Qel3vLskABqTJHHrN8pTm9xfnxwZYNXecQ8ReJSOtaCv3R++4qq6xKCfq+PKJunAZchfmta9u716m8k1l98nlnaf75qgo7l7kqCwMC8xF4Y5SGtnWINH/aPeiWP5l2iD9mj3PPF77C+RUitgrS6T0Y2tu8YHy55/pyTCp0IMT7vAbkDLUVM9kTG+29WlGe0jjZ97z/szQKGWhuTnf+lU7gfE/lGtQuhhHy/P9Rj8fUOhkdmLm0pb3zA2En7Fa3dbc08qEfar55rpPHEKpFAu75bTK2yG+TIKrop2FUMto4tKe9XELlad32+bgGmkcTOa0/xGmCRIRc4+ASb8iEJKD9iJ3MZIOMISUPOz8573nfsrrn8BmHA+tPWb9KRd3aY0R3qfyPVD602ddMh1R2f2hBE2A5KpYn9k1XBW3hSRb3lfvP1nbGJ8K2Tw5Hb9OmBo8Sl+j/Uk+bqZMiRujqvWs990HqIirr+ZQkvG9o/JzHi7R98LGWdNfFqkOrJP12MOvWbW5qpEOfVFF7ylB/whsR+Q+XdwqyXxhbdRu7FTqNE8nXa2id4m5jPVq2WPnqWq6p184GbUNndLUh6JypvIkXdi+4X7Y7ArlBJx9s8NFdRXFJLYvItrIqfjXa2V0sOVQq0Rg6SWdbvQdUMPS2UyoTxvRk1yZ3hRGod7fT4reSyb8m1d7xAj0XqqJLbew8XzyxfdZdHz8cmxwwE/V7QcPCj6kD54o8549/LPXUC49SHWEMp9Jdr/R8z15GdmIYwVlum3yh42adw18QOExAK7dLU+nO94XtWNlAue/7tkLNdaUQ6U3//eGdNy643ZQFdHTlChvYftSOHaxHwp+y8IaVXbWx8xTtQPToYIXrU2AFL4VlbeEgzFdPT058sSck/DBis9xu1DS6RcvLvyC6KDHr2py/NvmqeeGHmi7WeVusyYxCuBoPyW2aQWzRQeG/y5cOBH4gdWDFFRav0vIXJZTTq7xE7h5169xhVFmYGiSA/FaDiVYiyPZktBB/vYSzhS7bpPDPV6c3ty/kqNQ1G6jOE31K3blsX3dAB3NuV1mYKiaQl/y3NXh71ToZJurykrnrXdop6iFbP8r6U3rs2iciYf64y2Cn+kmk7RVV4LQtmgiftH4gd0xBTw19FVbus1i6u4b6jalHln/NpX/YMvEEqm316uK3X2hbWpq5SEeQqVVRnbQc1LD/t1pX42lf679TDn0t83Ddus3H55Ylvq1t4jfYfzfLNzc0jl/uIpvwGq1se9hFtiAj/JHC02OFP4G/uLm96zkRx3oCezafQ7QN5yNTH+edJzT2rKED2jj7PZXjw1SuSQdK2FZWTO438zzxVRa/HT6pyR4nnscFTpeNgFbmOlJkAzdSNj+If1xo6JXPg1rXvEIbwT9Opbs+e3z7uU8qFZl16zY3pTZ2/JNW6lEmM0jlry5UUlT6k2xI2qoMm/wq7biYC+bX0BMHfxxsUI24eUPXU1Ppzu1E8l1V59IBVrHDhsmjJsIHBBYm8OHmR5Zfu2ZDx6kLO7OrhYHf1+tA+e36z3kyQ2X3ax4P1LjO/u5q7ShLlG0fbWB/a/OGDntSQ8cO1PcFzNrT7EWyHZ9NMP1ey4qzFnAa6BITHxvI4SxHjeN+r/r/v7NOh/27QnX8Tyq9d6Al3XXJ6o1bTp42eVV4ae7qdOdzW9Kdv8ilb68QAAAQAElEQVRJzsq5F4f1oBzuC4ORefms6tZqWr+djbzA4tW8sfNyi+fKtu7DAz5tbd3LjUeqveu9ymevcvoOEZWsh9RNpQz8qRAB5LcKga64N4XtNaI8BX+GR4lbmzd2vHpauRkwFhclWto7Xq6TpL9lHbAOKDTDmcrhHXwziFTnn32ZG/pZ6DORQid0mu/7t6e0jxN0kNv6Fdpf+KH1o9TvFWrdDNPPGg/lfugmHFnqGYca+fkLaVl1Ulfr8QH6iAvpKMc1389dTkz2NK2b+on+4e2p9JY/UQV6u+v3AsYYtLR3fl4Hxy9dwNmCl9STawptywVd4eIsApIg78tE3E/RPh9OHVjRZ/18VaNJod8LmkJ/61XjyxI7daDqJQs6XfiiTmL579u94yan8cbcikZbLO6ez4k8He/8QWpj5zsCLa4q9MG6Pssi9pT64Tb7wlEsz1W0DcvDtVq0es4B0UFk8vlmZ/n5BYeI+c75L0e7kmvwHiChO6JpKSJtLIxJkUs45UIgnExDInmLSvxebTnM/blErtxPgJQj3IugU/6uQRoe0UG1bzSnt5w3e9AplT7vzFS689NaqT+k9/nHNIDuZRDRmJeXy6ySUj0ljU3CqqPfqY1inq2DavdPxW/16d1HTylrmxxUa9HBRo3jfeyJDRQ/k2L6iC+LutI6pmhATZkJMPOWvMc7NA9eox2mi6wDSXRRouDtpk0Nq3UgfCKP7h0gJtsGLWq++uJQf1+ISQpPJ/kKoXH9WsUeX51Kd91o8dMypVkVsVo1FyUsvhPnO6/JjTfqZAu/TS9EKWdUfMIIU1KPJv3So4CmUEYx2X7YBwOKLORsvXWCbf/k1IGmsVS6U9T6FleN5C+0s1R1WywNH3+oR8P8zYUiFfQaM73M4rkskXtE421xl5FEbsR4kMhHVc9qtTBLmADyW/0lvq04F+aPR4zZk5j521puPqplxxdb0h3PO+GUzWtnT3C0aVvOBpjturnTSdL9Ivx9ImpR62SEwr/A1ckjCEUmkGgc+wpR5PGNBmL+GD1xcF8hD23sPH96f8jaZDPaKvEsQtjPJP9UaG9QhI9QiPbcTH904PIbqfaO99gWn3alTe8lbaOdmWrvem9zunNHIiH3NfqJEgtuqOIfe8JY+1jRJrKI0kT+dZre92kb9G+trd2m8afJj/UXCyy0D6z95CER+pvJSy4/Omcmn4yc1i4+17jMnszWB4Tpg9GjIWdbP18H93/TnO54s6X39LpkKr0131+qdUhcC20+N7yzz3nR9+SisqhbH3rE9GmN04DFe83GjvXT4n14cZXeB9/XfskwkfxddNbxaEDbMB6O1ajFixIoIbpV5fVHv+Mz9/gNSb0B4lM4XdO+u3se15sr6qDmdJV2rPVSgYUdwy4CgT339uxnpsvL4bUm7s8euveGPeXQXac6PS0U/orJ75s96ETk2WSizuxT5KcNmPhre09quCEww8KEI/84sPv5HR6Onzeae0wr7RmDajpwZytutGE7vwKXKx7xyS5ykFmyBM7XsusHiaTs0YZnrpBPdQDcBn4n8+j6yGSYdiQ5+SXVo7e8fgcx0V68Oc0HOdviR+RlNW6+Wr0P9+YsvhPnyV5yPs19DIdCa7WTcpSLpux9vTep3L+rjd9Uu8Zbbx0X8m2yYW+1BxXhqwMCyG91kIhzoyCNiat0ICWOd9lZ+/PNQvy/yXziQZ3gmJoY1jqkszBBmtMBZruuoXiz2ogrS/nmZMNoHOHWoMCUm4CtfmafbBDuQAx+FfKa5dvp/SFrk8XcVvFF+C1D/X1RnmIqRNcnebBw4PblkfC/NvKhA9Yms8UG2ka7wxYbMNGpyuEo8TzniUG3IAWTkuUNthVQHPdpWoQ+b21ti79xMGv9RYqpD8zEXx4+qaHke+uCxXzpuRo+9qBNUP9XLDFnOkvT44uW3intY1lam51Kbya6hIjiWGhz3Zgs/4DqErXOxhf6hQrn1EY165n4i3nm+6fF+/DiKlX+crWe2uoxaBtWT1rEHJJIGS3pJe2xpZgHeqVvYtIh5phOV8d07fS/MRzvyiXz5Xo6IIbgLQ0VPvm2fZHt0R5nhEVn8m1/+UgVSJwBgq4CgfvYz3887DZg4o9drw1qK7cKSmrpSzMgJjRqKcHqP6y++Pz+3f09oTq/toetCF1Ftfk5kUdGnCY0NLoyJsv/TX+vU7vkjG3lIVJ4Wibi1lNLDh0i7EAA+c0BWpWLWN+Q8/x+DWYcA82qpiJG60n3LUIqEsIl5kmQ6A7t3HaTtrntqcogzhfdjQ4svmd4YNuP4giIxvt+1RPHgKeqmWtUf1X2ZWqofHHq/85NiSV8Rge2k5z8UA2NB9zn+f4b7H0vUVMtR006wbh0F2GjbRg1B1WnfKQJjYYc2XYOtq1KXLETndm/Pi5l8+qZ2NJqaN7roS/wXceMLXsotBgEYiXQeEhuY6K488/vJ7ezijWsUBaJgHYQ5a17d263RncoRfZYMfsU5eWStGgf5ta2aY8vEz5LlYB1NCs5gT4f568OH3fwJ/NdXOC8JJjspXxxbL+0gDdluMS0MpFcdryrZuuMMIs9oaZtJ1ctkeR8JrZJlUVhPzng8rlIMYgmbJNJP4+mAtK1QgD5rVZSKng4CwPNwn+tErUyMfqvUbYI0XjCLBKB4Uzrp4nlvYvkfRhv/2to5cFPqYCojWz8pL9Tldg7B/UnfsMiJ8SvNR6NE+WLvCcebWXR4mue/EeX/m9ZQlPDSm0xli/5P9co3Ke2ms0B9vk1caW59UOI+dNVEGF7msza5BUPCtqGgZHXjMNIExqDgz2HiPzbYoztnsmnPmJUOVdV03hyDxH/huL6MPVNsIhLIfS4EJjYS1LsaQoX8Xlk5Crbzmqei0v19D4msqdhFiX+2mK/JEoHMab9caPE3VYX/n/hFcgZjzWOVm1HIHx8IOFIYIwmVrovSkNwMsz26PO76NZbxyf/h/rZm+m1+ndRt1+aLMNscih42IWO1w5QpNWFtiUE+2wvzrRyILjf0V3qZIa8g8W3R+2ja3PTINmVI+9S0Xge9VdFIcwtSU6+VtMdi09CQKtxp8hvNZ6AxYJvgxFMXKWDjkdCzES/HJt4Kk+brUfO46hWCFyezx576N80HT9ZrSFm4i9qnfpW17ZYsXg9tGZZVs8H385XHYcxGuZ1YdxX2u3wcYe+yrG8YyH+kGtBckm2v++n8Wtemhpttb7v+bY10gNVSmC/J3KBTbTFGT5/WeJnxBTH9mquwbJJmtczUdkmTksEDG3DEoBq7XKkCQ2LrGbGXvuNx/LtE099xKNtPi0Tkw+xTcQI+WJ7Y8/nHc5XkIAkEz9X7+IqIMeE454g0dDVvhnJi/c2Evl2paPCxO8ezvTaQKi269x8j3l/3LCBOCDkPZ9J7D0bYWXXJ3OJJ4cVqnr3CGBoAh7LARucVcFb1FbaXN8wmn95YZWPu8+Lu/0Sy3tzSXm9Bn+X2nBGKPK7OaxzwsKvUY8Pqq2EKUxmDGX6Pk/MzmVnLAHVSTAd5HuXCF0Ri75gSmwy48W2Ii+Yc7iqGwLIb3WTlNMiIkOZlk9ae3DauWo7vG5Ulr8yYj1ZbXFaeuHR8mMo0/puEvp7IvLVVo2xQfehlQffHudkRiFyPT05jeg39LgsbQVVetLq07uPVv3VaSzNjx35KBNV1UQWx9D/rU7gixuqffdtv43zyS0aisXoT6m385oBz/O27B3oiz1ci7y92gEtTy+yfhDNG/UKXND7HH2RCnCukBfRJzS8xCAxPRxHeFlk62DhqY84tJXQIWxbE2m9WsJd6ctZStBAaWdwUQkC2VbPZtm3xeTXDdLYeEdMuupKjeTl8YYx/00aqS+prYixxtyQdmLVs8j3rVWkUultC5h2aOPkvOHM1u3i8T0ajyG1YYwikGeFEYDb+iVgg7M6qfFijWHsjV3VWdwIX62TGS978MEbItf5NtBje8KqR5V83HtcC4+Ls/19H9/f2jCsY/u/Vf9DGt68fn3ncSGF5jgfGtj2cxa/Wy9YnaU/ZTPjOvDxysJkBmk3omzeBFdsad84lv8rnRT/VHApR5cTefY5dr84aoBYjROop/xW40kRY/Avzw9ltn3CynNV6vSkoMqVxYhO1upAyUst35XFAyitMIHL89mB3s8QeReox4u1XaR6fdgU2jFD/b0fjH0yY8qLZUlbrLp16m/Mv6sbcrQ8Zp3xqtPBzqGVI/YU2D/Hq9hJmy1IeZuVdyqtRZ5+w8RKYGiwZ5COWtEtJNWyJfV1fj7Zsfe+rWXbXnhRxkGI9gv7f6rl6bWxJqCjMquj0RdxhFdlYpEnNMa9/IMsdHcM8crlSfpi0BNIxeRLvMOvzpyr/S5qWmGPZ869gjOVJxDjyhImucJmsSsfidrwUQc1R7IrR96moS13g2+i8a6dV6LL8+pfGDOv2+GBbT9i4jeoA19tWQ0T/ajhUP5w48R92zu+MI7B1LJGFsorRsAGaXWC4TmWv8rtaaGhf3TTS/S+jzyZMRVW2xNWJ/leqP8rsSjgAZ1A6BjO9H5T/ROyukKcHrk+ayQpZ6iOyGZoYPvNfj55DhH/hMrxYdohPj9jqL/3clUvaqvGaD4ayQ70vZOIX0VEZXlSRYQ/0DCWe5H6FVue1bDC1CABzQPIbzWYbiWCLFaeWxlHWtaVcFuJyzrwyO8ePm7kL2ygpBIewo/KEchmtv7fOI+nifg/aNE+fHNe6MzhqXZMmcJhfV/2+RJVX46tMY9lv8onNDTiNlmUzfR+VHy5UP8u1kTWA8z8J0OZPstzVdWGUybVbkKFL/u7q58YzvS9nsh7tgpm1C6GGWfiS3Rs5Xn7BnvCLnoMHd5hGwdheqUKVmJRwC2cT5493L/9BvWvagzahlWTFJECEnlCY/89fY/pQIfDKsc54d7FkqvYSk3bI5JZYljZKjdaITgnNjixaATYH/+tdm5sBXyUMBzQfN0TRcGSkL311nFr8JGQvVjrD/HHuayNdxnKbPs6lXfV1R+UzV8MZVpfrpXm4UG1wcGeQyzisvpp00gDnRs/Z2isVQKWryx/afjtZdPlaJQe1Dz86uHMmr8uR11nK5B0UL9Dw1/Gd4Lwf+hAxJlDOoGg/hw2CWJrA4QdTE8ysXUA+LCiCAfWaclmWl6i7ZFXqJo4FlmoGrJO0bt1EvXpwzu3/c5OVKmVbGbb95KcPFViXRnHN+sgwBnDA9s+pPfHSJXGvQqDVfdBQn6rwyS2Ms7KOtaBII3eIt3vhTLnKdqmvMwGQjUcMHVI4OH+G/+gddbbrH7RfmYl96Av9CW0rbB5/0DvvZVAayu4uTxbYx7nSy5ViTjE4IcM7+y7RtuPizCRNdlu7d/m8M7FGGK+NFVoG2Hr/zWM5p9CxG8nojKMa6jW4uZKbQtv1DrkExWsQ2Sov/dyFt/6YOWaxJnoj4zmtxSehCke98U+q+mOvshiJ0IU/yNProRdYwAAEABJREFUaJjnInSj/Uax2pn/7fBJTQ9F0RFK1lZnEm8PJVPMMUePezG1OOdOYHjnjVkS+ZG7BpPk7U35xn47WtDiohGQ7EDvz3RQ8lT9c6XaOIw2IvjtDaO5Py534z1bnlVXPpF8ThvBJyqb7xZ7smTyibRcSFishfbF1N2dDCkH53VN4PK8Tix+1hrDcQ4Mmy7Vearm4e8Uy8NxIZ0Y1G99jk6c/IXq1Htfv+Mxv7aBh2xm29se1oGI2SobxsmeDAn9hCkzvSh16pa22frc/1+eH+rv+0F25Ui7DpK8gIhvJrfPPh3Ue7eWO6u0U3RZrQzm25NGw5m+1yXIW89C/65RdxqUZKJfkkfPzGa2nTvUvy10uqq/MEuAAPJb/SWylXVa5n1Cy74WJn63xnCf2kqYX6PMqQTm6vLD6pdsf++FCV9OY6Jvaui0za/f8Zt92i76ezpqxRpthxXtS8Tv5RGNQ7Y1JvPZ5N4moSKfJl8o8radRfSW7dTD2n7UdsXbKOlttHZxwaPyfV1p+cr8M3/L5w00z0fA6hPl/7mG0XyLEF2s7so12E9afvxSfH6a9uFeam0T9aviZsgWex214mnqse24EdvCOLtXtA9pkzQ10R8x/sPoi2g2qD3jxRJk4TtVz2NqnY1WbtfYFhDOChwExZebVCzsgKKKTBqmhz1O3Df5Dz9VREBYfqbBGVPrZET87wwO9hxyEl6iQhODkr0vsYaYVtCuDfwME7+u0HjPbPucNSoqgdMajVkd9ExEHFDTsBYGFJMNY83ZTN/bTa+eK2oakg079ILLSqvnph4YO01lYUBgBoGpxlg+x2smB4ZdtgsYMVm7F4a1YWc6Z3hStj+X2z7V37V7v1AGELl2IHzWAQZmPiOb6T3PBh7mC/KuXb2PEInL489rOZd//nx6nc/bE2/9vT/NZradMybLV2pPRyc36MvaobqHhJ6YpdcGUHZr+H8sJG/hfHKDxjelg3qXLVTuzNIR5u8jHiezYQRmuy31f09m6wNDA73vzGZaj/E873SeGJi0J3f2FpF9hJhuJ5FPKZsLmvLJpqFM7/Oy9/XeqG4VmX7HaIT5nhjVQVUVEEB+q4JEiDkIVvZZGahlSCvp5Kaqt/e8HdDfOM1dWsD8o/jjLVrmbi5XmRNngKGrPAT27Oy7R+udi7Xd8iRmejkJX60+Wd2sP85GBxT5+0Jeh+Xj7EDvZ8rxdGzQ0FkbKptp2cws9hSpa7vMvJuMF3dljxupyacOsvds3Tms7WJrn2mb4C81UvaUr/5EM1qe3KP2H7XvuErLlJdYvoqmEdJxELAxiOFM7zc1TU7OC52qaXSZ6t2jNpJRPYfTW8uP59lThqpQT+v3IhkrYzSeH9E82DrZ9nZdFLDH5FXPqmG9VyrXh4wPHNqG8bGslCYvDo+Gd/berjfBk9Syq9VMb43OOIITWMe+gb5eDW+DWrdw9/eeYNtlBPYwvMOCxN5M760axqPUhg3nUSZbUBLTlw42XOoQDgt3t+3JGVMwSqpJiGeTGa6TVbu5IRH96Z2SoaxPB9YQ0wr6Yh1gOoqELigMOBUGnmj2gJw2bmmAmL5jDcPJzmG7dka/bhXrYtApXonJjzUsD6i18OrPYePrUdEBxd07bir5tNmee3v2671kg652f4Sxx2Uz220SWb0vbYYyvRerP2H0T7l1umetzFH/XMorzupgbukY1Y8LR1Yly/X992/bq2W1DQyvCjAwbIPF11kD1NxmdTDZZO1eWAzSdu8XyoBMb7uVCcL8l1pGfE1b+jag/EiRMNkKxhu1s/0hT+Ts7MqR5ZbnrROublVMvxcwWZ14zGZ6OawdmtjTeAHN0S7ZHux2P2i43jyc6T1NBzWO1uPp4Uzo/3XZTN+LhjN9X5p8lLtkfCOEKj86no/tHUYLh+PyvLWtNB9cls30nq92jdrpcbfj45XPWdmBvn/IDvReO1jmBQjsi1Pc92ac22+s8fvpwpyKXx2qcJlfPBRE1ubTdOtWy2GtlkGXzqc3/vP1k9/iZ1Nao6VV2PSddH+S1VWlfXBxoRPkOrmp/rxF7XGj+eRxOuD8LKvnWCe8taC0+kTbb2TtuLkeTEwgP6BtWK1b6D+J5a+1jjlT65dG1WfvMPhk4WnwuZI1fSbSPavlTpyRt7yhrE9Sy6FthduShXZLf+/l2YFtz9E2VKOw384kr53WdplvcHCvurlduX3Z3JucyjdlM9teOZzZqv3Qy53qHdUXs7l84inSTO/JtnhCSN6iHsy32MDuqd0WLxH6AhG/ama8tvVVcEsdKsfH2mfD/du+nc30nq193SYi/ylavrx1Wtmyt6i/U+UKyY+1PPmQyjzLX5Y8ZljbeGo/GaTvWFRviJNoH4SAdcSp2G4Rmkbv1jRfa2lmaWdpqE7sPig2RkBaf9i4h15bvPTW8IUylgcn296pI/e62DjIblVk97b+TJqJ/Dx7HGetyZueSVfz/rjkRZOZV2HsF+qnbRihL1JyzCEs9ghtxnnHpWKZ0AgbEbgHgXITyJM8Q/1YodbFbMu2eloBuYhCZorA4GDPIRtoytqAU3+vDjz1zh6Qs45hOtvf++phbRhWV+dweiXW9yJtwKxXa+Gd3rGq9IDiFFr8gkBAAtPz8bwDwzZYfL41QG0QuZxbSwUM9GFnViZY2ZDt732DdiRO03vweLXT70E7bs4O9D5zqL/vA3sH+m6p9Y7y4ciX6cBnWqOqXerGxxuXJWZP6qqq2jGrT+8+WohOcgmxzxL3Km+XYECmhgggv1VPYh0Y7Dkw1N/bY/WcDYhM1ic6Idxr7TirR2bagUJ7df1E3dL7t9n+vq8O9ffdhfqletK0ekNyeX64f3tmKNP3jeyRtktzkbaL5bk16uYsvfZmc29y1dQGK8JYbPHEcKbvSxrm+RYb2D21zuI1PND71mxm2/dqIF5FohrsVKGvm9l+51B/7xeGdFJvWCcnlM0atZa+M+1UuZLpe9FQf98HhrRMsknEYD7BVbUQsDSztBvSNNR0tvug2BgBa/1h4x56rSbTe9q93mfjIOs0rnZvH8nTE/m5SsdxqiW3zA0H2oZzmUQ7Q4QJjagEIV91BNat29zkEb/YOWAs36WeHtenO5y9hSAIgAAIgAAIVCmBfeNJWrLbMDJz5G0GqjRdEawqJID8VoWJUk9BQlxAAARAAARAAARqigDahsWTyyt+GmdBoHYJjC3ns4ToWU4xYNoh+Zztwe0kDiEQAIH6JIBYgUA9EBBfnuQUD6bd++7uscfnncSrQajp8VyDhuMotWHNCPv+UFghuF/aBJDflnb6I/YgAAIgAAIgAAK1TSDu0KNtGDdRPKERP1FoXGwCTL73ag1Eo9rQRny6bnjnjcOhBSEAAiAAAiAAArMINKc7/juV7hxOtXfepvarLemuS5rTXS9avXHLySvbulfOcl72v+x5TlsukcigBk7UBjat6c5NGve9anel0h1Xqf1s88aOV7e0d3Y3bzg3RZs22QRDYH1RHeYbkzaZ0eqg51FJeCXfi+SgFyIxEkB+ixFmNFWQBgEQAAEQAAEQAIFFJ4C24aInQdkDgCc0yo4YHlSSQEt7x+nM9ApHP4WJriJ7jZN+wYBA5QjAJxAAgXok4BF7Gq/VJPQ0Enq9kFzKJFd67N+7LJF7RAf7Re3DzenOHS3pzm9oHfbBlvaul6XS55259rRzTlBZrZb0OwbT1ta9XMPQQQ4fEbrbQcxEnqRfJxLxC9W+jZm/rbquZ69hKHWgaUzjnlf7oNprW9o7v5Bq73hDc/t5F65t717XZuGl+D45P3eaarN3iOhPKPPgslF6JJQEHC8WAeS3xSIPf0EABEAABEAABEIQgNMKEUDbsEKgF8Mb62gvhr/wEwTiJ7BpU4Mv9I+qeJVaF3Prshzd6iIIGRAAARAAARBwJHCczlqcKkR/JcL/IiKXE3l35MYb9+tA/zWpp15oTxY4qj4iNprInaHz9d1HzgQ+GvFI7g3sOpxDa4euVZFni9DfkPBXWLxf5ST3wEgiN9KS7nieXotuuruTquSValltOMN0765dvXgpeDhq5XMdTTPyWzR+kAYBEAABEAABEACBeiKAtmENp6YlXg0HH0EHgSMEUgeWv4SJLz5yJtwRM/1cBy3qchVmOBJwDQIgAAIgEAcBYb4nop4zaORgKqIOsu2ddML/3apnhdqwZqeXbNwZVsjjZFZlItWpOsnTqToim9b7xzcT01+6KNIJpq0qp0HRb5iqJeAhv1Vt2iBgIAACIAAClScAH0FgqRNA27D+cwAmNOo/jZdEDJvTW84j4i8SEZPb54CIf4WbKKRAAARAAARAYC4B8f37554NdaaFfHqdSrjWbYXJjJZHm95HTBepntBGSG7ac29P6HdIJMZy9hLxvaE9nCHAr1ud3tw+41TIP60nb3my7/HXVSzIu7XU2QxzMEF8y4wz+FOVBJDfqjJZECgQAAEQAAEQAAEQWBQCaBsuCvaKeooJjYrirlfPFjNeFyVSGztfz+T3aChWqnU1v8ye2LjDVRhyIAACIAACIDCbwORWTSOzz4f8/97Uxo53r1u3uSmkHDdv6Hpq6sCKPhH6QEjZw86Z6Ur9E/oJhV22TRNT1K2qWj1JXN7S3nW6hiGUsa26lNs/+b5/uwpuVOtibs4tS0Z9ysbFX8iEJID8FhIYnIMACIBAJAIQBgEQAIHqJoC2YXWnTxyhw4RGHBShI1YCqfR5zS3prg+l0l2vXL1xy8mzX4za1ta93M6rm0tS6b0DxPRVImpQ62p88eVr1NOTc1UAORAAARAAARCYTaAx5/XrubvUThi3b4+YPza+LHF/c7rz0pYNXefMrhdN7erTu49e2969rrn9vAvNXSrd+SB7ooP5crZdd7JMOySfu9FJlki0bv1fR9kjYkxnichdGp9rmtu7XtO64byT2rQdQDM+FyWs7aD2zFR7xxtSG7t+RU8c/INxU2fO7QMmuWLf3T2Pqw6Y6icgyG/Vn0gIIQiAAAiAAAiAAAhUiADahhUCvVjeeIvlcRh/4XZpEcjnEgkhuZhIvuuxf29uvHG/DmT4asXsSCI3YufVzaVKZr3aSIaJrmkc93sjKYEwCIAACIAACMwisGtX7yPM9PNZp13/rtb66hLx5MbZ9aLVjd5o7rGc5B5g8X5l7tSTNWojGfbp68M7b7R3YTjp4YbEdhXcrTYOcz6LfMv3vEFrB1icj9i9OSJPw+ndQcJfIZYL1cOIbVzu9yjxY9UDUyMEkN9qJKEQTBAIQABOQAAEQAAEQCAqAbQNoxKsbvmInb3qjhxCV5sE+KjEYxry0C8gVRkX4/u+fPLBB2+IuiWIi9+QAQEQAIE4CUBXFRKQhPcNDVZcg/qqqkKGaUfCS/5PFN+y92zdqRM6X4miY7FkheSLezJbH1gs/+FveALIb+GZQQIEQAAEQAAEQAAE6pXAEmgb1mvSBYoXJjQCYbaC7ugAAAsqSURBVIKjShLY10yHmGV/RfwU+uHw8Yd6KuIXPAEBEAABEFhyBKwhLUSfqrWIC/FHd/f3PBg13AlKfoV0ciSqnsrK880NDWNfr6yf8C0OAshvcVBcCjoQRxAAARAAARAAgaVAAG3D+k1lTGjUb9rWbsx6enLi84EKRGA/e/JBuvXW8Qr4BS9AoPYJIAYgAAJOBBpH81/QQf2fOQkvjtB/DR978PtxeG2TIpKXt6suX20tmAPs09/t3nHTQ7UQWIRxJgHkt5k88A8EQAAEQAAEQAAEnAnUgSDahnWQiPNEARMa84DB6cUlwET3lTkEvgi/Zai/764y+wP1IAACIAACS5yAbWuYpOTfKIZb1Fa7uW5Mlr8rzsn+4Z191zLJOzTi1T6pYW2Dvx7aue0mDStMjRKohvwWEB3yW0BQcAYCIAACIAACIAACrgTQNnQlV91ymNCo7vRZsqHzSco6ocHE7xke2PajJQu4OiOOUIEACIBA3RKw1UFJTr5YI1i9kxrCVzeM5l/+yMC1j2o44zQylOn7fJVPaowz0yvRNogz2RdNF/LboqGHxyAAAiAAAiAQmAAcgkClCKBtWCnSFfQHExoVhA2vghPwmEaCuw7nUgcsPji08qDtZy7hJOEaBEAABEAABNwJ2KSGThg8h4Qud9dSHkmd6P+3hrHcix588IaHy+MDFToSGvcXqv4/qK0mk2HxO4b6ey1daqBtUE3oqjYsyG9VmzQIGAiAAAiAAAiAAAhUnADahhVHXl4PMaFRXr7Q7khAB1aGVfSg2vgMkerjV+mABd6bESdV6AIBEAABEAhMwCYMsgOtr9KB/b9QocUf2Be6TYg7hzLb3qVhK9tiAo2rGckO9P4syckz9M+VahfbjAvRZXTUiqcNDWy/ebEDA/9jJ4D8FjtSKAQBEACBGiKAoIIACIDATAJoG87kUdP/MKFR08lXv4H3OJnV2D2iNhYjJF/XAZRTs5lt31OFOn6h3zAgAAIgAAIgsCgELs9nB3q/2zCab9EK6WINQkZthQ3rAL73bJ1cecZwZlvfdM/LfWxPqmQzvS9J+HIaE31T/av0uzVGlPtlyYax1uFM77uzv7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Ss8rND0/OT7syUPq531tqm9w/64Hofjcb78qrrfebi3H0S1pue791dMZHAQAk4LAlIQAISkIAEJCCBrSHALxm3ZrAOVAISkIAEdhLwkwQkIAEJSGALCeybMfOFfpJK4Qv/p8WKaJpJGpd3psajo7PkhzmJY/A9kuI0e6OkfEmfpFEhkiGOt5OOL/MauHoyiH5Y6oxyptj/ZxRHx/skLS0X0x1y2XzCgeQjSS8fVRYTwHEKh+vHxqwOczh/OmWeEsXZIYlSQACHyQKzQZngBPWqjOgTUSI0J1lKKEsd1HWWpWqYX4g9jT2U6I9EB1/m91+UuU6aoI9vSUr02iRrFVjT9v+k1QtG68iNY8z4/znptONdTi0t9Ik5gwtzuGxFlKUO6mpq/om2+tR0iHEz/hzWEuac9UJ56qG+WhWsaEx7RPslch97cp3qsKfc81Jo5h6e830RnNZwquYahklVv28fgwdGKZdkpnA/xEH/uOTiWMtc53BpwVEQR1/utzgCLmp76UYaLggDohHj2Hb31L0MA65b1hnXLXtBqmlML2UzpwAAEABJREFUmOtl1//N0wvmlpeTmtzvUu1gZRXebe039Im9l7lc5Trl5UN+dsDRexP37pJFw1j7tN67vn9UMV+F9zau977Pd9V6MF8CEpCABCQweAIOUAISkIAEukdgmV/EdW8U9kgCEpCABCQgAQlIoEsE7IsEukwAZ1eigZX08WUxImJhklYEJ9w3pmYi370kKQ4mfIlMdN8z5DNOuc9I+qkoUQCTNCoXT22viJ4uWio4FRP5cJE9TipEbP1SjO4abUouloreG31clC8NkygTBOB+z3x+RxTnxCRLyb1Sigh5TTnMpbpeyvkKe831WWjaezPW2K0yCpySl3F+TNGZQl1E/8KhZqZBjZM4ZuGg9dmUuUG0KeEFBeaaSJNEtG2q3kX14PT3sRiUvlQS05nybzlL9DPY5HBp6fr8Xygj+2CUfTDJykI9H00t3HuStC7j/uMgvEpjPEvg2HraVSrZcFn+GgDOr3V+b83LRkQdndV1WBBR+eGzMlc8x/2WqNC8UMU1smJ1rRXnnv661E404lX3glQzYt/mpZ5r86EB5S82vD/1rLL+GRcvJw3BST0oWpWm9xuusVU7zAtm3GfZe1eta1ye5wruozwPdOn67Nt67/r+MZ7veanrfR6Z2ef7Pt+zR+VZCUhAAhKQgAQkIAEJSKAJAtaxAoE6v+hcoRmLSkACEpCABCQgAQlIQAIS6ASBK6cXJV/Q4uTx+tiSJmlNHpaazxm9dZQ/q31s0l9H2xa+SMfRtY5z7s/TqbdHFwnOEYzpiBgRqShJk7Krrvvlf6JJ4sScQyUE+NmeKMVEHeM4p1aSK6Q0c83azOHWCQ7g1ygY9ddjQ/TEJIMX9k3W2Esz0lNGm5b9UyEOdpdOuqwQNRTHLBy02IuWrWdeOep8cjJxdmWN5LA1uW5qxpmOSOQ5XFnulBr+Kco8JqktlOvy/F8yI3pPtG6U4RRZKDhf8mIK0eYXGq6YiRMwkZKb6j9O3dzjWbMrdm3txc+VFh8frXMvIyLsvL9IwPMO0V6J/JpqWxH6yt5wk1ZqX71S7uXc01kXq9f2xxp4zuNZGadw9og/5tQ7wlmRF9YuXK/YXOvbJOeV0dNHlb0JdHG/4drB0Z1ngb17vNoZnllenSpoI8nGpW/rvev7R9WEut6rCO3M7+B87+ygnyQgAQlIQAISkIAEJCABCfSVAL9A7Gvf7bcE2idgCxKQgAQkIAEJSEACQyKA89ZlCgdEVNuhOt4R/ZEokHUdNT4cdkTOTDJTcAR6SnJw0E3SquA8iZMuEX5abagnld8s/cQpMUljgqMMEQxxbmqs0h5UhJMREaCvWdBX1uBXCuz6bgITotLiNNfmWHhh4PA0gHNAklrCOsUhC8esWgX3GJcf3CWmz45yT0nSuHCfggNORU1W/qBUhqNKklrS9fnHqRvHrqacmafhsC6JNk8703lNfD53Knl5tGnHtINTZ1PRTVPVWuQUaeUR0dII5jHdJThRf2PX0c7/WLu81FP3eWdnLWWf+B37M2PKvTNJZ4T9lOujrX4xbp4VlnV+3C+keBmh7pyn2ELBIfuQhRbbmdnF/eammQr2WH6OyGErwjp9bmpuax9P1UXSt/Xe9f2jCrrrvYrQzvy+z/fO0fhJAhKQQBUB8yUgAQlIQAISkMCaCfDLiTU3aXMSkIAEJCABCUhAAhKQgAQ2QoBIX6UOAJ9OD78fHaKcPYO6W7SuvCkFTozOEpxgiM5411mZLZ0jyitObDjmtdREL6rly/5Hp6dt/HyPU9PDUzfzm2TwMnZex9moarBElH5+jNqOsp0mNi44Xv37mnrBHv20tFUnQi1OPUQLxSErRdciOAL/R1qi7SSNCdfzi1MbTqFJGhXqxGmz7vXc5fnfN4SImszYctiaUD/3G5xXmmyEdf6CVMi6T9KocE+4e2qkjSS9EJzTb7xET4mOOWsv/uvUtU4nTZzEb54295INnWDu2U/bWF+TQ2KtLeP8yP750FTUVOToVLVD6u51OwoP8AProWv7DQ6zrB3WUNvI2ce5X3DfaLutWfX3bb2zXrq8f8xiPHmO/rveR6PS9Q6vPs/35Nx7LAEJSEACEpCABDZKwMYlIAEJzCOwjl9+zGvb8xKQgAQkIAEJSEACEpBAswSsbTGBsyUb54kklfLxWPwyqvyBAM6Ib//D4cz/r5az63LgS1N7BAfSx+TTn0S3Vfi5vulIm5Ms75UPzG+SwQkOuXxxjfMSTlzHZYRPisI0yUJ5YnI/Gh264DD6yAyyhEnMGpHrpRYi6iUpEpxO71Rk2awRbRKNvMla4dzm9Qxb7oWlfe7y/LPv8wJBW5FBpxmxTzwwJ2k3SSOCA+E+jdQ0uxJeZDlwdtZgzn41Izk6Oi04G7Gvn3w6o+XPB6X+U0W7IDhoc82voy84P3KvqDN2WLF/r6N/tjEadW2/Ya2wZlg7ozX9437Bcy0s1tTknmbaWu97Gmj4oOv7R9VwmeMu3V+7vt77Pt9V68F8CUhAAhKQgAQkIAEJSEACGyfAL/5X6IRFJSABCUhAAhKQgAQkIAEJ9IYATkmljk/H92ZU6+noMWnmi9FZcsacfFS0zs+Xn4v930TPHKUcX6KeOsdXjL45WkfuHOPrRJWdBL6Uj38fxSkdvijHnKuzvpmf+45GI5ydUl1n5bbpGREU6+hvUubb0S9ED42eI1oiR8bo8VHaSjJYYc3cL6OrE92QlxtukTKsNcqjXNvXzrmjoqWCIyTO01X2540BERiTFAv9oD+nTwn6h3J8o3z+cLSO/HOMLx5tW96WBugzLOkvTqKM/Zk5X0dY4xcqLEA7XZ7/y2Ucd4iWynjepxkSCfnXhZXQHu0Wmi9tNn0dsQ8T/f5hqbG0rzEd8cxzSQ4GrB/I2NjHk+yQi+XTNaIlMn19sfa5xnhuZD9jPkrqweYC+a9k74pZq8L+cN8aLYyfy8Z793jNPSB1/CRaIlePEftokkrhmYL+0U6l8W4D+vGQHDM25od5op7L5xxRlJMoSxBgfbPOp+e+7f2GSPSsmdIu8+zKX+pgL2TdLDv/1LHuFxdYp31a71xj9Ld0bta9f5T2a5ad631vKkOe771H65kaBDSVgAQkIAEJSEACEpCABJokwC8zmqzPuiQgAQk0Q8BaJCABCUhAAhKQQPMETlOjym/UsN0GU5xmfzxnoDjaXmpO3vRpHIuIlnaRZBwR/U507OR4Yo7/N4oT2kWT8mVvkkrh51qc5HAUrjTeAoPfZYw41OCEx592/X4+j4VjzuFwiVPa+HxVipPTlaqMtiSfNYpTybzrYUgYcGC5deGAWHd3jC3OgK9IylpLsku4ttlD/jqfsME2hwuFKHdXWWgx2hUN8B9Ho9FZoyXyvRixlv8qKf35UdKxcPyafLhMFBtsc1gpRP57UKz409VJGhf6QX+umZrpMyxzOPpt/sMJ/25JcXCi/zmsFBya/qLS6g8G65v/P7Q3+X/V/OOcR7S3kuio3Hdul8rH8z7N8D7JO3/0I9EqoT3apf0q22Xy6Sv3yOnriPskzyWPSKXcH4n2ncMimXd/hgMRHVkTY71BUY2j0btjN3Z0Hpcdp7zskOy1CdctL1tMNkhf2LuYr8nzs45xQp++vrDjGvtmDtjPmI8H57hEThujWc8j6+bNfap0b/yX9Jl9geey8d49XnOPTd65o/8dLZF7xggGSRYKzxTsbQuNJjJpn348OufY+5ifHI74axfvz8FNoqznryVVygisc7+Z7hFO7KyV6fPzPrP38ez6rBiwF7I+c7hn/om6f62cKLkX4oh889iuU/q23ru+fywzd673+dSGON/zR2uOBCQgAQlsnoA9kIAEJCABCWwpAb7I3NKhO2wJSEACEpCABLaRgGOWgAS2msB+haP/Rey+Fd0meW0GS7Q5nFlwbOHLayKt4gTxq+S9JTpLiIB7j1kZM87xpTnOE89L3tixIocz5ZM5e+UozpBJKgUHpHV/2V7ZqQ0Y4PiIc9fj0jZfRCeZKazx+yen1OGINYGDW1tOaelKLwTnZhzIt2V/4JpiL6iaHNYd0fcOi+Gia5s8rn8cWmNaKbePBVHnkswUnDhxwpuZOXXy2Hwm8imRMseOPTm1l5CHDdG8S50gcQxib9urshVPsGdeN3XQH/qVw5ny9py9V7RUcLwtse3y/OOkWuJMytrkJZIXZcCLGH4l+TeLsk6SLBScwHAUW2i0ROa4r1wjXCvzqvhsMrg2uDfnsFLOFYs2/8x3qm9ViBJNdD/uP/wem+iZvITy3bT69egHo9OCgyzPENPnpz8/JyeeEl20NpK9yyH+GTmY1VZO7xD2rLPsOLP+DzBijZS0DEv+CsKiZ4YfpCKiRxM9PocL5dLJZa9NMld4prheckmTVArzdKtY0Y8kcwXHcZytd+zdc623O2PT+w0v4HAPL5kF1ui/xpBn1yQzhWuYn1VKXwK6ZWrhZ5gkrQvrvE/rvev7xzIT5nofjeat9yHO9zJrxDISkIAEJCABCUhAAgMg4BAk0HUC/GKz6320fxKQgAQkIAEJSEACEpCABNZJgC95+SKvTptj21Pl4F1R6mhaX5h6mxbGiSPujVLxJ6JjBw0iqOGMeIGcw8nlmKSzhLwSRyXa4UtzopzNqmfWOZwgb5uMUkcLnFFKoralysHKUzMy1glrL4cLBQewJ8Xi9dESweFm3pe7JeX7bENEvqtmADgo/SzpqnJ0KtgnitPGIsXpk6iLMV27nD4t4hyapFJYd0Q3rDSMAWsTR94P57hKiGZ7wAIjHCqJYLvAZFcWjq5EB/virk9l/+EE+bcx/Xm0SvjdGmsDB8Iq29J8ON01xh+KlsgbYlQSATZmo5Jov12f/+tnIKeMVsmzY0CE1SSV8tVY8IJDleMra45rM+aNCpHgS6+jD6Tl10VLhHXJPlNi2yUbrluiQxMlmpeFuGdxXRA9k6iu50xncdpkf87hDuG5hKjIO05OfSA69ZNzbvzck8OFQtR0XmZaaLQ7s85fbNhdpNHkCqkNPkkWCs7/OB7DdaFhMhk/exJRhfNxrrDWqpwRSx2oaYQXtXihqHSe2LsPSUGeO5Mocwhscr9hT+L+PadrO07zjMozRskapSDX6Ks4qFB+vrl4hU1T2X1b713fP5aZl7bWO2uZPW9Rn7Dp8nof4nwvmg/zJCABCUhAAhKQgAQkIAEJbIwAXyJsrHEbXoWAZSUgAQlIQAISkIAEJCABCaxM4Pmp4fDovC++cYjBqXaWkyBR4nCKTfFKoZ1SJ6nJyohq+LDJEwuOidpGJN0FJoPOIpIgzkbz5nLW4IlGxp+LrnJIo+w58t+Fotso+2fQOPoRRRjH2nxcp2ykLSLbEVW7qnGc6J4bozrrjhcA3pgyVYIj5OXnGOEsTgS8Odk7Tj8mn0odXWO6RyjzxD2fFh9cI9nnizYl70lFON0mKZIfxgqnzSSVQrS0qnXc5fnnRZgSB1nW5kzyWbwAABAASURBVNND4zfRUsHJ+Z0FxkTSPkWBXakJzuOL7sXT9TAmnCunz8/6zP5VNd+zym36HNct0aHn9YP713uTCYskO4TzOJmfLWd5kQgHfZz8Xp7PvPTD9ULdn8/nOjLvZaXpOkr/YsN0uSY+4yxGFNmqutiz2d9mPd/NK4ujdIkTOcxxSpxXT4kDNWXpI88oOAfzuVS5v5ReH6V1Dslu0/sNz5Pz7u2TnJl/9nCu9cnzi45x5C5Zo1wn11lUUYN5fVrvcOn6/lF3alzvoxHzOmu9c35o8z3ynwSGQcBRSEACEpCABCQgAQkMkYAOukOcVcckAQlIYBUClpWABCQgAQlIYFsI4LxEBNVZzi0lDM4VoxKH2FXbIUrgB9NWlfAlI04hVXZDzf+fDAyH5iS15OOxLnFIgy9O0DHfSiGSK47mRG/8mxA4aXTIwlwT9atqjEfG4HPRuoID3f+lEI7lRO9+Xo4fEb15lOsYxzoc7J6Vz7Pkz3OSCHhJFgp9Ixr4QqM5mTgH0S/6OMdkz+kz5Ogvo03Jm1JRHce5mI8+zX8FyrxyPS8y7fL843DK/C/qP3m8XIJjDseliiPYfxUYXzQ2Z4o2JVwPJ9SsDGdJ1mhVMeZ7/u9/q0pvJn+V63bcY64for3yVw1enJP3juLUz17O9co+vuzzT6rqrOAYy/Vb1UEizeKQXmU3mc96OyInqridOzZokpnCCwCsy5mZEyd5SaL0xYOJYiOu42X3/cl6hnq86f2Ge/dZCuDy1wZKnv+nq+KZ4js5yfWPE/6Lcvy4KNc8zojcQ9i//yHn1iF9Wu992D/qzpnrfTSat96HON9114f2EpCABCQgge0i4GglIAEJSGCjBPr2C9qNwrJxCUhAAhKQgAQkIIHlCVhSAj0icPL0leiwSQYtyzgvTQLBGQ0nl8lzs45XbYfIaa+eVfGMc0TkIrrijKxBn8JpBgdd0roDxZHl9YWFmPMSp5rC6nppdpr0+iVRIjGWrP+Y9k5w3rxEYa+5vquctWZVRfRKIpASaZJIvQfHiGjZr0yKQx2ONTj35+NMwYmXfs7MnDhJFMVvT3yue4jTO3WUlLtajEr6FLOFwnWMY9JCoxmZOI/POF37FGPo8vyfNyMqufbeF7tfRuvKF1KAfTHJXCGCMy+pzDWomXFU7Jn3JMVCFNiqfhZX1jFDrrlVrtuODWet3TlnWitZm7ycQzTzmNcS9sSqueEZ+vwLasVhcUH2nixeAOEZcM+JGgc4936rhv02ma5tv5kD9eJzzk+fxtGWfW76fNVnnM/Zo3nRh+i1/PWDQ1II5/K3JuVlCNZ+3T03RZeSPq33PuwfdSfB9T4azVvvQ5zvuutDewlIQAISkIAEJCABCUigAQJWUUZAB90yTlpJQAISkIAEJCABCUhAAttDAAdEIjcOfcT8CfVlnJfGXC4zPqhIV22H6nG0mucESP5Yicq17/jDFqU4yxy3wnhx1Cnhi8MDjjcrNDWYojfJSHCKLokCF9NeyT7p7dmjJYIjTIldkzY49uLUW1InjsAldvNscOApreMiqeR00VWFyJ/fXaKSkmu4pNquz/+BJYOIDVFYk9QWnLeq+POcUOp0VdKB40uMpmyY799OnRvKx9Jrbh3jJVr6hdLQodHDol0XnBJPWdBJIm6zvxWY7jDhxYmSyNTznhG5dujjjkrnfMCxbk5W5WkiqBI9tdJwCw02vd/wrF6C/TMlRh236dt659rs8v6xzHS73udT6/N8zx+VORKQgAQkIAEJSEACEpCABDpKQAfdjk6M3eoyAfsmAQlIQAISkIAEJNBTAnW+6CVKZk+HWdztTxZb7m2IA1eJkxTOH8tEgpxusSRiG2VOm//4c51JtkpwlqlyKFsEhMib31tksDvvHEm3MUJxhj1TrpCzz4kOzWn51BnToj9Pnuxd8vP8jxNUkrUK1zlR8aoa/UEMjo2uILuKlkRUxfCs+Y+9MclKQsTAZbhShjlZqfEU7vr8l9x7MozRr/mvRWU/bKJ65oy5q1sXZVgrdct13f6n6eBXo5sSXgDA2f7u6cA7or+K8vx4SNI/i3ZdSq+Ptp27cfzCOXGaF3zPNH1yxmeuC55NZmQVneJFh02uo6JObsAIruwddZumTBP7Dc+QRO0sab+J+3dJO23a9G29d33/qDtXrvfFxIY234tHa64EJCCBIgIaSUACEpCABCQggfYI6KDbHltrloAEJCABCdQjoLUEJCABCbRN4BdpAIfRJJVS+uVxZUUdNVj2C8vxcE6eA5zkkiwUmDfxhf6P08qXo1WCo+QQI5pWjRtHm9K1Pasu5gknpFl5k+eYdxhPnuvK8YvSkZPUVMaCE9H+KXfN6KOjX4rWkevFmD+fnGQwAhfmumpAP4lBE9d3qqklOMGWRHjDQYtoj7Uqn2H8zZzD2TfJQiHyOlGmFxr1ILPL84/DH/NfgvG1MWJfrKs4BZY43+6X+pXmCbCn4AzYfM1713iynCISMs64RETnOud+eEzOPz165Wh7vztP5S1I6Qtmj0nbda8N7HGgvlLKVglOuDgnTttx/XLPnT4//Zl1wF8HmD5f57MRdOvQWo8t1xxroKo1IoSzBqrsup7PWPu03ru+f3R9vqf71/X17nxPz5ifJSABCUhAAhKQQFsErFcCEpBACPTtl4zpsiIBCUhAAhKQgAQkIAEJ1CGg7R4CX89R6Ze9fxlbHIGSDFLgsIrjA04X+xaQISorzk4FpgtNcArBCXWh0e5Mvgzdfbg1CVHicEZcdsA43JQ4QPNFLk6Iy7bTtXK/TIdwvjwh6VujD4meL3rVaB1H3fvGvsQBI2a9EOaYua7qLE7dOLNV2TWdX9o/1jRre9X22XvQqnpwEG/i92xN9buqv/PyS/luYv659+D4N6/v6zx/9jSG81WSlaSp++RKnehQ4ePTl1XuZyk+V7hGz5VcouF+KilrmBRn3BvmM2s/Sa+lK47j3BNnXR/skSXP1+y56CqTsWr5VdruatlN7zfs4SU/P3BtbuL5oul529h6X3IgXd8/6g7L9b6Y2NDme/FozZWABCQgAQlIQAISkIAEJLBhAvySoOkuWJ8EJCABCUhAAhKQgAQkIIEuEvhuOvWtaIlcKEaniw5VcFpAhzg+HCyHOK4ujmmIfcIZ/B0ZGE66xyYtkfPGCOeuJMoWEyDy7DZG8N7WKcfJEIfPbR1/W+Pm2YR9uMn6eXHnVqmQPR0H+ENzTOTcJEpLBLg+/N6hJbhWK4GBE3D/GPgETw3P+Z4C0vGPdk8CEpCABCQgAQlIQAISWJKAvyhbEpzFJCCBTRCwTQlIQAISkIAEJLASgR+l9GejJXL+GF0wWleI+nZQCuG0U6Knju27o+uW76RBoocmUSQggRkEiEp8cM4TxS1JpVw7FkSGS6JIQAKrE7CGLSVAxMMmn08uFY48+7006QFRRQISkIAEJCABCUhAAhKQgAQ6RcDOSEACEpCABIZPQAfd4c+xI5SABCQgAQlIoIqA+RKQwLYQ+E0G+rZoiRDJ5fIlhj21+Vn6/evosvK7FIRnks7JZzbYo5OmbTTJWuWcae1UUaVZAh9Jde+MlshFY3Sm6DbJyTNYosYmWauw9xBlc62NFjT289h8I7otsqn53xa+2zpOnk+4xpsY/01SyfuiOuYGQs+EZym0nW5ba9cJbNv9hbWOdn1e7F87BLZtvbdD0VolIAEJSEACEpCABCQwi4DnJNAhAjrodmgy7IoEJCABCUhAAhKQgAQk0DoBHO5woipp6MYxOm10aRlwQRxovlYwPhwWz1ZgV2VCNOI+fHF95gzk9NF1C2xgtGy7RHI+d0FhIhtukwPiL8Lk6GiJMO/Mf4lt121+mA7+JFolp4kB406yViECOH2sapQ1zdqusqvK5/pCq+yGkg/brs7/7wO5KefNVKUMmMANMrZXRE8WXbfwFxvW3ea4vVVevhrX0WZKdGT28Ko2uLcM5Z5aNdZtyi+df16UZA30nU3peBlrF9Z71/ePXqyHiU6Wzv+m1rvzPTFZHkpAAhKQgAQkIAEJSEACEmibgA66bRNeb/22JgEJSEACEpCABCQgAQksJvDFZB8TLZHLxOjaUWVvAnzhiLPm3jk7zxBdky+dd56t/wlHaZztqkrifN2EA2nfIhnBBkZVfOblM0+MeV7++DyOcURPHn/ehrTEUREOMGxirVNXibZpg2Pyrwoa2NSY2X9KHLyIKn26gnFUmZw1BmeIVgl7YhP7T1U7bed3ef55OeTrBQC4F1wydry40JYelPpPjCrdI7BvuvTo6Kq/98ZZ/cjU84Aoz4S3TFoim9wHji3pYGxwYG7r2qBeIvt/M+1MCw5hXMfT56c/80zCPWb6fJ3PB9Yx1nYtBErnn8408YIf9WxSS8fblfXe9f1jk3O5TNul80/dm1jvzjfkVQn0h4A9lYAEJCABCUhAAhLoOYFVf1HZ8+HbfQlIQAISKCOglQQkIAEJSGAwBH6ckRweLZX7xPCMUWUnARw1SxxQcNK4xM6iS306V0qVRJbCmebbsV1Vlo0MioPmqg4ly/QdNjBapixl+FKYaMccL1KiJpc41iyqY6h5rPVNzH0bPH+aSr8crRLGzNqrsms6HwfSEgddnGrP10Dj508dJXN7fOyG4LDZ9fk/IZyrhPliP66yM38mgd6fvEVGcOFoHflqjJ8SvUkU51Ic5thDrpbPj41+KMrek6TTUnJ9MADu+6TrVvZIWFe1S0RJ5qHKbl7+Psk4e1TpFoHS+afX5+C/nmvpeLuy3ru+f/RtOZTOP+PaxHp3viGvSkACEpCABLaegAAkIAEJSGBdBHTQXRdp25GABCQgAQlIQAIS2JuAZySwGQLvSrM/ipbIpWL0j1Ec0ZIoEwQ+OHG86PCKyTxFdBXBQYYvr6vq+EQMiGKZZCOCY+8m1gpsLrbCiHFkoo6qKnCq4cvmKruh5MOEPaBkPETsLHFaL6lr0zY4YZdEKaWfRJUkrauwfWUK/SCKY+v/JH1G9ODojaI41uL8e7IcTwsRdD8+fXLOZ/aOOVlFp+nn9YosR6OPxQ7n1iS9lq7P/2cK6LIPH1Bgp8lOAjig/n7nqd59Ipr8bQp7zb5959gSaZuXXP4hx6+OsicR+TCHrUobvLlP8xJVVce571fZtJX/+cKKl72/UD33j02OkT6oswiMRqXzz3Ptsj8/PCpN87zKtfz2HD87evfozaIXiRIZf9m6U7yWlI63C+u9D/tHLfgdMC6d/02sd+e7AwvELkhAAhKQgAQkIAEJSGCwBBzYXgR00N0LiSckIAEJSEACEpCABCQggYET+HTG9+JoqTw4hkRjw+Enh43KKVMbziRJeiefS49xbkmyUP4queeNLivwuVZh4Q/HDue9UdJVZNnoi01EC1623zgR0u+65XFAvHphIRygC00HYYbDFuu3ZDCsu5LroaSuTdvgoHd0YScuF7tlIpXiEHfNoNTaAAAQAElEQVSh3WX3T3rD6N2iz43iIPeFpN+Kss/gaJXDHVL6ggBre1b5HZUt+EDfrrAgfzLr/ZMfenzc9flnbeDYWIX4oBiwvyVRCgkQhb6EbWF1GzFj3z6woGVe1Lpy7J4T5a8rJFm7tMH7KxlFyV8SuHTseL5Ksnb5VGGLV41dSXT/mO0l3JvOstdZT3SBAC+zlPTjL2O0TKRnHG9xdiSKMvdw1tGdUtfTo6+IHhPlhSoi8V80x21Ln9Z7H/aPtuer6fq7vN4HN99NT571SUACEpCABCQgAQlIQAISaJKADrpN0rSubSbg2CUgAQlIQAISkIAE+kMA56Pnp7ulznT83PSS2N8u2pSTLvVcJfXh0MWXyDnsnRyXHpdEMsQRjyjEyzpKXT/tlESV+lXs3hJdJETEI6LWIhvymJ+6Tn04uuAEQPlNKI6If71EwxdMmZJ+E5GPP/Ed860QIrcekpESZS1JpXA94FBaadgTA+aaOa/qLk7pJdfndD2XzAkcdJMsFBwbvj/DAoeXEt6s79vPKF9yin3gDjGc/jPpObWXEHG41Gl4r8IdPNHl+f9yeKFJFgrRky+w0GLvTF5yeHNOT0Z23mTkxXRFqUngHLE/dbRKiNrNSz1VdpP5yzgLTpZfxzF/RaDkZRqiw+OkW6dP7IlPSwGeoyYjk3KvJDIp9XHPPFVsFsmxyeQaS7JQuEfwVxgWGs3IpH0cMmdkeaoDBIgoWnL/xsH6Okv0l5cCS16uoh+s4yWaqFWkT+u9D/tHLfgdMGaddXW9O98dWCB2QQIS6AUBOykBCUhAAhKQgAQaIcAXzY1UZCUSkIAEJCABCbRBwDolIAEJSKAlAh9NvURNS1Ik/OyEU+9jY40DT5Kl5dwp+arokdEDon0VIr+9obDzfx87ImQmqSVEwnt4YYl3xu7j0UVClNPvLjKYyMO5C2eUiVMLD0sdiRdWskIma/RfUh5H4SRFghPqfWOJE3WShULUSqKZLjQaSOYZMo4jonUcfD4Qe66JJIMQIo1XXU8MlGvkPjnAISpJkbDucHylbFUBounOchTGqeZdVYV35z8wKY5jSWoJZRhbSSGc/YhEVmLbB5suzz8O2+z3VRyJvHnPGNV5OYQXZoiqSlRoIi9y32IfmI68+NPUu8wLESmmtEyg1ImWOeSFrdLu8Ox341Lj5e1WLslzzpsKauGZ4f6xq7N380x2o5SZjkx6aM4RmZSXFIhMCttb59w8Ya8scY7mHvGIVLJvtI7cJsZXiirdJMD9+32FXbtH7PaLlgpr5pYxLnmufWvs1vHc1qf13of9I9PWK+nyene+e7WU7KwEJCABCUhAAsMj4IgkIIFtI8Av47ZtzI5XAhKQgAQkIAEJSEACEpAAThn/EQxENUpSLPeL5TFRHBRwMsthkZw0VkRzwmmCSJt9cPJIlyvllbH4UbRK+NnzuTGCQZIi4Qt5HD7OU2Q9Gr1gNBr9IrpI+CKSaEGLbMZ5t8gBDnpJKgW7p1ZatW9whTTxuGjJ2sSJ4SaxvW20RHCIwzGuxLZvNjhyEPUPp2yiZRLpCjal42A/eWOpceyIOvuzpJRbpDig1nGeSpWNCU4rpQ74RG++e1pmTSWplBvE4qbRKiEqLVHGZ9nhtPvCZMAvyUJhfg+PBVH1khQJjprsP6cssCZ694tjV9KXmPVCujz/cH59KJImWSh3Ti7OeiVrk33zn2J/8miVHB0DngWSKB0jgPNoSZcuG6MzRkuE9XOvGF4zWiLnKzFq0YZ7R8mzGXs3z7Ws/aruwIAXK0ojis/bu2mHZzWuYY6r9MIxeGS0pI8xG3Ef/3cO1M4S4P7Nzw8lHSQK/kNjWDr/RIbmxYwUWSjct0vX4MKKCjK7s94LOhuTru8f6WKvpOvr3fnu1XKysxKQgAQkIAEJSEACEpBAnwnwJela+m8jEpCABCQgAQlIQAISkIAEOkbgq+nPg6K/i9YRot4S1fHHKfTMKM4ARPfCCTcfdwlfJOPEQFRVHEeJJka0qGvtyh3Of0RUJRpwyYhwkuNLQBw8JlnNKssX7NheclbmjHPvzrmSL9r5kpQ/NRrzSsEx779itcipD4cVnA1xXmV8Md+4EO2R6K9EgZ3XGfjfO5kviZb8XgBHhmIHxNS5CcHRGIe9ZRRnRKL+vS0dhx/Xbw6L5UOxJIJukkEJDqqlDu1EUHxCRk+UySQzhesFBzec9UvWXVVU2vemlfdESwSHOeYIhzT6Ma8MeddLJnsKEVRzWCnsEyXRICsr6phBl+efueS6q0LGOjssRg+ILlqb5LGGbx67EuG+zjNAie0QbQ7MoHipIUnnpPTFK54zmHPmftEgcOIlgjK2i+wm8+reQybLzjquy5tnszfPqmjGuYfl3JOjp4nOk/Ezw4PnGUydx/mSqKFTp3d85AUQXsLYcXLOB+7LVc817N08j70mdXTleSxdUeYQeEfOE6k9SaWUzD+V/EX+e2m0ZP55bi/5KwGprhHp03rvw/7RyKSssZIur3fne40LYchNOTYJSEACEpCABCQgAQlIoJoAv6iuttJCAhKQQHcJ2DMJSEACEpCABCSwCoH/TmGcdJPUFpw67pJSOPV9OynOn2PnQBwaT8i510ZvF8U2yeCEMROJuNSJD6cVnKU+EhI4QuHYjFNFPo6IFEoEWKIM43iFIzTnq/TnMcD56sSkJULkwxI7bHDqw/6QfDhbdNxXvvy/YT7z55xxBsGZNx87I0R//XJ6gzMNHHGuycddjOk3/J+UE6W/E2CMlEkRZQYBWA7RWe8zGSuO2UmKhOijfNHPvjh5vZwipYmejTM/TmNcPzm1ULiunx8L9tQkM4Vr/lHJKX3J4kyx5U9avyUpL0tM9oNjrg2u6dclf5GzWrL3CBHRie7IXrjn5EAOujj/Y7Rcb08bf6hI2eeIqMnavH1sp+87zDvRPlm/ya4UnMp4dqg07KEB1x2R5qu6/mcx+M/ouaMI929eVuKaJdIs5zalREAnYmVJ+wfHiEjIt0qKI26SXcJ+wJ5FlO7v5Mzdom1IW7zZj56RDvMsmqRSGN/XYsWzDn+9YPzMwN49fi7jPse1FLOFwvPgc2KxaO9O9ggHXtbLqPDfouca+si+zrNK157HCoe3dWZcV6V7OHDmzT8/VxBl+Vkx+li05K9usDb5awml+0SqXVn6tN77sH+sPCFrrqDL6935XvNisDkJSEACEqhFQGMJSEACEpDAoAiU/GJtUAN2MBKQgAQkIAEJSKCMgFYSkMCWEOBLWhxMcSbo8pBxCPq3jnaQvj2mZt8uFvuXR3FsxrmOeSDKMNEwcZxLVrE8PJY49CYpEiJm4cBTZBwjHPWInEekt3FfibhKdE8i8MWkk0K/cV78YnrHl69jxvQb/jldJDgQMf5fF1lvnxGRm3EKGuLIWTOPz8BwbExSJOeIFZHFJ68XnGCIIH7j5JUKe/JHC4yJgoejTYHpHhOi6PIiANcxY0Q55tqoc0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"/></switch></g></g></g></g><g data-cell-id="50"><g><rect x="41" y="580" width="650" height="100" rx="15" ry="15" fill="#fff2cc" stroke="#d6b656" stroke-width="2" pointer-events="all" style="fill: light-dark(rgb(255, 242, 204), rgb(40, 29, 0)); stroke: light-dark(rgb(214, 182, 86), rgb(109, 81, 0));"/></g><g><g><switch><foreignObject style="overflow: visible; text-align: left;" pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 648px; height: 1px; padding-top: 630px; margin-left: 42px;"><div style="box-sizing: border-box; font-size: 0; text-align: center; color: #000000; "><div style="display: inline-block; font-size: 16px; font-family: Helvetica; color: light-dark(#000000, #ffffff); line-height: 1.2; pointer-events: all; white-space: normal; word-wrap: normal; ">Identical actor marginals: {m₁,m₂,m₃,m₄}<br />Identical pair-token multiset: {red×2, blue×2, gray×2}</div></div></div></foreignObject><image x="42" y="611.5" width="648" height="42" 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value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;13&quot; edge=&quot;1&quot; parent=&quot;3&quot; source=&quot;21&quot; style=&quot;fontFamily=Arial;edgeStyle=none;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#7A7F89;strokeWidth=4;endArrow=none;&quot; target=&quot;22&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;14&quot; edge=&quot;1&quot; parent=&quot;3&quot; source=&quot;21&quot; style=&quot;fontFamily=Arial;edgeStyle=none;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#4D83C4;strokeWidth=4;endArrow=none;&quot; target=&quot;23&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;15&quot; edge=&quot;1&quot; parent=&quot;3&quot; source=&quot;22&quot; style=&quot;fontFamily=Arial;edgeStyle=none;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#D95F5F;strokeWidth=4;endArrow=none;&quot; target=&quot;23&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;20&quot; parent=&quot;3&quot; style=&quot;fontFamily=Arial;ellipse;whiteSpace=wrap;html=1;aspect=fixed;align=center;verticalAlign=middle;fontSize=16;fontStyle=1;fillColor=#D5E8D4;strokeColor=#6D9E69;strokeWidth=2;&quot; value=&quot;A&amp;#xa;m&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt;&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;80&quot; width=&quot;80&quot; x=&quot;130&quot; y=&quot;80&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;21&quot; parent=&quot;3&quot; style=&quot;fontFamily=Arial;ellipse;whiteSpace=wrap;html=1;aspect=fixed;align=center;verticalAlign=middle;fontSize=16;fontStyle=1;fillColor=#D5E8D4;strokeColor=#6D9E69;strokeWidth=2;&quot; value=&quot;B&amp;#xa;m&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;80&quot; width=&quot;80&quot; x=&quot;490&quot; y=&quot;80&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;22&quot; parent=&quot;3&quot; style=&quot;fontFamily=Arial;ellipse;whiteSpace=wrap;html=1;aspect=fixed;align=center;verticalAlign=middle;fontSize=16;fontStyle=1;fillColor=#D5E8D4;strokeColor=#6D9E69;strokeWidth=2;&quot; value=&quot;C&amp;#xa;m&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;80&quot; width=&quot;80&quot; x=&quot;130&quot; y=&quot;280&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;23&quot; parent=&quot;3&quot; style=&quot;fontFamily=Arial;ellipse;whiteSpace=wrap;html=1;aspect=fixed;align=center;verticalAlign=middle;fontSize=16;fontStyle=1;fillColor=#D5E8D4;strokeColor=#6D9E69;strokeWidth=2;&quot; value=&quot;D&amp;#xa;m&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;80&quot; width=&quot;80&quot; x=&quot;490&quot; y=&quot;280&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;24&quot; parent=&quot;3&quot; style=&quot;fontFamily=Arial;rounded=1;whiteSpace=wrap;html=1;align=center;verticalAlign=middle;fontSize=14;fillColor=#E9F2FC;strokeColor=#6C8EBF;&quot; value=&quot;Every actor: 1 red + 1 blue + 1 gray incident relation&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;50&quot; width=&quot;440&quot; x=&quot;130&quot; y=&quot;390&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;4&quot; parent=&quot;1&quot; style=&quot;fontFamily=Arial;swimlane;startSize=42;rounded=1;html=1;whiteSpace=wrap;fontSize=18;fontStyle=1;fillColor=#F8FAFD;strokeColor=#7792B8;strokeWidth=2;container=1;pointerEvents=0;&quot; value=&quot;Group B: concentrated incidence&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;480&quot; width=&quot;700&quot; x=&quot;840&quot; y=&quot;80&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;30&quot; edge=&quot;1&quot; parent=&quot;4&quot; source=&quot;40&quot; style=&quot;fontFamily=Arial;edgeStyle=none;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#D95F5F;strokeWidth=4;endArrow=none;&quot; target=&quot;41&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;31&quot; edge=&quot;1&quot; parent=&quot;4&quot; source=&quot;40&quot; style=&quot;fontFamily=Arial;edgeStyle=none;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#D95F5F;strokeWidth=4;endArrow=none;&quot; target=&quot;42&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;32&quot; edge=&quot;1&quot; parent=&quot;4&quot; source=&quot;40&quot; style=&quot;fontFamily=Arial;edgeStyle=none;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#4D83C4;strokeWidth=4;endArrow=none;&quot; target=&quot;43&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;33&quot; edge=&quot;1&quot; parent=&quot;4&quot; source=&quot;41&quot; style=&quot;fontFamily=Arial;edgeStyle=none;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#4D83C4;strokeWidth=4;endArrow=none;&quot; target=&quot;42&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;34&quot; edge=&quot;1&quot; parent=&quot;4&quot; source=&quot;41&quot; style=&quot;fontFamily=Arial;edgeStyle=none;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#7A7F89;strokeWidth=4;endArrow=none;&quot; target=&quot;43&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;35&quot; edge=&quot;1&quot; parent=&quot;4&quot; source=&quot;42&quot; style=&quot;fontFamily=Arial;edgeStyle=none;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#7A7F89;strokeWidth=4;endArrow=none;&quot; target=&quot;43&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;40&quot; parent=&quot;4&quot; style=&quot;fontFamily=Arial;ellipse;whiteSpace=wrap;html=1;aspect=fixed;align=center;verticalAlign=middle;fontSize=16;fontStyle=1;fillColor=#D5E8D4;strokeColor=#6D9E69;strokeWidth=2;&quot; value=&quot;A&amp;#xa;m&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt;&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;80&quot; width=&quot;80&quot; x=&quot;130&quot; y=&quot;80&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;41&quot; parent=&quot;4&quot; style=&quot;fontFamily=Arial;ellipse;whiteSpace=wrap;html=1;aspect=fixed;align=center;verticalAlign=middle;fontSize=16;fontStyle=1;fillColor=#D5E8D4;strokeColor=#6D9E69;strokeWidth=2;&quot; value=&quot;B&amp;#xa;m&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;80&quot; width=&quot;80&quot; x=&quot;490&quot; y=&quot;80&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;42&quot; parent=&quot;4&quot; style=&quot;fontFamily=Arial;ellipse;whiteSpace=wrap;html=1;aspect=fixed;align=center;verticalAlign=middle;fontSize=16;fontStyle=1;fillColor=#D5E8D4;strokeColor=#6D9E69;strokeWidth=2;&quot; value=&quot;C&amp;#xa;m&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;80&quot; width=&quot;80&quot; x=&quot;130&quot; y=&quot;280&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;43&quot; parent=&quot;4&quot; style=&quot;fontFamily=Arial;ellipse;whiteSpace=wrap;html=1;aspect=fixed;align=center;verticalAlign=middle;fontSize=16;fontStyle=1;fillColor=#D5E8D4;strokeColor=#6D9E69;strokeWidth=2;&quot; value=&quot;D&amp;#xa;m&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;80&quot; width=&quot;80&quot; x=&quot;490&quot; y=&quot;280&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;44&quot; parent=&quot;4&quot; style=&quot;fontFamily=Arial;rounded=1;whiteSpace=wrap;html=1;align=center;verticalAlign=middle;fontSize=14;fillColor=#FCE8E8;strokeColor=#B85450;&quot; value=&quot;A: 2 red; D: 2 gray; B/C: mixed incident relations&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;50&quot; width=&quot;440&quot; x=&quot;130&quot; y=&quot;390&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;50&quot; parent=&quot;1&quot; style=&quot;fontFamily=Arial;rounded=1;whiteSpace=wrap;html=1;align=center;verticalAlign=middle;fontSize=16;fillColor=#FFF2CC;strokeColor=#D6B656;strokeWidth=2;&quot; value=&quot;Identical actor marginals: {m&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt;,m&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;,m&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;,m&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;}&amp;#xa;Identical pair-token multiset: {red×2, blue×2, gray×2}&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;100&quot; width=&quot;650&quot; x=&quot;90&quot; y=&quot;590&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;51&quot; parent=&quot;1&quot; style=&quot;fontFamily=Arial;rounded=1;whiteSpace=wrap;html=1;align=center;verticalAlign=middle;fontSize=18;fontStyle=1;fillColor=#F8CECC;strokeColor=#B85450;strokeWidth=2;&quot; value=&quot;Pair-bag mean&amp;#xa;identical&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;100&quot; width=&quot;260&quot; x=&quot;790&quot; y=&quot;590&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;52&quot; parent=&quot;1&quot; style=&quot;fontFamily=Arial;rounded=1;whiteSpace=wrap;html=1;align=center;verticalAlign=middle;fontSize=18;fontStyle=1;fillColor=#D5E8D4;strokeColor=#82B366;strokeWidth=2;&quot; value=&quot;PhaseSet incidence moments&amp;#xa;distinguishable&quot; vertex=&quot;1&quot;&gt;&#10;          &lt;mxGeometry height=&quot;100&quot; width=&quot;420&quot; x=&quot;1090&quot; y=&quot;590&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;53&quot; edge=&quot;1&quot; parent=&quot;1&quot; source=&quot;50&quot; style=&quot;fontFamily=Arial;edgeStyle=orthogonalEdgeStyle;rounded=1;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=#6B7280;strokeWidth=2;endArrow=block;endFill=1;exitX=1;exitY=0.35;exitDx=0;exitDy=0;entryX=0;entryY=0.35;entryDx=0;entryDy=0;&quot; target=&quot;51&quot; value=&quot;&quot;&gt;&#10;          &lt;mxGeometry relative=&quot;1&quot; as=&quot;geometry&quot; /&gt;&#10;        &lt;/mxCell&gt;&#10;        &lt;mxCell id=&quot;54&quot; edge=&quot;1&quot; parent=&quot;1&quot; source=&quot;50&quot; 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text-align: left;" pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 20px; margin-left: 741px;"><div style="box-sizing: border-box; font-size: 0; text-align: center; color: #17233C; "><div style="display: inline-block; font-size: 26px; font-family: Arial; color: light-dark(#17233C, #c6d0e5); line-height: 1.2; pointer-events: all; font-weight: bold; white-space: nowrap; ">Why pair-bagging is not enough</div></div></div></foreignObject><image x="542.5" y="5" width="397" height="37.5" 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"/></switch></g></g></g></g><g data-cell-id="50"><g><rect x="31" y="570" width="650" height="100" rx="15" ry="15" fill="#fff2cc" stroke="#d6b656" stroke-width="2" pointer-events="all" style="fill: light-dark(rgb(255, 242, 204), rgb(40, 29, 0)); stroke: light-dark(rgb(214, 182, 86), rgb(109, 81, 0));"/></g><g><g><switch><foreignObject style="overflow: visible; text-align: left;" pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 648px; height: 1px; padding-top: 620px; margin-left: 32px;"><div style="box-sizing: border-box; font-size: 0; text-align: center; color: #000000; "><div style="display: inline-block; font-size: 16px; font-family: Arial; color: light-dark(#000000, #ffffff); line-height: 1.2; pointer-events: all; white-space: normal; word-wrap: normal; ">Identical actor marginals: {m<sub>1</sub>,m<sub>2</sub>,m<sub>3</sub>,m<sub>4</sub>}<br />Identical pair-token multiset: {red×2, blue×2, gray×2}</div></div></div></foreignObject><image x="32" y="600" width="648" height="45" 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diff --git a/paper/BUILD_RECEIPT.json b/paper/BUILD_RECEIPT.json
new file mode 100644
index 0000000..e47b3c0
--- /dev/null
+++ b/paper/BUILD_RECEIPT.json
@@ -0,0 +1,16 @@
+{
+  "bibliography_sha256": "7db5537b9c85545e3fff89b29e17438936f0bcfb3e7d1ee2cd8de24f4de1a279",
+  "created_at_utc": "20260908T055718662230Z",
+  "ieeebib_sha256": "7e6ca0c8b72158d504a12bb091f817c07032a021ba41ca783cca4c2dd80d570b",
+  "overfull_hbox_count": 0,
+  "overfull_vbox_count": 0,
+  "pdf_bytes": 514458,
+  "pdf_sha256": "e6589c0e35da06b3b7b8f72c3fbacc99eaa44adf6be9c20465b4cb03fe3961a8",
+  "schema": "phaseset-paper-build-v1",
+  "scientific_results_created": false,
+  "source_sha256": "21f99fb638bce1d6451bf58fa75ce01a7f0d081c8573c4d82aaf1ec502c33f50",
+  "spconf_sha256": "dc5d632639040cb183f2ab62780f314845aa021be73048c8a9ec9c2072d64a86",
+  "submission_ready_asserted": false,
+  "underfull_hbox_count": 2,
+  "underfull_vbox_count": 1
+}
diff --git a/paper/README.md b/paper/README.md
index 694d411..e9d91f5 100644
--- a/paper/README.md
+++ b/paper/README.md
@@ -56,12 +56,18 @@ receipt. The script needs no `latexmk` or Perl and disables TeX shell escape.
 
 Verified on 2026-09-08 with MiKTeX pdfTeX/BibTeX: five pages, technical content
 ends on page 4, page 5 contains only references, no undefined references or
-overfull boxes, and all 20 font entries embedded. Two underfull horizontal
+overfull boxes, and all 18 font entries embedded with no Type 3 fallback. Two underfull horizontal
 boxes, one underfull vertical box, and a nonfatal `balance` warning remain
 reported in the logs. All 57 `HOLD` result cells remain visible. Inspect page
 boundaries, fonts, figures, citations, and claims after every content change.
 A generated PDF remains a `NO_RESULT / NOT_FOR_SUBMISSION` artifact.
 
+`BUILD_RECEIPT.json` binds the checked source, bibliography, official styles,
+and PDF by SHA-256. Both diagrams now have native editable PowerPoint objects,
+alongside draw.io/PDF/SVG/PNG exports. The architecture and method text show the
+implemented parallel fixed-activity periodic branch and learned group base;
+they do not assert that Morlet reads learned base features.
+
 ## Non-negotiable language
 
 - The official Embody 3D scale and the project’s 572 eligible captures are
diff --git a/paper/main.pdf b/paper/main.pdf
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diff --git a/paper/main.tex b/paper/main.tex
index 01bc96d..ee26a4c 100644
--- a/paper/main.tex
+++ b/paper/main.tex
@@ -96,8 +96,11 @@ \section{Related evidence}
 \section{PhaseSet}
 \subsection{Input and deterministic periodic relations}
 An input is a dynamically padded tensor of $K\!\geq\!2$ actors, 200 frames,
-and 22 body joints, with actor/joint/frame masks. A shared 512D actor encoder
-uses no actor ID. Motion is resampled to 20 Hz; an independently hashed 20-Hz
+and 22 body joints, with actor/joint/frame masks. The group base uses a shared
+512D actor encoder with no actor ID. In parallel, the periodic branch extracts
+fixed, mask-aware body-motion activity from skeleton coordinates; it does not
+apply Morlet kernels to learned base features. Motion is resampled to 20 Hz;
+an independently hashed 20-Hz
 Morlet bank retains PhasePair's six physical center frequencies while its
 legacy 30-Hz tap bytes remain unchanged. Each response is computed once per actor. For every
 valid unordered pair $\{i,j\}$ and band $b$, the inherited directed 13D
@@ -134,7 +137,8 @@ \subsection{Pair and incidence paths}
 \begin{figure*}[t]
 \centering
 \includegraphics[width=0.96\textwidth]{../figures/phaseset_architecture.pdf}
-\caption{Registered architecture (conceptual, no-result). Directed relations
+\caption{Implementation architecture (no-result). Fixed motion activity and
+the qualified learned group base are parallel paths. Directed relations
 feed a symmetric pair mean and incident-moment topology path; a hard $K{=}2$
 gate preserves dyadic equivalence. Six motion tokens align with six semantic
 text-band tokens on top of the frozen qualified base score.}
@@ -146,9 +150,9 @@ \subsection{Pair and incidence paths}
 g_b=m_b\,C\!\left(\bar p_b+\tanh(\lambda_b)q_b\right),
 \label{eq:token}
 \end{equation}
-where $m_b$ is the band-valid mask and $C_b$ is the common postprocessor. The
+where $m_b$ is the band-valid mask and $C$ is the common postprocessor. The
 capacity-matched pair-only control passes exact positive zero for $q_b$ through
-the same $C_b$. Fixed-tree float64 microblock reduction, compensated
+the same $C$. Fixed-tree float64 microblock reduction, compensated
 microblock accumulation, and fixed-order Chan merges make the output invariant
 to registered chunk sizes.
 
@@ -208,10 +212,8 @@ \section{Registered experiments: all results held}
 uses four alternating blocks: temporal self-attention is applied independently
 with shared weights, then each frame performs set attention across the valid
 actors, followed by PMA. No candidate receives an actor index, actor-specific
-tower, or actor-0 reference. The architecture decision uses the three-seed
-validation mean, not per-seed winners; an exact tie is resolved only by the
-registered resource order. Thus every residual system for a seed reads the
-same immutable winning-base checkpoint and cache.
+tower, or actor-0 reference. Every residual system reads the same immutable
+winning-base checkpoint and cache for its seed.
 
 Systems 01--08 each run at the same three seeds (24 more attempts), for a
 formal total of \textbf{33 training attempts}. Bases use the registered AdamW
diff --git a/scripts/render_editable_figures.py b/scripts/render_editable_figures.py
new file mode 100644
index 0000000..a01aae7
--- /dev/null
+++ b/scripts/render_editable_figures.py
@@ -0,0 +1,823 @@
+"""Render the two PhaseSet draw.io pages as native editable PowerPoint shapes.
+
+This is a deliberately small renderer for the closed shapes used by this
+project, not a generic draw.io importer. Publication PDF/SVG exports remain
+the responsibility of the native draw.io CLI. No slide-wide raster is used.
+"""
+from __future__ import annotations
+
+import argparse
+from datetime import datetime, timezone
+import hashlib
+from html.parser import HTMLParser
+import io
+import json
+import math
+import platform
+import posixpath
+from pathlib import Path
+import re
+import xml.etree.ElementTree as ET
+import zipfile
+import zlib
+
+import lxml.etree
+import pptx
+import pptx.api
+from pptx import Presentation
+from pptx.dml.color import RGBColor
+from pptx.enum.dml import MSO_LINE_DASH_STYLE
+from pptx.enum.shapes import MSO_AUTO_SHAPE_TYPE, MSO_CONNECTOR_TYPE
+from pptx.enum.text import MSO_ANCHOR, PP_ALIGN
+from pptx.oxml.xmlchemy import OxmlElement
+from pptx.util import Emu, Inches, Pt
+
+
+PYTHON_PPTX_VERSION = "1.0.2"
+DEFAULT_TEMPLATE_BYTES = 34_030
+DEFAULT_TEMPLATE_SHA256 = "e10cc9e120961f6bd4074a373c9c80d2a06c497157e8f4972977b7bea83a8f34"
+REGISTERED_SOURCES = {
+    "phaseset_motivation.drawio": (
+        "a0a25fbc8c55575d50c6ffa8d6add01ce780cc8842328bb3f6bba20c6509d18a"
+    ),
+    "phaseset_architecture.drawio": (
+        "1a61a503c935a72cbfbe1ce175ab54bf975eb3e0a2164018aeda80448f472a1a"
+    ),
+}
+REGISTERED_RUNTIME: dict[str, object] = {
+    "libxml2": [2, 11, 9],
+    "libxslt": [1, 1, 45],
+    "lxml": "6.1.1.0",
+    "machine": "AMD64",
+    "python": "3.12.14",
+    "python_implementation": "CPython",
+    "python_pptx": PYTHON_PPTX_VERSION,
+    "system": "Windows",
+    "zlib_build": "1.3.2",
+    "zlib_runtime": "1.3.2",
+}
+ZIP_TIMESTAMP = (1980, 1, 1, 0, 0, 0)
+ZIP_CREATE_SYSTEM = 0
+ZIP_EXTERNAL_ATTR = 0o600 << 16
+
+REL_NS = "http://schemas.openxmlformats.org/package/2006/relationships"
+CONTENT_TYPES_NS = "http://schemas.openxmlformats.org/package/2006/content-types"
+APP_NS = "http://schemas.openxmlformats.org/officeDocument/2006/extended-properties"
+PML_NS = "http://schemas.openxmlformats.org/presentationml/2006/main"
+DML_NS = "http://schemas.openxmlformats.org/drawingml/2006/main"
+
+THUMBNAIL_REL = "http://schemas.openxmlformats.org/package/2006/relationships/metadata/thumbnail"
+PRINTER_REL = "http://schemas.openxmlformats.org/officeDocument/2006/relationships/printerSettings"
+REMOVED_TEMPLATE_PARTS = frozenset(
+    {"docProps/thumbnail.jpeg", "ppt/printerSettings/printerSettings1.bin"}
+)
+RETAINED_PACKAGE_PARTS = frozenset(
+    {
+        "[Content_Types].xml",
+        "_rels/.rels",
+        "docProps/app.xml",
+        "docProps/core.xml",
+        "ppt/_rels/presentation.xml.rels",
+        "ppt/presProps.xml",
+        "ppt/presentation.xml",
+        "ppt/slideMasters/_rels/slideMaster1.xml.rels",
+        "ppt/slideMasters/slideMaster1.xml",
+        "ppt/slides/_rels/slide1.xml.rels",
+        "ppt/slides/slide1.xml",
+        "ppt/tableStyles.xml",
+        "ppt/theme/theme1.xml",
+        "ppt/viewProps.xml",
+    }
+    | {
+        f"ppt/slideLayouts/slideLayout{index}.xml"
+        for index in range(1, 12)
+    }
+    | {
+        f"ppt/slideLayouts/_rels/slideLayout{index}.xml.rels"
+        for index in range(1, 12)
+    }
+)
+TEMPLATE_OUTPUT_PARTS = RETAINED_PACKAGE_PARTS | REMOVED_TEMPLATE_PARTS
+
+ALLOWED_RELATIONSHIP_TYPES = frozenset(
+    {
+        "http://schemas.openxmlformats.org/officeDocument/2006/relationships/extended-properties",
+        "http://schemas.openxmlformats.org/officeDocument/2006/relationships/officeDocument",
+        "http://schemas.openxmlformats.org/officeDocument/2006/relationships/presProps",
+        "http://schemas.openxmlformats.org/officeDocument/2006/relationships/slide",
+        "http://schemas.openxmlformats.org/officeDocument/2006/relationships/slideLayout",
+        "http://schemas.openxmlformats.org/officeDocument/2006/relationships/slideMaster",
+        "http://schemas.openxmlformats.org/officeDocument/2006/relationships/tableStyles",
+        "http://schemas.openxmlformats.org/officeDocument/2006/relationships/theme",
+        "http://schemas.openxmlformats.org/officeDocument/2006/relationships/viewProps",
+        "http://schemas.openxmlformats.org/package/2006/relationships/metadata/core-properties",
+    }
+)
+EXPECTED_RELATIONSHIPS = frozenset(
+    {
+        (
+            "",
+            "rId1",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/officeDocument",
+            "ppt/presentation.xml",
+        ),
+        (
+            "",
+            "rId3",
+            "http://schemas.openxmlformats.org/package/2006/relationships/metadata/core-properties",
+            "docProps/core.xml",
+        ),
+        (
+            "",
+            "rId4",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/extended-properties",
+            "docProps/app.xml",
+        ),
+        (
+            "ppt/presentation.xml",
+            "rId1",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/slideMaster",
+            "slideMasters/slideMaster1.xml",
+        ),
+        (
+            "ppt/presentation.xml",
+            "rId3",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/presProps",
+            "presProps.xml",
+        ),
+        (
+            "ppt/presentation.xml",
+            "rId4",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/viewProps",
+            "viewProps.xml",
+        ),
+        (
+            "ppt/presentation.xml",
+            "rId5",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/theme",
+            "theme/theme1.xml",
+        ),
+        (
+            "ppt/presentation.xml",
+            "rId6",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/tableStyles",
+            "tableStyles.xml",
+        ),
+        (
+            "ppt/presentation.xml",
+            "rId7",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/slide",
+            "slides/slide1.xml",
+        ),
+        (
+            "ppt/slides/slide1.xml",
+            "rId1",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/slideLayout",
+            "../slideLayouts/slideLayout7.xml",
+        ),
+        (
+            "ppt/slideMasters/slideMaster1.xml",
+            "rId12",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/theme",
+            "../theme/theme1.xml",
+        ),
+    }
+    | {
+        (
+            f"ppt/slideLayouts/slideLayout{index}.xml",
+            "rId1",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/slideMaster",
+            "../slideMasters/slideMaster1.xml",
+        )
+        for index in range(1, 12)
+    }
+    | {
+        (
+            "ppt/slideMasters/slideMaster1.xml",
+            f"rId{index}",
+            "http://schemas.openxmlformats.org/officeDocument/2006/relationships/slideLayout",
+            f"../slideLayouts/slideLayout{index}.xml",
+        )
+        for index in range(1, 12)
+    }
+)
+DEFAULT_CONTENT_TYPES = {
+    "rels": "application/vnd.openxmlformats-package.relationships+xml",
+    "xml": "application/xml",
+}
+OVERRIDE_CONTENT_TYPES = {
+    "/docProps/app.xml": "application/vnd.openxmlformats-officedocument.extended-properties+xml",
+    "/docProps/core.xml": "application/vnd.openxmlformats-package.core-properties+xml",
+    "/ppt/presProps.xml": "application/vnd.openxmlformats-officedocument.presentationml.presProps+xml",
+    "/ppt/presentation.xml": "application/vnd.openxmlformats-officedocument.presentationml.presentation.main+xml",
+    "/ppt/slideMasters/slideMaster1.xml": "application/vnd.openxmlformats-officedocument.presentationml.slideMaster+xml",
+    "/ppt/slides/slide1.xml": "application/vnd.openxmlformats-officedocument.presentationml.slide+xml",
+    "/ppt/tableStyles.xml": "application/vnd.openxmlformats-officedocument.presentationml.tableStyles+xml",
+    "/ppt/theme/theme1.xml": "application/vnd.openxmlformats-officedocument.theme+xml",
+    "/ppt/viewProps.xml": "application/vnd.openxmlformats-officedocument.presentationml.viewProps+xml",
+    **{
+        f"/ppt/slideLayouts/slideLayout{index}.xml": (
+            "application/vnd.openxmlformats-officedocument.presentationml.slideLayout+xml"
+        )
+        for index in range(1, 12)
+    },
+}
+PRIVATE_PATH_RE = re.compile(
+    rb"(?:(?<![A-Za-z0-9])[A-Za-z]:[\\/]|/(?:home|media|Users)/|file:|javascript:)",
+    re.IGNORECASE,
+)
+
+
+def sha256_bytes(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def xml_bytes(root: ET.Element, default_namespace: str) -> bytes:
+    ET.register_namespace("", default_namespace)
+    return ET.tostring(root, encoding="utf-8", xml_declaration=True)
+
+
+def runtime_receipt() -> tuple[dict[str, object], Path]:
+    renderer = Path(__file__).resolve(strict=True)
+    template = Path(pptx.api.__file__).resolve(strict=True).parent / "templates" / "default.pptx"
+    template = template.resolve(strict=True)
+    template_raw = template.read_bytes()
+    if len(template_raw) != DEFAULT_TEMPLATE_BYTES or sha256_bytes(template_raw) != DEFAULT_TEMPLATE_SHA256:
+        raise RuntimeError("python-pptx default template identity mismatch")
+    runtime: dict[str, object] = {
+        "libxml2": list(lxml.etree.LIBXML_VERSION),
+        "libxslt": list(lxml.etree.LIBXSLT_VERSION),
+        "lxml": ".".join(str(value) for value in lxml.etree.LXML_VERSION),
+        "machine": platform.machine(),
+        "python": platform.python_version(),
+        "python_implementation": platform.python_implementation(),
+        "python_pptx": pptx.__version__,
+        "system": platform.system(),
+        "zlib_build": zlib.ZLIB_VERSION,
+        "zlib_runtime": zlib.ZLIB_RUNTIME_VERSION,
+    }
+    if runtime != REGISTERED_RUNTIME:
+        raise RuntimeError("registered canonical-build runtime identity mismatch")
+    receipt = {
+        "renderer": {
+            "bytes": renderer.stat().st_size,
+            "name": renderer.name,
+            "sha256": sha256_bytes(renderer.read_bytes()),
+        },
+        "runtime": runtime,
+        "template": {
+            "bytes": len(template_raw),
+            "name": "python-pptx-default.pptx",
+            "sha256": sha256_bytes(template_raw),
+        },
+    }
+    return receipt, template
+
+
+class LabelParser(HTMLParser):
+    def __init__(self, label: str) -> None:
+        super().__init__(convert_charrefs=True)
+        self.parts: list[tuple[str, bool]] = []
+        self.subscript = False
+        self.feed(label)
+
+    def handle_starttag(self, tag: str, attrs: list[tuple[str, str | None]]) -> None:
+        del attrs
+        if tag == "sub":
+            self.subscript = True
+        elif tag == "br":
+            self.parts.append(("\n", False))
+        else:
+            raise ValueError(f"unsupported label tag: {tag}")
+
+    def handle_endtag(self, tag: str) -> None:
+        if tag != "sub":
+            raise ValueError(f"unsupported closing label tag: {tag}")
+        self.subscript = False
+
+    def handle_data(self, data: str) -> None:
+        self.parts.append((data, self.subscript))
+
+
+def style(cell: ET.Element) -> dict[str, str]:
+    result = {}
+    for field in cell.get("style", "").split(";"):
+        if field:
+            key, _, value = field.partition("=")
+            result[key] = value
+    return result
+
+
+def read_zip_parts(raw: bytes) -> dict[str, bytes]:
+    with zipfile.ZipFile(io.BytesIO(raw)) as archive:
+        names = archive.namelist()
+        if len(names) != len(set(names)) or set(names) != TEMPLATE_OUTPUT_PARTS:
+            raise ValueError("python-pptx template part census mismatch")
+        if archive.comment:
+            raise ValueError("source package comment is not permitted")
+        return {name: archive.read(name) for name in names}
+
+
+def remove_relationship(parts: dict[str, bytes], rels_name: str, relationship_type: str) -> None:
+    root = ET.fromstring(parts[rels_name])
+    matches = [
+        item
+        for item in list(root)
+        if item.tag == f"{{{REL_NS}}}Relationship"
+        and item.get("Type") == relationship_type
+    ]
+    if len(matches) != 1:
+        raise ValueError("template relationship census mismatch")
+    root.remove(matches[0])
+    parts[rels_name] = xml_bytes(root, REL_NS)
+
+
+def sanitize_package(raw: bytes) -> dict[str, bytes]:
+    parts = read_zip_parts(raw)
+    remove_relationship(parts, "_rels/.rels", THUMBNAIL_REL)
+    remove_relationship(parts, "ppt/_rels/presentation.xml.rels", PRINTER_REL)
+    for name in REMOVED_TEMPLATE_PARTS:
+        parts.pop(name)
+
+    content_root = ET.fromstring(parts["[Content_Types].xml"])
+    removed_defaults: dict[str, str] = {}
+    for item in list(content_root):
+        if item.tag != f"{{{CONTENT_TYPES_NS}}}Default":
+            continue
+        extension = item.get("Extension", "")
+        if extension in {"bin", "jpeg"}:
+            removed_defaults[extension] = item.get("ContentType", "")
+            content_root.remove(item)
+    if removed_defaults != {
+        "bin": "application/vnd.openxmlformats-officedocument.presentationml.printerSettings",
+        "jpeg": "image/jpeg",
+    }:
+        raise ValueError("template content-type census mismatch")
+    parts["[Content_Types].xml"] = xml_bytes(content_root, CONTENT_TYPES_NS)
+
+    app_root = ET.fromstring(parts["docProps/app.xml"])
+    stale_fields = {"TotalTime", "Words", "Paragraphs", "HeadingPairs", "TitlesOfParts"}
+    for item in list(app_root):
+        if item.tag.rsplit("}", 1)[-1] in stale_fields:
+            app_root.remove(item)
+    truthful = {
+        "Application": "PhaseSet native figure renderer",
+        "AppVersion": "2.0000",
+        "PresentationFormat": "Custom",
+        "Slides": "1",
+        "Notes": "0",
+        "HiddenSlides": "0",
+        "MMClips": "0",
+        "HyperlinkBase": "",
+        "HyperlinksChanged": "false",
+        "LinksUpToDate": "false",
+        "SharedDoc": "false",
+    }
+    for name, value in truthful.items():
+        item = app_root.find(f"{{{APP_NS}}}{name}")
+        if item is None:
+            item = ET.SubElement(app_root, f"{{{APP_NS}}}{name}")
+        item.text = value
+    parts["docProps/app.xml"] = xml_bytes(app_root, APP_NS)
+    if set(parts) != RETAINED_PACKAGE_PARTS:
+        raise ValueError("retained package part census mismatch")
+    return parts
+
+
+def normalized_zip(parts: dict[str, bytes]) -> bytes:
+    output = io.BytesIO()
+    with zipfile.ZipFile(output, "w", compression=zipfile.ZIP_STORED) as target:
+        for name in sorted(parts):
+            info = zipfile.ZipInfo(name, ZIP_TIMESTAMP)
+            info.compress_type = zipfile.ZIP_STORED
+            info.create_system = ZIP_CREATE_SYSTEM
+            info.create_version = 20
+            info.extract_version = 20
+            info.flag_bits = 0
+            info.external_attr = ZIP_EXTERNAL_ATTR
+            info.internal_attr = 0
+            info.extra = b""
+            info.comment = b""
+            target.writestr(info, parts[name], compress_type=zipfile.ZIP_STORED)
+    return output.getvalue()
+
+
+def relationship_owner(rels_name: str) -> str:
+    if rels_name == "_rels/.rels":
+        return ""
+    prefix, local_name = rels_name.rsplit("/_rels/", 1)
+    if not local_name.endswith(".rels"):
+        raise ValueError("malformed relationship part name")
+    return f"{prefix}/{local_name[:-5]}"
+
+
+def validate_content_types(parts: dict[str, bytes]) -> None:
+    root = ET.fromstring(parts["[Content_Types].xml"])
+    if root.tag != f"{{{CONTENT_TYPES_NS}}}Types" or any(
+        item.tag
+        not in {
+            f"{{{CONTENT_TYPES_NS}}}Default",
+            f"{{{CONTENT_TYPES_NS}}}Override",
+        }
+        for item in root
+    ):
+        raise ValueError("retained content-type schema mismatch")
+    default_rows = root.findall(f"{{{CONTENT_TYPES_NS}}}Default")
+    override_rows = root.findall(f"{{{CONTENT_TYPES_NS}}}Override")
+    defaults = {
+        item.get("Extension", ""): item.get("ContentType", "")
+        for item in default_rows
+    }
+    overrides = {
+        item.get("PartName", ""): item.get("ContentType", "")
+        for item in override_rows
+    }
+    if (
+        len(defaults) != len(default_rows)
+        or len(overrides) != len(override_rows)
+        or defaults != DEFAULT_CONTENT_TYPES
+        or overrides != OVERRIDE_CONTENT_TYPES
+    ):
+        raise ValueError("retained content-type allowlist mismatch")
+
+
+def validate_relationships(parts: dict[str, bytes]) -> None:
+    relationship_rows: set[tuple[str, str, str, str]] = set()
+    for name in sorted(item for item in parts if item.endswith(".rels")):
+        root = ET.fromstring(parts[name])
+        if root.tag != f"{{{REL_NS}}}Relationships" or any(
+            item.tag != f"{{{REL_NS}}}Relationship" for item in root
+        ):
+            raise ValueError("retained relationship schema mismatch")
+        identifiers: set[str] = set()
+        for relationship in root.findall(f"{{{REL_NS}}}Relationship"):
+            identifier = relationship.get("Id", "")
+            relation_type = relationship.get("Type", "")
+            target = relationship.get("Target", "")
+            if (
+                not re.fullmatch(r"rId[1-9][0-9]*", identifier)
+                or identifier in identifiers
+                or relation_type not in ALLOWED_RELATIONSHIP_TYPES
+                or relationship.get("TargetMode") is not None
+                or not target
+                or "\\" in target
+                or ":" in target
+                or target.startswith("/")
+            ):
+                raise ValueError("retained relationship allowlist mismatch")
+            identifiers.add(identifier)
+            owner = relationship_owner(name)
+            relationship_rows.add((owner, identifier, relation_type, target))
+            resolved = posixpath.normpath(posixpath.join(posixpath.dirname(owner), target))
+            if resolved.startswith("../") or resolved not in parts:
+                raise ValueError("retained relationship target escapes package")
+    if relationship_rows != EXPECTED_RELATIONSHIPS:
+        raise ValueError("retained relationship tuple census mismatch")
+
+
+def validate_app_properties(parts: dict[str, bytes]) -> None:
+    root = ET.fromstring(parts["docProps/app.xml"])
+    expected = {
+        "Application": "PhaseSet native figure renderer",
+        "AppVersion": "2.0000",
+        "PresentationFormat": "Custom",
+        "Slides": "1",
+        "Notes": "0",
+        "HiddenSlides": "0",
+        "MMClips": "0",
+    }
+    for name, value in expected.items():
+        item = root.find(f"{{{APP_NS}}}{name}")
+        if item is None or (item.text or "") != value:
+            raise ValueError("extended property truthfulness mismatch")
+    for name in ("TotalTime", "Words", "Paragraphs", "HeadingPairs", "TitlesOfParts"):
+        if root.find(f"{{{APP_NS}}}{name}") is not None:
+            raise ValueError("stale extended property retained")
+
+
+def validate_hardened_package(
+    raw: bytes,
+    expected_shape_names: set[str],
+    expected_connector_names: set[str],
+) -> tuple[int, int]:
+    with zipfile.ZipFile(io.BytesIO(raw)) as archive:
+        names = archive.namelist()
+        if names != sorted(RETAINED_PACKAGE_PARTS) or len(names) != len(set(names)):
+            raise ValueError("normalized package part order or census mismatch")
+        if archive.comment:
+            raise ValueError("normalized package comment is not permitted")
+        for info in archive.infolist():
+            if (
+                info.date_time != ZIP_TIMESTAMP
+                or info.compress_type != zipfile.ZIP_STORED
+                or info.create_system != ZIP_CREATE_SYSTEM
+                or info.create_version != 20
+                or info.extract_version != 20
+                or info.flag_bits != 0
+                or info.external_attr != ZIP_EXTERNAL_ATTR
+                or info.internal_attr != 0
+                or info.extra
+                or info.comment
+            ):
+                raise ValueError("normalized ZIP metadata mismatch")
+        parts = {name: archive.read(name) for name in names}
+    for name, part in parts.items():
+        upper = part.upper()
+        if b"<!DOCTYPE" in upper or b"<!ENTITY" in upper or PRIVATE_PATH_RE.search(part):
+            raise ValueError(f"unsafe retained package text in {name}")
+    validate_content_types(parts)
+    validate_relationships(parts)
+    validate_app_properties(parts)
+
+    slide_xml = ET.fromstring(parts["ppt/slides/slide1.xml"])
+    ns = {"p": PML_NS, "a": DML_NS}
+    shape_names = [
+        item.get("name", "")
+        for item in slide_xml.findall(".//p:sp/p:nvSpPr/p:cNvPr", ns)
+    ]
+    connector_names = [
+        item.get("name", "")
+        for item in slide_xml.findall(".//p:cxnSp/p:nvCxnSpPr/p:cNvPr", ns)
+    ]
+    if (
+        len(shape_names) != len(set(shape_names))
+        or set(shape_names) != expected_shape_names
+        or len(connector_names) != len(set(connector_names))
+        or set(connector_names) != expected_connector_names
+    ):
+        raise ValueError("native object identity census is incomplete")
+    forbidden_tags = {
+        "pic", "graphicFrame", "oleObj", "control", "contentPart", "hlinkClick",
+        "hlinkHover", "video", "audio",
+    }
+    if any(item.tag.rsplit("}", 1)[-1] in forbidden_tags for item in slide_xml.iter()):
+        raise ValueError("forbidden slide object retained")
+    return len(shape_names), len(connector_names)
+
+
+def render(source: Path, destination: Path, template: Path) -> dict[str, object]:
+    raw = source.read_bytes()
+    source_sha256 = sha256_bytes(raw)
+    if REGISTERED_SOURCES.get(source.name) != source_sha256:
+        raise ValueError("registered figure source identity mismatch")
+    if (
+        len(raw) > 1024 * 1024
+        or b"<!DOCTYPE" in raw.upper()
+        or b"<!ENTITY" in raw.upper()
+        or PRIVATE_PATH_RE.search(raw)
+        or re.search(rb"https?://", raw, re.IGNORECASE)
+    ):
+        raise ValueError("unsupported source document")
+    tree = ET.fromstring(raw)
+    models = tree.findall("./diagram/mxGraphModel")
+    if len(models) != 1:
+        raise ValueError("exactly one uncompressed page is required")
+    model = models[0]
+    cell_rows = model.findall("./root/mxCell")
+    cell_ids = [cell.attrib["id"] for cell in cell_rows]
+    if len(cell_ids) != len(set(cell_ids)):
+        raise ValueError("duplicate draw.io cell id")
+    cells = dict(zip(cell_ids, cell_rows, strict=True))
+    width = float(model.attrib["pageWidth"])
+    height = float(model.attrib["pageHeight"])
+    prs = Presentation(str(template))
+    prs.slide_width = Inches(16)
+    scale = int(prs.slide_width) / width
+    prs.slide_height = Emu(round(height * scale))
+    slide = prs.slides.add_slide(prs.slide_layouts[6])
+    properties = prs.core_properties
+    properties.title = source.stem
+    properties.author = "PhaseSet contributors"
+    properties.last_modified_by = "PhaseSet figure renderer"
+    properties.subject = "Data-free methods figure; native editable objects"
+    properties.comments = "Source SHA256: " + source_sha256
+    properties.created = properties.modified = datetime(2000, 1, 1, tzinfo=timezone.utc)
+    boxes: dict[str, tuple[float, float, float, float]] = {}
+
+    def box(identifier: str) -> tuple[float, float, float, float]:
+        if identifier in boxes:
+            return boxes[identifier]
+        cell = cells[identifier]
+        geometry = cell.find("mxGeometry")
+        if geometry is None:
+            return (0, 0, 0, 0)
+        parent = cell.get("parent", "1")
+        px, py, _, _ = box(parent) if parent in cells and parent != identifier else (0, 0, 0, 0)
+        result = (
+            px + float(geometry.get("x", "0")),
+            py + float(geometry.get("y", "0")),
+            float(geometry.get("width", "0")),
+            float(geometry.get("height", "0")),
+        )
+        boxes[identifier] = result
+        return result
+
+    def emu(value: float) -> Emu:
+        return Emu(round(value * scale))
+
+    def font_points(value: float) -> Pt:
+        return Pt(value * scale / 12700)
+
+    def format_line(shape, settings: dict[str, str]) -> None:
+        shape.line.color.rgb = RGBColor.from_string(settings.get("strokeColor", "#000000").lstrip("#"))
+        shape.line.width = font_points(float(settings.get("strokeWidth", "1")))
+        if settings.get("dashed") == "1":
+            shape.line.dash_style = MSO_LINE_DASH_STYLE.DASH
+
+    def label(shape, text: str, settings: dict[str, str]) -> None:
+        frame = shape.text_frame
+        frame.clear()
+        frame.word_wrap = True
+        frame.margin_left = frame.margin_right = emu(3)
+        frame.margin_top = frame.margin_bottom = emu(1)
+        frame.vertical_anchor = MSO_ANCHOR.MIDDLE
+        paragraph = frame.paragraphs[0]
+        paragraph.alignment = PP_ALIGN.CENTER
+        paragraph.space_before = paragraph.space_after = Pt(0)
+        for text_part, subscript in LabelParser(text).parts:
+            for index, segment in enumerate(text_part.split("\n")):
+                if index:
+                    paragraph = frame.add_paragraph()
+                    paragraph.alignment = PP_ALIGN.CENTER
+                    paragraph.space_before = paragraph.space_after = Pt(0)
+                if segment:
+                    run = paragraph.add_run()
+                    run.text = segment
+                    run.font.name = "Arial"
+                    size = float(settings.get("fontSize", "14"))
+                    run.font.size = font_points(size * (0.75 if subscript else 1))
+                    run.font.bold = bool(int(settings.get("fontStyle", "0")) & 1)
+                    run.font.color.rgb = RGBColor.from_string(settings.get("fontColor", "#000000").lstrip("#"))
+                    if subscript:
+                        run._r.get_or_add_rPr().set("baseline", "-25000")
+
+    vertices = [cell for cell in cells.values() if cell.get("vertex") == "1"]
+    edges = [cell for cell in cells.values() if cell.get("edge") == "1"]
+    containers = [cell for cell in vertices if "swimlane" in style(cell)]
+    expected_shape_names = {
+        *("drawio-node-" + cell.attrib["id"] for cell in vertices),
+        *("drawio-header-" + cell.attrib["id"] for cell in containers),
+        *(
+            "drawio-edge-label-" + cell.attrib["id"]
+            for cell in edges
+            if cell.get("value")
+        ),
+    }
+    expected_connector_names: set[str] = set()
+    edge_segment_counts = {cell.attrib["id"]: 0 for cell in edges}
+
+    def vertex(cell: ET.Element, *, container: bool = False) -> None:
+        identifier = cell.attrib["id"]
+        settings = style(cell)
+        x, y, w, h = box(identifier)
+        if "text" in settings:
+            shape = slide.shapes.add_textbox(emu(x), emu(y), emu(w), emu(h))
+        else:
+            kind = (
+                MSO_AUTO_SHAPE_TYPE.OVAL if "ellipse" in settings else
+                MSO_AUTO_SHAPE_TYPE.ROUNDED_RECTANGLE if settings.get("rounded") == "1" else
+                MSO_AUTO_SHAPE_TYPE.RECTANGLE
+            )
+            shape = slide.shapes.add_shape(kind, emu(x), emu(y), emu(w), emu(h))
+            shape.fill.solid()
+            shape.fill.fore_color.rgb = RGBColor.from_string(settings.get("fillColor", "#FFFFFF").lstrip("#"))
+            format_line(shape, settings)
+            if kind == MSO_AUTO_SHAPE_TYPE.ROUNDED_RECTANGLE:
+                shape.adjustments[0] = 0.08
+        shape.name = "drawio-node-" + identifier
+        text = cell.get("value", "")
+        if container:
+            header = float(settings.get("startSize", "36"))
+            title = slide.shapes.add_textbox(emu(x), emu(y), emu(w), emu(header))
+            title.name = "drawio-header-" + identifier
+            label(title, text, settings)
+            separator = slide.shapes.add_connector(MSO_CONNECTOR_TYPE.STRAIGHT, emu(x), emu(y + header), emu(x + w), emu(y + header))
+            separator.name = "drawio-container-separator-" + identifier
+            expected_connector_names.add(separator.name)
+            format_line(separator, settings)
+        else:
+            label(shape, text, settings)
+
+    for cell in containers:
+        vertex(cell, container=True)
+
+    def endpoint(identifier: str, other: str, edge_style: dict[str, str], prefix: str) -> tuple[float, float]:
+        x, y, w, h = box(identifier)
+        if prefix + "X" in edge_style:
+            return x + float(edge_style[prefix + "X"]) * w, y + float(edge_style[prefix + "Y"]) * h
+        ox, oy, ow, oh = box(other)
+        dx, dy = ox + ow / 2 - x - w / 2, oy + oh / 2 - y - h / 2
+        if "ellipse" in style(cells[identifier]):
+            factor = 1 / math.sqrt((2 * dx / w) ** 2 + (2 * dy / h) ** 2)
+        else:
+            factor = 1 / max(abs(2 * dx / w), abs(2 * dy / h))
+        return x + w / 2 + dx * factor, y + h / 2 + dy * factor
+
+    for cell in edges:
+        settings = style(cell)
+        source_id, target_id = cell.attrib["source"], cell.attrib["target"]
+        start = endpoint(source_id, target_id, settings, "exit")
+        end = endpoint(target_id, source_id, settings, "entry")
+        geometry = cell.find("mxGeometry")
+        px, py, _, _ = box(cell.get("parent", "1"))
+        points = [] if geometry is None else [
+            (px + float(point.attrib["x"]), py + float(point.attrib["y"]))
+            for point in geometry.findall("./Array/mxPoint")
+        ]
+        if not points and settings.get("edgeStyle") == "orthogonalEdgeStyle" and start[0] != end[0] and start[1] != end[1]:
+            middle = (start[0] + end[0]) / 2
+            points = [(middle, start[1]), (middle, end[1])]
+        route = [start, *points, end]
+        for index, (a, b) in enumerate(zip(route, route[1:])):
+            if a == b:
+                continue
+            connector = slide.shapes.add_connector(MSO_CONNECTOR_TYPE.STRAIGHT, emu(a[0]), emu(a[1]), emu(b[0]), emu(b[1]))
+            connector.name = f"drawio-edge-{cell.attrib['id']}-segment-{index}"
+            expected_connector_names.add(connector.name)
+            edge_segment_counts[cell.attrib["id"]] += 1
+            format_line(connector, settings)
+            if index == len(route) - 2 and settings.get("endArrow", "none") != "none":
+                tail = OxmlElement("a:tailEnd")
+                tail.set("type", "triangle")
+                connector._element.spPr.get_or_add_ln().append(tail)
+        if cell.get("value"):
+            a, b = route[len(route) // 2 - 1:len(route) // 2 + 1]
+            tx, ty = (a[0] + b[0]) / 2, (a[1] + b[1]) / 2
+            tag = slide.shapes.add_textbox(emu(tx - 115), emu(ty - 12), emu(230), emu(24))
+            tag.name = "drawio-edge-label-" + cell.attrib["id"]
+            tag.fill.solid()
+            tag.fill.fore_color.rgb = RGBColor(255, 255, 255)
+            label(tag, cell.attrib["value"], settings)
+    for cell in vertices:
+        if cell not in containers:
+            vertex(cell)
+    if any(count < 1 for count in edge_segment_counts.values()):
+        raise ValueError("an editable edge produced no native connector segment")
+    output = io.BytesIO()
+    prs.save(output)
+    normalized = normalized_zip(sanitize_package(output.getvalue()))
+    shapes, connectors = validate_hardened_package(
+        normalized,
+        expected_shape_names,
+        expected_connector_names,
+    )
+    destination.write_bytes(normalized)
+    return {
+        "source": source.name, "source_sha256": source_sha256,
+        "pptx": destination.name, "pptx_sha256": hashlib.sha256(normalized).hexdigest(),
+        "native_shapes": shapes, "native_connector_segments": connectors,
+        "native_node_shapes": len(vertices),
+        "native_container_headers": len(containers),
+        "native_edge_labels": sum(bool(cell.get("value")) for cell in edges),
+        "source_edges": len(edges),
+        "package_parts": len(RETAINED_PACKAGE_PARTS),
+        "external_relationships": 0,
+        "raster_media": 0,
+        "empirical_result": False,
+    }
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--figure-dir", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    args = parser.parse_args()
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    destinations = tuple(
+        args.output_dir / name
+        for name in (
+            "phaseset_motivation.pptx",
+            "phaseset_architecture.pptx",
+            "EDITABLE_FIGURES.json",
+        )
+    )
+    if any(path.exists() for path in destinations):
+        raise FileExistsError("refusing to overwrite an existing figure receipt")
+    receipt, template = runtime_receipt()
+    rows = []
+    for stem in ("phaseset_motivation", "phaseset_architecture"):
+        rows.append(
+            render(
+                args.figure_dir / (stem + ".drawio"),
+                args.output_dir / (stem + ".pptx"),
+                template,
+            )
+        )
+    result = {
+        "schema": "phaseset-editable-figure-census-v2",
+        "figures": rows,
+        **receipt,
+        "package_policy": {
+            "compression": "ZIP_STORED",
+            "create_system": ZIP_CREATE_SYSTEM,
+            "external_attr": ZIP_EXTERNAL_ATTR,
+            "retained_part_count": len(RETAINED_PACKAGE_PARTS),
+            "timestamp": list(ZIP_TIMESTAMP),
+        },
+    }
+    (args.output_dir / "EDITABLE_FIGURES.json").write_text(json.dumps(result, sort_keys=True, indent=2) + "\n", encoding="ascii")
+    print(json.dumps(result, sort_keys=True))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/test_editable_figures.py b/tests/test_editable_figures.py
new file mode 100644
index 0000000..03e6f6c
--- /dev/null
+++ b/tests/test_editable_figures.py
@@ -0,0 +1,85 @@
+"""Pure-document checks: no model, data, or numerical experiment is run."""
+from __future__ import annotations
+
+import hashlib
+import json
+from pathlib import Path
+import xml.etree.ElementTree as ET
+import zipfile
+
+import pytest
+
+
+ROOT = Path(__file__).resolve().parents[1]
+NS = {
+    "p": "http://schemas.openxmlformats.org/presentationml/2006/main",
+    "a": "http://schemas.openxmlformats.org/drawingml/2006/main",
+    "r": "http://schemas.openxmlformats.org/package/2006/relationships",
+}
+
+
+@pytest.mark.parametrize("stem", ["phaseset_motivation", "phaseset_architecture"])
+def test_native_editable_figure_census_and_source_binding(stem: str) -> None:
+    figures = ROOT / "figures"
+    report = json.loads((figures / "EDITABLE_FIGURES.json").read_text(encoding="ascii"))
+    assert report["schema"] == "phaseset-editable-figure-census-v2"
+    renderer = (ROOT / "scripts" / report["renderer"]["name"]).read_bytes()
+    assert len(renderer) == report["renderer"]["bytes"]
+    assert hashlib.sha256(renderer).hexdigest() == report["renderer"]["sha256"]
+    assert report["runtime"]["python_pptx"] == "1.0.2"
+    assert report["template"]["sha256"] == "e10cc9e120961f6bd4074a373c9c80d2a06c497157e8f4972977b7bea83a8f34"
+    rows = [row for row in report["figures"] if row["source"] == stem + ".drawio"]
+    assert len(rows) == 1
+    row = rows[0]
+    source = (figures / row["source"]).read_bytes()
+    pptx = figures / row["pptx"]
+    assert hashlib.sha256(source).hexdigest() == row["source_sha256"]
+    assert hashlib.sha256(pptx.read_bytes()).hexdigest() == row["pptx_sha256"]
+    assert row["raster_media"] == 0 and row["empirical_result"] is False
+    model = ET.fromstring(source).find("./diagram/mxGraphModel")
+    assert model is not None
+    cells = model.findall("./root/mxCell")
+    vertices = [cell for cell in cells if cell.get("vertex") == "1"]
+    edges = [cell for cell in cells if cell.get("edge") == "1"]
+    with zipfile.ZipFile(pptx) as archive:
+        assert archive.testzip() is None
+        names = archive.namelist()
+        assert len(names) == len(set(names))
+        assert len(names) == report["package_policy"]["retained_part_count"] == row["package_parts"]
+        assert not any(name.startswith(("ppt/media/", "ppt/embeddings/")) for name in names)
+        assert not any("vba" in name.lower() for name in names)
+        assert not any("thumbnail" in name.lower() or "printersettings" in name.lower() for name in names)
+        for info in archive.infolist():
+            assert info.date_time == (1980, 1, 1, 0, 0, 0)
+            assert info.compress_type == zipfile.ZIP_STORED
+            assert info.create_system == report["package_policy"]["create_system"] == 0
+            assert info.external_attr == report["package_policy"]["external_attr"]
+            raw = archive.read(info)
+            assert b"<!DOCTYPE" not in raw and b"<!ENTITY" not in raw
+        for name in names:
+            if name.endswith(".rels"):
+                relationships = ET.fromstring(archive.read(name))
+                assert all(item.get("TargetMode") != "External" for item in relationships)
+        slide = ET.fromstring(archive.read("ppt/slides/slide1.xml"))
+        shapes = slide.findall(".//p:sp", NS)
+        connectors = slide.findall(".//p:cxnSp", NS)
+        assert len(shapes) == row["native_shapes"] >= len(vertices)
+        assert len(connectors) == row["native_connector_segments"] >= len(edges)
+        assert not slide.findall(".//p:pic", NS)
+        object_names = {item.get("name") for item in slide.findall(".//p:cNvPr", NS)}
+        assert all("drawio-node-" + cell.attrib["id"] in object_names for cell in vertices)
+        assert all(any(name and name.startswith("drawio-edge-" + cell.attrib["id"] + "-segment-")
+                       for name in object_names) for cell in edges)
+        assert slide.findall(".//a:t", NS), "labels must remain native text"
+        app = ET.fromstring(archive.read("docProps/app.xml"))
+        app_ns = {"app": "http://schemas.openxmlformats.org/officeDocument/2006/extended-properties"}
+        assert app.findtext("app:Slides", namespaces=app_ns) == "1"
+        core = archive.read("docProps/core.xml")
+        assert row["source_sha256"].encode("ascii") in core
+
+
+def test_embedded_pngs_have_complete_iend() -> None:
+    for stem in ("phaseset_motivation", "phaseset_architecture"):
+        raw = (ROOT / "figures" / (stem + ".png")).read_bytes()
+        assert raw.startswith(b"\x89PNG\r\n\x1a\n")
+        assert raw.endswith(b"\x00\x00\x00\x00IEND\xaeB`\x82")
diff --git a/tests/test_render_editable_figures.py b/tests/test_render_editable_figures.py
new file mode 100644
index 0000000..16433a8
--- /dev/null
+++ b/tests/test_render_editable_figures.py
@@ -0,0 +1,157 @@
+"""Document-only regression checks for the hardened native figure renderer."""
+
+from __future__ import annotations
+
+import hashlib
+import importlib.util
+import tempfile
+from pathlib import Path
+import unittest
+from unittest import mock
+import xml.etree.ElementTree as ET
+import zipfile
+
+import pytest
+
+pytest.importorskip("pptx", minversion="1.0.2")
+SPEC = importlib.util.spec_from_file_location(
+    "render_editable_figures", Path(__file__).resolve().parents[1] / "scripts" / "render_editable_figures.py"
+)
+assert SPEC is not None and SPEC.loader is not None
+renderer = importlib.util.module_from_spec(SPEC)
+SPEC.loader.exec_module(renderer)
+try:
+    renderer.runtime_receipt()
+except RuntimeError as error:
+    if str(error) == "registered canonical-build runtime identity mismatch":
+        pytest.skip("canonical PPTX regeneration requires its registered document runtime", allow_module_level=True)
+    raise
+
+
+HERE = Path(__file__).resolve().parents[1] / "figures"
+EXPECTED_SOURCES = {
+    "phaseset_motivation.drawio": (
+        "a0a25fbc8c55575d50c6ffa8d6add01ce780cc8842328bb3f6bba20c6509d18a"
+    ),
+    "phaseset_architecture.drawio": (
+        "1a61a503c935a72cbfbe1ce175ab54bf975eb3e0a2164018aeda80448f472a1a"
+    ),
+}
+
+
+class HardenedEditableFigureTests(unittest.TestCase):
+    def test_sources_are_unchanged_and_pptx_is_reproducible(self) -> None:
+        receipt, template = renderer.runtime_receipt()
+        self.assertEqual(receipt["runtime"], renderer.REGISTERED_RUNTIME)
+        self.assertEqual(renderer.REGISTERED_SOURCES, EXPECTED_SOURCES)
+        self.assertEqual(
+            receipt["template"]["sha256"],
+            renderer.DEFAULT_TEMPLATE_SHA256,
+        )
+        with tempfile.TemporaryDirectory(prefix="phaseset-pptx-hardened-test-") as temporary:
+            root = Path(temporary)
+            for filename, expected_sha256 in EXPECTED_SOURCES.items():
+                source = HERE / filename
+                self.assertEqual(hashlib.sha256(source.read_bytes()).hexdigest(), expected_sha256)
+                stem = source.stem
+                first = root / f"{stem}-first.pptx"
+                second = root / f"{stem}-second.pptx"
+                first_row = renderer.render(source, first, template)
+                second_row = renderer.render(source, second, template)
+                self.assertEqual(first.read_bytes(), second.read_bytes())
+                self.assertEqual(first_row["pptx_sha256"], second_row["pptx_sha256"])
+                self.assertEqual(first_row["raster_media"], 0)
+                self.assertEqual(first_row["external_relationships"], 0)
+                self._assert_package(first)
+
+    def test_unregistered_runtime_is_rejected(self) -> None:
+        with (
+            mock.patch.object(renderer.platform, "python_version", return_value="0.0.0-mutant"),
+            self.assertRaisesRegex(RuntimeError, "canonical-build runtime identity"),
+        ):
+            renderer.runtime_receipt()
+
+    def test_modified_source_is_rejected(self) -> None:
+        _, template = renderer.runtime_receipt()
+        source = HERE / "phaseset_motivation.drawio"
+        with tempfile.TemporaryDirectory(prefix="phaseset-pptx-source-mutant-") as temporary:
+            root = Path(temporary)
+            tree = ET.fromstring(source.read_bytes())
+            vertex = next(
+                item
+                for item in tree.findall("./diagram/mxGraphModel/root/mxCell")
+                if item.get("vertex") == "1"
+            )
+            original_id = vertex.attrib["id"]
+            mutant_id = original_id + "-tampered"
+            for item in tree.iter():
+                for attribute in ("id", "parent", "source", "target"):
+                    if item.get(attribute) == original_id:
+                        item.set(attribute, mutant_id)
+            mutant = root / source.name
+            mutant.write_bytes(ET.tostring(tree, encoding="utf-8"))
+            with self.assertRaisesRegex(ValueError, "figure source identity mismatch"):
+                renderer.render(mutant, root / "mutant.pptx", template)
+            renamed = root / "unregistered-name.drawio"
+            renamed.write_bytes(source.read_bytes())
+            with self.assertRaisesRegex(ValueError, "figure source identity mismatch"):
+                renderer.render(renamed, root / "renamed.pptx", template)
+
+    def test_repointed_relationship_is_rejected(self) -> None:
+        _, template = renderer.runtime_receipt()
+        source = HERE / "phaseset_motivation.drawio"
+        with tempfile.TemporaryDirectory(prefix="phaseset-pptx-opc-mutant-") as temporary:
+            destination = Path(temporary) / "canonical.pptx"
+            renderer.render(source, destination, template)
+            with zipfile.ZipFile(destination) as archive:
+                parts = {name: archive.read(name) for name in archive.namelist()}
+            relationships = ET.fromstring(parts["_rels/.rels"])
+            office_document = next(
+                item
+                for item in relationships
+                if item.get("Type", "").endswith("/officeDocument")
+            )
+            office_document.set("Target", "docProps/app.xml")
+            parts["_rels/.rels"] = renderer.xml_bytes(relationships, renderer.REL_NS)
+            mutant = renderer.normalized_zip(parts)
+            slide = ET.fromstring(parts["ppt/slides/slide1.xml"])
+            namespace = {"p": renderer.PML_NS}
+            shape_names = {
+                item.get("name", "")
+                for item in slide.findall(".//p:sp/p:nvSpPr/p:cNvPr", namespace)
+            }
+            connector_names = {
+                item.get("name", "")
+                for item in slide.findall(".//p:cxnSp/p:nvCxnSpPr/p:cNvPr", namespace)
+            }
+            with self.assertRaisesRegex(ValueError, "relationship tuple census"):
+                renderer.validate_hardened_package(mutant, shape_names, connector_names)
+
+    def _assert_package(self, path: Path) -> None:
+        presentation = renderer.Presentation(str(path))
+        self.assertEqual(len(presentation.slides), 1)
+        with zipfile.ZipFile(path) as archive:
+            self.assertIsNone(archive.testzip())
+            self.assertEqual(archive.namelist(), sorted(renderer.RETAINED_PACKAGE_PARTS))
+            self.assertFalse(renderer.REMOVED_TEMPLATE_PARTS & set(archive.namelist()))
+            self.assertEqual(archive.comment, b"")
+            for info in archive.infolist():
+                self.assertEqual(info.date_time, renderer.ZIP_TIMESTAMP)
+                self.assertEqual(info.compress_type, zipfile.ZIP_STORED)
+                self.assertEqual(info.create_system, renderer.ZIP_CREATE_SYSTEM)
+                self.assertEqual(info.external_attr, renderer.ZIP_EXTERNAL_ATTR)
+                self.assertEqual(info.flag_bits, 0)
+                self.assertEqual(info.extra, b"")
+                self.assertEqual(info.comment, b"")
+            parts = {name: archive.read(name) for name in archive.namelist()}
+        renderer.validate_content_types(parts)
+        renderer.validate_relationships(parts)
+        renderer.validate_app_properties(parts)
+        for part in parts.values():
+            self.assertNotIn(b"<!DOCTYPE", part.upper())
+            self.assertNotIn(b"<!ENTITY", part.upper())
+            self.assertIsNone(renderer.PRIVATE_PATH_RE.search(part))
+
+
+if __name__ == "__main__":
+    unittest.main()

From aaadd8891c10a9820ddad20e73182b4d337de13b Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 02:41:35 -0500
Subject: [PATCH 06/95] feat: add verified frozen CLIP text feature adapter

---
 RELEASE_FILES.sha256                    |    5 +-
 STATUS.md                               |   12 +-
 docs/FROZEN_CLIP_TEXT_ADAPTER.md        |  215 ++++
 src/phaseset_core/frozen_clip_text.py   | 1331 +++++++++++++++++++++++
 tests/test_phaseset_frozen_clip_text.py |  438 ++++++++
 5 files changed, 1997 insertions(+), 4 deletions(-)
 create mode 100644 docs/FROZEN_CLIP_TEXT_ADAPTER.md
 create mode 100644 src/phaseset_core/frozen_clip_text.py
 create mode 100644 tests/test_phaseset_frozen_clip_text.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 5332f5f..e1c4dda 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,7 +11,7 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-d7cb9c77abdcdd0061ce83f630541586111cea3d146d9fcf905bd84a45cbca71	12240	STATUS.md
+8b9d341db90149d930c38a1e0de506e04af4496ce6f16db86dfd84a237c4f703	12682	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
@@ -20,6 +20,7 @@ e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA
 3633e37c3eae3289d552a363e565bee027ebd83661f1c44ea136f9498de9e5af	19059	docs/EXECUTION_PROTOCOL.md
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
+e7bc4f9bace637106d73afe6be84e0695c5be78857218bc8b141cc8d5f009d52	11085	docs/FROZEN_CLIP_TEXT_ADAPTER.md
 3861d213e951e059a3fa044c9f4c87eaa93054aef84323315db67521865838f2	9883	docs/HOST_RUNTIME_INTEGRATION.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
@@ -124,6 +125,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
+51bdad872537b332e886646071d4381d00a09ddba5912c43ba8559e2675a4c79	48618	src/phaseset_core/frozen_clip_text.py
 22f9855b0f689c3f34de78ec47a53f15b1c9c8fbedaf57130d5ced1e5da561f4	67811	src/phaseset_core/host.py
 47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
@@ -178,6 +180,7 @@ a17a50e801c389b4e24b2840f2aafcb7b476cbef86e2e142ad0af6245a54718f	4515	tests/test
 8bd6e95a35523d108153f668f8919e6f22858e8283e6308041751c6c92d0f904	18605	tests/test_phaseset_evaluation.py
 aea41df712a25be78841e655a6fbd91fc96217ce92e60d3ab842f185fc39a58c	41553	tests/test_phaseset_execution.py
 dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/test_phaseset_experiments.py
+aaa4db9a4f9b9d0a4fdea4120170b27ac9185c455193b29d207b1a50b142e9fd	17118	tests/test_phaseset_frozen_clip_text.py
 4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
 a75f583fd0a233760b1a421ca51680afe4195c33f9ef4bce6e9a1737828f207e	14907	tests/test_phaseset_models.py
 e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test_phaseset_morlet.py
diff --git a/STATUS.md b/STATUS.md
index ecc0098..8e686f1 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -18,8 +18,9 @@ evidence, not training results or scientific claims.
 - A separate data-volume Python 3.12.12 / NumPy 2.4.6 / Torch 2.12.0+cu126
   environment is installed with a clean dependency check. The complete Linux
   CPU regression including the private-host lifecycle integration passed:
-  917 tests, 1 skipped, and 39 subtests, in 345.97 s. Source bytes were unchanged
-  before and after the full suite. Its separate 25 focused tests also passed.
+  932 tests, 1 skipped, and 39 subtests, in 287.60 s after adding the frozen
+  CLIP adapter. Source bytes were unchanged before and after the full suite.
+  The earlier host-only suite passed 917 tests and its 25 focused checks.
   This includes synthetic model/lifecycle checks, not main-data training.
 - A separately bounded shared-GPU functionality probe passed FP64 forward and
   FP32 forward/backward using 8x8 tensors. It did not stop existing workloads
@@ -47,7 +48,12 @@ evidence, not training results or scientific claims.
   confirmatory data. The pinned CLIP snapshot's eight files also match the
   previously acquired official SHA-256 values. Actual frozen CLIP text CPU
   loading and two public-caption forwards passed, with bitwise repeat outputs
-  and unchanged RNG. This does not qualify GPU execution or real data training.
+  and unchanged RNG. The production-shaped frozen text adapter subsequently
+  passed 15 server tests and three real-model forwards: the unchanged repeat
+  golden plus a separate 226-to-77-token long-caption observation. It verifies
+  the exact retained prefix and EOS, restores caller RNG, and emits text-free
+  receipts. See [frozen CLIP text adapter](docs/FROZEN_CLIP_TEXT_ADAPTER.md).
+  This does not qualify GPU text execution or real-data training.
 - Native Linux last-bit Morlet formula drift is repaired using strictly
   verified canonical coefficient bytes, while preserving both original hashes.
   Windows still requires its original exact formula digest; Linux requires its
diff --git a/docs/FROZEN_CLIP_TEXT_ADAPTER.md b/docs/FROZEN_CLIP_TEXT_ADAPTER.md
new file mode 100644
index 0000000..71c9d78
--- /dev/null
+++ b/docs/FROZEN_CLIP_TEXT_ADAPTER.md
@@ -0,0 +1,215 @@
+# PhaseSet frozen CLIP text adapter
+
+The frozen CPU feature path passed its registered Linux server observation on
+2026-09-08. Fifteen adapter tests passed; the actual pretrained model completed
+the two-forward golden repeat and one separate overlength forward. This is a
+feature-path engineering result, not a dataset, training, or quality result.
+
+Implementation: `src/phaseset_core/frozen_clip_text.py`; tests:
+`tests/test_phaseset_frozen_clip_text.py`. The server probe imported the canonical
+`phaseset_core.frozen_clip_text` module from a separately verified source copy.
+Model files and private execution receipts are not distributed in this tree.
+
+## Selection made for PhaseSet
+
+PhaseSet consumes the official frozen pretrained projection from
+`CLIPTextModelWithProjection`:
+
+```text
+caption
+  -> fixed CLIP tokenizer (77 tokens)
+  -> frozen CLIP text_model
+  -> final-LayerNorm pooled EOS representation
+  -> frozen official text_projection
+  -> raw finite contiguous CPU float32 [Q,512]
+```
+
+The adapter does not L2-normalize this output. The existing retrieval systems
+normalize embeddings at scoring time. It also does not add the legacy
+PhasePair bridge-owned trainable `text.project` layer. That old layer belonged
+to a different training design and is not needed to place the official CLIP
+projection in PhaseSet's registered 512-dimensional space.
+
+This choice does not eliminate PhaseSet's downstream `TextBandMLP` in residual
+retrieval. `TextBandMLP` remains part of the trainable PhaseSet retrieval head,
+where it converts an already-frozen global text embedding into band queries.
+It is not part of the CLIP tower and is not run by this adapter.
+
+The public `RetrievalTrainingBatch` boundary already enforces finite contiguous
+CPU float32 `[Q,D]`, detaches/clones it, requires one commitment per row, and
+uses commitments only for positive-family/lineage logic. This adapter tightens
+the producer width to exactly 512 and calls the installed training embedding
+validator before returning.
+
+## API
+
+```python
+adapter = load_frozen_clip_text_adapter(
+    verified_snapshot_path,
+    max_batch_size=64,
+)
+batch = adapter.encode(
+    ordered_caption_tuple,
+    ordered_caption_commitment_tuple,
+    batch_size=32,
+)
+text_embeddings = batch.embeddings
+receipt_bytes = batch.receipt.canonical_json_bytes()
+```
+
+`Q` is variable and preserves input order. The caller must choose an explicit
+batch size no larger than the load-time maximum; both are capped at 256.
+Caption count, individual UTF-8 bytes, and total UTF-8 bytes are bounded.
+Commitments must be unique exact `bytes[32]` rows. They are never passed to the
+tokenizer/model, never influence embeddings, and appear only as hex lineage in
+the path-free, text-free receipt.
+
+Long text uses the single frozen `clip_native_truncate_77` policy already used
+by the legacy loader. Each batch is first tokenized with special tokens and no
+truncation solely to measure the complete token count. The formal tokenizer
+call then uses `max_length=77`, `padding="max_length"`, and `truncation=True`;
+an overlength row retains CLIP's native prefix and tokenizer EOS token 49407.
+The adapter separately preserves the model configuration's pinned legacy
+`eos_token_id=2` compatibility branch; those two identities are intentionally
+not conflated. The receipt exposes the original and encoded token counts plus
+`truncated=true`,
+and binds the full caption UTF-8 digest and complete padded input-ID/attention-
+mask tensor digests. It never prints raw text or token IDs, so truncation cannot
+be silent even though the text itself remains private.
+
+The formal token tensor is also compared element-for-element with a local
+expectation constructed from the complete untruncated token row: short rows are
+padded without changing their tokens, while long rows retain the first 76
+native tokens and place tokenizer EOS 49407 at position 77. The attention mask
+must exactly match that expected row. A count/EOS-only match is insufficient.
+
+The authorized prepare-data host retains the complete caption. The 512-vector
+represents only CLIP's native truncated-prefix semantics when `truncated=true`;
+it is not claimed to preserve the full caption's meaning. Segmenting, LLM
+summarization, or multi-window aggregation would be a different scientific
+contract and is not performed here.
+
+Batch size and ordered chunk ranges are provenance. This implementation does
+not promise bitwise equality across different CPU batch sizes, so a different
+batch size produces a different frozen-cache key even when caption order is
+unchanged. The cache key binds method, source, runtime, snapshot, batch/chunk
+order, full-text digests, and tokenization digests, but excludes lineage
+commitments and every model/system identifier downstream of CLIP. B0/B1/B2 and
+all residual systems must reuse the same receipt-bound frozen embedding cache;
+they must not run separate text conversions.
+
+`FrozenClipTextBatch.embeddings` returns a fresh owned clone on every access so
+a caller cannot mutate the receipt-bound stored value through the public
+property. `RetrievalTrainingBatch` takes another snapshot at its boundary.
+
+## Snapshot and runtime trust
+
+The only accepted snapshot is `openai/clip-vit-base-patch32` revision
+`3d74acf9a28c67741b2f4f2ea7635f0aaf6f0268`. The module contains literal name,
+size, and SHA-256 pins for its eight official files. It requires an absolute,
+resolved, non-reparse directory containing exactly those eight regular,
+single-link files—no subdirectories, metadata, cache files, aliases, or extras.
+
+All eight files are hashed before and after model loading. They are hashed
+again before and after every complete `encode` call. This is a bounded
+best-effort stability check, not an operating-system atomic snapshot.
+
+The loader is exact and text-only:
+
+- `CLIPTokenizerFast.from_pretrained(local_path, local_files_only=True,
+  trust_remote_code=False)`;
+- `CLIPTextModelWithProjection.from_pretrained` with the same offline flags,
+  `low_cpu_mem_usage=False`, and loading information returned;
+- a temporary empty cache plus offline environment and blocked common network
+  connection entry points;
+- exact loading-info census: no missing/mismatched/error rows and only the
+  expected vision/logit keys discarded from the full checkpoint;
+- exact model config, 197 live text/projection parameters, one buffer, no vision
+  module, all parameters finite CPU float32 and permanently `requires_grad=False`;
+- `eval()` plus `torch.inference_mode()` for every forward;
+- live parameter/buffer content manifest checked before and after each encode;
+- caller Torch RNG preserved across load and every success or failure of
+  encode; any RNG advance during encode is a closed hold after the caller state
+  is restored.
+
+The implementation is deliberately pinned to the newly observed CPU identity:
+CPython 3.12.12, Linux x86-64 little-endian, Torch 2.12.0+cu126 with CUDA hidden,
+Transformers 4.57.3, tokenizers 0.22.2, huggingface-hub 0.36.2, and NumPy 2.4.6.
+It requires one intra-op/inter-op thread, deterministic algorithms, and highest
+float32 matmul precision. A different runtime is a new qualification lane, not
+a permissive fallback.
+
+The exact Transformers model/tokenizer sources and installed
+`phaseset_core.training` source are pinned. The adapter's own source is
+hashed before/after load and encode and recorded in every receipt. The receipt
+cross-binds runtime, source, snapshot, ordered caption text digests, lineage
+commitments, per-row embedding digests, and the full output digest without
+including captions or filesystem paths.
+
+## Prepare-data integration boundary
+
+The eventual prepare-data host, not this module, owns:
+
+1. authorized caption access and deterministic caption selection;
+2. split and positive-family assignment;
+3. caption commitments and manifest provenance;
+4. secure storage of raw text and frozen arrays;
+5. checking that the receipt and array digest still agree before constructing
+   `RetrievalTrainingBatch`.
+
+The adapter accepts captions already selected and authorized by the host. It
+cannot discover data, read caption files, infer splits, issue commitments,
+authorize training, or convert a receipt into authority. Its receipt always
+contains `authority=0`, `production=false`, and `training_authorized=false`.
+Successful feature preparation is not a scientific result or a data-rights
+witness.
+
+## Tested adapter coverage
+
+`test_phaseset_frozen_clip_text.py` uses a deterministic fake tokenizer/model
+with real Torch tensors; it never imports Transformers or loads CLIP. It covers:
+
+- variable ordered `Q`, explicit chunking, exact `[Q,512]` output;
+- native truncation above 77 tokens with original/encoded counts, a visible
+  truncation flag, exact retained-prefix tokens/mask, terminal EOS, and no raw
+  caption in the receipt;
+- commitments affecting lineage/receipt but never embedding values;
+- input-order preservation;
+- text/path-free authority-zero receipts;
+- literal offline loader arguments and frozen eval state;
+- extra snapshot files and post-load snapshot mutation;
+- non-finite forward output and live parameter mutation;
+- exact bounded integer batch size;
+- cache identity changing with batch/chunk order but not lineage commitments;
+- returned tensor ownership.
+- formal-token body mutation before forward and model RNG advance with caller-
+  state restoration.
+
+Additional coverage can include mutation tests for missing files, aliases/hardlinks,
+source/runtime drift, tokenizer shape/key drift, loading-info/config drift,
+caption UTF-8/size/count limits, duplicate commitments, output dtype/device/
+layout drift, network attempts, warning emission, and receipt canonicalization.
+
+## Observed server qualification
+
+The fifteen adapter tests passed in 11.45 seconds. The separate real-model
+observation completed with exit zero and empty stderr; source hashes were equal
+before and after execution. The two original public fixture rows produced
+bitwise-equal embeddings and receipts across repeated forwards, with the same
+previously observed combined output SHA-256:
+`4a2ab93dab393a38bd0e78ee737fa31813c8e35ef3fcb5f4f3108705a502f58e`.
+
+A third forward used a separate public overlength fixture. The original 226
+tokens became exactly 77, with the complete expected prefix, attention mask,
+and terminal tokenizer EOS 49407 checked before forwarding. Its observed output
+SHA-256 was
+`3f89662e5f6c06d4c7f9c1e2e77a78ad73dbe4da0a7be4a56a35df27060ebcba`.
+That value is an observation, not a self-created acceptance golden. Caller RNG
+state was preserved across all three calls. Independent same-family code review
+remains provisional; these actual tests close only the measured feature-path
+checks. Prepare-data integration, data rights, and formal experiments are separate.
+
+The full Linux regression with this adapter then passed 932 tests, one skipped,
+and 39 subtests in 287.60 seconds, with unchanged source bytes. The model weights
+are not required for that unit/regression suite; actual loading is verified by
+the separate private snapshot-bound observation above.
diff --git a/src/phaseset_core/frozen_clip_text.py b/src/phaseset_core/frozen_clip_text.py
new file mode 100644
index 0000000..fe58002
--- /dev/null
+++ b/src/phaseset_core/frozen_clip_text.py
@@ -0,0 +1,1331 @@
+"""Frozen, offline CLIP text projection adapter for PhaseSet.
+
+The adapter owns no optimizer, dataset discovery, download, split, or training
+surface.  It turns an ordered tuple of captions into the official pretrained
+CLIP text projection, returned as finite contiguous CPU float32 ``[Q, 512]``.
+Caption commitments are carried only as lineage and never enter tokenization or
+the model.  All public errors are closed codes so private paths or text cannot be
+accidentally serialized by a caller.
+"""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from dataclasses import dataclass
+import hashlib
+import http.client
+import inspect
+import json
+import os
+from pathlib import Path
+import platform
+import socket
+import stat
+import sys
+import tempfile
+import threading
+from typing import Any, Callable, Final, Iterator
+import urllib.request
+import warnings
+
+
+MODEL_ID: Final = "openai/clip-vit-base-patch32"
+REVISION: Final = "3d74acf9a28c67741b2f4f2ea7635f0aaf6f0268"
+METHOD_ID: Final = "phaseset-clip-native-truncate-77-pretrained-projection-v1"
+EMBEDDING_DIM: Final = 512
+TOKEN_LENGTH: Final = 77
+TOKEN_VOCAB_SIZE: Final = 49_408
+TOKEN_BOS_ID: Final = 49_406
+TOKEN_EOS_ID: Final = 49_407
+TOKEN_PAD_ID: Final = TOKEN_EOS_ID
+MAX_BATCH_SIZE: Final = 256
+MAX_CAPTIONS: Final = 16_384
+MAX_CAPTION_UTF8_BYTES: Final = 16_384
+MAX_TOTAL_CAPTION_UTF8_BYTES: Final = 16 * 1024 * 1024
+AUTHORITY: Final = 0
+PRODUCTION: Final = False
+TRAINING_AUTHORIZED: Final = False
+
+PINNED_FILES: Final = (
+    (
+        "pytorch_model.bin",
+        605_247_071,
+        "a63082132ba4f97a80bea76823f544493bffa8082296d62d71581a4feff1576f",
+    ),
+    (
+        "config.json",
+        4_186,
+        "b575ef3c36f2a057fa19e221650105052d61cc9c1a972ec15019c6261ec98770",
+    ),
+    (
+        "merges.txt",
+        524_657,
+        "f526393189112391ce6f9795d4695f704121ce452c3aad1f5335cc41337eba85",
+    ),
+    (
+        "preprocessor_config.json",
+        316,
+        "910e70b3956ac9879ebc90b22fb3bc8a75b6a0677814500101a4c072bd7857bd",
+    ),
+    (
+        "special_tokens_map.json",
+        389,
+        "f8c0d6c39aee3f8431078ef6646567b0aba7f2246e9c54b8b99d55c22b707cbf",
+    ),
+    (
+        "tokenizer_config.json",
+        592,
+        "34b7336e4bee12e0a9730eaf5189f582ef3c3eea5027f65730e5717256755aad",
+    ),
+    (
+        "tokenizer.json",
+        2_224_041,
+        "b556ac8c99757ffb677208af34bc8c6721572114111a6e0aaf5fa69ff0b8d842",
+    ),
+    (
+        "vocab.json",
+        862_328,
+        "5047b556ce86ccaf6aa22b3ffccfc52d391ea4accdab9c2f2407da5b742d4363",
+    ),
+)
+_OFFICIAL_PINNED_FILES: Final = PINNED_FILES
+TOTAL_SNAPSHOT_BYTES: Final = 608_863_580
+
+_EXPECTED_RUNTIME: Final = (
+    ("cuda_available", "false"),
+    ("cuda_visible_devices", "hidden"),
+    ("deterministic_algorithms", "true"),
+    ("device", "cpu"),
+    ("float32_matmul_precision", "highest"),
+    ("huggingface_hub", "0.36.2"),
+    ("interop_threads", "1"),
+    ("intraop_threads", "1"),
+    ("machine", "x86_64"),
+    ("numpy", "2.4.6"),
+    ("python", "3.12.12"),
+    ("python_implementation", "CPython"),
+    ("sys_byteorder", "little"),
+    ("system", "Linux"),
+    ("tokenizers", "0.22.2"),
+    ("torch", "2.12.0+cu126"),
+    ("torch_cuda_build", "12.6"),
+    ("transformers", "4.57.3"),
+)
+_EXPECTED_CONFIG: Final = (
+    ("attention_dropout", 0.0),
+    ("bos_token_id", 0),
+    ("eos_token_id", 2),
+    ("hidden_act", "quick_gelu"),
+    ("hidden_size", 512),
+    ("intermediate_size", 2048),
+    ("layer_norm_eps", 1e-05),
+    ("max_position_embeddings", 77),
+    ("model_type", "clip_text_model"),
+    ("num_attention_heads", 8),
+    ("num_hidden_layers", 12),
+    ("pad_token_id", 1),
+    ("projection_dim", 512),
+    ("vocab_size", 49_408),
+)
+_EXPECTED_TOKENIZER: Final = (
+    ("bos_token_id", TOKEN_BOS_ID),
+    ("eos_token_id", TOKEN_EOS_ID),
+    ("model_max_length", TOKEN_LENGTH),
+    ("pad_token_id", TOKEN_PAD_ID),
+    ("padding_side", "right"),
+    ("truncation_side", "right"),
+    ("unk_token_id", TOKEN_EOS_ID),
+)
+_EXPECTED_UNEXPECTED_COUNT: Final = 202
+_EXPECTED_UNEXPECTED_SHA256: Final = (
+    "354d27b4b81e1f6e9fc9e908b04fefd182847eeb35c980176b2260a8b572f597"
+)
+_EXPECTED_PARAMETER_COUNT: Final = 197
+_EXPECTED_BUFFER_COUNT: Final = 1
+_EXPECTED_SOURCE_FILES: Final = (
+    (
+        "transformers.modeling_clip",
+        50_315,
+        "3e779a99827618d6e6d57583ec4f5c0d765321dfa5054a4ffca6a56bb004d499",
+    ),
+    (
+        "transformers.tokenization_clip_fast",
+        6_766,
+        "67dff0f21a56d0dc18a45f8764f3fafa02ee2b0aeafeb70761c9623f9114dd53",
+    ),
+    (
+        "phaseset_core.training",
+        154_285,
+        "f75b9a59dfc2ccfc04ed5f418fb318f3bff6bc11cfe61021f5ff552d71a2ae7f",
+    ),
+)
+
+_CONSTRUCTION_SEAL = object()
+_GLOBAL_LOAD_LOCK = threading.Lock()
+
+
+class FrozenClipTextAdapterError(ValueError):
+    """Closed, path-free failure from the adapter boundary."""
+
+    def __init__(self, code: str) -> None:
+        if type(code) is not str or not code.startswith("HOLD_"):
+            raise TypeError("adapter error code must be a closed HOLD code")
+        self.code = code
+        super().__init__(code)
+
+
+def _hold(code: str) -> None:
+    raise FrozenClipTextAdapterError(code)
+
+
+def _canonical_json(value: object) -> bytes:
+    return json.dumps(
+        value,
+        ensure_ascii=True,
+        allow_nan=False,
+        sort_keys=True,
+        separators=(",", ":"),
+    ).encode("ascii")
+
+
+def _sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    try:
+        with path.open("rb") as handle:
+            while chunk := handle.read(8 * 1024 * 1024):
+                digest.update(chunk)
+    except OSError:
+        _hold("HOLD_FILE_UNREADABLE")
+    return digest.hexdigest()
+
+
+def _is_reparse(metadata: os.stat_result) -> bool:
+    attributes = getattr(metadata, "st_file_attributes", 0)
+    flag = getattr(stat, "FILE_ATTRIBUTE_REPARSE_POINT", 0)
+    return bool(attributes & flag)
+
+
+def _safe_absolute_directory(value: object) -> Path:
+    try:
+        supplied = Path(os.fspath(value))
+        resolved = supplied.resolve(strict=True)
+        metadata = supplied.lstat()
+    except (TypeError, ValueError, OSError, RuntimeError):
+        _hold("HOLD_SNAPSHOT_ROOT")
+    if (
+        not supplied.is_absolute()
+        or os.path.normcase(os.path.abspath(os.fspath(supplied)))
+        != os.path.normcase(os.fspath(resolved))
+        or not stat.S_ISDIR(metadata.st_mode)
+        or _is_reparse(metadata)
+    ):
+        _hold("HOLD_SNAPSHOT_ROOT")
+    return resolved
+
+
+def _scan_snapshot(
+    root: Path,
+    pins: tuple[tuple[str, int, str], ...] = PINNED_FILES,
+) -> tuple[tuple[str, int, str], ...]:
+    expected = {name: (size, digest) for name, size, digest in pins}
+    if len(expected) != len(pins) or len({name.casefold() for name in expected}) != len(pins):
+        _hold("HOLD_INTERNAL_PIN_SCHEMA")
+    try:
+        children = tuple(sorted(root.iterdir(), key=lambda path: path.name))
+    except OSError:
+        _hold("HOLD_SNAPSHOT_CENSUS")
+    names = tuple(path.name for path in children)
+    if names != tuple(sorted(expected)) or len({name.casefold() for name in names}) != len(names):
+        _hold("HOLD_SNAPSHOT_CENSUS")
+    rows: list[tuple[str, int, str]] = []
+    for child in children:
+        try:
+            metadata = child.lstat()
+            expected_size, expected_digest = expected[child.name]
+            resolved = child.resolve(strict=True)
+        except (KeyError, OSError, RuntimeError):
+            _hold("HOLD_SNAPSHOT_FILE_IDENTITY")
+        if (
+            not stat.S_ISREG(metadata.st_mode)
+            or _is_reparse(metadata)
+            or metadata.st_nlink != 1
+            or metadata.st_size != expected_size
+            or resolved.parent != root
+        ):
+            _hold("HOLD_SNAPSHOT_FILE_IDENTITY")
+        digest = _sha256_file(child)
+        if digest != expected_digest:
+            _hold("HOLD_SNAPSHOT_FILE_BYTES")
+        rows.append((child.name, expected_size, digest))
+    if sum(size for _name, size, _digest in rows) != sum(size for _name, size, _digest in pins):
+        _hold("HOLD_SNAPSHOT_TOTAL_BYTES")
+    return tuple(rows)
+
+
+def _snapshot_manifest_sha256(rows: tuple[tuple[str, int, str], ...]) -> str:
+    payload = {
+        "files": [
+            {"bytes": size, "name": name, "sha256": digest}
+            for name, size, digest in rows
+        ],
+        "model_id": MODEL_ID,
+        "revision": REVISION,
+        "schema": "phaseset-frozen-clip-snapshot-v1",
+    }
+    return hashlib.sha256(_canonical_json(payload)).hexdigest()
+
+
+def _source_row(
+    label: str,
+    path: Path,
+    *,
+    expected_bytes: int | None = None,
+    expected_sha256: str | None = None,
+) -> tuple[str, int, str]:
+    try:
+        resolved = path.resolve(strict=True)
+        metadata = path.lstat()
+    except (OSError, RuntimeError):
+        _hold("HOLD_SOURCE_IDENTITY")
+    if (
+        resolved != path
+        or not stat.S_ISREG(metadata.st_mode)
+        or _is_reparse(metadata)
+        or metadata.st_nlink != 1
+    ):
+        _hold("HOLD_SOURCE_IDENTITY")
+    digest = _sha256_file(path)
+    if (
+        (expected_bytes is not None and metadata.st_size != expected_bytes)
+        or (expected_sha256 is not None and digest != expected_sha256)
+    ):
+        _hold("HOLD_SOURCE_IDENTITY")
+    return label, metadata.st_size, digest
+
+
+def _adapter_source_row() -> tuple[str, int, str]:
+    try:
+        path = Path(__file__)
+        if not path.is_absolute():
+            _hold("HOLD_ADAPTER_SOURCE_IDENTITY")
+    except (TypeError, ValueError, OSError, RuntimeError):
+        _hold("HOLD_ADAPTER_SOURCE_IDENTITY")
+    return _source_row("phaseset_core.frozen_clip_text", path)
+
+
+@dataclass(frozen=True, slots=True)
+class _Backend:
+    torch: Any
+    tokenizer_type: type[Any]
+    model_type: type[Any]
+    batch_encoding_type: type[Any]
+    runtime_identity: tuple[tuple[str, str], ...]
+    source_rows: tuple[tuple[str, int, str], ...]
+    expected_config: tuple[tuple[str, object], ...]
+    unexpected_count: int
+    unexpected_sha256: str
+    expected_parameter_count: int
+    expected_buffer_count: int
+    validate_training_boundary: Callable[[Any], None]
+    validate_runtime: Callable[[], None]
+
+
+def _runtime_facts(
+    *,
+    huggingface_hub: Any,
+    numpy: Any,
+    tokenizers: Any,
+    torch: Any,
+    transformers: Any,
+) -> tuple[tuple[str, str], ...]:
+    return tuple(
+        sorted(
+            {
+                "cuda_available": str(bool(torch.cuda.is_available())).lower(),
+                "cuda_visible_devices": (
+                    "hidden"
+                    if os.environ.get("CUDA_VISIBLE_DEVICES") == ""
+                    else "not-hidden"
+                ),
+                "deterministic_algorithms": str(
+                    bool(torch.are_deterministic_algorithms_enabled())
+                ).lower(),
+                "device": "cpu",
+                "float32_matmul_precision": str(
+                    torch.get_float32_matmul_precision()
+                ),
+                "huggingface_hub": str(huggingface_hub.__version__),
+                "interop_threads": str(torch.get_num_interop_threads()),
+                "intraop_threads": str(torch.get_num_threads()),
+                "machine": platform.machine(),
+                "numpy": str(numpy.__version__),
+                "python": platform.python_version(),
+                "python_implementation": platform.python_implementation(),
+                "sys_byteorder": sys.byteorder,
+                "system": platform.system(),
+                "tokenizers": str(tokenizers.__version__),
+                "torch": str(torch.__version__),
+                "torch_cuda_build": str(torch.version.cuda),
+                "transformers": str(transformers.__version__),
+            }.items()
+        )
+    )
+
+
+def _real_backend() -> _Backend:
+    try:
+        import huggingface_hub
+        import numpy
+        import tokenizers
+        import torch
+        import transformers
+        from transformers import CLIPTextModelWithProjection, CLIPTokenizerFast
+        from transformers.tokenization_utils_base import BatchEncoding
+        from phaseset_core import training
+    except Exception:
+        _hold("HOLD_RUNTIME_IMPORT")
+
+    def validate_runtime() -> None:
+        observed = _runtime_facts(
+            huggingface_hub=huggingface_hub,
+            numpy=numpy,
+            tokenizers=tokenizers,
+            torch=torch,
+            transformers=transformers,
+        )
+        if (
+            observed != _EXPECTED_RUNTIME
+            or os.environ.get("CUDA_VISIBLE_DEVICES") != ""
+            or torch.cuda.is_available() is not False
+            or torch.get_num_threads() != 1
+            or torch.get_num_interop_threads() != 1
+            or not torch.are_deterministic_algorithms_enabled()
+            or torch.get_float32_matmul_precision() != "highest"
+        ):
+            _hold("HOLD_RUNTIME_IDENTITY")
+
+    validate_runtime()
+    try:
+        modeling_path = Path(inspect.getsourcefile(CLIPTextModelWithProjection) or "")
+        tokenizer_path = Path(inspect.getsourcefile(CLIPTokenizerFast) or "")
+        training_path = Path(str(training.__file__))
+    except (TypeError, ValueError):
+        _hold("HOLD_SOURCE_IDENTITY")
+    paths = (modeling_path, tokenizer_path, training_path)
+    source_rows = tuple(
+        _source_row(label, path, expected_bytes=size, expected_sha256=digest)
+        for (label, size, digest), path in zip(_EXPECTED_SOURCE_FILES, paths, strict=True)
+    )
+    if training.REGISTERED_EMBEDDING_DIM != EMBEDDING_DIM:
+        _hold("HOLD_TRAINING_BOUNDARY")
+
+    def validate_training_boundary(value: Any) -> None:
+        training._validate_embedding_pair(value, value, EMBEDDING_DIM)
+
+    return _Backend(
+        torch=torch,
+        tokenizer_type=CLIPTokenizerFast,
+        model_type=CLIPTextModelWithProjection,
+        batch_encoding_type=BatchEncoding,
+        runtime_identity=_EXPECTED_RUNTIME,
+        source_rows=source_rows,
+        expected_config=_EXPECTED_CONFIG,
+        unexpected_count=_EXPECTED_UNEXPECTED_COUNT,
+        unexpected_sha256=_EXPECTED_UNEXPECTED_SHA256,
+        expected_parameter_count=_EXPECTED_PARAMETER_COUNT,
+        expected_buffer_count=_EXPECTED_BUFFER_COUNT,
+        validate_training_boundary=validate_training_boundary,
+        validate_runtime=validate_runtime,
+    )
+
+
+@contextmanager
+def _offline_loader_environment() -> Iterator[tuple[Path, list[str]]]:
+    names = (
+        "HF_HOME",
+        "HF_HUB_CACHE",
+        "HF_HUB_DISABLE_TELEMETRY",
+        "HF_HUB_OFFLINE",
+        "TOKENIZERS_PARALLELISM",
+        "TRANSFORMERS_OFFLINE",
+    )
+    previous = {name: os.environ.get(name) for name in names}
+    attempts: list[str] = []
+    old_create_connection = socket.create_connection
+    old_urlopen = urllib.request.urlopen
+    old_http_connect = http.client.HTTPConnection.connect
+    old_https_connect = http.client.HTTPSConnection.connect
+
+    def blocked(*_args: object, **_kwargs: object) -> None:
+        attempts.append("blocked")
+        raise OSError("offline")
+
+    with tempfile.TemporaryDirectory(prefix="phaseset-frozen-clip-") as cache_text:
+        cache_root = Path(cache_text)
+        try:
+            os.environ.update(
+                {
+                    "HF_HOME": cache_text,
+                    "HF_HUB_CACHE": cache_text,
+                    "HF_HUB_DISABLE_TELEMETRY": "1",
+                    "HF_HUB_OFFLINE": "1",
+                    "TOKENIZERS_PARALLELISM": "false",
+                    "TRANSFORMERS_OFFLINE": "1",
+                }
+            )
+            socket.create_connection = blocked
+            urllib.request.urlopen = blocked
+            http.client.HTTPConnection.connect = blocked
+            http.client.HTTPSConnection.connect = blocked
+            yield cache_root, attempts
+        finally:
+            socket.create_connection = old_create_connection
+            urllib.request.urlopen = old_urlopen
+            http.client.HTTPConnection.connect = old_http_connect
+            http.client.HTTPSConnection.connect = old_https_connect
+            for name, value in previous.items():
+                if value is None:
+                    os.environ.pop(name, None)
+                else:
+                    os.environ[name] = value
+
+
+def _loading_info_digest(unexpected: tuple[str, ...]) -> str:
+    return hashlib.sha256("".join(key + "\n" for key in unexpected).encode("ascii")).hexdigest()
+
+
+def _validate_loading_info(info: object, backend: _Backend) -> None:
+    if type(info) is not dict:
+        _hold("HOLD_LOADING_INFO")
+    try:
+        unexpected = tuple(sorted(info.get("unexpected_keys", ())))
+        missing = tuple(info.get("missing_keys", ()))
+        mismatched = tuple(info.get("mismatched_keys", ()))
+        errors = tuple(info.get("error_msgs", ()))
+    except (AttributeError, TypeError):
+        _hold("HOLD_LOADING_INFO")
+    if (
+        any(type(value) is not str for value in unexpected)
+        or missing
+        or mismatched
+        or errors
+        or len(unexpected) != backend.unexpected_count
+        or _loading_info_digest(unexpected) != backend.unexpected_sha256
+        or any(
+            key != "logit_scale"
+            and not key.startswith("vision_model.")
+            and not key.startswith("visual_projection.")
+            for key in unexpected
+        )
+    ):
+        _hold("HOLD_LOADING_INFO")
+
+
+def _config_facts(model: object) -> tuple[tuple[str, object], ...]:
+    try:
+        config = model.config
+        facts = {
+            "attention_dropout": float(config.attention_dropout),
+            "bos_token_id": int(config.bos_token_id),
+            "eos_token_id": int(config.eos_token_id),
+            "hidden_act": str(config.hidden_act),
+            "hidden_size": int(config.hidden_size),
+            "intermediate_size": int(config.intermediate_size),
+            "layer_norm_eps": float(config.layer_norm_eps),
+            "max_position_embeddings": int(config.max_position_embeddings),
+            "model_type": str(config.model_type),
+            "num_attention_heads": int(config.num_attention_heads),
+            "num_hidden_layers": int(config.num_hidden_layers),
+            "pad_token_id": int(config.pad_token_id),
+            "projection_dim": int(config.projection_dim),
+            "vocab_size": int(config.vocab_size),
+        }
+    except (AttributeError, TypeError, ValueError, OverflowError):
+        _hold("HOLD_MODEL_CONFIG")
+    return tuple(sorted(facts.items()))
+
+
+def _tokenizer_facts(tokenizer: object) -> tuple[tuple[str, object], ...]:
+    try:
+        facts = {
+            "bos_token_id": int(tokenizer.bos_token_id),
+            "eos_token_id": int(tokenizer.eos_token_id),
+            "model_max_length": int(tokenizer.model_max_length),
+            "pad_token_id": int(tokenizer.pad_token_id),
+            "padding_side": str(tokenizer.padding_side),
+            "truncation_side": str(tokenizer.truncation_side),
+            "unk_token_id": int(tokenizer.unk_token_id),
+        }
+    except (AttributeError, TypeError, ValueError, OverflowError):
+        _hold("HOLD_TOKENIZER_CONFIG")
+    return tuple(sorted(facts.items()))
+
+
+def _tensor_bytes(tensor: Any) -> bytes:
+    try:
+        return tensor.detach().to(device="cpu").contiguous().numpy().tobytes(order="C")
+    except Exception:
+        _hold("HOLD_TENSOR_BYTES")
+
+
+def _cpu_rng_state(torch: Any) -> Any:
+    try:
+        return torch.get_rng_state().clone()
+    except Exception:
+        _hold("HOLD_RUNTIME_RNG")
+
+
+@contextmanager
+def _preserve_cpu_rng(torch: Any) -> Iterator[None]:
+    """Detect CPU RNG use while restoring caller state on every exit path."""
+
+    caller_state = _cpu_rng_state(torch)
+    restored_manually = False
+    try:
+        with torch.random.fork_rng(devices=[], enabled=True):
+            guarded_state = _cpu_rng_state(torch)
+            yield
+            try:
+                unchanged = bool(torch.equal(guarded_state, torch.get_rng_state()))
+            except Exception:
+                _hold("HOLD_RUNTIME_RNG")
+            if not unchanged:
+                _hold("HOLD_RUNTIME_RNG")
+    finally:
+        try:
+            restored = bool(torch.equal(caller_state, torch.get_rng_state()))
+            if not restored:
+                torch.set_rng_state(caller_state)
+                restored_manually = True
+        except Exception:
+            raise FrozenClipTextAdapterError("HOLD_RUNTIME_RNG") from None
+        if restored_manually:
+            _hold("HOLD_RUNTIME_RNG")
+
+
+def _live_model_manifest(model: object, backend: _Backend) -> str:
+    torch = backend.torch
+    try:
+        parameters = tuple(model.named_parameters())
+        buffers = tuple(model.named_buffers())
+        modules = tuple(model.named_modules())
+    except Exception:
+        _hold("HOLD_MODEL_REGISTRY")
+    if (
+        len(parameters) != backend.expected_parameter_count
+        or len(buffers) != backend.expected_buffer_count
+        or any("vision" in name.casefold() for name, _module in modules)
+    ):
+        _hold("HOLD_MODEL_REGISTRY")
+    rows: list[dict[str, object]] = []
+    seen: set[int] = set()
+    for kind, values in (("parameter", parameters), ("buffer", buffers)):
+        for index, (name, tensor) in enumerate(values):
+            if (
+                type(name) is not str
+                or not name
+                or id(tensor) in seen
+                or type(tensor) not in (torch.Tensor, torch.nn.Parameter)
+                or tensor.device.type != "cpu"
+                or not tensor.is_contiguous()
+                or not bool(torch.isfinite(tensor).all().item())
+            ):
+                _hold("HOLD_MODEL_REGISTRY")
+            seen.add(id(tensor))
+            if kind == "parameter" and (
+                type(tensor) is not torch.nn.Parameter
+                or tensor.dtype != torch.float32
+                or tensor.requires_grad
+                or tensor.grad is not None
+            ):
+                _hold("HOLD_MODEL_NOT_FROZEN")
+            rows.append(
+                {
+                    "dtype": str(tensor.dtype),
+                    "index": index,
+                    "kind": kind,
+                    "name": name,
+                    "numel": int(tensor.numel()),
+                    "sha256": hashlib.sha256(_tensor_bytes(tensor)).hexdigest(),
+                    "shape": list(tensor.shape),
+                    "stride": list(tensor.stride()),
+                }
+            )
+    return hashlib.sha256(_canonical_json(rows)).hexdigest()
+
+
+def _source_manifest_sha256(rows: tuple[tuple[str, int, str], ...]) -> str:
+    return hashlib.sha256(
+        _canonical_json(
+            [
+                {"bytes": size, "label": label, "sha256": digest}
+                for label, size, digest in rows
+            ]
+        )
+    ).hexdigest()
+
+
+def _runtime_manifest_sha256(rows: tuple[tuple[str, str], ...]) -> str:
+    return hashlib.sha256(_canonical_json(dict(rows))).hexdigest()
+
+
+def _load_components(root: Path, backend: _Backend) -> tuple[object, object]:
+    torch = backend.torch
+    try:
+        rng_before = torch.get_rng_state().clone()
+    except Exception:
+        _hold("HOLD_RUNTIME_RNG")
+    with _GLOBAL_LOAD_LOCK:
+        with _offline_loader_environment() as (cache_root, attempts):
+            try:
+                with torch.random.fork_rng(devices=[], enabled=True):
+                    torch.manual_seed(0)
+                    with warnings.catch_warnings(record=True) as caught:
+                        warnings.simplefilter("always")
+                        tokenizer = backend.tokenizer_type.from_pretrained(
+                            str(root),
+                            cache_dir=str(cache_root),
+                            local_files_only=True,
+                            trust_remote_code=False,
+                        )
+                        model, loading_info = backend.model_type.from_pretrained(
+                            str(root),
+                            cache_dir=str(cache_root),
+                            local_files_only=True,
+                            trust_remote_code=False,
+                            low_cpu_mem_usage=False,
+                            output_loading_info=True,
+                        )
+            except FrozenClipTextAdapterError:
+                raise
+            except Exception:
+                _hold("HOLD_MODEL_LOAD")
+            if attempts or tuple(cache_root.rglob("*")):
+                _hold("HOLD_NETWORK_OR_CACHE_ACTIVITY")
+            if caught:
+                _hold("HOLD_MODEL_LOAD_WARNING")
+    if not bool(torch.equal(rng_before, torch.get_rng_state())):
+        _hold("HOLD_RUNTIME_RNG")
+    if type(tokenizer) is not backend.tokenizer_type or type(model) is not backend.model_type:
+        _hold("HOLD_MODEL_TYPE")
+    _validate_loading_info(loading_info, backend)
+    if _config_facts(model) != backend.expected_config:
+        _hold("HOLD_MODEL_CONFIG")
+    if _tokenizer_facts(tokenizer) != _EXPECTED_TOKENIZER:
+        _hold("HOLD_TOKENIZER_CONFIG")
+    return tokenizer, model
+
+
+def _freeze_model(model: object, backend: _Backend) -> None:
+    try:
+        model.eval()
+        for parameter in model.parameters():
+            parameter.requires_grad_(False)
+    except Exception:
+        _hold("HOLD_MODEL_NOT_FROZEN")
+    if model.training or any(parameter.requires_grad for parameter in model.parameters()):
+        _hold("HOLD_MODEL_NOT_FROZEN")
+
+
+def _validate_captions_and_lineage(
+    captions: object,
+    commitments: object,
+) -> tuple[tuple[str, ...], tuple[bytes, ...], tuple[str, ...]]:
+    if type(captions) is not tuple or not 1 <= len(captions) <= MAX_CAPTIONS:
+        _hold("HOLD_CAPTION_BATCH")
+    if type(commitments) is not tuple or len(commitments) != len(captions):
+        _hold("HOLD_CAPTION_LINEAGE")
+    encoded_rows: list[bytes] = []
+    checked_commitments: list[bytes] = []
+    for caption, commitment in zip(captions, commitments, strict=True):
+        if type(caption) is not str or not caption or "\x00" in caption:
+            _hold("HOLD_CAPTION_VALUE")
+        try:
+            encoded = caption.encode("utf-8", errors="strict")
+        except UnicodeError:
+            _hold("HOLD_CAPTION_VALUE")
+        if not 1 <= len(encoded) <= MAX_CAPTION_UTF8_BYTES:
+            _hold("HOLD_CAPTION_VALUE")
+        if type(commitment) is not bytes or len(commitment) != 32:
+            _hold("HOLD_CAPTION_LINEAGE")
+        encoded_rows.append(encoded)
+        checked_commitments.append(commitment)
+    if sum(map(len, encoded_rows)) > MAX_TOTAL_CAPTION_UTF8_BYTES:
+        _hold("HOLD_CAPTION_BATCH")
+    checked_lineage = tuple(checked_commitments)
+    if len(set(checked_lineage)) != len(checked_lineage):
+        _hold("HOLD_CAPTION_LINEAGE")
+    text_digests = tuple(hashlib.sha256(value).hexdigest() for value in encoded_rows)
+    return captions, checked_lineage, text_digests
+
+
+def _validate_token_batch(value: object, count: int, backend: _Backend) -> None:
+    torch = backend.torch
+    if type(value) is not backend.batch_encoding_type or set(value) != {
+        "input_ids",
+        "attention_mask",
+    }:
+        _hold("HOLD_TOKEN_BATCH")
+    for name in ("input_ids", "attention_mask"):
+        tensor = value[name]
+        if (
+            type(tensor) is not torch.Tensor
+            or tensor.dtype != torch.int64
+            or tuple(tensor.shape) != (count, TOKEN_LENGTH)
+            or tensor.device.type != "cpu"
+            or not tensor.is_contiguous()
+        ):
+            _hold("HOLD_TOKEN_BATCH")
+    input_ids = value["input_ids"]
+    attention_mask = value["attention_mask"]
+    if (
+        not bool(((input_ids >= 0) & (input_ids < TOKEN_VOCAB_SIZE)).all().item())
+        or not bool(((attention_mask == 0) | (attention_mask == 1)).all().item())
+    ):
+        _hold("HOLD_TOKEN_BATCH")
+
+
+def _untruncated_token_rows(
+    value: object,
+    count: int,
+    backend: _Backend,
+) -> tuple[tuple[int, ...], ...]:
+    if type(value) is not backend.batch_encoding_type or set(value) != {"input_ids"}:
+        _hold("HOLD_TOKEN_LENGTH_PROBE")
+    rows = value["input_ids"]
+    if type(rows) is not list or len(rows) != count:
+        _hold("HOLD_TOKEN_LENGTH_PROBE")
+    checked_rows: list[tuple[int, ...]] = []
+    for row in rows:
+        if (
+            type(row) is not list
+            or not row
+            or row[0] != TOKEN_BOS_ID
+            or row[-1] != TOKEN_EOS_ID
+            or any(
+                type(token) is not int or not 0 <= token < TOKEN_VOCAB_SIZE
+                for token in row
+            )
+        ):
+            _hold("HOLD_TOKEN_LENGTH_PROBE")
+        checked_rows.append(tuple(row))
+    return tuple(checked_rows)
+
+
+def _validate_embedding(value: object, count: int, backend: _Backend) -> Any:
+    torch = backend.torch
+    if (
+        type(value) is not torch.Tensor
+        or value.dtype != torch.float32
+        or tuple(value.shape) != (count, EMBEDDING_DIM)
+        or value.device.type != "cpu"
+        or value.requires_grad
+        or not value.is_contiguous()
+        or not bool(torch.isfinite(value).all().item())
+    ):
+        _hold("HOLD_EMBEDDING_BOUNDARY")
+    try:
+        backend.validate_training_boundary(value)
+    except FrozenClipTextAdapterError:
+        raise
+    except Exception:
+        _hold("HOLD_TRAINING_BOUNDARY")
+    return value
+
+
+@dataclass(frozen=True, slots=True)
+class FrozenClipTextReceipt:
+    """Path-free, text-free provenance for one ordered embedding batch."""
+
+    batch_size: int
+    caption_rows: tuple[tuple[int, str, str, int, int, bool, str, str, str], ...]
+    chunk_ranges: tuple[tuple[int, int], ...]
+    frozen_embedding_cache_key_sha256: str
+    live_model_manifest_sha256: str
+    output_bytes: int
+    output_sha256: str
+    output_shape: tuple[int, int]
+    output_stride: tuple[int, int]
+    runtime_identity: tuple[tuple[str, str], ...]
+    runtime_manifest_sha256: str
+    snapshot_files: tuple[tuple[str, int, str], ...]
+    snapshot_manifest_sha256: str
+    source_files: tuple[tuple[str, int, str], ...]
+    source_manifest_sha256: str
+    status: str = "ENCODED_AUTHORITY0"
+    schema: str = "phaseset-frozen-clip-text-receipt-v1"
+
+    def to_public_dict(self) -> dict[str, object]:
+        return {
+            "authority": AUTHORITY,
+            "batch_size": self.batch_size,
+            "caption_count": len(self.caption_rows),
+            "caption_rows": [
+                {
+                    "index": index,
+                    "caption_commitment": commitment,
+                    "caption_utf8_sha256": text_digest,
+                    "original_token_count_with_special_tokens": original_token_count,
+                    "encoded_token_count_with_special_tokens": encoded_token_count,
+                    "truncated": truncated,
+                    "input_ids_int64_sha256": input_ids_digest,
+                    "attention_mask_int64_sha256": attention_mask_digest,
+                    "embedding_float32_sha256": embedding_digest,
+                }
+                for (
+                    index,
+                    commitment,
+                    text_digest,
+                    original_token_count,
+                    encoded_token_count,
+                    truncated,
+                    input_ids_digest,
+                    attention_mask_digest,
+                    embedding_digest,
+                ) in self.caption_rows
+            ],
+            "chunk_ranges": [list(value) for value in self.chunk_ranges],
+            "embedding_selection": "official_pretrained_text_projection_raw_float32",
+            "frozen_embedding_cache_key_sha256": self.frozen_embedding_cache_key_sha256,
+            "live_model_manifest_sha256": self.live_model_manifest_sha256,
+            "model_id": MODEL_ID,
+            "method_id": METHOD_ID,
+            "output": {
+                "bytes": self.output_bytes,
+                "device": "cpu",
+                "dtype": "torch.float32",
+                "finite": True,
+                "requires_grad": False,
+                "sha256": self.output_sha256,
+                "shape": list(self.output_shape),
+                "stride": list(self.output_stride),
+            },
+            "production": PRODUCTION,
+            "revision": REVISION,
+            "runtime": dict(self.runtime_identity),
+            "runtime_manifest_sha256": self.runtime_manifest_sha256,
+            "schema": self.schema,
+            "snapshot": {
+                "file_count": len(self.snapshot_files),
+                "files": [
+                    {"bytes": size, "name": name, "sha256": digest}
+                    for name, size, digest in self.snapshot_files
+                ],
+                "manifest_sha256": self.snapshot_manifest_sha256,
+            },
+            "source": {
+                "files": [
+                    {"bytes": size, "label": label, "sha256": digest}
+                    for label, size, digest in self.source_files
+                ],
+                "manifest_sha256": self.source_manifest_sha256,
+            },
+            "status": self.status,
+            "training_authorized": TRAINING_AUTHORIZED,
+            "tokenization": {
+                "add_special_tokens": True,
+                "long_caption_policy": "clip_native_truncate_77",
+                "max_tokens_including_special_tokens": TOKEN_LENGTH,
+                "padding": "max_length",
+                "preserve_terminal_eos": True,
+                "truncation": True,
+            },
+        }
+
+    def canonical_json_bytes(self) -> bytes:
+        return _canonical_json(self.to_public_dict()) + b"\n"
+
+    @property
+    def sha256(self) -> str:
+        return hashlib.sha256(self.canonical_json_bytes()).hexdigest()
+
+
+class FrozenClipTextBatch:
+    """Snapshot-owned frozen embeddings plus immutable lineage and receipt."""
+
+    __slots__ = ("__embeddings", "__lineage", "__receipt")
+
+    def __init__(
+        self,
+        embeddings: Any,
+        lineage: tuple[bytes, ...],
+        receipt: FrozenClipTextReceipt,
+        *,
+        _seal: object,
+    ) -> None:
+        if _seal is not _CONSTRUCTION_SEAL:
+            raise TypeError("FrozenClipTextBatch construction is internal")
+        self.__embeddings = embeddings.detach().clone().contiguous()
+        self.__lineage = lineage
+        self.__receipt = receipt
+
+    @property
+    def embeddings(self) -> Any:
+        return self.__embeddings.detach().clone().contiguous()
+
+    @property
+    def caption_commitments(self) -> tuple[bytes, ...]:
+        return self.__lineage
+
+    @property
+    def receipt(self) -> FrozenClipTextReceipt:
+        return self.__receipt
+
+    def __repr__(self) -> str:
+        return (
+            f"FrozenClipTextBatch(Q={len(self.__lineage)}, D={EMBEDDING_DIM}, "
+            f"receipt_sha256={self.__receipt.sha256!r})"
+        )
+
+
+class FrozenClipTextAdapter:
+    """Opaque frozen CLIP text tower loaded only from the pinned local snapshot."""
+
+    __slots__ = (
+        "_backend",
+        "_lock",
+        "_max_batch_size",
+        "_model",
+        "_model_manifest_sha256",
+        "_pins",
+        "_snapshot_root",
+        "_snapshot_rows",
+        "_source_rows",
+        "_tokenizer",
+    )
+
+    def __init__(
+        self,
+        *,
+        backend: _Backend,
+        max_batch_size: int,
+        model: object,
+        model_manifest_sha256: str,
+        pins: tuple[tuple[str, int, str], ...],
+        snapshot_root: Path,
+        snapshot_rows: tuple[tuple[str, int, str], ...],
+        source_rows: tuple[tuple[str, int, str], ...],
+        tokenizer: object,
+        _seal: object,
+    ) -> None:
+        if _seal is not _CONSTRUCTION_SEAL:
+            raise TypeError("FrozenClipTextAdapter construction is internal")
+        self._backend = backend
+        self._lock = threading.Lock()
+        self._max_batch_size = max_batch_size
+        self._model = model
+        self._model_manifest_sha256 = model_manifest_sha256
+        self._pins = pins
+        self._snapshot_root = snapshot_root
+        self._snapshot_rows = snapshot_rows
+        self._source_rows = source_rows
+        self._tokenizer = tokenizer
+
+    @property
+    def max_batch_size(self) -> int:
+        return self._max_batch_size
+
+    def __repr__(self) -> str:
+        return (
+            f"FrozenClipTextAdapter(model_id={MODEL_ID!r}, revision={REVISION!r}, "
+            f"max_batch_size={self._max_batch_size}, authority=0)"
+        )
+
+    def encode(
+        self,
+        captions: tuple[str, ...],
+        caption_commitments: tuple[bytes, ...],
+        *,
+        batch_size: int,
+    ) -> FrozenClipTextBatch:
+        try:
+            return self._encode(captions, caption_commitments, batch_size=batch_size)
+        except FrozenClipTextAdapterError:
+            raise
+        except Exception:
+            raise FrozenClipTextAdapterError("HOLD_UNEXPECTED_ENCODE") from None
+
+    def _encode(
+        self,
+        captions: object,
+        caption_commitments: object,
+        *,
+        batch_size: object,
+    ) -> FrozenClipTextBatch:
+        checked_captions, lineage, text_digests = _validate_captions_and_lineage(
+            captions, caption_commitments
+        )
+        if (
+            type(batch_size) is not int
+            or not 1 <= batch_size <= self._max_batch_size
+            or batch_size > MAX_BATCH_SIZE
+        ):
+            _hold("HOLD_BATCH_SIZE")
+        torch = self._backend.torch
+        with self._lock, _preserve_cpu_rng(torch):
+            self._backend.validate_runtime()
+            if _adapter_source_row() != self._source_rows[-1]:
+                _hold("HOLD_ADAPTER_SOURCE_CHANGED")
+            before = _scan_snapshot(self._snapshot_root, self._pins)
+            if before != self._snapshot_rows:
+                _hold("HOLD_SNAPSHOT_CHANGED")
+            if _live_model_manifest(self._model, self._backend) != self._model_manifest_sha256:
+                _hold("HOLD_MODEL_CHANGED")
+            _freeze_model(self._model, self._backend)
+            chunks: list[Any] = []
+            token_rows: list[tuple[int, int, bool, str, str]] = []
+            chunk_ranges: list[tuple[int, int]] = []
+            for start in range(0, len(checked_captions), batch_size):
+                stop = min(start + batch_size, len(checked_captions))
+                chunk = list(checked_captions[start:stop])
+                chunk_ranges.append((start, stop))
+                try:
+                    untruncated = self._tokenizer(
+                        chunk,
+                        add_special_tokens=True,
+                        padding=False,
+                        truncation=False,
+                        return_attention_mask=False,
+                        verbose=False,
+                    )
+                    original_token_rows = _untruncated_token_rows(
+                        untruncated, stop - start, self._backend
+                    )
+                    encoded = self._tokenizer(
+                        chunk,
+                        add_special_tokens=True,
+                        max_length=TOKEN_LENGTH,
+                        padding="max_length",
+                        truncation=True,
+                        return_tensors="pt",
+                    )
+                except FrozenClipTextAdapterError:
+                    raise
+                except Exception:
+                    _hold("HOLD_TOKENIZE")
+                _validate_token_batch(encoded, stop - start, self._backend)
+                if _tokenizer_facts(self._tokenizer) != _EXPECTED_TOKENIZER:
+                    _hold("HOLD_TOKENIZER_CONFIG")
+                eos_token_id = int(self._tokenizer.eos_token_id)
+                for local_index, original_tokens in enumerate(original_token_rows):
+                    original_token_count = len(original_tokens)
+                    input_row = encoded["input_ids"][local_index : local_index + 1].contiguous()
+                    mask_row = encoded["attention_mask"][local_index : local_index + 1].contiguous()
+                    encoded_token_count = int(mask_row.sum().item())
+                    truncated = original_token_count > TOKEN_LENGTH
+                    if truncated:
+                        expected_tokens = (
+                            *original_tokens[: TOKEN_LENGTH - 1],
+                            eos_token_id,
+                        )
+                    else:
+                        expected_tokens = (
+                            *original_tokens,
+                            *(TOKEN_PAD_ID,) * (TOKEN_LENGTH - original_token_count),
+                        )
+                    expected_input = torch.tensor(
+                        expected_tokens,
+                        dtype=torch.int64,
+                        device="cpu",
+                    ).reshape(1, TOKEN_LENGTH)
+                    expected_mask = torch.zeros_like(mask_row)
+                    expected_mask[0, :encoded_token_count] = 1
+                    if (
+                        encoded_token_count != min(original_token_count, TOKEN_LENGTH)
+                        or not bool(torch.equal(input_row, expected_input))
+                        or not bool(torch.equal(mask_row, expected_mask))
+                    ):
+                        _hold("HOLD_TOKEN_LENGTH_PROBE")
+                    token_rows.append(
+                        (
+                            original_token_count,
+                            encoded_token_count,
+                            truncated,
+                            hashlib.sha256(_tensor_bytes(input_row)).hexdigest(),
+                            hashlib.sha256(_tensor_bytes(mask_row)).hexdigest(),
+                        )
+                    )
+                try:
+                    with torch.inference_mode():
+                        text_output = self._model.text_model(
+                            input_ids=encoded["input_ids"],
+                            attention_mask=encoded["attention_mask"],
+                        )
+                        pooled = text_output.pooler_output
+                        projected = self._model.text_projection(pooled).contiguous()
+                except Exception:
+                    _hold("HOLD_TEXT_FORWARD")
+                chunks.append(_validate_embedding(projected, stop - start, self._backend))
+            embeddings = torch.cat(chunks, dim=0).contiguous().detach().clone()
+            _validate_embedding(embeddings, len(checked_captions), self._backend)
+            if _live_model_manifest(self._model, self._backend) != self._model_manifest_sha256:
+                _hold("HOLD_MODEL_CHANGED")
+            after = _scan_snapshot(self._snapshot_root, self._pins)
+            if after != before:
+                _hold("HOLD_SNAPSHOT_CHANGED")
+            if _adapter_source_row() != self._source_rows[-1]:
+                _hold("HOLD_ADAPTER_SOURCE_CHANGED")
+
+        output_bytes = _tensor_bytes(embeddings)
+        row_digests = tuple(
+            hashlib.sha256(_tensor_bytes(embeddings[index : index + 1].contiguous())).hexdigest()
+            for index in range(len(checked_captions))
+        )
+        caption_rows = tuple(
+            (
+                index,
+                commitment.hex(),
+                text_digest,
+                original_token_count,
+                encoded_token_count,
+                truncated,
+                input_ids_digest,
+                attention_mask_digest,
+                output_digest,
+            )
+            for index, (
+                commitment,
+                text_digest,
+                (
+                    original_token_count,
+                    encoded_token_count,
+                    truncated,
+                    input_ids_digest,
+                    attention_mask_digest,
+                ),
+                output_digest,
+            ) in enumerate(
+                zip(lineage, text_digests, token_rows, row_digests, strict=True)
+            )
+        )
+        snapshot_manifest = _snapshot_manifest_sha256(self._snapshot_rows)
+        source_manifest = _source_manifest_sha256(self._source_rows)
+        runtime_manifest = _runtime_manifest_sha256(self._backend.runtime_identity)
+        cache_key_payload = {
+            "batch_size": batch_size,
+            "caption_rows": [
+                {
+                    "attention_mask_int64_sha256": row[7],
+                    "caption_utf8_sha256": row[2],
+                    "encoded_token_count_with_special_tokens": row[4],
+                    "index": row[0],
+                    "input_ids_int64_sha256": row[6],
+                    "original_token_count_with_special_tokens": row[3],
+                    "truncated": row[5],
+                }
+                for row in caption_rows
+            ],
+            "chunk_ranges": [list(value) for value in chunk_ranges],
+            "method_id": METHOD_ID,
+            "model_id": MODEL_ID,
+            "revision": REVISION,
+            "runtime_manifest_sha256": runtime_manifest,
+            "snapshot_manifest_sha256": snapshot_manifest,
+            "source_manifest_sha256": source_manifest,
+            "token_length": TOKEN_LENGTH,
+            "truncation": True,
+        }
+        cache_key = hashlib.sha256(_canonical_json(cache_key_payload)).hexdigest()
+        receipt = FrozenClipTextReceipt(
+            batch_size=batch_size,
+            caption_rows=caption_rows,
+            chunk_ranges=tuple(chunk_ranges),
+            frozen_embedding_cache_key_sha256=cache_key,
+            live_model_manifest_sha256=self._model_manifest_sha256,
+            output_bytes=len(output_bytes),
+            output_sha256=hashlib.sha256(output_bytes).hexdigest(),
+            output_shape=tuple(embeddings.shape),
+            output_stride=tuple(embeddings.stride()),
+            runtime_identity=self._backend.runtime_identity,
+            runtime_manifest_sha256=runtime_manifest,
+            snapshot_files=self._snapshot_rows,
+            snapshot_manifest_sha256=snapshot_manifest,
+            source_files=self._source_rows,
+            source_manifest_sha256=source_manifest,
+        )
+        return FrozenClipTextBatch(
+            embeddings,
+            lineage,
+            receipt,
+            _seal=_CONSTRUCTION_SEAL,
+        )
+
+
+def _load_frozen_clip_text_adapter(
+    snapshot_root: object,
+    *,
+    max_batch_size: object,
+    backend: _Backend,
+    pins: tuple[tuple[str, int, str], ...],
+) -> FrozenClipTextAdapter:
+    if type(max_batch_size) is not int or not 1 <= max_batch_size <= MAX_BATCH_SIZE:
+        _hold("HOLD_BATCH_SIZE")
+    backend.validate_runtime()
+    root = _safe_absolute_directory(snapshot_root)
+    before = _scan_snapshot(root, pins)
+    adapter_source = _adapter_source_row()
+    tokenizer, model = _load_components(root, backend)
+    _freeze_model(model, backend)
+    model_manifest = _live_model_manifest(model, backend)
+    after = _scan_snapshot(root, pins)
+    if before != after:
+        _hold("HOLD_SNAPSHOT_CHANGED")
+    if _adapter_source_row() != adapter_source:
+        _hold("HOLD_ADAPTER_SOURCE_CHANGED")
+    source_rows = backend.source_rows + (adapter_source,)
+    return FrozenClipTextAdapter(
+        backend=backend,
+        max_batch_size=max_batch_size,
+        model=model,
+        model_manifest_sha256=model_manifest,
+        pins=pins,
+        snapshot_root=root,
+        snapshot_rows=before,
+        source_rows=source_rows,
+        tokenizer=tokenizer,
+        _seal=_CONSTRUCTION_SEAL,
+    )
+
+
+def load_frozen_clip_text_adapter(
+    snapshot_root: str | os.PathLike[str],
+    *,
+    max_batch_size: int,
+) -> FrozenClipTextAdapter:
+    """Load the fixed text-only CLIP projection from an exact local snapshot.
+
+    The caller must explicitly configure the qualified CPU runtime, including
+    hidden CUDA and one deterministic Torch thread.  This function performs no
+    download and returns no authority beyond frozen feature preparation.
+    """
+
+    try:
+        backend = _real_backend()
+        return _load_frozen_clip_text_adapter(
+            snapshot_root,
+            max_batch_size=max_batch_size,
+            backend=backend,
+            pins=_OFFICIAL_PINNED_FILES,
+        )
+    except FrozenClipTextAdapterError:
+        raise
+    except Exception:
+        raise FrozenClipTextAdapterError("HOLD_UNEXPECTED_LOAD") from None
+
+
+__all__ = [
+    "AUTHORITY",
+    "EMBEDDING_DIM",
+    "FrozenClipTextAdapter",
+    "FrozenClipTextAdapterError",
+    "FrozenClipTextBatch",
+    "FrozenClipTextReceipt",
+    "MAX_BATCH_SIZE",
+    "MAX_CAPTIONS",
+    "METHOD_ID",
+    "MODEL_ID",
+    "PRODUCTION",
+    "REVISION",
+    "TRAINING_AUTHORIZED",
+    "load_frozen_clip_text_adapter",
+]
diff --git a/tests/test_phaseset_frozen_clip_text.py b/tests/test_phaseset_frozen_clip_text.py
new file mode 100644
index 0000000..031904b
--- /dev/null
+++ b/tests/test_phaseset_frozen_clip_text.py
@@ -0,0 +1,438 @@
+"""Mock-only tests for the proposed public PhaseSet frozen CLIP adapter.
+
+These tests use Torch tensors but never import Transformers, load a checkpoint,
+open a network connection, or run the real CLIP model.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from pathlib import Path
+import shutil
+import subprocess
+import sys
+
+import pytest
+import torch
+
+from phaseset_core import frozen_clip_text as adapter_module
+
+
+MODULE_PATH = Path(str(adapter_module.__file__)).resolve(strict=True)
+
+
+class _Config:
+    attention_dropout = 0.0
+    bos_token_id = 0
+    eos_token_id = 2
+    hidden_act = "quick_gelu"
+    hidden_size = 512
+    intermediate_size = 2048
+    layer_norm_eps = 1e-05
+    max_position_embeddings = 77
+    model_type = "clip_text_model"
+    num_attention_heads = 8
+    num_hidden_layers = 12
+    pad_token_id = 1
+    projection_dim = 512
+    vocab_size = 49_408
+
+
+class _Output:
+    def __init__(self, pooler_output: torch.Tensor) -> None:
+        self.pooler_output = pooler_output
+
+
+class _TextModel(torch.nn.Module):
+    def __init__(self, owner: "_Model") -> None:
+        super().__init__()
+        object.__setattr__(self, "_owner", owner)
+
+    def forward(
+        self,
+        *,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> _Output:
+        del attention_mask
+        self._owner.forward_count += 1
+        if self._owner.advance_rng:
+            _ = torch.rand(())
+        base = input_ids[:, 1:2].to(dtype=torch.float32)
+        axis = torch.arange(512, dtype=torch.float32).unsqueeze(0) / 1024.0
+        output = base + axis
+        if self._owner.return_nonfinite:
+            output[0, 0] = torch.nan
+        return _Output(output)
+
+
+class _Model(torch.nn.Module):
+    last_model: "_Model | None" = None
+    load_calls: list[dict[str, object]] = []
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.config = _Config()
+        self.advance_rng = False
+        self.forward_count = 0
+        self.return_nonfinite = False
+        self.text_model = _TextModel(self)
+        self.text_projection = torch.nn.Linear(512, 512, bias=False)
+        with torch.no_grad():
+            self.text_projection.weight.copy_(torch.eye(512, dtype=torch.float32))
+        self.register_buffer("position_ids", torch.arange(77, dtype=torch.int64))
+
+    @classmethod
+    def from_pretrained(cls, _root: str, **kwargs: object) -> tuple["_Model", dict[str, object]]:
+        cls.load_calls.append(dict(kwargs))
+        model = cls()
+        cls.last_model = model
+        return model, {
+            "unexpected_keys": [],
+            "missing_keys": [],
+            "mismatched_keys": [],
+            "error_msgs": [],
+        }
+
+
+class _Tokenizer:
+    load_calls: list[dict[str, object]] = []
+    scramble_formal_middle = False
+    wrong_terminal_eos = False
+    bos_token_id = 49_406
+    eos_token_id = 49_407
+    model_max_length = 77
+    pad_token_id = 49_407
+    padding_side = "right"
+    truncation_side = "right"
+    unk_token_id = 49_407
+
+    @classmethod
+    def from_pretrained(cls, _root: str, **kwargs: object) -> "_Tokenizer":
+        cls.load_calls.append(dict(kwargs))
+        return cls()
+
+    def __call__(self, captions: list[str], **kwargs: object) -> object:
+        token_rows = []
+        for caption in captions:
+            body = [
+                int.from_bytes(hashlib.sha256(word.encode("utf-8")).digest()[:4], "little")
+                % 49_000
+                for word in caption.split()
+            ]
+            token_rows.append([49_406, *body, 49_407])
+        if "return_tensors" not in kwargs:
+            assert kwargs == {
+                "add_special_tokens": True,
+                "padding": False,
+                "truncation": False,
+                "return_attention_mask": False,
+                "verbose": False,
+            }
+            return {"input_ids": token_rows}
+        assert kwargs == {
+            "add_special_tokens": True,
+            "max_length": 77,
+            "padding": "max_length",
+            "truncation": True,
+            "return_tensors": "pt",
+        }
+        rows = torch.full((len(captions), 77), 49_407, dtype=torch.int64)
+        masks = torch.zeros_like(rows)
+        for index, tokens in enumerate(token_rows):
+            if len(tokens) > 77:
+                tokens = [*tokens[:76], 49_407]
+            if self.scramble_formal_middle and len(tokens) > 2:
+                tokens[1] = (tokens[1] + 1) % 49_000
+            if self.wrong_terminal_eos:
+                tokens[-1] = 2
+            rows[index, : len(tokens)] = torch.tensor(tokens, dtype=torch.int64)
+            masks[index, : len(tokens)] = 1
+        return {"input_ids": rows, "attention_mask": masks}
+
+
+def _commitment(label: str) -> bytes:
+    return hashlib.sha256(("lineage:" + label).encode("ascii")).digest()
+
+
+def _fixture(tmp_path: Path, *, max_batch_size: int = 3):
+    _Model.last_model = None
+    _Model.load_calls.clear()
+    _Tokenizer.load_calls.clear()
+    _Tokenizer.scramble_formal_middle = False
+    _Tokenizer.wrong_terminal_eos = False
+    snapshot = tmp_path / "snapshot"
+    snapshot.mkdir()
+    contents = {"a.bin": b"official-a", "b.json": b"{}\n"}
+    pins = []
+    for name, raw in contents.items():
+        path = snapshot / name
+        path.write_bytes(raw)
+        pins.append((name, len(raw), hashlib.sha256(raw).hexdigest()))
+    boundary_calls: list[tuple[int, int]] = []
+
+    def validate_boundary(value: torch.Tensor) -> None:
+        assert type(value) is torch.Tensor
+        assert value.dtype == torch.float32
+        assert value.device.type == "cpu"
+        assert value.is_contiguous()
+        assert not value.requires_grad
+        assert value.shape[1] == 512
+        boundary_calls.append(tuple(value.shape))
+
+    backend = adapter_module._Backend(
+        torch=torch,
+        tokenizer_type=_Tokenizer,
+        model_type=_Model,
+        batch_encoding_type=dict,
+        runtime_identity=(("runtime", "mock-only"),),
+        source_rows=(("mock.backend", 1, "0" * 64),),
+        expected_config=adapter_module._EXPECTED_CONFIG,
+        unexpected_count=0,
+        unexpected_sha256=hashlib.sha256(b"").hexdigest(),
+        expected_parameter_count=1,
+        expected_buffer_count=1,
+        validate_training_boundary=validate_boundary,
+        validate_runtime=lambda: None,
+    )
+    adapter = adapter_module._load_frozen_clip_text_adapter(
+        snapshot.resolve(),
+        max_batch_size=max_batch_size,
+        backend=backend,
+        pins=tuple(pins),
+    )
+    assert _Model.last_model is not None
+    return adapter, snapshot, boundary_calls, _Model.last_model
+
+
+def test_ordered_variable_q_and_explicit_chunk_bound(tmp_path: Path) -> None:
+    adapter, _snapshot, boundary_calls, model = _fixture(tmp_path, max_batch_size=3)
+    captions = ("first private row", "second private row", "third private row", "fourth")
+    lineage = tuple(_commitment(str(index)) for index in range(len(captions)))
+
+    result = adapter.encode(captions, lineage, batch_size=2)
+
+    assert tuple(result.embeddings.shape) == (4, 512)
+    assert result.embeddings.dtype == torch.float32
+    assert result.embeddings.device.type == "cpu"
+    assert result.embeddings.is_contiguous()
+    assert not result.embeddings.requires_grad
+    assert model.forward_count == 2
+    assert boundary_calls == [(2, 512), (2, 512), (4, 512)]
+    assert result.caption_commitments == lineage
+    assert result.receipt.output_shape == (4, 512)
+    assert result.receipt.output_stride == (512, 1)
+
+
+def test_lineage_never_changes_embeddings_and_order_is_preserved(tmp_path: Path) -> None:
+    adapter, _snapshot, _boundary_calls, _model = _fixture(tmp_path)
+    captions = ("alpha private text", "beta private text", "gamma private text")
+    first_lineage = tuple(_commitment("a" + str(index)) for index in range(3))
+    second_lineage = tuple(_commitment("b" + str(index)) for index in range(3))
+
+    first = adapter.encode(captions, first_lineage, batch_size=2)
+    second = adapter.encode(captions, second_lineage, batch_size=2)
+    different_batch = adapter.encode(captions, first_lineage, batch_size=3)
+    reversed_rows = adapter.encode(
+        tuple(reversed(captions)), tuple(reversed(first_lineage)), batch_size=2
+    )
+
+    assert torch.equal(first.embeddings, second.embeddings)
+    assert first.receipt.output_sha256 == second.receipt.output_sha256
+    assert (
+        first.receipt.frozen_embedding_cache_key_sha256
+        == second.receipt.frozen_embedding_cache_key_sha256
+    )
+    assert first.receipt.sha256 != second.receipt.sha256
+    assert (
+        first.receipt.frozen_embedding_cache_key_sha256
+        != different_batch.receipt.frozen_embedding_cache_key_sha256
+    )
+    assert torch.equal(first.embeddings.flip(0), reversed_rows.embeddings)
+
+
+def test_receipt_is_path_free_text_free_and_authority_zero(tmp_path: Path) -> None:
+    adapter, snapshot, _boundary_calls, _model = _fixture(tmp_path)
+    caption = "never serialize this private sentence"
+    result = adapter.encode((caption,), (_commitment("one"),), batch_size=1)
+    raw = result.receipt.canonical_json_bytes()
+    decoded = json.loads(raw)
+
+    assert caption.encode("utf-8") not in raw
+    assert str(snapshot).encode("utf-8") not in raw
+    assert decoded["authority"] == 0
+    assert decoded["production"] is False
+    assert decoded["training_authorized"] is False
+    assert decoded["caption_rows"][0]["caption_utf8_sha256"] == hashlib.sha256(
+        caption.encode("utf-8")
+    ).hexdigest()
+    assert decoded["embedding_selection"] == "official_pretrained_text_projection_raw_float32"
+
+
+def test_loader_is_literal_offline_text_only(tmp_path: Path) -> None:
+    adapter, _snapshot, _boundary_calls, model = _fixture(tmp_path)
+    assert adapter.max_batch_size == 3
+    assert model.training is False
+    assert all(parameter.requires_grad is False for parameter in model.parameters())
+    for call in (_Tokenizer.load_calls[0], _Model.load_calls[0]):
+        assert call["local_files_only"] is True
+        assert call["trust_remote_code"] is False
+        assert isinstance(call["cache_dir"], str)
+    assert _Model.load_calls[0]["low_cpu_mem_usage"] is False
+    assert _Model.load_calls[0]["output_loading_info"] is True
+
+
+def test_extra_file_and_snapshot_mutation_fail_without_path_leak(tmp_path: Path) -> None:
+    snapshot = tmp_path / "bad-snapshot"
+    snapshot.mkdir()
+    raw = b"bound"
+    expected = snapshot / "one.bin"
+    expected.write_bytes(raw)
+    (snapshot / "extra.bin").write_bytes(b"extra")
+    pins = (("one.bin", len(raw), hashlib.sha256(raw).hexdigest()),)
+    backend = adapter_module._Backend(
+        torch=torch,
+        tokenizer_type=_Tokenizer,
+        model_type=_Model,
+        batch_encoding_type=dict,
+        runtime_identity=(("runtime", "mock-only"),),
+        source_rows=(("mock.backend", 1, "0" * 64),),
+        expected_config=adapter_module._EXPECTED_CONFIG,
+        unexpected_count=0,
+        unexpected_sha256=hashlib.sha256(b"").hexdigest(),
+        expected_parameter_count=1,
+        expected_buffer_count=1,
+        validate_training_boundary=lambda _value: None,
+        validate_runtime=lambda: None,
+    )
+    with pytest.raises(adapter_module.FrozenClipTextAdapterError) as caught:
+        adapter_module._load_frozen_clip_text_adapter(
+            snapshot.resolve(), max_batch_size=1, backend=backend, pins=pins
+        )
+    assert caught.value.code == "HOLD_SNAPSHOT_CENSUS"
+    assert str(snapshot) not in str(caught.value)
+
+    (snapshot / "extra.bin").unlink()
+    adapter = adapter_module._load_frozen_clip_text_adapter(
+        snapshot.resolve(), max_batch_size=1, backend=backend, pins=pins
+    )
+    expected.write_bytes(b"xxxxx")
+    with pytest.raises(adapter_module.FrozenClipTextAdapterError) as changed:
+        adapter.encode(("row",), (_commitment("row"),), batch_size=1)
+    assert changed.value.code == "HOLD_SNAPSHOT_FILE_BYTES"
+    assert str(snapshot) not in str(changed.value)
+
+
+def test_nonfinite_forward_and_live_parameter_mutation_are_rejected(tmp_path: Path) -> None:
+    adapter, _snapshot, _boundary_calls, model = _fixture(tmp_path)
+    model.return_nonfinite = True
+    with pytest.raises(adapter_module.FrozenClipTextAdapterError) as nonfinite:
+        adapter.encode(("row",), (_commitment("row"),), batch_size=1)
+    assert nonfinite.value.code == "HOLD_EMBEDDING_BOUNDARY"
+
+    model.return_nonfinite = False
+    with torch.no_grad():
+        model.text_projection.weight[0, 0] += 1.0
+    with pytest.raises(adapter_module.FrozenClipTextAdapterError) as mutated:
+        adapter.encode(("row",), (_commitment("row"),), batch_size=1)
+    assert mutated.value.code == "HOLD_MODEL_CHANGED"
+
+
+@pytest.mark.parametrize("batch_size", [0, 4, True])
+def test_batch_size_is_exact_and_bounded(tmp_path: Path, batch_size: object) -> None:
+    adapter, _snapshot, _boundary_calls, _model = _fixture(tmp_path, max_batch_size=3)
+    with pytest.raises(adapter_module.FrozenClipTextAdapterError) as caught:
+        adapter.encode(
+            ("row",), (_commitment("row"),), batch_size=batch_size  # type: ignore[arg-type]
+        )
+    assert caught.value.code == "HOLD_BATCH_SIZE"
+
+
+def test_batch_owns_immutable_snapshot_of_returned_tensor(tmp_path: Path) -> None:
+    adapter, _snapshot, _boundary_calls, _model = _fixture(tmp_path)
+    result = adapter.encode(("row",), (_commitment("row"),), batch_size=1)
+    first = result.embeddings
+    first.zero_()
+    assert not torch.equal(first, result.embeddings)
+
+
+def test_long_caption_uses_visible_native_truncation(tmp_path: Path) -> None:
+    adapter, _snapshot, _boundary_calls, model = _fixture(tmp_path)
+    caption = " ".join(f"word{index}" for index in range(76))
+    result = adapter.encode((caption,), (_commitment("long"),), batch_size=1)
+    row = result.receipt.to_public_dict()["caption_rows"][0]
+    assert row["original_token_count_with_special_tokens"] == 78
+    assert row["encoded_token_count_with_special_tokens"] == 77
+    assert row["truncated"] is True
+    assert result.receipt.to_public_dict()["tokenization"] == {
+        "add_special_tokens": True,
+        "long_caption_policy": "clip_native_truncate_77",
+        "max_tokens_including_special_tokens": 77,
+        "padding": "max_length",
+        "preserve_terminal_eos": True,
+        "truncation": True,
+    }
+    assert model.forward_count == 1
+
+
+def test_wrong_native_terminal_eos_is_rejected_before_forward(tmp_path: Path) -> None:
+    adapter, _snapshot, _boundary_calls, model = _fixture(tmp_path)
+    _Tokenizer.wrong_terminal_eos = True
+    with pytest.raises(adapter_module.FrozenClipTextAdapterError) as caught:
+        adapter.encode(("one row",), (_commitment("one"),), batch_size=1)
+    assert caught.value.code == "HOLD_TOKEN_LENGTH_PROBE"
+    assert model.forward_count == 0
+
+
+def test_formal_token_body_must_equal_untruncated_native_prefix(tmp_path: Path) -> None:
+    adapter, _snapshot, _boundary_calls, model = _fixture(tmp_path)
+    _Tokenizer.scramble_formal_middle = True
+    with pytest.raises(adapter_module.FrozenClipTextAdapterError) as caught:
+        adapter.encode(("one row",), (_commitment("one"),), batch_size=1)
+    assert caught.value.code == "HOLD_TOKEN_LENGTH_PROBE"
+    assert model.forward_count == 0
+
+
+def test_encode_rng_advance_fails_and_restores_caller_state(tmp_path: Path) -> None:
+    adapter, _snapshot, _boundary_calls, model = _fixture(tmp_path)
+    caller_state = torch.get_rng_state().clone()
+    model.advance_rng = True
+    with pytest.raises(adapter_module.FrozenClipTextAdapterError) as caught:
+        adapter.encode(("one row",), (_commitment("one"),), batch_size=1)
+    assert caught.value.code == "HOLD_RUNTIME_RNG"
+    assert torch.equal(caller_state, torch.get_rng_state())
+    assert model.forward_count == 1
+
+
+def test_planned_public_package_import_is_lazy_in_subprocess(tmp_path: Path) -> None:
+    package = tmp_path / "src" / "phaseset_core"
+    package.mkdir(parents=True)
+    (package / "__init__.py").write_bytes(b"")
+    shutil.copyfile(MODULE_PATH, package / "frozen_clip_text.py")
+    source_root = str(tmp_path / "src")
+    program = (
+        "import json,sys;"
+        f"sys.path.insert(0,{source_root!r});"
+        "from phaseset_core.frozen_clip_text import "
+        "EMBEDDING_DIM,METHOD_ID,load_frozen_clip_text_adapter;"
+        "print(json.dumps({'dim':EMBEDDING_DIM,'method':METHOD_ID,"
+        "'torch_eager':'torch' in sys.modules,"
+        "'transformers_eager':'transformers' in sys.modules,"
+        "'callable':callable(load_frozen_clip_text_adapter)},sort_keys=True))"
+    )
+    completed = subprocess.run(
+        [sys.executable, "-I", "-B", "-c", program],
+        check=False,
+        capture_output=True,
+        text=True,
+    )
+    assert completed.returncode == 0, completed.stderr
+    assert completed.stderr == ""
+    assert json.loads(completed.stdout) == {
+        "callable": True,
+        "dim": 512,
+        "method": "phaseset-clip-native-truncate-77-pretrained-projection-v1",
+        "torch_eager": False,
+        "transformers_eager": False,
+    }

From fefd9664a74789c02abeefdfcdaf8196065ba4f6 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 03:26:02 -0500
Subject: [PATCH 07/95] feat: resume residual attempts with verified checkpoint
 copies

---
 RELEASE_FILES.sha256                   |  10 +-
 STATUS.md                              |  26 +-
 docs/HOST_RUNTIME_INTEGRATION.md       |  19 +-
 docs/RESIDUAL_RESUME.md                |  73 ++
 src/phaseset_core/cli.py               |   2 +
 src/phaseset_core/host.py              | 277 ++++++-
 tests/test_phaseset_residual_resume.py | 966 +++++++++++++++++++++++++
 7 files changed, 1340 insertions(+), 33 deletions(-)
 create mode 100644 docs/RESIDUAL_RESUME.md
 create mode 100644 tests/test_phaseset_residual_resume.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index e1c4dda..e5acef1 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,7 +11,7 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-8b9d341db90149d930c38a1e0de506e04af4496ce6f16db86dfd84a237c4f703	12682	STATUS.md
+68a1113a30603aef022aeacfd6b45db82c97b9a4e8eeabf695f97ef66bf3a4a7	14030	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
@@ -21,7 +21,7 @@ e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
 e7bc4f9bace637106d73afe6be84e0695c5be78857218bc8b141cc8d5f009d52	11085	docs/FROZEN_CLIP_TEXT_ADAPTER.md
-3861d213e951e059a3fa044c9f4c87eaa93054aef84323315db67521865838f2	9883	docs/HOST_RUNTIME_INTEGRATION.md
+cc8c2059d499a52f40d40f3be6c75d6e46ef4065a3035aa26e89c41cb7b2cc41	10342	docs/HOST_RUNTIME_INTEGRATION.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
@@ -51,6 +51,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 ecb85faa3b442450f6ecc8d6f2fff77baf19e833254fa8a715127d9127742f63	13825	docs/PHASESET_RESEARCH_CONTRACT.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
+abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
 8d689f49b2704ff7c5988eff8a7e3e062f209b85d18425ccdba1e1099581079b	10658	docs/phasepair_research_contract_20260824_165840.md
 8d4b03326029b2a06478295b7328d61181d022604b1465bccd3a6cfcc8ce9704	2155	figures/EDITABLE_FIGURES.json
 efb48515925d5412bc840a5ae07415cda4645591deaa1ab6aca90e1e4f528591	4208	figures/README.md
@@ -117,7 +118,7 @@ dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phase
 2e5fb5280ed27a46c587d07a60ce663fce676cbdd30b059ec34882b4507d157f	604	src/phaseset_core/__init__.py
 9668a89535264a3ed6b58ffc7ac31b4904a4d8f68636d405e15cbf7368096716	8437	src/phaseset_core/_morlet_oracle_bytes.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
-707ad75839dd8e2a6fb3ed82536d66ccebb6aa049bcab12f198a9d6311623cb2	15896	src/phaseset_core/cli.py
+c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 9636324cff46e2a89bb93b71f8c3a4cc07544f8dd0e10e2da60d5a2ffbc7b356	25892	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
@@ -126,7 +127,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
 51bdad872537b332e886646071d4381d00a09ddba5912c43ba8559e2675a4c79	48618	src/phaseset_core/frozen_clip_text.py
-22f9855b0f689c3f34de78ec47a53f15b1c9c8fbedaf57130d5ced1e5da561f4	67811	src/phaseset_core/host.py
+074bd1c13b1cd98b200e98c2e85b23431b0ffcc5f4f3b7eb9f5a230e2017ec0f	77870	src/phaseset_core/host.py
 47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
@@ -190,6 +191,7 @@ e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test
 f6837c3ac23f4209cb3ec3eadb899d0bdcd4b4259c901b021254ffe7b3688492	8482	tests/test_phaseset_preprocessing.py
 64016f9369540dae8b87654f43d09df9b9c07a3c4af06fa45567008b57140193	45251	tests/test_phaseset_production.py
 a1fbdfe55e50f67bb6568d5eba276e24beeb5a44373c516f0bd7758bd5742127	12486	tests/test_phaseset_publication.py
+005a91d472a715e98b3a733cde102ecffc70222e1572d6100bb18f87ce9e3361	33889	tests/test_phaseset_residual_resume.py
 3f7e4d75e8bd29e6b90c8742d8b5aeccdbe08f454257c43a05c14165ae01d327	16439	tests/test_phaseset_scaling.py
 9147a58a884062ca0b549e33aa7141c9e3d8729db1132f328673d59e93b13177	6762	tests/test_phaseset_split_audit.py
 d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/test_phaseset_statistics.py
diff --git a/STATUS.md b/STATUS.md
index 8e686f1..15900f9 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -18,8 +18,9 @@ evidence, not training results or scientific claims.
 - A separate data-volume Python 3.12.12 / NumPy 2.4.6 / Torch 2.12.0+cu126
   environment is installed with a clean dependency check. The complete Linux
   CPU regression including the private-host lifecycle integration passed:
-  932 tests, 1 skipped, and 39 subtests, in 287.60 s after adding the frozen
-  CLIP adapter. Source bytes were unchanged before and after the full suite.
+  948 tests, 2 skipped, and 39 subtests, in 376.00 s after adding residual
+  host resume. Source bytes were unchanged before and after the full suite.
+  The earlier frozen-CLIP integration passed 932 tests and 39 subtests.
   The earlier host-only suite passed 917 tests and its 25 focused checks.
   This includes synthetic model/lifecycle checks, not main-data training.
 - A separately bounded shared-GPU functionality probe passed FP64 forward and
@@ -36,6 +37,14 @@ evidence, not training results or scientific claims.
   CUDA context-usable total were incorrectly equated; that failed receipt is
   retained. The corrected memory instrumentation did not relax a model gate.
   This does not qualify BF16, the learned group base, or the full training runtime.
+- Separate actual-width B0/B1/B2 FP32 CUDA forward/backward lanes passed the
+  registered parameter census, physical permutation/padding equality, finite
+  gradients, frozen-text preservation, and CPU/CUDA tolerance checks. Their
+  small analytic fixtures are not training or performance results. A subsequent
+  residual retrieval lane failed its frozen-base CPU/CUDA embedding comparison
+  after zero-residual scoring checks. That failed receipt is preserved;
+  inference-backend diagnosis is ongoing, with no tolerance relaxation or
+  frozen-base inference/full-runtime qualification claimed.
 - The native Embody loader and concrete licensed SMPL-X body-22 evaluator are
   implemented. Seven adapter contract tests passed on Linux/Python 3.12.12/
   NumPy 2.4.6; these use explicit fixtures, not licensed assets or main data.
@@ -54,6 +63,11 @@ evidence, not training results or scientific claims.
   the exact retained prefix and EOS, restores caller RNG, and emits text-free
   receipts. See [frozen CLIP text adapter](docs/FROZEN_CLIP_TEXT_ADAPTER.md).
   This does not qualify GPU text execution or real-data training.
+- The exact latest text-adapter commit was downloaded from anonymous codeload,
+  manifest/content/link checked, built offline into a wheel, and installed into
+  a separate server target. All 56 wheel Python modules match the public Git
+  index. Three real CLIP forwards from that installed wheel produced a complete
+  observation byte-identical to the previously verified source copy.
 - Native Linux last-bit Morlet formula drift is repaired using strictly
   verified canonical coefficient bytes, while preserving both original hashes.
   Windows still requires its original exact formula digest; Linux requires its
@@ -66,8 +80,12 @@ evidence, not training results or scientific claims.
   overfull boxes. All result cells and pending author metadata remain explicit.
 - The concrete private-host bridge now connects base/residual training to
   live checkpoint receipts, a periodic heartbeat, and a whole-attempt OS lease.
-  Base resume verifies the predecessor checkpoint chain, including process-loss
-  recovery; residual resume and the remaining host commands are not yet wired.
+  Base and residual resume verify the predecessor checkpoint chain, including
+  process-loss recovery and authenticated latest/older-best materialization.
+  Fifty-one focused server tests passed, including actual tiny residual
+  optimization/checkpoint/resume equality and zero execution on invalid prior
+  checkpoint bytes. See [resume details](docs/RESIDUAL_RESUME.md).
+  The remaining preparation/qualification/evaluation host commands are not yet wired.
   Four actual tiny server fits verified observer behavior without changing
   checkpoint/model/loss/validation outputs. These are engineering fixtures,
   not formal training attempts. GitHub CI passed both Windows runtimes and the
diff --git a/docs/HOST_RUNTIME_INTEGRATION.md b/docs/HOST_RUNTIME_INTEGRATION.md
index 2e4041f..ee7d0db 100644
--- a/docs/HOST_RUNTIME_INTEGRATION.md
+++ b/docs/HOST_RUNTIME_INTEGRATION.md
@@ -69,11 +69,15 @@ terminalization but are re-raised. If the failure or terminal cannot be
 persisted (for example, disk failure), the host propagates the error and does
 not manufacture a backend artifact.
 
-`resume` currently accepts only a base attempt.  The predecessor checkpoint
+`resume` accepts base and residual attempts. The predecessor checkpoint
 must be under the same attempt root; the harness verifies its terminal and
 checkpoint receipt, creates a distinct resume-linked attempt with
 `AttemptStore.create_resumed`, and passes that exact `ResumeRecord` to the
-training runtime.
+training runtime. Residual resume also requires `--base-checkpoint` and
+`--periodic-cache`; base resume rejects those arguments. The host verifies and
+copies the latest and any older best checkpoint into the new attempt before
+decoding owned authenticated bytes. Failed partial copies are retained, not
+deleted by a path-based cleanup race. See [resume details](RESIDUAL_RESUME.md).
 
 ## Executable invocation
 
@@ -141,15 +145,16 @@ rights assertion when Embody approval is unknown.
   prepared-data receipt.
 - `run-residual` is wired through the strict qualified-base loader, canonical
   periodic-cache record, energy floors, canonical all-system capacity audit,
-  closed residual constructor, and the same attempt ledger. Residual resume is
-  still rejected: the host has no residual resume dispatch or reconstruction
-  path and does not claim otherwise.
+  closed residual constructor, and the same attempt ledger. Residual resume
+  reconstructs those same dependencies and passes the verified predecessor
+  record to the unchanged runtime. Historical cache-file SHA provenance is not
+  retroactively claimed when only energy floors were consumed.
 - `prepare-data`, `audit-split`, `qualify-base`, `build-periodic-cache`,
   `evaluate`, `bootstrap`, and `render-paper` are not
   implemented here.  Calling one raises before a `BackendExecution` exists.
 - The public `resume` parser has no stop-after-step option. It accepts B0/B1/B2
-  and registered final system IDs; the host currently implements base resume
-  only and recovers identity from the verified attempt ledger.
+  and registered final system IDs; the host implements base and residual resume
+  and recovers identity from the verified attempt ledger.
 - The current public training runtime does not accept a precomputed periodic
   descriptor cache. The host authenticates the complete cache payload and
   consumes its energy floors, but periodic descriptors are recomputed by the
diff --git a/docs/RESIDUAL_RESUME.md b/docs/RESIDUAL_RESUME.md
new file mode 100644
index 0000000..81e909a
--- /dev/null
+++ b/docs/RESIDUAL_RESUME.md
@@ -0,0 +1,73 @@
+# Base and residual host resume
+
+The private host supports both registered base-qualification and residual
+training attempts through `phaseset resume`. The public CLI still requires the
+existing authenticated host adapter; this feature does not manufacture data
+rights, a qualified base, or an experiment result.
+
+## Command contract
+
+Base resume accepts an existing predecessor checkpoint and a new attempt
+directory. Residual resume additionally requires `--base-checkpoint` and
+`--periodic-cache`, the same artifact inputs as `run-residual`. Base resume
+rejects these residual-only arguments. Resume has no stop-after-step option.
+
+The verified predecessor run ID determines the seed, system, and legal role.
+An explicit system argument must agree. Both attempts must share their private
+attempt root. Symlinked paths, absent artifacts, incompatible roles, or a
+checkpoint other than the terminal-selected checkpoint are rejected.
+
+The residual host reconstructs, in order, the base qualification, selected
+frozen base, validated periodic-cache energy floors, seed capacity audit, and
+registered residual constructor. The unchanged training runtime then verifies
+its configuration, data, code/environment, initialization, optimizer, RNG, and
+resume-chain bindings before continuing.
+
+## Immutable attempts and checkpoint copies
+
+Resume creates a new attempt rather than rewriting its predecessor. A process
+lease excludes a simultaneous writer. Process-loss recovery acquires the same
+lease and uses already persisted checkpoint evidence; it does not guess from
+PID age or invent a missing checkpoint.
+
+The host copies the authenticated latest checkpoint into the new attempt with
+exclusive creation, a regular-file descriptor check, streaming SHA-256, and
+fsync. It reads and rehashes the bytes from the same open destination descriptor
+before decoding an owned byte buffer with `weights_only=True`. Unverified
+checkpoint bytes are never passed to the decoder.
+
+If that payload refers to an older best-validation checkpoint, only the named,
+digest-bound best file is copied too. This allows the unchanged runtime to find
+its best checkpoint under the new attempt directory. The copies do not replace
+the predecessor ledger or alter checkpoint payloads.
+
+A failed copy is retained inside the new failed attempt for diagnosis. The host
+does not use a check-then-unlink sequence that could delete a replacement file.
+Failure artifacts and a truthful FAILED terminal are written; retained partial
+bytes are not accepted as a resumable checkpoint. The predecessor is unchanged.
+
+## Cache provenance boundary
+
+Existing checkpoints bind the consumed six float64 energy floors through the
+registered residual initialization, together with the selected frozen base,
+qualification, capacity audit, and factory/code identity. They do not bind the
+historical periodic-cache filename, complete file SHA, or descriptors that the
+runtime did not consume. Resume validates the supplied cache and reconstructs
+the same consumed state; it does not retroactively claim file provenance.
+
+## Observed verification
+
+On the registered Linux server, 51 focused tests passed in 50.29 seconds with
+unchanged source bytes. Coverage includes role/artifact rejection, real attempt
+ledgers, latest and older-best materialization, changed checkpoint bytes rejected
+before decoding, zero runtime calls on invalid prior-best input, failure
+terminalization, and a tiny genuine optimization/checkpoint/interruption/resume
+comparison against uninterrupted execution.
+
+Two earlier test-fixture failures were retained and corrected without changing
+the host implementation: an incomplete fake report and an outdated deletion
+expectation. The tiny actual runtime test already passed in that first attempt.
+These are engineering tests, not formal PhaseSet training or scientific results.
+
+The complete server regression with this integration then passed 948 tests,
+two skipped, and 39 subtests in 376.00 seconds, again with unchanged source bytes.
diff --git a/src/phaseset_core/cli.py b/src/phaseset_core/cli.py
index cb5680f..0b1447e 100644
--- a/src/phaseset_core/cli.py
+++ b/src/phaseset_core/cli.py
@@ -289,6 +289,8 @@ def build_parser() -> argparse.ArgumentParser:
     resume.add_argument("--attempt-dir", type=Path)
     resume.add_argument("--checkpoint", dest="resume_checkpoint", type=Path)
     resume.add_argument("--system-id", choices=_RESUME_SYSTEM_IDS)
+    resume.add_argument("--base-checkpoint", type=Path)
+    resume.add_argument("--periodic-cache", type=Path)
 
     return parser
 
diff --git a/src/phaseset_core/host.py b/src/phaseset_core/host.py
index f86b65e..5d25138 100644
--- a/src/phaseset_core/host.py
+++ b/src/phaseset_core/host.py
@@ -7,7 +7,7 @@
 existing ``ExecutionHold`` boundary.
 
 The implemented command surface is intentionally small: ``preflight``,
-``run-base``, ``run-residual``, and base ``resume``. Other commands raise
+``run-base``, ``run-residual``, and base/residual ``resume``. Other commands raise
 before a ``BackendExecution`` can be manufactured.
 """
 
@@ -19,6 +19,7 @@
 import io
 import json
 import os
+import stat
 import sys
 import threading
 from collections.abc import Iterable, Mapping, Sequence
@@ -879,22 +880,205 @@ def _write_once_json(path: Path, value: object) -> Path:
     return path
 
 
+def _copy_verified_checkpoint(
+    source: Path,
+    destination: Path,
+    *,
+    expected_sha256: str,
+) -> tuple[Path, bytes]:
+    """Materialize already-verified checkpoint bytes under a new attempt."""
+
+    _reject_symlink_components(source, "resume checkpoint source")
+    _reject_symlink_components(destination.parent, "resume checkpoint destination")
+    if (
+        not source.is_file()
+        or not destination.parent.is_dir()
+        or destination.exists()
+        or destination.is_symlink()
+        or destination.name != source.name
+    ):
+        raise HostConfigurationError("resume checkpoint materialization path is invalid")
+    flags = os.O_RDONLY
+    if hasattr(os, "O_BINARY"):
+        flags |= os.O_BINARY
+    if hasattr(os, "O_NOFOLLOW"):
+        flags |= os.O_NOFOLLOW
+    destination_flags = os.O_RDWR | os.O_CREAT | os.O_EXCL
+    if hasattr(os, "O_BINARY"):
+        destination_flags |= os.O_BINARY
+    if hasattr(os, "O_NOFOLLOW"):
+        destination_flags |= os.O_NOFOLLOW
+    source_descriptor: int | None = None
+    destination_descriptor: int | None = None
+    digest = hashlib.sha256()
+    owned_raw = b""
+    try:
+        source_descriptor = os.open(source, flags)
+        source_metadata = os.fstat(source_descriptor)
+        if not stat.S_ISREG(source_metadata.st_mode):
+            raise HostConfigurationError(
+                "resume checkpoint source descriptor is not a regular file"
+            )
+        destination_descriptor = os.open(destination, destination_flags, 0o600)
+        destination_metadata = os.fstat(destination_descriptor)
+        if not stat.S_ISREG(destination_metadata.st_mode):
+            raise HostConfigurationError(
+                "resume checkpoint destination descriptor is not a regular file"
+            )
+        with os.fdopen(source_descriptor, "rb", closefd=False) as reader:
+            with os.fdopen(destination_descriptor, "wb", closefd=False) as writer:
+                while chunk := reader.read(1024 * 1024):
+                    digest.update(chunk)
+                    writer.write(chunk)
+                writer.flush()
+                os.fsync(writer.fileno())
+        if digest.hexdigest() != expected_sha256:
+            raise HostConfigurationError(
+                "resume checkpoint materialization digest mismatch"
+            )
+        os.lseek(destination_descriptor, 0, os.SEEK_SET)
+        owned_chunks: list[bytes] = []
+        owned_digest = hashlib.sha256()
+        while chunk := os.read(destination_descriptor, 1024 * 1024):
+            owned_chunks.append(chunk)
+            owned_digest.update(chunk)
+        if owned_digest.hexdigest() != expected_sha256:
+            raise HostConfigurationError(
+                "resume checkpoint materialized bytes failed verification"
+            )
+        owned_raw = b"".join(owned_chunks)
+        if os.name == "posix":
+            directory_flags = os.O_RDONLY
+            if hasattr(os, "O_DIRECTORY"):
+                directory_flags |= os.O_DIRECTORY
+            directory_descriptor = os.open(destination.parent, directory_flags)
+            try:
+                os.fsync(directory_descriptor)
+            finally:
+                os.close(directory_descriptor)
+    except Exception as error:
+        if source_descriptor is not None:
+            with suppress(OSError):
+                os.close(source_descriptor)
+            source_descriptor = None
+        if destination_descriptor is not None:
+            with suppress(OSError):
+                os.close(destination_descriptor)
+            destination_descriptor = None
+        if isinstance(error, HostConfigurationError):
+            raise
+        raise HostConfigurationError(
+            "resume checkpoint materialization failed"
+        ) from error
+    except BaseException:
+        if source_descriptor is not None:
+            with suppress(OSError):
+                os.close(source_descriptor)
+            source_descriptor = None
+        if destination_descriptor is not None:
+            with suppress(OSError):
+                os.close(destination_descriptor)
+            destination_descriptor = None
+        raise
+    finally:
+        if source_descriptor is not None:
+            os.close(source_descriptor)
+        if destination_descriptor is not None:
+            os.close(destination_descriptor)
+    return destination, owned_raw
+
+
+def _materialize_resume_checkpoints(
+    checkpoint: Path,
+    checkpoint_receipt: CheckpointRecord,
+    destination_directory: Path,
+) -> Path:
+    """Copy only ledger-bound latest/best checkpoint bytes into a new attempt."""
+
+    _reject_symlink_components(
+        destination_directory, "resume checkpoint destination directory"
+    )
+    destination_directory.mkdir(mode=0o700, exist_ok=False)
+    latest, latest_raw = _copy_verified_checkpoint(
+        checkpoint,
+        destination_directory / checkpoint.name,
+        expected_sha256=checkpoint_receipt.checkpoint_payload_sha256,
+    )
+    payload = _decode_runtime_checkpoint_bytes(latest_raw)
+    best_name = payload.get("best_checkpoint_name")
+    best_sha256 = payload.get("best_checkpoint_sha256")
+    if best_name is not None and (
+        type(best_name) is not str
+        or Path(best_name).name != best_name
+        or not best_name.startswith("checkpoint-")
+        or not best_name.endswith(".pt")
+    ):
+        raise HostConfigurationError("resume checkpoint best-checkpoint name is invalid")
+    if best_sha256 is not None and (
+        type(best_sha256) is not str
+        or len(best_sha256) != 64
+        or any(character not in "0123456789abcdef" for character in best_sha256)
+    ):
+        raise HostConfigurationError("resume checkpoint best-checkpoint digest is invalid")
+    if best_name is None and best_sha256 is not None:
+        raise HostConfigurationError("resume checkpoint best digest has no name")
+    if best_name is None or best_name == checkpoint.name:
+        if best_sha256 not in (None, checkpoint_receipt.checkpoint_payload_sha256):
+            raise HostConfigurationError(
+                "resume checkpoint self-selected best digest is inconsistent"
+            )
+        return latest
+    if best_sha256 is None:
+        raise HostConfigurationError("resume checkpoint prior best lacks a digest")
+    best_source = checkpoint.parent / best_name
+    _copy_verified_checkpoint(
+        best_source,
+        destination_directory / best_name,
+        expected_sha256=best_sha256,
+    )
+    return latest
+
+
 def _torch_value_sha256(value: object) -> str:
     stream = io.BytesIO()
     torch.save(value, stream)
     return _sha256_bytes(stream.getvalue())
 
 
-def _load_runtime_checkpoint(path: Path) -> dict[str, object]:
+def _decode_runtime_checkpoint_bytes(raw: bytes) -> dict[str, object]:
     try:
-        payload = torch.load(path, map_location="cpu", weights_only=False)
-    except TypeError:  # torch 2.0 does not expose weights_only.
-        payload = torch.load(path, map_location="cpu")
+        payload = torch.load(
+            io.BytesIO(raw),
+            map_location="cpu",
+            weights_only=True,
+        )
+    except Exception as error:
+        raise HostConfigurationError(
+            "training checkpoint is truncated or unreadable"
+        ) from error
     if type(payload) is not dict:
         raise HostConfigurationError("training checkpoint payload is not an object")
     return payload
 
 
+def _load_runtime_checkpoint(
+    path: Path,
+    *,
+    expected_sha256: str | None = None,
+) -> dict[str, object]:
+    if path.is_symlink() or not path.is_file():
+        raise HostConfigurationError(
+            "training checkpoint must be a regular non-symlink file"
+        )
+    raw = path.read_bytes()
+    if (
+        expected_sha256 is not None
+        and _sha256_bytes(raw) != expected_sha256
+    ):
+        raise HostConfigurationError("training checkpoint artifact digest mismatch")
+    return _decode_runtime_checkpoint_bytes(raw)
+
+
 def _checkpoint_record(
     checkpoint_path: Path,
     checkpoint_sha256: str,
@@ -1085,7 +1269,7 @@ def stop(self) -> None:
 
 
 class PhaseSetHostBackend:
-    """Request-bound backend for real base/residual training and base resume."""
+    """Request-bound backend for real base/residual training and resume."""
 
     def __init__(self, config: HostConfig, request: cli.CLICommandRequest) -> None:
         self._config = config
@@ -1170,6 +1354,9 @@ def _execute_residual_runtime(
         base_checkpoint: Path,
         periodic_cache: Path,
         stop_after_global_step: int | None,
+        resume_checkpoint: Path | None = None,
+        resume_attempt_root: Path | None = None,
+        resume_record: ResumeRecord | None = None,
     ) -> TrainingReport:
         artifacts = self._config.residual_artifacts
         if artifacts is None:
@@ -1223,6 +1410,9 @@ def _execute_residual_runtime(
         return runtime.fit(
             train,
             val,
+            resume_checkpoint=resume_checkpoint,
+            resume_attempt_root=resume_attempt_root,
+            resume_record=resume_record,
             stop_after_global_step=stop_after_global_step,
         )
 
@@ -1367,6 +1557,8 @@ def _resume_base(self, intent: CommandIntent) -> BackendExecution:
             raise HostConfigurationError(
                 "resume requires --attempt-dir and --checkpoint"
             )
+        if request.stop_after_global_step is not None:
+            raise HostConfigurationError("resume does not accept a stop parameter")
         _reject_symlink_components(
             request.attempt_directory, "resume attempt directory"
         )
@@ -1402,9 +1594,35 @@ def _resume_base(self, intent: CommandIntent) -> BackendExecution:
                 "resume checkpoint differs from predecessor receipt"
             )
         role, seed, system_id = parse_run_id(predecessor_attempt.run_id)
-        if role != "BASE_QUALIFICATION":
+        base_checkpoint: Path | None = None
+        periodic_cache: Path | None = None
+        if role == "BASE_QUALIFICATION":
+            if request.base_checkpoint is not None or request.periodic_cache is not None:
+                raise HostConfigurationError(
+                    "base resume rejects base-checkpoint and periodic-cache"
+                )
+        elif role == "RESIDUAL_TRAIN":
+            if request.base_checkpoint is None or request.periodic_cache is None:
+                raise HostConfigurationError(
+                    "residual resume requires --base-checkpoint and --periodic-cache"
+                )
+            _reject_symlink_components(
+                request.base_checkpoint, "qualified base checkpoint"
+            )
+            base_checkpoint = request.base_checkpoint.resolve()
+            if not base_checkpoint.is_file():
+                raise HostConfigurationError(
+                    "qualified base checkpoint must be a regular existing file"
+                )
+            _reject_symlink_components(request.periodic_cache, "periodic cache")
+            periodic_cache = request.periodic_cache.resolve()
+            if not periodic_cache.is_dir():
+                raise HostConfigurationError(
+                    "periodic cache must be a regular existing directory"
+                )
+        else:
             raise HostConfigurationError(
-                "initial host resume supports base attempts only"
+                "host resume supports only base and residual attempts"
             )
         if request.system_id is not None and request.system_id != system_id:
             raise HostConfigurationError("resume system differs from predecessor run")
@@ -1443,17 +1661,40 @@ def _resume_base(self, intent: CommandIntent) -> BackendExecution:
             live_ledger = _LiveAttemptLedger(store, resumed_attempt)
             try:
                 live_ledger.start()
-                report = self._execute_base_runtime(
-                    run_id=predecessor_attempt.run_id,
-                    system_id=system_id,
-                    seed=seed,
-                    checkpoint_directory=new_directory / "model-checkpoints",
-                    live_ledger=live_ledger,
-                    resume_checkpoint=checkpoint,
-                    resume_attempt_root=new_directory.parent,
-                    resume_record=resume,
-                    stop_after_global_step=request.stop_after_global_step,
+                materialized_checkpoint = _materialize_resume_checkpoints(
+                    checkpoint,
+                    checkpoint_receipt,
+                    new_directory / "model-checkpoints",
                 )
+                if role == "BASE_QUALIFICATION":
+                    report = self._execute_base_runtime(
+                        run_id=predecessor_attempt.run_id,
+                        system_id=system_id,
+                        seed=seed,
+                        checkpoint_directory=new_directory / "model-checkpoints",
+                        live_ledger=live_ledger,
+                        resume_checkpoint=materialized_checkpoint,
+                        resume_attempt_root=new_directory.parent,
+                        resume_record=resume,
+                        stop_after_global_step=None,
+                    )
+                else:
+                    if base_checkpoint is None or periodic_cache is None:
+                        raise HostConfigurationError(
+                            "residual resume artifacts were not resolved"
+                        )
+                    report = self._execute_residual_runtime(
+                        system_id=system_id,
+                        seed=seed,
+                        checkpoint_directory=new_directory / "model-checkpoints",
+                        live_ledger=live_ledger,
+                        base_checkpoint=base_checkpoint,
+                        periodic_cache=periodic_cache,
+                        stop_after_global_step=None,
+                        resume_checkpoint=materialized_checkpoint,
+                        resume_attempt_root=new_directory.parent,
+                        resume_record=resume,
+                    )
                 live_ledger.ensure_checkpoint(report.latest_checkpoint)
                 live_ledger.stop()
                 return self._close_attempt(
diff --git a/tests/test_phaseset_residual_resume.py b/tests/test_phaseset_residual_resume.py
new file mode 100644
index 0000000..2d1155f
--- /dev/null
+++ b/tests/test_phaseset_residual_resume.py
@@ -0,0 +1,966 @@
+"""Host residual-resume contract and tiny real-runtime integration tests."""
+
+from __future__ import annotations
+
+import argparse
+from dataclasses import asdict, dataclass, replace
+import hashlib
+import json
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import cli, host, training
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+from phaseset_core.execution import (
+    AttemptRecord,
+    AttemptStore,
+    CommandIntent,
+    ResumeRecord,
+    TerminalRecord,
+    artifact_sha256,
+    canonical_attempt_bytes,
+    parse_checkpoint_bytes,
+)
+from phaseset_core.models import ActorMeanBase, PhaseSetEncoder
+from phaseset_core.objectives import PhaseSetRetrievalHead
+from phaseset_core.production import BackendExecution
+from phaseset_core.training import (
+    PhaseSetTrainingRuntime,
+    ResidualRetrievalSystem,
+    RetrievalTrainingBatch,
+    TrainingConfig,
+)
+
+
+def _digest(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _sha(path: Path) -> str:
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def _intent() -> CommandIntent:
+    return CommandIntent(
+        command="resume",
+        run_id=None,
+        seed=None,
+        split=None,
+        plan_sha256="a" * 64,
+    )
+
+
+def _host_config(tmp_path: Path) -> host.HostConfig:
+    return host.HostConfig(
+        path=tmp_path / "host.json",
+        prepared_index=tmp_path / "prepared-index.json",
+        receipt_record=tmp_path / "receipts.json",
+        receipt_artifacts={},
+        source_tree_sha256="d" * 64,
+        device="cpu",
+        request_bf16=False,
+        bf16_runtime_qualified=False,
+        edge_budget=4,
+        checkpoint_every_updates=1,
+        max_batch_bytes=1_000_000,
+        resume_retry_class="INFRA_TRANSIENT",
+        corrective_change_sha256="e" * 64,
+        residual_artifacts=None,
+    )
+
+
+def _predecessor(
+    root: Path,
+    *,
+    attempt_id: str,
+    run_id: str,
+) -> tuple[AttemptStore, Path]:
+    attempt = AttemptRecord(
+        attempt_id=attempt_id,
+        run_id=run_id,
+        created_at_utc="2026-09-08T12:00:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    store = AttemptStore.create(root, attempt)
+    checkpoint = store.path / "model-checkpoints" / "checkpoint-000000000001-000002-validation.pt"
+    checkpoint.parent.mkdir()
+    torch.save(
+        {
+            "best_checkpoint_name": checkpoint.name,
+            "best_checkpoint_sha256": None,
+            "best_validation_metric": 1.0,
+            "epoch": 1,
+            "global_step": 1,
+            "model": {},
+            "optimizer": {},
+            "rng": {},
+            "train_manifest_sha256": "5" * 64,
+            "update_index": 0,
+            "updates_per_epoch": 1,
+            "val_manifest_sha256": "6" * 64,
+        },
+        checkpoint,
+    )
+    receipt = host._checkpoint_record(
+        checkpoint,
+        _sha(checkpoint),
+        attempt_id=attempt.attempt_id,
+        run_id=attempt.run_id,
+        written_at_utc="2026-09-08T12:00:01Z",
+        parent_checkpoint_receipt_sha256=None,
+    )
+    receipt_sha256 = store.write_checkpoint(receipt)
+    store.write_terminal(
+        TerminalRecord(
+            attempt_id=attempt.attempt_id,
+            run_id=attempt.run_id,
+            completed_at_utc="2026-09-08T12:00:02Z",
+            outcome="FAILED",
+            attempt_receipt_sha256=artifact_sha256(canonical_attempt_bytes(attempt)),
+            latest_heartbeat_sha256=None,
+            latest_checkpoint_receipt_sha256=receipt_sha256,
+            failure_code="CONTROLLED_INTERRUPTION",
+        )
+    )
+    return store, checkpoint
+
+
+def _predecessor_with_prior_best(
+    root: Path,
+    *,
+    attempt_id: str,
+    run_id: str,
+    best_case: str = "valid",
+) -> tuple[AttemptStore, Path, Path]:
+    attempt = AttemptRecord(
+        attempt_id=attempt_id,
+        run_id=run_id,
+        created_at_utc="2026-09-08T12:10:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    store = AttemptStore.create(root, attempt)
+    checkpoint_directory = store.path / "model-checkpoints"
+    checkpoint_directory.mkdir()
+    best = checkpoint_directory / "checkpoint-000000000002-000003-validation.pt"
+    torch.save(
+        {
+            "best_checkpoint_name": best.name,
+            "best_checkpoint_sha256": None,
+            "best_validation_metric": 0.75,
+            "epoch": 1,
+            "global_step": 2,
+            "model": {"best": torch.tensor([2.0])},
+            "optimizer": {"state": {}},
+            "rng": {},
+            "train_manifest_sha256": "5" * 64,
+            "update_index": 0,
+            "updates_per_epoch": 2,
+            "val_manifest_sha256": "6" * 64,
+        },
+        best,
+    )
+    best_sha256 = _sha(best)
+    if best_case == "missing":
+        best.unlink()
+    elif best_case == "wrong-digest":
+        best_sha256 = "0" * 64
+    elif best_case != "valid":
+        raise AssertionError("unknown best-checkpoint fixture")
+    latest = checkpoint_directory / "checkpoint-000000000003-000004-update.pt"
+    torch.save(
+        {
+            "best_checkpoint_name": best.name,
+            "best_checkpoint_sha256": best_sha256,
+            "best_validation_metric": 0.75,
+            "epoch": 1,
+            "global_step": 3,
+            "model": {"latest": torch.tensor([3.0])},
+            "optimizer": {"state": {}},
+            "rng": {},
+            "train_manifest_sha256": "5" * 64,
+            "update_index": 1,
+            "updates_per_epoch": 2,
+            "val_manifest_sha256": "6" * 64,
+        },
+        latest,
+    )
+    receipt = host._checkpoint_record(
+        latest,
+        _sha(latest),
+        attempt_id=attempt.attempt_id,
+        run_id=attempt.run_id,
+        written_at_utc="2026-09-08T12:10:01Z",
+        parent_checkpoint_receipt_sha256=None,
+    )
+    receipt_sha256 = store.write_checkpoint(receipt)
+    store.write_terminal(
+        TerminalRecord(
+            attempt_id=attempt.attempt_id,
+            run_id=attempt.run_id,
+            completed_at_utc="2026-09-08T12:10:02Z",
+            outcome="FAILED",
+            attempt_receipt_sha256=artifact_sha256(canonical_attempt_bytes(attempt)),
+            latest_heartbeat_sha256=None,
+            latest_checkpoint_receipt_sha256=receipt_sha256,
+            failure_code="CONTROLLED_INTERRUPTION",
+        )
+    )
+    return store, latest, best
+
+
+def _resume_request(
+    root: Path,
+    checkpoint: Path,
+    *,
+    attempt_id: str,
+    system_id: str,
+    base_checkpoint: Path | None = None,
+    periodic_cache: Path | None = None,
+) -> cli.CLICommandRequest:
+    return cli.CLICommandRequest(
+        command="resume",
+        system_id=system_id,
+        attempt_directory=root / attempt_id,
+        resume_checkpoint=checkpoint,
+        base_checkpoint=base_checkpoint,
+        periodic_cache=periodic_cache,
+    )
+
+
+def test_resume_cli_carries_residual_artifacts_but_has_no_stop_option(
+    tmp_path: Path,
+) -> None:
+    parser = cli.build_parser()
+    checkpoint = tmp_path / "checkpoint.pt"
+    base = tmp_path / "base.pt"
+    cache = tmp_path / "cache"
+    attempt = tmp_path / "attempt"
+    namespace = parser.parse_args(
+        [
+            "resume",
+            "--attempt-dir",
+            str(attempt),
+            "--checkpoint",
+            str(checkpoint),
+            "--system-id",
+            "08",
+            "--base-checkpoint",
+            str(base),
+            "--periodic-cache",
+            str(cache),
+        ]
+    )
+    request = cli._build_command_request(namespace)
+    assert request.attempt_directory == attempt
+    assert request.resume_checkpoint == checkpoint
+    assert request.base_checkpoint == base
+    assert request.periodic_cache == cache
+    assert request.stop_after_global_step is None
+
+    subparsers = next(
+        action for action in parser._actions if isinstance(action, argparse._SubParsersAction)
+    )
+    assert "--stop-after-global-step" not in subparsers.choices["resume"].format_help()
+
+
+def test_base_resume_rejects_residual_artifact_paths(tmp_path: Path) -> None:
+    root = tmp_path / "attempts"
+    root.mkdir()
+    _store, checkpoint = _predecessor(
+        root,
+        attempt_id="base-first",
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B0",
+    )
+    extra = tmp_path / "extra-base.pt"
+    extra.write_bytes(b"not-consumed")
+    request = _resume_request(
+        root,
+        checkpoint,
+        attempt_id="base-resume",
+        system_id="B0",
+        base_checkpoint=extra,
+    )
+    backend = host.PhaseSetHostBackend(_host_config(tmp_path), request)
+    with pytest.raises(host.HostConfigurationError, match="base resume rejects"):
+        backend._resume_base(_intent())
+    assert not request.attempt_directory.exists()
+
+
+@pytest.mark.parametrize("case", ["missing", "base-is-directory", "cache-is-file"])
+def test_residual_resume_requires_explicit_regular_artifacts(
+    tmp_path: Path,
+    case: str,
+) -> None:
+    root = tmp_path / "attempts"
+    root.mkdir()
+    _store, checkpoint = _predecessor(
+        root,
+        attempt_id="residual-first",
+        run_id="phaseset-run-v1/RESIDUAL_TRAIN/1729/08",
+    )
+    base = tmp_path / "base.pt"
+    base.write_bytes(b"base")
+    cache = tmp_path / "cache"
+    cache.mkdir()
+    if case == "missing":
+        base_value = None
+        cache_value = None
+        message = "requires --base-checkpoint"
+    elif case == "base-is-directory":
+        base_value = cache
+        cache_value = cache
+        message = "regular existing file"
+    else:
+        base_value = base
+        cache_value = base
+        message = "regular existing directory"
+    request = _resume_request(
+        root,
+        checkpoint,
+        attempt_id=f"residual-resume-{case}",
+        system_id="08",
+        base_checkpoint=base_value,
+        periodic_cache=cache_value,
+    )
+    backend = host.PhaseSetHostBackend(_host_config(tmp_path), request)
+    with pytest.raises(host.HostConfigurationError, match=message):
+        backend._resume_base(_intent())
+    assert not request.attempt_directory.exists()
+
+
+def test_residual_resume_dispatches_verified_new_ledger_and_runtime_arguments(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    root = tmp_path / "attempts"
+    root.mkdir()
+    _store, checkpoint, prior_best = _predecessor_with_prior_best(
+        root,
+        attempt_id="residual-first",
+        run_id="phaseset-run-v1/RESIDUAL_TRAIN/1729/08",
+    )
+    base = tmp_path / "base.pt"
+    base.write_bytes(b"qualified-base")
+    cache = tmp_path / "cache"
+    cache.mkdir()
+    request = _resume_request(
+        root,
+        checkpoint,
+        attempt_id="residual-second",
+        system_id="08",
+        base_checkpoint=base,
+        periodic_cache=cache,
+    )
+    backend = host.PhaseSetHostBackend(_host_config(tmp_path), request)
+    captured: dict[str, object] = {}
+    fake_report = SimpleNamespace(latest_checkpoint=object())
+    artifact = tmp_path / "dispatch.json"
+    artifact.write_bytes(b"{}\n")
+
+    def execute_residual(_self: object, **kwargs: object) -> object:
+        captured.update(kwargs)
+        return fake_report
+
+    def close_attempt(
+        _self: object,
+        _store: object,
+        _attempt: object,
+        report: object,
+        _ledger: object,
+    ) -> BackendExecution:
+        assert report is fake_report
+        return BackendExecution(
+            outcome="COMPLETED",
+            completed_at_utc="2026-09-08T12:00:03Z",
+            artifacts=(artifact,),
+        )
+
+    monkeypatch.setattr(
+        host.PhaseSetHostBackend,
+        "_execute_residual_runtime",
+        execute_residual,
+    )
+    monkeypatch.setattr(host._LiveAttemptLedger, "ensure_checkpoint", lambda *_: None)
+    monkeypatch.setattr(host.PhaseSetHostBackend, "_close_attempt", close_attempt)
+    result = backend._resume_base(_intent())
+
+    assert result.outcome == "COMPLETED"
+    assert captured["system_id"] == "08"
+    assert captured["seed"] == 1729
+    assert captured["base_checkpoint"] == base.resolve()
+    assert captured["periodic_cache"] == cache.resolve()
+    assert captured["stop_after_global_step"] is None
+    assert captured["resume_attempt_root"] == root.resolve()
+    assert type(captured["resume_record"]) is ResumeRecord
+    copied = captured["resume_checkpoint"]
+    assert copied == request.attempt_directory / "model-checkpoints" / checkpoint.name
+    assert isinstance(copied, Path) and _sha(copied) == _sha(checkpoint)
+    materialized = request.attempt_directory / "model-checkpoints"
+    assert {path.name for path in materialized.iterdir()} == {
+        checkpoint.name,
+        prior_best.name,
+    }
+    assert _sha(materialized / prior_best.name) == _sha(prior_best)
+    assert (request.attempt_directory / "resume.json").is_file()
+
+
+def test_materialization_rejects_digest_change_before_deserialization(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    root = tmp_path / "attempts"
+    root.mkdir()
+    _store, checkpoint = _predecessor(
+        root,
+        attempt_id="residual-first",
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B0",
+    )
+    load_calls = 0
+    original_materialize = host._materialize_resume_checkpoints
+    changed_bytes = b"changed-after-predecessor-validation"
+
+    def forbidden_load(*_args: object, **_kwargs: object) -> object:
+        nonlocal load_calls
+        load_calls += 1
+        raise AssertionError("digest mismatch must precede checkpoint deserialization")
+
+    def mutate_then_materialize(
+        source: Path,
+        receipt: object,
+        destination: Path,
+    ) -> Path:
+        source.write_bytes(changed_bytes)
+        return original_materialize(source, receipt, destination)  # type: ignore[arg-type]
+
+    monkeypatch.setattr(host.torch, "load", forbidden_load)
+    monkeypatch.setattr(host, "_materialize_resume_checkpoints", mutate_then_materialize)
+    request = _resume_request(
+        root,
+        checkpoint,
+        attempt_id="base-resume-copy-race",
+        system_id="B0",
+    )
+    result = host.PhaseSetHostBackend(_host_config(tmp_path), request)._resume_base(
+        _intent()
+    )
+
+    assert result.outcome == "FAILED"
+    assert load_calls == 0
+    materialized = request.attempt_directory / "model-checkpoints" / checkpoint.name
+    assert materialized.read_bytes() == changed_bytes
+    failure = json.loads((request.attempt_directory / "failure.json").read_text())
+    terminal = host.parse_terminal_bytes(
+        (request.attempt_directory / "terminal.json").read_bytes()
+    )
+    assert failure["failure_code"] == "HOST_EXECUTION_FAILED"
+    assert failure["result_claimed"] is False
+    assert terminal.outcome == "FAILED"
+    assert terminal.failure_code == "HOST_EXECUTION_FAILED"
+
+
+@pytest.mark.parametrize("best_case", ["missing", "wrong-digest"])
+def test_bad_prior_best_terminalizes_new_attempt_before_runtime(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    best_case: str,
+) -> None:
+    root = tmp_path / "attempts"
+    root.mkdir()
+    _store, checkpoint, prior_best = _predecessor_with_prior_best(
+        root,
+        attempt_id="residual-first",
+        run_id="phaseset-run-v1/RESIDUAL_TRAIN/1729/08",
+        best_case=best_case,
+    )
+    base = tmp_path / "base.pt"
+    base.write_bytes(b"qualified-base")
+    cache = tmp_path / "cache"
+    cache.mkdir()
+    request = _resume_request(
+        root,
+        checkpoint,
+        attempt_id=f"residual-resume-{best_case}",
+        system_id="08",
+        base_checkpoint=base,
+        periodic_cache=cache,
+    )
+    backend = host.PhaseSetHostBackend(_host_config(tmp_path), request)
+
+    runtime_calls: list[bool] = []
+
+    def forbidden_runtime(*_args: object, **_kwargs: object) -> object:
+        runtime_calls.append(True)
+        raise AssertionError("bad prior best must fail before runtime construction")
+
+    monkeypatch.setattr(
+        host.PhaseSetHostBackend,
+        "_execute_residual_runtime",
+        forbidden_runtime,
+    )
+    result = backend._resume_base(_intent())
+
+    assert result.outcome == "FAILED"
+    assert runtime_calls == []
+    failure = json.loads((request.attempt_directory / "failure.json").read_text())
+    terminal = host.parse_terminal_bytes(
+        (request.attempt_directory / "terminal.json").read_bytes()
+    )
+    assert failure["failure_code"] == "HOST_EXECUTION_FAILED"
+    assert failure["result_claimed"] is False
+    assert terminal.outcome == "FAILED"
+    assert terminal.failure_code == "HOST_EXECUTION_FAILED"
+    assert terminal.latest_checkpoint_receipt_sha256 is None
+    materialized = request.attempt_directory / "model-checkpoints"
+    expected_names = {checkpoint.name}
+    if best_case == "wrong-digest":
+        expected_names.add(prior_best.name)
+        assert (materialized / prior_best.name).read_bytes() == prior_best.read_bytes()
+    assert {path.name for path in materialized.iterdir()} == expected_names
+
+
+def test_residual_runtime_rebuilds_registered_chain_and_forwards_resume_binding(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    qualification_path = tmp_path / "qualification.json"
+    audit_path = tmp_path / "capacity-audit.json"
+    base_checkpoint = tmp_path / "selected-base.pt"
+    periodic_cache = tmp_path / "periodic-cache"
+    checkpoint_directory = tmp_path / "model-checkpoints"
+    resume_checkpoint = tmp_path / "resume.pt"
+    for path in (qualification_path, audit_path, base_checkpoint, resume_checkpoint):
+        path.write_bytes(b"fixture")
+    periodic_cache.mkdir()
+    checkpoint_directory.mkdir()
+    config = replace(
+        _host_config(tmp_path),
+        residual_artifacts=host.ResidualArtifacts(
+            qualification=qualification_path,
+            capacity_audits={1729: audit_path},
+        ),
+    )
+    backend = host.PhaseSetHostBackend(
+        config,
+        cli.CLICommandRequest(command="resume", system_id="08"),
+    )
+    resume_record = object()
+    train_source = object()
+    val_source = object()
+    qualification = object()
+    qualified_base = object()
+    capacity_audit = object()
+    system = object()
+    binding = object()
+    report = object()
+    energy_floors = np.arange(6, dtype=np.float64)
+    events: list[tuple[str, object]] = []
+
+    def load_qualification(path: Path) -> tuple[object, str]:
+        events.append(("qualification", path))
+        return qualification, "a" * 64
+
+    def load_base(path: Path, **kwargs: object) -> object:
+        events.append(("base", (path, kwargs)))
+        return qualified_base
+
+    def load_cache(path: Path, **kwargs: object) -> np.ndarray:
+        events.append(("cache", (path, kwargs)))
+        return energy_floors
+
+    def load_audit(path: Path) -> tuple[object, str]:
+        events.append(("audit", path))
+        return capacity_audit, "b" * 64
+
+    def load_sources(_self: host.HostConfig) -> tuple[object, object]:
+        events.append(("sources", _self))
+        return train_source, val_source
+
+    def construct(system_id: str, *args: object, **kwargs: object):
+        events.append(("constructor", (system_id, args, kwargs)))
+        return system, binding
+
+    class FakeRuntime:
+        def __init__(self, *args: object, **kwargs: object) -> None:
+            events.append(("runtime", (args, kwargs)))
+
+        def fit(self, *args: object, **kwargs: object) -> object:
+            events.append(("fit", (args, kwargs)))
+            return report
+
+    monkeypatch.setattr(host, "_load_base_qualification", load_qualification)
+    monkeypatch.setattr(host, "load_qualified_frozen_base", load_base)
+    monkeypatch.setattr(host, "_load_periodic_cache", load_cache)
+    monkeypatch.setattr(host, "_load_capacity_audit", load_audit)
+    monkeypatch.setattr(host.HostConfig, "load_sources", load_sources)
+    monkeypatch.setattr(host, "construct_registered_residual_seed_bound_system", construct)
+    monkeypatch.setattr(host, "PhaseSetTrainingRuntime", FakeRuntime)
+    ledger = SimpleNamespace(observe_checkpoint=object())
+
+    result = backend._execute_residual_runtime(
+        system_id="08",
+        seed=1729,
+        checkpoint_directory=checkpoint_directory,
+        live_ledger=ledger,
+        base_checkpoint=base_checkpoint,
+        periodic_cache=periodic_cache,
+        stop_after_global_step=None,
+        resume_checkpoint=resume_checkpoint,
+        resume_attempt_root=tmp_path,
+        resume_record=resume_record,  # type: ignore[arg-type]
+    )
+
+    assert result is report
+    assert [name for name, _ in events] == [
+        "qualification",
+        "base",
+        "cache",
+        "audit",
+        "sources",
+        "constructor",
+        "runtime",
+        "fit",
+    ]
+    assert events[0][1] == qualification_path
+    base_args = events[1][1]
+    assert isinstance(base_args, tuple) and base_args[0] == base_checkpoint
+    assert base_args[1] == {
+        "qualification": qualification,
+        "seed": 1729,
+        "expected_qualification_sha256": "a" * 64,
+    }
+    cache_args = events[2][1]
+    assert isinstance(cache_args, tuple) and cache_args[0] == periodic_cache
+    assert cache_args[1] == {
+        "seed": 1729,
+        "qualification": qualification,
+        "qualification_sha256": "a" * 64,
+    }
+    constructor_args = events[5][1]
+    assert isinstance(constructor_args, tuple) and constructor_args[0] == "08"
+    assert constructor_args[1][0] is qualified_base
+    assert constructor_args[2]["energy_floors"] is energy_floors
+    assert constructor_args[2]["residual_capacity_audit"] is capacity_audit
+    assert constructor_args[2]["expected_qualification_sha256"] == "a" * 64
+    assert constructor_args[2]["expected_capacity_audit_sha256"] == "b" * 64
+    runtime_args = events[6][1]
+    assert isinstance(runtime_args, tuple)
+    assert runtime_args[0][0] is system
+    assert runtime_args[0][2] == checkpoint_directory
+    assert runtime_args[1]["initialization_binding"] is binding
+    assert runtime_args[1]["checkpoint_observer"] is ledger.observe_checkpoint
+    fit_args = events[7][1]
+    assert isinstance(fit_args, tuple)
+    assert fit_args[0] == (train_source, val_source)
+    assert fit_args[1] == {
+        "resume_checkpoint": resume_checkpoint,
+        "resume_attempt_root": tmp_path,
+        "resume_record": resume_record,
+        "stop_after_global_step": None,
+    }
+
+
+@pytest.mark.parametrize(
+    ("broken", "expected_calls"),
+    [("base", ["qualification", "base"]), ("cache", ["qualification", "base", "cache"])],
+)
+def test_residual_runtime_stops_before_training_on_semantically_wrong_artifact(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    broken: str,
+    expected_calls: list[str],
+) -> None:
+    qualification_path = tmp_path / "qualification.json"
+    audit_path = tmp_path / "capacity-audit.json"
+    base_checkpoint = tmp_path / "selected-base.pt"
+    periodic_cache = tmp_path / "periodic-cache"
+    for path in (qualification_path, audit_path, base_checkpoint):
+        path.write_bytes(b"fixture")
+    periodic_cache.mkdir()
+    config = replace(
+        _host_config(tmp_path),
+        residual_artifacts=host.ResidualArtifacts(
+            qualification=qualification_path,
+            capacity_audits={1729: audit_path},
+        ),
+    )
+    backend = host.PhaseSetHostBackend(
+        config,
+        cli.CLICommandRequest(command="resume", system_id="08"),
+    )
+    calls: list[str] = []
+
+    def load_qualification(_path: Path) -> tuple[object, str]:
+        calls.append("qualification")
+        return object(), "a" * 64
+
+    def load_base(*_args: object, **_kwargs: object) -> object:
+        calls.append("base")
+        if broken == "base":
+            raise host.HostConfigurationError("qualified base is wrong")
+        return object()
+
+    def load_cache(*_args: object, **_kwargs: object) -> np.ndarray:
+        calls.append("cache")
+        raise host.HostConfigurationError("periodic cache is wrong")
+
+    def forbidden(*_args: object, **_kwargs: object) -> object:
+        raise AssertionError("semantic artifact failure must precede training")
+
+    monkeypatch.setattr(host, "_load_base_qualification", load_qualification)
+    monkeypatch.setattr(host, "load_qualified_frozen_base", load_base)
+    monkeypatch.setattr(host, "_load_periodic_cache", load_cache)
+    monkeypatch.setattr(host, "_load_capacity_audit", forbidden)
+    monkeypatch.setattr(host.HostConfig, "load_sources", forbidden)
+    monkeypatch.setattr(host, "construct_registered_residual_seed_bound_system", forbidden)
+    monkeypatch.setattr(host, "PhaseSetTrainingRuntime", forbidden)
+
+    with pytest.raises(host.HostConfigurationError, match="is wrong"):
+        backend._execute_residual_runtime(
+            system_id="08",
+            seed=1729,
+            checkpoint_directory=tmp_path / "model-checkpoints",
+            live_ledger=SimpleNamespace(observe_checkpoint=object()),
+            base_checkpoint=base_checkpoint,
+            periodic_cache=periodic_cache,
+            stop_after_global_step=None,
+        )
+    assert calls == expected_calls
+
+
+def _groups() -> PreparedGroupBatch:
+    rows = 2
+    actors = 2
+    frames = 9
+    skeletons = np.zeros((rows, actors, frames, 22, 3), dtype=np.float32)
+    grid = np.arange(frames, dtype=np.float32)
+    actor_commitments: list[tuple[bytes, ...]] = []
+    group_commitments: list[bytes] = []
+    for row in range(rows):
+        actor_ids = tuple(_digest(f"actor-{row}-{actor}") for actor in range(actors))
+        actor_commitments.append(actor_ids)
+        group_commitments.append(group_commitment(actor_ids))
+        for actor in range(actors):
+            for joint in range(22):
+                skeletons[row, actor, :, joint, 0] = np.sin(
+                    np.float32(0.19 + 0.03 * actor) * grid + np.float32(0.01 * joint + 0.02 * row)
+                )
+                skeletons[row, actor, :, joint, 2] = np.cos(
+                    np.float32(0.11 + 0.02 * row) * grid + np.float32(0.01 * joint)
+                )
+    return PreparedGroupBatch(
+        skeletons,
+        np.ones((rows, actors), dtype=np.bool_),
+        np.ones((rows, frames), dtype=np.bool_),
+        np.ones((rows, actors, frames, 22), dtype=np.bool_),
+        tuple(actor_commitments),
+        tuple(group_commitments),
+    )
+
+
+def _batch(split: str) -> RetrievalTrainingBatch:
+    groups = _groups()
+    positive_ids = (_digest("positive-0"), _digest("positive-1"))
+    text = torch.randn(
+        (2, 8),
+        generator=torch.Generator().manual_seed(701),
+        dtype=torch.float32,
+    ).contiguous()
+    return RetrievalTrainingBatch(
+        groups,
+        text,
+        positive_ids,
+        positive_ids,
+        (_digest("text-0"), _digest("text-1")),
+        split,  # type: ignore[arg-type]
+    )
+
+
+@dataclass
+class _Source:
+    split: str
+    manifest_sha256: str
+
+    def iter_epoch(self, *, epoch: int, seed: int):
+        del epoch, seed
+        return (_batch(self.split),)
+
+
+def _sources() -> tuple[_Source, _Source]:
+    return (
+        _Source("train", hashlib.sha256(b"tiny-train").hexdigest()),
+        _Source("val", hashlib.sha256(b"tiny-val").hexdigest()),
+    )
+
+
+def _residual_system(initialization_seed: int) -> ResidualRetrievalSystem:
+    torch.manual_seed(initialization_seed)
+    return ResidualRetrievalSystem(
+        ActorMeanBase(hidden_dim=8, heads=2, ffn_dim=16, dropout=0.2),
+        PhaseSetEncoder(embedding_dim=8, hidden_dim=8, edge_budget=4),
+        PhaseSetRetrievalHead(
+            embedding_dim=8,
+            text_hidden_dim=8,
+            residual_lambda_init=0.2,
+        ),
+        embedding_dim=8,
+    )
+
+
+def _state(module: torch.nn.Module) -> dict[str, torch.Tensor]:
+    return {name: value.detach().cpu().clone() for name, value in module.state_dict().items()}
+
+
+def _assert_state_equal(left: dict[str, torch.Tensor], right: dict[str, torch.Tensor]) -> None:
+    assert left.keys() == right.keys()
+    for name in left:
+        assert torch.equal(left[name], right[name]), name
+
+
+def _report_semantics(report: object) -> dict[str, object]:
+    value = asdict(report)  # type: ignore[arg-type]
+    for name in ("latest_checkpoint", "best_checkpoint"):
+        checkpoint = value[name]
+        if checkpoint is not None:
+            checkpoint["path"] = Path(checkpoint["path"]).name
+    return value
+
+
+def test_tiny_real_residual_new_ledger_resume_matches_uninterrupted(
+    tmp_path: Path,
+) -> None:
+    config = TrainingConfig(
+        stage="residual",
+        seed=1729,
+        effective_global_batch=2,
+        edge_budget=4,
+        checkpoint_every_updates=1,
+        synthetic_contract=True,
+    )
+    uninterrupted_system = _residual_system(991)
+    uninterrupted_runtime = PhaseSetTrainingRuntime(
+        uninterrupted_system,
+        config,
+        tmp_path / "uninterrupted",
+    )
+    uninterrupted_report = uninterrupted_runtime.fit(*_sources(), stop_after_global_step=2)
+
+    attempt_root = tmp_path / "attempts"
+    attempt_root.mkdir()
+    run_id = "phaseset-run-v1/RESIDUAL_TRAIN/1729/08"
+    predecessor_attempt = AttemptRecord(
+        attempt_id="tiny-residual-first",
+        run_id=run_id,
+        created_at_utc="2026-09-08T13:00:00Z",
+        plan_sha256="1" * 64,
+        matrix_sha256="2" * 64,
+        training_config_sha256="3" * 64,
+        source_tree_sha256="4" * 64,
+    )
+    predecessor = AttemptStore.create(attempt_root, predecessor_attempt)
+    predecessor_ledger = host._LiveAttemptLedger(predecessor, predecessor_attempt)
+    predecessor_ledger.start()
+    first_system = _residual_system(991)
+    first_runtime = PhaseSetTrainingRuntime(
+        first_system,
+        config,
+        predecessor.path / "model-checkpoints",
+        checkpoint_observer=predecessor_ledger.observe_checkpoint,
+    )
+    first_report = first_runtime.fit(*_sources(), stop_after_global_step=1)
+    predecessor_ledger.ensure_checkpoint(first_report.latest_checkpoint)
+    predecessor_ledger.stop()
+    assert predecessor_ledger.latest_checkpoint_receipt_sha256 is not None
+    predecessor_terminal_sha256 = predecessor.write_terminal(
+        TerminalRecord(
+            attempt_id=predecessor_attempt.attempt_id,
+            run_id=run_id,
+            completed_at_utc="2026-09-08T13:00:01Z",
+            outcome="FAILED",
+            attempt_receipt_sha256=artifact_sha256(canonical_attempt_bytes(predecessor_attempt)),
+            latest_heartbeat_sha256=predecessor_ledger.latest_heartbeat_sha256,
+            latest_checkpoint_receipt_sha256=(predecessor_ledger.latest_checkpoint_receipt_sha256),
+            failure_code="CONTROLLED_INTERRUPTION",
+        )
+    )
+
+    resumed_attempt = AttemptRecord(
+        attempt_id="tiny-residual-second",
+        run_id=run_id,
+        created_at_utc="2026-09-08T13:00:02Z",
+        plan_sha256=predecessor_attempt.plan_sha256,
+        matrix_sha256=predecessor_attempt.matrix_sha256,
+        training_config_sha256=predecessor_attempt.training_config_sha256,
+        source_tree_sha256=predecessor_attempt.source_tree_sha256,
+    )
+    resume = ResumeRecord(
+        new_attempt_id=resumed_attempt.attempt_id,
+        predecessor_attempt_id=predecessor_attempt.attempt_id,
+        run_id=run_id,
+        created_at_utc=resumed_attempt.created_at_utc,
+        predecessor_terminal_sha256=predecessor_terminal_sha256,
+        checkpoint_receipt_sha256=(predecessor_ledger.latest_checkpoint_receipt_sha256),
+        corrective_change_sha256="7" * 64,
+        retry_class="INFRA_TRANSIENT",
+    )
+    resumed_store = AttemptStore.create_resumed(attempt_root, resumed_attempt, resume)
+    checkpoint_receipt_path = sorted((predecessor.path / "checkpoints").glob("checkpoint-*.json"))[
+        -1
+    ]
+    checkpoint_receipt = parse_checkpoint_bytes(checkpoint_receipt_path.read_bytes())
+    resumed_ledger = host._LiveAttemptLedger(resumed_store, resumed_attempt)
+    resumed_ledger.start()
+    materialized = host._materialize_resume_checkpoints(
+        first_report.latest_checkpoint.path,
+        checkpoint_receipt,
+        resumed_store.path / "model-checkpoints",
+    )
+    resumed_system = _residual_system(991)
+    resumed_runtime = PhaseSetTrainingRuntime(
+        resumed_system,
+        config,
+        resumed_store.path / "model-checkpoints",
+        checkpoint_observer=resumed_ledger.observe_checkpoint,
+    )
+    resumed_report = resumed_runtime.fit(
+        *_sources(),
+        resume_checkpoint=materialized,
+        resume_attempt_root=attempt_root,
+        resume_record=resume,
+        stop_after_global_step=2,
+    )
+    resumed_ledger.ensure_checkpoint(resumed_report.latest_checkpoint)
+    resumed_ledger.stop()
+
+    _assert_state_equal(_state(uninterrupted_system), _state(resumed_system))
+    assert training._stable_hash(
+        uninterrupted_runtime._checked_optimizer.state_dict()
+    ) == training._stable_hash(resumed_runtime._checked_optimizer.state_dict())
+    assert training._stable_hash(
+        uninterrupted_runtime._checked_scheduler.state_dict()
+    ) == training._stable_hash(resumed_runtime._checked_scheduler.state_dict())
+    assert _report_semantics(uninterrupted_report) == _report_semantics(resumed_report)
+    assert resumed_ledger.latest_checkpoint_payload_sha256 == (
+        resumed_report.latest_checkpoint.sha256
+    )
+    payload = torch.load(
+        resumed_report.latest_checkpoint.path,
+        map_location="cpu",
+        weights_only=True,
+    )
+    assert {
+        "frozen_base_checkpoint_sha256",
+        "frozen_base_state_sha256",
+        "qualified_base_selection_sha256",
+        "residual_capacity_audit_sha256",
+    }.issubset(payload)
+    assert not any("periodic_cache" in key or "descriptor" in key for key in payload)

From 3380bf16e93a14e189cb14a30d8b02f4321fe04f Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 07:03:42 -0500
Subject: [PATCH 08/95] Connect immutable prepared-data v2 to the verified host
 runtime

---
 RELEASE_FILES.sha256                          |   10 +-
 STATUS.md                                     |   14 +-
 docs/HOST_RUNTIME_INTEGRATION.md              |   26 +-
 docs/PREPARED_DATA_V2.md                      |   95 ++
 src/phaseset_core/host.py                     |   27 +-
 src/phaseset_core/prepared_data_v2.py         | 1087 +++++++++++++++++
 tests/test_phaseset_prepared_data_v2.py       |  377 ++++++
 ...test_phaseset_prepared_host_integration.py |  422 +++++++
 8 files changed, 2048 insertions(+), 10 deletions(-)
 create mode 100644 docs/PREPARED_DATA_V2.md
 create mode 100644 src/phaseset_core/prepared_data_v2.py
 create mode 100644 tests/test_phaseset_prepared_data_v2.py
 create mode 100644 tests/test_phaseset_prepared_host_integration.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index e5acef1..a366720 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,7 +11,7 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-68a1113a30603aef022aeacfd6b45db82c97b9a4e8eeabf695f97ef66bf3a4a7	14030	STATUS.md
+bdf1eda2c32be748566637fdd79d0c9b695d1f856a8f60c1ee0f4d3eceab9ca0	14758	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
@@ -21,7 +21,7 @@ e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
 e7bc4f9bace637106d73afe6be84e0695c5be78857218bc8b141cc8d5f009d52	11085	docs/FROZEN_CLIP_TEXT_ADAPTER.md
-cc8c2059d499a52f40d40f3be6c75d6e46ef4065a3035aa26e89c41cb7b2cc41	10342	docs/HOST_RUNTIME_INTEGRATION.md
+39589bf5cba54b0626fedef87e41cb436da7c1cab4cb3e4975afea720d45b62e	11899	docs/HOST_RUNTIME_INTEGRATION.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
@@ -50,6 +50,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 ecb85faa3b442450f6ecc8d6f2fff77baf19e833254fa8a715127d9127742f63	13825	docs/PHASESET_RESEARCH_CONTRACT.md
+74d36ae75a863a3f73b13918c9cbc7ca40fbd70fd19451188b564bba1fcee95e	5053	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
 8d689f49b2704ff7c5988eff8a7e3e062f209b85d18425ccdba1e1099581079b	10658	docs/phasepair_research_contract_20260824_165840.md
@@ -127,12 +128,13 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
 51bdad872537b332e886646071d4381d00a09ddba5912c43ba8559e2675a4c79	48618	src/phaseset_core/frozen_clip_text.py
-074bd1c13b1cd98b200e98c2e85b23431b0ffcc5f4f3b7eb9f5a230e2017ec0f	77870	src/phaseset_core/host.py
+25871fe486090edd2ac49f82018dcced116e35c85d99178b251bc8862dfdbe70	78628	src/phaseset_core/host.py
 47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phaseset_core/pipeline.py
+c675143d6ba9bc98976041ff02c0fa6ae6ef7bfd122122d0063eeb85f62c8c88	43972	src/phaseset_core/prepared_data_v2.py
 273225e6696351ccb16a3a9e7a653b55af18c64b316a4e5d80c7b265625cc5c1	17929	src/phaseset_core/preprocessing.py
 044b381ba73e73f476918d353f29c7cbee38cdca32ee612826549fef60671bdc	73146	src/phaseset_core/production.py
 6f60feada83ebf1a55635bb657a5a3998e9fc77fa85012aad46e4f8d90d344db	37785	src/phaseset_core/publication.py
@@ -188,6 +190,8 @@ e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test
 6d7005585882e25be9479542aac96a5b2aeb1bddc5619a0f37c57f61f2cbe6c3	6348	tests/test_phaseset_objectives.py
 52ccd9d4ea8351d5aaaa82eb99d84c6bd240c8b6147a506fd65dbca3d1394331	11547	tests/test_phaseset_periodic.py
 6ebbf2ece33ff2390036e5ce55dcd34a0459211b79922219971c27bee37844a0	10278	tests/test_phaseset_pipeline.py
+2cb88d371c1dbd2f52d37c4b68f4715fb40ca4deec2e23215300bc18efa6e0b0	15678	tests/test_phaseset_prepared_data_v2.py
+aae917cd95bde322f4ec7cea693fd5d0c926d51f0a366e7be160426472ad7973	15770	tests/test_phaseset_prepared_host_integration.py
 f6837c3ac23f4209cb3ec3eadb899d0bdcd4b4259c901b021254ffe7b3688492	8482	tests/test_phaseset_preprocessing.py
 64016f9369540dae8b87654f43d09df9b9c07a3c4af06fa45567008b57140193	45251	tests/test_phaseset_production.py
 a1fbdfe55e50f67bb6568d5eba276e24beeb5a44373c516f0bd7758bd5742127	12486	tests/test_phaseset_publication.py
diff --git a/STATUS.md b/STATUS.md
index 15900f9..f7bd5c7 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -18,8 +18,10 @@ evidence, not training results or scientific claims.
 - A separate data-volume Python 3.12.12 / NumPy 2.4.6 / Torch 2.12.0+cu126
   environment is installed with a clean dependency check. The complete Linux
   CPU regression including the private-host lifecycle integration passed:
-  948 tests, 2 skipped, and 39 subtests, in 376.00 s after adding residual
-  host resume. Source bytes were unchanged before and after the full suite.
+  964 tests, 2 skipped, and 39 subtests, in 286.06 s after adding prepared-data
+  v2 host dispatch. Its focused integration passed 94 tests. Source bytes were
+  unchanged before and after both suites. The prior residual-resume integration
+  passed 948 tests and 39 subtests.
   The earlier frozen-CLIP integration passed 932 tests and 39 subtests.
   The earlier host-only suite passed 917 tests and its 25 focused checks.
   This includes synthetic model/lifecycle checks, not main-data training.
@@ -48,6 +50,14 @@ evidence, not training results or scientific claims.
 - The native Embody loader and concrete licensed SMPL-X body-22 evaluator are
   implemented. Seven adapter contract tests passed on Linux/Python 3.12.12/
   NumPy 2.4.6; these use explicit fixtures, not licensed assets or main data.
+- The immutable prepared-data v2 writer/reader now connects canonical body-22
+  windows and frozen variable-count text features to the private-host factory.
+  It verifies the exact consumed index/manifest/payload bytes, rejects split
+  overlap, and applies reproducible shared-group yaw from original bytes each
+  training epoch. Validation is unrotated. Window-positive families support the
+  auxiliary task; the main holistic capture validation route is not yet wired.
+  Actor-set commitments must not be used as unique capture/window row IDs.
+  See [prepared-data contract](docs/PREPARED_DATA_V2.md).
 - Neither Embody approval nor a licensed neutral model has been observed.
   Open-license Multi-TPC acquisition completed with its official byte count and
   MD5 verified. Its archive inventory has 322 files. The bounded audit confirms
diff --git a/docs/HOST_RUNTIME_INTEGRATION.md b/docs/HOST_RUNTIME_INTEGRATION.md
index ee7d0db..5a7b14f 100644
--- a/docs/HOST_RUNTIME_INTEGRATION.md
+++ b/docs/HOST_RUNTIME_INTEGRATION.md
@@ -20,7 +20,14 @@ It never creates authority, rights, runtime, or execution receipts.  If an
 artifact is absent or changed, the factory raises the existing
 `ExecutionHold` codes and the public CLI remains authority zero.
 
-`PrivatePreparedDataSource` maps one immutable split manifest to the public
+The factory dispatches v1 and v2 prepared indexes using the actual index bytes
+bound to the private prepared-data receipt. The v2 loader also verifies each
+consumed split manifest before decoding its numeric batches. Malformed batch
+semantics are translated into the existing execution hold before a training
+backend is constructed. The legacy v1 loader now checks its consumed manifest
+against the index digest rather than relying on a separate earlier path read.
+
+`PrivatePreparedDataSource` maps a v1 immutable split manifest to the public
 `TrainingDataSource` protocol.  Each NPZ contains exactly:
 
 - `skeletons`: float32 `[B,K,200,22,3]`
@@ -34,6 +41,12 @@ artifact is absent or changed, the factory raises the existing
 - `text_positive_ids`: uint8 `[Q,32]`
 - `text_commitments`: uint8 `[Q,32]`
 
+The v2 contract fixes the text width at 512, accepts variable caption counts,
+and supplies epoch-specific deterministic whole-group yaw from immutable
+unrotated training bytes. Its validation source is unrotated. See
+[prepared-data v2](PREPARED_DATA_V2.md) for the writer, bounded numeric format,
+split checks, and provenance limits. Neither loader establishes data rights.
+
 `run-base` uses the formal seed-bound B0--B2 constructor and
 `PhaseSetTrainingRuntime`.  Its checkpoint directory must be
 `<attempt-root>/<attempt-id>/model-checkpoints`, allowing the harness to build
@@ -143,6 +156,12 @@ rights assertion when Embody approval is unknown.
 - The Embody-format conversion is outside this harness.  It must produce the
   exact prepared index/split/NPZ seam above and bind it to the authenticated
   prepared-data receipt.
+- The current training validation path is still window-oriented. V2 positive
+  families identify windows; actor-set group commitments are lineage and can
+  repeat across windows or captures. Neither is a substitute for the main
+  holistic capture gallery. Complete capture-window census, fixed embedding
+  aggregation, and capture-level scoring must be connected before formal base
+  selection on that task.
 - `run-residual` is wired through the strict qualified-base loader, canonical
   periodic-cache record, energy floors, canonical all-system capacity audit,
   closed residual constructor, and the same attempt ledger. Residual resume
@@ -187,3 +206,8 @@ Report/failure JSON is file-fsynced and, on POSIX, parent-directory-fsynced
 before a terminal is persisted. A failed fsync cannot create a success terminal.
 Actual Embody rights, full model/runtime qualification, all registered runs,
 and sealed test remain separate requirements.
+
+The subsequent v2 prepared-source integration passed 94 focused server tests
+and a separate complete suite of 964 tests, 2 skipped, and 39 subtests. These
+include actual malformed numeric payloads and manifest/index replacement
+cases; they do not assert a completed preparation command or a real-data run.
diff --git a/docs/PREPARED_DATA_V2.md b/docs/PREPARED_DATA_V2.md
new file mode 100644
index 0000000..ab1692b
--- /dev/null
+++ b/docs/PREPARED_DATA_V2.md
@@ -0,0 +1,95 @@
+# Prepared motion-text batches and epoch augmentation
+
+`phaseset_core.prepared_data_v2` connects already prepared body-22 motion
+windows to already frozen CLIP text features. It writes immutable numeric
+training artifacts and reads them as `TrainingDataSource` objects. It does not
+download or authorize data, decode SMPL-X, generate captions, or run CLIP.
+
+## Interface
+
+`PreparedMotionTextBlock` contains canonical `PreparedGroupSample` values,
+one `FrozenClipTextBatch`, variable per-motion `text_counts`, and independent
+uint64 `window_ordinals`. Every motion receives at least one text row; no fixed
+three-caption or two-caption assumption is made. The supplied frozen text
+receipt must bind the actual embedding bytes, row order, and commitments.
+
+`write_prepared_training_tree_v2(...)` takes separate train/val blocks, an edge
+budget, optional microbatch-size bound, and explicit artifact-size bounds. It
+creates a new tree and returns its index and census digests. Existing output
+roots cannot be reused. Failed partial builds are preserved, not admitted as
+completed datasets.
+
+`load_prepared_training_sources_v2(...)` requires the expected index digest
+and returns train and val sources. Each split constructor verifies the exact
+manifest bytes it consumes against the digest in that index. Test cannot be
+inserted into this training-only index.
+
+## Numeric artifact contract
+
+Every uncompressed NPZ has exactly ten arrays:
+
+| Name | Type and shape |
+|---|---|
+| `skeletons` | float32 `[B,K_pad,200,22,3]` |
+| `actor_mask` | bool `[B,K_pad]` |
+| `frame_mask` | bool `[B,200]` |
+| `track_mask` | bool `[B,K_pad,200,22]` |
+| `actor_commitments` | uint8 `[B,K_pad,32]` |
+| `group_commitments` | uint8 `[B,32]` |
+| `text_embeddings` | float32 `[Q,512]` |
+| `motion_positive_ids` | uint8 `[B,32]` |
+| `text_positive_ids` | uint8 `[Q,32]` |
+| `text_commitments` | uint8 `[Q,32]` |
+
+Padding is exact positive zero. Storage uses fixed-order ZIP_STORED members
+and NPY v1 headers. Loading checks member identity, shape/type, declared byte
+length, CRC, and decoded-size bounds before array allocation; pickle is not
+accepted. The writer rejects train/val overlap in window, ordinal, caption,
+and actor commitments. Upstream participant/capture split verification remains
+necessary; these numeric checks do not establish participant identity.
+
+## Epoch behavior
+
+Stored motion always has exact positive-zero augmentation yaw. Each training
+epoch rereads the original authenticated bytes and applies the registered
+`deterministic_group_yaw(seed, epoch, window_ordinal)` once, synchronously to
+all actors. The angle is not derived from any lineage commitment. Rotation is
+computed in float64 and cast to float32, then all invalid positions are reset
+to exact positive zero. No augmented sample becomes the next epoch's input.
+
+Validation is unrotated. Masks, lineage, positive families, caption identity,
+and frozen text values do not change. This storage-based path is not claimed
+bitwise equal to rotating earlier, before preprocessing's final float32 cast.
+
+## Provenance and integration boundary
+
+The private build census binds index/batch/window/source/text digests and the
+independent ordinals. It contains no raw captions, source paths, or participant
+identifiers and is not a data-rights certificate. Keep the entire prepared tree,
+its commitments, and its census private.
+
+The private-host factory recognizes the v2 index and passes the actual
+receipt-bound index digest into this loader. A replaced index or manifest, or
+invalid decoded training batch, is rejected before backend construction. The
+legacy v1 loader remains supported and also binds the manifest bytes consumed.
+
+Positive families in this storage format identify individual windows. They
+support the auxiliary window-caption task, not the main holistic capture task.
+Actor-set group commitments are lineage, not unique capture/window row IDs.
+Main validation must separately encode every admitted window in a capture,
+aggregate embeddings with the fixed capture mean, and score one capture gallery
+against official holistic text. That training/host route is not yet connected.
+
+This module alone is not the complete `prepare-data` command. Native capture
+loading, licensed body-model conversion, preprocessing rejection/tail census,
+caption provenance, and the preparation command composition must also be wired
+and verified before real training. See [the frozen text adapter](FROZEN_CLIP_TEXT_ADAPTER.md)
+for text feature semantics and [host integration](HOST_RUNTIME_INTEGRATION.md)
+for the execution lifecycle.
+
+Server tests exercise analytic numeric artifacts, variable caption counts,
+epoch replay, padding zeros, validation invariance, split isolation, immutable
+creation, and invalid/replaced manifests and ZIP payloads. They are regression
+evidence, not real-data experiments or scientific results. The final combined
+host integration passed 94 focused tests and a separate complete Linux server
+suite of 964 tests, 2 skipped, and 39 subtests, with unchanged source bytes.
diff --git a/src/phaseset_core/host.py b/src/phaseset_core/host.py
index 5d25138..becb1c2 100644
--- a/src/phaseset_core/host.py
+++ b/src/phaseset_core/host.py
@@ -62,12 +62,19 @@
     PrivateReceiptAssertions,
     ProductionRuntimeAdapter,
 )
+from phaseset_core.prepared_data_v2 import (
+    PREPARED_INDEX_V2_SCHEMA,
+    PreparedDataV2Error,
+    load_prepared_training_sources_v2,
+)
 from phaseset_core.training import (
     CheckpointArtifact,
     PhaseSetTrainingRuntime,
     ResidualCapacityAudit,
     RetrievalTrainingBatch,
     TrainingConfig,
+    TrainingDataSource,
+    TrainingRuntimeError,
     TrainingReport,
     construct_registered_base_seed_bound_system,
     construct_registered_residual_seed_bound_system,
@@ -818,16 +825,28 @@ def validate_runtime(self) -> None:
 
     def load_sources(
         self,
-    ) -> tuple[PrivatePreparedDataSource, PrivatePreparedDataSource]:
+    ) -> tuple[TrainingDataSource, TrainingDataSource]:
         raw, value = _load_json_object(self.prepared_index, "prepared index")
         _closed_keys(value, {"schema", "train", "val"}, "prepared index")
-        if value["schema"] != PREPARED_INDEX_SCHEMA:
+        schema = value["schema"]
+        if schema not in {PREPARED_INDEX_SCHEMA, PREPARED_INDEX_V2_SCHEMA}:
             raise HostConfigurationError("prepared index schema is not registered")
         receipts = self.load_receipts()
         if _sha256_bytes(raw) != receipts.prepared_data_manifest_sha256:
             raise HostConfigurationError(
                 "prepared index differs from authenticated receipt"
             )
+        if schema == PREPARED_INDEX_V2_SCHEMA:
+            try:
+                return load_prepared_training_sources_v2(
+                    self.prepared_index,
+                    expected_index_sha256=receipts.prepared_data_manifest_sha256,
+                    max_batch_bytes=self.max_batch_bytes,
+                )
+            except (PreparedDataV2Error, TrainingRuntimeError) as error:
+                raise HostConfigurationError(
+                    "prepared v2 source is invalid"
+                ) from error
         sources: list[PrivatePreparedDataSource] = []
         for split in ("train", "val"):
             item = value[split]
@@ -839,11 +858,11 @@ def load_sources(
             path = _resolve_under(
                 self.prepared_index.parent, item["path"], f"{split} manifest"
             )
-            if _sha256_file(path) != item["sha256"]:
-                raise HostConfigurationError(f"{split} manifest digest mismatch")
             source = PrivatePreparedDataSource(
                 path, max_batch_bytes=self.max_batch_bytes
             )
+            if source.manifest_sha256 != item["sha256"]:
+                raise HostConfigurationError(f"{split} manifest digest mismatch")
             if source.split != split:
                 raise HostConfigurationError(
                     f"{split} manifest declares the wrong split"
diff --git a/src/phaseset_core/prepared_data_v2.py b/src/phaseset_core/prepared_data_v2.py
new file mode 100644
index 0000000..1f0af66
--- /dev/null
+++ b/src/phaseset_core/prepared_data_v2.py
@@ -0,0 +1,1087 @@
+"""Private-server prepared-data v2 writer and epoch-yaw data source.
+
+This authority-zero module composes already-prepared numeric motion windows
+with already-frozen CLIP text batches.  It never locates a dataset, reads raw
+caption text, loads a body model, grants rights, or executes a text model.
+
+The ten-array NPZ payload is an unaugmented canonical float32 window.  A v2
+split manifest binds one independent global uint64 window ordinal per motion.
+The training source rereads the authenticated NPZ for every epoch and applies
+the existing group_yaw_v2 angle once from (seed, epoch, ordinal).  Validation
+is never augmented.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+import hashlib
+import io
+import json
+import math
+import os
+from pathlib import Path
+import stat
+import struct
+from typing import Final, Literal, Mapping
+import zipfile
+
+import numpy as np
+import torch
+
+from phaseset_core.contracts import PreparedGroupBatch
+from phaseset_core.frozen_clip_text import FrozenClipTextBatch, FrozenClipTextReceipt
+from phaseset_core.pipeline import (
+    PreparedGroupSample,
+    collate_group_samples,
+    deterministic_group_yaw,
+    edge_budget_batches,
+)
+from phaseset_core.training import RetrievalTrainingBatch
+
+
+AUTHORITY: Final = 0
+PREPARED_INDEX_V2_SCHEMA: Final = "phaseset-prepared-index-v2"
+PREPARED_SPLIT_V2_SCHEMA: Final = "phaseset-prepared-training-split-v2"
+TRAIN_AUGMENTATION: Final = "group_yaw_v2"
+VALIDATION_AUGMENTATION: Final = "none"
+EMBEDDING_DIM: Final = 512
+TARGET_FRAMES: Final = 200
+BODY_JOINTS: Final = 22
+MAX_JSON_BYTES: Final = 64 * 1024 * 1024
+DEFAULT_MAX_BATCH_BYTES: Final = 2**31
+DEFAULT_MAX_DECODED_BATCH_BYTES: Final = 2**31
+ABSOLUTE_MAX_DECODED_BATCH_BYTES: Final = 8 * 2**30
+NPZ_KEY_ORDER: Final = (
+    "skeletons",
+    "actor_mask",
+    "frame_mask",
+    "track_mask",
+    "actor_commitments",
+    "group_commitments",
+    "text_embeddings",
+    "motion_positive_ids",
+    "text_positive_ids",
+    "text_commitments",
+)
+NPZ_KEYS: Final = frozenset(NPZ_KEY_ORDER)
+NPZ_DTYPES: Final = {
+    "skeletons": np.dtype(np.float32),
+    "actor_mask": np.dtype(np.bool_),
+    "frame_mask": np.dtype(np.bool_),
+    "track_mask": np.dtype(np.bool_),
+    "actor_commitments": np.dtype(np.uint8),
+    "group_commitments": np.dtype(np.uint8),
+    "text_embeddings": np.dtype(np.float32),
+    "motion_positive_ids": np.dtype(np.uint8),
+    "text_positive_ids": np.dtype(np.uint8),
+    "text_commitments": np.dtype(np.uint8),
+}
+Split = Literal["train", "val"]
+
+
+class PreparedDataV2Error(ValueError):
+    """The typed input, immutable artifact, or v2 manifest is invalid."""
+
+
+def _sha256_bytes(value: bytes) -> str:
+    return hashlib.sha256(value).hexdigest()
+
+
+def _sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        while chunk := stream.read(1024 * 1024):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise PreparedDataV2Error(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _bytes32(value: object, label: str) -> bytes:
+    if type(value) is not bytes or len(value) != 32:
+        raise PreparedDataV2Error(f"{label} must be exact bytes[32]")
+    return value
+
+
+def _uint64(value: object, label: str) -> int:
+    if type(value) is not int or not 0 <= value < 2**64:
+        raise PreparedDataV2Error(f"{label} must be an exact uint64")
+    return value
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _unique_object(pairs: list[tuple[str, object]]) -> dict[str, object]:
+    output: dict[str, object] = {}
+    for key, value in pairs:
+        if key in output:
+            raise PreparedDataV2Error("JSON contains a duplicate key")
+        output[key] = value
+    return output
+
+
+def _reject_constant(value: str) -> object:
+    raise PreparedDataV2Error(f"JSON contains forbidden constant {value}")
+
+
+def _read_bounded(path: Path, *, maximum: int, label: str) -> bytes:
+    if type(maximum) is not int or maximum < 1:
+        raise PreparedDataV2Error("byte maximum must be a positive exact int")
+    if path.is_symlink() or not path.is_file():
+        raise PreparedDataV2Error(f"{label} must be a regular non-symlink file")
+    size = path.stat().st_size
+    if not 1 <= size <= maximum:
+        raise PreparedDataV2Error(f"{label} is empty or exceeds its byte bound")
+    with path.open("rb") as stream:
+        raw = stream.read(maximum + 1)
+    if len(raw) != size or len(raw) > maximum:
+        raise PreparedDataV2Error(f"{label} changed while it was read")
+    return raw
+
+
+def _load_json(path: Path, *, label: str) -> tuple[bytes, dict[str, object]]:
+    raw = _read_bounded(path, maximum=MAX_JSON_BYTES, label=label)
+    try:
+        value = json.loads(
+            raw.decode("ascii"),
+            object_pairs_hook=_unique_object,
+            parse_constant=_reject_constant,
+        )
+    except PreparedDataV2Error:
+        raise
+    except (UnicodeError, json.JSONDecodeError) as error:
+        raise PreparedDataV2Error(f"{label} must be strict ASCII JSON") from error
+    if type(value) is not dict:
+        raise PreparedDataV2Error(f"{label} must be a JSON object")
+    if raw != _canonical_json_bytes(value):
+        raise PreparedDataV2Error(f"{label} must use canonical JSON bytes")
+    return raw, value
+
+
+def _closed(value: Mapping[str, object], keys: set[str], label: str) -> None:
+    if set(value) != keys:
+        raise PreparedDataV2Error(f"{label} keys are not closed")
+
+
+def _reject_symlink_components(path: Path, label: str) -> None:
+    for component in (path, *path.parents):
+        if component.is_symlink():
+            raise PreparedDataV2Error(f"{label} may not traverse a symlink")
+
+
+def _resolve_under(root: Path, value: object, label: str) -> Path:
+    if type(value) is not str or not value or "\x00" in value or Path(value).is_absolute():
+        raise PreparedDataV2Error(f"{label} must be a relative path")
+    unresolved = root / value
+    _reject_symlink_components(unresolved, label)
+    candidate = unresolved.resolve()
+    try:
+        candidate.relative_to(root.resolve())
+    except ValueError as error:
+        raise PreparedDataV2Error(f"{label} escapes its artifact root") from error
+    if not candidate.is_file():
+        raise PreparedDataV2Error(f"{label} must be an existing regular file")
+    return candidate
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().cpu().contiguous().view(torch.uint8).numpy().tobytes()
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class PreparedMotionTextBlock:
+    """One frozen CLIP batch partitioned across one or more motion windows.
+
+    ``text_counts[i]`` assigns a nonempty consecutive text-row range to
+    ``samples[i]``.  This supports official holistic text (Q=1), machine-fused
+    variants (often Q=2), and other already-authorized variable-positive text
+    without fixing a caption count in the writer.
+    """
+
+    samples: tuple[PreparedGroupSample, ...] = field(repr=False)
+    text_batch: FrozenClipTextBatch = field(repr=False)
+    text_counts: tuple[int, ...]
+    window_ordinals: tuple[int, ...]
+
+    def __post_init__(self) -> None:
+        if type(self.samples) is not tuple or not self.samples:
+            raise PreparedDataV2Error("samples must be one nonempty exact tuple")
+        if any(type(sample) is not PreparedGroupSample for sample in self.samples):
+            raise PreparedDataV2Error("samples must contain exact PreparedGroupSample values")
+        for sample in self.samples:
+            if struct.pack(">d", float(sample.augmentation_yaw)) != b"\x00" * 8:
+                raise PreparedDataV2Error("stored motion samples must have exact +0 yaw")
+        if type(self.text_batch) is not FrozenClipTextBatch:
+            raise PreparedDataV2Error("text_batch must be exact FrozenClipTextBatch")
+        if (
+            type(self.text_counts) is not tuple
+            or len(self.text_counts) != len(self.samples)
+            or any(type(count) is not int or count < 1 for count in self.text_counts)
+        ):
+            raise PreparedDataV2Error("text_counts must assign at least one row per motion")
+        if type(self.window_ordinals) is not tuple or len(self.window_ordinals) != len(
+            self.samples
+        ):
+            raise PreparedDataV2Error("window_ordinals must cover every motion")
+        ordinals = tuple(
+            _uint64(value, f"window_ordinals[{index}]")
+            for index, value in enumerate(self.window_ordinals)
+        )
+        if len(set(ordinals)) != len(ordinals):
+            raise PreparedDataV2Error("window ordinals must be unique within a block")
+        windows = tuple(sample.window_sha256 for sample in self.samples)
+        if len(set(windows)) != len(windows):
+            raise PreparedDataV2Error("motion windows must be unique within a block")
+
+        embeddings = self.text_batch.embeddings
+        lineage = self.text_batch.caption_commitments
+        receipt = self.text_batch.receipt
+        if (
+            type(embeddings) is not torch.Tensor
+            or embeddings.dtype != torch.float32
+            or embeddings.device.type != "cpu"
+            or embeddings.requires_grad
+            or embeddings.ndim != 2
+            or embeddings.shape[1] != EMBEDDING_DIM
+            or not embeddings.is_contiguous()
+            or not bool(torch.isfinite(embeddings).all().item())
+        ):
+            raise PreparedDataV2Error("frozen text embedding contract is invalid")
+        if sum(self.text_counts) != int(embeddings.shape[0]):
+            raise PreparedDataV2Error("text_counts must consume every frozen text row")
+        if (
+            type(lineage) is not tuple
+            or len(lineage) != int(embeddings.shape[0])
+            or any(type(value) is not bytes or len(value) != 32 for value in lineage)
+            or len(set(lineage)) != len(lineage)
+        ):
+            raise PreparedDataV2Error("frozen text lineage is invalid")
+        if type(receipt) is not FrozenClipTextReceipt:
+            raise PreparedDataV2Error("frozen text receipt type is invalid")
+        output = _tensor_bytes(embeddings)
+        rows = receipt.caption_rows
+        if (
+            type(rows) is not tuple
+            or len(rows) != len(lineage)
+            or any(type(row) is not tuple or len(row) != 9 for row in rows)
+        ):
+            raise PreparedDataV2Error("frozen text receipt caption census is invalid")
+        receipt_lineage = tuple(row[1] for row in rows)
+        row_embedding_digests = tuple(row[8] for row in rows)
+        expected_row_digests = tuple(
+            _sha256_bytes(_tensor_bytes(embeddings[index : index + 1]))
+            for index in range(len(lineage))
+        )
+        if (
+            receipt.batch_size != len(lineage)
+            or tuple(row[0] for row in rows) != tuple(range(len(lineage)))
+            or receipt.output_shape != tuple(embeddings.shape)
+            or receipt.output_stride != tuple(embeddings.stride())
+            or receipt.output_bytes != len(output)
+            or receipt.output_sha256 != _sha256_bytes(output)
+            or receipt_lineage != tuple(value.hex() for value in lineage)
+            or row_embedding_digests != expected_row_digests
+        ):
+            raise PreparedDataV2Error("frozen text receipt does not bind the supplied batch")
+
+    def __repr__(self) -> str:
+        return (
+            f"PreparedMotionTextBlock(motions={len(self.samples)}, "
+            f"texts={sum(self.text_counts)}, <numeric/text hidden>)"
+        )
+
+
+@dataclass(frozen=True, slots=True)
+class PreparedDataBuildResult:
+    """Authority-zero identity of one newly written artifact tree."""
+
+    root: Path
+    index_path: Path
+    index_sha256: str
+    census_path: Path
+    census_sha256: str
+    train_manifest_sha256: str
+    val_manifest_sha256: str
+    batch_count: int
+    motion_count: int
+    text_count: int
+    authority: int = AUTHORITY
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class _MotionTextRow:
+    sample: PreparedGroupSample = field(repr=False)
+    embeddings: np.ndarray = field(repr=False)
+    text_commitments: tuple[bytes, ...] = field(repr=False)
+    positive_family: bytes = field(repr=False)
+    window_ordinal: int
+    clip_receipt_sha256: str
+
+
+@dataclass(frozen=True, slots=True)
+class _ArtifactEntry:
+    path: Path
+    sha256: str
+    window_ordinals: tuple[int, ...]
+
+
+def _expand_blocks(blocks: object) -> tuple[_MotionTextRow, ...]:
+    if type(blocks) is not tuple or not blocks:
+        raise PreparedDataV2Error("each split requires a nonempty exact block tuple")
+    if any(type(block) is not PreparedMotionTextBlock for block in blocks):
+        raise PreparedDataV2Error("blocks must contain exact PreparedMotionTextBlock values")
+    rows: list[_MotionTextRow] = []
+    seen_windows: set[str] = set()
+    seen_ordinals: set[int] = set()
+    seen_text: set[bytes] = set()
+    for block in blocks:
+        embeddings = block.text_batch.embeddings.detach().cpu().contiguous().numpy()
+        commitments = block.text_batch.caption_commitments
+        start = 0
+        for sample, count, ordinal in zip(
+            block.samples,
+            block.text_counts,
+            block.window_ordinals,
+            strict=True,
+        ):
+            stop = start + count
+            if sample.window_sha256 in seen_windows:
+                raise PreparedDataV2Error("a split repeats a motion window")
+            if ordinal in seen_ordinals:
+                raise PreparedDataV2Error("a split repeats a global window ordinal")
+            selected_commitments = tuple(commitments[start:stop])
+            if seen_text.intersection(selected_commitments):
+                raise PreparedDataV2Error("a split repeats a text commitment")
+            family = bytes.fromhex(sample.window_sha256)
+            rows.append(
+                _MotionTextRow(
+                    sample=sample,
+                    embeddings=np.ascontiguousarray(embeddings[start:stop]),
+                    text_commitments=selected_commitments,
+                    positive_family=family,
+                    window_ordinal=ordinal,
+                    clip_receipt_sha256=block.text_batch.receipt.sha256,
+                )
+            )
+            seen_windows.add(sample.window_sha256)
+            seen_ordinals.add(ordinal)
+            seen_text.update(selected_commitments)
+            start = stop
+        if start != len(commitments):
+            raise AssertionError("text partition did not consume its frozen batch")
+    return tuple(rows)
+
+
+def _pack32(values: tuple[bytes, ...]) -> np.ndarray:
+    return np.ascontiguousarray(
+        np.asarray([list(_bytes32(value, "commitment")) for value in values], dtype=np.uint8)
+    )
+
+
+def _batch_arrays(rows: tuple[_MotionTextRow, ...]) -> dict[str, np.ndarray]:
+    samples = tuple(row.sample for row in rows)
+    groups = collate_group_samples(samples)
+    batch_size, padded_actors = groups.actor_mask.shape
+    actor_commitments = np.zeros((batch_size, padded_actors, 32), dtype=np.uint8)
+    for batch_index, commitments in enumerate(groups.actor_commitments):
+        for actor_index, commitment in enumerate(commitments):
+            if commitment is not None:
+                actor_commitments[batch_index, actor_index] = np.frombuffer(
+                    commitment, dtype=np.uint8
+                )
+    text_embeddings = np.ascontiguousarray(
+        np.concatenate(tuple(row.embeddings for row in rows), axis=0),
+        dtype=np.float32,
+    )
+    motion_positive_ids = _pack32(tuple(row.positive_family for row in rows))
+    text_positive_ids = _pack32(
+        tuple(
+            row.positive_family
+            for row in rows
+            for _ in range(len(row.text_commitments))
+        )
+    )
+    text_commitments = _pack32(
+        tuple(value for row in rows for value in row.text_commitments)
+    )
+    arrays = {
+        "skeletons": np.ascontiguousarray(groups.skeletons, dtype=np.float32),
+        "actor_mask": np.ascontiguousarray(groups.actor_mask, dtype=np.bool_),
+        "frame_mask": np.ascontiguousarray(groups.frame_mask, dtype=np.bool_),
+        "track_mask": np.ascontiguousarray(groups.track_mask, dtype=np.bool_),
+        "actor_commitments": actor_commitments,
+        "group_commitments": _pack32(groups.group_commitments),
+        "text_embeddings": text_embeddings,
+        "motion_positive_ids": motion_positive_ids,
+        "text_positive_ids": text_positive_ids,
+        "text_commitments": text_commitments,
+    }
+    _arrays_to_training_batch(arrays, split="train")
+    return arrays
+
+
+def _decoded_nbytes(arrays: Mapping[str, np.ndarray]) -> int:
+    return sum(int(value.nbytes) for value in arrays.values())
+
+
+def _write_npz_once(
+    path: Path,
+    arrays: Mapping[str, np.ndarray],
+    *,
+    max_decoded_bytes: int,
+) -> str:
+    if set(arrays) != NPZ_KEYS:
+        raise AssertionError("internal NPZ key census changed")
+    if _decoded_nbytes(arrays) > max_decoded_bytes:
+        raise PreparedDataV2Error("decoded prepared batch exceeds its byte bound")
+    flags = os.O_RDWR | os.O_CREAT | os.O_EXCL
+    if hasattr(os, "O_NOFOLLOW"):
+        flags |= os.O_NOFOLLOW
+    descriptor = os.open(path, flags, 0o600)
+    try:
+        with os.fdopen(descriptor, "w+b", closefd=True) as output:
+            with zipfile.ZipFile(output, mode="w", compression=zipfile.ZIP_STORED) as archive:
+                for name in NPZ_KEY_ORDER:
+                    info = zipfile.ZipInfo(
+                        filename=name + ".npy",
+                        date_time=(1980, 1, 1, 0, 0, 0),
+                    )
+                    info.compress_type = zipfile.ZIP_STORED
+                    info.create_system = 3
+                    info.external_attr = (stat.S_IFREG | 0o600) << 16
+                    with archive.open(info, mode="w", force_zip64=True) as member:
+                        np.lib.format.write_array(
+                            member,
+                            np.ascontiguousarray(arrays[name]),
+                            version=(1, 0),
+                            allow_pickle=False,
+                        )
+            output.flush()
+            os.fsync(output.fileno())
+    except BaseException:
+        try:
+            os.close(descriptor)
+        except OSError:
+            pass
+        raise
+    os.chmod(path, 0o400)
+    return _sha256_file(path)
+
+
+def _write_json_once(path: Path, value: object) -> str:
+    raw = _canonical_json_bytes(value)
+    flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL
+    if hasattr(os, "O_NOFOLLOW"):
+        flags |= os.O_NOFOLLOW
+    descriptor = os.open(path, flags, 0o600)
+    with os.fdopen(descriptor, "wb", closefd=True) as output:
+        output.write(raw)
+        output.flush()
+        os.fsync(output.fileno())
+    os.chmod(path, 0o400)
+    return _sha256_bytes(raw)
+
+
+def _validate_resource_bounds(max_batch_bytes: object, max_decoded_bytes: object) -> None:
+    if type(max_batch_bytes) is not int or not 1 <= max_batch_bytes <= 2**63 - 1:
+        raise PreparedDataV2Error("max_batch_bytes is outside its safety bound")
+    if type(max_decoded_bytes) is not int or not (
+        1 <= max_decoded_bytes <= ABSOLUTE_MAX_DECODED_BATCH_BYTES
+    ):
+        raise PreparedDataV2Error("max_decoded_batch_bytes is outside its safety bound")
+
+
+def write_prepared_training_tree_v2(
+    root: str | Path,
+    *,
+    train_blocks: tuple[PreparedMotionTextBlock, ...],
+    val_blocks: tuple[PreparedMotionTextBlock, ...],
+    max_total_edges: int,
+    max_batch_size: int | None = None,
+    max_batch_bytes: int = DEFAULT_MAX_BATCH_BYTES,
+    max_decoded_batch_bytes: int = DEFAULT_MAX_DECODED_BATCH_BYTES,
+) -> PreparedDataBuildResult:
+    """Write one new immutable train/val tree; existing roots are never reused."""
+
+    _validate_resource_bounds(max_batch_bytes, max_decoded_batch_bytes)
+    destination = Path(root)
+    if not destination.is_absolute():
+        raise PreparedDataV2Error("prepared root must be absolute")
+    if destination != destination.resolve(strict=False):
+        raise PreparedDataV2Error("prepared root must be canonical")
+    _reject_symlink_components(destination.parent, "prepared root parent")
+    if destination.exists() or destination.is_symlink():
+        raise PreparedDataV2Error("prepared root must be new")
+    expanded = {
+        "train": _expand_blocks(train_blocks),
+        "val": _expand_blocks(val_blocks),
+    }
+    all_windows: set[str] = set()
+    all_ordinals: set[int] = set()
+    all_text: set[bytes] = set()
+    all_actors: dict[str, set[bytes]] = {}
+    for split in ("train", "val"):
+        rows = expanded[split]
+        windows = {row.sample.window_sha256 for row in rows}
+        ordinals = {row.window_ordinal for row in rows}
+        texts = {value for row in rows for value in row.text_commitments}
+        actors = {value for row in rows for value in row.sample.actor_commitments}
+        if (
+            all_windows.intersection(windows)
+            or all_ordinals.intersection(ordinals)
+            or all_text.intersection(texts)
+            or any(all_actors[prior].intersection(actors) for prior in all_actors)
+        ):
+            raise PreparedDataV2Error("train and val inputs are not strictly isolated")
+        all_windows.update(windows)
+        all_ordinals.update(ordinals)
+        all_text.update(texts)
+        all_actors[split] = actors
+
+    plans = {
+        split: edge_budget_batches(
+            tuple(row.sample for row in expanded[split]),
+            max_total_edges=max_total_edges,
+            max_batch_size=max_batch_size,
+        )
+        for split in ("train", "val")
+    }
+
+    destination.mkdir(mode=0o700, parents=False, exist_ok=False)
+    split_results: dict[str, tuple[str, int, int, int]] = {}
+    census_splits: dict[str, list[dict[str, object]]] = {}
+    for split, augmentation in (
+        ("train", TRAIN_AUGMENTATION),
+        ("val", VALIDATION_AUGMENTATION),
+    ):
+        rows = expanded[split]
+
+        row_by_window = {row.sample.window_sha256: row for row in rows}
+        planned = plans[split]
+        split_directory = destination / split
+        split_directory.mkdir(mode=0o700, exist_ok=False)
+        manifest_rows = []
+        census_rows = []
+        split_motion_count = 0
+        split_text_count = 0
+        for batch_index, samples in enumerate(planned):
+            selected = tuple(row_by_window[sample.window_sha256] for sample in samples)
+            arrays = _batch_arrays(selected)
+            batch_path = split_directory / f"batch-{batch_index:08d}.npz"
+            digest = _write_npz_once(
+                batch_path,
+                arrays,
+                max_decoded_bytes=max_decoded_batch_bytes,
+            )
+            if batch_path.stat().st_size > max_batch_bytes:
+                raise PreparedDataV2Error("prepared batch exceeds max_batch_bytes")
+            # Parse exactly what was written before admitting it to a manifest.
+            _load_npz_training_batch(
+                batch_path,
+                expected_sha256=digest,
+                split=split,
+                max_batch_bytes=max_batch_bytes,
+                max_decoded_bytes=max_decoded_batch_bytes,
+            )
+            relative = batch_path.relative_to(destination).as_posix()
+            manifest_rows.append(
+                {
+                    "path": relative,
+                    "sha256": digest,
+                    "window_ordinals": [row.window_ordinal for row in selected],
+                }
+            )
+            census_rows.append(
+                {
+                    "batch_sha256": digest,
+                    "motions": [
+                        {
+                            "clip_receipt_sha256": row.clip_receipt_sha256,
+                            "group_commitment": row.sample.group_commitment.hex(),
+                            "positive_family": row.positive_family.hex(),
+                            "source_sha256": row.sample.source_sha256,
+                            "text_commitments": [
+                                value.hex() for value in row.text_commitments
+                            ],
+                            "window_ordinal": row.window_ordinal,
+                            "window_sha256": row.sample.window_sha256,
+                        }
+                        for row in selected
+                    ],
+                    "path": relative,
+                }
+            )
+            split_motion_count += len(selected)
+            split_text_count += int(arrays["text_embeddings"].shape[0])
+        manifest_path = destination / f"{split}.json"
+        manifest_sha = _write_json_once(
+            manifest_path,
+            {
+                "augmentation": augmentation,
+                "batches": manifest_rows,
+                "schema": PREPARED_SPLIT_V2_SCHEMA,
+                "split": split,
+            },
+        )
+        split_results[split] = (
+            manifest_sha,
+            len(manifest_rows),
+            split_motion_count,
+            split_text_count,
+        )
+        census_splits[split] = census_rows
+
+    index_path = destination / "prepared-index.json"
+    index_sha = _write_json_once(
+        index_path,
+        {
+            "schema": PREPARED_INDEX_V2_SCHEMA,
+            "train": {"path": "train.json", "sha256": split_results["train"][0]},
+            "val": {"path": "val.json", "sha256": split_results["val"][0]},
+        },
+    )
+    census_path = destination / "prepared-census.json"
+    census_sha = _write_json_once(
+        census_path,
+        {
+            "authority": AUTHORITY,
+            "index_sha256": index_sha,
+            "result_claimed": False,
+            "schema": "phaseset-prepared-data-build-census-v2",
+            "splits": census_splits,
+        },
+    )
+    return PreparedDataBuildResult(
+        root=destination.resolve(),
+        index_path=index_path.resolve(),
+        index_sha256=index_sha,
+        census_path=census_path.resolve(),
+        census_sha256=census_sha,
+        train_manifest_sha256=split_results["train"][0],
+        val_manifest_sha256=split_results["val"][0],
+        batch_count=split_results["train"][1] + split_results["val"][1],
+        motion_count=split_results["train"][2] + split_results["val"][2],
+        text_count=split_results["train"][3] + split_results["val"][3],
+    )
+
+
+def _zip_member_census(raw: bytes, *, max_decoded_bytes: int) -> None:
+    try:
+        with zipfile.ZipFile(io.BytesIO(raw), mode="r") as archive:
+            infos = archive.infolist()
+            names = tuple(info.filename for info in infos)
+            if (
+                names != tuple(name + ".npy" for name in NPZ_KEY_ORDER)
+                or len(set(names)) != len(names)
+            ):
+                raise PreparedDataV2Error("prepared NPZ member census or order changed")
+            total = 0
+            decoded = 0
+            for name, info in zip(NPZ_KEY_ORDER, infos, strict=True):
+                if (
+                    info.is_dir()
+                    or info.flag_bits & 0x1
+                    or info.compress_type != zipfile.ZIP_STORED
+                    or info.compress_size != info.file_size
+                    or info.file_size < 1
+                ):
+                    raise PreparedDataV2Error("prepared NPZ member is not bounded ZIP_STORED")
+                total += info.file_size
+                if total > max_decoded_bytes:
+                    raise PreparedDataV2Error("prepared NPZ decoded size exceeds its bound")
+                with archive.open(info, mode="r") as member:
+                    version = np.lib.format.read_magic(member)
+                    if version != (1, 0):
+                        raise PreparedDataV2Error("prepared NPY header version changed")
+                    shape, fortran_order, dtype = np.lib.format.read_array_header_1_0(
+                        member
+                    )
+                    if (
+                        fortran_order
+                        or dtype != NPZ_DTYPES[name]
+                        or dtype.hasobject
+                        or any(type(dimension) is not int or dimension < 0 for dimension in shape)
+                    ):
+                        raise PreparedDataV2Error("prepared NPY header contract changed")
+                    payload_bytes = math.prod(shape) * dtype.itemsize
+                    if member.tell() + payload_bytes != info.file_size:
+                        raise PreparedDataV2Error("prepared NPY payload length is inconsistent")
+                    decoded += payload_bytes
+                    if decoded > max_decoded_bytes:
+                        raise PreparedDataV2Error(
+                            "prepared array payloads exceed their decoded byte bound"
+                        )
+            if archive.testzip() is not None:
+                raise PreparedDataV2Error("prepared NPZ CRC check failed")
+    except PreparedDataV2Error:
+        raise
+    except (OSError, zipfile.BadZipFile) as error:
+        raise PreparedDataV2Error("prepared batch is not a valid bounded NPZ") from error
+
+
+def _bytes32_rows(value: np.ndarray, label: str) -> tuple[bytes, ...]:
+    if value.dtype != np.uint8 or value.ndim != 2 or value.shape[1] != 32:
+        raise PreparedDataV2Error(f"{label} must be uint8 [N,32]")
+    return tuple(bytes(row) for row in np.ascontiguousarray(value))
+
+
+def _actor_rows(
+    value: np.ndarray,
+    actor_mask: np.ndarray,
+) -> tuple[tuple[bytes | None, ...], ...]:
+    if (
+        value.dtype != np.uint8
+        or value.ndim != 3
+        or value.shape != (*actor_mask.shape, 32)
+    ):
+        raise PreparedDataV2Error("actor_commitments must be uint8 [B,K,32]")
+    rows = []
+    for batch_index in range(value.shape[0]):
+        commitments = []
+        for actor_index in range(value.shape[1]):
+            packed = bytes(value[batch_index, actor_index])
+            if bool(actor_mask[batch_index, actor_index]):
+                commitments.append(packed)
+            else:
+                if packed != b"\x00" * 32:
+                    raise PreparedDataV2Error(
+                        "invalid actor commitment slots must be exact zero"
+                    )
+                commitments.append(None)
+        rows.append(tuple(commitments))
+    return tuple(rows)
+
+
+def _arrays_to_training_batch(
+    arrays: Mapping[str, np.ndarray],
+    *,
+    split: Split,
+) -> RetrievalTrainingBatch:
+    if set(arrays) != NPZ_KEYS:
+        raise PreparedDataV2Error("prepared batch NPZ keys are not closed")
+    if any(arrays[name].dtype != dtype for name, dtype in NPZ_DTYPES.items()):
+        raise PreparedDataV2Error("prepared batch contains an unexpected dtype")
+    actor_mask = np.ascontiguousarray(arrays["actor_mask"])
+    groups = PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(arrays["skeletons"]),
+        actor_mask=actor_mask,
+        frame_mask=np.ascontiguousarray(arrays["frame_mask"]),
+        track_mask=np.ascontiguousarray(arrays["track_mask"]),
+        actor_commitments=_actor_rows(arrays["actor_commitments"], actor_mask),
+        group_commitments=_bytes32_rows(
+            arrays["group_commitments"], "group_commitments"
+        ),
+    )
+    text = np.ascontiguousarray(arrays["text_embeddings"])
+    if text.ndim != 2 or text.shape[1] != EMBEDDING_DIM:
+        raise PreparedDataV2Error("text_embeddings must be float32 [Q,512]")
+    return RetrievalTrainingBatch(
+        groups=groups,
+        text_embeddings=torch.from_numpy(text),
+        motion_positive_ids=_bytes32_rows(
+            arrays["motion_positive_ids"], "motion_positive_ids"
+        ),
+        text_positive_ids=_bytes32_rows(
+            arrays["text_positive_ids"], "text_positive_ids"
+        ),
+        text_commitments=_bytes32_rows(
+            arrays["text_commitments"], "text_commitments"
+        ),
+        split=split,
+    )
+
+
+def _load_npz_training_batch(
+    path: Path,
+    *,
+    expected_sha256: str,
+    split: Split,
+    max_batch_bytes: int,
+    max_decoded_bytes: int,
+) -> RetrievalTrainingBatch:
+    raw = _read_bounded(path, maximum=max_batch_bytes, label="prepared batch")
+    if _sha256_bytes(raw) != expected_sha256:
+        raise PreparedDataV2Error("prepared batch digest mismatch")
+    _zip_member_census(raw, max_decoded_bytes=max_decoded_bytes)
+    try:
+        with np.load(io.BytesIO(raw), allow_pickle=False) as archive:
+            if set(archive.files) != NPZ_KEYS:
+                raise PreparedDataV2Error("prepared batch NPZ keys are not closed")
+            arrays = {
+                name: np.ascontiguousarray(np.array(archive[name], copy=True, subok=False))
+                for name in NPZ_KEY_ORDER
+            }
+    except PreparedDataV2Error:
+        raise
+    except (OSError, ValueError, MemoryError) as error:
+        raise PreparedDataV2Error("prepared batch is not a safe numeric NPZ") from error
+    if _decoded_nbytes(arrays) > max_decoded_bytes:
+        raise PreparedDataV2Error("decoded prepared arrays exceed their byte bound")
+    return _arrays_to_training_batch(arrays, split=split)
+
+
+def _rotate_training_batch(
+    batch: RetrievalTrainingBatch,
+    *,
+    seed: int,
+    epoch: int,
+    ordinals: tuple[int, ...],
+) -> RetrievalTrainingBatch:
+    groups = batch.groups
+    if len(ordinals) != groups.batch_size:
+        raise PreparedDataV2Error("window ordinal count differs from motion count")
+    source = np.asarray(groups.skeletons, dtype=np.float64)
+    rotated = np.empty(source.shape, dtype=np.float64)
+    for row, ordinal in enumerate(ordinals):
+        yaw = deterministic_group_yaw(seed=seed, epoch=epoch, window_ordinal=ordinal)
+        cosine = math.cos(yaw)
+        sine = math.sin(yaw)
+        x_axis = source[row, ..., 0]
+        z_axis = source[row, ..., 2]
+        rotated[row, ..., 0] = cosine * x_axis + sine * z_axis
+        rotated[row, ..., 1] = source[row, ..., 1]
+        rotated[row, ..., 2] = -sine * x_axis + cosine * z_axis
+    skeletons = np.ascontiguousarray(rotated, dtype=np.float32)
+    valid = (
+        groups.actor_mask[:, :, None, None]
+        & groups.frame_mask[:, None, :, None]
+        & groups.track_mask
+    )
+    skeletons[~np.broadcast_to(valid[..., None], skeletons.shape)] = np.float32(0.0)
+    rebuilt = PreparedGroupBatch(
+        skeletons=skeletons,
+        actor_mask=np.ascontiguousarray(groups.actor_mask),
+        frame_mask=np.ascontiguousarray(groups.frame_mask),
+        track_mask=np.ascontiguousarray(groups.track_mask),
+        actor_commitments=groups.actor_commitments,
+        group_commitments=groups.group_commitments,
+    )
+    return RetrievalTrainingBatch(
+        groups=rebuilt,
+        text_embeddings=batch.text_embeddings,
+        motion_positive_ids=batch.motion_positive_ids,
+        text_positive_ids=batch.text_positive_ids,
+        text_commitments=batch.text_commitments,
+        split=batch.split,
+    )
+
+
+class PreparedTrainingDataSourceV2:
+    """Strict v2 TrainingDataSource with nonaccumulating epoch yaw."""
+
+    def __init__(
+        self,
+        manifest: str | Path,
+        *,
+        expected_manifest_sha256: str,
+        max_batch_bytes: int = DEFAULT_MAX_BATCH_BYTES,
+        max_decoded_batch_bytes: int = DEFAULT_MAX_DECODED_BATCH_BYTES,
+    ) -> None:
+        _validate_resource_bounds(max_batch_bytes, max_decoded_batch_bytes)
+        expected_manifest = _lower_sha256(
+            expected_manifest_sha256,
+            "expected_manifest_sha256",
+        )
+        unresolved = Path(manifest)
+        _reject_symlink_components(unresolved, "prepared split manifest")
+        path = unresolved.resolve(strict=True)
+        raw, value = _load_json(path, label="prepared split manifest")
+        manifest_sha256 = _sha256_bytes(raw)
+        if manifest_sha256 != expected_manifest:
+            raise PreparedDataV2Error("prepared split manifest digest mismatch")
+        _closed(
+            value,
+            {"augmentation", "batches", "schema", "split"},
+            "prepared split manifest",
+        )
+        if value["schema"] != PREPARED_SPLIT_V2_SCHEMA:
+            raise PreparedDataV2Error("prepared split schema is not v2")
+        split = value["split"]
+        if split not in ("train", "val"):
+            raise PreparedDataV2Error("prepared training split must be train or val")
+        expected_augmentation = (
+            TRAIN_AUGMENTATION if split == "train" else VALIDATION_AUGMENTATION
+        )
+        if value["augmentation"] != expected_augmentation:
+            raise PreparedDataV2Error("prepared split augmentation policy changed")
+        rows = value["batches"]
+        if type(rows) is not list or not rows:
+            raise PreparedDataV2Error("prepared split must list at least one batch")
+        entries: list[_ArtifactEntry] = []
+        seen_paths: set[Path] = set()
+        seen_ordinals: set[int] = set()
+        motion_families: set[bytes] = set()
+        text_commitments: set[bytes] = set()
+        actor_commitments: set[bytes] = set()
+        for index, item in enumerate(rows):
+            if type(item) is not dict:
+                raise PreparedDataV2Error(f"batch row {index} must be an object")
+            _closed(item, {"path", "sha256", "window_ordinals"}, f"batch row {index}")
+            batch_path = _resolve_under(path.parent, item["path"], f"batch row {index}")
+            digest = _lower_sha256(item["sha256"], f"batch row {index} sha256")
+            raw_ordinals = item["window_ordinals"]
+            if type(raw_ordinals) is not list or not raw_ordinals:
+                raise PreparedDataV2Error("every batch must bind window ordinals")
+            ordinals = tuple(
+                _uint64(value, f"batch row {index} ordinal") for value in raw_ordinals
+            )
+            if batch_path in seen_paths or seen_ordinals.intersection(ordinals):
+                raise PreparedDataV2Error("prepared split repeats a path or window ordinal")
+            batch = _load_npz_training_batch(
+                batch_path,
+                expected_sha256=digest,
+                split=split,
+                max_batch_bytes=max_batch_bytes,
+                max_decoded_bytes=max_decoded_batch_bytes,
+            )
+            if len(ordinals) != batch.motion_count:
+                raise PreparedDataV2Error("window ordinal count differs from motion count")
+            families = set(batch.motion_positive_ids)
+            captions = set(batch.text_commitments)
+            actors = {
+                value
+                for actor_row in batch.groups.actor_commitments
+                for value in actor_row
+                if value is not None
+            }
+            if (
+                len(families) != batch.motion_count
+                or motion_families.intersection(families)
+                or len(captions) != len(batch.text_commitments)
+                or text_commitments.intersection(captions)
+            ):
+                raise PreparedDataV2Error("prepared split repeats a motion or text identity")
+            seen_paths.add(batch_path)
+            seen_ordinals.update(ordinals)
+            motion_families.update(families)
+            text_commitments.update(captions)
+            actor_commitments.update(actors)
+            entries.append(_ArtifactEntry(batch_path, digest, ordinals))
+        self.split: Split = split
+        self.manifest_sha256 = manifest_sha256
+        self._entries = tuple(entries)
+        self._max_batch_bytes = max_batch_bytes
+        self._max_decoded_bytes = max_decoded_batch_bytes
+        self._window_ordinals = frozenset(seen_ordinals)
+        self._motion_families = frozenset(motion_families)
+        self._text_commitments = frozenset(text_commitments)
+        self._actor_commitments = frozenset(actor_commitments)
+
+    def iter_epoch(self, *, epoch: int, seed: int):
+        checked_epoch = _uint64(epoch, "epoch")
+        checked_seed = _uint64(seed, "seed")
+        prefix = bytes.fromhex(self.manifest_sha256) + checked_seed.to_bytes(
+            16, "big", signed=True
+        )
+        ordered = sorted(
+            enumerate(self._entries),
+            key=lambda row: hashlib.sha256(
+                prefix
+                + checked_epoch.to_bytes(8, "big")
+                + row[0].to_bytes(8, "big")
+            ).digest(),
+        )
+        for _, entry in ordered:
+            original = _load_npz_training_batch(
+                entry.path,
+                expected_sha256=entry.sha256,
+                split=self.split,
+                max_batch_bytes=self._max_batch_bytes,
+                max_decoded_bytes=self._max_decoded_bytes,
+            )
+            if self.split == "train":
+                yield _rotate_training_batch(
+                    original,
+                    seed=checked_seed,
+                    epoch=checked_epoch,
+                    ordinals=entry.window_ordinals,
+                )
+            else:
+                yield original
+
+
+def load_prepared_training_sources_v2(
+    index: str | Path,
+    *,
+    expected_index_sha256: str,
+    max_batch_bytes: int = DEFAULT_MAX_BATCH_BYTES,
+    max_decoded_batch_bytes: int = DEFAULT_MAX_DECODED_BATCH_BYTES,
+) -> tuple[PreparedTrainingDataSourceV2, PreparedTrainingDataSourceV2]:
+    """Load one exact v2 index and prove strict train/validation isolation."""
+
+    unresolved = Path(index)
+    _reject_symlink_components(unresolved, "prepared index")
+    path = unresolved.resolve(strict=True)
+    raw, value = _load_json(path, label="prepared index")
+    if _sha256_bytes(raw) != _lower_sha256(
+        expected_index_sha256, "expected_index_sha256"
+    ):
+        raise PreparedDataV2Error("prepared index differs from its expected digest")
+    _closed(value, {"schema", "train", "val"}, "prepared index")
+    if value["schema"] != PREPARED_INDEX_V2_SCHEMA:
+        raise PreparedDataV2Error("prepared index schema is not v2")
+    sources = []
+    for split in ("train", "val"):
+        row = value[split]
+        if type(row) is not dict:
+            raise PreparedDataV2Error(f"prepared index {split} row must be an object")
+        _closed(row, {"path", "sha256"}, f"prepared index {split} row")
+        manifest = _resolve_under(path.parent, row["path"], f"{split} manifest")
+        digest = _lower_sha256(row["sha256"], f"{split} manifest sha256")
+        source = PreparedTrainingDataSourceV2(
+            manifest,
+            expected_manifest_sha256=digest,
+            max_batch_bytes=max_batch_bytes,
+            max_decoded_batch_bytes=max_decoded_batch_bytes,
+        )
+        if source.split != split:
+            raise PreparedDataV2Error(f"{split} manifest declares the wrong split")
+        sources.append(source)
+    train, val = sources
+    if (
+        train._window_ordinals & val._window_ordinals
+        or train._motion_families & val._motion_families
+        or train._text_commitments & val._text_commitments
+        or train._actor_commitments & val._actor_commitments
+    ):
+        raise PreparedDataV2Error("prepared train and val identities are not disjoint")
+    return train, val
+
+
+__all__ = [
+    "ABSOLUTE_MAX_DECODED_BATCH_BYTES",
+    "AUTHORITY",
+    "DEFAULT_MAX_BATCH_BYTES",
+    "DEFAULT_MAX_DECODED_BATCH_BYTES",
+    "NPZ_KEYS",
+    "NPZ_KEY_ORDER",
+    "PREPARED_INDEX_V2_SCHEMA",
+    "PREPARED_SPLIT_V2_SCHEMA",
+    "PreparedDataBuildResult",
+    "PreparedDataV2Error",
+    "PreparedMotionTextBlock",
+    "PreparedTrainingDataSourceV2",
+    "TRAIN_AUGMENTATION",
+    "VALIDATION_AUGMENTATION",
+    "load_prepared_training_sources_v2",
+    "write_prepared_training_tree_v2",
+]
diff --git a/tests/test_phaseset_prepared_data_v2.py b/tests/test_phaseset_prepared_data_v2.py
new file mode 100644
index 0000000..150d805
--- /dev/null
+++ b/tests/test_phaseset_prepared_data_v2.py
@@ -0,0 +1,377 @@
+"""Server-only numeric contract tests for prepared_data_v2.
+
+The fixtures contain only analytic arrays and a sealed synthetic
+FrozenClipTextBatch.  They do not load CLIP/SMPL-X or access a dataset.
+"""
+from __future__ import annotations
+
+from dataclasses import replace
+import hashlib
+import json
+import math
+import os
+from pathlib import Path
+import zipfile
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import frozen_clip_text as clip
+from phaseset_core import prepared_data_v2 as prepared
+from phaseset_core.contracts import group_commitment
+from phaseset_core.pipeline import PreparedGroupSample, deterministic_group_yaw
+from phaseset_core.preprocessing import WindowDecision
+
+
+def _digest(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _sample(label: str, *, actors: int) -> PreparedGroupSample:
+    commitments = tuple(sorted(_digest(f"{label}/actor/{index}") for index in range(actors)))
+    skeletons = np.zeros((actors, 200, 22, 3), dtype=np.float32)
+    grid = np.arange(200, dtype=np.float32)
+    for actor in range(actors):
+        for joint in range(22):
+            skeletons[actor, :, joint, 0] = grid * np.float32(0.01) + actor
+            skeletons[actor, :, joint, 1] = np.float32(joint * 0.03)
+            skeletons[actor, :, joint, 2] = grid * np.float32(-0.004) + actor * 2
+    track = np.ones((actors, 200, 22), dtype=np.bool_)
+    source = hashlib.sha256((label + "/source").encode("ascii")).hexdigest()
+    window = hashlib.sha256((label + "/window").encode("ascii")).hexdigest()
+    return PreparedGroupSample(
+        skeletons=np.ascontiguousarray(skeletons),
+        track_mask=track,
+        actor_commitments=commitments,
+        group_commitment=group_commitment(commitments),
+        source_sha256=source,
+        window_sha256=window,
+        source_start_frame=0,
+        augmentation_yaw=0.0,
+        decision=WindowDecision(True, "ACCEPT", 300, actors, 0, 1.0, 0, False),
+    )
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().contiguous().view(torch.uint8).numpy().tobytes()
+
+
+def _text_batch(label: str, count: int) -> clip.FrozenClipTextBatch:
+    values = torch.arange(count * 512, dtype=torch.float32).reshape(count, 512)
+    embeddings = (torch.sin(values * 0.001) + len(label)).contiguous()
+    commitments = tuple(_digest(f"{label}/caption/{index}") for index in range(count))
+    rows = tuple(
+        (
+            index,
+            commitment.hex(),
+            hashlib.sha256(f"{label}/text/{index}".encode("ascii")).hexdigest(),
+            3,
+            3,
+            False,
+            hashlib.sha256(f"{label}/tokens/{index}".encode("ascii")).hexdigest(),
+            hashlib.sha256(f"{label}/mask/{index}".encode("ascii")).hexdigest(),
+            hashlib.sha256(_tensor_bytes(embeddings[index : index + 1])).hexdigest(),
+        )
+        for index, commitment in enumerate(commitments)
+    )
+    output = _tensor_bytes(embeddings)
+    receipt = clip.FrozenClipTextReceipt(
+        batch_size=count,
+        caption_rows=rows,
+        chunk_ranges=((0, count),),
+        frozen_embedding_cache_key_sha256="1" * 64,
+        live_model_manifest_sha256="2" * 64,
+        output_bytes=len(output),
+        output_sha256=hashlib.sha256(output).hexdigest(),
+        output_shape=tuple(embeddings.shape),
+        output_stride=tuple(embeddings.stride()),
+        runtime_identity=(("fixture", "server-only"),),
+        runtime_manifest_sha256="3" * 64,
+        snapshot_files=(),
+        snapshot_manifest_sha256="4" * 64,
+        source_files=(),
+        source_manifest_sha256="5" * 64,
+    )
+    return clip.FrozenClipTextBatch(
+        embeddings,
+        commitments,
+        receipt,
+        _seal=clip._CONSTRUCTION_SEAL,
+    )
+
+
+def _block(
+    label: str,
+    counts: tuple[int, ...],
+    ordinals: tuple[int, ...],
+    *,
+    actors: tuple[int, ...] | None = None,
+) -> prepared.PreparedMotionTextBlock:
+    actor_counts = actors or tuple(2 + index % 2 for index in range(len(counts)))
+    samples = tuple(
+        _sample(f"{label}/{index}", actors=actor_count)
+        for index, actor_count in enumerate(actor_counts)
+    )
+    return prepared.PreparedMotionTextBlock(
+        samples=samples,
+        text_batch=_text_batch(label, sum(counts)),
+        text_counts=counts,
+        window_ordinals=ordinals,
+    )
+
+
+def _write_tree(root: Path):
+    return prepared.write_prepared_training_tree_v2(
+        root,
+        train_blocks=(_block("train", (1, 3), (10, 11), actors=(2, 3)),),
+        val_blocks=(_block("val", (2,), (20,), actors=(2,)),),
+        max_total_edges=4,
+        max_batch_size=2,
+        max_batch_bytes=32 * 1024 * 1024,
+        max_decoded_batch_bytes=32 * 1024 * 1024,
+    )
+
+
+def _canonical_json(value: object) -> bytes:
+    return (
+        json.dumps(value, sort_keys=True, separators=(",", ":"), allow_nan=False).encode(
+            "ascii"
+        )
+        + b"\n"
+    )
+
+
+def test_write_load_variable_q_and_exact_ten_key_npz(tmp_path: Path) -> None:
+    result = _write_tree(tmp_path / "prepared")
+    assert result.authority == 0
+    assert (result.batch_count, result.motion_count, result.text_count) == (2, 3, 6)
+    assert hashlib.sha256(result.index_path.read_bytes()).hexdigest() == result.index_sha256
+    assert hashlib.sha256(result.census_path.read_bytes()).hexdigest() == result.census_sha256
+
+    with np.load(result.root / "train" / "batch-00000000.npz", allow_pickle=False) as data:
+        assert tuple(data.files) == prepared.NPZ_KEY_ORDER
+        assert set(data.files) == prepared.NPZ_KEYS
+        assert data["text_embeddings"].shape == (4, 512)
+        families = tuple(bytes(row) for row in data["text_positive_ids"])
+        assert families.count(bytes(data["motion_positive_ids"][0])) == 1
+        assert families.count(bytes(data["motion_positive_ids"][1])) == 3
+
+    train, val = prepared.load_prepared_training_sources_v2(
+        result.index_path,
+        expected_index_sha256=result.index_sha256,
+        max_batch_bytes=32 * 1024 * 1024,
+        max_decoded_batch_bytes=32 * 1024 * 1024,
+    )
+    assert train.split == "train"
+    assert val.split == "val"
+    assert sum(batch.motion_count for batch in train.iter_epoch(epoch=0, seed=1729)) == 2
+    assert sum(batch.motion_count for batch in val.iter_epoch(epoch=0, seed=1729)) == 1
+
+
+def test_train_yaw_is_epoch_bound_nonaccumulating_and_preserves_metadata(
+    tmp_path: Path,
+) -> None:
+    result = _write_tree(tmp_path / "prepared")
+    train, _ = prepared.load_prepared_training_sources_v2(
+        result.index_path,
+        expected_index_sha256=result.index_sha256,
+        max_batch_bytes=32 * 1024 * 1024,
+        max_decoded_batch_bytes=32 * 1024 * 1024,
+    )
+    epoch0 = tuple(train.iter_epoch(epoch=0, seed=1729))[0]
+    epoch1 = tuple(train.iter_epoch(epoch=1, seed=1729))[0]
+    replay = tuple(train.iter_epoch(epoch=0, seed=1729))[0]
+    assert np.array_equal(epoch0.groups.skeletons, replay.groups.skeletons)
+    assert not np.array_equal(epoch0.groups.skeletons, epoch1.groups.skeletons)
+    assert np.array_equal(epoch0.groups.actor_mask, epoch1.groups.actor_mask)
+    assert np.array_equal(epoch0.groups.frame_mask, epoch1.groups.frame_mask)
+    assert np.array_equal(epoch0.groups.track_mask, epoch1.groups.track_mask)
+    assert epoch0.groups.actor_commitments == epoch1.groups.actor_commitments
+    assert epoch0.motion_positive_ids == epoch1.motion_positive_ids
+    assert epoch0.text_positive_ids == epoch1.text_positive_ids
+    assert epoch0.text_commitments == epoch1.text_commitments
+    assert torch.equal(epoch0.text_embeddings, epoch1.text_embeddings)
+    with np.load(result.root / "train" / "batch-00000000.npz", allow_pickle=False) as data:
+        source = np.asarray(data["skeletons"], dtype=np.float64)
+        expected = np.empty_like(source)
+        for row, ordinal in enumerate((10, 11)):
+            yaw = deterministic_group_yaw(seed=1729, epoch=0, window_ordinal=ordinal)
+            cosine = math.cos(yaw)
+            sine = math.sin(yaw)
+            expected[row, ..., 0] = cosine * source[row, ..., 0] + sine * source[row, ..., 2]
+            expected[row, ..., 1] = source[row, ..., 1]
+            expected[row, ..., 2] = -sine * source[row, ..., 0] + cosine * source[row, ..., 2]
+        expected = np.ascontiguousarray(expected, dtype=np.float32)
+        valid = (
+            data["actor_mask"][:, :, None, None]
+            & data["frame_mask"][:, None, :, None]
+            & data["track_mask"]
+        )
+        expected[~np.broadcast_to(valid[..., None], expected.shape)] = np.float32(0.0)
+    assert np.array_equal(epoch0.groups.skeletons, expected)
+    invalid = ~epoch0.groups.actor_mask[:, :, None, None, None]
+    padded = np.broadcast_to(invalid, epoch0.groups.skeletons.shape)
+    assert np.all(epoch0.groups.skeletons.view(np.uint32)[padded] == 0)
+
+
+def test_validation_is_exact_unaugmented_npz_content(tmp_path: Path) -> None:
+    result = _write_tree(tmp_path / "prepared")
+    _, val = prepared.load_prepared_training_sources_v2(
+        result.index_path,
+        expected_index_sha256=result.index_sha256,
+        max_batch_bytes=32 * 1024 * 1024,
+        max_decoded_batch_bytes=32 * 1024 * 1024,
+    )
+    batch = tuple(val.iter_epoch(epoch=29, seed=31415))[0]
+    with np.load(result.root / "val" / "batch-00000000.npz", allow_pickle=False) as data:
+        assert np.array_equal(batch.groups.skeletons, data["skeletons"])
+        assert torch.equal(batch.text_embeddings, torch.from_numpy(data["text_embeddings"]))
+
+
+def test_writer_is_new_root_only_and_leaves_no_overwrite_path(tmp_path: Path) -> None:
+    root = tmp_path / "prepared"
+    _write_tree(root)
+    before = hashlib.sha256((root / "prepared-index.json").read_bytes()).hexdigest()
+    with pytest.raises(prepared.PreparedDataV2Error, match="must be new"):
+        _write_tree(root)
+    assert hashlib.sha256((root / "prepared-index.json").read_bytes()).hexdigest() == before
+
+
+def test_block_rejects_noncanonical_yaw_partition_and_receipt_drift() -> None:
+    sample = _sample("bad", actors=2)
+    text = _text_batch("bad", 2)
+    with pytest.raises(prepared.PreparedDataV2Error, match="text_counts"):
+        prepared.PreparedMotionTextBlock((sample,), text, (1,), (7,))
+    with pytest.raises(prepared.PreparedDataV2Error, match=r"exact \+0 yaw"):
+        prepared.PreparedMotionTextBlock(
+            (replace(sample, augmentation_yaw=0.25),), text, (2,), (7,)
+        )
+    bad_receipt = replace(text.receipt, output_sha256="0" * 64)
+    forged = clip.FrozenClipTextBatch(
+        text.embeddings,
+        text.caption_commitments,
+        bad_receipt,
+        _seal=clip._CONSTRUCTION_SEAL,
+    )
+    with pytest.raises(prepared.PreparedDataV2Error, match="receipt"):
+        prepared.PreparedMotionTextBlock((sample,), forged, (2,), (7,))
+
+
+def test_writer_rejects_train_val_participant_or_ordinal_leakage(tmp_path: Path) -> None:
+    train = _block("train", (1,), (10,))
+    same_actor_sample = replace(
+        _sample("val", actors=2),
+        actor_commitments=train.samples[0].actor_commitments,
+        group_commitment=train.samples[0].group_commitment,
+    )
+    val = prepared.PreparedMotionTextBlock(
+        (same_actor_sample,), _text_batch("val", 1), (1,), (20,)
+    )
+    with pytest.raises(prepared.PreparedDataV2Error, match="strictly isolated"):
+        prepared.write_prepared_training_tree_v2(
+            tmp_path / "actor-leak",
+            train_blocks=(train,),
+            val_blocks=(val,),
+            max_total_edges=4,
+        )
+    with pytest.raises(prepared.PreparedDataV2Error, match="strictly isolated"):
+        prepared.write_prepared_training_tree_v2(
+            tmp_path / "ordinal-leak",
+            train_blocks=(train,),
+            val_blocks=(_block("fresh-val", (1,), (10,)),),
+            max_total_edges=4,
+        )
+
+
+def test_loader_rejects_test_or_wrong_augmentation_manifest(tmp_path: Path) -> None:
+    result = _write_tree(tmp_path / "prepared")
+    manifest = result.root / "val.json"
+    os.chmod(manifest, 0o600)
+    value = json.loads(manifest.read_text(encoding="ascii"))
+    value["split"] = "test"
+    value["augmentation"] = "none"
+    manifest.write_bytes(_canonical_json(value))
+    os.chmod(manifest, 0o400)
+    with pytest.raises(prepared.PreparedDataV2Error, match="train or val"):
+        prepared.PreparedTrainingDataSourceV2(
+            manifest,
+            expected_manifest_sha256=hashlib.sha256(manifest.read_bytes()).hexdigest(),
+            max_batch_bytes=32 * 1024 * 1024,
+            max_decoded_batch_bytes=32 * 1024 * 1024,
+        )
+
+    os.chmod(manifest, 0o600)
+    value["split"] = "val"
+    value["augmentation"] = "group_yaw_v2"
+    manifest.write_bytes(_canonical_json(value))
+    os.chmod(manifest, 0o400)
+    with pytest.raises(prepared.PreparedDataV2Error, match="policy changed"):
+        prepared.PreparedTrainingDataSourceV2(
+            manifest,
+            expected_manifest_sha256=hashlib.sha256(manifest.read_bytes()).hexdigest(),
+            max_batch_bytes=32 * 1024 * 1024,
+            max_decoded_batch_bytes=32 * 1024 * 1024,
+        )
+
+
+def test_index_rejects_manifest_swapped_at_constructor_boundary(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    result = _write_tree(tmp_path / "prepared")
+    target = (result.root / "train.json").resolve()
+    changed = json.loads(target.read_text(encoding="ascii"))
+    changed["batches"][0]["sha256"] = "0" * 64
+    changed_raw = _canonical_json(changed)
+    real_constructor = prepared.PreparedTrainingDataSourceV2
+    swapped = False
+
+    def swapping_constructor(
+        manifest: str | Path,
+        *,
+        expected_manifest_sha256: str,
+        max_batch_bytes: int,
+        max_decoded_batch_bytes: int,
+    ) -> prepared.PreparedTrainingDataSourceV2:
+        nonlocal swapped
+        if Path(manifest).resolve() == target and not swapped:
+            os.chmod(target, 0o600)
+            target.write_bytes(changed_raw)
+            os.chmod(target, 0o400)
+            swapped = True
+        return real_constructor(
+            manifest,
+            expected_manifest_sha256=expected_manifest_sha256,
+            max_batch_bytes=max_batch_bytes,
+            max_decoded_batch_bytes=max_decoded_batch_bytes,
+        )
+
+    monkeypatch.setattr(prepared, "PreparedTrainingDataSourceV2", swapping_constructor)
+    with pytest.raises(prepared.PreparedDataV2Error, match="manifest digest mismatch"):
+        prepared.load_prepared_training_sources_v2(
+            result.index_path,
+            expected_index_sha256=result.index_sha256,
+            max_batch_bytes=32 * 1024 * 1024,
+            max_decoded_batch_bytes=32 * 1024 * 1024,
+        )
+    assert swapped
+
+
+def test_loader_rejects_compressed_or_oversized_npz_before_numpy_load(tmp_path: Path) -> None:
+    path = tmp_path / "bad.npz"
+    with zipfile.ZipFile(path, mode="w", compression=zipfile.ZIP_DEFLATED) as archive:
+        for name in prepared.NPZ_KEY_ORDER:
+            archive.writestr(name + ".npy", b"not-an-array")
+    raw = path.read_bytes()
+    with pytest.raises(prepared.PreparedDataV2Error, match="ZIP_STORED"):
+        prepared._zip_member_census(raw, max_decoded_bytes=1024 * 1024)
+    oversized = tmp_path / "oversized.npz"
+    with zipfile.ZipFile(oversized, mode="w", compression=zipfile.ZIP_STORED) as archive:
+        for name in prepared.NPZ_KEY_ORDER:
+            archive.writestr(name + ".npy", b"not-an-array")
+    with pytest.raises(prepared.PreparedDataV2Error, match="decoded size"):
+        prepared._zip_member_census(
+            oversized.read_bytes(),
+            max_decoded_bytes=1,
+        )
diff --git a/tests/test_phaseset_prepared_host_integration.py b/tests/test_phaseset_prepared_host_integration.py
new file mode 100644
index 0000000..559855f
--- /dev/null
+++ b/tests/test_phaseset_prepared_host_integration.py
@@ -0,0 +1,422 @@
+"""Private host dispatch tests for canonical prepared-data v1 and v2 sources."""
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import frozen_clip_text as clip
+from phaseset_core import host
+from phaseset_core import prepared_data_v2 as prepared
+from phaseset_core.contracts import group_commitment
+from phaseset_core.pipeline import PreparedGroupSample
+from phaseset_core.preprocessing import WindowDecision
+
+
+MAX_BATCH_BYTES = 32 * 1024 * 1024
+
+
+def _sha(path: Path) -> str:
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def _digest(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _canonical_json(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _write_json(path: Path, value: object) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_bytes(_canonical_json(value))
+
+
+def _write_v1_batch(path: Path, label: str) -> None:
+    actor_ids = (_digest(f"{label}/actor/0"), _digest(f"{label}/actor/1"))
+    family = _digest(f"{label}/family")
+    np.savez(
+        path,
+        skeletons=np.zeros((1, 2, 200, 22, 3), dtype=np.float32),
+        actor_mask=np.ones((1, 2), dtype=np.bool_),
+        frame_mask=np.ones((1, 200), dtype=np.bool_),
+        track_mask=np.ones((1, 2, 200, 22), dtype=np.bool_),
+        actor_commitments=np.asarray([[list(row) for row in actor_ids]], dtype=np.uint8),
+        group_commitments=np.asarray(
+            [list(group_commitment(actor_ids))], dtype=np.uint8
+        ),
+        text_embeddings=np.asarray([[1.0, 0.0]], dtype=np.float32),
+        motion_positive_ids=np.asarray([list(family)], dtype=np.uint8),
+        text_positive_ids=np.asarray([list(family)], dtype=np.uint8),
+        text_commitments=np.asarray([list(_digest(f"{label}/text"))], dtype=np.uint8),
+    )
+
+
+def _write_v1_tree(root: Path) -> tuple[Path, Path, Path]:
+    manifests: dict[str, Path] = {}
+    for split, labels in (("train", ("a", "b")), ("val", ("a",))):
+        rows = []
+        for label in labels:
+            batch = root / f"{split}-{label}.npz"
+            _write_v1_batch(batch, f"{split}/{label}")
+            rows.append({"path": batch.name, "sha256": _sha(batch)})
+        manifest = root / f"{split}.json"
+        _write_json(
+            manifest,
+            {
+                "batches": rows,
+                "schema": host.PREPARED_SPLIT_SCHEMA,
+                "split": split,
+            },
+        )
+        manifests[split] = manifest
+    index = root / "prepared-index.json"
+    _write_json(
+        index,
+        {
+            "schema": host.PREPARED_INDEX_SCHEMA,
+            "train": {
+                "path": manifests["train"].name,
+                "sha256": _sha(manifests["train"]),
+            },
+            "val": {
+                "path": manifests["val"].name,
+                "sha256": _sha(manifests["val"]),
+            },
+        },
+    )
+    return manifests["train"], manifests["val"], index
+
+
+def _sample(
+    label: str,
+    *,
+    capture_label: str | None = None,
+    source_start_frame: int = 0,
+) -> PreparedGroupSample:
+    capture = capture_label or label
+    actors = tuple(
+        sorted((_digest(f"{capture}/actor/0"), _digest(f"{capture}/actor/1")))
+    )
+    skeletons = np.zeros((2, 200, 22, 3), dtype=np.float32)
+    skeletons[:, :, :, 0] = np.arange(200, dtype=np.float32)[None, :, None]
+    skeletons[1, :, :, 2] = np.float32(1.0)
+    return PreparedGroupSample(
+        skeletons=np.ascontiguousarray(skeletons),
+        track_mask=np.ones((2, 200, 22), dtype=np.bool_),
+        actor_commitments=actors,
+        group_commitment=group_commitment(actors),
+        source_sha256=hashlib.sha256(f"{capture}/source".encode("ascii")).hexdigest(),
+        window_sha256=hashlib.sha256(f"{label}/window".encode("ascii")).hexdigest(),
+        source_start_frame=source_start_frame,
+        augmentation_yaw=0.0,
+        decision=WindowDecision(True, "ACCEPT", 300, 2, 0, 1.0, 0, False),
+    )
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().contiguous().view(torch.uint8).numpy().tobytes()
+
+
+def _text_batch(label: str, count: int = 1) -> clip.FrozenClipTextBatch:
+    embeddings = (
+        torch.arange(count * 512, dtype=torch.float32).reshape(count, 512).contiguous()
+    )
+    commitments = tuple(_digest(f"{label}/caption/{index}") for index in range(count))
+    output = _tensor_bytes(embeddings)
+    receipt = clip.FrozenClipTextReceipt(
+        batch_size=count,
+        caption_rows=tuple(
+            (
+                index,
+                commitment.hex(),
+                hashlib.sha256(f"{label}/text/{index}".encode("ascii")).hexdigest(),
+                3,
+                3,
+                False,
+                hashlib.sha256(f"{label}/tokens/{index}".encode("ascii")).hexdigest(),
+                hashlib.sha256(f"{label}/mask/{index}".encode("ascii")).hexdigest(),
+                hashlib.sha256(_tensor_bytes(embeddings[index : index + 1])).hexdigest(),
+            )
+            for index, commitment in enumerate(commitments)
+        ),
+        chunk_ranges=((0, count),),
+        frozen_embedding_cache_key_sha256="1" * 64,
+        live_model_manifest_sha256="2" * 64,
+        output_bytes=len(output),
+        output_sha256=hashlib.sha256(output).hexdigest(),
+        output_shape=tuple(embeddings.shape),
+        output_stride=tuple(embeddings.stride()),
+        runtime_identity=(("fixture", "server-only"),),
+        runtime_manifest_sha256="3" * 64,
+        snapshot_files=(),
+        snapshot_manifest_sha256="4" * 64,
+        source_files=(),
+        source_manifest_sha256="5" * 64,
+    )
+    return clip.FrozenClipTextBatch(
+        embeddings,
+        commitments,
+        receipt,
+        _seal=clip._CONSTRUCTION_SEAL,
+    )
+
+
+def _write_v2_tree(root: Path):
+    train = prepared.PreparedMotionTextBlock(
+        samples=(
+            _sample("train/window-0", capture_label="train/capture", source_start_frame=0),
+            _sample(
+                "train/window-1",
+                capture_label="train/capture",
+                source_start_frame=300,
+            ),
+        ),
+        text_batch=_text_batch("train", 2),
+        text_counts=(1, 1),
+        window_ordinals=(10, 11),
+    )
+    val = prepared.PreparedMotionTextBlock(
+        samples=(_sample("val"),),
+        text_batch=_text_batch("val"),
+        text_counts=(1,),
+        window_ordinals=(20,),
+    )
+    return prepared.write_prepared_training_tree_v2(
+        root,
+        train_blocks=(train,),
+        val_blocks=(val,),
+        max_total_edges=4,
+        max_batch_size=2,
+        max_batch_bytes=MAX_BATCH_BYTES,
+        max_decoded_batch_bytes=MAX_BATCH_BYTES,
+    )
+
+
+def _host_config(root: Path, index: Path) -> host.HostConfig:
+    common = root / "receipt-artifact.bin"
+    common.write_bytes(b"fixture-only receipt artifact\n")
+    artifacts: dict[str, str] = {}
+    digests: dict[str, str] = {}
+    for field in host.RECEIPT_DIGEST_FIELDS:
+        artifact = index if field == "prepared_data_manifest_sha256" else common
+        artifacts[field] = artifact.relative_to(root).as_posix()
+        digests[field] = _sha(artifact)
+    receipt = root / "receipt-record.json"
+    _write_json(
+        receipt,
+        {
+            "admitted_at_utc": "2026-09-08T12:00:00Z",
+            "authority": 1,
+            **digests,
+        },
+    )
+    config = root / "host.json"
+    _write_json(
+        config,
+        {
+            "prepared_index": index.relative_to(root).as_posix(),
+            "receipts": {
+                "artifacts": artifacts,
+                "record": receipt.name,
+            },
+            "runtime": {
+                "bf16_runtime_qualified": False,
+                "checkpoint_every_updates": 1,
+                "corrective_change_sha256": hashlib.sha256(b"none").hexdigest(),
+                "device": "cpu",
+                "edge_budget": 32768,
+                "max_batch_bytes": MAX_BATCH_BYTES,
+                "request_bf16": False,
+                "resume_retry_class": "INFRA_TRANSIENT",
+            },
+            "schema": host.HOST_CONFIG_SCHEMA,
+            "source_tree_sha256": hashlib.sha256(b"source-tree").hexdigest(),
+        },
+    )
+    return host.HostConfig.load(config)
+
+
+def _replace_v2_positive_family(result: prepared.PreparedDataBuildResult) -> None:
+    batch = result.root / "train" / "batch-00000000.npz"
+    with np.load(batch, allow_pickle=False) as archive:
+        arrays = {name: np.array(archive[name], copy=True) for name in archive.files}
+    arrays["text_positive_ids"][0] = np.frombuffer(
+        _digest("foreign-positive-family"), dtype=np.uint8
+    )
+    os.chmod(batch, 0o600)
+    np.savez(batch, **arrays)
+
+    manifest = result.root / "train.json"
+    os.chmod(manifest, 0o600)
+    manifest_value = json.loads(manifest.read_text(encoding="ascii"))
+    manifest_value["batches"][0]["sha256"] = _sha(batch)
+    manifest.write_bytes(_canonical_json(manifest_value))
+
+    os.chmod(result.index_path, 0o600)
+    index_value = json.loads(result.index_path.read_text(encoding="ascii"))
+    index_value["train"]["sha256"] = _sha(manifest)
+    result.index_path.write_bytes(_canonical_json(index_value))
+
+
+def test_host_preserves_v1_dispatch_and_binds_consumed_manifests(tmp_path: Path) -> None:
+    train_manifest, val_manifest, index = _write_v1_tree(tmp_path)
+    train, val = _host_config(tmp_path, index).load_sources()
+    assert type(train) is host.PrivatePreparedDataSource
+    assert type(val) is host.PrivatePreparedDataSource
+    assert train.manifest_sha256 == _sha(train_manifest)
+    assert val.manifest_sha256 == _sha(val_manifest)
+    assert len(tuple(train.iter_epoch(epoch=0, seed=1729))) == 2
+    assert len(tuple(val.iter_epoch(epoch=0, seed=1729))) == 1
+
+
+def test_host_v1_rejects_manifest_swap_at_constructor_boundary(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    train_manifest, _, index = _write_v1_tree(tmp_path)
+    changed = json.loads(train_manifest.read_text(encoding="ascii"))
+    changed["batches"] = list(reversed(changed["batches"]))
+    changed_raw = _canonical_json(changed)
+    real_constructor = host.PrivatePreparedDataSource
+    swapped = False
+
+    def swapping_constructor(
+        manifest: str | Path,
+        *,
+        max_batch_bytes: int,
+    ) -> host.PrivatePreparedDataSource:
+        nonlocal swapped
+        if Path(manifest).resolve() == train_manifest.resolve() and not swapped:
+            train_manifest.write_bytes(changed_raw)
+            swapped = True
+        return real_constructor(manifest, max_batch_bytes=max_batch_bytes)
+
+    monkeypatch.setattr(host, "PrivatePreparedDataSource", swapping_constructor)
+    with pytest.raises(host.HostConfigurationError, match="manifest digest mismatch"):
+        _host_config(tmp_path, index).load_sources()
+    assert swapped
+
+
+def test_host_dispatches_v2_and_returns_training_sources(tmp_path: Path) -> None:
+    result = _write_v2_tree(tmp_path / "prepared")
+    train, val = _host_config(tmp_path, result.index_path).load_sources()
+    assert type(train) is prepared.PreparedTrainingDataSourceV2
+    assert type(val) is prepared.PreparedTrainingDataSourceV2
+    assert train.split == "train"
+    assert val.split == "val"
+    train_batches = tuple(train.iter_epoch(epoch=2, seed=1729))
+    assert len(train_batches) == 1
+    assert train_batches[0].motion_count == 2
+    assert train_batches[0].groups.group_commitments[0] == (
+        train_batches[0].groups.group_commitments[1]
+    )
+    assert train_batches[0].motion_positive_ids[0] != (
+        train_batches[0].motion_positive_ids[1]
+    )
+    assert len(tuple(val.iter_epoch(epoch=2, seed=1729))) == 1
+
+
+def test_host_v2_translates_invalid_positive_family_to_hold_before_runtime(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    result = _write_v2_tree(tmp_path / "prepared")
+    _replace_v2_positive_family(result)
+    config = _host_config(tmp_path, result.index_path)
+    with pytest.raises(host.HostConfigurationError, match="prepared v2 source") as error:
+        config.load_sources()
+    assert isinstance(error.value.__cause__, host.TrainingRuntimeError)
+
+    def forbidden_backend(*args: object, **kwargs: object) -> object:
+        raise AssertionError("runtime backend was constructed for invalid prepared data")
+
+    monkeypatch.setattr(host, "PhaseSetHostBackend", forbidden_backend)
+    factory = host.RuntimeAdapterFactory(config.path)
+    with pytest.raises(host.ExecutionHold) as held:
+        factory(host.cli.CLICommandRequest(command="preflight"))
+    assert held.value.hold_codes == (
+        "HOLD_PREPARED_DATA_MANIFEST_ABSENT",
+        "HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",
+    )
+
+
+def test_host_rejects_unknown_index_schema(tmp_path: Path) -> None:
+    result = _write_v2_tree(tmp_path / "prepared")
+    os.chmod(result.index_path, 0o600)
+    value = json.loads(result.index_path.read_text(encoding="ascii"))
+    value["schema"] = "phaseset-prepared-index-unknown"
+    result.index_path.write_bytes(_canonical_json(value))
+    with pytest.raises(host.HostConfigurationError, match="schema is not registered"):
+        _host_config(tmp_path, result.index_path).load_sources()
+
+
+def test_host_rejects_test_split_in_authenticated_v2_tree(tmp_path: Path) -> None:
+    result = _write_v2_tree(tmp_path / "prepared")
+    val_manifest = result.root / "val.json"
+    os.chmod(val_manifest, 0o600)
+    value = json.loads(val_manifest.read_text(encoding="ascii"))
+    value["split"] = "test"
+    val_manifest.write_bytes(_canonical_json(value))
+    os.chmod(result.index_path, 0o600)
+    index_value = json.loads(result.index_path.read_text(encoding="ascii"))
+    index_value["val"]["sha256"] = _sha(val_manifest)
+    result.index_path.write_bytes(_canonical_json(index_value))
+    with pytest.raises(host.HostConfigurationError, match="prepared v2 source") as error:
+        _host_config(tmp_path, result.index_path).load_sources()
+    assert isinstance(error.value.__cause__, prepared.PreparedDataV2Error)
+    assert "train or val" in str(error.value.__cause__)
+
+
+def test_host_v2_rejects_index_swap_after_receipt_check(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    result = _write_v2_tree(tmp_path / "prepared")
+    original_loader = host.load_prepared_training_sources_v2
+    changed = json.loads(result.index_path.read_text(encoding="ascii"))
+    changed["train"]["sha256"] = "0" * 64
+    changed_raw = _canonical_json(changed)
+    swapped = False
+
+    def swapping_loader(
+        index: str | Path,
+        *,
+        expected_index_sha256: str,
+        max_batch_bytes: int,
+    ) -> tuple[
+        prepared.PreparedTrainingDataSourceV2,
+        prepared.PreparedTrainingDataSourceV2,
+    ]:
+        nonlocal swapped
+        if not swapped:
+            os.chmod(result.index_path, 0o600)
+            result.index_path.write_bytes(changed_raw)
+            swapped = True
+        return original_loader(
+            index,
+            expected_index_sha256=expected_index_sha256,
+            max_batch_bytes=max_batch_bytes,
+        )
+
+    monkeypatch.setattr(host, "load_prepared_training_sources_v2", swapping_loader)
+    with pytest.raises(host.HostConfigurationError, match="prepared v2 source") as error:
+        _host_config(tmp_path, result.index_path).load_sources()
+    assert isinstance(error.value.__cause__, prepared.PreparedDataV2Error)
+    assert "expected digest" in str(error.value.__cause__)
+    assert swapped

From 9456c520563895b2bed89c0db5f97c67f0be22ca Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 07:07:51 -0500
Subject: [PATCH 09/95] Freeze native attention fastpath after server inference
 requalification

---
 RELEASE_FILES.sha256                    | 13 +++---
 STATUS.md                               | 16 +++++--
 docs/FROZEN_CLIP_TEXT_ADAPTER.md        | 12 ++++-
 docs/FROZEN_MHA_RUNTIME.md              | 59 +++++++++++++++++++++++++
 src/phaseset_core/frozen_clip_text.py   |  9 +++-
 src/phaseset_core/training.py           |  7 +++
 tests/test_phaseset_frozen_clip_text.py | 35 +++++++++++++++
 tests/test_phaseset_training.py         | 10 ++++-
 8 files changed, 148 insertions(+), 13 deletions(-)
 create mode 100644 docs/FROZEN_MHA_RUNTIME.md

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index a366720..7ccd1cc 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,7 +11,7 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-bdf1eda2c32be748566637fdd79d0c9b695d1f856a8f60c1ee0f4d3eceab9ca0	14758	STATUS.md
+d1288616901689bf297a4fb355c58b47ad9b937b9e42c38f190f951512e87a5d	15503	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
@@ -20,7 +20,8 @@ e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA
 3633e37c3eae3289d552a363e565bee027ebd83661f1c44ea136f9498de9e5af	19059	docs/EXECUTION_PROTOCOL.md
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
-e7bc4f9bace637106d73afe6be84e0695c5be78857218bc8b141cc8d5f009d52	11085	docs/FROZEN_CLIP_TEXT_ADAPTER.md
+dc990975cd2feac83d7b6df822cd838270a16b7bea50753e1cebfdd3242b3fe0	11748	docs/FROZEN_CLIP_TEXT_ADAPTER.md
+4bad38f50a4d6ee1650316bfcfc074b1a8b0748d5daa52168a17b7d749d0ddab	3374	docs/FROZEN_MHA_RUNTIME.md
 39589bf5cba54b0626fedef87e41cb436da7c1cab4cb3e4975afea720d45b62e	11899	docs/HOST_RUNTIME_INTEGRATION.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
@@ -127,7 +128,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
-51bdad872537b332e886646071d4381d00a09ddba5912c43ba8559e2675a4c79	48618	src/phaseset_core/frozen_clip_text.py
+68a8484727b5e9be568aca0b19840e5b45f2c3db5a90817d276d56291739a4e4	48867	src/phaseset_core/frozen_clip_text.py
 25871fe486090edd2ac49f82018dcced116e35c85d99178b251bc8862dfdbe70	78628	src/phaseset_core/host.py
 47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
@@ -142,7 +143,7 @@ c675143d6ba9bc98976041ff02c0fa6ae6ef7bfd122122d0063eeb85f62c8c88	43972	src/phase
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 0c8f52f8822f4e3822e5124ce794d482bb09d0545c73f5c6b341c1bbd7bc86f2	35557	src/phaseset_core/streaming_autograd.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
-f75b9a59dfc2ccfc04ed5f418fb318f3bff6bc11cfe61021f5ff552d71a2ae7f	154285	src/phaseset_core/training.py
+b1bbc5061a02aac1226ab00fd6eb970bf786cfe32b4d40ff0999dbebaaec60b4	154645	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
@@ -183,7 +184,7 @@ a17a50e801c389b4e24b2840f2aafcb7b476cbef86e2e142ad0af6245a54718f	4515	tests/test
 8bd6e95a35523d108153f668f8919e6f22858e8283e6308041751c6c92d0f904	18605	tests/test_phaseset_evaluation.py
 aea41df712a25be78841e655a6fbd91fc96217ce92e60d3ab842f185fc39a58c	41553	tests/test_phaseset_execution.py
 dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/test_phaseset_experiments.py
-aaa4db9a4f9b9d0a4fdea4120170b27ac9185c455193b29d207b1a50b142e9fd	17118	tests/test_phaseset_frozen_clip_text.py
+937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
 4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
 a75f583fd0a233760b1a421ca51680afe4195c33f9ef4bce6e9a1737828f207e	14907	tests/test_phaseset_models.py
 e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test_phaseset_morlet.py
@@ -201,7 +202,7 @@ a1fbdfe55e50f67bb6568d5eba276e24beeb5a44373c516f0bd7758bd5742127	12486	tests/tes
 d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/test_phaseset_statistics.py
 e6e9600a244af88f731e9cc981f19478b5760db8c254dfd8455960ffe789ef1a	12321	tests/test_phaseset_streaming_autograd.py
 ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test_phaseset_torch_periodic.py
-0e7f9c3b6b04fb94826cc1005460a291f92a6cbe385e80004ff50f169f0ddbee	52009	tests/test_phaseset_training.py
+280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
 45b42266bc1c42c36af7eb7d07be2fc85d8b1d0a539ffb3674f98b47e1327254	4973	tests/test_release_artifact_audit.py
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
diff --git a/STATUS.md b/STATUS.md
index f7bd5c7..b7cb0dc 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -44,9 +44,15 @@ evidence, not training results or scientific claims.
   gradients, frozen-text preservation, and CPU/CUDA tolerance checks. Their
   small analytic fixtures are not training or performance results. A subsequent
   residual retrieval lane failed its frozen-base CPU/CUDA embedding comparison
-  after zero-residual scoring checks. That failed receipt is preserved;
-  inference-backend diagnosis is ongoing, with no tolerance relaxation or
-  frozen-base inference/full-runtime qualification claimed.
+  after zero-residual scoring checks. That failed receipt is preserved. A
+  same-weight diagnostic isolated an observed native-MHA-fastpath dependency.
+  The frozen runtime now disables that fastpath on CPU and CUDA and restores
+  caller flags. With unchanged tolerance, the new CUDA retrieval-head seam
+  passed: frozen-base maximum difference 7.153e-7, score difference 4.471e-8,
+  and finite nonzero exercised head gradients. This did not run the periodic
+  core or an optimizer. A subsequent B0 repeat stopped before model creation
+  for insufficient free memory; new-policy B1/B2 and full-runtime qualification
+  remain pending. See [runtime policy](docs/FROZEN_MHA_RUNTIME.md).
 - The native Embody loader and concrete licensed SMPL-X body-22 evaluator are
   implemented. Seven adapter contract tests passed on Linux/Python 3.12.12/
   NumPy 2.4.6; these use explicit fixtures, not licensed assets or main data.
@@ -73,6 +79,9 @@ evidence, not training results or scientific claims.
   the exact retained prefix and EOS, restores caller RNG, and emits text-free
   receipts. See [frozen CLIP text adapter](docs/FROZEN_CLIP_TEXT_ADAPTER.md).
   This does not qualify GPU text execution or real-data training.
+  The MHA-policy successor separately passed 33 focused/949 complete server
+  tests and three official CLIP forwards, preserving the original output
+  golden and RNG under correctly changed code/runtime/receipt identities.
 - The exact latest text-adapter commit was downloaded from anonymous codeload,
   manifest/content/link checked, built offline into a wheel, and installed into
   a separate server target. All 56 wheel Python modules match the public Git
@@ -149,6 +158,7 @@ data, model assets, connection details, and download material are not public.
 - A frozen numerical runtime with deterministic-algorithm errors, highest
   float32 matmul precision, TF32/reduced-precision reductions disabled,
   deterministic cuDNN, a mandatory deterministic CUDA workspace setting, and
+  disabled native MHA fastpath on CPU/CUDA,
   an environment-v2 hardware/runtime/build/thread inventory. Live drift is
   rejected and ambient host flags are restored on exit.
 - A differentiable small-batch Torch CPU skeleton/Morlet oracle for input-
diff --git a/docs/FROZEN_CLIP_TEXT_ADAPTER.md b/docs/FROZEN_CLIP_TEXT_ADAPTER.md
index 71c9d78..22e7a31 100644
--- a/docs/FROZEN_CLIP_TEXT_ADAPTER.md
+++ b/docs/FROZEN_CLIP_TEXT_ADAPTER.md
@@ -136,7 +136,9 @@ The implementation is deliberately pinned to the newly observed CPU identity:
 CPython 3.12.12, Linux x86-64 little-endian, Torch 2.12.0+cu126 with CUDA hidden,
 Transformers 4.57.3, tokenizers 0.22.2, huggingface-hub 0.36.2, and NumPy 2.4.6.
 It requires one intra-op/inter-op thread, deterministic algorithms, and highest
-float32 matmul precision. A different runtime is a new qualification lane, not
+float32 matmul precision, and native MHA fastpath disabled. The caller must
+install that false flag; the adapter checks and records it without changing it.
+A different runtime is a new qualification lane, not
 a permissive fallback.
 
 The exact Transformers model/tokenizer sources and installed
@@ -213,3 +215,11 @@ The full Linux regression with this adapter then passed 932 tests, one skipped,
 and 39 subtests in 287.60 seconds, with unchanged source bytes. The model weights
 are not required for that unit/regression suite; actual loading is verified by
 the separate private snapshot-bound observation above.
+
+After the [frozen MHA policy](FROZEN_MHA_RUNTIME.md) changed the pinned training
+source and runtime identity, a separate server requalification passed 33
+focused tests and 949 full-suite tests, 2 skipped, and 39 subtests. Three actual
+official CLIP forwards preserved both output values above and caller RNG.
+The new receipt contains the new source/runtime identity and false MHA flag;
+it is intentionally not byte-identical to the historical receipt. These checks
+do not qualify CUDA text, full training, or real-data experiments.
diff --git a/docs/FROZEN_MHA_RUNTIME.md b/docs/FROZEN_MHA_RUNTIME.md
new file mode 100644
index 0000000..a131611
--- /dev/null
+++ b/docs/FROZEN_MHA_RUNTIME.md
@@ -0,0 +1,59 @@
+# Frozen attention runtime policy
+
+PhaseSet's numerical context disables PyTorch's native MHA fastpath on both
+CPU and CUDA. It captures the caller's boolean, installs and checks `False`,
+and restores the original value on normal and exceptional exit. The training
+environment digest includes this policy; older code/environment-bound
+checkpoints and qualifications are not evidence for the new runtime.
+
+This is a runtime change, not a change to model layers, weights, masks, loss,
+optimizer, or the registered CPU/CUDA tolerance (`rtol=1e-4, atol=1e-5`). It is
+applied around the actual training/validation context, not just a test branch.
+
+## Observed reason
+
+A preserved same-weight B0 inference diagnostic found 336 group-embedding
+elements outside that tolerance with native fastpath enabled (maximum absolute
+difference about 2.26e-4). Disabling it reduced the exceedance count to zero
+(maximum about 7.15e-7). The direct temporal comparison exhibited the same
+pattern with both an all-false padding mask and no mask. This establishes the
+observed runtime dependency; it is not a claim about all hardware or a general
+vendor defect.
+
+The original residual-retrieval failure remains a failed historical attempt.
+The diagnostic did not overwrite it or change its acceptance threshold.
+
+## Subsequent registered-server observations
+
+- Focused CPU regression: 33 tests passed. A separate full suite passed 949
+  tests, 2 skipped, and 39 subtests, with unchanged source bytes.
+- Actual official CLIP CPU requalification: three forwards passed. The old
+  two-row output golden and separate long-caption output were unchanged,
+  and caller RNG was preserved. Source/runtime/receipt identities changed
+  correctly; complete old and new receipts are not byte-identical.
+- A new actual-width FP32 CUDA residual-retrieval engineering observation
+  passed. Frozen-base embedding maximum CPU/CUDA difference was 7.153e-7;
+  score difference was 4.471e-8; loss difference was zero. The zero-initialized
+  residual equaled the frozen base and the exercised residual text-head
+  parameters received finite nonzero gradients. The frozen text fixture was
+  unchanged. Peak allocated memory was 108,373,504 bytes under a 2 GiB cap.
+
+The last observation exercises the closed retrieval-head seam with analytic
+features, not the periodic core or an optimizer. It does not qualify full
+training, BF16, performance, data access, or a winning base checkpoint. A
+subsequent B0 requalification stopped before model construction because no GPU
+met the fixed 8 GiB free-memory requirement. B1/B2 were not run under the new
+policy after that stop. Their older observations remain historical, not new
+policy qualifications. Existing workloads were not interrupted.
+
+## CLIP and empirical boundaries
+
+The frozen text adapter pins the entire installed training module, records
+`mha_fastpath_enabled=false`, and rejects a live true flag. Its caller must
+configure the validated CPU runtime before loading it; the adapter does not
+silently alter global flags. See [the text adapter](FROZEN_CLIP_TEXT_ADAPTER.md).
+
+Main holistic validation must still aggregate every admitted window into one
+capture embedding and use true capture identities. Window scores and repeating
+actor-set lineage IDs are not a substitute. This runtime repair is not a
+completed empirical qualification or a scientific result.
diff --git a/src/phaseset_core/frozen_clip_text.py b/src/phaseset_core/frozen_clip_text.py
index fe58002..872bcc5 100644
--- a/src/phaseset_core/frozen_clip_text.py
+++ b/src/phaseset_core/frozen_clip_text.py
@@ -101,6 +101,7 @@
     ("interop_threads", "1"),
     ("intraop_threads", "1"),
     ("machine", "x86_64"),
+    ("mha_fastpath_enabled", "false"),
     ("numpy", "2.4.6"),
     ("python", "3.12.12"),
     ("python_implementation", "CPython"),
@@ -155,8 +156,8 @@
     ),
     (
         "phaseset_core.training",
-        154_285,
-        "f75b9a59dfc2ccfc04ed5f418fb318f3bff6bc11cfe61021f5ff552d71a2ae7f",
+        154_645,
+        "b1bbc5061a02aac1226ab00fd6eb970bf786cfe32b4d40ff0999dbebaaec60b4",
     ),
 )
 
@@ -358,6 +359,9 @@ def _runtime_facts(
                 "interop_threads": str(torch.get_num_interop_threads()),
                 "intraop_threads": str(torch.get_num_threads()),
                 "machine": platform.machine(),
+                "mha_fastpath_enabled": str(
+                    bool(torch.backends.mha.get_fastpath_enabled())
+                ).lower(),
                 "numpy": str(numpy.__version__),
                 "python": platform.python_version(),
                 "python_implementation": platform.python_implementation(),
@@ -401,6 +405,7 @@ def validate_runtime() -> None:
             or torch.get_num_interop_threads() != 1
             or not torch.are_deterministic_algorithms_enabled()
             or torch.get_float32_matmul_precision() != "highest"
+            or torch.backends.mha.get_fastpath_enabled() is not False
         ):
             _hold("HOLD_RUNTIME_IDENTITY")
 
diff --git a/src/phaseset_core/training.py b/src/phaseset_core/training.py
index 3896af8..13208f8 100644
--- a/src/phaseset_core/training.py
+++ b/src/phaseset_core/training.py
@@ -1908,6 +1908,7 @@ def _capture_numerical_runtime_flags() -> dict[str, object]:
             torch.is_deterministic_algorithms_warn_only_enabled()
         ),
         "float32_matmul_precision": torch.get_float32_matmul_precision(),
+        "mha_fastpath_enabled": bool(torch.backends.mha.get_fastpath_enabled()),
     }
     for owner, name, _expected in _backend_flag_targets():
         owner_name = (
@@ -1920,6 +1921,7 @@ def _capture_numerical_runtime_flags() -> dict[str, object]:
 def _install_frozen_numerical_runtime() -> None:
     torch.use_deterministic_algorithms(True, warn_only=False)
     torch.set_float32_matmul_precision("highest")
+    torch.backends.mha.set_fastpath_enabled(False)
     for owner, name, expected in _backend_flag_targets():
         setattr(owner, name, expected)
 
@@ -1935,6 +1937,9 @@ def _restore_numerical_runtime_flags(flags: Mapping[str, object]) -> None:
     precision = flags.get("float32_matmul_precision")
     if type(precision) is str:
         torch.set_float32_matmul_precision(precision)
+    mha_fastpath = flags.get("mha_fastpath_enabled")
+    if type(mha_fastpath) is bool:
+        torch.backends.mha.set_fastpath_enabled(mha_fastpath)
     deterministic = flags.get("deterministic_algorithms")
     warn_only = flags.get("deterministic_warn_only")
     if type(deterministic) is bool and type(warn_only) is bool:
@@ -1947,6 +1952,7 @@ def _assert_frozen_numerical_runtime(device: torch.device) -> None:
         "deterministic_algorithms": True,
         "deterministic_warn_only": False,
         "float32_matmul_precision": "highest",
+        "mha_fastpath_enabled": False,
     }
     for owner, name, value in _backend_flag_targets():
         owner_name = (
@@ -2000,6 +2006,7 @@ def training_environment_sha256() -> str:
         "deterministic_algorithms": True,
         "deterministic_warn_only": False,
         "float32_matmul_precision": "highest",
+        "mha_fastpath_enabled": False,
         "backend_flags": {
             (
                 f"{'cuda.matmul' if owner is torch.backends.cuda.matmul else 'cudnn'}.{name}"
diff --git a/tests/test_phaseset_frozen_clip_text.py b/tests/test_phaseset_frozen_clip_text.py
index 031904b..07d2b64 100644
--- a/tests/test_phaseset_frozen_clip_text.py
+++ b/tests/test_phaseset_frozen_clip_text.py
@@ -12,6 +12,7 @@
 import shutil
 import subprocess
 import sys
+from types import SimpleNamespace
 
 import pytest
 import torch
@@ -283,6 +284,40 @@ def test_loader_is_literal_offline_text_only(tmp_path: Path) -> None:
     assert _Model.load_calls[0]["output_loading_info"] is True
 
 
+def test_runtime_identity_binds_mha_fastpath_and_restores_caller_flag() -> None:
+    original = bool(torch.backends.mha.get_fastpath_enabled())
+    versions = SimpleNamespace(__version__="mock-only")
+    try:
+        torch.backends.mha.set_fastpath_enabled(False)
+        disabled = dict(
+            adapter_module._runtime_facts(
+                huggingface_hub=versions,
+                numpy=versions,
+                tokenizers=versions,
+                torch=torch,
+                transformers=versions,
+            )
+        )
+        assert disabled["mha_fastpath_enabled"] == "false"
+        assert dict(adapter_module._EXPECTED_RUNTIME)["mha_fastpath_enabled"] == "false"
+
+        torch.backends.mha.set_fastpath_enabled(True)
+        enabled = dict(
+            adapter_module._runtime_facts(
+                huggingface_hub=versions,
+                numpy=versions,
+                tokenizers=versions,
+                torch=torch,
+                transformers=versions,
+            )
+        )
+        assert enabled["mha_fastpath_enabled"] == "true"
+        assert enabled != disabled
+    finally:
+        torch.backends.mha.set_fastpath_enabled(original)
+    assert bool(torch.backends.mha.get_fastpath_enabled()) is original
+
+
 def test_extra_file_and_snapshot_mutation_fail_without_path_leak(tmp_path: Path) -> None:
     snapshot = tmp_path / "bad-snapshot"
     snapshot.mkdir()
diff --git a/tests/test_phaseset_training.py b/tests/test_phaseset_training.py
index 9d98e0b..a2dc1ba 100644
--- a/tests/test_phaseset_training.py
+++ b/tests/test_phaseset_training.py
@@ -982,6 +982,7 @@ def test_numerical_runtime_is_frozen_checked_and_restored(
     try:
         torch.use_deterministic_algorithms(False, warn_only=True)
         torch.set_float32_matmul_precision("medium")
+        torch.backends.mha.set_fastpath_enabled(True)
         ambient = training_module._capture_numerical_runtime_flags()
 
         formal_config = TrainingConfig(stage="base", seed=1729)
@@ -997,13 +998,20 @@ def test_numerical_runtime_is_frozen_checked_and_restored(
         )
         with training_module._frozen_numerical_runtime(torch.device("cpu")):
             training_module._assert_frozen_numerical_runtime(torch.device("cpu"))
+            assert torch.backends.mha.get_fastpath_enabled() is False
             formal_runtime._numeric_runtime_active = True
-            torch.set_float32_matmul_precision("medium")
+            torch.backends.mha.set_fastpath_enabled(True)
             with pytest.raises(TrainingRuntimeError, match="numerical runtime flags changed"):
                 formal_runtime._assert_live_formal_binding(require_initial_state=True)
             formal_runtime._numeric_runtime_active = False
         assert training_module._capture_numerical_runtime_flags() == ambient
 
+        with pytest.raises(RuntimeError, match="intentional numerical runtime exit"):
+            with training_module._frozen_numerical_runtime(torch.device("cpu")):
+                assert torch.backends.mha.get_fastpath_enabled() is False
+                raise RuntimeError("intentional numerical runtime exit")
+        assert training_module._capture_numerical_runtime_flags() == ambient
+
         synthetic_config = TrainingConfig(
             stage="base",
             seed=1729,

From e3c73c031c0034701277870ca99fbbcbdd037294 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 08:15:01 -0500
Subject: [PATCH 10/95] Integrate capture-level validation with snapshot-bound
 training and resume

---
 RELEASE_FILES.sha256                         |  23 +-
 STATUS.md                                    |  35 +-
 docs/CAPTURE_VALIDATION.md                   | 112 +++
 docs/FROZEN_CLIP_TEXT_ADAPTER.md             |   9 +
 docs/FROZEN_MHA_RUNTIME.md                   |  18 +-
 docs/HOST_RUNTIME_INTEGRATION.md             |  13 +-
 docs/LEGACY_TEST_STABILITY.md                |  28 +
 docs/PREPARED_DATA_V2.md                     |  13 +-
 src/phaseset_core/capture_pooling.py         | 214 +++++
 src/phaseset_core/capture_validation.py      | 825 +++++++++++++++++++
 src/phaseset_core/frozen_clip_text.py        |   4 +-
 src/phaseset_core/training.py                |  52 +-
 tests/test_phasepair_core_clip_resolution.py |  11 +
 tests/test_phaseset_capture_pooling.py       | 201 +++++
 tests/test_phaseset_capture_training.py      | 187 +++++
 tests/test_phaseset_capture_validation.py    | 326 ++++++++
 16 files changed, 2031 insertions(+), 40 deletions(-)
 create mode 100644 docs/CAPTURE_VALIDATION.md
 create mode 100644 docs/LEGACY_TEST_STABILITY.md
 create mode 100644 src/phaseset_core/capture_pooling.py
 create mode 100644 src/phaseset_core/capture_validation.py
 create mode 100644 tests/test_phaseset_capture_pooling.py
 create mode 100644 tests/test_phaseset_capture_training.py
 create mode 100644 tests/test_phaseset_capture_validation.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 7ccd1cc..73e42b7 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,18 +11,20 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-d1288616901689bf297a4fb355c58b47ad9b937b9e42c38f190f951512e87a5d	15503	STATUS.md
+b0a0c70901c228ad99c2fa8adf039245388b1b5a80358778413162d143370395	16883	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
+6d562907df5ae5a598401eb6ba622775ae03d4b81795671fe0c86942174e90e8	6781	docs/CAPTURE_VALIDATION.md
 e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA_PROTOCOL.md
 2a18716b57d21362672faaaf76944821764302064b7d7d931f9ec6a0d3f4c598	5564	docs/EMBODY_ADAPTER_INTEGRATION.md
 3633e37c3eae3289d552a363e565bee027ebd83661f1c44ea136f9498de9e5af	19059	docs/EXECUTION_PROTOCOL.md
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
-dc990975cd2feac83d7b6df822cd838270a16b7bea50753e1cebfdd3242b3fe0	11748	docs/FROZEN_CLIP_TEXT_ADAPTER.md
-4bad38f50a4d6ee1650316bfcfc074b1a8b0748d5daa52168a17b7d749d0ddab	3374	docs/FROZEN_MHA_RUNTIME.md
-39589bf5cba54b0626fedef87e41cb436da7c1cab4cb3e4975afea720d45b62e	11899	docs/HOST_RUNTIME_INTEGRATION.md
+a96ee04e959510fbcde26e704dc11f096b8fdf3bbe6789772be7b96ab3034190	12305	docs/FROZEN_CLIP_TEXT_ADAPTER.md
+f2c59540bfc4dcdbb5013b76eb20090a7dd61c805523121995ed9206c843f9c8	3711	docs/FROZEN_MHA_RUNTIME.md
+7612d39a01ca67d4a74bc5fb71bacd78e30d7512e9facb46cc331185ff5e65c4	11994	docs/HOST_RUNTIME_INTEGRATION.md
+8bd042f717f9a70e888ff79758aae991043404952243dc9dfd60282e4ecb29a1	1712	docs/LEGACY_TEST_STABILITY.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
@@ -51,7 +53,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 ecb85faa3b442450f6ecc8d6f2fff77baf19e833254fa8a715127d9127742f63	13825	docs/PHASESET_RESEARCH_CONTRACT.md
-74d36ae75a863a3f73b13918c9cbc7ca40fbd70fd19451188b564bba1fcee95e	5053	docs/PREPARED_DATA_V2.md
+d45d19f444c7d90a820695f5e8315b4485d660f965c13ebeab5d2b5a31d7e265	5453	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
 8d689f49b2704ff7c5988eff8a7e3e062f209b85d18425ccdba1e1099581079b	10658	docs/phasepair_research_contract_20260824_165840.md
@@ -120,6 +122,8 @@ dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phase
 2e5fb5280ed27a46c587d07a60ce663fce676cbdd30b059ec34882b4507d157f	604	src/phaseset_core/__init__.py
 9668a89535264a3ed6b58ffc7ac31b4904a4d8f68636d405e15cbf7368096716	8437	src/phaseset_core/_morlet_oracle_bytes.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
+d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
+85e5e80bc3b9d21b5fb8865923d27cb6254c077ac2319afe38dad8b79d785bd2	34366	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 9636324cff46e2a89bb93b71f8c3a4cc07544f8dd0e10e2da60d5a2ffbc7b356	25892	src/phaseset_core/controls.py
@@ -128,7 +132,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
-68a8484727b5e9be568aca0b19840e5b45f2c3db5a90817d276d56291739a4e4	48867	src/phaseset_core/frozen_clip_text.py
+9ccd454368f85eedb5eeebfdc2c87d0f4b5f76fc141426ea77cec0487444cc41	48867	src/phaseset_core/frozen_clip_text.py
 25871fe486090edd2ac49f82018dcced116e35c85d99178b251bc8862dfdbe70	78628	src/phaseset_core/host.py
 47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
@@ -143,7 +147,7 @@ c675143d6ba9bc98976041ff02c0fa6ae6ef7bfd122122d0063eeb85f62c8c88	43972	src/phase
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 0c8f52f8822f4e3822e5124ce794d482bb09d0545c73f5c6b341c1bbd7bc86f2	35557	src/phaseset_core/streaming_autograd.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
-b1bbc5061a02aac1226ab00fd6eb970bf786cfe32b4d40ff0999dbebaaec60b4	154645	src/phaseset_core/training.py
+9f6c21e354a17facda89a542d53e39013477c5e1b1a259781e2446cd394f6d06	157160	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
@@ -152,7 +156,7 @@ af9b5adc42d82b9a0aa4ed744bb069e36b7dd041c1957fdc97d17eb814a0fc0c	17284	tests/tes
 ad630675cc642350a988d92de6176053d1bfab5f81cbc49548cb0b5dbeb679d9	23596	tests/test_phasepair_core_batching_readiness.py
 3b4b3654d38d9b99983e924d838b7236b6d2e8b27554298762be1e8c4fcd00c4	10736	tests/test_phasepair_core_caption_processing.py
 0f247c766ce4ff3ea8be87429696f4b7e021d5ea9965fe352542e3ca1371f7ea	19115	tests/test_phasepair_core_checkpoint.py
-d1f96da586cdc53c5036fb9d105e57e4be0f9580ab55924351ff23b36823a58d	33016	tests/test_phasepair_core_clip_resolution.py
+c61ce0ab46726427abe866e61bbd1cf450cf28063072909b5d80718a23c4b645	33787	tests/test_phasepair_core_clip_resolution.py
 09e9f151cc5c23a50434bf5db8d27823f97e260e03263c056ac8e1e06306fbc3	28076	tests/test_phasepair_core_contracts.py
 046b55ea6c6daaff89f278850b0093deb27c9c4cfeabd0831bc589986cb8698f	20812	tests/test_phasepair_core_evaluation.py
 394950bc154f69faa859236fafab4f9591d6ab738695a0dea2bc34396fddfbf9	29559	tests/test_phasepair_core_execution_schema.py
@@ -175,6 +179,9 @@ b801d46fb184d99c65825d7f9bf91cbced022418600a95d421c5d9244dbded13	19373	tests/tes
 8f31d46a9ff2d4ca96fcd34dc6fd617933f2e604c0dd6e95e0d10ec9db487074	10255	tests/test_phasepair_core_validation_gallery.py
 4da1d6c1b05bc485b9afb78776264ae7757744f104c37b947cdce0280cdfb172	2347	tests/test_phasepair_posix_checkpoint_ownership.py
 fa34950fb26f63d5d1ba169499e6e4af49c9b691f33c5cb3aac6e961bdac9e55	18384	tests/test_phaseset_caption_fusion.py
+c88e5b91823b22ebb7a4019acb328040c6b42be7acc03738949439e4efbe443f	10126	tests/test_phaseset_capture_pooling.py
+a02d460a28bd6aeb372fa66e779cb4a65c86536ab951ccd3b6c30ed8091cbd15	8371	tests/test_phaseset_capture_training.py
+beacbd78c68771001b60ff7c6271e6d99c92d24d83f251e1074fc4ce50d1216c	15511	tests/test_phaseset_capture_validation.py
 5dbbb81d6725f1185f4c12dc85653b6eac1e47ae2ffb7fb36f5b951eb8dea632	8848	tests/test_phaseset_cli.py
 c3060c5567214d195e024152f2d427ff3a63f3cc5fef1ded01e73d6887a3431e	13653	tests/test_phaseset_cli_production_bridge.py
 a53fe5c6b53adb5c80421842f806fc45e8305360348d7065aa0ddfb0b9316dcb	6546	tests/test_phaseset_contracts.py
diff --git a/STATUS.md b/STATUS.md
index b7cb0dc..ef17fd5 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -17,10 +17,11 @@ evidence, not training results or scientific claims.
   read-only inventory is complete. Existing workloads were not interrupted.
 - A separate data-volume Python 3.12.12 / NumPy 2.4.6 / Torch 2.12.0+cu126
   environment is installed with a clean dependency check. The complete Linux
-  CPU regression including the private-host lifecycle integration passed:
-  964 tests, 2 skipped, and 39 subtests, in 286.06 s after adding prepared-data
-  v2 host dispatch. Its focused integration passed 94 tests. Source bytes were
-  unchanged before and after both suites. The prior residual-resume integration
+  CPU regression including capture-level training selection passed:
+  1012 tests, 2 skipped, and 39 subtests, in 286.46 s. Its focused integration
+  passed 84 tests and 6 subtests. Source bytes were unchanged before and after
+  both suites. The prior prepared-data v2 host integration passed 94 focused /
+  964 complete tests. The prior residual-resume integration
   passed 948 tests and 39 subtests.
   The earlier frozen-CLIP integration passed 932 tests and 39 subtests.
   The earlier host-only suite passed 917 tests and its 25 focused checks.
@@ -51,8 +52,10 @@ evidence, not training results or scientific claims.
   passed: frozen-base maximum difference 7.153e-7, score difference 4.471e-8,
   and finite nonzero exercised head gradients. This did not run the periodic
   core or an optimizer. A subsequent B0 repeat stopped before model creation
-  for insufficient free memory; new-policy B1/B2 and full-runtime qualification
-  remain pending. See [runtime policy](docs/FROZEN_MHA_RUNTIME.md).
+  for insufficient free memory. After memory recovered, a distinct retry chain
+  passed retrieval/B0/B1/B2 with the same reviewed source and tolerance; the
+  original resource failure remains preserved. Full-training and BF16
+  qualification remain pending. See [runtime policy](docs/FROZEN_MHA_RUNTIME.md).
 - The native Embody loader and concrete licensed SMPL-X body-22 evaluator are
   implemented. Seven adapter contract tests passed on Linux/Python 3.12.12/
   NumPy 2.4.6; these use explicit fixtures, not licensed assets or main data.
@@ -61,9 +64,23 @@ evidence, not training results or scientific claims.
   It verifies the exact consumed index/manifest/payload bytes, rejects split
   overlap, and applies reproducible shared-group yaw from original bytes each
   training epoch. Validation is unrotated. Window-positive families support the
-  auxiliary task; the main holistic capture validation route is not yet wired.
+  auxiliary task; the private-host holistic capture source is not yet wired.
   Actor-set commitments must not be used as unique capture/window row IDs.
   See [prepared-data contract](docs/PREPARED_DATA_V2.md).
+- The complete-window capture evaluator now pools unnormalized embeddings
+  before one full-gallery scoring call and preserves exact capture-macro R@1
+  fractions. The training API uses this concrete val-only source for checkpoint
+  selection and binds its full census on resume. The standalone caller passed
+  42 focused / 997 complete server tests; the training integration passed 54
+  focused tests including actual optimizer/checkpoint/resume equality, followed
+  by complete 1002- and 1012-test regressions and actual CLIP requalifications. Two
+  legacy fixture failures from the first full attempt are retained and explained
+  in [test stability](docs/LEGACY_TEST_STABILITY.md). These are analytic lifecycle
+  checks, not formal runs. Actual scored-census consistency and scoped live
+  capture-function drift checks were added after provisional composition review.
+  Private-host disk composition
+  and licensed source/caption provenance remain incomplete. See
+  [capture validation](docs/CAPTURE_VALIDATION.md).
 - Neither Embody approval nor a licensed neutral model has been observed.
   Open-license Multi-TPC acquisition completed with its official byte count and
   MD5 verified. Its archive inventory has 322 files. The bounded audit confirms
@@ -82,7 +99,9 @@ evidence, not training results or scientific claims.
   The MHA-policy successor separately passed 33 focused/949 complete server
   tests and three official CLIP forwards, preserving the original output
   golden and RNG under correctly changed code/runtime/receipt identities.
-- The exact latest text-adapter commit was downloaded from anonymous codeload,
+  The capture-training successor also passed three official CLIP forwards with
+  both prior output digests and RNG unchanged under its new source identity.
+- The earlier text-adapter commit was downloaded from anonymous codeload,
   manifest/content/link checked, built offline into a wheel, and installed into
   a separate server target. All 56 wheel Python modules match the public Git
   index. Three real CLIP forwards from that installed wheel produced a complete
diff --git a/docs/CAPTURE_VALIDATION.md b/docs/CAPTURE_VALIDATION.md
new file mode 100644
index 0000000..5e367c8
--- /dev/null
+++ b/docs/CAPTURE_VALIDATION.md
@@ -0,0 +1,112 @@
+# Holistic capture validation and training selection
+
+`capture_pooling.py` and `capture_validation.py` implement the main task's
+numeric model-to-gallery path. `PhaseSetTrainingRuntime.fit(train_source,
+capture_validation_source)` uses it for validation checkpoint selection. The
+private-host on-disk capture source and the upstream licensed-data preparation
+remain separate integration work; this is not a declaration of data readiness.
+
+## Complete capture inputs
+
+`CaptureWindowPlan` binds a separate capture identity, distinct accepted-window
+identities, and chronological source starts on the 300-frame/30-fps grid.
+Upstream preparation must freeze the complete accepted nonoverlapping window
+census before model outputs exist. Rejected windows and tails belong in its
+audit; a plan cannot be assembled from whichever outputs happened to finish.
+
+`CaptureValidationWindow` supplies one complete body-22 window as B=1,
+200-frame `PreparedGroupBatch`. All windows of a capture must preserve its
+valid actor set, group commitment, and K. Padding and input actor positions may
+differ. Several distinct captures may legitimately share the same actor set;
+actor-set group commitments are never used as unique capture or window row IDs.
+
+`CaptureValidationCapture` combines the complete plan/windows with an exact
+`FrozenClipTextBatch` and its participant-component label. Its text receipt
+binds the actual feature bytes, but cannot establish that unseen text came
+from an official human holistic annotation. That provenance and data rights
+must be supplied by the private source audit. Variable positive text counts are
+supported; no fixed three-caption assumption is introduced.
+
+`CaptureValidationSource` is val-only and requires at least two captures.
+It canonicalizes capture and window order and checks duplicate/missing/foreign
+identities. Test cannot be relabeled through a split argument. Source plans,
+commitments, embeddings, captions, and component membership remain private.
+
+## Encoding and pooling
+
+The evaluator encodes every admitted window at B=1 with a registered retrieval
+system interface and width 512. It uses the existing frozen numerical context,
+eval/no-grad, and the configured qualified precision. CPU/CUDA device aliases
+are resolved to actual tensor devices. Caller training modes and numerical
+flags are restored even when an encoder fails.
+
+Base embeddings are summed chronologically with the fixed adjacent float64
+binary tree and divided by the complete accepted-window count. Periodic tokens
+use the same tree but divide by each band's supported-window count. Results
+are cast once to float32; unsupported bands remain exact positive zero.
+
+Only after pooling does the existing retrieval scorer normalize features and
+compute the full capture/text gallery in FP32. The implementation does not
+average normalized window embeddings, window scores, or window recall values.
+
+An explicit byte budget covers supplied encoded arrays, and the configured
+edge budget admits every pair in every window. CUDA also checks a static
+model/gallery tensor estimate; this is not a total allocator/RSS guarantee.
+Resource failures reject the complete operation, never sample windows,
+captures, people, or edges. CUDA OOM remains an explicit resource failure.
+
+## Metrics and checkpoints
+
+The gallery contains one motion row per capture and every admitted holistic
+text positive. The existing evaluator produces integer R@1 hits. Exact
+`Fraction` arithmetic computes each capture's T2M and M2T contributions and
+their equally weighted capture mean. The primary metric is their half-sum;
+captions are not independent capture clusters. Variable-positive symmetric
+InfoNCE is computed on this same full gallery.
+
+Training admission snapshots the complete validation census, including the
+upstream manifest and window/text feature identities. The existing checkpoint
+`val_manifest_sha256` stores that combined census digest. A changed gallery
+is rejected on resume, and a capture source cannot become a training iterator.
+At each epoch the training loop uses this evaluator's capture primary metric
+for the normal best-checkpoint selection and records all encoded window edges.
+It first creates an owned snapshot and computes edge counts from that snapshot,
+then verifies the evaluator's returned scored census before consuming its metric.
+Formal runtime checks also bind the capture class/runner and the key imported
+pooling, evaluation, and objective functions. This is scoped live-function drift
+detection, not a security guarantee against arbitrary Python execution.
+
+The checkpoint schema retains its historical float metric field; it is not
+presented as an exact qualification artifact. Qualification must recompute
+exact fractions from the selected checkpoint and full capture gallery. The
+window-oriented validation source remains available for the separately
+declared auxiliary/legacy task and is not main-task evidence.
+
+## Observed scope and remaining work
+
+The standalone caller's focused server suite passed 42 tests, including actual
+512-wide B0 and PhaseSet encoders; its full regression passed 997 tests,
+2 skipped, and 39 subtests. The subsequent training integration passed 54
+focused tests, including actual tiny optimizer/checkpoint/new-runtime resume
+equality and rejection of a changed capture census. Full regression also
+exposed two legacy fixture-timing/lifetime failures
+in a complete run (1000 passed, two failed), preserved as a failed attempt.
+The [test-only stability repair](LEGACY_TEST_STABILITY.md) subsequently passed
+74 focused tests and 6 subtests, then 1002 complete tests, 2 skipped, and
+39 subtests in 297.31 seconds, with unchanged source bytes. Three actual
+official CLIP forwards requalified the changed training source while retaining
+the pre-existing output golden, long-caption observation, and caller RNG.
+An available same-author provisional composition review then identified the
+scored-census and live-function consistency gaps described above. Their narrow
+repair passed 84 focused tests and 6 subtests, then 1012 complete tests,
+2 skipped, and 39 subtests in 286.46 seconds. Three more actual CLIP forwards
+retained the original output values and caller RNG with correctly updated
+source identities. Earlier attempts are not overwritten. Initial review used
+the ARIS local-only fallback; the subsequent same-author review remains
+provisional, not independent scientific acceptance.
+
+These tests use labeled analytic fixtures. They do not substitute for native
+Embody captures, official holistic descriptions, a rights grant, complete host
+command composition, the nine-run base qualification, or sealed evaluation.
+See [host integration](HOST_RUNTIME_INTEGRATION.md) and
+[prepared window storage](PREPARED_DATA_V2.md).
diff --git a/docs/FROZEN_CLIP_TEXT_ADAPTER.md b/docs/FROZEN_CLIP_TEXT_ADAPTER.md
index 22e7a31..de8f350 100644
--- a/docs/FROZEN_CLIP_TEXT_ADAPTER.md
+++ b/docs/FROZEN_CLIP_TEXT_ADAPTER.md
@@ -223,3 +223,12 @@ official CLIP forwards preserved both output values above and caller RNG.
 The new receipt contains the new source/runtime identity and false MHA flag;
 it is intentionally not byte-identical to the historical receipt. These checks
 do not qualify CUDA text, full training, or real-data experiments.
+
+The later capture-training integration changed the exact training source to
+157160 bytes, SHA-256
+`9f6c21e354a17facda89a542d53e39013477c5e1b1a259781e2446cd394f6d06`.
+After its 1012-test complete server regression passed, another three official
+CLIP forwards passed. Both output digests above, repeated embedding equality,
+and caller RNG were unchanged. The adapter receipt correctly records the new
+training and adapter source identities; no golden was regenerated. This is a
+source-bound CPU feature-path requalification, not a real-data training result.
diff --git a/docs/FROZEN_MHA_RUNTIME.md b/docs/FROZEN_MHA_RUNTIME.md
index a131611..7439bc3 100644
--- a/docs/FROZEN_MHA_RUNTIME.md
+++ b/docs/FROZEN_MHA_RUNTIME.md
@@ -42,9 +42,12 @@ The last observation exercises the closed retrieval-head seam with analytic
 features, not the periodic core or an optimizer. It does not qualify full
 training, BF16, performance, data access, or a winning base checkpoint. A
 subsequent B0 requalification stopped before model construction because no GPU
-met the fixed 8 GiB free-memory requirement. B1/B2 were not run under the new
-policy after that stop. Their older observations remain historical, not new
-policy qualifications. Existing workloads were not interrupted.
+met the fixed 8 GiB free-memory requirement. Once memory became available, a
+separate resource-retry chain repeated retrieval and passed B0, B1, and B2 with
+the same reviewed source and unchanged numerical checks. That chain used new
+attempt directories; the failed resource receipt was not overwritten. These
+are bounded FP32 forward/backward observations, not optimizer, BF16, throughput,
+or full-training qualification. Existing workloads were not interrupted.
 
 ## CLIP and empirical boundaries
 
@@ -53,7 +56,8 @@ The frozen text adapter pins the entire installed training module, records
 configure the validated CPU runtime before loading it; the adapter does not
 silently alter global flags. See [the text adapter](FROZEN_CLIP_TEXT_ADAPTER.md).
 
-Main holistic validation must still aggregate every admitted window into one
-capture embedding and use true capture identities. Window scores and repeating
-actor-set lineage IDs are not a substitute. This runtime repair is not a
-completed empirical qualification or a scientific result.
+The [capture evaluator](CAPTURE_VALIDATION.md) now aggregates every admitted
+window into one capture embedding and uses true capture identities in the
+training API. Its private-host disk composition remains unfinished. Window
+scores and repeating actor-set lineage IDs are not a substitute. This runtime
+repair is not a completed empirical qualification or a scientific result.
diff --git a/docs/HOST_RUNTIME_INTEGRATION.md b/docs/HOST_RUNTIME_INTEGRATION.md
index 5a7b14f..e25f23a 100644
--- a/docs/HOST_RUNTIME_INTEGRATION.md
+++ b/docs/HOST_RUNTIME_INTEGRATION.md
@@ -156,12 +156,13 @@ rights assertion when Embody approval is unknown.
 - The Embody-format conversion is outside this harness.  It must produce the
   exact prepared index/split/NPZ seam above and bind it to the authenticated
   prepared-data receipt.
-- The current training validation path is still window-oriented. V2 positive
-  families identify windows; actor-set group commitments are lineage and can
-  repeat across windows or captures. Neither is a substitute for the main
-  holistic capture gallery. Complete capture-window census, fixed embedding
-  aggregation, and capture-level scoring must be connected before formal base
-  selection on that task.
+- The training API now accepts the concrete val-only capture source described
+  in [capture validation](CAPTURE_VALIDATION.md), with complete-window pooling
+  and checkpoint-census binding. This host's v1/v2 on-disk prepared loaders still
+  return window sources. Their positive families and repeating actor-set
+  commitments cannot substitute for the main holistic capture gallery. The
+  private-host capture-source disk composition remains to be connected before
+  formal base selection on that task.
 - `run-residual` is wired through the strict qualified-base loader, canonical
   periodic-cache record, energy floors, canonical all-system capacity audit,
   closed residual constructor, and the same attempt ledger. Residual resume
diff --git a/docs/LEGACY_TEST_STABILITY.md b/docs/LEGACY_TEST_STABILITY.md
new file mode 100644
index 0000000..2faff32
--- /dev/null
+++ b/docs/LEGACY_TEST_STABILITY.md
@@ -0,0 +1,28 @@
+# Legacy CLIP test determinism
+
+A complete server run during capture-training integration reported 1000 passed
+tests and two failures in the existing legacy CLIP-resolution tests. That
+failed attempt is retained. The production scanner, lease implementation,
+canonical model pins, and old immutable release are unchanged.
+
+The late equal-length file-mutation test specifically exercises the closing
+metadata sweep after the changed file has already been hashed in pass two.
+It previously assumed that a very fast same-length write necessarily changes
+the filesystem's observed timestamps. The fixture now explicitly advances its
+owned temporary file's mtime and verifies that change. The real byte mutation,
+16-open expectation, and required `HOLD_FILE_TOCTOU` result are unchanged. This
+does not turn the production scanner's documented best-effort observations
+into an atomic snapshot guarantee.
+
+The weak-lease registry test now collects unreachable earlier test cycles
+before measuring its baseline, just as it already collects them after removing
+the 256 test assessments. Otherwise automatic collection during those 256
+allocations can remove a pre-existing unreachable lease and invalidate the
+relative-count assertion. All retention and release checks remain in place.
+
+The corrected focused server integration passed 74 tests and 6 subtests.
+The subsequent complete suite passed 1002 tests, 2 skipped, and 39 subtests
+in 297.31 seconds; source bytes were unchanged across execution.
+These are test-fixture changes, not relaxed model tolerances or weaker data
+admission checks. Review was local-only under ARIS's unavailable-delegation
+fallback; actual server outcomes remain the deterministic evidence.
diff --git a/docs/PREPARED_DATA_V2.md b/docs/PREPARED_DATA_V2.md
index ab1692b..4a4002d 100644
--- a/docs/PREPARED_DATA_V2.md
+++ b/docs/PREPARED_DATA_V2.md
@@ -41,7 +41,9 @@ Every uncompressed NPZ has exactly ten arrays:
 | `text_positive_ids` | uint8 `[Q,32]` |
 | `text_commitments` | uint8 `[Q,32]` |
 
-Padding is exact positive zero. Storage uses fixed-order ZIP_STORED members
+Padding is exact positive zero. Output creation is write-once with read-only
+file permissions; this is not operating-system immutability. Authenticated
+digests bind the bytes actually consumed. Storage uses fixed-order ZIP_STORED members
 and NPY v1 headers. Loading checks member identity, shape/type, declared byte
 length, CRC, and decoded-size bounds before array allocation; pickle is not
 accepted. The writer rejects train/val overlap in window, ordinal, caption,
@@ -65,8 +67,9 @@ bitwise equal to rotating earlier, before preprocessing's final float32 cast.
 
 The private build census binds index/batch/window/source/text digests and the
 independent ordinals. It contains no raw captions, source paths, or participant
-identifiers and is not a data-rights certificate. Keep the entire prepared tree,
-its commitments, and its census private.
+raw identifiers and is not a data-rights certificate. Stable commitments may
+still be linkable; they are not an anonymity guarantee. Keep the entire
+prepared tree, its commitments, and its census private.
 
 The private-host factory recognizes the v2 index and passes the actual
 receipt-bound index digest into this loader. A replaced index or manifest, or
@@ -78,7 +81,9 @@ support the auxiliary window-caption task, not the main holistic capture task.
 Actor-set group commitments are lineage, not unique capture/window row IDs.
 Main validation must separately encode every admitted window in a capture,
 aggregate embeddings with the fixed capture mean, and score one capture gallery
-against official holistic text. That training/host route is not yet connected.
+against official holistic text. The [capture validation API](CAPTURE_VALIDATION.md)
+now implements that operation and training checkpoint selection, but the
+private-host capture-source disk route is not yet connected to this storage.
 
 This module alone is not the complete `prepare-data` command. Native capture
 loading, licensed body-model conversion, preprocessing rejection/tail census,
diff --git a/src/phaseset_core/capture_pooling.py b/src/phaseset_core/capture_pooling.py
new file mode 100644
index 0000000..f732bd8
--- /dev/null
+++ b/src/phaseset_core/capture_pooling.py
@@ -0,0 +1,214 @@
+"""Deterministic capture-level pooling of already encoded motion windows.
+
+Window identity is independent of actor-set identity. This numeric seam does
+not encode motion, score a gallery, establish participant identity, select
+windows, or authorize access to sealed evaluation data.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from dataclasses import dataclass
+
+import numpy as np
+
+EMBEDDING_DIM = 512
+BAND_COUNT = 6
+SOURCE_WINDOW_FRAMES = 300
+DEFAULT_MAX_ENCODED_BYTES = 256 * 1024 * 1024
+
+
+class CapturePoolingError(ValueError):
+    """The supplied encoded windows differ from the complete capture plan."""
+
+
+class CapturePoolingResourceLimit(CapturePoolingError):
+    """An explicit memory bound was exceeded; no windows were sampled."""
+
+
+def _commitment(value: object) -> bytes:
+    if type(value) is not bytes or len(value) != 32:
+        raise CapturePoolingError("identity must be exact bytes[32]")
+    return value
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class CaptureWindowPlan:
+    """The complete accepted-window census, frozen before any model score.
+
+    Rejected windows and trailing source frames belong to the upstream
+    preprocessing census. This plan may not be reconstructed by filtering
+    available model outputs. A capture with no accepted window is ineligible.
+    """
+
+    capture_commitment: bytes
+    window_commitments: tuple[bytes, ...]
+    source_start_frames: tuple[int, ...]
+
+    def __post_init__(self) -> None:
+        _commitment(self.capture_commitment)
+        if type(self.window_commitments) is not tuple or not self.window_commitments:
+            raise CapturePoolingError("a capture must have its complete window census")
+        for value in self.window_commitments:
+            _commitment(value)
+        if len(set(self.window_commitments)) != len(self.window_commitments):
+            raise CapturePoolingError("window identities must be unique")
+        if (
+            type(self.source_start_frames) is not tuple
+            or len(self.source_start_frames) != len(self.window_commitments)
+            or any(
+                type(value) is not int
+                or not 0 <= value < 2**64
+                or value % SOURCE_WINDOW_FRAMES
+                for value in self.source_start_frames
+            )
+            or tuple(sorted(set(self.source_start_frames))) != self.source_start_frames
+        ):
+            raise CapturePoolingError("window starts must be ordered nonoverlapping 10s grid positions")
+
+    @property
+    def sha256(self) -> str:
+        raw = json.dumps(
+            {
+                "capture_commitment": self.capture_commitment.hex(),
+                "schema": "phaseset-capture-window-plan-v1",
+                "source_start_frames": self.source_start_frames,
+                "window_commitments": tuple(value.hex() for value in self.window_commitments),
+            },
+            sort_keys=True,
+            separators=(",", ":"),
+        ).encode("ascii")
+        return hashlib.sha256(raw).hexdigest()
+
+    def __repr__(self) -> str:
+        return f"CaptureWindowPlan(windows={len(self.window_commitments)}, <identities hidden>)"
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class CapturePooledEncoding:
+    """One unnormalized capture encoding; scoring normalizes after pooling."""
+
+    capture_commitment: bytes
+    plan_sha256: str
+    window_count: int
+    base_embedding: np.ndarray
+    tokens: np.ndarray | None
+    band_mask: np.ndarray | None
+    valid_window_count: np.ndarray | None
+
+    def __repr__(self) -> str:
+        return (
+            f"CapturePooledEncoding(windows={self.window_count}, "
+            f"periodic={self.tokens is not None}, <identity/features hidden>)"
+        )
+
+
+def _require_array(value: object, dtype: np.dtype, shape: tuple[int, ...], label: str) -> np.ndarray:
+    if (
+        type(value) is not np.ndarray
+        or value.dtype != dtype
+        or value.shape != shape
+        or not value.flags.c_contiguous
+    ):
+        raise CapturePoolingError(f"{label} dtype, shape, or contiguous layout is invalid")
+    return value
+
+
+def _tree_sum(values: np.ndarray) -> np.ndarray:
+    """Fixed adjacent binary tree in float64, carrying an odd final node."""
+    level = np.array(values, dtype=np.float64, order="C", copy=True)
+    while len(level) > 1:
+        pairs = len(level) // 2
+        next_level = np.empty(((len(level) + 1) // 2, *level.shape[1:]), dtype=np.float64)
+        np.add(level[: 2 * pairs : 2], level[1 : 2 * pairs : 2], out=next_level[:pairs])
+        if len(level) % 2:
+            next_level[-1] = level[-1]
+        level = next_level
+    return level[0]
+
+
+def _readonly(value: np.ndarray) -> np.ndarray:
+    value.setflags(write=False)
+    return value
+
+
+def pool_capture_windows(
+    plan: CaptureWindowPlan,
+    *,
+    window_commitments: tuple[bytes, ...],
+    base_embeddings: np.ndarray,
+    tokens: np.ndarray | None = None,
+    band_mask: np.ndarray | None = None,
+    max_encoded_bytes: int = DEFAULT_MAX_ENCODED_BYTES,
+) -> CapturePooledEncoding:
+    """Pool exactly every planned window, independent of input row ordering.
+
+    Inputs are already encoded CPU float32 features. Canonical source-time
+    order fixes the float64 reduction tree. Base embeddings average over all
+    accepted windows. Each periodic band averages over its valid windows;
+    an unsupported capture band has exact positive-zero output. No per-window
+    normalization or score averaging is performed.
+
+    The resource bound counts supplied encoded-array bytes, not total process
+    RSS. Temporary float64 reduction storage is O(W*D), never actor-pair dense.
+    """
+
+    if type(plan) is not CaptureWindowPlan:
+        raise CapturePoolingError("plan must be exact CaptureWindowPlan")
+    if type(max_encoded_bytes) is not int or max_encoded_bytes < 1:
+        raise CapturePoolingError("max_encoded_bytes must be a positive integer")
+    if type(window_commitments) is not tuple:
+        raise CapturePoolingError("window identities must be a tuple")
+    for value in window_commitments:
+        _commitment(value)
+    count = len(plan.window_commitments)
+    if (
+        len(window_commitments) != count
+        or len(set(window_commitments)) != count
+        or set(window_commitments) != set(plan.window_commitments)
+    ):
+        raise CapturePoolingError("encoded windows must exactly match the complete capture census")
+    base = _require_array(base_embeddings, np.dtype(np.float32), (count, EMBEDDING_DIM), "base")
+    if (tokens is None) != (band_mask is None):
+        raise CapturePoolingError("periodic tokens and mask must be supplied together")
+    periodic = None
+    mask = None
+    encoded_bytes = base.nbytes
+    if tokens is not None:
+        periodic = _require_array(tokens, np.dtype(np.float32), (count, BAND_COUNT, EMBEDDING_DIM), "tokens")
+        mask = _require_array(band_mask, np.dtype(np.bool_), (count, BAND_COUNT), "band_mask")
+        encoded_bytes += periodic.nbytes + mask.nbytes
+    if encoded_bytes > max_encoded_bytes:
+        raise CapturePoolingResourceLimit("RESOURCE_LIMIT: encoded capture exceeds the byte budget")
+    if not np.isfinite(base).all() or (periodic is not None and not np.isfinite(periodic).all()):
+        raise CapturePoolingError("encoded features must be finite")
+    if periodic is not None:
+        invalid = periodic[~mask]
+        if np.any(invalid != 0.0) or np.any(np.signbit(invalid)):
+            raise CapturePoolingError("unsupported window bands must have exact positive-zero tokens")
+    positions = {value: index for index, value in enumerate(window_commitments)}
+    order = np.array([positions[value] for value in plan.window_commitments], dtype=np.int64)
+    base_mean = np.ascontiguousarray((_tree_sum(base[order]) / count).astype(np.float32))
+    pooled_tokens = None
+    pooled_mask = None
+    valid_counts = None
+    if periodic is not None:
+        canonical_mask = mask[order]
+        valid_counts = canonical_mask.sum(axis=0, dtype=np.int64)
+        pooled_mask = np.ascontiguousarray(valid_counts > 0)
+        denominator = np.maximum(valid_counts, 1)[:, None]
+        pooled_tokens = np.ascontiguousarray((_tree_sum(periodic[order]) / denominator).astype(np.float32))
+        pooled_tokens[~pooled_mask] = np.float32(0.0)
+        _readonly(pooled_tokens)
+        _readonly(pooled_mask)
+        _readonly(valid_counts)
+    return CapturePooledEncoding(
+        capture_commitment=plan.capture_commitment,
+        plan_sha256=plan.sha256,
+        window_count=count,
+        base_embedding=_readonly(base_mean),
+        tokens=pooled_tokens,
+        band_mask=pooled_mask,
+        valid_window_count=valid_counts,
+    )
diff --git a/src/phaseset_core/capture_validation.py b/src/phaseset_core/capture_validation.py
new file mode 100644
index 0000000..e4e0124
--- /dev/null
+++ b/src/phaseset_core/capture_validation.py
@@ -0,0 +1,825 @@
+"""Capture-level validation seam for already prepared PhaseSet windows.
+
+This module composes the registered motion-system interfaces, frozen CLIP text
+batches, and the separately audited fixed-tree capture pooling kernel.  It does
+not select windows or captions, load checkpoints, qualify weights, or expose a
+test split.  Capture and window commitments are source identities; actor-group
+commitments remain participant-lineage metadata and are never substituted for
+window identity.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from fractions import Fraction
+import hashlib
+import json
+from typing import Final, Literal
+
+import numpy as np
+import torch
+from torch import Tensor
+
+from .capture_pooling import (
+    BAND_COUNT,
+    EMBEDDING_DIM,
+    CapturePoolingError,
+    CapturePoolingResourceLimit,
+    CaptureWindowPlan,
+    pool_capture_windows,
+)
+from .contracts import PreparedGroupBatch, validate_prepared_group_batch
+from .evaluation import (
+    RetrievalDataset,
+    capture_r1_contributions,
+    validate_retrieval_dataset,
+)
+from .frozen_clip_text import FrozenClipTextBatch, FrozenClipTextReceipt
+from .objectives import variable_positive_symmetric_infonce
+from .training import (
+    BaseRetrievalSystem,
+    ResidualRetrievalSystem,
+    TrainingConfig,
+    TrainingRuntimeError,
+    _assert_frozen_numerical_runtime,
+    _autocast_context,
+    _frozen_numerical_runtime,
+    resolve_precision,
+)
+
+
+STATUS: Final = "CAPTURE_VALIDATION_SEAM_AUTHORITY0"
+SCHEMA: Final = "phaseset-capture-validation-source-v1"
+DEFAULT_MAX_ENCODED_BYTES: Final = 256 * 1024 * 1024
+DEFAULT_MAX_CUDA_STATIC_TENSOR_BYTES: Final = 2 * 1024 * 1024 * 1024
+
+
+class CaptureValidationError(ValueError):
+    """The supplied validation census or runtime result is inconsistent."""
+
+
+class CaptureValidationResourceLimit(CaptureValidationError):
+    """A declared resource budget was exceeded without sampling the census."""
+
+
+def _sha256(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _raw32(value: object, label: str) -> bytes:
+    if type(value) is not bytes or len(value) != 32:
+        raise CaptureValidationError(f"{label} must be exact built-in bytes[32]")
+    return value
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise CaptureValidationError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _array_sha256(value: np.ndarray) -> str:
+    return _sha256(value.tobytes(order="C"))
+
+
+def _tensor_bytes(value: Tensor) -> bytes:
+    return (
+        value.detach()
+        .to(device="cpu")
+        .contiguous()
+        .view(torch.uint8)
+        .numpy()
+        .tobytes(order="C")
+    )
+
+
+def _positive_int(value: object, label: str) -> int:
+    if type(value) is not int or value < 1:
+        raise CaptureValidationError(f"{label} must be an exact positive int")
+    return value
+
+
+def _validate_text_batch(
+    value: object,
+) -> tuple[Tensor, tuple[bytes, ...], str]:
+    """Rebind a sealed CLIP batch to its receipt and return a CPU snapshot."""
+
+    if type(value) is not FrozenClipTextBatch:
+        raise CaptureValidationError("holistic_text must be exact FrozenClipTextBatch")
+    embeddings = value.embeddings
+    lineage = value.caption_commitments
+    receipt = value.receipt
+    if type(embeddings) is not Tensor:
+        raise CaptureValidationError("frozen text embeddings must be exact torch.Tensor")
+    if (
+        embeddings.dtype != torch.float32
+        or embeddings.device.type != "cpu"
+        or embeddings.ndim != 2
+        or tuple(embeddings.shape)[1:] != (EMBEDDING_DIM,)
+        or int(embeddings.shape[0]) < 1
+        or embeddings.requires_grad
+        or not embeddings.is_contiguous()
+        or not bool(torch.isfinite(embeddings).all().item())
+    ):
+        raise CaptureValidationError(
+            "frozen text must be finite contiguous non-grad CPU float32 [Q,512]"
+        )
+    count = int(embeddings.shape[0])
+    if type(lineage) is not tuple or len(lineage) != count:
+        raise CaptureValidationError("frozen text lineage does not match its rows")
+    commitments = tuple(
+        _raw32(item, f"caption_commitments[{index}]")
+        for index, item in enumerate(lineage)
+    )
+    if len(set(commitments)) != count:
+        raise CaptureValidationError("caption commitments must be unique within a capture")
+    if type(receipt) is not FrozenClipTextReceipt:
+        raise CaptureValidationError("frozen text receipt type is invalid")
+    if receipt.schema != "phaseset-frozen-clip-text-receipt-v1":
+        raise CaptureValidationError("frozen text receipt schema is invalid")
+    if receipt.status != "ENCODED_AUTHORITY0":
+        raise CaptureValidationError("frozen text receipt status is invalid")
+    raw = _tensor_bytes(embeddings)
+    if (
+        receipt.output_shape != tuple(embeddings.shape)
+        or receipt.output_stride != tuple(embeddings.stride())
+        or receipt.output_bytes != len(raw)
+        or receipt.output_sha256 != _sha256(raw)
+    ):
+        raise CaptureValidationError("frozen text output differs from its receipt")
+    if type(receipt.batch_size) is not int or receipt.batch_size < 1:
+        raise CaptureValidationError("frozen text receipt batch size is invalid")
+    if type(receipt.caption_rows) is not tuple or len(receipt.caption_rows) != count:
+        raise CaptureValidationError("frozen text receipt caption census is invalid")
+    for index, (commitment, row) in enumerate(
+        zip(commitments, receipt.caption_rows, strict=True)
+    ):
+        if type(row) is not tuple or len(row) != 9:
+            raise CaptureValidationError("frozen text receipt caption row is invalid")
+        if row[0] != index or row[1] != commitment.hex():
+            raise CaptureValidationError("frozen text receipt lineage is reordered")
+        row_raw = _tensor_bytes(embeddings[index : index + 1].contiguous())
+        if row[8] != _sha256(row_raw):
+            raise CaptureValidationError("frozen text receipt row digest mismatch")
+    if type(receipt.chunk_ranges) is not tuple or not receipt.chunk_ranges:
+        raise CaptureValidationError("frozen text receipt chunk census is invalid")
+    position = 0
+    for chunk in receipt.chunk_ranges:
+        if (
+            type(chunk) is not tuple
+            or len(chunk) != 2
+            or type(chunk[0]) is not int
+            or type(chunk[1]) is not int
+            or chunk[0] != position
+            or not chunk[0] < chunk[1] <= count
+            or chunk[1] - chunk[0] > receipt.batch_size
+        ):
+            raise CaptureValidationError("frozen text receipt chunks do not partition rows")
+        position = chunk[1]
+    if position != count:
+        raise CaptureValidationError("frozen text receipt chunks do not cover all rows")
+    try:
+        receipt_sha256 = _lower_sha256(receipt.sha256, "frozen text receipt SHA-256")
+    except (TypeError, ValueError, OverflowError) as error:
+        raise CaptureValidationError("frozen text receipt is not canonicalizable") from error
+    return embeddings.detach().clone().contiguous(), commitments, receipt_sha256
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class CaptureValidationWindow:
+    """One actual accepted-window identity and its B=1 prepared motion row."""
+
+    window_commitment: bytes
+    groups: PreparedGroupBatch = field(repr=False)
+
+    def __post_init__(self) -> None:
+        window = _raw32(self.window_commitment, "window_commitment")
+        try:
+            groups = validate_prepared_group_batch(self.groups)
+        except (TypeError, ValueError) as error:
+            raise CaptureValidationError("window prepared group is invalid") from error
+        if groups.batch_size != 1:
+            raise CaptureValidationError("capture validation encodes exactly B=1 per window")
+        if groups.skeletons.shape[2] != 200 or not bool(groups.frame_mask.all()):
+            raise CaptureValidationError("capture validation requires complete 200-frame windows")
+        object.__setattr__(self, "window_commitment", window)
+        object.__setattr__(self, "groups", groups)
+
+    def __repr__(self) -> str:
+        return "CaptureValidationWindow(B=1, <identity/features hidden>)"
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class CaptureValidationCapture:
+    """One complete capture plan, all accepted windows, and holistic text rows.
+
+    ``plan.capture_commitment`` is the source/capture identity.  The CLIP type
+    and receipt prove an exact frozen embedding batch, not that its unseen text
+    is the official holistic annotation; the caller must bind that fact in the
+    supplied source manifest.
+    """
+
+    plan: CaptureWindowPlan
+    windows: tuple[CaptureValidationWindow, ...] = field(repr=False)
+    holistic_text: FrozenClipTextBatch = field(repr=False)
+    component_label: str
+
+    def __post_init__(self) -> None:
+        if type(self.plan) is not CaptureWindowPlan:
+            raise CaptureValidationError("plan must be exact CaptureWindowPlan")
+        try:
+            plan = CaptureWindowPlan(
+                self.plan.capture_commitment,
+                self.plan.window_commitments,
+                self.plan.source_start_frames,
+            )
+        except (TypeError, ValueError) as error:
+            raise CaptureValidationError("capture window plan is invalid") from error
+        if type(self.windows) is not tuple or not self.windows:
+            raise CaptureValidationError("capture windows must be a nonempty tuple")
+        rebuilt: list[CaptureValidationWindow] = []
+        for value in self.windows:
+            if type(value) is not CaptureValidationWindow:
+                raise CaptureValidationError("capture window type is invalid")
+            rebuilt.append(CaptureValidationWindow(value.window_commitment, value.groups))
+        by_identity = {item.window_commitment: item for item in rebuilt}
+        if (
+            len(by_identity) != len(rebuilt)
+            or len(rebuilt) != len(plan.window_commitments)
+            or set(by_identity) != set(plan.window_commitments)
+        ):
+            raise CaptureValidationError(
+                "capture windows must exactly equal the complete accepted-window plan"
+            )
+        canonical = tuple(by_identity[value] for value in plan.window_commitments)
+        reference = canonical[0].groups
+        reference_actors = frozenset(
+            item for item in reference.actor_commitments[0] if item is not None
+        )
+        reference_group = reference.group_commitments[0]
+        reference_k = reference.actor_counts[0]
+        for window in canonical[1:]:
+            groups = window.groups
+            actors = frozenset(
+                item for item in groups.actor_commitments[0] if item is not None
+            )
+            if (
+                actors != reference_actors
+                or groups.group_commitments[0] != reference_group
+                or groups.actor_counts[0] != reference_k
+            ):
+                raise CaptureValidationError(
+                    "all windows in a capture must preserve valid actor set, group, and K"
+                )
+        _validate_text_batch(self.holistic_text)
+        if type(self.component_label) is not str or not self.component_label:
+            raise CaptureValidationError("component_label must be a nonempty exact string")
+        object.__setattr__(self, "plan", plan)
+        object.__setattr__(self, "windows", canonical)
+
+    @property
+    def capture_commitment(self) -> bytes:
+        return self.plan.capture_commitment
+
+    @property
+    def group_commitment(self) -> bytes:
+        return self.windows[0].groups.group_commitments[0]
+
+    @property
+    def actor_commitment_set(self) -> frozenset[bytes]:
+        return frozenset(
+            item
+            for item in self.windows[0].groups.actor_commitments[0]
+            if item is not None
+        )
+
+    @property
+    def group_size(self) -> int:
+        return self.windows[0].groups.actor_counts[0]
+
+    def __repr__(self) -> str:
+        return (
+            f"CaptureValidationCapture(windows={len(self.windows)}, "
+            f"captions={len(self.holistic_text.caption_commitments)}, "
+            "<identities/features hidden>)"
+        )
+
+
+def _window_census_row(
+    window: CaptureValidationWindow,
+    source_start_frame: int,
+) -> dict[str, object]:
+    groups = window.groups
+    return {
+        "actor_commitments": sorted(
+            item.hex() for item in groups.actor_commitments[0] if item is not None
+        ),
+        "actor_mask_sha256": _array_sha256(groups.actor_mask),
+        "frame_mask_sha256": _array_sha256(groups.frame_mask),
+        "group_commitment": groups.group_commitments[0].hex(),
+        "group_size": groups.actor_counts[0],
+        "skeletons_float32_sha256": _array_sha256(groups.skeletons),
+        "source_start_frame": source_start_frame,
+        "track_mask_sha256": _array_sha256(groups.track_mask),
+        "window_commitment": window.window_commitment.hex(),
+    }
+
+
+def _capture_census_row(capture: CaptureValidationCapture) -> dict[str, object]:
+    _, captions, receipt_sha256 = _validate_text_batch(capture.holistic_text)
+    return {
+        "caption_commitments": [value.hex() for value in sorted(captions)],
+        "capture_commitment": capture.capture_commitment.hex(),
+        "component_label": capture.component_label,
+        "frozen_text_receipt_sha256": receipt_sha256,
+        "group_commitment": capture.group_commitment.hex(),
+        "group_size": capture.group_size,
+        "plan_sha256": capture.plan.sha256,
+        "windows": [
+            _window_census_row(window, start)
+            for window, start in zip(
+                capture.windows,
+                capture.plan.source_start_frames,
+                strict=True,
+            )
+        ],
+    }
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class CaptureValidationSource:
+    """Validated val-only source with a complete canonical capture census."""
+
+    split: Literal["val"]
+    manifest_sha256: str
+    captures: tuple[CaptureValidationCapture, ...] = field(repr=False)
+
+    def __post_init__(self) -> None:
+        if type(self.split) is not str or self.split != "val":
+            raise CaptureValidationError("capture validation source must be exactly split='val'")
+        manifest = _lower_sha256(self.manifest_sha256, "manifest_sha256")
+        if type(self.captures) is not tuple or len(self.captures) < 2:
+            raise CaptureValidationError("capture validation needs at least two captures")
+        rebuilt: list[CaptureValidationCapture] = []
+        for value in self.captures:
+            if type(value) is not CaptureValidationCapture:
+                raise CaptureValidationError("capture source row type is invalid")
+            rebuilt.append(
+                CaptureValidationCapture(
+                    value.plan,
+                    value.windows,
+                    value.holistic_text,
+                    value.component_label,
+                )
+            )
+        ordered = tuple(sorted(rebuilt, key=lambda value: value.capture_commitment))
+        capture_ids = [value.capture_commitment for value in ordered]
+        if len(set(capture_ids)) != len(capture_ids):
+            raise CaptureValidationError("capture identities must be globally unique")
+        seen_windows: set[bytes] = set()
+        seen_captions: set[bytes] = set()
+        for capture in ordered:
+            windows = set(capture.plan.window_commitments)
+            _, captions, _ = _validate_text_batch(capture.holistic_text)
+            caption_set = set(captions)
+            if seen_windows.intersection(windows):
+                raise CaptureValidationError("window identities must be globally unique")
+            if seen_captions.intersection(caption_set):
+                raise CaptureValidationError("caption identities must be globally unique")
+            seen_windows.update(windows)
+            seen_captions.update(caption_set)
+        object.__setattr__(self, "manifest_sha256", manifest)
+        object.__setattr__(self, "captures", ordered)
+
+    @property
+    def census_sha256(self) -> str:
+        value = {
+            "authority": 0,
+            "captures": [_capture_census_row(capture) for capture in self.captures],
+            "manifest_sha256": self.manifest_sha256,
+            "schema": SCHEMA,
+            "split": self.split,
+        }
+        return _sha256(_canonical_json_bytes(value))
+
+    def __repr__(self) -> str:
+        return (
+            f"CaptureValidationSource(split='val', captures={len(self.captures)}, "
+            f"census_sha256={self.census_sha256!r}, <identities/features hidden>)"
+        )
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class CaptureValidationResult:
+    """Actual capture-gallery scores and exact endpoint accounting."""
+
+    dataset: RetrievalDataset = field(repr=False)
+    text_to_motion_capture_r1: Fraction
+    motion_to_text_capture_r1: Fraction
+    primary_capture_r1: Fraction
+    loss: float
+    source_census_sha256: str
+    scores_float64_sha256: str
+    system_id: str
+    stage: Literal["base", "residual"]
+    device: str
+    precision_mode: Literal["FP32", "BF16"]
+    encoded_bytes: int
+    cuda_static_tensor_bytes: int | None
+    status: str = STATUS
+
+    def __repr__(self) -> str:
+        return (
+            f"CaptureValidationResult(stage={self.stage!r}, system_id={self.system_id!r}, "
+            f"shape={self.dataset.scores.shape}, primary={self.primary_capture_r1}, "
+            f"source_census_sha256={self.source_census_sha256!r})"
+        )
+
+
+def _snapshot_source(value: object) -> CaptureValidationSource:
+    if type(value) is not CaptureValidationSource:
+        raise CaptureValidationError("source must be exact CaptureValidationSource")
+    return CaptureValidationSource(value.split, value.manifest_sha256, value.captures)
+
+
+def _system_tensor_bytes(system: torch.nn.Module) -> int:
+    return sum(
+        int(value.numel()) * int(value.element_size())
+        for value in (*tuple(system.parameters()), *tuple(system.buffers()))
+    )
+
+
+def _require_system_device(system: torch.nn.Module, device: torch.device) -> None:
+    tensors = (*tuple(system.parameters()), *tuple(system.buffers()))
+    if not tensors or any(value.device != device for value in tensors):
+        raise CaptureValidationError(
+            "system parameters and buffers must already reside on the configured device"
+        )
+
+
+def _concrete_device(specification: str) -> torch.device:
+    device = torch.device(specification)
+    if device.type == "cpu":
+        return torch.device("cpu")
+    if device.type == "cuda" and device.index is None:
+        return torch.device("cuda", torch.cuda.current_device())
+    return device
+
+
+def _expected_encoded_bytes(
+    source: CaptureValidationSource,
+    *,
+    residual: bool,
+) -> int:
+    windows = sum(len(capture.windows) for capture in source.captures)
+    per_window = EMBEDDING_DIM * np.dtype(np.float32).itemsize
+    if residual:
+        per_window += BAND_COUNT * EMBEDDING_DIM * np.dtype(np.float32).itemsize
+        per_window += BAND_COUNT * np.dtype(np.bool_).itemsize
+    return windows * per_window
+
+
+def _cuda_static_bytes(
+    system: torch.nn.Module,
+    source: CaptureValidationSource,
+    *,
+    residual: bool,
+) -> int:
+    capture_count = len(source.captures)
+    caption_count = sum(
+        len(capture.holistic_text.caption_commitments) for capture in source.captures
+    )
+    total = _system_tensor_bytes(system)
+    total += capture_count * EMBEDDING_DIM * 4
+    if residual:
+        total += capture_count * BAND_COUNT * EMBEDDING_DIM * 4
+        total += capture_count * BAND_COUNT
+    total += caption_count * EMBEDDING_DIM * 4
+    total += capture_count * caption_count * (4 + 1)
+    return total
+
+
+def _finite_cpu_float32(value: Tensor, shape: tuple[int, ...], label: str) -> np.ndarray:
+    if type(value) is not Tensor or value.dtype != torch.float32:
+        raise CaptureValidationError(f"{label} must return an exact float32 tensor")
+    output = value.detach().to(device="cpu").contiguous()
+    if tuple(output.shape) != shape or not bool(torch.isfinite(output).all().item()):
+        raise CaptureValidationError(f"{label} output shape or finiteness is invalid")
+    return np.array(output.numpy(), dtype=np.float32, copy=True, order="C", subok=False)
+
+
+def _capture_fractions(
+    dataset: RetrievalDataset,
+) -> tuple[Fraction, Fraction, Fraction]:
+    rows = capture_r1_contributions(dataset)
+    count = len(rows)
+    text = sum(
+        (Fraction(sum(row.text_to_motion_hits), len(row.text_to_motion_hits)) for row in rows),
+        start=Fraction(0, 1),
+    ) / count
+    motion = sum(
+        (
+            Fraction(sum(row.motion_to_text_hits), len(row.motion_to_text_hits))
+            for row in rows
+        ),
+        start=Fraction(0, 1),
+    ) / count
+    return text, motion, (text + motion) / 2
+
+
+def run_capture_validation(
+    system: BaseRetrievalSystem | ResidualRetrievalSystem,
+    config: TrainingConfig,
+    source: CaptureValidationSource,
+    *,
+    max_encoded_bytes: int = DEFAULT_MAX_ENCODED_BYTES,
+    max_cuda_static_tensor_bytes: int = DEFAULT_MAX_CUDA_STATIC_TENSOR_BYTES,
+) -> CaptureValidationResult:
+    """Encode all planned windows, pool captures, and score one full gallery.
+
+    Each accepted window is encoded independently at B=1.  The byte budgets
+    reject the complete workload; they never select a subset.  The CUDA budget
+    covers model parameters/buffers plus explicit final gallery tensors.  It is
+    not a claim about transient workspace or allocator peaks; CUDA OOM is
+    translated to ``RESOURCE_LIMIT``.
+    """
+
+    if not isinstance(system, (BaseRetrievalSystem, ResidualRetrievalSystem)):
+        raise CaptureValidationError("system must use a registered retrieval interface")
+    if type(config) is not TrainingConfig:
+        raise CaptureValidationError("config must be exact TrainingConfig")
+    checked_source = _snapshot_source(source)
+    encoded_limit = _positive_int(max_encoded_bytes, "max_encoded_bytes")
+    cuda_limit = _positive_int(
+        max_cuda_static_tensor_bytes,
+        "max_cuda_static_tensor_bytes",
+    )
+    residual = isinstance(system, ResidualRetrievalSystem)
+    expected_stage = "residual" if residual else "base"
+    if config.stage != expected_stage:
+        raise CaptureValidationError("training config stage differs from retrieval system")
+    if system.embedding_dim != EMBEDDING_DIM:
+        raise CaptureValidationError("capture validation requires registered width 512")
+    if type(system.system_id) is not str or not system.system_id:
+        raise CaptureValidationError("retrieval system_id is invalid")
+    device = _concrete_device(config.device)
+    _require_system_device(system, device)
+    precision = resolve_precision(config)
+    expected_bytes = _expected_encoded_bytes(checked_source, residual=residual)
+    if expected_bytes > encoded_limit:
+        raise CaptureValidationResourceLimit(
+            "RESOURCE_LIMIT: complete encoded-window arrays exceed max_encoded_bytes"
+        )
+    cuda_static_bytes = None
+    if device.type == "cuda":
+        cuda_static_bytes = _cuda_static_bytes(
+            system,
+            checked_source,
+            residual=residual,
+        )
+        if cuda_static_bytes > cuda_limit:
+            raise CaptureValidationResourceLimit(
+                "RESOURCE_LIMIT: explicit CUDA tensors exceed max_cuda_static_tensor_bytes"
+            )
+    for capture in checked_source.captures:
+        for window in capture.windows:
+            k = window.groups.actor_counts[0]
+            edges = k * (k - 1) // 2
+            if edges > config.edge_budget:
+                raise CaptureValidationResourceLimit(
+                    "RESOURCE_LIMIT: an accepted window exceeds config.edge_budget"
+                )
+
+    modes = tuple((module, bool(module.training)) for module in system.modules())
+    pooled_base: list[np.ndarray] = []
+    pooled_tokens: list[np.ndarray] = []
+    pooled_masks: list[np.ndarray] = []
+    text_rows: list[Tensor] = []
+    caption_commitments: list[bytes] = []
+    positive_motion_indices: list[tuple[int, ...]] = []
+    logits: Tensor | None = None
+    loss_value: float | None = None
+    try:
+        with _frozen_numerical_runtime(device), torch.no_grad():
+            system.eval()
+            for motion_index, capture in enumerate(checked_source.captures):
+                base_rows: list[np.ndarray] = []
+                token_rows: list[np.ndarray] = []
+                mask_rows: list[np.ndarray] = []
+                for window in capture.windows:
+                    with _autocast_context(precision, device):
+                        if residual:
+                            assert isinstance(system, ResidualRetrievalSystem)
+                            tokens, band_mask = system.encode_trainable(window.groups)
+                            base = system.encode_frozen_base(window.groups)
+                        else:
+                            assert isinstance(system, BaseRetrievalSystem)
+                            base = system.encode_trainable(window.groups)
+                    base_rows.append(
+                        _finite_cpu_float32(base, (1, EMBEDDING_DIM), "base encoder")[0]
+                    )
+                    _assert_frozen_numerical_runtime(device)
+                    if residual:
+                        token_array = _finite_cpu_float32(
+                            tokens,
+                            (1, BAND_COUNT, EMBEDDING_DIM),
+                            "periodic encoder",
+                        )[0]
+                        if (
+                            type(band_mask) is not Tensor
+                            or band_mask.dtype != torch.bool
+                            or band_mask.device != device
+                            or tuple(band_mask.shape) != (1, BAND_COUNT)
+                        ):
+                            raise CaptureValidationError(
+                                "periodic encoder must return a bool [1,6] device mask"
+                            )
+                        mask_array = np.array(
+                            band_mask.detach().to(device="cpu").contiguous().numpy(),
+                            dtype=np.bool_,
+                            copy=True,
+                            order="C",
+                            subok=False,
+                        )
+                        if mask_array.shape != (1, BAND_COUNT):
+                            raise CaptureValidationError(
+                                "periodic encoder band mask shape is invalid"
+                            )
+                        token_rows.append(token_array)
+                        mask_rows.append(mask_array[0])
+                try:
+                    pooled = pool_capture_windows(
+                        capture.plan,
+                        window_commitments=tuple(
+                            window.window_commitment for window in capture.windows
+                        ),
+                        base_embeddings=np.ascontiguousarray(
+                            np.stack(base_rows), dtype=np.float32
+                        ),
+                        tokens=(
+                            np.ascontiguousarray(np.stack(token_rows), dtype=np.float32)
+                            if residual
+                            else None
+                        ),
+                        band_mask=(
+                            np.ascontiguousarray(np.stack(mask_rows), dtype=np.bool_)
+                            if residual
+                            else None
+                        ),
+                        max_encoded_bytes=encoded_limit,
+                    )
+                except CapturePoolingResourceLimit as error:
+                    raise CaptureValidationResourceLimit(str(error)) from error
+                except CapturePoolingError as error:
+                    raise CaptureValidationError("capture pooling rejected encoder output") from error
+                pooled_base.append(pooled.base_embedding)
+                if residual:
+                    if pooled.tokens is None or pooled.band_mask is None:
+                        raise CaptureValidationError("residual capture pooling omitted tokens")
+                    pooled_tokens.append(pooled.tokens)
+                    pooled_masks.append(pooled.band_mask)
+                text, commitments, _ = _validate_text_batch(capture.holistic_text)
+                order = tuple(sorted(range(len(commitments)), key=lambda i: commitments[i]))
+                text_rows.extend(text[index : index + 1] for index in order)
+                caption_commitments.extend(commitments[index] for index in order)
+                positive_motion_indices.extend((motion_index,) for _ in order)
+
+            motion = torch.from_numpy(
+                np.ascontiguousarray(np.stack(pooled_base), dtype=np.float32)
+            ).to(device=device).contiguous()
+            text_gallery = torch.cat(text_rows).to(device=device).contiguous()
+            positive = torch.zeros(
+                (len(checked_source.captures), len(caption_commitments)),
+                dtype=torch.bool,
+                device=device,
+            )
+            for caption_index, row in enumerate(positive_motion_indices):
+                positive[row[0], caption_index] = True
+            if residual:
+                assert isinstance(system, ResidualRetrievalSystem)
+                tokens_gallery = torch.from_numpy(
+                    np.ascontiguousarray(np.stack(pooled_tokens), dtype=np.float32)
+                ).to(device=device).contiguous()
+                masks_gallery = torch.from_numpy(
+                    np.ascontiguousarray(np.stack(pooled_masks), dtype=np.bool_)
+                ).to(device=device).contiguous()
+                logits = system.scores(
+                    tokens_gallery,
+                    masks_gallery,
+                    motion,
+                    text_gallery,
+                )
+            else:
+                assert isinstance(system, BaseRetrievalSystem)
+                logits = system.scores(motion, text_gallery)
+            if (
+                type(logits) is not Tensor
+                or logits.dtype != torch.float32
+                or logits.device != device
+                or tuple(logits.shape)
+                != (len(checked_source.captures), len(caption_commitments))
+                or not logits.is_contiguous()
+                or logits.requires_grad
+                or not bool(torch.isfinite(logits).all().item())
+            ):
+                raise CaptureValidationError("full-gallery score tensor is invalid")
+            _, _, objective = variable_positive_symmetric_infonce(logits, positive)
+            loss_value = float(objective.detach().cpu().item())
+            if not np.isfinite(loss_value):
+                raise CaptureValidationError("capture-gallery loss is not finite")
+            _assert_frozen_numerical_runtime(device)
+    except torch.cuda.OutOfMemoryError as error:
+        raise CaptureValidationResourceLimit(
+            "RESOURCE_LIMIT: CUDA exhausted memory while evaluating the complete census"
+        ) from error
+    finally:
+        for module, mode in modes:
+            module.training = mode
+
+    if logits is None or loss_value is None:
+        raise TrainingRuntimeError("capture validation did not produce a gallery")
+    score_array = np.ascontiguousarray(
+        logits.detach().to(device="cpu").numpy(),
+        dtype=np.float64,
+    )
+    dataset = validate_retrieval_dataset(
+        RetrievalDataset(
+            scores=score_array,
+            motion_commitments=tuple(
+                capture.capture_commitment for capture in checked_source.captures
+            ),
+            caption_commitments=tuple(caption_commitments),
+            positive_motion_indices=tuple(positive_motion_indices),
+            group_sizes=np.ascontiguousarray(
+                [capture.group_size for capture in checked_source.captures],
+                dtype=np.int64,
+            ),
+            component_labels=tuple(
+                capture.component_label for capture in checked_source.captures
+            ),
+        )
+    )
+    text_r1, motion_r1, primary_r1 = _capture_fractions(dataset)
+    return CaptureValidationResult(
+        dataset=dataset,
+        text_to_motion_capture_r1=text_r1,
+        motion_to_text_capture_r1=motion_r1,
+        primary_capture_r1=primary_r1,
+        loss=loss_value,
+        source_census_sha256=checked_source.census_sha256,
+        scores_float64_sha256=_array_sha256(dataset.scores),
+        system_id=system.system_id,
+        stage=expected_stage,
+        device=str(device),
+        precision_mode=precision.mode,
+        encoded_bytes=expected_bytes,
+        cuda_static_tensor_bytes=cuda_static_bytes,
+    )
+
+
+# Match the existing formal runtime's live-function drift checks. This is not
+# a security boundary against arbitrary Python execution or a rights grant.
+_CAPTURE_RUNTIME_BINDINGS = tuple(
+    (name, globals()[name])
+    for name in (
+        "CaptureValidationSource", "run_capture_validation", "_snapshot_source",
+        "_capture_fractions", "pool_capture_windows", "capture_r1_contributions",
+        "validate_retrieval_dataset", "variable_positive_symmetric_infonce",
+    )
+)
+
+
+def _verified_capture_validation_bindings():
+    for name, expected in _CAPTURE_RUNTIME_BINDINGS:
+        if globals().get(name) is not expected:
+            raise TrainingRuntimeError(f"capture runtime function identity changed: {name}")
+    return _CAPTURE_RUNTIME_BINDINGS[0][1], _CAPTURE_RUNTIME_BINDINGS[1][1]
+
+
+__all__ = [
+    "CaptureValidationCapture",
+    "CaptureValidationError",
+    "CaptureValidationResourceLimit",
+    "CaptureValidationResult",
+    "CaptureValidationSource",
+    "CaptureValidationWindow",
+    "run_capture_validation",
+]
diff --git a/src/phaseset_core/frozen_clip_text.py b/src/phaseset_core/frozen_clip_text.py
index 872bcc5..af82117 100644
--- a/src/phaseset_core/frozen_clip_text.py
+++ b/src/phaseset_core/frozen_clip_text.py
@@ -156,8 +156,8 @@
     ),
     (
         "phaseset_core.training",
-        154_645,
-        "b1bbc5061a02aac1226ab00fd6eb970bf786cfe32b4d40ff0999dbebaaec60b4",
+        157_160,
+        "9f6c21e354a17facda89a542d53e39013477c5e1b1a259781e2446cd394f6d06",
     ),
 )
 
diff --git a/src/phaseset_core/training.py b/src/phaseset_core/training.py
index 13208f8..6465ff4 100644
--- a/src/phaseset_core/training.py
+++ b/src/phaseset_core/training.py
@@ -26,7 +26,7 @@
 import struct
 import sys
 import types
-from typing import Final, Literal, Protocol, runtime_checkable
+from typing import TYPE_CHECKING, Final, Literal, Protocol, runtime_checkable
 import uuid
 
 import numpy as np
@@ -36,6 +36,9 @@
 
 from phaseset_core import execution as execution_module
 
+if TYPE_CHECKING:
+    from .capture_validation import CaptureValidationSource
+
 from .contracts import PreparedGroupBatch, validate_prepared_group_batch
 from .execution import (
     ExecutionContractError,
@@ -669,6 +672,9 @@ def _assert_registered_method_integrity(
 ) -> None:
     """Reject class monkeypatching and instance-level method shadowing."""
 
+    from .capture_validation import _verified_capture_validation_bindings
+
+    _verified_capture_validation_bindings()
     for name, expected in _FORMAL_RUNTIME_CONSTANTS.items():
         if globals().get(name) != expected or type(globals().get(name)) is not type(
             expected
@@ -1297,7 +1303,22 @@ class _CacheRow:
     rng_before: dict[str, object]
 
 
-def _source_identity(source: object, split: Split) -> tuple[TrainingDataSource, str]:
+def _source_identity(
+    source: object, split: Split,
+) -> tuple[TrainingDataSource | CaptureValidationSource, str]:
+    # Lazy import avoids the capture evaluator's intentional system-interface
+    # dependency on this module. Only the concrete val-only source is admitted.
+    from .capture_validation import _verified_capture_validation_bindings
+
+    CaptureValidationSource, _ = _verified_capture_validation_bindings()
+
+    if type(source) is CaptureValidationSource:
+        if split != "val":
+            raise TrainingRuntimeError("capture validation cannot be a training source")
+        checked = CaptureValidationSource(source.split, source.manifest_sha256, source.captures)
+        # The census binds the complete window/feature/text plan AND upstream
+        # manifest, so resume cannot substitute a different capture gallery.
+        return checked, checked.census_sha256
     if not isinstance(source, TrainingDataSource):
         raise TypeError("data source must implement TrainingDataSource")
     if source.split != split:
@@ -3275,8 +3296,26 @@ def _train_update(self, microbatches: tuple[RetrievalTrainingBatch, ...]) -> flo
 
     def _validation(
         self,
-        source: TrainingDataSource,
+        source: TrainingDataSource | CaptureValidationSource,
     ) -> tuple[float, float, int, int]:
+        from .capture_validation import CaptureValidationSource, run_capture_validation
+
+        if not self.config.synthetic_contract:
+            from .capture_validation import _verified_capture_validation_bindings
+
+            CaptureValidationSource, run_capture_validation = _verified_capture_validation_bindings()
+        if type(source) is CaptureValidationSource:
+            checked = CaptureValidationSource(source.split, source.manifest_sha256, source.captures)
+            if checked.census_sha256 != self._val_manifest:
+                raise TrainingRuntimeError("capture validation census changed after fit admission")
+            edges = tuple(
+                window.groups.actor_counts[0] * (window.groups.actor_counts[0] - 1) // 2
+                for capture in checked.captures for window in capture.windows
+            )
+            result = run_capture_validation(self.system, self.config, checked)
+            if result.source_census_sha256 != self._val_manifest:
+                raise TrainingRuntimeError("scored capture census differs from admitted validation")
+            return float(result.primary_capture_r1), result.loss, sum(edges), max(edges)
         batches = _materialize_epoch(
             source,
             split="val",
@@ -3406,7 +3445,7 @@ def _report(self, status: Literal["COMPLETED", "INTERRUPTED"]) -> TrainingReport
     def fit(
         self,
         train_source: TrainingDataSource,
-        val_source: TrainingDataSource,
+        val_source: TrainingDataSource | CaptureValidationSource,
         *,
         resume_checkpoint: str | Path | None = None,
         resume_checkpoint_sha256: str | None = None,
@@ -3418,7 +3457,10 @@ def fit(
 
         Validation alone selects the best checkpoint.  There is intentionally
         no test-source argument, and any source or batch labelled ``test`` is
-        rejected before a model forward.
+        rejected before a model forward. A concrete CaptureValidationSource
+        uses the complete holistic capture gallery; the existing window source
+        remains available for the separately declared auxiliary task. Capture
+        checkpoints bind the complete source census rather than actor-set IDs.
         """
 
         self._assert_live_formal_binding(require_initial_state=True)
diff --git a/tests/test_phasepair_core_clip_resolution.py b/tests/test_phasepair_core_clip_resolution.py
index b41adba..b33bc6e 100644
--- a/tests/test_phasepair_core_clip_resolution.py
+++ b/tests/test_phasepair_core_clip_resolution.py
@@ -586,6 +586,7 @@ def test_second_pass_late_equal_length_mutation_of_first_file_is_rejected(self)
         pins, total = tiny_snapshot(self.temporary.path)
         first_file = self.temporary.path / "pytorch_model.bin"
         original = first_file.read_bytes()
+        original_stat = first_file.stat()
         open_count = 0
         fd_ordinals: dict[int, int] = {}
         mutated = False
@@ -605,6 +606,13 @@ def mutating_read(fd: int, count: int) -> bytes:
                 replacement = bytes((original[0] ^ 1,)) + original[1:]
                 self.assertEqual(len(replacement), len(original))
                 first_file.write_bytes(replacement)
+                # Equal-length writes may share one filesystem timestamp tick.
+                # This test targets the closing METADATA sweep, so make that
+                # observable premise explicit instead of depending on elapsed
+                # wall time. The production scanner remains best-effort.
+                os.utime(first_file, ns=(original_stat.st_atime_ns,
+                                       original_stat.st_mtime_ns + 2_000_000_000))
+                self.assertNotEqual(first_file.stat().st_mtime_ns, original_stat.st_mtime_ns)
                 mutated = True
             return os.read(fd, count)
 
@@ -744,6 +752,9 @@ def test_canonical_rescans_and_rejects_files_changed_after_issue(self) -> None:
             clip.canonical_assessment_bytes(assessment)
 
     def test_weak_lease_registry_returns_to_baseline_after_256_assessments(self) -> None:
+        # Earlier tests may leave unreachable exception/closure cycles. They
+        # must be collected before, not during, this relative-count assertion.
+        gc.collect()
         baseline = clip._issued_lease_count_for_tests()
         missing = self.temporary.path / "lease-missing"
         assessments = [clip.assess_local_clip_snapshot(missing) for _ in range(256)]
diff --git a/tests/test_phaseset_capture_pooling.py b/tests/test_phaseset_capture_pooling.py
new file mode 100644
index 0000000..43a372c
--- /dev/null
+++ b/tests/test_phaseset_capture_pooling.py
@@ -0,0 +1,201 @@
+"""Numeric-only tests of complete capture pooling, not model evaluation."""
+
+import hashlib
+from dataclasses import replace
+from itertools import permutations
+import struct
+
+import numpy as np
+import pytest
+from phaseset_core.capture_pooling import (
+    CapturePoolingError,
+    CapturePoolingResourceLimit,
+    CaptureWindowPlan,
+    pool_capture_windows,
+)
+
+
+def _identity(value: str) -> bytes:
+    return hashlib.sha256(value.encode("ascii")).digest()
+
+
+def _fixture(count=3):
+    ids = tuple(_identity(f"capture/window/{index}") for index in range(count))
+    plan = CaptureWindowPlan(_identity("capture"), ids, tuple(300 * index for index in range(count)))
+    base = np.broadcast_to(np.arange(1, count + 1, dtype=np.float32)[:, None], (count, 512)).copy()
+    tokens = np.broadcast_to(base[:, None, :], (count, 6, 512)).copy()
+    mask = np.ones((count, 6), dtype=np.bool_)
+    mask[:, 5] = False
+    mask[0, 1] = False
+    tokens[~mask] = 0.0
+    return plan, ids, base, tokens, mask
+
+
+def test_base_and_band_means_precede_normalization():
+    plan, ids, base, tokens, mask = _fixture()
+    output = pool_capture_windows(plan, window_commitments=ids, base_embeddings=base, tokens=tokens, band_mask=mask)
+    np.testing.assert_array_equal(output.base_embedding, np.full(512, 2.0, dtype=np.float32))
+    np.testing.assert_array_equal(output.tokens[0], np.full(512, 2.0, dtype=np.float32))
+    np.testing.assert_array_equal(output.tokens[1], np.full(512, 2.5, dtype=np.float32))
+    np.testing.assert_array_equal(output.valid_window_count, [3, 2, 3, 3, 3, 0])
+    assert output.band_mask.tolist() == [True, True, True, True, True, False]
+    assert output.tokens[5].tobytes() == bytes(512 * 4)
+    assert output.plan_sha256 == plan.sha256
+    assert output.capture_commitment == plan.capture_commitment
+    assert output.window_count == 3
+    for array in (output.base_embedding, output.tokens, output.band_mask, output.valid_window_count):
+        assert not array.flags.writeable
+
+
+def test_every_input_permutation_is_bitwise_equal():
+    plan, ids, base, tokens, mask = _fixture(5)
+    # Cancellation makes a canonical reduction order material.
+    base[:, 0] = np.array([1e20, 1.0, -1e20, 3.0, 2.0], dtype=np.float32)
+    baseline = pool_capture_windows(plan, window_commitments=ids, base_embeddings=base, tokens=tokens, band_mask=mask)
+    for permutation in permutations(range(5)):
+        indices = list(permutation)
+        observed = pool_capture_windows(
+            plan,
+            window_commitments=tuple(ids[index] for index in indices),
+            base_embeddings=base[indices],
+            tokens=tokens[indices],
+            band_mask=mask[indices],
+        )
+        for name in ("base_embedding", "tokens", "band_mask", "valid_window_count"):
+            assert getattr(observed, name).tobytes() == getattr(baseline, name).tobytes()
+
+
+def test_literal_binary64_tree_and_chronology_have_independent_bit_oracle():
+    ids = tuple(bytes([value]) * 32 for value in (3, 1, 4, 0, 2))
+    plan = CaptureWindowPlan(bytes(32), ids, (0, 300, 600, 900, 1200))
+    base = np.zeros((5, 512), dtype=np.float32)
+    base[:, 0] = [2**54, 1, -(2**54), 1, 1]
+    base[:, 1] = [2**24, 1, -(2**24), 1, 1]
+    output = pool_capture_windows(plan, window_commitments=ids, base_embeddings=base)
+    # Hand-expanded adjacent binary64 tree, not the implementation's helper.
+    expected_zero = ((float(2**54) + 1.0) + (float(-(2**54)) + 1.0) + 1.0) / 5.0
+    expected_one = ((float(2**24) + 1.0) + (float(-(2**24)) + 1.0) + 1.0) / 5.0
+    assert struct.pack("<f", expected_zero) == bytes.fromhex("cdcc4c3e")  # 0.2
+    assert struct.pack("<f", expected_one) == bytes.fromhex("9a99193f")  # 0.6
+    assert output.base_embedding[:2].tobytes() == bytes.fromhex("cdcc4c3e9a99193f")
+    sequential = 0.0
+    for value in base[:, 0]:
+        sequential += float(value)
+    assert struct.pack("<f", sequential / 5.0) != bytes.fromhex("cdcc4c3e")
+    assert struct.pack("<f", float(np.sum(base[:, 0].astype(np.float64))) / 5.0) != bytes.fromhex("cdcc4c3e")
+    a, b, c, d, e = base[:, 1]
+    wrong_float32_tree = np.float32(np.float32(np.float32(a + b) + np.float32(c + d)) + e)
+    assert struct.pack("<f", float(wrong_float32_tree) / 5.0) != bytes.fromhex("9a99193f")
+    # Sorting by private window digest instead of plan chronology changes the tree.
+    by_digest = sorted(range(5), key=lambda index: ids[index])
+    a, b, c, d, e = (float(base[index, 0]) for index in by_digest)
+    wrong_order_tree = ((a + b) + (c + d) + e) / 5.0
+    assert struct.pack("<f", wrong_order_tree) != bytes.fromhex("cdcc4c3e")
+
+
+def test_plan_digest_literal_golden_binds_all_identity_fields_and_schema():
+    first, second = bytes.fromhex("11" * 32), bytes.fromhex("22" * 32)
+    plan = CaptureWindowPlan(bytes(32), (first, second), (0, 600))
+    payload = (
+        b'{"capture_commitment":"' + b"00" * 32
+        + b'","schema":"phaseset-capture-window-plan-v1","source_start_frames":[0,600],'
+        + b'"window_commitments":["' + b"11" * 32 + b'","' + b"22" * 32 + b'"]}'
+    )
+    expected = "8db60469ea75a3e4d1cbf7f7e64000322dcdb4ef8b85b82d4c12e022bf203f3e"
+    assert hashlib.sha256(payload).hexdigest() == expected
+    assert plan.sha256 == expected
+    mutants = (
+        replace(plan, capture_commitment=bytes.fromhex("33" * 32)),
+        replace(plan, window_commitments=(bytes.fromhex("33" * 32), second)),
+        replace(plan, window_commitments=(second, first)),
+        replace(plan, source_start_frames=(300, 600)),
+        replace(plan, source_start_frames=(0, 900)),
+    )
+    assert all(mutant.sha256 != expected for mutant in mutants)
+    assert len({mutant.sha256 for mutant in mutants}) == len(mutants)
+    wrong_schema = payload.replace(b"phaseset-capture-window-plan-v1", b"phaseset-capture-window-plan-v2")
+    assert hashlib.sha256(wrong_schema).hexdigest() != expected
+
+
+@pytest.mark.parametrize("mode", ["missing", "duplicate", "foreign"])
+def test_missing_duplicate_or_foreign_window_rejected(mode):
+    plan, ids, base, _, _ = _fixture()
+    if mode == "missing":
+        supplied, arrays = ids[:-1], base[:-1]
+    elif mode == "duplicate":
+        supplied, arrays = (ids[0], ids[0], ids[2]), base
+    else:
+        supplied, arrays = (*ids[:-1], _identity("foreign")), base
+    with pytest.raises(CapturePoolingError, match="complete capture census"):
+        pool_capture_windows(plan, window_commitments=supplied, base_embeddings=arrays)
+
+
+def test_single_window_base_only_and_distinct_capture_identity():
+    plan, ids, base, _, _ = _fixture(1)
+    output = pool_capture_windows(plan, window_commitments=ids, base_embeddings=base)
+    assert output.base_embedding.tobytes() == base[0].tobytes()
+    assert output.tokens is None and output.band_mask is None and output.valid_window_count is None
+    # There is deliberately no actor-set identity in this interface.
+    other = replace(plan, capture_commitment=_identity("other-capture-same-actors"))
+    assert other.sha256 != plan.sha256
+    assert pool_capture_windows(other, window_commitments=ids, base_embeddings=base).capture_commitment != output.capture_commitment
+
+
+def test_all_invalid_bands_are_positive_zero_without_mutating_inputs():
+    plan, ids, base, tokens, mask = _fixture()
+    tokens.fill(0.0)
+    mask.fill(False)
+    before = tuple(value.tobytes() for value in (base, tokens, mask))
+    output = pool_capture_windows(plan, window_commitments=ids, base_embeddings=base, tokens=tokens, band_mask=mask)
+    assert output.tokens.tobytes() == bytes(6 * 512 * 4)
+    assert not output.band_mask.any()
+    assert not output.valid_window_count.any()
+    assert before == tuple(value.tobytes() for value in (base, tokens, mask))
+
+
+@pytest.mark.parametrize("value", [-0.0, 1.0, np.nan])
+def test_invalid_band_payload_cannot_be_hidden(value):
+    plan, ids, base, tokens, mask = _fixture()
+    tokens[0, 5, 0] = value
+    with pytest.raises(CapturePoolingError):
+        pool_capture_windows(plan, window_commitments=ids, base_embeddings=base, tokens=tokens, band_mask=mask)
+
+
+def test_explicit_resource_limit_does_not_drop_windows():
+    plan, ids, base, tokens, mask = _fixture()
+    size = base.nbytes + tokens.nbytes + mask.nbytes
+    with pytest.raises(CapturePoolingResourceLimit, match="RESOURCE_LIMIT"):
+        pool_capture_windows(plan, window_commitments=ids, base_embeddings=base, tokens=tokens, band_mask=mask, max_encoded_bytes=size - 1)
+    assert pool_capture_windows(plan, window_commitments=ids, base_embeddings=base, tokens=tokens, band_mask=mask, max_encoded_bytes=size).window_count == 3
+
+
+def test_plan_rejects_empty_repeated_offgrid_and_reordered_starts():
+    plan, ids, _, _, _ = _fixture()
+    for starts in ((0, 0, 600), (0, 301, 600), (600, 300, 0), (0, True, 600)):
+        with pytest.raises(CapturePoolingError):
+            CaptureWindowPlan(plan.capture_commitment, ids, starts)
+    with pytest.raises(CapturePoolingError):
+        CaptureWindowPlan(plan.capture_commitment, (), ())
+
+
+def test_input_types_shapes_finiteness_and_optional_fields():
+    plan, ids, base, tokens, mask = _fixture()
+    for array in (base.astype(np.float64), base[:, ::2], base.copy(order="F")):
+        with pytest.raises(CapturePoolingError):
+            pool_capture_windows(plan, window_commitments=ids, base_embeddings=array)
+    broken = base.copy()
+    broken[0, 0] = np.inf
+    with pytest.raises(CapturePoolingError, match="finite"):
+        pool_capture_windows(plan, window_commitments=ids, base_embeddings=broken)
+    for partial in ({"tokens": tokens}, {"band_mask": mask}):
+        with pytest.raises(CapturePoolingError, match="together"):
+            pool_capture_windows(plan, window_commitments=ids, base_embeddings=base, **partial)
+
+
+def test_plan_and_output_repr_do_not_expose_lineage_or_features():
+    plan, ids, base, _, _ = _fixture()
+    output = pool_capture_windows(plan, window_commitments=ids, base_embeddings=base)
+    for rendered in (repr(plan), repr(output)):
+        assert plan.capture_commitment.hex() not in rendered
+        assert ids[0].hex() not in rendered
+        assert "array(" not in rendered
diff --git a/tests/test_phaseset_capture_training.py b/tests/test_phaseset_capture_training.py
new file mode 100644
index 0000000..8536c5e
--- /dev/null
+++ b/tests/test_phaseset_capture_training.py
@@ -0,0 +1,187 @@
+"""Actual tiny optimization/checkpoint/resume with the holistic capture caller."""
+from __future__ import annotations
+
+from dataclasses import dataclass, replace
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+from torch import nn
+
+from phaseset_core import training
+from phaseset_core.capture_validation import CaptureValidationSource, run_capture_validation
+from phaseset_core.models import GroupBaseOutput
+from test_phaseset_capture_validation import _groups, _id, _source
+
+
+class _TinyLearnedBase(nn.Module):
+    """A small trainable interface fixture, not a registered architecture result."""
+    system_id = "B0"
+
+    def __init__(self):
+        super().__init__()
+        self.projection = nn.Linear(2, 512)
+        self.dropout = nn.Dropout(0.2)
+
+    def forward(self, groups):
+        values = torch.tensor(np.array(groups.skeletons[:, 0, 0, 0, :2], copy=True))
+        embeddings = self.projection(self.dropout(values)).contiguous()
+        actors = embeddings[:, None, :].expand(-1, 3, -1).contiguous()
+        return GroupBaseOutput(embeddings, actors, torch.ones(actors.shape[:2], dtype=torch.bool))
+
+
+@dataclass
+class _TrainSource:
+    split: str = "train"
+    manifest_sha256: str = "b" * 64
+
+    def iter_epoch(self, *, epoch, seed):
+        for index, coordinate in enumerate(((1.0, 0.0), (0.0, 1.0))):
+            text = torch.zeros((1, 512), dtype=torch.float32)
+            text[0, index] = 1.0
+            key = _id(f"train-window/{index}")
+            yield training.RetrievalTrainingBatch(_groups(*coordinate), text, (key,), (key,),
+                (_id(f"train-text/{index}"),), "train")
+
+
+def _runtime(path: Path):
+    with torch.random.fork_rng():
+        torch.manual_seed(811)
+        system = training.BaseRetrievalSystem(_TinyLearnedBase(), embedding_dim=512)
+    config = training.TrainingConfig(stage="base", seed=1729, synthetic_contract=True,
+                                     effective_global_batch=2)
+    return training.PhaseSetTrainingRuntime(system, config, checkpoint_directory=path)
+
+
+def _payload(path: Path):
+    return torch.load(path, map_location="cpu", weights_only=True)
+
+
+def _equal_nested(left, right):
+    if isinstance(left, torch.Tensor):
+        assert torch.equal(left, right)
+    elif isinstance(left, dict):
+        assert left.keys() == right.keys()
+        for key in left:
+            _equal_nested(left[key], right[key])
+    elif isinstance(left, (list, tuple)):
+        assert type(left) is type(right) and len(left) == len(right)
+        for a, b in zip(left, right, strict=True):
+            _equal_nested(a, b)
+    else:
+        assert left == right
+
+
+def test_capture_gallery_selects_validation_checkpoint_and_binds_complete_census(tmp_path):
+    runtime = _runtime(tmp_path / "one")
+    source = _source()
+    report = runtime.fit(_TrainSource(), source, stop_after_global_step=1)
+    assert report.status == "INTERRUPTED"
+    assert report.completed_epochs == 1
+    assert report.best_checkpoint is not None
+    assert report.val_manifest_sha256 == source.census_sha256
+    assert report.validation_edges_seen == 9  # three K=3 windows, not two captures
+    payload = _payload(report.latest_checkpoint.path)
+    actual = run_capture_validation(runtime.system, runtime.config, source)
+    assert payload["best_validation_metric"] == float(actual.primary_capture_r1)
+    assert payload["val_manifest_sha256"] == source.census_sha256
+    assert payload["val_manifest_sha256"] != source.manifest_sha256
+
+
+def test_capture_checkpoint_resume_equals_uninterrupted_optimizer_and_model(tmp_path):
+    source = _source()
+    complete = _runtime(tmp_path / "complete")
+    expected = complete.fit(_TrainSource(), source, stop_after_global_step=2)
+    resumed = _runtime(tmp_path / "resumed")
+    partial = resumed.fit(_TrainSource(), source, stop_after_global_step=1)
+    # A new runtime reconstructs and checks state from the checkpoint, rather
+    # than continuing the in-memory optimizer left by the first fit call.
+    restarted = _runtime(tmp_path / "resumed")
+    observed = restarted.fit(_TrainSource(), source,
+        resume_checkpoint=partial.latest_checkpoint.path,
+        resume_checkpoint_sha256=partial.latest_checkpoint.sha256, stop_after_global_step=2)
+    left, right = _payload(expected.latest_checkpoint.path), _payload(observed.latest_checkpoint.path)
+    for field in ("model", "optimizer", "scheduler", "rng", "epoch", "update_index",
+                  "global_step", "best_validation_metric", "val_manifest_sha256"):
+        _equal_nested(left[field], right[field])
+    assert expected.best_validation_metric == observed.best_validation_metric
+    assert expected.validation_edges_seen == observed.validation_edges_seen == 18
+
+
+def test_changed_capture_census_is_rejected_on_resume(tmp_path):
+    source = _source()
+    runtime = _runtime(tmp_path / "changed")
+    report = runtime.fit(_TrainSource(), source, stop_after_global_step=1)
+    changed = CaptureValidationSource("val", "d" * 64, source.captures)
+    restarted = _runtime(tmp_path / "changed")
+    with pytest.raises(training.TrainingCheckpointError, match="val_manifest"):
+        restarted.fit(_TrainSource(), changed, resume_checkpoint=report.latest_checkpoint.path,
+            resume_checkpoint_sha256=report.latest_checkpoint.sha256, stop_after_global_step=2)
+
+
+def test_capture_source_cannot_enter_training_iterator(tmp_path):
+    runtime = _runtime(tmp_path / "invalid-train")
+    with pytest.raises(training.TrainingRuntimeError, match="cannot be a training source"):
+        runtime.fit(_source(), _source(), stop_after_global_step=1)
+
+
+def test_live_capture_census_drift_rejected_before_encoding(tmp_path):
+    runtime = _runtime(tmp_path / "drift")
+    source = _source()
+    runtime._val_manifest = source.census_sha256
+    changed = replace(source, manifest_sha256="e" * 64)
+    with pytest.raises(training.TrainingRuntimeError, match="census changed"):
+        runtime._validation(changed)
+
+
+def test_scored_capture_census_is_checked_before_using_metric(tmp_path, monkeypatch):
+    from phaseset_core import capture_validation as capture_module
+
+    runtime = _runtime(tmp_path / "scored-drift")
+    source = _source()
+    runtime._val_manifest = source.census_sha256
+    original = capture_module.run_capture_validation
+
+    def swapped(system, config, checked):
+        return original(system, config, replace(checked, manifest_sha256="f" * 64))
+
+    monkeypatch.setattr(capture_module, "run_capture_validation", swapped)
+    with pytest.raises(training.TrainingRuntimeError, match="scored capture census"):
+        runtime._validation(source)
+
+
+def test_edge_count_uses_the_scored_snapshot_not_live_original(tmp_path, monkeypatch):
+    from phaseset_core import capture_validation as capture_module
+
+    runtime = _runtime(tmp_path / "edge-snapshot")
+    source = _source()
+    runtime._val_manifest = source.census_sha256
+    original = capture_module.run_capture_validation
+
+    def mutate_original(system, config, checked):
+        assert checked is not source
+        result = original(system, config, checked)
+        object.__setattr__(source, "captures", source.captures[:1])
+        return result
+
+    monkeypatch.setattr(capture_module, "run_capture_validation", mutate_original)
+    _, _, total_edges, maximum_edges = runtime._validation(source)
+    assert total_edges == 9 and maximum_edges == 3
+
+
+@pytest.mark.parametrize("name", (
+    "CaptureValidationSource", "run_capture_validation", "_snapshot_source",
+    "_capture_fractions", "pool_capture_windows", "capture_r1_contributions",
+    "validate_retrieval_dataset", "variable_positive_symmetric_infonce",
+))
+def test_formal_runtime_rejects_capture_function_drift(tmp_path, monkeypatch, name):
+    from phaseset_core import capture_validation as capture_module
+
+    config = training.TrainingConfig(stage="base", seed=1729)
+    system, binding = training.construct_registered_base_seed_bound_system("B0", config)
+    runtime = training.PhaseSetTrainingRuntime(system, config, tmp_path / name,
+                                              initialization_binding=binding)
+    monkeypatch.setattr(capture_module, name, lambda *args, **kwargs: None)
+    with pytest.raises(training.TrainingRuntimeError, match="capture runtime function identity"):
+        runtime._assert_live_formal_binding(require_initial_state=True)
diff --git a/tests/test_phaseset_capture_validation.py b/tests/test_phaseset_capture_validation.py
new file mode 100644
index 0000000..22b79ac
--- /dev/null
+++ b/tests/test_phaseset_capture_validation.py
@@ -0,0 +1,326 @@
+"""Server-only capture-gallery contracts; analytic fixtures are not results."""
+from __future__ import annotations
+
+from dataclasses import replace
+from fractions import Fraction
+import hashlib
+
+import numpy as np
+import pytest
+import torch
+from torch import nn
+
+from phaseset_core import capture_validation as cv
+from phaseset_core import frozen_clip_text as clip
+from phaseset_core.capture_pooling import CaptureWindowPlan
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+from phaseset_core.models import ActorMeanBase, GroupBaseOutput, GroupTokenOutput, PhaseSetEncoder
+from phaseset_core.objectives import PhaseSetRetrievalHead
+from phaseset_core.training import BaseRetrievalSystem, ResidualRetrievalSystem, TrainingConfig
+
+
+def _id(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _groups(x: float, y: float, *, pad: int = 0, reverse: bool = False) -> PreparedGroupBatch:
+    actors = tuple(_id(f"shared/actor/{index}") for index in range(3))
+    values = np.zeros((1, 3 + pad, 200, 22, 3), dtype=np.float32)
+    values[0, :3, :, :, 0] = x
+    values[0, :3, :, :, 1] = y
+    values[0, :3, :, :, 2] = np.arange(200, dtype=np.float32)[None, :, None] * 0.01
+    mask = np.zeros((1, 3 + pad), dtype=np.bool_)
+    mask[:, :3] = True
+    track = np.zeros((1, 3 + pad, 200, 22), dtype=np.bool_)
+    track[:, :3] = True
+    keys = actors + (None,) * pad
+    if reverse:
+        order = tuple(reversed(range(3 + pad)))
+        values = np.ascontiguousarray(values[:, order])
+        mask = np.ascontiguousarray(mask[:, order])
+        track = np.ascontiguousarray(track[:, order])
+        keys = tuple(keys[index] for index in order)
+    return PreparedGroupBatch(values, mask, np.ones((1, 200), dtype=np.bool_), track,
+                              (keys,), (group_commitment(actors),))
+
+
+def _bytes(value: torch.Tensor) -> bytes:
+    return value.detach().contiguous().view(torch.uint8).numpy().tobytes()
+
+
+def _text(label: str, axes: tuple[int, ...]) -> clip.FrozenClipTextBatch:
+    values = torch.zeros((len(axes), 512), dtype=torch.float32)
+    for row, axis in enumerate(axes):
+        values[row, axis] = 1.0
+    keys = tuple(_id(f"{label}/caption/{row}") for row in range(len(axes)))
+    rows = tuple((row, key.hex(), _id(f"{label}/text/{row}").hex(), 3, 3, False,
+                  _id(f"tokens/{row}").hex(), _id(f"mask/{row}").hex(),
+                  hashlib.sha256(_bytes(values[row:row + 1])).hexdigest())
+                 for row, key in enumerate(keys))
+    raw = _bytes(values)
+    receipt = clip.FrozenClipTextReceipt(
+        batch_size=len(axes), caption_rows=rows, chunk_ranges=((0, len(axes)),),
+        frozen_embedding_cache_key_sha256="1" * 64, live_model_manifest_sha256="2" * 64,
+        output_bytes=len(raw), output_sha256=hashlib.sha256(raw).hexdigest(),
+        output_shape=tuple(values.shape), output_stride=tuple(values.stride()),
+        runtime_identity=(("fixture", "server-only-not-official-text"),),
+        runtime_manifest_sha256="3" * 64, snapshot_files=(), snapshot_manifest_sha256="4" * 64,
+        source_files=(), source_manifest_sha256="5" * 64,
+    )
+    return clip.FrozenClipTextBatch(values, keys, receipt, _seal=clip._CONSTRUCTION_SEAL)
+
+
+def _capture(label: str, coordinates: tuple[tuple[float, float], ...],
+             axes: tuple[int, ...], *, reverse: bool = False, pad: int = 0):
+    keys = tuple(_id(f"{label}/window/{row}") for row in range(len(coordinates)))
+    windows = tuple(cv.CaptureValidationWindow(key, _groups(*xy, pad=pad, reverse=reverse))
+                    for key, xy in zip(keys, coordinates, strict=True))
+    plan = CaptureWindowPlan(_id(label), keys, tuple(300 * row for row in range(len(keys))))
+    return cv.CaptureValidationCapture(plan, windows[::-1] if reverse else windows,
+                                       _text(label, axes), "C00")
+
+
+def _source(*, reverse: bool = False, pad: int = 0):
+    captures = (_capture("A", ((10.0, 0.0), (0.0, 2.0)), (0, 0), reverse=reverse, pad=pad),
+                _capture("B", ((0.0, 2.0),), (1,), reverse=reverse, pad=pad))
+    return cv.CaptureValidationSource("val", "a" * 64, captures[::-1] if reverse else captures)
+
+
+class _AnalyticBase(nn.Module):
+    """Controllable encoder output, only for an independent pooling/scoring oracle."""
+    system_id = "B0"
+
+    def __init__(self):
+        super().__init__()
+        self.anchor = nn.Parameter(torch.zeros(1))
+        self.calls = 0
+        self.fail = False
+        self.dropout = nn.Dropout(0.5)
+
+    def forward(self, groups):
+        self.calls += 1
+        if self.fail:
+            raise RuntimeError("injected encoder failure")
+        assert not self.training
+        assert not torch.backends.mha.get_fastpath_enabled()
+        value = torch.zeros((1, 512), dtype=torch.float32, device=self.anchor.device)
+        value[0, :2] = torch.tensor(groups.skeletons[0, 0, 0, 0, :2].copy(), device=value.device)
+        actors = value[:, None, :].expand(1, groups.actor_counts[0], 512).contiguous()
+        return GroupBaseOutput(value, actors, torch.ones(actors.shape[:2], dtype=torch.bool))
+
+
+class _RecordingBaseSystem(BaseRetrievalSystem):
+    def __init__(self):
+        super().__init__(_AnalyticBase(), embedding_dim=512)
+        self.scored = []
+
+    def scores(self, motion, text):
+        self.scored.append((motion.clone(), text.clone()))
+        assert not torch.backends.mha.get_fastpath_enabled()
+        return super().scores(motion, text)
+
+
+class _AnalyticPeriodic(nn.Module):
+    system_id = "08"
+    embedding_dim = 512
+
+    def __init__(self):
+        super().__init__()
+        self.anchor = nn.Parameter(torch.zeros(1))
+        self.bad_mask = False
+
+    def forward(self, groups):
+        x = float(groups.skeletons[0, 0, 0, 0, 0])
+        tokens = torch.zeros((1, 6, 512), dtype=torch.float32)
+        mask = torch.zeros((1, 6), dtype=torch.bool)
+        if x > 0:
+            tokens[0, 0, 0] = x
+            mask[0, 0] = True
+        if self.bad_mask:
+            mask = mask.float()
+        counts = torch.zeros((1, 6), dtype=torch.int64)
+        return GroupTokenOutput(tokens, mask, tokens.clone(), torch.zeros_like(tokens), counts, counts)
+
+
+class _RecordingResidual(ResidualRetrievalSystem):
+    def scores(self, tokens, mask, base, text):
+        self.scored_tokens = tokens.clone()
+        self.scored_mask = mask.clone()
+        return super().scores(tokens, mask, base, text)
+
+
+def _config(stage="base", **kwargs):
+    return TrainingConfig(stage=stage, seed=1729, **kwargs)
+
+
+def test_pool_before_normalization_one_capture_gallery_and_variable_caption_counts():
+    system = _RecordingBaseSystem()
+    source = _source()
+    result = cv.run_capture_validation(system, _config(), source)
+    assert system.group_base.calls == 3
+    assert len(system.scored) == 1
+    motion, text = system.scored[0]
+    row_a = tuple(c.capture_commitment for c in source.captures).index(_id("A"))
+    row_b = 1 - row_a
+    assert torch.equal(motion[row_a, :2], torch.tensor([5.0, 1.0]))
+    assert torch.equal(motion[row_b, :2], torch.tensor([0.0, 2.0]))
+    assert text.shape == (3, 512)
+    assert result.dataset.scores.shape == (2, 3)
+    assert len(set(result.dataset.motion_commitments)) == 2
+    assert source.captures[0].group_commitment == source.captures[1].group_commitment
+    assert result.text_to_motion_capture_r1 == result.motion_to_text_capture_r1 == Fraction(1)
+    assert result.primary_capture_r1 == Fraction(1)
+    assert result.encoded_bytes == 3 * 512 * 4
+    assert result.source_census_sha256 == source.census_sha256
+    assert np.isfinite(result.loss)
+
+
+def test_capture_window_actor_order_and_padding_preserve_scores():
+    system = _RecordingBaseSystem()
+    left = cv.run_capture_validation(system, _config(), _source())
+    right = cv.run_capture_validation(system, _config(), _source(reverse=True, pad=5))
+    assert left.dataset.motion_commitments == right.dataset.motion_commitments
+    assert left.dataset.caption_commitments == right.dataset.caption_commitments
+    assert left.scores_float64_sha256 == right.scores_float64_sha256
+
+
+def test_modes_flags_rng_and_state_restored_after_success_and_failure():
+    system = _RecordingBaseSystem().train()
+    system.group_base.dropout.eval()
+    modes = tuple(module.training for module in system.modules())
+    state = {key: value.clone() for key, value in system.state_dict().items()}
+    rng = torch.random.get_rng_state().clone()
+    old_flag = torch.backends.mha.get_fastpath_enabled()
+    try:
+        torch.backends.mha.set_fastpath_enabled(True)
+        cv.run_capture_validation(system, _config(), _source())
+        assert torch.backends.mha.get_fastpath_enabled() is True
+        assert modes == tuple(module.training for module in system.modules())
+        system.group_base.fail = True
+        with pytest.raises(RuntimeError, match="injected encoder failure"):
+            cv.run_capture_validation(system, _config(), _source())
+        assert torch.backends.mha.get_fastpath_enabled() is True
+        assert modes == tuple(module.training for module in system.modules())
+    finally:
+        torch.backends.mha.set_fastpath_enabled(old_flag)
+    assert torch.equal(rng, torch.random.get_rng_state())
+    assert all(torch.equal(value, system.state_dict()[key]) for key, value in state.items())
+    assert all(parameter.grad is None for parameter in system.parameters())
+
+
+@pytest.mark.parametrize("split", ["test", "train"])
+def test_source_rejects_other_splits(split):
+    with pytest.raises(cv.CaptureValidationError, match="split='val'"):
+        replace(_source(), split=split)
+
+
+def test_complete_plan_required_and_duplicate_ids_rejected():
+    source = _source()
+    capture = next(c for c in source.captures if len(c.windows) == 2)
+    with pytest.raises(cv.CaptureValidationError, match="complete accepted-window plan"):
+        replace(capture, windows=capture.windows[:1])
+    with pytest.raises(cv.CaptureValidationError, match="globally unique"):
+        replace(source, captures=(source.captures[0], source.captures[0]))
+    with pytest.raises(cv.CaptureValidationError, match="globally unique"):
+        replace(source, captures=(source.captures[0], replace(source.captures[1],
+                       holistic_text=source.captures[0].holistic_text)))
+
+
+def test_rejects_short_window_and_changed_actor_group():
+    groups = _groups(1.0, 2.0)
+    short = PreparedGroupBatch(np.ascontiguousarray(groups.skeletons[:, :, :100]),
+        groups.actor_mask, np.ascontiguousarray(groups.frame_mask[:, :100]),
+        np.ascontiguousarray(groups.track_mask[:, :, :100]), groups.actor_commitments,
+        groups.group_commitments)
+    with pytest.raises(cv.CaptureValidationError, match="200-frame"):
+        cv.CaptureValidationWindow(_id("short"), short)
+    source = _source()
+    capture = next(c for c in source.captures if len(c.windows) == 2)
+    keys = tuple(_id(f"other/{i}") for i in range(3))
+    other = PreparedGroupBatch(groups.skeletons, groups.actor_mask, groups.frame_mask,
+                               groups.track_mask, (keys,), (group_commitment(keys),))
+    with pytest.raises(cv.CaptureValidationError, match="valid actor set"):
+        replace(capture, windows=(capture.windows[0],
+                                  replace(capture.windows[1], groups=other)))
+
+
+@pytest.mark.parametrize("limit", [1, 6143])
+def test_encoded_budget_rejects_before_any_forward(limit):
+    system = _RecordingBaseSystem()
+    with pytest.raises(cv.CaptureValidationResourceLimit, match="max_encoded_bytes"):
+        cv.run_capture_validation(system, _config(), _source(), max_encoded_bytes=limit)
+    assert system.group_base.calls == 0
+
+
+def test_edge_budget_rejects_all_edges_before_any_forward():
+    system = _RecordingBaseSystem()
+    with pytest.raises(cv.CaptureValidationResourceLimit, match="edge_budget"):
+        cv.run_capture_validation(system, _config(edge_budget=2), _source())
+    assert system.group_base.calls == 0
+
+
+def test_concrete_device_resolves_cuda_alias_and_cpu_index(monkeypatch):
+    monkeypatch.setattr(torch.cuda, "current_device", lambda: 3)
+    assert cv._concrete_device("cuda") == torch.device("cuda:3")
+    assert cv._concrete_device("cuda:1") == torch.device("cuda:1")
+    assert cv._concrete_device("cpu:0") == torch.device("cpu")
+
+
+def test_encoder_dtype_is_not_silently_cast():
+    with pytest.raises(cv.CaptureValidationError, match="float32 tensor"):
+        cv._finite_cpu_float32(torch.ones((1, 512), dtype=torch.float64), (1, 512), "fixture")
+
+
+def test_residual_masked_pooling_and_bool_mask_contract():
+    with torch.random.fork_rng():
+        torch.manual_seed(1729)
+        system = _RecordingResidual(_AnalyticBase(), _AnalyticPeriodic(),
+             PhaseSetRetrievalHead(embedding_dim=512, text_hidden_dim=512), embedding_dim=512)
+    base = cv.run_capture_validation(_RecordingBaseSystem(), _config(), _source())
+    result = cv.run_capture_validation(system, _config("residual"), _source())
+    assert np.array_equal(result.dataset.scores, base.dataset.scores)
+    assert result.primary_capture_r1 == Fraction(1)
+    assert result.encoded_bytes == 3 * (512 * 4 + 6 * 512 * 4 + 6)
+    row_a = result.dataset.motion_commitments.index(_id("A"))
+    assert system.scored_tokens[row_a, 0, 0].item() == 10.0
+    assert system.scored_mask.sum().item() == 1
+    invalid = system.scored_tokens[~system.scored_mask]
+    assert torch.count_nonzero(invalid).item() == 0
+    assert not torch.signbit(invalid).any().item()
+    system.periodic_encoder.bad_mask = True
+    with pytest.raises(cv.CaptureValidationError, match="bool"):
+        cv.run_capture_validation(system, _config("residual"), _source())
+
+
+@pytest.mark.parametrize("stage", ["base", "residual"])
+def test_actual_width_native_encoder_capture_validation(stage):
+    with torch.random.fork_rng():
+        torch.manual_seed(1729)
+        base = ActorMeanBase()
+        if stage == "base":
+            system = BaseRetrievalSystem(base, embedding_dim=512)
+        else:
+            system = ResidualRetrievalSystem(base, PhaseSetEncoder(),
+                PhaseSetRetrievalHead(embedding_dim=512, text_hidden_dim=512), embedding_dim=512)
+    source = cv.CaptureValidationSource("val", "c" * 64,
+        (_capture("native-A", ((1.0, 2.0), (0.0, 1.0)), (0,)),
+         _capture("native-B", ((2.0, 1.0),), (1,))))
+    result = cv.run_capture_validation(system, _config(stage), source)
+    repeat = cv.run_capture_validation(system, _config(stage), source)
+    assert result.dataset.scores.shape == (2, 2)
+    assert np.isfinite(result.dataset.scores).all()
+    assert result.scores_float64_sha256 == repeat.scores_float64_sha256
+    assert result.primary_capture_r1 == repeat.primary_capture_r1
+    assert all(parameter.grad is None for parameter in system.parameters())
+
+
+def test_capture_macro_fraction_does_not_weight_captions_as_independent_captures():
+    from phaseset_core.evaluation import RetrievalDataset
+    dataset = RetrievalDataset(
+        scores=np.array([[2.0, 0.0, 0.0], [1.0, 3.0, 4.0]], dtype=np.float64),
+        motion_commitments=(_id("a"), _id("b")),
+        caption_commitments=(_id("a0"), _id("a1"), _id("b0")),
+        positive_motion_indices=((0,), (0,), (1,)),
+        group_sizes=np.array([3, 3], dtype=np.int64), component_labels=("C00", "C00"))
+    # Capture A text success=1/2, B=1; both motion queries retrieve a positive.
+    assert cv._capture_fractions(dataset) == (Fraction(3, 4), Fraction(1), Fraction(7, 8))

From ed9e75a217fa5509cd6def71f51bedae8ca63c68 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 09:50:13 -0500
Subject: [PATCH 11/95] Add receipt-bound capture storage and verified
 full-width CUDA lifecycle evidence

---
 RELEASE_FILES.sha256                          |   11 +-
 STATUS.md                                     |   28 +-
 docs/CAPTURE_PREPARED_STORAGE.md              |   64 +
 docs/CUDA_LIFECYCLE_OBSERVATION.md            |   62 +
 docs/FROZEN_CLIP_TEXT_ADAPTER.md              |   31 +
 src/phaseset_core/capture_prepared_storage.py | 1049 +++++++++++++++++
 src/phaseset_core/frozen_clip_text.py         |  598 ++++++++++
 .../test_phaseset_capture_prepared_storage.py |  552 +++++++++
 ...st_phaseset_frozen_clip_cache_rehydrate.py |  396 +++++++
 9 files changed, 2784 insertions(+), 7 deletions(-)
 create mode 100644 docs/CAPTURE_PREPARED_STORAGE.md
 create mode 100644 docs/CUDA_LIFECYCLE_OBSERVATION.md
 create mode 100644 src/phaseset_core/capture_prepared_storage.py
 create mode 100644 tests/test_phaseset_capture_prepared_storage.py
 create mode 100644 tests/test_phaseset_frozen_clip_cache_rehydrate.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 73e42b7..59b8311 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,17 +11,19 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-b0a0c70901c228ad99c2fa8adf039245388b1b5a80358778413162d143370395	16883	STATUS.md
+00c359b7f0a16dfe232e430bb02a9659f9d180b7641684d948de94647ce1b451	18439	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
+a9d6a4875760c58a0075e9e4e6b7a5f147da9762410fde953d648895ec159a76	3398	docs/CAPTURE_PREPARED_STORAGE.md
 6d562907df5ae5a598401eb6ba622775ae03d4b81795671fe0c86942174e90e8	6781	docs/CAPTURE_VALIDATION.md
+e9331b64297d6c4586ccb22022c972fad657ac2b3f9202203be62b067a38ca82	3686	docs/CUDA_LIFECYCLE_OBSERVATION.md
 e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA_PROTOCOL.md
 2a18716b57d21362672faaaf76944821764302064b7d7d931f9ec6a0d3f4c598	5564	docs/EMBODY_ADAPTER_INTEGRATION.md
 3633e37c3eae3289d552a363e565bee027ebd83661f1c44ea136f9498de9e5af	19059	docs/EXECUTION_PROTOCOL.md
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
-a96ee04e959510fbcde26e704dc11f096b8fdf3bbe6789772be7b96ab3034190	12305	docs/FROZEN_CLIP_TEXT_ADAPTER.md
+97b7b7e68b7896220b38661b094505b47c711a431f546e6da9772cfedcf6beaa	14238	docs/FROZEN_CLIP_TEXT_ADAPTER.md
 f2c59540bfc4dcdbb5013b76eb20090a7dd61c805523121995ed9206c843f9c8	3711	docs/FROZEN_MHA_RUNTIME.md
 7612d39a01ca67d4a74bc5fb71bacd78e30d7512e9facb46cc331185ff5e65c4	11994	docs/HOST_RUNTIME_INTEGRATION.md
 8bd042f717f9a70e888ff79758aae991043404952243dc9dfd60282e4ecb29a1	1712	docs/LEGACY_TEST_STABILITY.md
@@ -123,6 +125,7 @@ dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phase
 9668a89535264a3ed6b58ffc7ac31b4904a4d8f68636d405e15cbf7368096716	8437	src/phaseset_core/_morlet_oracle_bytes.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
 d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
+6789c7aef0be5225ba0d4534596d5d7d95e126cf494eceb212651b5d5eff5742	43901	src/phaseset_core/capture_prepared_storage.py
 85e5e80bc3b9d21b5fb8865923d27cb6254c077ac2319afe38dad8b79d785bd2	34366	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
@@ -132,7 +135,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
-9ccd454368f85eedb5eeebfdc2c87d0f4b5f76fc141426ea77cec0487444cc41	48867	src/phaseset_core/frozen_clip_text.py
+e0242fc212e83f2d3b694734fadb62ab76c4ef90c3c5b04df503f5a7c907a3e0	68520	src/phaseset_core/frozen_clip_text.py
 25871fe486090edd2ac49f82018dcced116e35c85d99178b251bc8862dfdbe70	78628	src/phaseset_core/host.py
 47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
@@ -180,6 +183,7 @@ b801d46fb184d99c65825d7f9bf91cbced022418600a95d421c5d9244dbded13	19373	tests/tes
 4da1d6c1b05bc485b9afb78776264ae7757744f104c37b947cdce0280cdfb172	2347	tests/test_phasepair_posix_checkpoint_ownership.py
 fa34950fb26f63d5d1ba169499e6e4af49c9b691f33c5cb3aac6e961bdac9e55	18384	tests/test_phaseset_caption_fusion.py
 c88e5b91823b22ebb7a4019acb328040c6b42be7acc03738949439e4efbe443f	10126	tests/test_phaseset_capture_pooling.py
+53d9629d112fb6c835e8cec6f6b4eb5ccc70a2c9f86862099404fb7769b7bec3	19344	tests/test_phaseset_capture_prepared_storage.py
 a02d460a28bd6aeb372fa66e779cb4a65c86536ab951ccd3b6c30ed8091cbd15	8371	tests/test_phaseset_capture_training.py
 beacbd78c68771001b60ff7c6271e6d99c92d24d83f251e1074fc4ce50d1216c	15511	tests/test_phaseset_capture_validation.py
 5dbbb81d6725f1185f4c12dc85653b6eac1e47ae2ffb7fb36f5b951eb8dea632	8848	tests/test_phaseset_cli.py
@@ -191,6 +195,7 @@ a17a50e801c389b4e24b2840f2aafcb7b476cbef86e2e142ad0af6245a54718f	4515	tests/test
 8bd6e95a35523d108153f668f8919e6f22858e8283e6308041751c6c92d0f904	18605	tests/test_phaseset_evaluation.py
 aea41df712a25be78841e655a6fbd91fc96217ce92e60d3ab842f185fc39a58c	41553	tests/test_phaseset_execution.py
 dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/test_phaseset_experiments.py
+04df5b4a8d75bed006f16b56910e4082d4104a45cc8aeb11d6932ede645777cb	14111	tests/test_phaseset_frozen_clip_cache_rehydrate.py
 937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
 4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
 a75f583fd0a233760b1a421ca51680afe4195c33f9ef4bce6e9a1737828f207e	14907	tests/test_phaseset_models.py
diff --git a/STATUS.md b/STATUS.md
index ef17fd5..232a5ec 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -17,9 +17,10 @@ evidence, not training results or scientific claims.
   read-only inventory is complete. Existing workloads were not interrupted.
 - A separate data-volume Python 3.12.12 / NumPy 2.4.6 / Torch 2.12.0+cu126
   environment is installed with a clean dependency check. The complete Linux
-  CPU regression including capture-level training selection passed:
-  1012 tests, 2 skipped, and 39 subtests, in 286.46 s. Its focused integration
-  passed 84 tests and 6 subtests. Source bytes were unchanged before and after
+  CPU regression including capture storage and frozen-text restoration passed:
+  1043 tests, 2 skipped, and 39 subtests, in 298.12 s. Its focused integration
+  passed 110 tests. The preceding capture-training integration passed 1012
+  complete / 84 focused tests and 6 focused subtests. Source bytes were unchanged before and after
   both suites. The prior prepared-data v2 host integration passed 94 focused /
   964 complete tests. The prior residual-resume integration
   passed 948 tests and 39 subtests.
@@ -54,8 +55,17 @@ evidence, not training results or scientific claims.
   core or an optimizer. A subsequent B0 repeat stopped before model creation
   for insufficient free memory. After memory recovered, a distinct retry chain
   passed retrieval/B0/B1/B2 with the same reviewed source and tolerance; the
-  original resource failure remains preserved. Full-training and BF16
+  original resource failure remains preserved. Formal-training and BF16
   qualification remain pending. See [runtime policy](docs/FROZEN_MHA_RUNTIME.md).
+- Full-width FP32 CUDA lifecycle checks subsequently passed for B0/B1/B2 and
+  system 08: width 512, 8 heads, FFN 2048, full 200-frame body22 windows and analytic
+  K2/K3 groups. Three uninterrupted optimizer updates exactly matched a newly
+  constructed runtime restoring update 2 and completing update 3, including
+  model, optimizer, scheduler, RNG, cursor and manifests. All four lanes stayed
+  within a 2 GiB allocator cap; B2 retained all four social-temporal layers.
+  These bounded fixtures used effective batch 2, completed no epoch, selected
+  no validation checkpoint, and are not formal runs or performance benchmarks.
+  See [observed lifecycle scope](docs/CUDA_LIFECYCLE_OBSERVATION.md).
 - The native Embody loader and concrete licensed SMPL-X body-22 evaluator are
   implemented. Seven adapter contract tests passed on Linux/Python 3.12.12/
   NumPy 2.4.6; these use explicit fixtures, not licensed assets or main data.
@@ -81,6 +91,16 @@ evidence, not training results or scientific claims.
   Private-host disk composition
   and licensed source/caption provenance remain incomplete. See
   [capture validation](docs/CAPTURE_VALIDATION.md).
+- A bounded validation-only capture storage writer/reader now preserves the
+  complete window plan, original padding, variable caption counts and original
+  frozen text receipts. Receipt-bound restoration performs no CLIP inference.
+  An actual official-model cache probe caught a snapshot-file ordering bug that
+  the initial structural fixtures had missed. The failed attempt is preserved;
+  the corrected source passed the 110 / 1043 tests above plus three official
+  forwards and two exact disk-restoration roundtrips, with no extra forward.
+  The original output goldens and observed CPU Torch RNG were unchanged.
+  Storage is not a rights, annotation, split or anonymization proof. See
+  [capture storage](docs/CAPTURE_PREPARED_STORAGE.md).
 - Neither Embody approval nor a licensed neutral model has been observed.
   Open-license Multi-TPC acquisition completed with its official byte count and
   MD5 verified. Its archive inventory has 322 files. The bounded audit confirms
diff --git a/docs/CAPTURE_PREPARED_STORAGE.md b/docs/CAPTURE_PREPARED_STORAGE.md
new file mode 100644
index 0000000..66f8212
--- /dev/null
+++ b/docs/CAPTURE_PREPARED_STORAGE.md
@@ -0,0 +1,64 @@
+# Prepared capture-validation storage
+
+`phaseset_core.capture_prepared_storage` persists an already admitted,
+validation-only `CaptureValidationSource` without discarding windows or
+re-encoding text. It preserves full chronological 200-frame body22 windows,
+original actor padding, variable caption counts, and each original frozen-CLIP
+feature batch and canonical encoding receipt.
+
+```python
+from phaseset_core.capture_prepared_storage import (
+    load_capture_validation_source,
+    write_capture_validation_source,
+)
+
+written = write_capture_validation_source(new_absolute_private_root, source)
+restored = load_capture_validation_source(
+    written.manifest_path,
+    expected_manifest_sha256=written.manifest_sha256,
+)
+assert restored.census_sha256 == source.census_sha256
+```
+
+The caller must retain the expected manifest digest through its independently
+authenticated preparation record. Reading a digest from the same untrusted
+artifact is not authentication. The writer requires a new root; existing
+artifacts are not overwritten.
+
+The manifest binds exact capture/window/caption counts, complete window plans,
+the upstream manifest and source census, every numeric artifact, original text
+receipts, caption lineage, output feature hashes and embedding cache keys.
+Window arrays are deterministic uncompressed NPZ/NPY1.0, with no pickle or
+object arrays. Text features and receipt JSON are stored separately.
+
+The reader hashes and decodes the same bounded byte buffers. It checks closed
+canonical JSON, file and directory census, regular non-symlink file identity,
+stable opened-descriptor metadata, ZIP metadata and CRC, NPY headers and payload
+lengths, exact dtypes/shapes/masks, and final reconstructed source census.
+Header-based logical materialized-byte limits are enforced before NumPy loads
+the arrays. Resource failures reject the complete source; they never sample
+windows, people or captions. These limits are not a total-process-RSS bound.
+Root ancestors must be trusted and stable; the loader does not claim
+descriptor-relative protection against hostile writable ancestors.
+
+Frozen text restoration calls only
+`rehydrate_frozen_clip_text_batch(embeddings, receipt_json_bytes=...,
+expected_receipt_sha256=...)` in the owning text module. That API verifies the
+complete original canonical receipt, full/per-row feature bytes, fixed model
+and tokenizer identity, historical encoding source/runtime manifests,
+caption lineage, chunk ranges and cache key before reconstructing the owned
+batch. It does not load CLIP or perform inference. Historical training/adapter
+source identities remain historical; live adapter loading retains its own
+strict current source pins.
+
+This is private data storage, not an anonymization or data-release format.
+Capture, actor, group, caption and component commitments can remain linkable.
+The upstream controller must ensure component labels contain no identifying
+text. Stored bodies, features, captions, operational paths and receipts do not
+belong in the public repository.
+
+Storage integrity does not establish data rights, participant-disjointness,
+official holistic annotation identity, model quality, or permission to open
+the sealed test split. This API accepts validation only. Cross-source training
+and validation admission belongs to the host and split-audit layer; a pair of
+individually valid sources must not be assumed disjoint.
diff --git a/docs/CUDA_LIFECYCLE_OBSERVATION.md b/docs/CUDA_LIFECYCLE_OBSERVATION.md
new file mode 100644
index 0000000..cf3429d
--- /dev/null
+++ b/docs/CUDA_LIFECYCLE_OBSERVATION.md
@@ -0,0 +1,62 @@
+# Bounded full-width CUDA lifecycle observation
+
+This is engineering evidence for public commit
+`e3c73c031c0034701277870ca99fbbcbdd037294`, not a real-data result or completed
+formal training qualification. No dataset, external checkpoint or licensed
+body-model asset was used. The four lanes ran on an NVIDIA RTX A6000 using
+Python3.12.12, NumPy2.4.6, Torch2.12.0+cu126 and CUDA12.6, with FP32,
+deterministic backend settings and the frozen disabled-MHA-fastpath policy.
+
+Each lane used the actual registered architecture: width512,8 attention heads,
+FFN2048, full200-frame body22 windows, analytic K2/K3 groups, and frozen analytic
+text embeddings. The effective batch was2 for this engineering fixture, not
+the formal128-window batch. System08 retained its six-band periodic encoder,
+incidence topology and text-band head. No reduced-width substitute was used.
+
+The lifecycle was:3 uninterrupted optimizer updates; an independent2-update
+run; a third newly constructed runtime restoring its update2 checkpoint and
+performing update3. Three updates allow initially zero residual/topology gates
+to begin passing nonzero topology gradients. Every lane used fresh work and
+checkpoint directories. No epoch was completed and no validation metric or
+selected checkpoint was produced.
+
+| Observation | B0 | B1 | B2 | System08 |
+|---|---:|---:|---:|---:|
+| Parameter elements, including frozen base | 6351361 | 9504257 | 20014593 | 8197639 |
+| Peak allocated CUDA bytes | 259429888 | 326669824 | 641133568 | 138454528 |
+| Checkpoint files retained | 4 | 4 | 4 | 4 |
+| Checkpoint bytes retained | 305078852 | 456479908 | 961194980 | 190464852 |
+| Uninterrupted vs resumed decoded payload/model/optimizer/scheduler/RNG/cursor/manifests | exact | exact | exact | exact |
+
+For B0/B1/B2, the group encoder and logit scale had finite nonzero gradients and
+changed state. For08, the topology encoder, six topology-gate elements,
+text-band MLP and residual score gate had finite nonzero gradients and changed
+state. Its frozen base retained exactly the same state with no gradients.
+Frozen fixture text bytes and non-gradient state were preserved in every lane.
+All source files remained unchanged, and all canonical observation validators
+and all local receipt-member checks passed.
+
+Private receipt SHA-256 commitments (operational files and checkpoints are not
+public release assets):
+
+- B0: `6517734b854e74ea42982a9055de08256d14fe4876cdd91b38c091a8a92346b0`
+- B1: `a0df9f67b5bb47fa957f36b8adc3ea68c412a5dae2a5c950daeaa2decd7f5e8f`
+- B2: `fe0914cac54e6ae88f01f104489c44086d3d6f2cde8dd281cc1afe17f0fea671`
+- System08: `4815471fc85c3dbbbf8afd2798295fee5327b437a82f6fadfa6cb0c259b70d1d`
+
+B1/B2 were a distinct reviewed extension with explicit PMA and four-layer
+social-temporal parameter censuses. Their wrapper revalidated the successful
+B0/08 receipts before execution. Both preserved the full200-frame fixture and
+the same2GiB cap; no architecture was reduced to fit. All60 local receipt
+members across the four lanes were checked after their transfers completed.
+
+Raw checkpoint archive bytes also happened to match for the uninterrupted and
+resumed runs in each lane. Only decoded-state equality was required; this does
+not promise equal serialization bytes across independent hardware or runtimes.
+
+These figures are not throughput, latency or memory-efficiency benchmarks:
+the device was shared, a2GiB allocator cap was imposed, and only this bounded
+fixture was observed. BF16, formal initialization/base
+qualification, real-data overfit, convergence, native-data scores and empirical
+paper readiness remain separate requirements. The synthetic losses are not
+reported as retrieval quality.
diff --git a/docs/FROZEN_CLIP_TEXT_ADAPTER.md b/docs/FROZEN_CLIP_TEXT_ADAPTER.md
index de8f350..9bb0362 100644
--- a/docs/FROZEN_CLIP_TEXT_ADAPTER.md
+++ b/docs/FROZEN_CLIP_TEXT_ADAPTER.md
@@ -232,3 +232,34 @@ CLIP forwards passed. Both output digests above, repeated embedding equality,
 and caller RNG were unchanged. The adapter receipt correctly records the new
 training and adapter source identities; no golden was regenerated. This is a
 source-bound CPU feature-path requalification, not a real-data training result.
+
+## Receipt-bound disk restoration
+
+`rehydrate_frozen_clip_text_batch` restores an already encoded batch from its
+owned CPU float32 features and complete original canonical JSON receipt. The
+caller supplies the independently retained expected receipt digest. Closed
+field/type validation checks all official file pins, historical source/runtime
+manifests, caption lineage, chunk ranges, cache identity, per-row feature hashes
+and the complete feature hash. It does not load CLIP or run inference. A
+historical receipt is not rewritten to claim today's encoding source.
+
+The first separate real-model restoration probe found a snapshot-order bug:
+the live encoder records files in filename order, whereas restoration compared
+against pin declaration order. Unit fixtures had repeated that wrong ordering.
+The failed attempt is retained. Restoration and fixtures were corrected to the
+encoder's actual ordering; a negative case now rejects declaration-order rows.
+No official pin, live encoding behavior or output golden was changed.
+
+The corrected integration passed110 focused tests and1043 complete tests,
+2 existing skips and39 subtests. Three subsequent actual official forwards
+preserved both original output digests above. Two separately persisted cache
+batches then restored exact features, lineage, complete receipts and cache
+keys, with no extra forward and unchanged observed CPU Torch RNG state.
+Source comparisons passed, and all44 receipt members were independently
+rehashed after transfer. The actual cache-probe private receipt commitment is
+`b8dfe62f3773f81ff117d31da703cf60130eaf0b6890407bc203cdff35bbd2ac`.
+Private cache payloads and raw operational receipts are not public assets.
+
+See [prepared capture storage](CAPTURE_PREPARED_STORAGE.md) for the bounded
+on-disk consumer. Neither restoration nor a matching receipt proves caption
+rights, official annotation identity, participant-disjointness or model quality.
diff --git a/src/phaseset_core/capture_prepared_storage.py b/src/phaseset_core/capture_prepared_storage.py
new file mode 100644
index 0000000..5d73783
--- /dev/null
+++ b/src/phaseset_core/capture_prepared_storage.py
@@ -0,0 +1,1049 @@
+"""Immutable val-only storage for prepared capture-validation sources.
+
+This authority-zero seam persists already-prepared numeric windows and already
+frozen CLIP features.  It neither selects accepted windows nor handles caption
+text, datasets, body-model assets, checkpoints, or evaluation test data.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+import io
+import json
+import math
+import os
+from pathlib import Path
+import stat
+from typing import Final, Mapping
+import zipfile
+
+import numpy as np
+import torch
+
+from .capture_pooling import CaptureWindowPlan
+from .capture_validation import (
+    CaptureValidationCapture,
+    CaptureValidationSource,
+    CaptureValidationWindow,
+)
+from .contracts import PreparedGroupBatch
+from .frozen_clip_text import rehydrate_frozen_clip_text_batch
+
+
+AUTHORITY: Final = 0
+SCHEMA: Final = "phaseset-capture-prepared-storage-v1"
+MANIFEST_NAME: Final = "capture-validation-manifest.json"
+WINDOW_KEYS: Final = (
+    "skeletons",
+    "actor_mask",
+    "frame_mask",
+    "track_mask",
+    "actor_commitments",
+    "group_commitments",
+)
+TEXT_KEYS: Final = ("embeddings",)
+WINDOW_DTYPES: Final = {
+    "skeletons": np.dtype(np.float32),
+    "actor_mask": np.dtype(np.bool_),
+    "frame_mask": np.dtype(np.bool_),
+    "track_mask": np.dtype(np.bool_),
+    "actor_commitments": np.dtype(np.uint8),
+    "group_commitments": np.dtype(np.uint8),
+}
+TEXT_DTYPES: Final = {"embeddings": np.dtype(np.float32)}
+MAX_JSON_BYTES: Final = 64 * 1024 * 1024
+ABSOLUTE_MAX_FILE_BYTES: Final = 8 * 2**30
+ABSOLUTE_MAX_TOTAL_MATERIALIZED_BYTES: Final = 64 * 2**30
+
+
+class CapturePreparedStorageError(ValueError):
+    """A source or consumed artifact violates the closed storage contract."""
+
+
+class CapturePreparedStorageResourceLimit(CapturePreparedStorageError):
+    """A complete source exceeds a declared bound; no rows are sampled."""
+
+
+@dataclass(frozen=True, slots=True)
+class CaptureStorageLimits:
+    """Explicit complete-census allocation and cardinality bounds."""
+
+    max_file_bytes: int = 2**31
+    max_decoded_file_bytes: int = 2**31
+    max_total_materialized_bytes: int = 8 * 2**30
+    max_captures: int = 100_000
+    max_windows: int = 1_000_000
+    max_captions: int = 1_000_000
+
+    def __post_init__(self) -> None:
+        values = (
+            self.max_file_bytes,
+            self.max_decoded_file_bytes,
+            self.max_total_materialized_bytes,
+            self.max_captures,
+            self.max_windows,
+            self.max_captions,
+        )
+        if any(type(value) is not int or value < 1 for value in values):
+            raise CapturePreparedStorageError("storage limits must be positive exact ints")
+        if self.max_file_bytes > ABSOLUTE_MAX_FILE_BYTES:
+            raise CapturePreparedStorageError("max_file_bytes exceeds the absolute bound")
+        if self.max_decoded_file_bytes > ABSOLUTE_MAX_FILE_BYTES:
+            raise CapturePreparedStorageError(
+                "max_decoded_file_bytes exceeds the absolute bound"
+            )
+        if self.max_total_materialized_bytes > ABSOLUTE_MAX_TOTAL_MATERIALIZED_BYTES:
+            raise CapturePreparedStorageError(
+                "max_total_materialized_bytes exceeds the absolute bound"
+            )
+
+
+@dataclass(frozen=True, slots=True)
+class CaptureStorageBuildResult:
+    root: Path
+    manifest_path: Path
+    manifest_sha256: str
+    source_census_sha256: str
+    capture_count: int
+    window_count: int
+    caption_count: int
+    total_materialized_bytes: int
+    authority: int = AUTHORITY
+
+
+def _sha256(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise CapturePreparedStorageError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _raw32(value: object, label: str) -> bytes:
+    if type(value) is not bytes or len(value) != 32:
+        raise CapturePreparedStorageError(f"{label} must be exact bytes[32]")
+    return value
+
+
+def _commitment_hex(value: object, label: str) -> bytes:
+    return bytes.fromhex(_lower_sha256(value, label))
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _unique_object(pairs: list[tuple[str, object]]) -> dict[str, object]:
+    output: dict[str, object] = {}
+    for key, value in pairs:
+        if key in output:
+            raise CapturePreparedStorageError("JSON contains a duplicate key")
+        output[key] = value
+    return output
+
+
+def _reject_constant(value: str) -> object:
+    raise CapturePreparedStorageError(f"JSON contains forbidden constant {value}")
+
+
+def _parse_json(raw: bytes, label: str) -> dict[str, object]:
+    try:
+        value = json.loads(
+            raw.decode("ascii"),
+            object_pairs_hook=_unique_object,
+            parse_constant=_reject_constant,
+        )
+        canonical = _canonical_json_bytes(value)
+    except CapturePreparedStorageError:
+        raise
+    except (UnicodeError, TypeError, ValueError, OverflowError, RecursionError):
+        raise CapturePreparedStorageError(f"{label} must be strict ASCII JSON") from None
+    if type(value) is not dict or raw != canonical:
+        raise CapturePreparedStorageError(f"{label} must be a canonical JSON object")
+    return value
+
+
+def _closed(value: Mapping[str, object], keys: set[str], label: str) -> None:
+    if set(value) != keys:
+        raise CapturePreparedStorageError(f"{label} keys are not closed")
+
+
+def _positive_int(value: object, label: str) -> int:
+    if type(value) is not int or value < 1:
+        raise CapturePreparedStorageError(f"{label} must be a positive exact int")
+    return value
+
+
+def _uint64(value: object, label: str) -> int:
+    if type(value) is not int or not 0 <= value < 2**64:
+        raise CapturePreparedStorageError(f"{label} must be an exact uint64")
+    return value
+
+
+def _reject_symlink_components(path: Path, label: str) -> None:
+    for component in (path, *path.parents):
+        if component.is_symlink():
+            raise CapturePreparedStorageError(f"{label} may not traverse a symlink")
+
+
+def _canonical_existing_root(root: Path) -> Path:
+    if not isinstance(root, Path) or not root.is_absolute():
+        raise CapturePreparedStorageError("artifact root must be an absolute pathlib.Path")
+    _reject_symlink_components(root, "artifact root")
+    resolved = root.resolve()
+    if not resolved.is_dir():
+        raise CapturePreparedStorageError("artifact root must be an existing directory")
+    return resolved
+
+
+def _resolve_under(root: Path, value: object, label: str) -> Path:
+    if (
+        type(value) is not str
+        or not value
+        or "\x00" in value
+        or "\\" in value
+        or Path(value).is_absolute()
+    ):
+        raise CapturePreparedStorageError(f"{label} must be a canonical relative path")
+    parts = value.split("/")
+    if any(part in {"", ".", ".."} for part in parts):
+        raise CapturePreparedStorageError(f"{label} contains an invalid path component")
+    unresolved = root.joinpath(*parts)
+    _reject_symlink_components(unresolved, label)
+    resolved = unresolved.resolve()
+    try:
+        resolved.relative_to(root)
+    except ValueError as error:
+        raise CapturePreparedStorageError(f"{label} escapes the artifact root") from error
+    if not resolved.is_file():
+        raise CapturePreparedStorageError(f"{label} must be a regular file")
+    return resolved
+
+
+def _stat_identity(value: os.stat_result) -> tuple[int, int, int, int, int]:
+    return (
+        value.st_dev,
+        value.st_ino,
+        value.st_size,
+        value.st_mtime_ns,
+        value.st_ctime_ns,
+    )
+
+
+def _read_bounded(path: Path, *, maximum: int, label: str) -> bytes:
+    descriptor = -1
+    try:
+        path_before = os.lstat(path)
+        if not stat.S_ISREG(path_before.st_mode):
+            raise CapturePreparedStorageError(
+                f"{label} must be a regular non-symlink file"
+            )
+        flags = os.O_RDONLY
+        if hasattr(os, "O_BINARY"):
+            flags |= os.O_BINARY
+        if hasattr(os, "O_NOFOLLOW"):
+            flags |= os.O_NOFOLLOW
+        descriptor = os.open(path, flags)
+        opened_before = os.fstat(descriptor)
+        if (
+            not stat.S_ISREG(opened_before.st_mode)
+            or _stat_identity(path_before) != _stat_identity(opened_before)
+        ):
+            raise CapturePreparedStorageError(f"{label} changed before it was opened")
+        size = opened_before.st_size
+        if not 1 <= size <= maximum:
+            raise CapturePreparedStorageResourceLimit(f"{label} exceeds its byte bound")
+        raw = bytearray()
+        while len(raw) <= size:
+            block = os.read(descriptor, min(1024 * 1024, size + 1 - len(raw)))
+            if not block:
+                break
+            raw.extend(block)
+        opened_after = os.fstat(descriptor)
+        path_after = os.lstat(path)
+        if (
+            not stat.S_ISREG(path_after.st_mode)
+            or _stat_identity(opened_before) != _stat_identity(opened_after)
+            or _stat_identity(opened_after) != _stat_identity(path_after)
+            or len(raw) != size
+        ):
+            raise CapturePreparedStorageError(f"{label} changed while it was read")
+        return bytes(raw)
+    except CapturePreparedStorageError:
+        raise
+    except OSError:
+        raise CapturePreparedStorageError(f"{label} could not be read safely") from None
+    finally:
+        if descriptor >= 0:
+            try:
+                os.close(descriptor)
+            except OSError:
+                pass
+
+
+def _write_once(path: Path, raw: bytes) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL
+    if hasattr(os, "O_NOFOLLOW"):
+        flags |= os.O_NOFOLLOW
+    descriptor = os.open(path, flags, 0o600)
+    try:
+        with os.fdopen(descriptor, "wb") as stream:
+            descriptor = -1
+            stream.write(raw)
+            stream.flush()
+            os.fsync(stream.fileno())
+    finally:
+        if descriptor >= 0:
+            os.close(descriptor)
+    path.chmod(stat.S_IRUSR)
+
+
+def _npy_bytes(value: np.ndarray) -> bytes:
+    output = io.BytesIO()
+    np.lib.format.write_array(output, value, version=(1, 0), allow_pickle=False)
+    return output.getvalue()
+
+
+def _npz_bytes(arrays: Mapping[str, np.ndarray], keys: tuple[str, ...]) -> bytes:
+    output = io.BytesIO()
+    with zipfile.ZipFile(output, mode="w", compression=zipfile.ZIP_STORED) as archive:
+        for key in keys:
+            info = zipfile.ZipInfo(f"{key}.npy", date_time=(1980, 1, 1, 0, 0, 0))
+            info.compress_type = zipfile.ZIP_STORED
+            info.create_system = 3
+            info.external_attr = (stat.S_IFREG | 0o600) << 16
+            archive.writestr(info, _npy_bytes(arrays[key]))
+    return output.getvalue()
+
+
+def _array_decoded_bytes(arrays: Mapping[str, np.ndarray]) -> int:
+    return sum(int(value.nbytes) for value in arrays.values())
+
+
+def _decode_npz(
+    raw: bytes,
+    *,
+    keys: tuple[str, ...],
+    dtypes: Mapping[str, np.dtype],
+    limits: CaptureStorageLimits,
+    materialized_byte_limit: int,
+    label: str,
+) -> tuple[dict[str, np.ndarray], int]:
+    if type(materialized_byte_limit) is not int or materialized_byte_limit < 1:
+        raise CapturePreparedStorageResourceLimit(
+            f"{label} has no remaining materialized-byte budget"
+        )
+    expected_names = [f"{key}.npy" for key in keys]
+    try:
+        with zipfile.ZipFile(io.BytesIO(raw), mode="r") as archive:
+            infos = archive.infolist()
+            if [value.filename for value in infos] != expected_names:
+                raise CapturePreparedStorageError(f"{label} member census is invalid")
+            decoded_from_headers = 0
+            for key, info in zip(keys, infos, strict=True):
+                if (
+                    info.compress_type != zipfile.ZIP_STORED
+                    or info.compress_size != info.file_size
+                    or info.flag_bits & 0x1
+                    or info.is_dir()
+                    or info.file_size < 1
+                    or info.date_time != (1980, 1, 1, 0, 0, 0)
+                    or info.create_system != 3
+                    or info.external_attr != (stat.S_IFREG | 0o600) << 16
+                    or info.file_size > limits.max_decoded_file_bytes
+                ):
+                    raise CapturePreparedStorageError(f"{label} ZIP metadata is invalid")
+            if sum(info.file_size for info in infos) > limits.max_decoded_file_bytes:
+                raise CapturePreparedStorageResourceLimit(
+                    f"{label} decoded archive exceeds its byte bound"
+                )
+            for key, info in zip(keys, infos, strict=True):
+                with archive.open(info, mode="r") as member:
+                    version = np.lib.format.read_magic(member)
+                    if version != (1, 0):
+                        raise CapturePreparedStorageError(
+                            f"{label} NPY header version is invalid"
+                        )
+                    shape, fortran_order, dtype = np.lib.format.read_array_header_1_0(
+                        member
+                    )
+                    if (
+                        fortran_order
+                        or dtype != dtypes[key]
+                        or dtype.hasobject
+                        or any(type(dimension) is not int or dimension < 0 for dimension in shape)
+                    ):
+                        raise CapturePreparedStorageError(
+                            f"{label} NPY header contract is invalid"
+                        )
+                    payload_bytes = math.prod(shape) * dtype.itemsize
+                    if member.tell() + payload_bytes != info.file_size:
+                        raise CapturePreparedStorageError(
+                            f"{label} NPY payload length is inconsistent"
+                        )
+                    decoded_from_headers += payload_bytes
+                    if (
+                        decoded_from_headers > limits.max_decoded_file_bytes
+                        or decoded_from_headers > materialized_byte_limit
+                    ):
+                        raise CapturePreparedStorageResourceLimit(
+                            f"{label} decoded arrays exceed their byte bound"
+                        )
+            if archive.testzip() is not None:
+                raise CapturePreparedStorageError(f"{label} CRC check failed")
+            arrays: dict[str, np.ndarray] = {}
+            with np.load(io.BytesIO(raw), allow_pickle=False) as loaded:
+                if list(loaded.files) != list(keys):
+                    raise CapturePreparedStorageError(f"{label} array census is invalid")
+                for key in keys:
+                    value = loaded[key]
+                    if (
+                        type(value) is not np.ndarray
+                        or value.dtype != dtypes[key]
+                        or not value.flags.c_contiguous
+                    ):
+                        raise CapturePreparedStorageError(
+                            f"{label} array {key!r} has an invalid dtype or layout"
+                        )
+                    arrays[key] = np.array(value, copy=True, order="C", subok=False)
+    except CapturePreparedStorageError:
+        raise
+    except (OSError, ValueError, EOFError, zipfile.BadZipFile) as error:
+        raise CapturePreparedStorageError(f"{label} is not a valid canonical NPZ") from error
+    decoded = _array_decoded_bytes(arrays)
+    if (
+        decoded != decoded_from_headers
+        or decoded > limits.max_decoded_file_bytes
+        or decoded > materialized_byte_limit
+    ):
+        raise CapturePreparedStorageResourceLimit(
+            f"{label} decoded arrays exceed their byte bound"
+        )
+    return arrays, decoded
+
+
+def _window_arrays(groups: PreparedGroupBatch) -> dict[str, np.ndarray]:
+    k_pad = groups.padded_actor_count
+    actors = np.zeros((1, k_pad, 32), dtype=np.uint8)
+    for index, commitment in enumerate(groups.actor_commitments[0]):
+        if commitment is not None:
+            actors[0, index] = np.frombuffer(commitment, dtype=np.uint8)
+    group = np.frombuffer(groups.group_commitments[0], dtype=np.uint8).reshape(1, 32)
+    return {
+        "skeletons": np.array(groups.skeletons, copy=True, order="C"),
+        "actor_mask": np.array(groups.actor_mask, copy=True, order="C"),
+        "frame_mask": np.array(groups.frame_mask, copy=True, order="C"),
+        "track_mask": np.array(groups.track_mask, copy=True, order="C"),
+        "actor_commitments": actors,
+        "group_commitments": np.array(group, copy=True, order="C"),
+    }
+
+
+def _window_expected_decoded_bytes(groups: PreparedGroupBatch) -> int:
+    return (
+        int(groups.skeletons.nbytes)
+        + int(groups.actor_mask.nbytes)
+        + int(groups.frame_mask.nbytes)
+        + int(groups.track_mask.nbytes)
+        + groups.padded_actor_count * 32
+        + 32
+    )
+
+
+def _groups_from_arrays(arrays: Mapping[str, np.ndarray]) -> PreparedGroupBatch:
+    skeletons = arrays["skeletons"]
+    if skeletons.ndim != 5 or skeletons.shape[0] != 1:
+        raise CapturePreparedStorageError("window skeleton shape must be [1,K_pad,T,22,3]")
+    _, k_pad, frames, joints, coordinates = skeletons.shape
+    if frames != 200 or joints != 22 or coordinates != 3 or k_pad < 2:
+        raise CapturePreparedStorageError("window skeleton dimensions are invalid")
+    shapes = {
+        "actor_mask": (1, k_pad),
+        "frame_mask": (1, 200),
+        "track_mask": (1, k_pad, 200, 22),
+        "actor_commitments": (1, k_pad, 32),
+        "group_commitments": (1, 32),
+    }
+    for key, shape in shapes.items():
+        if arrays[key].shape != shape:
+            raise CapturePreparedStorageError(f"window array {key!r} shape is invalid")
+    actors: list[bytes | None] = []
+    for index in range(k_pad):
+        raw = arrays["actor_commitments"][0, index].tobytes(order="C")
+        actors.append(raw if bool(arrays["actor_mask"][0, index]) else None)
+        if not bool(arrays["actor_mask"][0, index]) and raw != bytes(32):
+            raise CapturePreparedStorageError(
+                "invalid actor commitment slots must be exact zero"
+            )
+    try:
+        return PreparedGroupBatch(
+            skeletons,
+            arrays["actor_mask"],
+            arrays["frame_mask"],
+            arrays["track_mask"],
+            (tuple(actors),),
+            (arrays["group_commitments"][0].tobytes(order="C"),),
+        )
+    except (TypeError, ValueError) as error:
+        raise CapturePreparedStorageError("window batch contract is invalid") from error
+
+
+def _snapshot_source(source: CaptureValidationSource) -> CaptureValidationSource:
+    if type(source) is not CaptureValidationSource:
+        raise CapturePreparedStorageError("source must be exact CaptureValidationSource")
+    try:
+        checked = CaptureValidationSource(
+            source.split,
+            source.manifest_sha256,
+            source.captures,
+        )
+    except (TypeError, ValueError) as error:
+        raise CapturePreparedStorageError("capture validation source is invalid") from error
+    rebuilt: list[CaptureValidationCapture] = []
+    for capture in checked.captures:
+        receipt_raw = capture.holistic_text.receipt.canonical_json_bytes()
+        try:
+            text = rehydrate_frozen_clip_text_batch(
+                capture.holistic_text.embeddings,
+                receipt_json_bytes=receipt_raw,
+                expected_receipt_sha256=_sha256(receipt_raw),
+            )
+            rebuilt.append(
+                CaptureValidationCapture(
+                    capture.plan,
+                    capture.windows,
+                    text,
+                    capture.component_label,
+                )
+            )
+        except Exception as error:
+            raise CapturePreparedStorageError(
+                "capture source contains a non-rehydratable frozen text batch"
+            ) from error
+    try:
+        snapshot = CaptureValidationSource("val", checked.manifest_sha256, tuple(rebuilt))
+    except (TypeError, ValueError) as error:
+        raise CapturePreparedStorageError("capture source snapshot is invalid") from error
+    if snapshot.census_sha256 != checked.census_sha256:
+        raise CapturePreparedStorageError("capture source changed during snapshotting")
+    return snapshot
+
+
+def _count_source(source: CaptureValidationSource) -> tuple[int, int, int]:
+    return (
+        len(source.captures),
+        sum(len(capture.windows) for capture in source.captures),
+        sum(len(capture.holistic_text.caption_commitments) for capture in source.captures),
+    )
+
+
+def _check_counts(counts: tuple[int, int, int], limits: CaptureStorageLimits) -> None:
+    if counts[0] > limits.max_captures:
+        raise CapturePreparedStorageResourceLimit("capture census exceeds its bound")
+    if counts[1] > limits.max_windows:
+        raise CapturePreparedStorageResourceLimit("window census exceeds its bound")
+    if counts[2] > limits.max_captions:
+        raise CapturePreparedStorageResourceLimit("caption census exceeds its bound")
+
+
+def _add_materialized(total: int, amount: int, limits: CaptureStorageLimits) -> int:
+    result = total + amount
+    if result > limits.max_total_materialized_bytes:
+        raise CapturePreparedStorageResourceLimit(
+            "complete source exceeds the total materialized-byte bound"
+        )
+    return result
+
+
+def write_capture_validation_source(
+    root: Path,
+    source: CaptureValidationSource,
+    *,
+    limits: CaptureStorageLimits = CaptureStorageLimits(),
+) -> CaptureStorageBuildResult:
+    """Write one new immutable artifact tree and return its exact receipt."""
+
+    if type(limits) is not CaptureStorageLimits:
+        raise CapturePreparedStorageError("limits must be exact CaptureStorageLimits")
+    checked = _snapshot_source(source)
+    counts = _count_source(checked)
+    _check_counts(counts, limits)
+    if not isinstance(root, Path) or not root.is_absolute():
+        raise CapturePreparedStorageError("new artifact root must be an absolute Path")
+    if root != root.resolve(strict=False):
+        raise CapturePreparedStorageError("new artifact root must be canonical")
+    _reject_symlink_components(root.parent, "artifact root parent")
+    parent = root.parent.resolve()
+    if not parent.is_dir():
+        raise CapturePreparedStorageError("artifact root parent must exist")
+    canonical = parent / root.name
+    if canonical.exists() or canonical.is_symlink():
+        raise CapturePreparedStorageError("new artifact root must not exist")
+    canonical.mkdir(mode=0o700, parents=False, exist_ok=False)
+
+    capture_rows: list[dict[str, object]] = []
+    total = 0
+    for capture_index, capture in enumerate(checked.captures):
+        window_rows: list[dict[str, object]] = []
+        for window_index, (window, source_start) in enumerate(
+            zip(capture.windows, capture.plan.source_start_frames, strict=True)
+        ):
+            relative = (
+                f"captures/capture-{capture_index:06d}/window-{window_index:06d}.npz"
+            )
+            expected_decoded = _window_expected_decoded_bytes(window.groups)
+            if expected_decoded > limits.max_decoded_file_bytes:
+                raise CapturePreparedStorageResourceLimit(
+                    "window decoded arrays exceed their byte bound"
+                )
+            total = _add_materialized(total, expected_decoded, limits)
+            arrays = _window_arrays(window.groups)
+            decoded = _array_decoded_bytes(arrays)
+            if decoded != expected_decoded:
+                raise CapturePreparedStorageError("window decoded-byte preflight changed")
+            raw = _npz_bytes(arrays, WINDOW_KEYS)
+            if len(raw) > limits.max_file_bytes:
+                raise CapturePreparedStorageResourceLimit("window NPZ exceeds its byte bound")
+            path = canonical.joinpath(*relative.split("/"))
+            _write_once(path, raw)
+            consumed = _read_bounded(path, maximum=limits.max_file_bytes, label="window NPZ")
+            rebuilt, rebuilt_bytes = _decode_npz(
+                consumed,
+                keys=WINDOW_KEYS,
+                dtypes=WINDOW_DTYPES,
+                limits=limits,
+                materialized_byte_limit=decoded,
+                label="window NPZ",
+            )
+            _groups_from_arrays(rebuilt)
+            if rebuilt_bytes != decoded:
+                raise CapturePreparedStorageError("window decoded-byte census changed")
+            window_rows.append(
+                {
+                    "decoded_bytes": decoded,
+                    "path": relative,
+                    "sha256": _sha256(consumed),
+                    "source_start_frame": source_start,
+                    "window_commitment": window.window_commitment.hex(),
+                }
+            )
+
+        expected_text_decoded = capture.holistic_text.receipt.output_bytes
+        if (
+            type(expected_text_decoded) is not int
+            or expected_text_decoded < 1
+            or expected_text_decoded > limits.max_decoded_file_bytes
+        ):
+            raise CapturePreparedStorageResourceLimit(
+                "text decoded arrays exceed their byte bound"
+            )
+        total = _add_materialized(total, expected_text_decoded, limits)
+        embeddings = capture.holistic_text.embeddings
+        text_array = embeddings.detach().cpu().contiguous().numpy().copy(order="C")
+        text_arrays = {"embeddings": text_array}
+        text_decoded = _array_decoded_bytes(text_arrays)
+        if text_decoded != expected_text_decoded:
+            raise CapturePreparedStorageError("text decoded-byte preflight changed")
+        text_relative = f"captures/capture-{capture_index:06d}/text.npz"
+        text_raw = _npz_bytes(text_arrays, TEXT_KEYS)
+        if len(text_raw) > limits.max_file_bytes:
+            raise CapturePreparedStorageResourceLimit("text NPZ exceeds its byte bound")
+        text_path = canonical.joinpath(*text_relative.split("/"))
+        _write_once(text_path, text_raw)
+        consumed_text = _read_bounded(
+            text_path, maximum=limits.max_file_bytes, label="text NPZ"
+        )
+        _, rebuilt_text_bytes = _decode_npz(
+            consumed_text,
+            keys=TEXT_KEYS,
+            dtypes=TEXT_DTYPES,
+            limits=limits,
+            materialized_byte_limit=text_decoded,
+            label="text NPZ",
+        )
+        if rebuilt_text_bytes != text_decoded:
+            raise CapturePreparedStorageError("text decoded-byte census changed")
+
+        receipt_raw = capture.holistic_text.receipt.canonical_json_bytes()
+        if len(receipt_raw) > min(MAX_JSON_BYTES, limits.max_file_bytes):
+            raise CapturePreparedStorageResourceLimit("text receipt exceeds its byte bound")
+        total = _add_materialized(total, len(receipt_raw), limits)
+        receipt_relative = f"captures/capture-{capture_index:06d}/text-receipt.json"
+        receipt_path = canonical.joinpath(*receipt_relative.split("/"))
+        _write_once(receipt_path, receipt_raw)
+        consumed_receipt = _read_bounded(
+            receipt_path,
+            maximum=min(MAX_JSON_BYTES, limits.max_file_bytes),
+            label="text receipt",
+        )
+        if consumed_receipt != receipt_raw:
+            raise CapturePreparedStorageError("text receipt bytes changed after writing")
+        capture_rows.append(
+            {
+                "capture_commitment": capture.capture_commitment.hex(),
+                "component_label": capture.component_label,
+                "plan_sha256": capture.plan.sha256,
+                "text": {
+                    "cache_key_sha256": (
+                        capture.holistic_text.receipt.frozen_embedding_cache_key_sha256
+                    ),
+                    "caption_commitments": [
+                        value.hex() for value in capture.holistic_text.caption_commitments
+                    ],
+                    "caption_count": len(capture.holistic_text.caption_commitments),
+                    "decoded_bytes": text_decoded,
+                    "path": text_relative,
+                    "receipt_path": receipt_relative,
+                    "receipt_sha256": _sha256(consumed_receipt),
+                    "sha256": _sha256(consumed_text),
+                    "output_sha256": capture.holistic_text.receipt.output_sha256,
+                },
+                "windows": window_rows,
+            }
+        )
+
+    manifest_value = {
+        "authority": AUTHORITY,
+        "captures": capture_rows,
+        "counts": {
+            "captions": counts[2],
+            "captures": counts[0],
+            "windows": counts[1],
+        },
+        "schema": SCHEMA,
+        "source_census_sha256": checked.census_sha256,
+        "split": "val",
+        "upstream_manifest_sha256": checked.manifest_sha256,
+    }
+    manifest_raw = _canonical_json_bytes(manifest_value)
+    if len(manifest_raw) > min(MAX_JSON_BYTES, limits.max_file_bytes):
+        raise CapturePreparedStorageResourceLimit("storage manifest exceeds its byte bound")
+    total = _add_materialized(total, len(manifest_raw), limits)
+    manifest_path = canonical / MANIFEST_NAME
+    _write_once(manifest_path, manifest_raw)
+    consumed_manifest = _read_bounded(
+        manifest_path,
+        maximum=min(MAX_JSON_BYTES, limits.max_file_bytes),
+        label="storage manifest",
+    )
+    _parse_json(consumed_manifest, "storage manifest")
+    return CaptureStorageBuildResult(
+        root=canonical,
+        manifest_path=manifest_path,
+        manifest_sha256=_sha256(consumed_manifest),
+        source_census_sha256=checked.census_sha256,
+        capture_count=counts[0],
+        window_count=counts[1],
+        caption_count=counts[2],
+        total_materialized_bytes=total,
+    )
+
+
+def _manifest_counts(value: object, limits: CaptureStorageLimits) -> tuple[int, int, int]:
+    if type(value) is not dict:
+        raise CapturePreparedStorageError("manifest counts must be an object")
+    _closed(value, {"captures", "windows", "captions"}, "manifest counts")
+    counts = (
+        _positive_int(value["captures"], "capture count"),
+        _positive_int(value["windows"], "window count"),
+        _positive_int(value["captions"], "caption count"),
+    )
+    _check_counts(counts, limits)
+    return counts
+
+
+def load_capture_validation_source(
+    manifest_path: Path,
+    *,
+    expected_manifest_sha256: str,
+    limits: CaptureStorageLimits = CaptureStorageLimits(),
+) -> CaptureValidationSource:
+    """Consume one authenticated canonical manifest and all referenced bytes."""
+
+    if type(limits) is not CaptureStorageLimits:
+        raise CapturePreparedStorageError("limits must be exact CaptureStorageLimits")
+    expected = _lower_sha256(expected_manifest_sha256, "expected manifest SHA-256")
+    if not isinstance(manifest_path, Path) or not manifest_path.is_absolute():
+        raise CapturePreparedStorageError("manifest_path must be an absolute Path")
+    root = _canonical_existing_root(manifest_path.parent)
+    resolved_manifest = _resolve_under(root, manifest_path.name, "storage manifest")
+    if resolved_manifest.name != MANIFEST_NAME:
+        raise CapturePreparedStorageError("storage manifest filename is not canonical")
+    manifest_raw = _read_bounded(
+        resolved_manifest,
+        maximum=min(
+            MAX_JSON_BYTES,
+            limits.max_file_bytes,
+            limits.max_total_materialized_bytes,
+        ),
+        label="storage manifest",
+    )
+    if _sha256(manifest_raw) != expected:
+        raise CapturePreparedStorageError("consumed storage manifest SHA-256 mismatch")
+    manifest = _parse_json(manifest_raw, "storage manifest")
+    _closed(
+        manifest,
+        {
+            "authority",
+            "captures",
+            "counts",
+            "schema",
+            "source_census_sha256",
+            "split",
+            "upstream_manifest_sha256",
+        },
+        "storage manifest",
+    )
+    if (
+        type(manifest["authority"]) is not int
+        or manifest["authority"] != AUTHORITY
+        or manifest["schema"] != SCHEMA
+        or manifest["split"] != "val"
+    ):
+        raise CapturePreparedStorageError("storage manifest literals are invalid")
+    source_census = _lower_sha256(manifest["source_census_sha256"], "source census")
+    upstream = _lower_sha256(manifest["upstream_manifest_sha256"], "upstream manifest")
+    counts = _manifest_counts(manifest["counts"], limits)
+    rows = manifest["captures"]
+    if type(rows) is not list or len(rows) != counts[0]:
+        raise CapturePreparedStorageError("capture rows do not match their census")
+    total = _add_materialized(0, len(manifest_raw), limits)
+    captures: list[CaptureValidationCapture] = []
+    seen_paths: set[str] = {MANIFEST_NAME}
+    observed_windows = 0
+    observed_captions = 0
+    prior_capture_id: bytes | None = None
+    for capture_index, raw_capture in enumerate(rows):
+        if type(raw_capture) is not dict:
+            raise CapturePreparedStorageError("capture row must be an object")
+        _closed(
+            raw_capture,
+            {
+                "capture_commitment",
+                "component_label",
+                "plan_sha256",
+                "text",
+                "windows",
+            },
+            "capture row",
+        )
+        capture_id = _commitment_hex(
+            raw_capture["capture_commitment"],
+            "capture commitment",
+        )
+        if prior_capture_id is not None and capture_id <= prior_capture_id:
+            raise CapturePreparedStorageError(
+                "capture rows must be in unique commitment order"
+            )
+        prior_capture_id = capture_id
+        component = raw_capture["component_label"]
+        if type(component) is not str or not component:
+            raise CapturePreparedStorageError("component label must be a nonempty string")
+        plan_sha = _lower_sha256(raw_capture["plan_sha256"], "plan SHA-256")
+        window_rows = raw_capture["windows"]
+        if type(window_rows) is not list or not window_rows:
+            raise CapturePreparedStorageError("capture must contain every planned window")
+        observed_windows += len(window_rows)
+        if observed_windows > limits.max_windows:
+            raise CapturePreparedStorageResourceLimit("window census exceeds its bound")
+        window_ids: list[bytes] = []
+        starts: list[int] = []
+        windows: list[CaptureValidationWindow] = []
+        for window_index, raw_window in enumerate(window_rows):
+            if type(raw_window) is not dict:
+                raise CapturePreparedStorageError("window row must be an object")
+            _closed(
+                raw_window,
+                {
+                    "decoded_bytes",
+                    "path",
+                    "sha256",
+                    "source_start_frame",
+                    "window_commitment",
+                },
+                "window row",
+            )
+            decoded_expected = _positive_int(
+                raw_window["decoded_bytes"], "window decoded bytes"
+            )
+            if decoded_expected > limits.max_decoded_file_bytes:
+                raise CapturePreparedStorageResourceLimit(
+                    "window decoded-byte declaration exceeds its bound"
+                )
+            expected_relative = (
+                f"captures/capture-{capture_index:06d}/window-{window_index:06d}.npz"
+            )
+            if raw_window["path"] != expected_relative or expected_relative in seen_paths:
+                raise CapturePreparedStorageError("window path census is not canonical")
+            seen_paths.add(expected_relative)
+            path = _resolve_under(root, expected_relative, "window NPZ")
+            raw = _read_bounded(path, maximum=limits.max_file_bytes, label="window NPZ")
+            if _sha256(raw) != _lower_sha256(raw_window["sha256"], "window SHA-256"):
+                raise CapturePreparedStorageError("consumed window NPZ SHA-256 mismatch")
+            arrays, decoded = _decode_npz(
+                raw,
+                keys=WINDOW_KEYS,
+                dtypes=WINDOW_DTYPES,
+                limits=limits,
+                materialized_byte_limit=(
+                    limits.max_total_materialized_bytes - total
+                ),
+                label="window NPZ",
+            )
+            if decoded != decoded_expected:
+                raise CapturePreparedStorageError("window decoded-byte census mismatch")
+            total = _add_materialized(total, decoded, limits)
+            window_id = _commitment_hex(
+                raw_window["window_commitment"],
+                "window commitment",
+            )
+            window_ids.append(window_id)
+            starts.append(_uint64(raw_window["source_start_frame"], "window start"))
+            windows.append(CaptureValidationWindow(window_id, _groups_from_arrays(arrays)))
+
+        try:
+            plan = CaptureWindowPlan(capture_id, tuple(window_ids), tuple(starts))
+        except (TypeError, ValueError) as error:
+            raise CapturePreparedStorageError("capture window plan is invalid") from error
+        if plan.sha256 != plan_sha:
+            raise CapturePreparedStorageError("capture plan SHA-256 mismatch")
+
+        text_row = raw_capture["text"]
+        if type(text_row) is not dict:
+            raise CapturePreparedStorageError("text row must be an object")
+        _closed(
+            text_row,
+            {
+                "caption_commitments",
+                "caption_count",
+                "cache_key_sha256",
+                "decoded_bytes",
+                "output_sha256",
+                "path",
+                "receipt_path",
+                "receipt_sha256",
+                "sha256",
+            },
+            "text row",
+        )
+        caption_count = _positive_int(text_row["caption_count"], "caption count")
+        observed_captions += caption_count
+        if observed_captions > limits.max_captions:
+            raise CapturePreparedStorageResourceLimit("caption census exceeds its bound")
+        declared_commitments = text_row["caption_commitments"]
+        if type(declared_commitments) is not list or len(declared_commitments) != caption_count:
+            raise CapturePreparedStorageError("caption commitment census is invalid")
+        commitments: list[bytes] = []
+        for value in declared_commitments:
+            commitments.append(_commitment_hex(value, "caption commitment"))
+        if len(set(commitments)) != caption_count:
+            raise CapturePreparedStorageError("caption commitments are not unique")
+        text_relative = f"captures/capture-{capture_index:06d}/text.npz"
+        receipt_relative = f"captures/capture-{capture_index:06d}/text-receipt.json"
+        if (
+            text_row["path"] != text_relative
+            or text_row["receipt_path"] != receipt_relative
+            or text_relative in seen_paths
+            or receipt_relative in seen_paths
+        ):
+            raise CapturePreparedStorageError("text path census is not canonical")
+        seen_paths.update((text_relative, receipt_relative))
+        text_raw = _read_bounded(
+            _resolve_under(root, text_relative, "text NPZ"),
+            maximum=limits.max_file_bytes,
+            label="text NPZ",
+        )
+        if _sha256(text_raw) != _lower_sha256(text_row["sha256"], "text SHA-256"):
+            raise CapturePreparedStorageError("consumed text NPZ SHA-256 mismatch")
+        text_arrays, text_decoded = _decode_npz(
+            text_raw,
+            keys=TEXT_KEYS,
+            dtypes=TEXT_DTYPES,
+            limits=limits,
+            materialized_byte_limit=(limits.max_total_materialized_bytes - total),
+            label="text NPZ",
+        )
+        if text_decoded != _positive_int(text_row["decoded_bytes"], "text decoded bytes"):
+            raise CapturePreparedStorageError("text decoded-byte census mismatch")
+        embeddings = text_arrays["embeddings"]
+        if embeddings.shape != (caption_count, 512) or not bool(np.isfinite(embeddings).all()):
+            raise CapturePreparedStorageError("text embeddings must be finite [Q,512]")
+        total = _add_materialized(total, text_decoded, limits)
+        remaining = limits.max_total_materialized_bytes - total
+        receipt_raw = _read_bounded(
+            _resolve_under(root, receipt_relative, "text receipt"),
+            maximum=min(MAX_JSON_BYTES, limits.max_file_bytes, remaining),
+            label="text receipt",
+        )
+        receipt_sha = _lower_sha256(text_row["receipt_sha256"], "receipt SHA-256")
+        cache_key_sha = _lower_sha256(text_row["cache_key_sha256"], "cache key SHA-256")
+        output_sha = _lower_sha256(text_row["output_sha256"], "output SHA-256")
+        if _sha256(receipt_raw) != receipt_sha:
+            raise CapturePreparedStorageError("consumed text receipt SHA-256 mismatch")
+        total = _add_materialized(total, len(receipt_raw), limits)
+        try:
+            text_batch = rehydrate_frozen_clip_text_batch(
+                torch.from_numpy(embeddings),
+                receipt_json_bytes=receipt_raw,
+                expected_receipt_sha256=receipt_sha,
+            )
+        except Exception as error:
+            raise CapturePreparedStorageError("frozen text cache rehydration failed") from error
+        if text_batch.caption_commitments != tuple(commitments):
+            raise CapturePreparedStorageError("rehydrated caption lineage differs from manifest")
+        if (
+            text_batch.receipt.frozen_embedding_cache_key_sha256 != cache_key_sha
+            or text_batch.receipt.output_sha256 != output_sha
+        ):
+            raise CapturePreparedStorageError("rehydrated text digests differ from manifest")
+        try:
+            captures.append(
+                CaptureValidationCapture(plan, tuple(windows), text_batch, component)
+            )
+        except (TypeError, ValueError) as error:
+            raise CapturePreparedStorageError("capture validation row is invalid") from error
+
+    if (len(captures), observed_windows, observed_captions) != counts:
+        raise CapturePreparedStorageError("observed artifact census differs from manifest")
+    expected_directories = {"captures"} | {
+        f"captures/capture-{index:06d}" for index in range(counts[0])
+    }
+    observed_files: set[str] = set()
+    observed_directories: set[str] = set()
+    for path in root.rglob("*"):
+        if path.is_symlink():
+            raise CapturePreparedStorageError("artifact tree contains a symlink")
+        relative = path.relative_to(root).as_posix()
+        if path.is_file():
+            observed_files.add(relative)
+        elif path.is_dir():
+            observed_directories.add(relative)
+        else:
+            raise CapturePreparedStorageError("artifact tree contains a special file")
+    if observed_files != seen_paths or observed_directories != expected_directories:
+        raise CapturePreparedStorageError("artifact tree census is not closed")
+    try:
+        source = CaptureValidationSource("val", upstream, tuple(captures))
+    except (TypeError, ValueError) as error:
+        raise CapturePreparedStorageError("rehydrated capture source is invalid") from error
+    if source.census_sha256 != source_census:
+        raise CapturePreparedStorageError("rehydrated source census SHA-256 mismatch")
+    return source
diff --git a/src/phaseset_core/frozen_clip_text.py b/src/phaseset_core/frozen_clip_text.py
index af82117..1a140cd 100644
--- a/src/phaseset_core/frozen_clip_text.py
+++ b/src/phaseset_core/frozen_clip_text.py
@@ -42,6 +42,7 @@
 MAX_CAPTIONS: Final = 16_384
 MAX_CAPTION_UTF8_BYTES: Final = 16_384
 MAX_TOTAL_CAPTION_UTF8_BYTES: Final = 16 * 1024 * 1024
+_MAX_REHYDRATION_RECEIPT_BYTES: Final = 32 * 1024 * 1024
 AUTHORITY: Final = 0
 PRODUCTION: Final = False
 TRAINING_AUTHORIZED: Final = False
@@ -983,6 +984,602 @@ def __repr__(self) -> str:
         )
 
 
+def _rehydration_unique_object(
+    pairs: list[tuple[str, object]],
+) -> dict[str, object]:
+    value: dict[str, object] = {}
+    for key, item in pairs:
+        if type(key) is not str or key in value:
+            _hold("HOLD_REHYDRATION_RECEIPT_JSON")
+        value[key] = item
+    return value
+
+
+def _rehydration_reject_constant(_value: str) -> object:
+    _hold("HOLD_REHYDRATION_RECEIPT_JSON")
+
+
+def _rehydration_closed_dict(
+    value: object,
+    keys: frozenset[str],
+    code: str,
+) -> dict[str, object]:
+    if type(value) is not dict or set(value) != keys:
+        _hold(code)
+    return value
+
+
+def _rehydration_sha256(value: object, code: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        _hold(code)
+    return value
+
+
+def _rehydration_positive_int(value: object, maximum: int, code: str) -> int:
+    if type(value) is not int or not 1 <= value <= maximum:
+        _hold(code)
+    return value
+
+
+def _rehydration_exact_literals(
+    value: object,
+    expected: dict[str, object],
+    code: str,
+) -> dict[str, object]:
+    checked = _rehydration_closed_dict(value, frozenset(expected), code)
+    if any(
+        type(checked[key]) is not type(item) or checked[key] != item
+        for key, item in expected.items()
+    ):
+        _hold(code)
+    return checked
+
+
+def _rehydration_caption_rows(
+    value: object,
+    *,
+    count: int,
+) -> tuple[
+    tuple[tuple[int, str, str, int, int, bool, str, str, str], ...],
+    tuple[bytes, ...],
+]:
+    if type(value) is not list or len(value) != count:
+        _hold("HOLD_REHYDRATION_CAPTION_ROWS")
+    keys = frozenset(
+        {
+            "attention_mask_int64_sha256",
+            "caption_commitment",
+            "caption_utf8_sha256",
+            "embedding_float32_sha256",
+            "encoded_token_count_with_special_tokens",
+            "index",
+            "input_ids_int64_sha256",
+            "original_token_count_with_special_tokens",
+            "truncated",
+        }
+    )
+    rows: list[tuple[int, str, str, int, int, bool, str, str, str]] = []
+    lineage: list[bytes] = []
+    for index, item in enumerate(value):
+        row = _rehydration_closed_dict(
+            item,
+            keys,
+            "HOLD_REHYDRATION_CAPTION_ROWS",
+        )
+        if type(row["index"]) is not int or row["index"] != index:
+            _hold("HOLD_REHYDRATION_CAPTION_ROWS")
+        commitment = _rehydration_sha256(
+            row["caption_commitment"],
+            "HOLD_REHYDRATION_LINEAGE",
+        )
+        text_digest = _rehydration_sha256(
+            row["caption_utf8_sha256"],
+            "HOLD_REHYDRATION_CAPTION_ROWS",
+        )
+        input_digest = _rehydration_sha256(
+            row["input_ids_int64_sha256"],
+            "HOLD_REHYDRATION_CAPTION_ROWS",
+        )
+        mask_digest = _rehydration_sha256(
+            row["attention_mask_int64_sha256"],
+            "HOLD_REHYDRATION_CAPTION_ROWS",
+        )
+        output_digest = _rehydration_sha256(
+            row["embedding_float32_sha256"],
+            "HOLD_REHYDRATION_OUTPUT",
+        )
+        original_count = _rehydration_positive_int(
+            row["original_token_count_with_special_tokens"],
+            MAX_CAPTION_UTF8_BYTES + 2,
+            "HOLD_REHYDRATION_CAPTION_ROWS",
+        )
+        encoded_count = row["encoded_token_count_with_special_tokens"]
+        truncated = row["truncated"]
+        if (
+            original_count < 2
+            or type(encoded_count) is not int
+            or encoded_count != min(original_count, TOKEN_LENGTH)
+            or type(truncated) is not bool
+            or truncated != (original_count > TOKEN_LENGTH)
+        ):
+            _hold("HOLD_REHYDRATION_CAPTION_ROWS")
+        rows.append(
+            (
+                index,
+                commitment,
+                text_digest,
+                original_count,
+                encoded_count,
+                truncated,
+                input_digest,
+                mask_digest,
+                output_digest,
+            )
+        )
+        lineage.append(bytes.fromhex(commitment))
+    checked_lineage = tuple(lineage)
+    if len(set(checked_lineage)) != count:
+        _hold("HOLD_REHYDRATION_LINEAGE")
+    return tuple(rows), checked_lineage
+
+
+def _rehydration_chunk_ranges(
+    value: object,
+    *,
+    count: int,
+    batch_size: int,
+) -> tuple[tuple[int, int], ...]:
+    if type(value) is not list:
+        _hold("HOLD_REHYDRATION_CHUNKS")
+    expected = tuple(
+        (start, min(start + batch_size, count))
+        for start in range(0, count, batch_size)
+    )
+    checked: list[tuple[int, int]] = []
+    for item in value:
+        if (
+            type(item) is not list
+            or len(item) != 2
+            or type(item[0]) is not int
+            or type(item[1]) is not int
+        ):
+            _hold("HOLD_REHYDRATION_CHUNKS")
+        checked.append((item[0], item[1]))
+    if tuple(checked) != expected:
+        _hold("HOLD_REHYDRATION_CHUNKS")
+    return tuple(checked)
+
+
+def _rehydration_snapshot(
+    value: object,
+) -> tuple[tuple[str, int, str], ...]:
+    snapshot = _rehydration_closed_dict(
+        value,
+        frozenset({"file_count", "files", "manifest_sha256"}),
+        "HOLD_REHYDRATION_SNAPSHOT",
+    )
+    files = snapshot["files"]
+    if type(files) is not list or len(files) != len(_OFFICIAL_PINNED_FILES):
+        _hold("HOLD_REHYDRATION_SNAPSHOT")
+    rows: list[tuple[str, int, str]] = []
+    for item in files:
+        row = _rehydration_closed_dict(
+            item,
+            frozenset({"bytes", "name", "sha256"}),
+            "HOLD_REHYDRATION_SNAPSHOT",
+        )
+        name = row["name"]
+        size = row["bytes"]
+        if type(name) is not str or not name or type(size) is not int or size < 1:
+            _hold("HOLD_REHYDRATION_SNAPSHOT")
+        rows.append(
+            (
+                name,
+                size,
+                _rehydration_sha256(
+                    row["sha256"],
+                    "HOLD_REHYDRATION_SNAPSHOT",
+                ),
+            )
+        )
+    checked = tuple(rows)
+    if (
+        type(snapshot["file_count"]) is not int
+        or snapshot["file_count"] != len(checked)
+        or checked != tuple(sorted(_OFFICIAL_PINNED_FILES))
+        or _rehydration_sha256(
+            snapshot["manifest_sha256"],
+            "HOLD_REHYDRATION_SNAPSHOT",
+        )
+        != _snapshot_manifest_sha256(checked)
+    ):
+        _hold("HOLD_REHYDRATION_SNAPSHOT")
+    return checked
+
+
+def _rehydration_source(
+    value: object,
+) -> tuple[tuple[str, int, str], ...]:
+    source = _rehydration_closed_dict(
+        value,
+        frozenset({"files", "manifest_sha256"}),
+        "HOLD_REHYDRATION_SOURCE",
+    )
+    files = source["files"]
+    if type(files) is not list or len(files) != 4:
+        _hold("HOLD_REHYDRATION_SOURCE")
+    rows: list[tuple[str, int, str]] = []
+    for item in files:
+        row = _rehydration_closed_dict(
+            item,
+            frozenset({"bytes", "label", "sha256"}),
+            "HOLD_REHYDRATION_SOURCE",
+        )
+        label = row["label"]
+        size = row["bytes"]
+        if type(label) is not str or not label or type(size) is not int or size < 1:
+            _hold("HOLD_REHYDRATION_SOURCE")
+        rows.append(
+            (
+                label,
+                size,
+                _rehydration_sha256(
+                    row["sha256"],
+                    "HOLD_REHYDRATION_SOURCE",
+                ),
+            )
+        )
+    checked = tuple(rows)
+    if (
+        checked[:2] != _EXPECTED_SOURCE_FILES[:2]
+        or checked[2][0] != "phaseset_core.training"
+        or checked[3][0] != "phaseset_core.frozen_clip_text"
+        or _rehydration_sha256(
+            source["manifest_sha256"],
+            "HOLD_REHYDRATION_SOURCE",
+        )
+        != _source_manifest_sha256(checked)
+    ):
+        _hold("HOLD_REHYDRATION_SOURCE")
+    return checked
+
+
+def _rehydration_runtime(value: object) -> tuple[tuple[str, str], ...]:
+    runtime = _rehydration_closed_dict(
+        value,
+        frozenset(key for key, _item in _EXPECTED_RUNTIME),
+        "HOLD_REHYDRATION_RUNTIME",
+    )
+    if any(type(item) is not str for item in runtime.values()):
+        _hold("HOLD_REHYDRATION_RUNTIME")
+    checked = tuple(sorted(runtime.items()))
+    if checked != _EXPECTED_RUNTIME:
+        _hold("HOLD_REHYDRATION_RUNTIME")
+    return checked
+
+
+def _rehydration_cache_key(
+    *,
+    batch_size: int,
+    caption_rows: tuple[
+        tuple[int, str, str, int, int, bool, str, str, str], ...
+    ],
+    chunk_ranges: tuple[tuple[int, int], ...],
+    runtime_manifest_sha256: str,
+    snapshot_manifest_sha256: str,
+    source_manifest_sha256: str,
+) -> str:
+    payload = {
+        "batch_size": batch_size,
+        "caption_rows": [
+            {
+                "attention_mask_int64_sha256": row[7],
+                "caption_utf8_sha256": row[2],
+                "encoded_token_count_with_special_tokens": row[4],
+                "index": row[0],
+                "input_ids_int64_sha256": row[6],
+                "original_token_count_with_special_tokens": row[3],
+                "truncated": row[5],
+            }
+            for row in caption_rows
+        ],
+        "chunk_ranges": [list(value) for value in chunk_ranges],
+        "method_id": METHOD_ID,
+        "model_id": MODEL_ID,
+        "revision": REVISION,
+        "runtime_manifest_sha256": runtime_manifest_sha256,
+        "snapshot_manifest_sha256": snapshot_manifest_sha256,
+        "source_manifest_sha256": source_manifest_sha256,
+        "token_length": TOKEN_LENGTH,
+        "truncation": True,
+    }
+    return hashlib.sha256(_canonical_json(payload)).hexdigest()
+
+
+def _rehydrate_frozen_clip_text_batch(
+    embeddings: object,
+    *,
+    receipt_json_bytes: object,
+    expected_receipt_sha256: object,
+) -> FrozenClipTextBatch:
+    if (
+        type(receipt_json_bytes) is not bytes
+        or not 1 <= len(receipt_json_bytes) <= _MAX_REHYDRATION_RECEIPT_BYTES
+    ):
+        _hold("HOLD_REHYDRATION_RECEIPT_BYTES")
+    expected_digest = _rehydration_sha256(
+        expected_receipt_sha256,
+        "HOLD_REHYDRATION_EXPECTED_RECEIPT",
+    )
+    if hashlib.sha256(receipt_json_bytes).hexdigest() != expected_digest:
+        _hold("HOLD_REHYDRATION_EXPECTED_RECEIPT")
+    try:
+        parsed = json.loads(
+            receipt_json_bytes.decode("ascii"),
+            object_pairs_hook=_rehydration_unique_object,
+            parse_constant=_rehydration_reject_constant,
+        )
+    except FrozenClipTextAdapterError:
+        raise
+    except (UnicodeError, TypeError, ValueError, OverflowError):
+        _hold("HOLD_REHYDRATION_RECEIPT_JSON")
+    if receipt_json_bytes != _canonical_json(parsed) + b"\n":
+        _hold("HOLD_REHYDRATION_RECEIPT_JSON")
+    receipt_value = _rehydration_closed_dict(
+        parsed,
+        frozenset(
+            {
+                "authority",
+                "batch_size",
+                "caption_count",
+                "caption_rows",
+                "chunk_ranges",
+                "embedding_selection",
+                "frozen_embedding_cache_key_sha256",
+                "live_model_manifest_sha256",
+                "method_id",
+                "model_id",
+                "output",
+                "production",
+                "revision",
+                "runtime",
+                "runtime_manifest_sha256",
+                "schema",
+                "snapshot",
+                "source",
+                "status",
+                "tokenization",
+                "training_authorized",
+            }
+        ),
+        "HOLD_REHYDRATION_RECEIPT_SCHEMA",
+    )
+    literals = {
+        "authority": AUTHORITY,
+        "embedding_selection": "official_pretrained_text_projection_raw_float32",
+        "method_id": METHOD_ID,
+        "model_id": MODEL_ID,
+        "production": PRODUCTION,
+        "revision": REVISION,
+        "schema": "phaseset-frozen-clip-text-receipt-v1",
+        "status": "ENCODED_AUTHORITY0",
+        "training_authorized": TRAINING_AUTHORIZED,
+    }
+    if any(
+        type(receipt_value[key]) is not type(item) or receipt_value[key] != item
+        for key, item in literals.items()
+    ):
+        _hold("HOLD_REHYDRATION_RECEIPT_SCHEMA")
+    _rehydration_exact_literals(
+        receipt_value["tokenization"],
+        {
+            "add_special_tokens": True,
+            "long_caption_policy": "clip_native_truncate_77",
+            "max_tokens_including_special_tokens": TOKEN_LENGTH,
+            "padding": "max_length",
+            "preserve_terminal_eos": True,
+            "truncation": True,
+        },
+        "HOLD_REHYDRATION_TOKENIZATION",
+    )
+    caption_rows_value = receipt_value["caption_rows"]
+    if type(caption_rows_value) is not list:
+        _hold("HOLD_REHYDRATION_CAPTION_ROWS")
+    count = len(caption_rows_value)
+    if not 1 <= count <= MAX_CAPTIONS:
+        _hold("HOLD_REHYDRATION_CAPTION_ROWS")
+    if type(receipt_value["caption_count"]) is not int or receipt_value[
+        "caption_count"
+    ] != count:
+        _hold("HOLD_REHYDRATION_CAPTION_ROWS")
+    batch_size = _rehydration_positive_int(
+        receipt_value["batch_size"],
+        MAX_BATCH_SIZE,
+        "HOLD_REHYDRATION_CHUNKS",
+    )
+    caption_rows, lineage = _rehydration_caption_rows(
+        caption_rows_value,
+        count=count,
+    )
+    chunk_ranges = _rehydration_chunk_ranges(
+        receipt_value["chunk_ranges"],
+        count=count,
+        batch_size=batch_size,
+    )
+    output = _rehydration_closed_dict(
+        receipt_value["output"],
+        frozenset(
+            {
+                "bytes",
+                "device",
+                "dtype",
+                "finite",
+                "requires_grad",
+                "sha256",
+                "shape",
+                "stride",
+            }
+        ),
+        "HOLD_REHYDRATION_OUTPUT",
+    )
+    if (
+        type(output["bytes"]) is not int
+        or output["bytes"] != count * EMBEDDING_DIM * 4
+        or type(output["device"]) is not str
+        or output["device"] != "cpu"
+        or type(output["dtype"]) is not str
+        or output["dtype"] != "torch.float32"
+        or type(output["finite"]) is not bool
+        or output["finite"] is not True
+        or type(output["requires_grad"]) is not bool
+        or output["requires_grad"] is not False
+        or type(output["shape"]) is not list
+        or output["shape"] != [count, EMBEDDING_DIM]
+        or any(type(item) is not int for item in output["shape"])
+        or type(output["stride"]) is not list
+        or output["stride"] != [EMBEDDING_DIM, 1]
+        or any(type(item) is not int for item in output["stride"])
+    ):
+        _hold("HOLD_REHYDRATION_OUTPUT")
+    output_sha256 = _rehydration_sha256(
+        output["sha256"],
+        "HOLD_REHYDRATION_OUTPUT",
+    )
+    try:
+        import torch
+    except Exception:
+        _hold("HOLD_REHYDRATION_RUNTIME_IMPORT")
+    if (
+        type(embeddings) is not torch.Tensor
+        or embeddings.dtype != torch.float32
+        or embeddings.device.type != "cpu"
+        or tuple(embeddings.shape) != (count, EMBEDDING_DIM)
+        or tuple(embeddings.stride()) != (EMBEDDING_DIM, 1)
+        or embeddings.requires_grad
+        or not embeddings.is_contiguous()
+    ):
+        _hold("HOLD_REHYDRATION_OUTPUT")
+    try:
+        frozen_embeddings = embeddings.detach().clone().contiguous()
+    except Exception:
+        _hold("HOLD_REHYDRATION_OUTPUT")
+    if not bool(torch.isfinite(frozen_embeddings).all().item()):
+        _hold("HOLD_REHYDRATION_OUTPUT")
+    output_bytes = _tensor_bytes(frozen_embeddings)
+    if (
+        len(output_bytes) != output["bytes"]
+        or hashlib.sha256(output_bytes).hexdigest() != output_sha256
+    ):
+        _hold("HOLD_REHYDRATION_OUTPUT")
+    row_bytes = EMBEDDING_DIM * 4
+    if any(
+        hashlib.sha256(
+            output_bytes[index * row_bytes : (index + 1) * row_bytes]
+        ).hexdigest()
+        != row[8]
+        for index, row in enumerate(caption_rows)
+    ):
+        _hold("HOLD_REHYDRATION_OUTPUT")
+    snapshot_files = _rehydration_snapshot(receipt_value["snapshot"])
+    source_files = _rehydration_source(receipt_value["source"])
+    runtime_identity = _rehydration_runtime(receipt_value["runtime"])
+    snapshot_manifest = _snapshot_manifest_sha256(snapshot_files)
+    source_manifest = _source_manifest_sha256(source_files)
+    runtime_manifest = _runtime_manifest_sha256(runtime_identity)
+    if (
+        _rehydration_sha256(
+            receipt_value["snapshot"]["manifest_sha256"],
+            "HOLD_REHYDRATION_SNAPSHOT",
+        )
+        != snapshot_manifest
+        or _rehydration_sha256(
+            receipt_value["source"]["manifest_sha256"],
+            "HOLD_REHYDRATION_SOURCE",
+        )
+        != source_manifest
+        or _rehydration_sha256(
+            receipt_value["runtime_manifest_sha256"],
+            "HOLD_REHYDRATION_RUNTIME",
+        )
+        != runtime_manifest
+    ):
+        _hold("HOLD_REHYDRATION_PROVENANCE")
+    cache_key = _rehydration_sha256(
+        receipt_value["frozen_embedding_cache_key_sha256"],
+        "HOLD_REHYDRATION_CACHE_KEY",
+    )
+    if cache_key != _rehydration_cache_key(
+        batch_size=batch_size,
+        caption_rows=caption_rows,
+        chunk_ranges=chunk_ranges,
+        runtime_manifest_sha256=runtime_manifest,
+        snapshot_manifest_sha256=snapshot_manifest,
+        source_manifest_sha256=source_manifest,
+    ):
+        _hold("HOLD_REHYDRATION_CACHE_KEY")
+    live_model_manifest = _rehydration_sha256(
+        receipt_value["live_model_manifest_sha256"],
+        "HOLD_REHYDRATION_PROVENANCE",
+    )
+    receipt = FrozenClipTextReceipt(
+        batch_size=batch_size,
+        caption_rows=caption_rows,
+        chunk_ranges=chunk_ranges,
+        frozen_embedding_cache_key_sha256=cache_key,
+        live_model_manifest_sha256=live_model_manifest,
+        output_bytes=len(output_bytes),
+        output_sha256=output_sha256,
+        output_shape=(count, EMBEDDING_DIM),
+        output_stride=(EMBEDDING_DIM, 1),
+        runtime_identity=runtime_identity,
+        runtime_manifest_sha256=runtime_manifest,
+        snapshot_files=snapshot_files,
+        snapshot_manifest_sha256=snapshot_manifest,
+        source_files=source_files,
+        source_manifest_sha256=source_manifest,
+    )
+    if (
+        receipt.canonical_json_bytes() != receipt_json_bytes
+        or receipt.sha256 != expected_digest
+    ):
+        _hold("HOLD_REHYDRATION_RECEIPT_REBUILD")
+    return FrozenClipTextBatch(
+        frozen_embeddings,
+        lineage,
+        receipt,
+        _seal=_CONSTRUCTION_SEAL,
+    )
+
+
+def rehydrate_frozen_clip_text_batch(
+    embeddings: Any,
+    *,
+    receipt_json_bytes: bytes,
+    expected_receipt_sha256: str,
+) -> FrozenClipTextBatch:
+    """Restore one exact cached batch without re-running the CLIP text tower.
+
+    ``expected_receipt_sha256`` must come from an independently authenticated
+    prepared-data manifest. Rehydration preserves the original encoding
+    source/runtime identities and grants no data, training, or result authority.
+    """
+
+    try:
+        return _rehydrate_frozen_clip_text_batch(
+            embeddings,
+            receipt_json_bytes=receipt_json_bytes,
+            expected_receipt_sha256=expected_receipt_sha256,
+        )
+    except FrozenClipTextAdapterError:
+        raise
+    except Exception:
+        raise FrozenClipTextAdapterError("HOLD_UNEXPECTED_REHYDRATION") from None
+
+
 class FrozenClipTextAdapter:
     """Opaque frozen CLIP text tower loaded only from the pinned local snapshot."""
 
@@ -1333,4 +1930,5 @@ def load_frozen_clip_text_adapter(
     "REVISION",
     "TRAINING_AUTHORIZED",
     "load_frozen_clip_text_adapter",
+    "rehydrate_frozen_clip_text_batch",
 ]
diff --git a/tests/test_phaseset_capture_prepared_storage.py b/tests/test_phaseset_capture_prepared_storage.py
new file mode 100644
index 0000000..7181c99
--- /dev/null
+++ b/tests/test_phaseset_capture_prepared_storage.py
@@ -0,0 +1,552 @@
+"""Server-only contracts for immutable prepared capture-validation storage."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+import hashlib
+import json
+import os
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import capture_prepared_storage as storage
+from phaseset_core import frozen_clip_text as clip
+from phaseset_core.capture_pooling import CaptureWindowPlan
+from phaseset_core.capture_validation import (
+    CaptureValidationCapture,
+    CaptureValidationSource,
+    CaptureValidationWindow,
+)
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+
+
+EXPECTED_RUNTIME = (
+    ("cuda_available", "false"),
+    ("cuda_visible_devices", "hidden"),
+    ("deterministic_algorithms", "true"),
+    ("device", "cpu"),
+    ("float32_matmul_precision", "highest"),
+    ("huggingface_hub", "0.36.2"),
+    ("interop_threads", "1"),
+    ("intraop_threads", "1"),
+    ("machine", "x86_64"),
+    ("mha_fastpath_enabled", "false"),
+    ("numpy", "2.4.6"),
+    ("python", "3.12.12"),
+    ("python_implementation", "CPython"),
+    ("sys_byteorder", "little"),
+    ("system", "Linux"),
+    ("tokenizers", "0.22.2"),
+    ("torch", "2.12.0+cu126"),
+    ("torch_cuda_build", "12.6"),
+    ("transformers", "4.57.3"),
+)
+EXPECTED_SOURCE_PREFIX = (
+    (
+        "transformers.modeling_clip",
+        50_315,
+        "3e779a99827618d6e6d57583ec4f5c0d765321dfa5054a4ffca6a56bb004d499",
+    ),
+    (
+        "transformers.tokenization_clip_fast",
+        6_766,
+        "67dff0f21a56d0dc18a45f8764f3fafa02ee2b0aeafeb70761c9623f9114dd53",
+    ),
+    (
+        "phaseset_core.training",
+        157_160,
+        "9f6c21e354a17facda89a542d53e39013477c5e1b1a259781e2446cd394f6d06",
+    ),
+)
+
+
+def _id(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _canonical(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _digest(value: object) -> str:
+    raw = json.dumps(
+        value,
+        sort_keys=True,
+        separators=(",", ":"),
+        ensure_ascii=True,
+        allow_nan=False,
+    ).encode("ascii")
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().contiguous().view(torch.uint8).numpy().tobytes(order="C")
+
+
+def _frozen_text(label: str, axes: tuple[int, ...]) -> clip.FrozenClipTextBatch:
+    embeddings = torch.zeros((len(axes), 512), dtype=torch.float32)
+    for row, axis in enumerate(axes):
+        embeddings[row, axis] = float(row + 1)
+    rows = tuple(
+        (
+            row,
+            _id(f"{label}/caption/{row}").hex(),
+            _id(f"{label}/caption-bytes/{row}").hex(),
+            3,
+            3,
+            False,
+            _id(f"{label}/tokens/{row}").hex(),
+            _id(f"{label}/mask/{row}").hex(),
+            hashlib.sha256(_tensor_bytes(embeddings[row : row + 1])).hexdigest(),
+        )
+        for row in range(len(axes))
+    )
+    snapshot = tuple(sorted(clip.PINNED_FILES))
+    source = EXPECTED_SOURCE_PREFIX + (
+        (
+            "phaseset_core.frozen_clip_text",
+            68_448,
+            _id("candidate frozen clip source").hex(),
+        ),
+    )
+    snapshot_manifest = _digest(
+        {
+            "files": [
+                {"bytes": size, "name": name, "sha256": digest}
+                for name, size, digest in snapshot
+            ],
+            "model_id": clip.MODEL_ID,
+            "revision": clip.REVISION,
+            "schema": "phaseset-frozen-clip-snapshot-v1",
+        }
+    )
+    source_manifest = _digest(
+        [
+            {"bytes": size, "label": name, "sha256": digest}
+            for name, size, digest in source
+        ]
+    )
+    runtime_manifest = _digest(dict(EXPECTED_RUNTIME))
+    chunks = ((0, len(axes)),)
+    cache_key = _digest(
+        {
+            "batch_size": len(axes),
+            "caption_rows": [
+                {
+                    "attention_mask_int64_sha256": row[7],
+                    "caption_utf8_sha256": row[2],
+                    "encoded_token_count_with_special_tokens": row[4],
+                    "index": row[0],
+                    "input_ids_int64_sha256": row[6],
+                    "original_token_count_with_special_tokens": row[3],
+                    "truncated": row[5],
+                }
+                for row in rows
+            ],
+            "chunk_ranges": [list(value) for value in chunks],
+            "method_id": clip.METHOD_ID,
+            "model_id": clip.MODEL_ID,
+            "revision": clip.REVISION,
+            "runtime_manifest_sha256": runtime_manifest,
+            "snapshot_manifest_sha256": snapshot_manifest,
+            "source_manifest_sha256": source_manifest,
+            "token_length": clip.TOKEN_LENGTH,
+            "truncation": True,
+        }
+    )
+    raw = _tensor_bytes(embeddings)
+    receipt = clip.FrozenClipTextReceipt(
+        batch_size=len(axes),
+        caption_rows=rows,
+        chunk_ranges=chunks,
+        frozen_embedding_cache_key_sha256=cache_key,
+        live_model_manifest_sha256=_id("fixture live model manifest").hex(),
+        output_bytes=len(raw),
+        output_sha256=hashlib.sha256(raw).hexdigest(),
+        output_shape=tuple(embeddings.shape),
+        output_stride=tuple(embeddings.stride()),
+        runtime_identity=EXPECTED_RUNTIME,
+        runtime_manifest_sha256=runtime_manifest,
+        snapshot_files=snapshot,
+        snapshot_manifest_sha256=snapshot_manifest,
+        source_files=source,
+        source_manifest_sha256=source_manifest,
+    )
+    receipt_raw = receipt.canonical_json_bytes()
+    return clip.rehydrate_frozen_clip_text_batch(
+        embeddings,
+        receipt_json_bytes=receipt_raw,
+        expected_receipt_sha256=hashlib.sha256(receipt_raw).hexdigest(),
+    )
+
+
+def _groups(label: str, value: float, *, pad: int) -> PreparedGroupBatch:
+    actors = tuple(_id(f"{label}/actor/{index}") for index in range(3))
+    skeletons = np.zeros((1, 3 + pad, 200, 22, 3), dtype=np.float32)
+    skeletons[0, :3, :, :, 0] = value
+    skeletons[0, :3, :, :, 1] = np.arange(200, dtype=np.float32)[None, :, None]
+    actor_mask = np.zeros((1, 3 + pad), dtype=np.bool_)
+    actor_mask[:, :3] = True
+    frame_mask = np.ones((1, 200), dtype=np.bool_)
+    track_mask = np.zeros((1, 3 + pad, 200, 22), dtype=np.bool_)
+    track_mask[:, :3] = True
+    return PreparedGroupBatch(
+        skeletons,
+        actor_mask,
+        frame_mask,
+        track_mask,
+        (actors + (None,) * pad,),
+        (group_commitment(actors),),
+    )
+
+
+def _capture(
+    label: str,
+    values: tuple[float, ...],
+    axes: tuple[int, ...],
+) -> CaptureValidationCapture:
+    window_ids = tuple(_id(f"{label}/window/{index}") for index in range(len(values)))
+    plan = CaptureWindowPlan(
+        _id(f"{label}/capture"),
+        window_ids,
+        tuple(300 * index for index in range(len(values))),
+    )
+    windows = tuple(
+        CaptureValidationWindow(
+            window_id,
+            _groups(label, value, pad=index % 2),
+        )
+        for index, (window_id, value) in enumerate(zip(window_ids, values, strict=True))
+    )
+    return CaptureValidationCapture(plan, windows[::-1], _frozen_text(label, axes), "C00")
+
+
+def _source() -> CaptureValidationSource:
+    return CaptureValidationSource(
+        "val",
+        _id("accepted-window-and-caption-manifest").hex(),
+        (
+            _capture("capture-b", (3.0,), (7,)),
+            _capture("capture-a", (1.0, 2.0), (2, 4)),
+        ),
+    )
+
+
+def _write(tmp_path: Path) -> tuple[Path, storage.CaptureStorageBuildResult]:
+    root = (tmp_path / "capture-store").resolve()
+    result = storage.write_capture_validation_source(root, _source())
+    return root, result
+
+
+def _rewrite_manifest(path: Path, mutate) -> str:
+    value = json.loads(path.read_text(encoding="ascii"))
+    mutate(value)
+    raw = _canonical(value)
+    os.chmod(path, 0o600)
+    path.write_bytes(raw)
+    os.chmod(path, 0o400)
+    return hashlib.sha256(raw).hexdigest()
+
+
+def test_roundtrip_preserves_complete_capture_source(tmp_path: Path) -> None:
+    original = _source()
+    root, result = _write(tmp_path)
+    loaded = storage.load_capture_validation_source(
+        result.manifest_path,
+        expected_manifest_sha256=result.manifest_sha256,
+    )
+
+    assert loaded.census_sha256 == original.census_sha256 == result.source_census_sha256
+    assert loaded.manifest_sha256 == original.manifest_sha256
+    assert result.capture_count == 2
+    assert result.window_count == 3
+    assert result.caption_count == 3
+    assert sorted(len(value.windows) for value in loaded.captures) == [1, 2]
+    multi_window = next(value for value in loaded.captures if len(value.windows) == 2)
+    assert tuple(value.groups.padded_actor_count for value in multi_window.windows) == (
+        3,
+        4,
+    )
+    for before, after in zip(original.captures, loaded.captures, strict=True):
+        assert before.plan == after.plan
+        assert before.holistic_text.caption_commitments == (
+            after.holistic_text.caption_commitments
+        )
+        assert before.holistic_text.receipt.canonical_json_bytes() == (
+            after.holistic_text.receipt.canonical_json_bytes()
+        )
+        assert torch.equal(before.holistic_text.embeddings, after.holistic_text.embeddings)
+    manifest_raw = result.manifest_path.read_bytes()
+    assert str(root).encode("utf-8") not in manifest_raw
+    assert b"caption-a" not in manifest_raw
+
+
+def test_writer_is_new_root_only_and_count_bounds_are_complete(tmp_path: Path) -> None:
+    root, _ = _write(tmp_path)
+    with pytest.raises(storage.CapturePreparedStorageError):
+        storage.write_capture_validation_source(root, _source())
+    with pytest.raises(storage.CapturePreparedStorageResourceLimit):
+        storage.write_capture_validation_source(
+            (tmp_path / "bounded").resolve(),
+            _source(),
+            limits=storage.CaptureStorageLimits(max_windows=2),
+        )
+    assert not (tmp_path / "bounded").exists()
+
+
+def test_manifest_digest_and_referenced_file_bytes_are_authenticated(tmp_path: Path) -> None:
+    root, result = _write(tmp_path)
+    with pytest.raises(storage.CapturePreparedStorageError, match="manifest SHA-256"):
+        storage.load_capture_validation_source(
+            result.manifest_path,
+            expected_manifest_sha256="0" * 64,
+        )
+
+    window = root / "captures" / "capture-000000" / "window-000000.npz"
+    os.chmod(window, 0o600)
+    raw = bytearray(window.read_bytes())
+    raw[-1] ^= 1
+    window.write_bytes(raw)
+    with pytest.raises(storage.CapturePreparedStorageError, match="window NPZ SHA-256"):
+        storage.load_capture_validation_source(
+            result.manifest_path,
+            expected_manifest_sha256=result.manifest_sha256,
+        )
+
+
+@pytest.mark.parametrize(
+    ("window_count", "error_pattern"),
+    [(1, "every planned window"), (2, "plan SHA-256")],
+)
+def test_manifest_cannot_silently_drop_an_accepted_window(
+    tmp_path: Path, window_count: int, error_pattern: str,
+) -> None:
+    _, result = _write(tmp_path)
+
+    def mutate(value: dict[str, object]) -> None:
+        captures = value["captures"]
+        assert isinstance(captures, list)
+        row = next(row for row in captures if len(row["windows"]) == window_count)
+        assert isinstance(row, dict)
+        windows = row["windows"]
+        assert isinstance(windows, list)
+        windows.pop()
+        counts = value["counts"]
+        assert isinstance(counts, dict)
+        counts["windows"] -= 1
+
+    rebound = _rewrite_manifest(result.manifest_path, mutate)
+    with pytest.raises(storage.CapturePreparedStorageError, match=error_pattern):
+        storage.load_capture_validation_source(
+            result.manifest_path,
+            expected_manifest_sha256=rebound,
+        )
+
+
+def test_text_feature_swap_fails_against_bound_receipt(tmp_path: Path) -> None:
+    root, result = _write(tmp_path)
+    left = root / "captures" / "capture-000000" / "text.npz"
+    right = root / "captures" / "capture-000001" / "text.npz"
+    left_raw = left.read_bytes()
+    right_raw = right.read_bytes()
+    os.chmod(left, 0o600)
+    os.chmod(right, 0o600)
+    left.write_bytes(right_raw)
+    right.write_bytes(left_raw)
+
+    def mutate(value: dict[str, object]) -> None:
+        captures = value["captures"]
+        assert isinstance(captures, list)
+        first = captures[0]
+        second = captures[1]
+        assert isinstance(first, dict) and isinstance(second, dict)
+        first_text = first["text"]
+        second_text = second["text"]
+        assert isinstance(first_text, dict) and isinstance(second_text, dict)
+        first_text["sha256"], second_text["sha256"] = (
+            hashlib.sha256(right_raw).hexdigest(),
+            hashlib.sha256(left_raw).hexdigest(),
+        )
+        first_text["decoded_bytes"], second_text["decoded_bytes"] = (
+            second_text["decoded_bytes"],
+            first_text["decoded_bytes"],
+        )
+
+    rebound = _rewrite_manifest(result.manifest_path, mutate)
+    with pytest.raises(storage.CapturePreparedStorageError, match="text embeddings|rehydration"):
+        storage.load_capture_validation_source(
+            result.manifest_path,
+            expected_manifest_sha256=rebound,
+        )
+
+
+def test_artifact_tree_is_closed_and_text_limits_are_enforced(tmp_path: Path) -> None:
+    root, result = _write(tmp_path)
+    extra = root / "unreferenced.bin"
+    extra.write_bytes(b"not admitted")
+    with pytest.raises(storage.CapturePreparedStorageError, match="tree census"):
+        storage.load_capture_validation_source(
+            result.manifest_path,
+            expected_manifest_sha256=result.manifest_sha256,
+        )
+    extra.unlink()
+
+    with pytest.raises(storage.CapturePreparedStorageResourceLimit):
+        storage.load_capture_validation_source(
+            result.manifest_path,
+            expected_manifest_sha256=result.manifest_sha256,
+            limits=replace(storage.CaptureStorageLimits(), max_captions=2),
+        )
+
+
+def test_bounded_read_rejects_leaf_replacement_between_lstat_and_open(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    leaf = tmp_path / "leaf.bin"
+    replacement = tmp_path / "replacement.bin"
+    leaf.write_bytes(b"original")
+    replacement.write_bytes(b"replaced")
+    real_open = storage.os.open
+    calls = 0
+
+    def swapping_open(path: Path, flags: int, *args: object) -> int:
+        nonlocal calls
+        calls += 1
+        if hasattr(os, "O_NOFOLLOW"):
+            assert flags & os.O_NOFOLLOW
+        os.replace(replacement, leaf)
+        return real_open(path, flags, *args)
+
+    monkeypatch.setattr(storage.os, "open", swapping_open)
+    with pytest.raises(storage.CapturePreparedStorageError, match="changed before"):
+        storage._read_bounded(leaf, maximum=64, label="fixture")
+    assert calls == 1
+
+
+@pytest.mark.parametrize("mutation", ["authority", "capture", "window", "caption"])
+def test_manifest_literals_and_commitment_hex_are_exact(
+    tmp_path: Path,
+    mutation: str,
+) -> None:
+    _, result = _write(tmp_path)
+
+    def mutate(value: dict[str, object]) -> None:
+        captures = value["captures"]
+        assert isinstance(captures, list)
+        first = captures[0]
+        assert isinstance(first, dict)
+        if mutation == "authority":
+            value["authority"] = False
+        elif mutation == "capture":
+            first["capture_commitment"] = first["capture_commitment"].upper()
+        elif mutation == "window":
+            windows = first["windows"]
+            assert isinstance(windows, list) and isinstance(windows[0], dict)
+            windows[0]["window_commitment"] += " "
+        else:
+            text = first["text"]
+            assert isinstance(text, dict)
+            commitments = text["caption_commitments"]
+            assert isinstance(commitments, list)
+            commitments[0] = commitments[0].upper()
+
+    rebound = _rewrite_manifest(result.manifest_path, mutate)
+    with pytest.raises(storage.CapturePreparedStorageError):
+        storage.load_capture_validation_source(
+            result.manifest_path,
+            expected_manifest_sha256=rebound,
+        )
+
+
+@pytest.mark.parametrize("relabel_only", [False, True])
+def test_capture_rows_must_remain_in_canonical_commitment_order(
+    tmp_path: Path, relabel_only: bool,
+) -> None:
+    _, result = _write(tmp_path)
+
+    def mutate(value: dict[str, object]) -> None:
+        captures = value["captures"]
+        assert isinstance(captures, list)
+        if not relabel_only:
+            captures.reverse()
+            return
+        reversed_ids = [row["capture_commitment"] for row in captures][::-1]
+        for row, capture_id in zip(captures, reversed_ids, strict=True):
+            row["capture_commitment"] = capture_id
+            windows = row["windows"]
+            row["plan_sha256"] = CaptureWindowPlan(
+                bytes.fromhex(capture_id),
+                tuple(bytes.fromhex(window["window_commitment"]) for window in windows),
+                tuple(window["source_start_frame"] for window in windows),
+            ).sha256
+
+    rebound = _rewrite_manifest(result.manifest_path, mutate)
+    expected_error = "commitment order" if relabel_only else "window path census"
+    with pytest.raises(storage.CapturePreparedStorageError, match=expected_error):
+        storage.load_capture_validation_source(
+            result.manifest_path,
+            expected_manifest_sha256=rebound,
+        )
+
+
+def test_total_budget_rejects_header_before_numpy_materialization(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    _, result = _write(tmp_path)
+    manifest_bytes = len(result.manifest_path.read_bytes())
+    calls = 0
+
+    def forbidden_load(*args: object, **kwargs: object) -> object:
+        nonlocal calls
+        calls += 1
+        raise AssertionError("NumPy materialized an over-budget member")
+
+    monkeypatch.setattr(storage.np, "load", forbidden_load)
+    with pytest.raises(storage.CapturePreparedStorageResourceLimit):
+        storage.load_capture_validation_source(
+            result.manifest_path,
+            expected_manifest_sha256=result.manifest_sha256,
+            limits=replace(
+                storage.CaptureStorageLimits(),
+                max_total_materialized_bytes=manifest_bytes + 1,
+            ),
+        )
+    assert calls == 0
+
+
+def test_writer_checks_total_before_window_array_copy(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    calls = 0
+
+    def forbidden_arrays(*args: object, **kwargs: object) -> object:
+        nonlocal calls
+        calls += 1
+        raise AssertionError("writer copied an over-budget window")
+
+    monkeypatch.setattr(storage, "_window_arrays", forbidden_arrays)
+    with pytest.raises(storage.CapturePreparedStorageResourceLimit):
+        storage.write_capture_validation_source(
+            (tmp_path / "writer-budget").resolve(),
+            _source(),
+            limits=replace(
+                storage.CaptureStorageLimits(),
+                max_total_materialized_bytes=1,
+            ),
+        )
+    assert calls == 0
diff --git a/tests/test_phaseset_frozen_clip_cache_rehydrate.py b/tests/test_phaseset_frozen_clip_cache_rehydrate.py
new file mode 100644
index 0000000..9a4b661
--- /dev/null
+++ b/tests/test_phaseset_frozen_clip_cache_rehydrate.py
@@ -0,0 +1,396 @@
+"""Pure cache-integrity tests; these do not claim actual CLIP inference."""
+
+import hashlib
+import json
+from pathlib import Path
+
+import pytest
+import torch
+from phaseset_core import frozen_clip_text as clip
+
+
+HISTORICAL_ADAPTER_ROW = (
+    "phaseset_core.frozen_clip_text",
+    48_867,
+    "9ccd454368f85eedb5eeebfdc2c87d0f4b5f76fc141426ea77cec0487444cc41",
+)
+V2_HISTORICAL_ADAPTER_ROW = (
+    "phaseset_core.frozen_clip_text",
+    48_867,
+    "1fbe41cce5db731a5d96fc87f7426eacee52382a45ad5bc96a2c3fbd769756e7",
+)
+HISTORICAL_TRAINING_ROW = (
+    "phaseset_core.training",
+    156_450,
+    "01f5a48be6b4dc9604770d570ff04c0614cdbb9ec90ebfea91afef642384da62",
+)
+
+
+def _canonical(value: object) -> bytes:
+    return json.dumps(
+        value,
+        ensure_ascii=True,
+        allow_nan=False,
+        sort_keys=True,
+        separators=(",", ":"),
+    ).encode("ascii")
+
+
+def _sha(value: bytes) -> str:
+    return hashlib.sha256(value).hexdigest()
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().cpu().contiguous().numpy().tobytes(order="C")
+
+
+def _snapshot_manifest(rows: tuple[tuple[str, int, str], ...]) -> str:
+    return _sha(
+        _canonical(
+            {
+                "files": [
+                    {"bytes": size, "name": name, "sha256": digest}
+                    for name, size, digest in rows
+                ],
+                "model_id": clip.MODEL_ID,
+                "revision": clip.REVISION,
+                "schema": "phaseset-frozen-clip-snapshot-v1",
+            }
+        )
+    )
+
+
+def _source_manifest(rows: tuple[tuple[str, int, str], ...]) -> str:
+    return _sha(
+        _canonical(
+            [
+                {"bytes": size, "label": label, "sha256": digest}
+                for label, size, digest in rows
+            ]
+        )
+    )
+
+
+def _runtime_manifest(rows: tuple[tuple[str, str], ...]) -> str:
+    return _sha(_canonical(dict(rows)))
+
+
+def _fixture(
+    count: int = 2,
+    batch_size: int = 1,
+    *,
+    training_row: tuple[str, int, str] | None = None,
+    adapter_row: tuple[str, int, str] = HISTORICAL_ADAPTER_ROW,
+):
+    embeddings = torch.arange(count * clip.EMBEDDING_DIM, dtype=torch.float32).reshape(
+        count,
+        clip.EMBEDDING_DIM,
+    )
+    embeddings = (torch.sin(embeddings * 0.01) + 0.25).contiguous()
+    raw = _tensor_bytes(embeddings)
+    caption_rows = []
+    for index in range(count):
+        row_raw = raw[
+            index * clip.EMBEDDING_DIM * 4 : (index + 1) * clip.EMBEDDING_DIM * 4
+        ]
+        caption_rows.append(
+            (
+                index,
+                _sha(f"caption-lineage/{index}".encode("ascii")),
+                _sha(f"caption-text/{index}".encode("ascii")),
+                5 + index,
+                5 + index,
+                False,
+                _sha(f"input-ids/{index}".encode("ascii")),
+                _sha(f"attention-mask/{index}".encode("ascii")),
+                _sha(row_raw),
+            )
+        )
+    chunks = tuple(
+        (start, min(start + batch_size, count))
+        for start in range(0, count, batch_size)
+    )
+    snapshot = tuple(sorted(clip._OFFICIAL_PINNED_FILES))
+    source = (
+        *clip._EXPECTED_SOURCE_FILES[:2],
+        training_row or clip._EXPECTED_SOURCE_FILES[2],
+        adapter_row,
+    )
+    runtime = clip._EXPECTED_RUNTIME
+    snapshot_sha = _snapshot_manifest(snapshot)
+    source_sha = _source_manifest(source)
+    runtime_sha = _runtime_manifest(runtime)
+    cache_payload = {
+        "batch_size": batch_size,
+        "caption_rows": [
+            {
+                "attention_mask_int64_sha256": row[7],
+                "caption_utf8_sha256": row[2],
+                "encoded_token_count_with_special_tokens": row[4],
+                "index": row[0],
+                "input_ids_int64_sha256": row[6],
+                "original_token_count_with_special_tokens": row[3],
+                "truncated": row[5],
+            }
+            for row in caption_rows
+        ],
+        "chunk_ranges": [list(value) for value in chunks],
+        "method_id": clip.METHOD_ID,
+        "model_id": clip.MODEL_ID,
+        "revision": clip.REVISION,
+        "runtime_manifest_sha256": runtime_sha,
+        "snapshot_manifest_sha256": snapshot_sha,
+        "source_manifest_sha256": source_sha,
+        "token_length": clip.TOKEN_LENGTH,
+        "truncation": True,
+    }
+    receipt = clip.FrozenClipTextReceipt(
+        batch_size=batch_size,
+        caption_rows=tuple(caption_rows),
+        chunk_ranges=chunks,
+        frozen_embedding_cache_key_sha256=_sha(_canonical(cache_payload)),
+        live_model_manifest_sha256=_sha(b"synthetic-structural-test-model"),
+        output_bytes=len(raw),
+        output_sha256=_sha(raw),
+        output_shape=tuple(embeddings.shape),
+        output_stride=tuple(embeddings.stride()),
+        runtime_identity=runtime,
+        runtime_manifest_sha256=runtime_sha,
+        snapshot_files=snapshot,
+        snapshot_manifest_sha256=snapshot_sha,
+        source_files=source,
+        source_manifest_sha256=source_sha,
+    )
+    return embeddings, receipt
+
+
+def _rehydrate(embeddings: torch.Tensor, public: dict[str, object]):
+    raw = _canonical(public) + b"\n"
+    return clip.rehydrate_frozen_clip_text_batch(
+        embeddings,
+        receipt_json_bytes=raw,
+        expected_receipt_sha256=_sha(raw),
+    )
+
+
+def test_rehydrates_owned_snapshot_without_model_or_current_source_relabel(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    embeddings, receipt = _fixture()
+    rng_before = torch.get_rng_state().clone()
+
+    def forbidden(*args: object, **kwargs: object) -> object:
+        raise AssertionError("rehydration attempted model/runtime construction")
+
+    monkeypatch.setattr(clip, "_real_backend", forbidden)
+    monkeypatch.setattr(clip, "_load_components", forbidden)
+    restored = clip.rehydrate_frozen_clip_text_batch(
+        embeddings,
+        receipt_json_bytes=receipt.canonical_json_bytes(),
+        expected_receipt_sha256=receipt.sha256,
+    )
+    assert type(restored) is clip.FrozenClipTextBatch
+    assert restored.receipt.canonical_json_bytes() == receipt.canonical_json_bytes()
+    assert restored.receipt.source_files[-1] == HISTORICAL_ADAPTER_ROW
+    assert restored.receipt.source_files[-1][2] != _sha(
+        Path(clip.__file__).read_bytes()
+    )
+    assert restored.caption_commitments == tuple(
+        bytes.fromhex(row[1]) for row in receipt.caption_rows
+    )
+    assert torch.equal(restored.embeddings, embeddings)
+    embeddings.zero_()
+    assert not torch.equal(restored.embeddings, embeddings)
+    assert torch.equal(torch.get_rng_state(), rng_before)
+
+
+def test_rehydrates_authenticated_older_training_source_without_relabel() -> None:
+    embeddings, receipt = _fixture(
+        training_row=HISTORICAL_TRAINING_ROW,
+        adapter_row=V2_HISTORICAL_ADAPTER_ROW,
+    )
+    restored = clip.rehydrate_frozen_clip_text_batch(
+        embeddings,
+        receipt_json_bytes=receipt.canonical_json_bytes(),
+        expected_receipt_sha256=receipt.sha256,
+    )
+    assert restored.receipt.source_files[2] == HISTORICAL_TRAINING_ROW
+    assert restored.receipt.source_files[3] == V2_HISTORICAL_ADAPTER_ROW
+    assert restored.receipt.source_manifest_sha256 == receipt.source_manifest_sha256
+    assert restored.receipt.frozen_embedding_cache_key_sha256 == (
+        receipt.frozen_embedding_cache_key_sha256
+    )
+
+
+@pytest.mark.parametrize(
+    ("field", "replacement"),
+    [("label", "phaseset_core.foreign"), ("bytes", "156450")],
+)
+def test_historical_source_rows_keep_exact_label_and_types(
+    field: str,
+    replacement: object,
+) -> None:
+    embeddings, receipt = _fixture(training_row=HISTORICAL_TRAINING_ROW)
+    public = receipt.to_public_dict()
+    public["source"]["files"][2][field] = replacement  # type: ignore[index]
+    if field == "label":
+        source = public["source"]  # type: ignore[assignment]
+        source["manifest_sha256"] = _sha(  # type: ignore[index]
+            _canonical(source["files"])  # type: ignore[index]
+        )
+    with pytest.raises(clip.FrozenClipTextAdapterError) as error:
+        _rehydrate(embeddings, public)
+    assert error.value.code == "HOLD_REHYDRATION_SOURCE"
+
+
+def test_expected_receipt_digest_and_canonical_bytes_are_external_gates() -> None:
+    embeddings, receipt = _fixture()
+    with pytest.raises(clip.FrozenClipTextAdapterError) as mismatch:
+        clip.rehydrate_frozen_clip_text_batch(
+            embeddings,
+            receipt_json_bytes=receipt.canonical_json_bytes(),
+            expected_receipt_sha256="0" * 64,
+        )
+    assert mismatch.value.code == "HOLD_REHYDRATION_EXPECTED_RECEIPT"
+
+    noncanonical = receipt.canonical_json_bytes()[:-1] + b" \n"
+    with pytest.raises(clip.FrozenClipTextAdapterError) as whitespace:
+        clip.rehydrate_frozen_clip_text_batch(
+            embeddings,
+            receipt_json_bytes=noncanonical,
+            expected_receipt_sha256=_sha(noncanonical),
+        )
+    assert whitespace.value.code == "HOLD_REHYDRATION_RECEIPT_JSON"
+
+    duplicate = receipt.canonical_json_bytes().replace(
+        b'{"authority":0,',
+        b'{"authority":0,"authority":0,',
+        1,
+    )
+    with pytest.raises(clip.FrozenClipTextAdapterError) as duplicate_error:
+        clip.rehydrate_frozen_clip_text_batch(
+            embeddings,
+            receipt_json_bytes=duplicate,
+            expected_receipt_sha256=_sha(duplicate),
+        )
+    assert duplicate_error.value.code == "HOLD_REHYDRATION_RECEIPT_JSON"
+
+
+@pytest.mark.parametrize(
+    ("path", "replacement", "code"),
+    [
+        (("schema",), "foreign-schema", "HOLD_REHYDRATION_RECEIPT_SCHEMA"),
+        (("caption_rows", 0, "index"), 1, "HOLD_REHYDRATION_CAPTION_ROWS"),
+        (("chunk_ranges",), [[0, 2]], "HOLD_REHYDRATION_CHUNKS"),
+        (("output", "stride"), [1, 512], "HOLD_REHYDRATION_OUTPUT"),
+        (("runtime_manifest_sha256",), "0" * 64, "HOLD_REHYDRATION_PROVENANCE"),
+        (
+            ("frozen_embedding_cache_key_sha256",),
+            "0" * 64,
+            "HOLD_REHYDRATION_CACHE_KEY",
+        ),
+    ],
+)
+def test_closed_receipt_semantics_fail_after_authenticated_mutation(
+    path: tuple[object, ...],
+    replacement: object,
+    code: str,
+) -> None:
+    embeddings, receipt = _fixture(batch_size=1)
+    public = receipt.to_public_dict()
+    target = public
+    for key in path[:-1]:
+        target = target[key]  # type: ignore[index,assignment]
+    target[path[-1]] = replacement  # type: ignore[index]
+    with pytest.raises(clip.FrozenClipTextAdapterError) as error:
+        _rehydrate(embeddings, public)
+    assert error.value.code == code
+
+
+def test_lineage_provenance_and_row_output_digests_are_rebound() -> None:
+    embeddings, receipt = _fixture()
+    public = receipt.to_public_dict()
+    public["caption_rows"][1]["caption_commitment"] = public["caption_rows"][0][
+        "caption_commitment"
+    ]
+    with pytest.raises(clip.FrozenClipTextAdapterError) as duplicate:
+        _rehydrate(embeddings, public)
+    assert duplicate.value.code == "HOLD_REHYDRATION_LINEAGE"
+
+    public = receipt.to_public_dict()
+    public["caption_rows"][0]["embedding_float32_sha256"] = "0" * 64
+    with pytest.raises(clip.FrozenClipTextAdapterError) as row_digest:
+        _rehydrate(embeddings, public)
+    assert row_digest.value.code == "HOLD_REHYDRATION_OUTPUT"
+
+    public = receipt.to_public_dict()
+    public["source"]["manifest_sha256"] = "0" * 64
+    with pytest.raises(clip.FrozenClipTextAdapterError) as source:
+        _rehydrate(embeddings, public)
+    assert source.value.code == "HOLD_REHYDRATION_SOURCE"
+
+    public = receipt.to_public_dict()
+    public["snapshot"]["files"][0]["sha256"] = "0" * 64
+    with pytest.raises(clip.FrozenClipTextAdapterError) as snapshot:
+        _rehydrate(embeddings, public)
+    assert snapshot.value.code == "HOLD_REHYDRATION_SNAPSHOT"
+
+
+def test_tensor_bytes_type_shape_finiteness_and_gradient_are_exact() -> None:
+    embeddings, receipt = _fixture()
+
+    def call(value: object) -> None:
+        clip.rehydrate_frozen_clip_text_batch(
+            value,
+            receipt_json_bytes=receipt.canonical_json_bytes(),
+            expected_receipt_sha256=receipt.sha256,
+        )
+
+    mutants = (
+        embeddings.to(torch.float64),
+        embeddings[:, ::2],
+        embeddings.detach().clone().requires_grad_(True),
+    )
+    for mutant in mutants:
+        with pytest.raises(clip.FrozenClipTextAdapterError) as error:
+            call(mutant)
+        assert error.value.code == "HOLD_REHYDRATION_OUTPUT"
+    nonfinite = embeddings.clone()
+    nonfinite[0, 0] = torch.nan
+    with pytest.raises(clip.FrozenClipTextAdapterError) as error:
+        call(nonfinite)
+    assert error.value.code == "HOLD_REHYDRATION_OUTPUT"
+    changed = embeddings.clone()
+    changed[0, 0] += 1.0
+    with pytest.raises(clip.FrozenClipTextAdapterError) as error:
+        call(changed)
+    assert error.value.code == "HOLD_REHYDRATION_OUTPUT"
+
+
+def test_snapshot_requires_the_actual_encoder_filename_order() -> None:
+    embeddings, receipt = _fixture()
+    canonical_names = [row[0] for row in receipt.snapshot_files]
+    assert canonical_names == sorted(canonical_names)
+    assert canonical_names != [row[0] for row in clip._OFFICIAL_PINNED_FILES]
+    public = receipt.to_public_dict()
+    files_by_name = {row["name"]: row for row in public["snapshot"]["files"]}
+    public["snapshot"]["files"] = [
+        files_by_name[name] for name, _size, _digest in clip._OFFICIAL_PINNED_FILES
+    ]
+    public["snapshot"]["manifest_sha256"] = _snapshot_manifest(
+        clip._OFFICIAL_PINNED_FILES
+    )
+    with pytest.raises(clip.FrozenClipTextAdapterError) as error:
+        _rehydrate(embeddings, public)
+    assert error.value.code == "HOLD_REHYDRATION_SNAPSHOT"
+
+
+def test_direct_batch_construction_remains_sealed() -> None:
+    embeddings, receipt = _fixture(1)
+    with pytest.raises(TypeError, match="internal"):
+        clip.FrozenClipTextBatch(
+            embeddings,
+            (bytes.fromhex(receipt.caption_rows[0][1]),),
+            receipt,
+            _seal=object(),
+        )

From 1731e9a5d045b7be93b0e78c061fbd11fea2e56b Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 10:23:36 -0500
Subject: [PATCH 12/95] Wire complete-capture validation into host and fix
 Windows storage identity checks

---
 RELEASE_FILES.sha256                          |  11 +-
 STATUS.md                                     |  33 +-
 docs/HOST_RUNTIME_INTEGRATION.md              |  72 +-
 src/phaseset_core/capture_prepared_storage.py |  13 +-
 src/phaseset_core/host.py                     | 324 +++++++-
 .../test_phaseset_capture_prepared_storage.py |  65 ++
 tests/test_phaseset_host_capture_storage.py   | 689 ++++++++++++++++++
 7 files changed, 1177 insertions(+), 30 deletions(-)
 create mode 100644 tests/test_phaseset_host_capture_storage.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 59b8311..4e8a0cf 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,7 +11,7 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-00c359b7f0a16dfe232e430bb02a9659f9d180b7641684d948de94647ce1b451	18439	STATUS.md
+4870fc5e2037406bb915aa84f4b6c1c78c5bf7000ae8db7c129e48fa2fb24243	20110	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
@@ -25,7 +25,7 @@ e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
 97b7b7e68b7896220b38661b094505b47c711a431f546e6da9772cfedcf6beaa	14238	docs/FROZEN_CLIP_TEXT_ADAPTER.md
 f2c59540bfc4dcdbb5013b76eb20090a7dd61c805523121995ed9206c843f9c8	3711	docs/FROZEN_MHA_RUNTIME.md
-7612d39a01ca67d4a74bc5fb71bacd78e30d7512e9facb46cc331185ff5e65c4	11994	docs/HOST_RUNTIME_INTEGRATION.md
+fad4d9e8d1a51bf443af94b536b7aee0b8031cf42edab58190db7b174a768e0e	15565	docs/HOST_RUNTIME_INTEGRATION.md
 8bd042f717f9a70e888ff79758aae991043404952243dc9dfd60282e4ecb29a1	1712	docs/LEGACY_TEST_STABILITY.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
@@ -125,7 +125,7 @@ dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phase
 9668a89535264a3ed6b58ffc7ac31b4904a4d8f68636d405e15cbf7368096716	8437	src/phaseset_core/_morlet_oracle_bytes.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
 d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
-6789c7aef0be5225ba0d4534596d5d7d95e126cf494eceb212651b5d5eff5742	43901	src/phaseset_core/capture_prepared_storage.py
+3a60f7a68d9de9b0ded88274a631e1e4195b4d12689a60a7f9ae93401a36af16	44353	src/phaseset_core/capture_prepared_storage.py
 85e5e80bc3b9d21b5fb8865923d27cb6254c077ac2319afe38dad8b79d785bd2	34366	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
@@ -136,7 +136,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
 e0242fc212e83f2d3b694734fadb62ab76c4ef90c3c5b04df503f5a7c907a3e0	68520	src/phaseset_core/frozen_clip_text.py
-25871fe486090edd2ac49f82018dcced116e35c85d99178b251bc8862dfdbe70	78628	src/phaseset_core/host.py
+7fcc111d269ed22db55ba2c3a5bee8d6c79f3c850c42eca5ff8aaa08b6e809e1	90636	src/phaseset_core/host.py
 47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
@@ -183,7 +183,7 @@ b801d46fb184d99c65825d7f9bf91cbced022418600a95d421c5d9244dbded13	19373	tests/tes
 4da1d6c1b05bc485b9afb78776264ae7757744f104c37b947cdce0280cdfb172	2347	tests/test_phasepair_posix_checkpoint_ownership.py
 fa34950fb26f63d5d1ba169499e6e4af49c9b691f33c5cb3aac6e961bdac9e55	18384	tests/test_phaseset_caption_fusion.py
 c88e5b91823b22ebb7a4019acb328040c6b42be7acc03738949439e4efbe443f	10126	tests/test_phaseset_capture_pooling.py
-53d9629d112fb6c835e8cec6f6b4eb5ccc70a2c9f86862099404fb7769b7bec3	19344	tests/test_phaseset_capture_prepared_storage.py
+ec0609bf54766ffec53a1d7e4af7e778e2c2efbd9f4d2c4c06984814a066ca7f	21782	tests/test_phaseset_capture_prepared_storage.py
 a02d460a28bd6aeb372fa66e779cb4a65c86536ab951ccd3b6c30ed8091cbd15	8371	tests/test_phaseset_capture_training.py
 beacbd78c68771001b60ff7c6271e6d99c92d24d83f251e1074fc4ce50d1216c	15511	tests/test_phaseset_capture_validation.py
 5dbbb81d6725f1185f4c12dc85653b6eac1e47ae2ffb7fb36f5b951eb8dea632	8848	tests/test_phaseset_cli.py
@@ -198,6 +198,7 @@ dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/tes
 04df5b4a8d75bed006f16b56910e4082d4104a45cc8aeb11d6932ede645777cb	14111	tests/test_phaseset_frozen_clip_cache_rehydrate.py
 937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
 4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
+19d68d4141d9c026649181ac2c93933db2431acf34cd8c0ab44af0f28ceb27b0	23146	tests/test_phaseset_host_capture_storage.py
 a75f583fd0a233760b1a421ca51680afe4195c33f9ef4bce6e9a1737828f207e	14907	tests/test_phaseset_models.py
 e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test_phaseset_morlet.py
 6d7005585882e25be9479542aac96a5b2aeb1bddc5619a0f37c57f61f2cbe6c3	6348	tests/test_phaseset_objectives.py
diff --git a/STATUS.md b/STATUS.md
index 232a5ec..56053fa 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -17,9 +17,11 @@ evidence, not training results or scientific claims.
   read-only inventory is complete. Existing workloads were not interrupted.
 - A separate data-volume Python 3.12.12 / NumPy 2.4.6 / Torch 2.12.0+cu126
   environment is installed with a clean dependency check. The complete Linux
-  CPU regression including capture storage and frozen-text restoration passed:
-  1043 tests, 2 skipped, and 39 subtests, in 298.12 s. Its focused integration
-  passed 110 tests. The preceding capture-training integration passed 1012
+  CPU regression including capture host composition, storage portability and
+  frozen-text restoration passed: 1077 tests, 2 skipped, and 39 subtests, in
+  296.55 s. Its focused integration passed 103 tests. The preceding capture
+  host integration passed 96 focused / 1070 complete tests; the earlier storage
+  integration passed 110 focused / 1043 complete tests. The preceding capture-training integration passed 1012
   complete / 84 focused tests and 6 focused subtests. Source bytes were unchanged before and after
   both suites. The prior prepared-data v2 host integration passed 94 focused /
   964 complete tests. The prior residual-resume integration
@@ -74,7 +76,8 @@ evidence, not training results or scientific claims.
   It verifies the exact consumed index/manifest/payload bytes, rejects split
   overlap, and applies reproducible shared-group yaw from original bytes each
   training epoch. Validation is unrotated. Window-positive families support the
-  auxiliary task; the private-host holistic capture source is not yet wired.
+  auxiliary task; a separate combined index now connects the private-host
+  holistic capture source without relabeling auxiliary validation data.
   Actor-set commitments must not be used as unique capture/window row IDs.
   See [prepared-data contract](docs/PREPARED_DATA_V2.md).
 - The complete-window capture evaluator now pools unnormalized embeddings
@@ -88,8 +91,11 @@ evidence, not training results or scientific claims.
   in [test stability](docs/LEGACY_TEST_STABILITY.md). These are analytic lifecycle
   checks, not formal runs. Actual scored-census consistency and scoped live
   capture-function drift checks were added after provisional composition review.
-  Private-host disk composition
-  and licensed source/caption provenance remain incomplete. See
+  The private host now composes authenticated window training and capture
+  validation from disk, checking actual actor/caption/window overlap before
+  backend construction and forwarding the exact capture source through both
+  base/residual and resume paths. Licensed source/caption provenance and
+  participant-disjoint input preparation remain incomplete. See
   [capture validation](docs/CAPTURE_VALIDATION.md).
 - A bounded validation-only capture storage writer/reader now preserves the
   complete window plan, original padding, variable caption counts and original
@@ -101,6 +107,15 @@ evidence, not training results or scientific claims.
   The original output goldens and observed CPU Torch RNG were unchanged.
   Storage is not a rights, annotation, split or anonymization proof. See
   [capture storage](docs/CAPTURE_PREPARED_STORAGE.md).
+- The preceding capture-storage commit failed 14 tests on each Windows CI
+  runtime while its Ubuntu audit succeeded. A read-only filesystem diagnostic
+  found different path-stat and descriptor-stat `ctime` values for the same
+  unchanged file. The correction preserves complete before/after checks within
+  each interface, compares stable identity fields across interfaces, and keeps
+  all content-digest and bounded-read checks. Seven added cases cover this
+  distinction and actual drift. The 103 / 1077 server tests above and the
+  unchanged real CLIP cache observation passed. Windows acceptance of this
+  correction requires its own GitHub CI result; no failed check was skipped.
 - Neither Embody approval nor a licensed neutral model has been observed.
   Open-license Multi-TPC acquisition completed with its official byte count and
   MD5 verified. Its archive inventory has 322 files. The bounded audit confirms
@@ -126,6 +141,12 @@ evidence, not training results or scientific claims.
   a separate server target. All 56 wheel Python modules match the public Git
   index. Three real CLIP forwards from that installed wheel produced a complete
   observation byte-identical to the previously verified source copy.
+- The subsequent public capture-storage commit was also anonymously downloaded,
+  checked against its 223-file manifest, and built offline into a separate
+  wheel target. All 60 Python modules matched the source. Three actual CLIP
+  forwards and two disk restorations from the installed wheel produced the
+  same complete observation as the server source. This Linux packaging check
+  does not close the Windows filesystem-portability failure described above.
 - Native Linux last-bit Morlet formula drift is repaired using strictly
   verified canonical coefficient bytes, while preserving both original hashes.
   Windows still requires its original exact formula digest; Linux requires its
diff --git a/docs/HOST_RUNTIME_INTEGRATION.md b/docs/HOST_RUNTIME_INTEGRATION.md
index e25f23a..b38873b 100644
--- a/docs/HOST_RUNTIME_INTEGRATION.md
+++ b/docs/HOST_RUNTIME_INTEGRATION.md
@@ -47,6 +47,47 @@ unrotated training bytes. Its validation source is unrotated. See
 [prepared-data v2](PREPARED_DATA_V2.md) for the writer, bounded numeric format,
 split checks, and provenance limits. Neither loader establishes data rights.
 
+## Main holistic capture validation
+
+The additional closed `phaseset-host-capture-validation-index-v1` composes an
+existing prepared training index with validation-only
+[capture storage](CAPTURE_PREPARED_STORAGE.md). The combined index bytes are
+bound to the same private prepared-data receipt. Both the outer index and the
+referenced training index must equal canonical ASCII JSON bytes, not merely
+parse to equivalent objects. Duplicate keys and noncanonical encodings are
+rejected even when a supplied digest matches them.
+
+Its `train` row has exactly `kind/path/sha256`. The kind is
+`prepared-training-index-v1` or `prepared-training-index-v2` and must match the
+actual referenced schema. That complete old index is validated, then only its
+training source is used. Its auxiliary window-validation source is never
+relabeled as holistic capture data.
+
+Its `val` row has exactly `kind/path/sha256/source_census_sha256/`
+`upstream_manifest_sha256`, with kind `capture-validation-storage-v1`. The host
+loads the actual storage bytes with the retained expected digest and checks
+the reconstructed source type, `val` split, complete census and upstream
+manifest. The runtime receives this `CaptureValidationSource` unchanged.
+Base and residual runs, including both resume paths, use the same composition.
+Checkpoint and scoring-time census binding remain in the existing training API.
+
+Before constructing the backend, the host streams every actual training batch
+and rejects actor/track or caption commitments shared with capture validation.
+Prepared v2 additionally compares its documented window-positive IDs with
+capture window commitments; v1 arbitrary positive families are not falsely
+treated as window IDs. Source manifests/censuses are checked before and after
+the scan. The fixed scan order uses local deterministic source RNG and preserves
+Python, NumPy and Torch global RNG. Actor commitments are not participant IDs:
+this check complements, but never replaces, the authenticated participant split
+audit.
+
+Preflight materializes the bounded capture source and streams the training
+tree once. The single-batch byte limit still applies; train scan I/O is linear
+in total artifact bytes, and there is no new aggregate train-byte limit.
+Capture identity sets are linear in the materialized validation census. This
+operation neither loads CLIP nor opens the sealed test split. Direct legacy
+v1/v2 window-task dispatch retains its prior behavior.
+
 `run-base` uses the formal seed-bound B0--B2 constructor and
 `PhaseSetTrainingRuntime`.  Its checkpoint directory must be
 `<attempt-root>/<attempt-id>/model-checkpoints`, allowing the harness to build
@@ -156,13 +197,10 @@ rights assertion when Embody approval is unknown.
 - The Embody-format conversion is outside this harness.  It must produce the
   exact prepared index/split/NPZ seam above and bind it to the authenticated
   prepared-data receipt.
-- The training API now accepts the concrete val-only capture source described
-  in [capture validation](CAPTURE_VALIDATION.md), with complete-window pooling
-  and checkpoint-census binding. This host's v1/v2 on-disk prepared loaders still
-  return window sources. Their positive families and repeating actor-set
-  commitments cannot substitute for the main holistic capture gallery. The
-  private-host capture-source disk composition remains to be connected before
-  formal base selection on that task.
+- The main capture disk composition above is implemented. Producing its inputs
+  from licensed captures and verified official holistic captions remains the
+  upstream preparation responsibility. Auxiliary window positive families and
+  repeating actor-set commitments never substitute for actual capture row IDs.
 - `run-residual` is wired through the strict qualified-base loader, canonical
   periodic-cache record, energy floors, canonical all-system capacity audit,
   closed residual constructor, and the same attempt ledger. Residual resume
@@ -212,3 +250,23 @@ The subsequent v2 prepared-source integration passed 94 focused server tests
 and a separate complete suite of 964 tests, 2 skipped, and 39 subtests. These
 include actual malformed numeric payloads and manifest/index replacement
 cases; they do not assert a completed preparation command or a real-data run.
+
+The complete-capture host composition subsequently passed 96 focused tests
+and 1070 complete Linux server tests, with 2 skips and 39 subtests. A later
+storage-portability correction passed 103 focused / 1077 complete tests,
+again with 2 skips and 39 subtests, plus the unchanged official CLIP forward
+and disk-restoration observation. Source comparisons passed throughout.
+The new tests include canonical index bytes, real train/validation commitment
+overlap, unchanged ambient RNG, and exact dispatch through base/residual/resume.
+They use explicit analytic fixtures and do not establish data access or a
+completed real training run.
+
+The portability correction follows observed failures on both Windows CI
+versions at the preceding capture-storage commit. On the inspected Windows
+runtime, path stat and descriptor stat returned different `ctime` semantics
+for one unchanged file. Cross-interface identity checks now compare device,
+inode, size and modification time; each interface separately retains its full
+before/after identity check including `ctime`. Content digests, bounded reads,
+regular-file and symlink checks remain mandatory. Tests simulate the stable
+cross-interface difference and reject drift within either interface. Windows
+CI for the correction is a separate external acceptance check.
diff --git a/src/phaseset_core/capture_prepared_storage.py b/src/phaseset_core/capture_prepared_storage.py
index 5d73783..08d2eb6 100644
--- a/src/phaseset_core/capture_prepared_storage.py
+++ b/src/phaseset_core/capture_prepared_storage.py
@@ -247,6 +247,12 @@ def _stat_identity(value: os.stat_result) -> tuple[int, int, int, int, int]:
     )
 
 
+def _cross_interface_identity(value: os.stat_result) -> tuple[int, int, int, int]:
+    # Windows path and descriptor APIs can expose different ctime semantics.
+    # Check ctime within each API before/after, never across the two APIs.
+    return (value.st_dev, value.st_ino, value.st_size, value.st_mtime_ns)
+
+
 def _read_bounded(path: Path, *, maximum: int, label: str) -> bytes:
     descriptor = -1
     try:
@@ -264,7 +270,8 @@ def _read_bounded(path: Path, *, maximum: int, label: str) -> bytes:
         opened_before = os.fstat(descriptor)
         if (
             not stat.S_ISREG(opened_before.st_mode)
-            or _stat_identity(path_before) != _stat_identity(opened_before)
+            or _cross_interface_identity(path_before)
+            != _cross_interface_identity(opened_before)
         ):
             raise CapturePreparedStorageError(f"{label} changed before it was opened")
         size = opened_before.st_size
@@ -281,7 +288,9 @@ def _read_bounded(path: Path, *, maximum: int, label: str) -> bytes:
         if (
             not stat.S_ISREG(path_after.st_mode)
             or _stat_identity(opened_before) != _stat_identity(opened_after)
-            or _stat_identity(opened_after) != _stat_identity(path_after)
+            or _stat_identity(path_before) != _stat_identity(path_after)
+            or _cross_interface_identity(opened_after)
+            != _cross_interface_identity(path_after)
             or len(raw) != size
         ):
             raise CapturePreparedStorageError(f"{label} changed while it was read")
diff --git a/src/phaseset_core/host.py b/src/phaseset_core/host.py
index becb1c2..27642a8 100644
--- a/src/phaseset_core/host.py
+++ b/src/phaseset_core/host.py
@@ -32,6 +32,12 @@
 import numpy as np
 import torch
 from phaseset_core import cli
+from phaseset_core.capture_prepared_storage import (
+    CapturePreparedStorageError,
+    CaptureStorageLimits,
+    load_capture_validation_source,
+)
+from phaseset_core.capture_validation import CaptureValidationSource
 from phaseset_core.contracts import PreparedGroupBatch
 from phaseset_core.execution import (
     AttemptRecord,
@@ -57,16 +63,16 @@
     canonical_base_qualification_bytes,
     parse_run_id,
 )
-from phaseset_core.production import (
-    BackendExecution,
-    PrivateReceiptAssertions,
-    ProductionRuntimeAdapter,
-)
 from phaseset_core.prepared_data_v2 import (
     PREPARED_INDEX_V2_SCHEMA,
     PreparedDataV2Error,
     load_prepared_training_sources_v2,
 )
+from phaseset_core.production import (
+    BackendExecution,
+    PrivateReceiptAssertions,
+    ProductionRuntimeAdapter,
+)
 from phaseset_core.training import (
     CheckpointArtifact,
     PhaseSetTrainingRuntime,
@@ -74,8 +80,8 @@
     RetrievalTrainingBatch,
     TrainingConfig,
     TrainingDataSource,
-    TrainingRuntimeError,
     TrainingReport,
+    TrainingRuntimeError,
     construct_registered_base_seed_bound_system,
     construct_registered_residual_seed_bound_system,
     load_qualified_frozen_base,
@@ -84,6 +90,10 @@
 HOST_CONFIG_SCHEMA = "phaseset-private-host-v1"
 PREPARED_INDEX_SCHEMA = "phaseset-prepared-index-v1"
 PREPARED_SPLIT_SCHEMA = "phaseset-prepared-training-split-v1"
+CAPTURE_VALIDATION_INDEX_SCHEMA = "phaseset-host-capture-validation-index-v1"
+CAPTURE_VALIDATION_KIND = "capture-validation-storage-v1"
+PREPARED_TRAIN_V1_KIND = "prepared-training-index-v1"
+PREPARED_TRAIN_V2_KIND = "prepared-training-index-v2"
 PERIODIC_CACHE_MAX_BYTES = 1024 * 1024
 PREPARED_BATCH_KEYS = frozenset(
     {
@@ -220,6 +230,17 @@ def _sha256_bytes(value: bytes) -> str:
     return hashlib.sha256(value).hexdigest()
 
 
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise HostConfigurationError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
 def _sha256_file(path: Path) -> str:
     digest = hashlib.sha256()
     with path.open("rb") as stream:
@@ -258,6 +279,19 @@ def _load_json_object(path: Path, label: str) -> tuple[bytes, dict[str, object]]
     return raw, value
 
 
+def _require_canonical_json_object(
+    raw: bytes,
+    value: Mapping[str, object],
+    label: str,
+) -> None:
+    try:
+        canonical = _canonical_json_bytes(value)
+    except (TypeError, ValueError, OverflowError, RecursionError) as error:
+        raise HostConfigurationError(f"{label} is not canonical JSON") from error
+    if raw != canonical:
+        raise HostConfigurationError(f"{label} bytes are not canonical")
+
+
 def _closed_keys(value: Mapping[str, object], expected: set[str], label: str) -> None:
     if set(value) != expected:
         raise HostConfigurationError(f"{label} keys are not closed")
@@ -825,11 +859,15 @@ def validate_runtime(self) -> None:
 
     def load_sources(
         self,
-    ) -> tuple[TrainingDataSource, TrainingDataSource]:
+    ) -> tuple[TrainingDataSource, TrainingDataSource | CaptureValidationSource]:
         raw, value = _load_json_object(self.prepared_index, "prepared index")
         _closed_keys(value, {"schema", "train", "val"}, "prepared index")
         schema = value["schema"]
-        if schema not in {PREPARED_INDEX_SCHEMA, PREPARED_INDEX_V2_SCHEMA}:
+        if type(schema) is not str or schema not in {
+            PREPARED_INDEX_SCHEMA,
+            PREPARED_INDEX_V2_SCHEMA,
+            CAPTURE_VALIDATION_INDEX_SCHEMA,
+        }:
             raise HostConfigurationError("prepared index schema is not registered")
         receipts = self.load_receipts()
         if _sha256_bytes(raw) != receipts.prepared_data_manifest_sha256:
@@ -847,6 +885,9 @@ def load_sources(
                 raise HostConfigurationError(
                     "prepared v2 source is invalid"
                 ) from error
+        if schema == CAPTURE_VALIDATION_INDEX_SCHEMA:
+            _require_canonical_json_object(raw, value, "capture-validation index")
+            return self._load_capture_validation_sources(value)
         sources: list[PrivatePreparedDataSource] = []
         for split in ("train", "val"):
             item = value[split]
@@ -870,6 +911,269 @@ def load_sources(
             sources.append(source)
         return sources[0], sources[1]
 
+    def _load_capture_validation_sources(
+        self,
+        value: Mapping[str, object],
+    ) -> tuple[TrainingDataSource, CaptureValidationSource]:
+        train_row = value["train"]
+        val_row = value["val"]
+        if type(train_row) is not dict or type(val_row) is not dict:
+            raise HostConfigurationError(
+                "capture-validation index train and val rows must be objects"
+            )
+        _closed_keys(
+            train_row,
+            {"kind", "path", "sha256"},
+            "capture-validation training row",
+        )
+        _closed_keys(
+            val_row,
+            {
+                "kind",
+                "path",
+                "sha256",
+                "source_census_sha256",
+                "upstream_manifest_sha256",
+            },
+            "capture-validation val row",
+        )
+        train_kind = train_row["kind"]
+        if type(train_kind) is not str or train_kind not in {
+            PREPARED_TRAIN_V1_KIND,
+            PREPARED_TRAIN_V2_KIND,
+        }:
+            raise HostConfigurationError(
+                "capture-validation training kind is not registered"
+            )
+        if type(val_row["kind"]) is not str or val_row["kind"] != CAPTURE_VALIDATION_KIND:
+            raise HostConfigurationError(
+                "capture-validation val kind is not registered"
+            )
+        train_digest = _lower_sha256(
+            train_row["sha256"],
+            "capture-validation training index SHA-256",
+        )
+        val_digest = _lower_sha256(
+            val_row["sha256"],
+            "capture-validation storage manifest SHA-256",
+        )
+        expected_census = _lower_sha256(
+            val_row["source_census_sha256"],
+            "capture-validation source census SHA-256",
+        )
+        expected_upstream = _lower_sha256(
+            val_row["upstream_manifest_sha256"],
+            "capture-validation upstream manifest SHA-256",
+        )
+        train_path = _resolve_under(
+            self.prepared_index.parent,
+            train_row["path"],
+            "capture-validation training index",
+        )
+        val_path = _resolve_under(
+            self.prepared_index.parent,
+            val_row["path"],
+            "capture-validation storage manifest",
+        )
+        if train_path == val_path:
+            raise HostConfigurationError(
+                "capture-validation train and val artifacts must be distinct"
+            )
+        train = self._load_registered_training_index(
+            train_path,
+            expected_sha256=train_digest,
+            expected_kind=train_kind,
+        )
+        try:
+            val = load_capture_validation_source(
+                val_path,
+                expected_manifest_sha256=val_digest,
+                limits=CaptureStorageLimits(
+                    max_file_bytes=self.max_batch_bytes,
+                    max_decoded_file_bytes=self.max_batch_bytes,
+                ),
+            )
+        except CapturePreparedStorageError as error:
+            raise HostConfigurationError(
+                "capture-validation val storage is invalid"
+            ) from error
+        if type(val) is not CaptureValidationSource or val.split != "val":
+            raise HostConfigurationError(
+                "capture-validation loader did not return an exact val source"
+            )
+        if val.census_sha256 != expected_census:
+            raise HostConfigurationError(
+                "capture-validation source census differs from its index"
+            )
+        if val.manifest_sha256 != expected_upstream:
+            raise HostConfigurationError(
+                "capture-validation upstream manifest differs from its index"
+            )
+        if not isinstance(train, TrainingDataSource) or train.split != "train":
+            raise HostConfigurationError(
+                "capture-validation training index did not produce a train source"
+            )
+        self._reject_capture_training_identity_overlap(
+            train,
+            val,
+            compare_window_identities=(train_kind == PREPARED_TRAIN_V2_KIND),
+        )
+        return train, val
+
+    @staticmethod
+    def _reject_capture_training_identity_overlap(
+        train: TrainingDataSource,
+        val: CaptureValidationSource,
+        *,
+        compare_window_identities: bool,
+    ) -> None:
+        """Read every concrete train batch and reject exact val identity reuse.
+
+        Actor commitments identify prepared tracks, not participant identities.
+        Participant-level disjointness remains the authenticated split audit's
+        responsibility. Prepared-data v2, unlike v1, freezes each motion positive
+        identity as the source window SHA-256, so only v2 admits the window check.
+        """
+
+        train_manifest = _lower_sha256(
+            train.manifest_sha256,
+            "capture-validation train manifest SHA-256",
+        )
+        val_census = val.census_sha256
+        val_actors = {
+            actor
+            for capture in val.captures
+            for window in capture.windows
+            for actor in window.groups.actor_commitments[0]
+            if actor is not None
+        }
+        val_captions = {
+            commitment
+            for capture in val.captures
+            for commitment in capture.holistic_text.caption_commitments
+        }
+        val_windows = {
+            commitment
+            for capture in val.captures
+            for commitment in capture.plan.window_commitments
+        }
+        batch_count = 0
+        try:
+            # Registered v1/v2 sources expose their complete immutable artifact
+            # census through iter_epoch. Seed/epoch zero only fixes deterministic
+            # order and v2 yaw; neither path consumes a global RNG.
+            for batch in train.iter_epoch(epoch=0, seed=0):
+                if type(batch) is not RetrievalTrainingBatch or batch.split != "train":
+                    raise HostConfigurationError(
+                        "capture-validation train identity scan returned an invalid batch"
+                    )
+                batch_count += 1
+                train_actors = {
+                    actor
+                    for row in batch.groups.actor_commitments
+                    for actor in row
+                    if actor is not None
+                }
+                if train_actors & val_actors:
+                    raise HostConfigurationError(
+                        "capture-validation train and val reuse an actor/track identity"
+                    )
+                if set(batch.text_commitments) & val_captions:
+                    raise HostConfigurationError(
+                        "capture-validation train and val reuse a caption identity"
+                    )
+                if (
+                    compare_window_identities
+                    and set(batch.motion_positive_ids) & val_windows
+                ):
+                    raise HostConfigurationError(
+                        "capture-validation train and val reuse a window identity"
+                    )
+        except HostConfigurationError:
+            raise
+        except Exception as error:
+            raise HostConfigurationError(
+                "capture-validation train identity scan failed"
+            ) from error
+        if batch_count == 0:
+            raise HostConfigurationError(
+                "capture-validation train identity scan returned no batches"
+            )
+        if train.manifest_sha256 != train_manifest:
+            raise HostConfigurationError(
+                "capture-validation train source changed during identity scan"
+            )
+        if val.census_sha256 != val_census:
+            raise HostConfigurationError(
+                "capture-validation val source changed during identity scan"
+            )
+
+    def _load_registered_training_index(
+        self,
+        path: Path,
+        *,
+        expected_sha256: str,
+        expected_kind: str,
+    ) -> TrainingDataSource:
+        raw, value = _load_json_object(path, "referenced training index")
+        if _sha256_bytes(raw) != expected_sha256:
+            raise HostConfigurationError(
+                "referenced training index differs from its exact digest"
+            )
+        _require_canonical_json_object(raw, value, "referenced training index")
+        _closed_keys(
+            value,
+            {"schema", "train", "val"},
+            "referenced training index",
+        )
+        expected_schema = {
+            PREPARED_TRAIN_V1_KIND: PREPARED_INDEX_SCHEMA,
+            PREPARED_TRAIN_V2_KIND: PREPARED_INDEX_V2_SCHEMA,
+        }[expected_kind]
+        if value["schema"] != expected_schema:
+            raise HostConfigurationError(
+                "referenced training index schema differs from its kind"
+            )
+        if expected_schema == PREPARED_INDEX_V2_SCHEMA:
+            try:
+                train, _auxiliary_window_val = load_prepared_training_sources_v2(
+                    path,
+                    expected_index_sha256=expected_sha256,
+                    max_batch_bytes=self.max_batch_bytes,
+                )
+            except (PreparedDataV2Error, TrainingRuntimeError) as error:
+                raise HostConfigurationError(
+                    "referenced prepared v2 training source is invalid"
+                ) from error
+            return train
+        sources: list[PrivatePreparedDataSource] = []
+        for split in ("train", "val"):
+            row = value[split]
+            if type(row) is not dict:
+                raise HostConfigurationError(
+                    f"referenced prepared v1 {split} row must be an object"
+                )
+            _closed_keys(
+                row,
+                {"path", "sha256"},
+                f"referenced prepared v1 {split} row",
+            )
+            split_manifest = _resolve_under(
+                path.parent,
+                row["path"],
+                f"referenced prepared v1 {split} manifest",
+            )
+            source = PrivatePreparedDataSource(
+                split_manifest,
+                max_batch_bytes=self.max_batch_bytes,
+            )
+            if source.manifest_sha256 != row["sha256"] or source.split != split:
+                raise HostConfigurationError(
+                    f"referenced prepared v1 {split} source differs from its index"
+                )
+            sources.append(source)
+        return sources[0]
+
 
 def _jsonable(value: object) -> object:
     if isinstance(value, Path):
@@ -1889,8 +2193,8 @@ def __call__(self, request: cli.CLICommandRequest) -> ProductionRuntimeAdapter:
             config = HostConfig.load(self._config_path)
             receipts = config.load_receipts()
             config.validate_runtime()
-            # Preflight must prove that the authenticated prepared index and all
-            # immutable batch digests are present, without materializing tensors.
+            # Preflight verifies the authenticated prepared tree, materializes the
+            # bounded capture source, and streams every concrete train batch once.
             config.load_sources()
         except (HostConfigurationError, OSError, TypeError, ValueError) as error:
             text = str(error).lower()
diff --git a/tests/test_phaseset_capture_prepared_storage.py b/tests/test_phaseset_capture_prepared_storage.py
index 7181c99..7389e2e 100644
--- a/tests/test_phaseset_capture_prepared_storage.py
+++ b/tests/test_phaseset_capture_prepared_storage.py
@@ -7,6 +7,7 @@
 import json
 import os
 from pathlib import Path
+from types import SimpleNamespace
 
 import numpy as np
 import pytest
@@ -436,6 +437,70 @@ def swapping_open(path: Path, flags: int, *args: object) -> int:
     assert calls == 1
 
 
+def _stat_view(value: os.stat_result, **changes: int) -> SimpleNamespace:
+    fields = {
+        name: getattr(value, name)
+        for name in (
+            "st_mode", "st_dev", "st_ino", "st_size", "st_mtime_ns", "st_ctime_ns"
+        )
+    }
+    fields.update(changes)
+    return SimpleNamespace(**fields)
+
+
+def test_bounded_read_allows_stable_cross_api_ctime_difference(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    leaf = tmp_path / "ctime.bin"
+    leaf.write_bytes(b"unchanged")
+    real_fstat = storage.os.fstat
+
+    def distinct_ctime(descriptor: int) -> SimpleNamespace:
+        observed = real_fstat(descriptor)
+        return _stat_view(observed, st_ctime_ns=observed.st_ctime_ns + 1234567)
+
+    monkeypatch.setattr(storage.os, "fstat", distinct_ctime)
+    assert storage._read_bounded(leaf, maximum=64, label="fixture") == b"unchanged"
+
+
+@pytest.mark.parametrize("interface", ["lstat", "fstat"])
+def test_bounded_read_still_rejects_same_api_ctime_drift(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch, interface: str,
+) -> None:
+    leaf = tmp_path / "ctime.bin"
+    leaf.write_bytes(b"unchanged")
+    original = getattr(storage.os, interface)
+    calls = 0
+
+    def drifting_ctime(*args: object, **kwargs: object) -> SimpleNamespace:
+        nonlocal calls
+        observed = original(*args, **kwargs)
+        calls += 1
+        return _stat_view(observed, st_ctime_ns=observed.st_ctime_ns + calls)
+
+    monkeypatch.setattr(storage.os, interface, drifting_ctime)
+    with pytest.raises(storage.CapturePreparedStorageError, match="changed while"):
+        storage._read_bounded(leaf, maximum=64, label="fixture")
+    assert calls == 2
+
+
+@pytest.mark.parametrize("field", ["st_dev", "st_ino", "st_size", "st_mtime_ns"])
+def test_bounded_read_still_binds_cross_api_file_identity(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch, field: str,
+) -> None:
+    leaf = tmp_path / "identity.bin"
+    leaf.write_bytes(b"unchanged")
+    real_fstat = storage.os.fstat
+
+    def mismatched_identity(descriptor: int) -> SimpleNamespace:
+        observed = real_fstat(descriptor)
+        return _stat_view(observed, **{field: getattr(observed, field) + 1})
+
+    monkeypatch.setattr(storage.os, "fstat", mismatched_identity)
+    with pytest.raises(storage.CapturePreparedStorageError, match="changed before"):
+        storage._read_bounded(leaf, maximum=64, label="fixture")
+
+
 @pytest.mark.parametrize("mutation", ["authority", "capture", "window", "caption"])
 def test_manifest_literals_and_commitment_hex_are_exact(
     tmp_path: Path,
diff --git a/tests/test_phaseset_host_capture_storage.py b/tests/test_phaseset_host_capture_storage.py
new file mode 100644
index 0000000..f77ae0b
--- /dev/null
+++ b/tests/test_phaseset_host_capture_storage.py
@@ -0,0 +1,689 @@
+"""Server-only host dispatch tests for prepared capture validation storage."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import random
+from dataclasses import replace
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+from phaseset_core import capture_prepared_storage as capture_storage
+from phaseset_core import host, prepared_data_v2
+from phaseset_core.capture_validation import CaptureValidationSource
+from phaseset_core.contracts import group_commitment
+from phaseset_core.training import TrainingDataSource
+from test_phaseset_capture_prepared_storage import _source as _capture_source
+from test_phaseset_prepared_host_integration import _sample as _v2_sample
+from test_phaseset_prepared_host_integration import _text_batch as _v2_text_batch
+
+
+def _sha(path: Path) -> str:
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def _id(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _write_json(path: Path, value: object) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        )
+        + "\n",
+        encoding="ascii",
+    )
+
+
+def _write_batch(
+    path: Path,
+    split: str,
+    *,
+    actors: tuple[bytes, bytes] | None = None,
+    text_commitment: bytes | None = None,
+) -> None:
+    actors = actors or (_id(f"{split}/actor/0"), _id(f"{split}/actor/1"))
+    family = _id(f"{split}/positive")
+    np.savez(
+        path,
+        skeletons=np.zeros((1, 2, 200, 22, 3), dtype=np.float32),
+        actor_mask=np.ones((1, 2), dtype=np.bool_),
+        frame_mask=np.ones((1, 200), dtype=np.bool_),
+        track_mask=np.ones((1, 2, 200, 22), dtype=np.bool_),
+        actor_commitments=np.asarray(
+            [[list(actors[0]), list(actors[1])]],
+            dtype=np.uint8,
+        ),
+        group_commitments=np.asarray(
+            [list(group_commitment(actors))],
+            dtype=np.uint8,
+        ),
+        text_embeddings=np.zeros((1, 512), dtype=np.float32),
+        motion_positive_ids=np.asarray([list(family)], dtype=np.uint8),
+        text_positive_ids=np.asarray([list(family)], dtype=np.uint8),
+        text_commitments=np.asarray(
+            [list(text_commitment or _id(f"{split}/text"))],
+            dtype=np.uint8,
+        ),
+    )
+
+
+def _prepared_v1_tree(
+    root: Path,
+    *,
+    train_actors: tuple[bytes, bytes] | None = None,
+    train_text_commitment: bytes | None = None,
+) -> Path:
+    root.mkdir(parents=True)
+    rows: dict[str, dict[str, str]] = {}
+    for split in ("train", "val"):
+        batch = root / f"{split}-000000.npz"
+        _write_batch(
+            batch,
+            split,
+            actors=train_actors if split == "train" else None,
+            text_commitment=(
+                train_text_commitment if split == "train" else None
+            ),
+        )
+        manifest = root / f"{split}.json"
+        _write_json(
+            manifest,
+            {
+                "batches": [{"path": batch.name, "sha256": _sha(batch)}],
+                "schema": host.PREPARED_SPLIT_SCHEMA,
+                "split": split,
+            },
+        )
+        rows[split] = {"path": manifest.name, "sha256": _sha(manifest)}
+    index = root / "prepared-index.json"
+    _write_json(
+        index,
+        {
+            "schema": host.PREPARED_INDEX_SCHEMA,
+            "train": rows["train"],
+            "val": rows["val"],
+        },
+    )
+    return index
+
+
+def _capture_host_index(
+    tmp_path: Path,
+    training_index: Path,
+    *,
+    train_kind: str,
+) -> tuple[Path, capture_storage.CaptureStorageBuildResult]:
+    source = _capture_source()
+    capture_result = capture_storage.write_capture_validation_source(
+        (tmp_path / "capture").resolve(),
+        source,
+    )
+    index = tmp_path / "capture-host-index.json"
+    _write_json(
+        index,
+        {
+            "schema": host.CAPTURE_VALIDATION_INDEX_SCHEMA,
+            "train": {
+                "kind": train_kind,
+                "path": training_index.relative_to(tmp_path).as_posix(),
+                "sha256": _sha(training_index),
+            },
+            "val": {
+                "kind": host.CAPTURE_VALIDATION_KIND,
+                "path": capture_result.manifest_path.relative_to(tmp_path).as_posix(),
+                "sha256": capture_result.manifest_sha256,
+                "source_census_sha256": capture_result.source_census_sha256,
+                "upstream_manifest_sha256": source.manifest_sha256,
+            },
+        },
+    )
+    return index, capture_result
+
+
+def _combined_tree(
+    tmp_path: Path,
+    *,
+    train_actors: tuple[bytes, bytes] | None = None,
+    train_text_commitment: bytes | None = None,
+) -> tuple[Path, capture_storage.CaptureStorageBuildResult]:
+    training_index = _prepared_v1_tree(
+        tmp_path / "prepared",
+        train_actors=train_actors,
+        train_text_commitment=train_text_commitment,
+    )
+    return _capture_host_index(
+        tmp_path,
+        training_index,
+        train_kind=host.PREPARED_TRAIN_V1_KIND,
+    )
+
+
+def _prepared_v2_tree(root: Path, overlap: str | None = None):
+    sample = _v2_sample(
+        "train-v2",
+        capture_label="capture-a" if overlap == "actor" else None,
+    )
+    if overlap == "window":
+        sample = replace(
+            sample,
+            window_sha256=_id("capture-a/window/0").hex(),
+        )
+    train_text = _v2_text_batch("capture-a" if overlap == "caption" else "train-v2")
+    train = prepared_data_v2.PreparedMotionTextBlock(
+        samples=(sample,),
+        text_batch=train_text,
+        text_counts=(1,),
+        window_ordinals=(10,),
+    )
+    auxiliary = prepared_data_v2.PreparedMotionTextBlock(
+        samples=(_v2_sample("auxiliary-v2"),),
+        text_batch=_v2_text_batch("auxiliary-v2"),
+        text_counts=(1,),
+        window_ordinals=(20,),
+    )
+    return prepared_data_v2.write_prepared_training_tree_v2(
+        root,
+        train_blocks=(train,),
+        val_blocks=(auxiliary,),
+        max_total_edges=1,
+        max_batch_size=1,
+        max_batch_bytes=10_000_000,
+        max_decoded_batch_bytes=10_000_000,
+    )
+
+
+def _numpy_rng_equal(left: tuple[object, ...], right: tuple[object, ...]) -> bool:
+    return (
+        left[0] == right[0]
+        and np.array_equal(left[1], right[1])
+        and left[2:] == right[2:]
+    )
+
+
+def _config(
+    index: Path,
+    *,
+    residual_artifacts: host.ResidualArtifacts | None = None,
+) -> host.HostConfig:
+    return host.HostConfig(
+        path=index.parent / "host.json",
+        prepared_index=index,
+        receipt_record=index,
+        receipt_artifacts={},
+        source_tree_sha256=_id("source tree").hex(),
+        device="cpu",
+        request_bf16=False,
+        bf16_runtime_qualified=False,
+        edge_budget=32_768,
+        checkpoint_every_updates=1,
+        max_batch_bytes=10_000_000,
+        resume_retry_class="INFRA_TRANSIENT",
+        corrective_change_sha256=_id("corrective change").hex(),
+        residual_artifacts=residual_artifacts,
+    )
+
+
+def _bind_receipt(
+    monkeypatch: pytest.MonkeyPatch,
+    digest: str,
+) -> None:
+    monkeypatch.setattr(
+        host.HostConfig,
+        "load_receipts",
+        lambda _self: SimpleNamespace(prepared_data_manifest_sha256=digest),
+    )
+
+
+def _rewrite_index(path: Path, mutate) -> str:
+    value = json.loads(path.read_text(encoding="ascii"))
+    mutate(value)
+    _write_json(path, value)
+    return _sha(path)
+
+
+def _make_noncanonical_json(raw: bytes, variant: str) -> bytes:
+    if variant == "whitespace":
+        assert raw.startswith(b"{")
+        return b"{ " + raw[1:]
+    if variant == "duplicate":
+        value = json.loads(raw)
+        duplicate = json.dumps(value["schema"], ensure_ascii=True).encode("ascii")
+        return b'{"schema":' + duplicate + b"," + raw[1:]
+    if variant == "nonascii":
+        assert b"\\u00e9" in raw
+        return raw.replace(b"\\u00e9", "é".encode(), 1)
+    raise AssertionError(f"unsupported fixture variant: {variant}")
+
+
+def test_capture_index_returns_training_protocol_and_exact_capture_val(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    index, result = _combined_tree(tmp_path)
+    _bind_receipt(monkeypatch, _sha(index))
+
+    train, val = _config(index).load_sources()
+
+    assert isinstance(train, TrainingDataSource)
+    assert train.split == "train"
+    assert type(val) is CaptureValidationSource
+    assert val.split == "val"
+    assert val.census_sha256 == result.source_census_sha256
+    assert val.manifest_sha256 == _capture_source().manifest_sha256
+    assert not hasattr(val, "iter_epoch")
+
+
+@pytest.mark.parametrize("variant", ("whitespace", "duplicate", "nonascii"))
+def test_capture_index_requires_exact_canonical_bytes_after_authentication(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    variant: str,
+) -> None:
+    prepared_root = tmp_path / ("prépared" if variant == "nonascii" else "prepared")
+    training_index = _prepared_v1_tree(prepared_root)
+    index, _ = _capture_host_index(
+        tmp_path,
+        training_index,
+        train_kind=host.PREPARED_TRAIN_V1_KIND,
+    )
+    index.write_bytes(_make_noncanonical_json(index.read_bytes(), variant))
+    _bind_receipt(monkeypatch, _sha(index))
+
+    with pytest.raises(host.HostConfigurationError, match="bytes are not canonical"):
+        _config(index).load_sources()
+
+
+@pytest.mark.parametrize("variant", ("whitespace", "duplicate", "nonascii"))
+def test_capture_index_requires_canonical_referenced_training_index(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    variant: str,
+) -> None:
+    training_index = _prepared_v1_tree(tmp_path / "prepared")
+    if variant == "nonascii":
+        original = training_index.parent / "train.json"
+        renamed = training_index.parent / "tréin.json"
+        original.rename(renamed)
+        _rewrite_index(
+            training_index,
+            lambda value: value["train"].update(path=renamed.name),
+        )
+    index, _ = _capture_host_index(
+        tmp_path,
+        training_index,
+        train_kind=host.PREPARED_TRAIN_V1_KIND,
+    )
+    training_index.write_bytes(
+        _make_noncanonical_json(training_index.read_bytes(), variant)
+    )
+    outer_digest = _rewrite_index(
+        index,
+        lambda value: value["train"].update(sha256=_sha(training_index)),
+    )
+    _bind_receipt(monkeypatch, outer_digest)
+
+    with pytest.raises(host.HostConfigurationError, match="bytes are not canonical"):
+        _config(index).load_sources()
+
+
+def test_existing_v1_index_behavior_is_unchanged(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    index = _prepared_v1_tree(tmp_path / "prepared")
+    _bind_receipt(monkeypatch, _sha(index))
+
+    train, val = _config(index).load_sources()
+
+    assert type(train) is host.PrivatePreparedDataSource
+    assert type(val) is host.PrivatePreparedDataSource
+    assert (train.split, val.split) == ("train", "val")
+
+
+def test_existing_v2_dispatch_keeps_its_authenticated_index_contract(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    index = tmp_path / "prepared-v2.json"
+    _write_json(
+        index,
+        {
+            "schema": host.PREPARED_INDEX_V2_SCHEMA,
+            "train": {"fixture": "not consumed after registered loader dispatch"},
+            "val": {"fixture": "not consumed after registered loader dispatch"},
+        },
+    )
+    expected = _sha(index)
+    _bind_receipt(monkeypatch, expected)
+    v1_index = _prepared_v1_tree(tmp_path / "v1-fixture")
+    v1_train = host.PrivatePreparedDataSource(v1_index.parent / "train.json")
+    v1_val = host.PrivatePreparedDataSource(v1_index.parent / "val.json")
+    calls: list[tuple[Path, str, int]] = []
+
+    def fake_loader(
+        path: Path,
+        *,
+        expected_index_sha256: str,
+        max_batch_bytes: int,
+    ) -> tuple[TrainingDataSource, TrainingDataSource]:
+        calls.append((path, expected_index_sha256, max_batch_bytes))
+        return v1_train, v1_val
+
+    monkeypatch.setattr(host, "load_prepared_training_sources_v2", fake_loader)
+    train, val = _config(index).load_sources()
+
+    assert (train, val) == (v1_train, v1_val)
+    assert calls == [(index, expected, 10_000_000)]
+
+
+def test_capture_index_accepts_only_explicit_matching_v2_training_kind(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    index, result = _combined_tree(tmp_path)
+    inner = tmp_path / "prepared" / "prepared-index.json"
+    inner_value = json.loads(inner.read_text(encoding="ascii"))
+    inner_value["schema"] = host.PREPARED_INDEX_V2_SCHEMA
+    _write_json(inner, inner_value)
+    outer = json.loads(index.read_text(encoding="ascii"))
+    outer["train"]["kind"] = host.PREPARED_TRAIN_V2_KIND
+    outer["train"]["sha256"] = _sha(inner)
+    _write_json(index, outer)
+    _bind_receipt(monkeypatch, _sha(index))
+    v1_train = host.PrivatePreparedDataSource(inner.parent / "train.json")
+    auxiliary_val = host.PrivatePreparedDataSource(inner.parent / "val.json")
+    calls: list[tuple[Path, str]] = []
+
+    def fake_loader(
+        path: Path,
+        *,
+        expected_index_sha256: str,
+        max_batch_bytes: int,
+    ) -> tuple[TrainingDataSource, TrainingDataSource]:
+        assert max_batch_bytes == 10_000_000
+        calls.append((path, expected_index_sha256))
+        return v1_train, auxiliary_val
+
+    monkeypatch.setattr(host, "load_prepared_training_sources_v2", fake_loader)
+    train, val = _config(index).load_sources()
+
+    assert train is v1_train
+    assert type(val) is CaptureValidationSource
+    assert val.census_sha256 == result.source_census_sha256
+    assert calls == [(inner, _sha(inner))]
+
+
+def test_capture_index_scans_actual_v2_without_consuming_global_rng(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    prepared = _prepared_v2_tree(tmp_path / "prepared-v2")
+    index, result = _capture_host_index(
+        tmp_path,
+        prepared.index_path,
+        train_kind=host.PREPARED_TRAIN_V2_KIND,
+    )
+    _bind_receipt(monkeypatch, _sha(index))
+    python_rng = random.getstate()
+    numpy_rng = np.random.get_state()
+    torch_rng = host.torch.get_rng_state().clone()
+
+    train, val = _config(index).load_sources()
+
+    assert train.split == "train"
+    assert type(val) is CaptureValidationSource
+    assert val.census_sha256 == result.source_census_sha256
+    assert random.getstate() == python_rng
+    assert _numpy_rng_equal(np.random.get_state(), numpy_rng)
+    assert host.torch.equal(host.torch.get_rng_state(), torch_rng)
+
+
+@pytest.mark.parametrize(
+    ("overlap", "message"),
+    (
+        ("actor", "actor/track identity"),
+        ("caption", "caption identity"),
+        ("window", "window identity"),
+    ),
+)
+def test_capture_index_rejects_actual_v2_train_identity_overlap(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    overlap: str,
+    message: str,
+) -> None:
+    prepared = _prepared_v2_tree(tmp_path / "prepared-v2", overlap)
+    index, _ = _capture_host_index(
+        tmp_path,
+        prepared.index_path,
+        train_kind=host.PREPARED_TRAIN_V2_KIND,
+    )
+    _bind_receipt(monkeypatch, _sha(index))
+
+    with pytest.raises(host.HostConfigurationError, match=message):
+        _config(index).load_sources()
+
+
+@pytest.mark.parametrize(
+    ("overlap", "message"),
+    (
+        ("actor", "actor/track identity"),
+        ("caption", "caption identity"),
+    ),
+)
+def test_capture_index_rejects_actual_v1_train_identity_overlap(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    overlap: str,
+    message: str,
+) -> None:
+    source = _capture_source()
+    val_actors = sorted(
+        actor
+        for capture in source.captures
+        for actor in capture.actor_commitment_set
+    )
+    val_captions = sorted(
+        commitment
+        for capture in source.captures
+        for commitment in capture.holistic_text.caption_commitments
+    )
+    index, _ = _combined_tree(
+        tmp_path,
+        train_actors=(val_actors[0], _id("train/distinct-actor"))
+        if overlap == "actor"
+        else None,
+        train_text_commitment=val_captions[0] if overlap == "caption" else None,
+    )
+    _bind_receipt(monkeypatch, _sha(index))
+
+    with pytest.raises(host.HostConfigurationError, match=message):
+        _config(index).load_sources()
+
+
+def test_capture_index_rejects_train_source_drift_during_full_identity_scan(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    index, _ = _combined_tree(tmp_path)
+    inner = tmp_path / "prepared" / "prepared-index.json"
+    actual = host.PrivatePreparedDataSource(inner.parent / "train.json")
+
+    class DriftingSource:
+        split = "train"
+
+        def __init__(self) -> None:
+            self.manifest_sha256 = actual.manifest_sha256
+
+        def iter_epoch(self, *, epoch: int, seed: int):
+            yield from actual.iter_epoch(epoch=epoch, seed=seed)
+            self.manifest_sha256 = "0" * 64
+
+    drifting = DriftingSource()
+    monkeypatch.setattr(
+        host.HostConfig,
+        "_load_registered_training_index",
+        lambda _self, _path, **_kwargs: drifting,
+    )
+    _bind_receipt(monkeypatch, _sha(index))
+
+    with pytest.raises(host.HostConfigurationError, match="changed during identity scan"):
+        _config(index).load_sources()
+
+
+@pytest.mark.parametrize(
+    ("mutation", "message"),
+    (
+        (lambda value: value["train"].update(kind=[]), "training kind"),
+        (lambda value: value["train"].update(kind=host.CAPTURE_VALIDATION_KIND), "training kind"),
+        (lambda value: value["val"].update(kind=host.PREPARED_TRAIN_V1_KIND), "val kind"),
+        (lambda value: value["train"].update(kind=host.PREPARED_TRAIN_V2_KIND), "schema"),
+        (lambda value: value["val"].update(sha256="0" * 64), "val storage"),
+        (
+            lambda value: value["val"].update(source_census_sha256="0" * 64),
+            "source census",
+        ),
+        (
+            lambda value: value["val"].update(upstream_manifest_sha256="0" * 64),
+            "upstream manifest",
+        ),
+    ),
+)
+def test_capture_index_rejects_mixed_kinds_and_identity_drift_before_backend(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    mutation,
+    message: str,
+) -> None:
+    index, _ = _combined_tree(tmp_path)
+    rebound = _rewrite_index(index, mutation)
+    _bind_receipt(monkeypatch, rebound)
+
+    with pytest.raises(host.HostConfigurationError, match=message):
+        _config(index).load_sources()
+
+
+def test_outer_index_digest_is_required_before_capture_artifact_loading(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    index, _ = _combined_tree(tmp_path)
+    _bind_receipt(monkeypatch, "0" * 64)
+    reached = False
+
+    def forbidden_loader(*_args: object, **_kwargs: object) -> object:
+        nonlocal reached
+        reached = True
+        raise AssertionError("capture loader must not run")
+
+    monkeypatch.setattr(host, "load_capture_validation_source", forbidden_loader)
+    with pytest.raises(host.HostConfigurationError, match="authenticated receipt"):
+        _config(index).load_sources()
+    assert reached is False
+
+
+class _FakeRuntime:
+    calls: list[tuple[object, object, dict[str, object]]] = []
+
+    def __init__(self, *_args: object, **_kwargs: object) -> None:
+        pass
+
+    def fit(self, train: object, val: object, **kwargs: object) -> str:
+        self.calls.append((train, val, kwargs))
+        return "FIT_RETURNED"
+
+
+def test_base_runtime_and_resume_forward_typed_capture_validation(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    index, result = _combined_tree(tmp_path)
+    _bind_receipt(monkeypatch, _sha(index))
+    monkeypatch.setattr(
+        host,
+        "construct_registered_base_seed_bound_system",
+        lambda _system_id, _config: (object(), object()),
+    )
+    _FakeRuntime.calls.clear()
+    monkeypatch.setattr(host, "PhaseSetTrainingRuntime", _FakeRuntime)
+    backend = host.PhaseSetHostBackend(_config(index), object())
+    resume = tmp_path / "checkpoint.pt"
+
+    returned = backend._execute_base_runtime(
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B0",
+        system_id="B0",
+        seed=1729,
+        checkpoint_directory=tmp_path / "model-checkpoints",
+        live_ledger=SimpleNamespace(observe_checkpoint=lambda _value: None),
+        resume_checkpoint=resume,
+        resume_attempt_root=tmp_path,
+        resume_record=None,
+    )
+
+    assert returned == "FIT_RETURNED"
+    train, val, kwargs = _FakeRuntime.calls[-1]
+    assert isinstance(train, TrainingDataSource) and train.split == "train"
+    assert type(val) is CaptureValidationSource
+    assert val.census_sha256 == result.source_census_sha256
+    assert kwargs["resume_checkpoint"] == resume
+    assert kwargs["resume_attempt_root"] == tmp_path
+
+
+def test_residual_runtime_and_resume_forward_typed_capture_validation(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    index, result = _combined_tree(tmp_path)
+    _bind_receipt(monkeypatch, _sha(index))
+    qualification = tmp_path / "qualification.json"
+    capacity = tmp_path / "capacity.json"
+    qualification.write_bytes(b"qualification")
+    capacity.write_bytes(b"capacity")
+    artifacts = host.ResidualArtifacts(
+        qualification=qualification,
+        capacity_audits={1729: capacity},
+    )
+    monkeypatch.setattr(host, "_load_base_qualification", lambda _path: (object(), "1" * 64))
+    monkeypatch.setattr(host, "load_qualified_frozen_base", lambda *_args, **_kwargs: object())
+    monkeypatch.setattr(host, "_load_periodic_cache", lambda *_args, **_kwargs: (1.0,) * 6)
+    monkeypatch.setattr(host, "_load_capacity_audit", lambda _path: (object(), "2" * 64))
+    monkeypatch.setattr(
+        host,
+        "construct_registered_residual_seed_bound_system",
+        lambda *_args, **_kwargs: (object(), object()),
+    )
+    _FakeRuntime.calls.clear()
+    monkeypatch.setattr(host, "PhaseSetTrainingRuntime", _FakeRuntime)
+    backend = host.PhaseSetHostBackend(_config(index, residual_artifacts=artifacts), object())
+    resume = tmp_path / "residual-checkpoint.pt"
+
+    returned = backend._execute_residual_runtime(
+        system_id="01",
+        seed=1729,
+        checkpoint_directory=tmp_path / "model-checkpoints",
+        live_ledger=SimpleNamespace(observe_checkpoint=lambda _value: None),
+        base_checkpoint=tmp_path / "base.pt",
+        periodic_cache=tmp_path / "periodic-cache",
+        stop_after_global_step=None,
+        resume_checkpoint=resume,
+        resume_attempt_root=tmp_path,
+        resume_record=None,
+    )
+
+    assert returned == "FIT_RETURNED"
+    train, val, kwargs = _FakeRuntime.calls[-1]
+    assert isinstance(train, TrainingDataSource) and train.split == "train"
+    assert type(val) is CaptureValidationSource
+    assert val.census_sha256 == result.source_census_sha256
+    assert kwargs["resume_checkpoint"] == resume
+    assert kwargs["resume_attempt_root"] == tmp_path

From db71a838bdd093a8bcd6eeff85450acfde62e43d Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 10:37:26 -0500
Subject: [PATCH 13/95] Keep capture-index fixtures canonical across Windows
 and Linux

---
 RELEASE_FILES.sha256                        |  4 ++--
 docs/LEGACY_TEST_STABILITY.md               | 17 +++++++++++++++++
 tests/test_phaseset_host_capture_storage.py |  8 ++++++--
 3 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 4e8a0cf..b7d2f47 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -26,7 +26,7 @@ dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPER
 97b7b7e68b7896220b38661b094505b47c711a431f546e6da9772cfedcf6beaa	14238	docs/FROZEN_CLIP_TEXT_ADAPTER.md
 f2c59540bfc4dcdbb5013b76eb20090a7dd61c805523121995ed9206c843f9c8	3711	docs/FROZEN_MHA_RUNTIME.md
 fad4d9e8d1a51bf443af94b536b7aee0b8031cf42edab58190db7b174a768e0e	15565	docs/HOST_RUNTIME_INTEGRATION.md
-8bd042f717f9a70e888ff79758aae991043404952243dc9dfd60282e4ecb29a1	1712	docs/LEGACY_TEST_STABILITY.md
+f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
@@ -198,7 +198,7 @@ dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/tes
 04df5b4a8d75bed006f16b56910e4082d4104a45cc8aeb11d6932ede645777cb	14111	tests/test_phaseset_frozen_clip_cache_rehydrate.py
 937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
 4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
-19d68d4141d9c026649181ac2c93933db2431acf34cd8c0ab44af0f28ceb27b0	23146	tests/test_phaseset_host_capture_storage.py
+14f6d4b54d78876822e6f96440a32360ea4d4654c1d18d8d8662fe0f4dc42bf8	23309	tests/test_phaseset_host_capture_storage.py
 a75f583fd0a233760b1a421ca51680afe4195c33f9ef4bce6e9a1737828f207e	14907	tests/test_phaseset_models.py
 e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test_phaseset_morlet.py
 6d7005585882e25be9479542aac96a5b2aeb1bddc5619a0f37c57f61f2cbe6c3	6348	tests/test_phaseset_objectives.py
diff --git a/docs/LEGACY_TEST_STABILITY.md b/docs/LEGACY_TEST_STABILITY.md
index 2faff32..656b8d6 100644
--- a/docs/LEGACY_TEST_STABILITY.md
+++ b/docs/LEGACY_TEST_STABILITY.md
@@ -26,3 +26,20 @@ in 297.31 seconds; source bytes were unchanged across execution.
 These are test-fixture changes, not relaxed model tolerances or weaker data
 admission checks. Review was local-only under ARIS's unavailable-delegation
 fallback; actual server outcomes remain the deterministic evidence.
+
+## Canonical capture-index fixture newlines
+
+The later complete-capture host integration passed its Linux tests but exposed
+18 failures on each Windows CI runtime. Its test-only JSON writer used the
+platform default newline translation: canonical text ending in LF became CRLF
+on Windows. The production byte-equality check correctly rejected the fixture
+before the intended dispatch or overlap assertion. This is distinct from the
+earlier storage-stat issue, whose tests passed in that same CI run.
+
+The fixture now supplies an explicit LF newline policy. Production code and
+canonical-byte acceptance are unchanged. Two additional authenticated cases
+insert actual CRLF at the outer and referenced training-index seams; both must
+still be rejected even with recomputed matching digests. The corrected focused
+Linux server suite passed 71 tests in 11.08 seconds with source bytes unchanged.
+The failed Windows logs are retained privately. Acceptance on Windows requires
+the subsequent actual CI result, not inference from this Linux observation.
diff --git a/tests/test_phaseset_host_capture_storage.py b/tests/test_phaseset_host_capture_storage.py
index f77ae0b..b69e73a 100644
--- a/tests/test_phaseset_host_capture_storage.py
+++ b/tests/test_phaseset_host_capture_storage.py
@@ -41,6 +41,7 @@ def _write_json(path: Path, value: object) -> None:
         )
         + "\n",
         encoding="ascii",
+        newline="\n",
     )
 
 
@@ -252,6 +253,9 @@ def _rewrite_index(path: Path, mutate) -> str:
 
 
 def _make_noncanonical_json(raw: bytes, variant: str) -> bytes:
+    if variant == "crlf":
+        assert raw.endswith(b"\n") and not raw.endswith(b"\r\n")
+        return raw[:-1] + b"\r\n"
     if variant == "whitespace":
         assert raw.startswith(b"{")
         return b"{ " + raw[1:]
@@ -283,7 +287,7 @@ def test_capture_index_returns_training_protocol_and_exact_capture_val(
     assert not hasattr(val, "iter_epoch")
 
 
-@pytest.mark.parametrize("variant", ("whitespace", "duplicate", "nonascii"))
+@pytest.mark.parametrize("variant", ("whitespace", "duplicate", "nonascii", "crlf"))
 def test_capture_index_requires_exact_canonical_bytes_after_authentication(
     tmp_path: Path,
     monkeypatch: pytest.MonkeyPatch,
@@ -303,7 +307,7 @@ def test_capture_index_requires_exact_canonical_bytes_after_authentication(
         _config(index).load_sources()
 
 
-@pytest.mark.parametrize("variant", ("whitespace", "duplicate", "nonascii"))
+@pytest.mark.parametrize("variant", ("whitespace", "duplicate", "nonascii", "crlf"))
 def test_capture_index_requires_canonical_referenced_training_index(
     tmp_path: Path,
     monkeypatch: pytest.MonkeyPatch,

From 9f1d4a29eef7c85294123ea16855da5a6d6ebdf0 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 10:58:12 -0500
Subject: [PATCH 14/95] Connect completed base checkpoints to full capture
 validation

---
 RELEASE_FILES.sha256                          |   14 +-
 STATUS.md                                     |   23 +
 docs/BASE_COHORT_VALIDATION.md                |   90 ++
 src/phaseset_core/base_cohort_resolver.py     | 1004 +++++++++++++++++
 src/phaseset_core/base_cohort_validation.py   |  968 ++++++++++++++++
 src/phaseset_core/execution.py                |  134 +++
 ...t_phaseset_base_cohort_checkpoint_state.py |  260 +++++
 tests/test_phaseset_base_cohort_resolver.py   |  646 +++++++++++
 ...test_phaseset_base_cohort_score_witness.py |  398 +++++++
 tests/test_phaseset_base_cohort_validation.py |  522 +++++++++
 tests/test_phaseset_execution.py              |  180 +++
 tests/test_phaseset_execution_chain_bound.py  |   41 +
 12 files changed, 4277 insertions(+), 3 deletions(-)
 create mode 100644 docs/BASE_COHORT_VALIDATION.md
 create mode 100644 src/phaseset_core/base_cohort_resolver.py
 create mode 100644 src/phaseset_core/base_cohort_validation.py
 create mode 100644 tests/test_phaseset_base_cohort_checkpoint_state.py
 create mode 100644 tests/test_phaseset_base_cohort_resolver.py
 create mode 100644 tests/test_phaseset_base_cohort_score_witness.py
 create mode 100644 tests/test_phaseset_base_cohort_validation.py
 create mode 100644 tests/test_phaseset_execution_chain_bound.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index b7d2f47..2d5d4ed 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,10 +11,11 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-4870fc5e2037406bb915aa84f4b6c1c78c5bf7000ae8db7c129e48fa2fb24243	20110	STATUS.md
+fbd12b9f5c12d46e4d536b7e67953fad6612d92a9692196b327b408505d1fc28	21749	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
+3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
 a9d6a4875760c58a0075e9e4e6b7a5f147da9762410fde953d648895ec159a76	3398	docs/CAPTURE_PREPARED_STORAGE.md
 6d562907df5ae5a598401eb6ba622775ae03d4b81795671fe0c86942174e90e8	6781	docs/CAPTURE_VALIDATION.md
 e9331b64297d6c4586ccb22022c972fad657ac2b3f9202203be62b067a38ca82	3686	docs/CUDA_LIFECYCLE_OBSERVATION.md
@@ -123,6 +124,8 @@ fa68bcab33d09588a8653151ad8e5741d5e6530263bcb633d871d7744598b194	10364	src/phase
 dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phasepair_core/validation_gallery.py
 2e5fb5280ed27a46c587d07a60ce663fce676cbdd30b059ec34882b4507d157f	604	src/phaseset_core/__init__.py
 9668a89535264a3ed6b58ffc7ac31b4904a4d8f68636d405e15cbf7368096716	8437	src/phaseset_core/_morlet_oracle_bytes.py
+d2d71766c1c6af95480d507960d7186bbec9ff8a424f6e79ce6611fd0aa2e611	38885	src/phaseset_core/base_cohort_resolver.py
+3d4f31b6a773081af41f1d9d45ee98986ea33ef6f954070564373f995af1f2e9	40593	src/phaseset_core/base_cohort_validation.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
 d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
 3a60f7a68d9de9b0ded88274a631e1e4195b4d12689a60a7f9ae93401a36af16	44353	src/phaseset_core/capture_prepared_storage.py
@@ -133,7 +136,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
-0c6cae2999670ce59e5a48f2ed33f7c89eca941cc35da338977c7d551e136024	77642	src/phaseset_core/execution.py
+eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
 e0242fc212e83f2d3b694734fadb62ab76c4ef90c3c5b04df503f5a7c907a3e0	68520	src/phaseset_core/frozen_clip_text.py
 7fcc111d269ed22db55ba2c3a5bee8d6c79f3c850c42eca5ff8aaa08b6e809e1	90636	src/phaseset_core/host.py
@@ -181,6 +184,10 @@ b801d46fb184d99c65825d7f9bf91cbced022418600a95d421c5d9244dbded13	19373	tests/tes
 59dde3593b46a2a84453a722adb6b8748cec8246c50699e49087193af47da182	8604	tests/test_phasepair_core_training_batch.py
 8f31d46a9ff2d4ca96fcd34dc6fd617933f2e604c0dd6e95e0d10ec9db487074	10255	tests/test_phasepair_core_validation_gallery.py
 4da1d6c1b05bc485b9afb78776264ae7757744f104c37b947cdce0280cdfb172	2347	tests/test_phasepair_posix_checkpoint_ownership.py
+6462129abd78cce74c4a2b21c50cefc7220b9092511dab13f5abb9b6277ea621	8467	tests/test_phaseset_base_cohort_checkpoint_state.py
+41535795e088b5374bf59b95491afa4d1aac08622cddf1aa7d76c5fb0af990fa	23471	tests/test_phaseset_base_cohort_resolver.py
+2cd1a40ebf6c10c83dae4f72bf41821d25ee153c8b3f05819ac664eb0a8f611e	14477	tests/test_phaseset_base_cohort_score_witness.py
+e4b3ebf50a8f0971431905cebd12829b5799f677333ec573bf5809b90209018a	19405	tests/test_phaseset_base_cohort_validation.py
 fa34950fb26f63d5d1ba169499e6e4af49c9b691f33c5cb3aac6e961bdac9e55	18384	tests/test_phaseset_caption_fusion.py
 c88e5b91823b22ebb7a4019acb328040c6b42be7acc03738949439e4efbe443f	10126	tests/test_phaseset_capture_pooling.py
 ec0609bf54766ffec53a1d7e4af7e778e2c2efbd9f4d2c4c06984814a066ca7f	21782	tests/test_phaseset_capture_prepared_storage.py
@@ -193,7 +200,8 @@ a74143036a41dd9d8779dc4a9b70fe20e5b38d013ef23778d13e73c11273dbd8	26066	tests/tes
 a17a50e801c389b4e24b2840f2aafcb7b476cbef86e2e142ad0af6245a54718f	4515	tests/test_phaseset_data.py
 2f2fa0bf4d10771573896cf5d46eaaeffaaa6d9c52ca647a9ab9aa32a1766c87	14442	tests/test_phaseset_embody_adapter.py
 8bd6e95a35523d108153f668f8919e6f22858e8283e6308041751c6c92d0f904	18605	tests/test_phaseset_evaluation.py
-aea41df712a25be78841e655a6fbd91fc96217ce92e60d3ab842f185fc39a58c	41553	tests/test_phaseset_execution.py
+28ae2930b6e953909d8ff422284d8c2c580f94b65e022b8b2d9101b2c9f3a454	49018	tests/test_phaseset_execution.py
+e79f2cb54fc18bccef9b94adeaf8c1811b913d8ba2a22f77e4f35ad9669ad6db	1324	tests/test_phaseset_execution_chain_bound.py
 dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/test_phaseset_experiments.py
 04df5b4a8d75bed006f16b56910e4082d4104a45cc8aeb11d6932ede645777cb	14111	tests/test_phaseset_frozen_clip_cache_rehydrate.py
 937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
diff --git a/STATUS.md b/STATUS.md
index 56053fa..89fd9a8 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -13,6 +13,22 @@ evidence, not training results or scientific claims.
 
 ## Server continuation
 
+- The completed-base resolver now reconstructs the exact nine registered
+  successful chains, preserves failed predecessors, authenticates terminal-bound
+  latest and distinct validation-best checkpoint bytes, and revalidates the
+  complete registered state. The complete-capture scorer strictly loads those
+  selected states, derives live parameter censuses and recomputes full-gallery
+  score identities and exact capture-macro fractions. Their integration passed
+  119 focused / 1119 complete server tests with 2 existing skips and 39 subtests.
+  A separate unmocked full-width B0/B1/B2 selected-state-to-gallery witness
+  passed all three lanes in 19.36 s on analytic K2/K3, T200 captures. It uses
+  explicit checkpoint-cursor and text fixtures, not completed formal epochs,
+  real captions or an actual nine-run cohort. Latency, qualification selection
+  and host command assembly remain separate unfinished steps. The merged source
+  then passed 1124 complete Linux server tests, 2 existing skips and 39 subtests
+  in 315.67 s with the source census unchanged. See
+  [completed-base capture scoring](docs/BASE_COHORT_VALIDATION.md).
+
 - Strict known-host SSH login to the registered host now succeeds. The initial
   read-only inventory is complete. Existing workloads were not interrupted.
 - A separate data-volume Python 3.12.12 / NumPy 2.4.6 / Torch 2.12.0+cu126
@@ -116,6 +132,13 @@ evidence, not training results or scientific claims.
   distinction and actual drift. The 103 / 1077 server tests above and the
   unchanged real CLIP cache observation passed. Windows acceptance of this
   correction requires its own GitHub CI result; no failed check was skipped.
+- The next Windows run passed those storage-stat cases but exposed a separate
+  test helper's default CRLF translation. The test-only LF correction and two
+  authenticated CRLF negatives passed 71 focused server tests. Subsequent
+  GitHub Actions for commit `db71a83` passed both Windows Python versions and
+  Ubuntu's public-tree/wheel audit. Both preceding failed runs remain retained;
+  production canonical-byte checks were not relaxed. See
+  [fixture stability](docs/LEGACY_TEST_STABILITY.md).
 - Neither Embody approval nor a licensed neutral model has been observed.
   Open-license Multi-TPC acquisition completed with its official byte count and
   MD5 verified. Its archive inventory has 322 files. The bounded audit confirms
diff --git a/docs/BASE_COHORT_VALIDATION.md b/docs/BASE_COHORT_VALIDATION.md
new file mode 100644
index 0000000..6d2cce9
--- /dev/null
+++ b/docs/BASE_COHORT_VALIDATION.md
@@ -0,0 +1,90 @@
+# Completed-base cohort and capture validation
+
+The completed-base resolver and capture scorer connect the registered training
+ledger to actual validation-selected model states. They do not select a winner
+without the separately required complete latency session and qualification
+assembly, and they do not establish data rights.
+
+## Completed attempts, latest checkpoints and selected checkpoints
+
+`resolve_completed_base_cohort` consumes the trusted host's attempt registry
+and admitted plan, matrix, installed-source, runtime and data bindings. It
+requires exactly one successful chain for each registered B0/B1/B2 by seed
+row. Failed and held predecessors remain in the validated resume chain rather
+than becoming additional score rows.
+
+The terminal-bound latest checkpoint is not automatically the best checkpoint.
+The resolver authenticates and decodes that payload, follows its exact best
+filename and digest, and independently validates the selected payload. It
+retains immutable owned bytes rather than returning a mutable checkpoint path.
+Latest and selected checkpoints must agree on best metric and schedule; the
+selected validation cursor and cumulative counters cannot exceed the latest
+state. Selected-best-at-write evidence alone is not a completed-run receipt.
+
+The checks reuse the actual registered constructors and checkpoint validators:
+closed headers, full model state, optimizer, scheduler, RNG, cursor, seed-bound
+initialization, code, environment and behavior. Bounded no-follow regular-file
+reads precede restricted CPU checkpoint decoding. The recursive attempt chain
+is rejected before a 65th entry, and checkpoint directory iteration has a fixed
+100,000-entry bound. A single payload is bounded at 2 GiB and all retained
+selected payloads at 4 GiB. Resource rejection never silently drops a base row.
+
+## Actual complete-capture scoring
+
+`score_resolved_base_cohort` accepts those resolved bytes and one concrete
+validation-only `CaptureValidationSource`. Its full census must equal the
+checkpoint's admitted validation binding. This census includes the upstream
+manifest, complete window plan, numeric inputs, text receipts and identities;
+the upstream manifest digest remains separately identified.
+
+For every registered row, the scorer revalidates the selected bytes, constructs
+the real seed-bound base, strictly loads its complete state, verifies the live
+state and behavior, and derives the full trainable parameter census including
+`logit_scale`. There is no random-checkpoint fallback, caller-supplied factory,
+parameter count or score input. Each architecture's parameter rows must agree
+across all three seeds.
+
+The existing audited [capture evaluator](CAPTURE_VALIDATION.md) encodes every
+accepted window, pools unnormalized embeddings per capture and evaluates one
+complete motion/text gallery. The scorer independently checks gallery IDs,
+positive relations, group sizes, component labels, score bytes and exact
+capture-macro R@1 fractions. It checks source census and selected-byte stability
+before and after scoring. One failed row prevents a complete cohort result.
+
+The complete float64 score matrix has a 256 MiB preflight bound. No capture,
+caption, window, actor or edge is sampled to fit this bound. Returned score
+bytes are immutable little-endian float64; canonical observation bytes bind
+their digest to the actual selected checkpoint, terminal, runtime and query
+census. These private query identities and scores are not automatically safe
+to publish.
+
+## Acceptance boundary
+
+These APIs emit software observations, not a completed qualification or new
+execution authority. The trusted controller must still combine actual nine-run
+terminals, scores and parameter censuses with one complete frozen latency
+session, then call the existing selector and production evidence assembly.
+No caller may authenticate a qualification merely by submitting matching
+hashes. The exact formal training-caption policy must also be clarified before
+real training; the scorer does not choose that policy or access sealed test.
+
+Server contract tests cover restricted checkpoint state validation, exact row
+ordering, latest/best linkage, immutable inputs, actual score-derived metrics,
+live parameter counts and malformed dependencies. Explicit analytic fixtures
+are not real training attempts or evidence of retrieval quality. In the
+observed integrated candidate, 119 focused and 1119 complete Linux server
+tests passed, with two existing skips and 39 subtests; source comparisons
+passed. A separate three-lane server witness subsequently passed for actual
+registered width-512 B0/B1/B2. Without replacing constructors, checkpoint
+validators, encoders, pooling or scoring, it loaded complete selected model
+states and scored two analytic T200 captures with K2/K3. The checkpoint cursor
+and single initialized optimizer-state row were explicit software fixtures,
+not formal epochs or a completed attempt ledger. Its text embeddings were
+analytic unit vectors, not captions encoded by CLIP. All three tests passed in
+19.36 seconds with source unchanged. This tests numeric integration only;
+later cross-platform/public-package checks retain their independent scope.
+The merged source, including that actual-width witness and the previously
+verified LF-only host fixtures, subsequently passed 1124 complete Linux server
+tests, two existing skips and 39 subtests in 315.67 seconds. The full source
+census was unchanged across the run. This merged regression does not turn the
+analytic checkpoints into completed formal training attempts.
diff --git a/src/phaseset_core/base_cohort_resolver.py b/src/phaseset_core/base_cohort_resolver.py
new file mode 100644
index 0000000..af7e65f
--- /dev/null
+++ b/src/phaseset_core/base_cohort_resolver.py
@@ -0,0 +1,1004 @@
+"""Resolve the completed registered base cohort without scoring it.
+
+This private controller helper consumes an already authenticated attempt-registry
+snapshot.  It validates the exact nine successful terminal chains through
+``execution.verified_completed_attempt_evidence``, retains their failed resume
+ancestry, and returns owned bytes for each validation-selected checkpoint.
+
+The result is deliberately authority zero.  It is neither a score artifact nor
+a qualification, and a registry row or digest supplied to this API is never
+treated as proof that an external registry is authentic.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+import io
+import json
+import os
+from pathlib import Path
+import re
+import stat
+from typing import Literal, Mapping
+
+import torch
+from torch import nn
+
+from phaseset_core import execution as execution_module
+from phaseset_core import training as training_module
+from phaseset_core.experiments import (
+    base_run_ids,
+    build_experiment_plan,
+    experiment_plan_sha256,
+    parse_run_id,
+)
+
+
+AUTHORITY = 0
+PRODUCTION = False
+RESULT_CLAIMED = False
+STATUS = "BASE_COHORT_CHECKPOINTS_RESOLVED_NO_SCORE_NO_QUALIFICATION"
+MAX_CHECKPOINT_BYTES = 2 * 1024**3
+MAX_SELECTED_COHORT_BYTES = 4 * 1024**3
+MAX_LEDGER_ARTIFACT_BYTES = 1024**2
+MAX_CHECKPOINT_DIRECTORY_ENTRIES = 100_000
+MAX_ATTEMPTS_PER_RUN = 64
+
+_ATTEMPT_ID = re.compile(r"[a-z0-9][a-z0-9._-]{0,63}\Z")
+_CHECKPOINT_NAME = re.compile(
+    r"checkpoint-(?P<step>[0-9]{12})-(?P<sequence>[0-9]{6})-"
+    r"(?P<reason>update|validation)\.pt\Z"
+)
+_CHECKPOINT_KEYS = frozenset(
+    {
+        "authority",
+        "behavior_sha256",
+        "best_checkpoint_name",
+        "best_checkpoint_sha256",
+        "best_validation_metric",
+        "checkpoint_sequence",
+        "code_artifact_sha256",
+        "config_sha256",
+        "environment_sha256",
+        "epoch",
+        "external_receipt_verified",
+        "factory_sha256",
+        "frozen_base_checkpoint_sha256",
+        "frozen_base_state_sha256",
+        "global_step",
+        "initial_optimizable_state_sha256",
+        "initialization_binding_sha256",
+        "last_train_loss",
+        "max_microbatch_edges",
+        "model",
+        "optimizer",
+        "precision_mode",
+        "production",
+        "qualified_base_selection_sha256",
+        "residual_capacity_audit_sha256",
+        "result_claimed",
+        "rng",
+        "scheduler",
+        "schema",
+        "seed",
+        "stage",
+        "state_digest",
+        "system_id",
+        "total_steps",
+        "train_edges_seen",
+        "train_manifest_sha256",
+        "update_index",
+        "updates_per_epoch",
+        "val_manifest_sha256",
+        "validation_edges_seen",
+    }
+)
+
+
+class BaseCohortResolutionError(ValueError):
+    """The registry, terminal cohort, or selected checkpoint is inconsistent."""
+
+
+@dataclass(frozen=True, slots=True)
+class BaseCohortAdmission:
+    """Expected bindings retained by the trusted private controller.
+
+    These values are comparison inputs only.  Constructing this object does not
+    authenticate their origin and does not confer execution or result authority.
+    """
+
+    plan_sha256: str
+    matrix_sha256: str
+    training_config_sha256: str
+    source_tree_sha256: str
+    train_manifest_sha256: str
+    val_manifest_sha256: str
+    device: str
+    request_bf16: bool
+    bf16_runtime_qualified: bool
+    edge_budget: int
+    checkpoint_every_updates: int
+
+
+@dataclass(frozen=True, slots=True)
+class BaseAttemptRegistryRow:
+    """One row from an externally authenticated cohort registry snapshot.
+
+    Rows for one run are ordered oldest-to-newest and must describe exactly one
+    resume chain.  A digest here is checked against actual terminal bytes but is
+    not accepted as evidence that the registry itself was authenticated.
+    """
+
+    attempt_id: str
+    run_id: str
+    terminal_outcome: Literal["FAILED", "HELD", "SUCCEEDED"]
+    terminal_sha256: str
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class FailedBaseAttempt:
+    attempt_id: str
+    terminal_sha256: str
+    terminal_outcome: Literal["FAILED", "HELD"]
+    failure_code: str
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class SelectedBaseCheckpoint:
+    """Owned immutable bytes for one fully checked validation-best payload."""
+
+    name: str
+    sha256: str
+    raw: bytes
+    epoch: int
+    global_step: int
+    state_digest: str
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class ResolvedBaseRun:
+    run_id: str
+    attempt_id: str
+    system_id: str
+    seed: int
+    terminal_sha256: str
+    latest_checkpoint_sha256: str
+    latest_checkpoint_name: str
+    selected_checkpoint: SelectedBaseCheckpoint
+    failed_predecessors: tuple[FailedBaseAttempt, ...]
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class ResolvedBaseCohort:
+    """Exact registered row order with no score, latency, or winner."""
+
+    rows: tuple[ResolvedBaseRun, ...]
+    plan_sha256: str
+    matrix_sha256: str
+    training_config_sha256: str
+    source_tree_sha256: str
+    train_manifest_sha256: str
+    val_manifest_sha256: str
+    authority: int = AUTHORITY
+    production: bool = PRODUCTION
+    result_claimed: bool = RESULT_CLAIMED
+    status: str = STATUS
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class _CheckedCheckpoint:
+    raw: bytes
+    sha256: str
+    name: str
+    epoch: int
+    update_index: int
+    global_step: int
+    checkpoint_sequence: int
+    updates_per_epoch: int
+    total_steps: int
+    train_edges_seen: int
+    validation_edges_seen: int
+    max_microbatch_edges: int
+    best_validation_metric: float | None
+    state_digest: str
+    best_name: str
+    best_sha256: str
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if type(value) is not str or len(value) != 64 or any(
+        character not in "0123456789abcdef" for character in value
+    ):
+        raise BaseCohortResolutionError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _attempt_id(value: object) -> str:
+    if (
+        type(value) is not str
+        or _ATTEMPT_ID.fullmatch(value) is None
+        or value in {".", ".."}
+    ):
+        raise BaseCohortResolutionError("attempt_id is not one safe path component")
+    return value
+
+
+def _exact_positive_int(value: object, label: str) -> int:
+    if type(value) is not int or value < 1 or value > (1 << 63) - 1:
+        raise BaseCohortResolutionError(f"{label} must be an exact positive int")
+    return value
+
+
+def _checked_admission(value: object) -> BaseCohortAdmission:
+    if type(value) is not BaseCohortAdmission:
+        raise TypeError("admission must be exact BaseCohortAdmission")
+    plan = build_experiment_plan()
+    expected_plan = experiment_plan_sha256(plan)
+    if value.plan_sha256 != expected_plan or value.matrix_sha256 != plan.matrix_sha256:
+        raise BaseCohortResolutionError("admission differs from the installed experiment plan")
+    for label in (
+        "training_config_sha256",
+        "source_tree_sha256",
+        "train_manifest_sha256",
+        "val_manifest_sha256",
+    ):
+        _lower_sha256(getattr(value, label), f"admission.{label}")
+    if type(value.device) is not str or not value.device:
+        raise BaseCohortResolutionError("admission.device must be a nonempty exact string")
+    if (
+        type(value.request_bf16) is not bool
+        or type(value.bf16_runtime_qualified) is not bool
+    ):
+        raise TypeError("admission precision switches must be exact bool values")
+    _exact_positive_int(value.edge_budget, "admission.edge_budget")
+    _exact_positive_int(
+        value.checkpoint_every_updates,
+        "admission.checkpoint_every_updates",
+    )
+    try:
+        # This closes all formal runtime knobs not represented as fields above.
+        training_module.TrainingConfig(
+            stage="base",
+            seed=1729,
+            device=value.device,
+            request_bf16=value.request_bf16,
+            bf16_runtime_qualified=value.bf16_runtime_qualified,
+            edge_budget=value.edge_budget,
+            checkpoint_every_updates=value.checkpoint_every_updates,
+            synthetic_contract=False,
+        )
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortResolutionError("admission training runtime is invalid") from error
+    return value
+
+
+def _checked_registry_rows(value: object) -> tuple[BaseAttemptRegistryRow, ...]:
+    if type(value) is not tuple:
+        raise TypeError("registry_rows must be an exact tuple")
+    if not value:
+        raise BaseCohortResolutionError("attempt registry snapshot is empty")
+    registered = frozenset(base_run_ids())
+    if len(value) > MAX_ATTEMPTS_PER_RUN * len(registered):
+        raise BaseCohortResolutionError("attempt registry exceeds its closed row bound")
+    seen_attempts: set[str] = set()
+    checked: list[BaseAttemptRegistryRow] = []
+    for ordinal, row in enumerate(value):
+        if type(row) is not BaseAttemptRegistryRow:
+            raise TypeError(f"registry_rows[{ordinal}] has the wrong exact type")
+        attempt = _attempt_id(row.attempt_id)
+        if attempt in seen_attempts:
+            raise BaseCohortResolutionError("attempt registry contains a duplicate attempt_id")
+        seen_attempts.add(attempt)
+        if type(row.run_id) is not str or row.run_id not in registered:
+            raise BaseCohortResolutionError("attempt registry contains an unregistered base run")
+        role, _seed, _system = parse_run_id(row.run_id)
+        if role != "BASE_QUALIFICATION":
+            raise BaseCohortResolutionError("attempt registry row is not a base run")
+        if row.terminal_outcome not in ("FAILED", "HELD", "SUCCEEDED"):
+            raise BaseCohortResolutionError("registry terminal outcome is outside the vocabulary")
+        _lower_sha256(row.terminal_sha256, "registry terminal_sha256")
+        checked.append(row)
+    successes = [row for row in checked if row.terminal_outcome == "SUCCEEDED"]
+    expected = base_run_ids()
+    if len(successes) != len(expected):
+        raise BaseCohortResolutionError("registry must contain exactly nine successful terminals")
+    for run_id in expected:
+        if sum(row.run_id == run_id for row in successes) != 1:
+            raise BaseCohortResolutionError(
+                "registry must contain exactly one successful terminal per base run"
+            )
+        if sum(row.run_id == run_id for row in checked) > MAX_ATTEMPTS_PER_RUN:
+            raise BaseCohortResolutionError(
+                "registry run history exceeds its closed attempt bound"
+            )
+    return tuple(checked)
+
+
+def _checked_directory(path: Path, label: str) -> Path:
+    try:
+        metadata = path.lstat()
+    except OSError as error:
+        raise BaseCohortResolutionError(f"{label} is absent") from error
+    if not stat.S_ISDIR(metadata.st_mode) or path.is_symlink():
+        raise BaseCohortResolutionError(f"{label} must be a non-symlink directory")
+    return path
+
+
+def _read_owned_file(
+    path: Path,
+    *,
+    label: str,
+    maximum_bytes: int,
+    expected_sha256: str | None = None,
+) -> tuple[bytes, str]:
+    """Read one stable regular leaf from one descriptor without following it."""
+
+    flags = os.O_RDONLY
+    for name in ("O_BINARY", "O_CLOEXEC", "O_NOFOLLOW"):
+        flags |= int(getattr(os, name, 0))
+    before_link = None
+    if not hasattr(os, "O_NOFOLLOW"):
+        try:
+            before_link = path.lstat()
+        except OSError as error:
+            raise BaseCohortResolutionError(f"{label} is absent") from error
+        if stat.S_ISLNK(before_link.st_mode):
+            raise BaseCohortResolutionError(f"{label} cannot be a symlink")
+    try:
+        descriptor = os.open(path, flags)
+    except OSError as error:
+        raise BaseCohortResolutionError(f"{label} could not be opened safely") from error
+    try:
+        before = os.fstat(descriptor)
+        if not stat.S_ISREG(before.st_mode):
+            raise BaseCohortResolutionError(f"{label} must be a regular file")
+        if before.st_size < 1 or before.st_size > maximum_bytes:
+            raise BaseCohortResolutionError(f"{label} exceeds its closed byte bound")
+        remaining = before.st_size
+        chunks: list[bytes] = []
+        while remaining:
+            chunk = os.read(descriptor, min(1024 * 1024, remaining))
+            if not chunk:
+                raise BaseCohortResolutionError(f"{label} ended before its stated size")
+            chunks.append(chunk)
+            remaining -= len(chunk)
+        if os.read(descriptor, 1):
+            raise BaseCohortResolutionError(f"{label} grew during its bounded read")
+        after = os.fstat(descriptor)
+    finally:
+        os.close(descriptor)
+    stable_fields = ("st_dev", "st_ino", "st_size", "st_mtime_ns", "st_ctime_ns")
+    if any(getattr(before, field) != getattr(after, field) for field in stable_fields):
+        raise BaseCohortResolutionError(f"{label} changed during its bounded read")
+    if before_link is not None:
+        try:
+            after_link = path.lstat()
+        except OSError as error:
+            raise BaseCohortResolutionError(f"{label} changed after its read") from error
+        if stat.S_ISLNK(after_link.st_mode) or any(
+            getattr(before_link, field) != getattr(after_link, field)
+            for field in stable_fields
+        ):
+            raise BaseCohortResolutionError(f"{label} changed around its fallback read")
+    raw = b"".join(chunks)
+    digest = hashlib.sha256(raw).hexdigest()
+    if expected_sha256 is not None and digest != _lower_sha256(
+        expected_sha256,
+        f"expected {label} sha256",
+    ):
+        raise BaseCohortResolutionError(f"{label} digest mismatch")
+    return raw, digest
+
+
+def _decode_checkpoint(raw: bytes, label: str) -> dict[str, object]:
+    try:
+        payload = torch.load(io.BytesIO(raw), map_location="cpu", weights_only=True)
+    except Exception as error:
+        raise BaseCohortResolutionError(f"{label} is not a restricted checkpoint") from error
+    if type(payload) is not dict or frozenset(payload) != _CHECKPOINT_KEYS:
+        raise BaseCohortResolutionError(f"{label} checkpoint keys are not closed")
+    return payload
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _torch_value_sha256(value: object) -> str:
+    stream = io.BytesIO()
+    torch.save(value, stream)
+    return hashlib.sha256(stream.getvalue()).hexdigest()
+
+
+def _exact_payload_int(
+    payload: Mapping[str, object],
+    name: str,
+    *,
+    minimum: int = 0,
+) -> int:
+    value = payload.get(name)
+    if type(value) is not int or value < minimum or value > (1 << 63) - 1:
+        raise BaseCohortResolutionError(f"checkpoint {name} is not an exact bounded int")
+    return value
+
+
+def _validate_rng_without_consuming(payload: Mapping[str, object]) -> None:
+    rng = payload.get("rng")
+    if not isinstance(rng, Mapping):
+        raise BaseCohortResolutionError("checkpoint RNG state is malformed")
+    ambient = training_module._capture_rng()
+    try:
+        training_module._restore_rng(rng)
+    except training_module.TrainingCheckpointError as error:
+        raise BaseCohortResolutionError("checkpoint RNG state is malformed") from error
+    finally:
+        training_module._restore_rng(ambient)
+
+
+def _validate_model_and_optimizer(
+    payload: Mapping[str, object],
+    *,
+    config: training_module.TrainingConfig,
+    system_id: str,
+    global_step: int,
+    total_steps: int,
+) -> None:
+    try:
+        system, binding = training_module.construct_registered_base_seed_bound_system(
+            system_id,
+            config,
+        )
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortResolutionError("registered base reconstruction failed") from error
+    expected_bindings = {
+        "behavior_sha256": binding.behavior_sha256,
+        "code_artifact_sha256": binding.code_artifact_sha256,
+        "environment_sha256": binding.environment_sha256,
+        "factory_sha256": binding.factory_sha256,
+        "initial_optimizable_state_sha256": binding.initial_optimizable_state_sha256,
+        "initialization_binding_sha256": binding.sha256,
+    }
+    for name, expected in expected_bindings.items():
+        if payload.get(name) != expected:
+            raise BaseCohortResolutionError(f"checkpoint {name} binding mismatch")
+
+    model = payload.get("model")
+    if not isinstance(model, Mapping):
+        raise BaseCohortResolutionError("checkpoint model state is malformed")
+    parameters = [parameter for parameter in system.parameters() if parameter.requires_grad]
+    if not parameters or any(not isinstance(parameter, nn.Parameter) for parameter in parameters):
+        raise BaseCohortResolutionError("registered base parameter census is malformed")
+    optimizer = torch.optim.AdamW(
+        parameters,
+        lr=config.learning_rate,
+        weight_decay=training_module.WEIGHT_DECAY,
+    )
+
+    def multiplier(step: int) -> float:
+        return training_module._learning_rate_multiplier(step, total_steps)
+
+    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, multiplier)
+    try:
+        training_module._validate_restored_optimizer_scheduler(
+            payload,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            config=config,
+            global_step=global_step,
+            total_steps=total_steps,
+        )
+        system.load_state_dict(model, strict=True)
+    except (KeyError, TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortResolutionError("checkpoint model/optimizer state is malformed") from error
+    if training_module.training_system_state_sha256(system) != training_module._stable_hash(
+        model
+    ):
+        raise BaseCohortResolutionError("loaded registered base differs from checkpoint bytes")
+    if training_module.training_system_behavior_sha256(system) != binding.behavior_sha256:
+        raise BaseCohortResolutionError("loaded registered base behavior changed")
+    _validate_rng_without_consuming(payload)
+
+
+def _validate_ledger_payload_binding(
+    payload: Mapping[str, object],
+    record: execution_module.CheckpointRecord,
+) -> None:
+    epoch = _exact_payload_int(payload, "epoch")
+    global_step = _exact_payload_int(payload, "global_step")
+    if record.epoch_index != epoch or record.global_step != global_step:
+        raise BaseCohortResolutionError("ledger/runtime checkpoint cursor mismatch")
+    required = {
+        "model_state_sha256": _torch_value_sha256(payload["model"]),
+        "optimizer_state_sha256": _torch_value_sha256(payload["optimizer"]),
+        "rng_state_sha256": _torch_value_sha256(payload["rng"]),
+        "dropout_state_sha256": _torch_value_sha256(payload["rng"]),
+        "sampler_state_sha256": hashlib.sha256(
+            _canonical_json_bytes(
+                {
+                    "epoch": epoch,
+                    "global_step": global_step,
+                    "update_index": _exact_payload_int(payload, "update_index"),
+                }
+            )
+        ).hexdigest(),
+        "dataloader_state_sha256": hashlib.sha256(
+            _canonical_json_bytes(
+                {
+                    "train_manifest_sha256": payload["train_manifest_sha256"],
+                    "val_manifest_sha256": payload["val_manifest_sha256"],
+                }
+            )
+        ).hexdigest(),
+        "validation_state_sha256": hashlib.sha256(
+            _canonical_json_bytes(
+                {
+                    "best_checkpoint_name": payload["best_checkpoint_name"],
+                    "best_checkpoint_sha256": payload["best_checkpoint_sha256"],
+                    "best_validation_metric": payload["best_validation_metric"],
+                }
+            )
+        ).hexdigest(),
+    }
+    for name, expected in required.items():
+        if getattr(record, name) != expected:
+            raise BaseCohortResolutionError(f"ledger/runtime checkpoint {name} mismatch")
+
+
+def _validate_checkpoint_payload(
+    raw: bytes,
+    *,
+    sha256: str,
+    name: str,
+    run_id: str,
+    admission: BaseCohortAdmission,
+    ledger_record: execution_module.CheckpointRecord | None,
+    require_completed_latest: bool,
+    require_selected_best: bool,
+) -> _CheckedCheckpoint:
+    match = _CHECKPOINT_NAME.fullmatch(name)
+    if match is None:
+        raise BaseCohortResolutionError("checkpoint filename is outside the runtime grammar")
+    role, seed, system_id = parse_run_id(run_id)
+    if role != "BASE_QUALIFICATION":
+        raise BaseCohortResolutionError("checkpoint run is not a registered base run")
+    payload = _decode_checkpoint(raw, name)
+    state_digest = payload.get("state_digest")
+    without_digest = dict(payload)
+    without_digest.pop("state_digest")
+    if (
+        type(state_digest) is not str
+        or state_digest != training_module._stable_hash(without_digest)
+    ):
+        raise BaseCohortResolutionError("checkpoint state digest mismatch")
+    if (
+        type(payload["authority"]) is not int
+        or payload["authority"] != 0
+        or payload["production"] is not False
+        or payload["result_claimed"] is not False
+        or payload["external_receipt_verified"] is not False
+    ):
+        raise BaseCohortResolutionError("checkpoint authority/result header is malformed")
+    if any(
+        payload[name_] is not None
+        for name_ in (
+            "frozen_base_checkpoint_sha256",
+            "frozen_base_state_sha256",
+            "qualified_base_selection_sha256",
+            "residual_capacity_audit_sha256",
+        )
+    ):
+        raise BaseCohortResolutionError("base checkpoint contains residual-only bindings")
+    config = training_module.TrainingConfig(
+        stage="base",
+        seed=seed,
+        device=admission.device,
+        request_bf16=admission.request_bf16,
+        bf16_runtime_qualified=admission.bf16_runtime_qualified,
+        edge_budget=admission.edge_budget,
+        checkpoint_every_updates=admission.checkpoint_every_updates,
+        synthetic_contract=False,
+    )
+    expected = {
+        "config_sha256": config.sha256,
+        "precision_mode": training_module.resolve_precision(config).mode,
+        "schema": training_module.CHECKPOINT_SCHEMA,
+        "seed": seed,
+        "stage": "base",
+        "system_id": system_id,
+        "train_manifest_sha256": admission.train_manifest_sha256,
+        "val_manifest_sha256": admission.val_manifest_sha256,
+    }
+    for field, value in expected.items():
+        if payload.get(field) != value:
+            raise BaseCohortResolutionError(f"checkpoint {field} binding mismatch")
+    updates_per_epoch = _exact_payload_int(payload, "updates_per_epoch", minimum=1)
+    total_steps = _exact_payload_int(payload, "total_steps", minimum=1)
+    if total_steps != training_module.BASE_EPOCHS * updates_per_epoch:
+        raise BaseCohortResolutionError("checkpoint total schedule is not the formal base schedule")
+    try:
+        cursor = training_module._validate_checkpoint_progress(
+            payload,
+            epochs=training_module.BASE_EPOCHS,
+            updates_per_epoch=updates_per_epoch,
+            total_steps=total_steps,
+            artifact_path=Path(name),
+            artifact_sha256=sha256,
+        )
+    except training_module.TrainingCheckpointError as error:
+        raise BaseCohortResolutionError("checkpoint cursor/state is malformed") from error
+    if int(match.group("step")) != cursor.global_step:
+        raise BaseCohortResolutionError("checkpoint filename step differs from payload")
+    if int(match.group("sequence")) != cursor.checkpoint_sequence:
+        raise BaseCohortResolutionError("checkpoint filename sequence differs from payload")
+    if require_completed_latest and (
+        match.group("reason") != "validation"
+        or cursor.epoch != training_module.BASE_EPOCHS
+        or cursor.update_index != 0
+        or cursor.global_step != total_steps
+        or cursor.best_validation_metric is None
+    ):
+        raise BaseCohortResolutionError("successful terminal does not point to a completed base checkpoint")
+    if cursor.best_checkpoint_name is None or cursor.best_checkpoint_sha256 is None:
+        raise BaseCohortResolutionError("checkpoint has no immutable validation-best pointer")
+    if require_selected_best and (
+        match.group("reason") != "validation"
+        or cursor.epoch < 1
+        or cursor.update_index != 0
+        or cursor.best_validation_metric is None
+        or cursor.best_checkpoint_name != name
+        or cursor.best_checkpoint_sha256 != sha256
+    ):
+        raise BaseCohortResolutionError("selected payload is not its best-at-write checkpoint")
+    _validate_model_and_optimizer(
+        payload,
+        config=config,
+        system_id=system_id,
+        global_step=cursor.global_step,
+        total_steps=total_steps,
+    )
+    if ledger_record is not None:
+        _validate_ledger_payload_binding(payload, ledger_record)
+    return _CheckedCheckpoint(
+        raw=raw,
+        sha256=sha256,
+        name=name,
+        epoch=cursor.epoch,
+        update_index=cursor.update_index,
+        global_step=cursor.global_step,
+        checkpoint_sequence=cursor.checkpoint_sequence,
+        updates_per_epoch=updates_per_epoch,
+        total_steps=total_steps,
+        train_edges_seen=cursor.train_edges_seen,
+        validation_edges_seen=cursor.validation_edges_seen,
+        max_microbatch_edges=cursor.max_microbatch_edges,
+        best_validation_metric=cursor.best_validation_metric,
+        state_digest=state_digest,
+        best_name=cursor.best_checkpoint_name,
+        best_sha256=cursor.best_checkpoint_sha256,
+    )
+
+
+def _validate_latest_selected_link(
+    latest: _CheckedCheckpoint,
+    selected: _CheckedCheckpoint,
+) -> None:
+    """Close invariants shared by the terminal and its validation-best payload."""
+
+    if latest.best_name != selected.name or latest.best_sha256 != selected.sha256:
+        raise BaseCohortResolutionError("latest checkpoint best pointer changed")
+    if (
+        latest.updates_per_epoch != selected.updates_per_epoch
+        or latest.total_steps != selected.total_steps
+    ):
+        raise BaseCohortResolutionError("latest/selected checkpoint schedule mismatch")
+    if (
+        latest.best_validation_metric is None
+        or selected.best_validation_metric is None
+        or latest.best_validation_metric != selected.best_validation_metric
+    ):
+        raise BaseCohortResolutionError("latest/selected validation-best metric mismatch")
+    if (
+        selected.epoch > latest.epoch
+        or selected.global_step > latest.global_step
+        or selected.checkpoint_sequence > latest.checkpoint_sequence
+    ):
+        raise BaseCohortResolutionError("selected checkpoint is later than terminal latest")
+    if (
+        selected.train_edges_seen > latest.train_edges_seen
+        or selected.validation_edges_seen > latest.validation_edges_seen
+        or selected.max_microbatch_edges > latest.max_microbatch_edges
+    ):
+        raise BaseCohortResolutionError("selected checkpoint counters exceed terminal latest")
+
+
+def _latest_checkpoint_file(
+    checkpoint_directory: Path,
+    *,
+    global_step: int,
+) -> Path:
+    candidates: list[Path] = []
+    try:
+        with os.scandir(checkpoint_directory) as entries:
+            for ordinal, entry in enumerate(entries, start=1):
+                if ordinal > MAX_CHECKPOINT_DIRECTORY_ENTRIES:
+                    raise BaseCohortResolutionError(
+                        "checkpoint directory exceeds its closed entry bound"
+                    )
+                match = _CHECKPOINT_NAME.fullmatch(entry.name)
+                if (
+                    match is not None
+                    and int(match.group("step")) == global_step
+                    and match.group("reason") == "validation"
+                ):
+                    candidates.append(checkpoint_directory / entry.name)
+    except BaseCohortResolutionError:
+        raise
+    except OSError as error:
+        raise BaseCohortResolutionError("checkpoint directory could not be enumerated") from error
+    if len(candidates) != 1:
+        raise BaseCohortResolutionError(
+            "terminal checkpoint step must have exactly one validation payload"
+        )
+    return candidates[0]
+
+
+def _owned_failed_predecessors(
+    attempt_root: Path,
+    evidence: execution_module.CompletedAttemptEvidence,
+    *,
+    admission: BaseCohortAdmission,
+) -> tuple[FailedBaseAttempt, ...]:
+    rows: list[FailedBaseAttempt] = []
+    resume = evidence.resume
+    visited = {evidence.attempt.attempt_id}
+    while resume is not None:
+        if len(rows) >= MAX_ATTEMPTS_PER_RUN - 1:
+            raise BaseCohortResolutionError("resume chain exceeds its closed attempt bound")
+        predecessor_id = _attempt_id(resume.predecessor_attempt_id)
+        if predecessor_id in visited:
+            raise BaseCohortResolutionError("resume chain contains a cycle")
+        visited.add(predecessor_id)
+        directory = _checked_directory(attempt_root / predecessor_id, "predecessor attempt")
+        attempt_raw, _ = _read_owned_file(
+            directory / "attempt.json",
+            label="predecessor attempt receipt",
+            maximum_bytes=MAX_LEDGER_ARTIFACT_BYTES,
+        )
+        terminal_raw, terminal_sha = _read_owned_file(
+            directory / "terminal.json",
+            label="predecessor terminal",
+            maximum_bytes=MAX_LEDGER_ARTIFACT_BYTES,
+            expected_sha256=resume.predecessor_terminal_sha256,
+        )
+        try:
+            attempt = execution_module.parse_attempt_bytes(attempt_raw)
+            terminal = execution_module.parse_terminal_bytes(terminal_raw)
+        except (TypeError, ValueError) as error:
+            raise BaseCohortResolutionError("predecessor ledger artifact is malformed") from error
+        if (
+            attempt.attempt_id != predecessor_id
+            or attempt.run_id != evidence.attempt.run_id
+            or attempt.plan_sha256 != admission.plan_sha256
+            or attempt.matrix_sha256 != admission.matrix_sha256
+            or attempt.training_config_sha256 != admission.training_config_sha256
+            or attempt.source_tree_sha256 != admission.source_tree_sha256
+            or terminal.attempt_id != predecessor_id
+            or terminal.run_id != evidence.attempt.run_id
+            or terminal.outcome == "SUCCEEDED"
+            or terminal.failure_code is None
+            or terminal.attempt_receipt_sha256 != hashlib.sha256(attempt_raw).hexdigest()
+            or terminal.latest_checkpoint_receipt_sha256 != resume.checkpoint_receipt_sha256
+        ):
+            raise BaseCohortResolutionError("failed predecessor identity/binding is inconsistent")
+        rows.append(
+            FailedBaseAttempt(
+                attempt_id=predecessor_id,
+                terminal_sha256=terminal_sha,
+                terminal_outcome=terminal.outcome,
+                failure_code=terminal.failure_code,
+            )
+        )
+        resume_path = directory / "resume.json"
+        if resume_path.exists() or resume_path.is_symlink():
+            resume_raw, _ = _read_owned_file(
+                resume_path,
+                label="predecessor resume receipt",
+                maximum_bytes=MAX_LEDGER_ARTIFACT_BYTES,
+            )
+            try:
+                predecessor_resume = execution_module.parse_resume_bytes(resume_raw)
+            except (TypeError, ValueError) as error:
+                raise BaseCohortResolutionError("predecessor resume receipt is malformed") from error
+            if (
+                predecessor_resume.new_attempt_id != predecessor_id
+                or predecessor_resume.run_id != evidence.attempt.run_id
+                or predecessor_resume.created_at_utc != attempt.created_at_utc
+            ):
+                raise BaseCohortResolutionError("predecessor resume identity is inconsistent")
+            resume = predecessor_resume
+        else:
+            resume = None
+    rows.reverse()
+    return tuple(rows)
+
+
+def _resolve_registered_run(
+    attempt_root: Path,
+    run_id: str,
+    registry_history: tuple[BaseAttemptRegistryRow, ...],
+    admission: BaseCohortAdmission,
+    *,
+    selected_maximum_bytes: int = MAX_CHECKPOINT_BYTES,
+) -> ResolvedBaseRun:
+    success = [row for row in registry_history if row.terminal_outcome == "SUCCEEDED"]
+    if len(success) != 1 or registry_history[-1] != success[0]:
+        raise BaseCohortResolutionError("successful registry row must close its run history")
+    success_row = success[0]
+    try:
+        evidence = execution_module.verified_completed_attempt_evidence(
+            attempt_root,
+            success_row.attempt_id,
+        )
+    except (TypeError, ValueError) as error:
+        raise BaseCohortResolutionError("successful attempt chain did not verify") from error
+    attempt = evidence.attempt
+    if (
+        attempt.run_id != run_id
+        or attempt.plan_sha256 != admission.plan_sha256
+        or attempt.matrix_sha256 != admission.matrix_sha256
+        or attempt.training_config_sha256 != admission.training_config_sha256
+        or attempt.source_tree_sha256 != admission.source_tree_sha256
+        or evidence.terminal_sha256 != success_row.terminal_sha256
+    ):
+        raise BaseCohortResolutionError("successful attempt differs from cohort admission/registry")
+    failed = _owned_failed_predecessors(attempt_root, evidence, admission=admission)
+    actual_history = tuple(
+        (row.attempt_id, row.terminal_outcome, row.terminal_sha256) for row in failed
+    ) + ((success_row.attempt_id, "SUCCEEDED", evidence.terminal_sha256),)
+    expected_history = tuple(
+        (row.attempt_id, row.terminal_outcome, row.terminal_sha256)
+        for row in registry_history
+    )
+    if actual_history != expected_history:
+        raise BaseCohortResolutionError("registry history differs from the verified resume chain")
+
+    checkpoint_directory = _checked_directory(
+        attempt_root / attempt.attempt_id / "model-checkpoints",
+        "successful checkpoint directory",
+    )
+    latest_path = _latest_checkpoint_file(
+        checkpoint_directory,
+        global_step=evidence.latest_checkpoint.global_step,
+    )
+    latest_raw, latest_sha = _read_owned_file(
+        latest_path,
+        label="terminal-bound latest checkpoint",
+        maximum_bytes=MAX_CHECKPOINT_BYTES,
+        expected_sha256=evidence.latest_checkpoint_payload_sha256,
+    )
+    latest = _validate_checkpoint_payload(
+        latest_raw,
+        sha256=latest_sha,
+        name=latest_path.name,
+        run_id=run_id,
+        admission=admission,
+        ledger_record=evidence.latest_checkpoint,
+        require_completed_latest=True,
+        require_selected_best=False,
+    )
+    selected_path = checkpoint_directory / latest.best_name
+    selected_raw, selected_sha = _read_owned_file(
+        selected_path,
+        label="validation-selected checkpoint",
+        maximum_bytes=min(MAX_CHECKPOINT_BYTES, selected_maximum_bytes),
+        expected_sha256=latest.best_sha256,
+    )
+    selected = _validate_checkpoint_payload(
+        selected_raw,
+        sha256=selected_sha,
+        name=selected_path.name,
+        run_id=run_id,
+        admission=admission,
+        ledger_record=None,
+        require_completed_latest=False,
+        require_selected_best=True,
+    )
+    _validate_latest_selected_link(latest, selected)
+    _role, seed, system_id = parse_run_id(run_id)
+    return ResolvedBaseRun(
+        run_id=run_id,
+        attempt_id=attempt.attempt_id,
+        system_id=system_id,
+        seed=seed,
+        terminal_sha256=evidence.terminal_sha256,
+        latest_checkpoint_sha256=latest.sha256,
+        latest_checkpoint_name=latest.name,
+        selected_checkpoint=SelectedBaseCheckpoint(
+            name=selected.name,
+            sha256=selected.sha256,
+            raw=selected.raw,
+            epoch=selected.epoch,
+            global_step=selected.global_step,
+            state_digest=selected.state_digest,
+        ),
+        failed_predecessors=failed,
+    )
+
+
+def resolve_completed_base_cohort(
+    attempt_root: str | Path,
+    *,
+    registry_rows: tuple[BaseAttemptRegistryRow, ...],
+    admission: BaseCohortAdmission,
+) -> ResolvedBaseCohort:
+    """Resolve exactly nine terminal chains and their validation-best payloads.
+
+    ``registry_rows`` must come from the controller's separately authenticated,
+    stable private registry snapshot.  This function validates the snapshot's
+    claims against ledger/checkpoint bytes, but cannot authenticate the source
+    of the snapshot and never treats its hashes as a qualification grant.
+    """
+
+    checked_admission = _checked_admission(admission)
+    checked_registry = _checked_registry_rows(registry_rows)
+    root = _checked_directory(Path(attempt_root), "attempt root")
+    rows: list[ResolvedBaseRun] = []
+    selected_bytes = 0
+    for run_id in base_run_ids():
+        history = tuple(row for row in checked_registry if row.run_id == run_id)
+        remaining = MAX_SELECTED_COHORT_BYTES - selected_bytes
+        if remaining < 1:
+            raise BaseCohortResolutionError(
+                "selected checkpoint cohort exceeds its closed byte bound"
+            )
+        resolved = _resolve_registered_run(
+            root,
+            run_id,
+            history,
+            checked_admission,
+            selected_maximum_bytes=remaining,
+        )
+        selected_bytes += len(resolved.selected_checkpoint.raw)
+        if selected_bytes > MAX_SELECTED_COHORT_BYTES:
+            raise AssertionError("selected checkpoint byte accounting exceeded its bound")
+        rows.append(resolved)
+    canonical = tuple(rows)
+    if tuple(row.run_id for row in canonical) != base_run_ids():
+        raise AssertionError("resolved base cohort lost the registered order")
+    return ResolvedBaseCohort(
+        rows=canonical,
+        plan_sha256=checked_admission.plan_sha256,
+        matrix_sha256=checked_admission.matrix_sha256,
+        training_config_sha256=checked_admission.training_config_sha256,
+        source_tree_sha256=checked_admission.source_tree_sha256,
+        train_manifest_sha256=checked_admission.train_manifest_sha256,
+        val_manifest_sha256=checked_admission.val_manifest_sha256,
+    )
+
+
+__all__ = [
+    "AUTHORITY",
+    "BaseAttemptRegistryRow",
+    "BaseCohortAdmission",
+    "BaseCohortResolutionError",
+    "FailedBaseAttempt",
+    "MAX_CHECKPOINT_BYTES",
+    "MAX_SELECTED_COHORT_BYTES",
+    "PRODUCTION",
+    "RESULT_CLAIMED",
+    "ResolvedBaseCohort",
+    "ResolvedBaseRun",
+    "STATUS",
+    "SelectedBaseCheckpoint",
+    "resolve_completed_base_cohort",
+]
diff --git a/src/phaseset_core/base_cohort_validation.py b/src/phaseset_core/base_cohort_validation.py
new file mode 100644
index 0000000..3e5d1a3
--- /dev/null
+++ b/src/phaseset_core/base_cohort_validation.py
@@ -0,0 +1,968 @@
+"""Score a resolved nine-base cohort on one frozen capture validation census.
+
+This private seam starts from ``base_cohort_resolver`` owned selected-best
+checkpoint bytes.  It reconstructs each registered seed-bound B0/B1/B2 system,
+revalidates the selected payload, loads exactly that model state, and delegates
+the complete capture gallery to the audited capture-validation runtime.
+
+The output is authority zero and deliberately stops before latency measurement,
+``BaseScore`` construction, winner selection, or production authorization.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+from fractions import Fraction
+import hashlib
+import json
+from typing import Final
+
+import numpy as np
+import torch
+from torch import nn
+
+from . import base_cohort_resolver as resolver_module
+from . import capture_validation as capture_module
+from . import evaluation as evaluation_module
+from . import training as training_module
+from .experiments import base_run_ids, parse_run_id
+
+
+AUTHORITY: Final = 0
+PRODUCTION: Final = False
+RESULT_CLAIMED: Final = False
+OBSERVATION_SCHEMA: Final = "phaseset-base-capture-score-observation-v1"
+COHORT_SCHEMA: Final = "phaseset-base-cohort-validation-v1"
+OBSERVATION_STATUS: Final = "ACTUAL_GALLERY_SCORED_NO_LATENCY_NO_QUALIFICATION"
+COHORT_STATUS: Final = "NINE_BASE_GALLERIES_SCORED_NO_WINNER_NO_QUALIFICATION"
+MAX_SCORE_MATRIX_BYTES: Final = capture_module.DEFAULT_MAX_ENCODED_BYTES
+
+
+class BaseCohortValidationError(ValueError):
+    """The resolved cohort, selected model, or scored gallery is inconsistent."""
+
+
+def _sha256(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise BaseCohortValidationError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _raw32(value: object, label: str) -> bytes:
+    if type(value) is not bytes or len(value) != 32:
+        raise BaseCohortValidationError(f"{label} must be exact bytes[32]")
+    return value
+
+
+def _positive_int(value: object, label: str) -> int:
+    if type(value) is not int or value < 1 or value > (1 << 63) - 1:
+        raise BaseCohortValidationError(f"{label} must be an exact positive int")
+    return value
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+@dataclass(frozen=True, slots=True)
+class BaseParameterRow:
+    """One live named parameter in canonical module order."""
+
+    name: str
+    shape: tuple[int, ...]
+    dtype: str
+    numel: int
+
+    def __post_init__(self) -> None:
+        if type(self.name) is not str or not self.name:
+            raise BaseCohortValidationError("parameter name must be a nonempty string")
+        if (
+            type(self.shape) is not tuple
+            or any(type(axis) is not int or axis < 0 for axis in self.shape)
+        ):
+            raise BaseCohortValidationError("parameter shape must be an exact int tuple")
+        if type(self.dtype) is not str or self.dtype != "torch.float32":
+            raise BaseCohortValidationError("registered base parameter dtype must be float32")
+        expected = 1
+        for axis in self.shape:
+            expected *= axis
+        if _positive_int(self.numel, "parameter numel") != expected:
+            raise BaseCohortValidationError("parameter numel differs from its shape")
+
+
+@dataclass(frozen=True, slots=True)
+class BaseParameterCensus:
+    """Complete live parameter census derived after selected-state loading."""
+
+    system_id: str
+    rows: tuple[BaseParameterRow, ...]
+    total: int
+    loaded_state_sha256: str
+
+    def __post_init__(self) -> None:
+        if self.system_id not in training_module.REGISTERED_BASE_SYSTEM_IDS:
+            raise BaseCohortValidationError("parameter census system_id is not registered")
+        if type(self.rows) is not tuple or not self.rows:
+            raise BaseCohortValidationError("parameter census must be a nonempty tuple")
+        if any(type(row) is not BaseParameterRow for row in self.rows):
+            raise BaseCohortValidationError("parameter census row type is invalid")
+        names = tuple(row.name for row in self.rows)
+        if names != tuple(sorted(names)) or len(set(names)) != len(names):
+            raise BaseCohortValidationError(
+                "parameter census names must be unique and lexically ordered"
+            )
+        if _positive_int(self.total, "parameter total") != sum(
+            row.numel for row in self.rows
+        ):
+            raise BaseCohortValidationError("parameter total differs from its rows")
+        _lower_sha256(self.loaded_state_sha256, "loaded_state_sha256")
+
+    @property
+    def sha256(self) -> str:
+        return _sha256(
+            _canonical_json_bytes(
+                {
+                    "loaded_state_sha256": self.loaded_state_sha256,
+                    "rows": [
+                        {
+                            "dtype": row.dtype,
+                            "name": row.name,
+                            "numel": row.numel,
+                            "shape": list(row.shape),
+                        }
+                        for row in self.rows
+                    ],
+                    "schema": "phaseset-base-parameter-census-v1",
+                    "system_id": self.system_id,
+                    "total": self.total,
+                }
+            )
+        )
+
+
+def _fraction_payload(value: Fraction) -> dict[str, int]:
+    if type(value) is not Fraction or not Fraction(0, 1) <= value <= Fraction(1, 1):
+        raise BaseCohortValidationError("capture R1 must be an exact fraction in [0,1]")
+    return {"denominator": value.denominator, "numerator": value.numerator}
+
+
+def _dataset_from_observation(
+    *,
+    scores_float64le: bytes,
+    score_shape: tuple[int, int],
+    motion_commitments: tuple[bytes, ...],
+    caption_commitments: tuple[bytes, ...],
+    positive_motion_indices: tuple[tuple[int, ...], ...],
+    group_sizes: tuple[int, ...],
+    component_labels: tuple[str, ...],
+) -> evaluation_module.RetrievalDataset:
+    if type(scores_float64le) is not bytes:
+        raise BaseCohortValidationError("score bytes must be exact immutable bytes")
+    if (
+        type(score_shape) is not tuple
+        or len(score_shape) != 2
+        or any(type(axis) is not int or axis < 2 for axis in score_shape)
+    ):
+        raise BaseCohortValidationError("score shape must be exact [M>=2,Q>=2]")
+    expected = score_shape[0] * score_shape[1] * np.dtype("<f8").itemsize
+    if expected > MAX_SCORE_MATRIX_BYTES:
+        raise BaseCohortValidationError(
+            "complete score matrix exceeds its fixed observation byte bound"
+        )
+    if len(scores_float64le) != expected:
+        raise BaseCohortValidationError("score bytes differ from declared float64 shape")
+    try:
+        scores = (
+            np.frombuffer(scores_float64le, dtype="<f8")
+            .reshape(score_shape)
+            .astype(np.float64, copy=True, order="C")
+        )
+        dataset = evaluation_module.validate_retrieval_dataset(
+            evaluation_module.RetrievalDataset(
+                scores=scores,
+                motion_commitments=motion_commitments,
+                caption_commitments=caption_commitments,
+                positive_motion_indices=positive_motion_indices,
+                group_sizes=np.ascontiguousarray(group_sizes, dtype=np.int64),
+                component_labels=component_labels,
+            )
+        )
+    except (TypeError, ValueError, OverflowError) as error:
+        raise BaseCohortValidationError("score observation dataset is invalid") from error
+    return dataset
+
+
+def _capture_fractions(
+    dataset: evaluation_module.RetrievalDataset,
+) -> tuple[Fraction, Fraction, Fraction]:
+    rows = evaluation_module.capture_r1_contributions(dataset)
+    if not rows:
+        raise BaseCohortValidationError("capture score contribution census is empty")
+    count = len(rows)
+    text = sum(
+        (
+            Fraction(sum(row.text_to_motion_hits), len(row.text_to_motion_hits))
+            for row in rows
+        ),
+        start=Fraction(0, 1),
+    ) / count
+    motion = sum(
+        (
+            Fraction(sum(row.motion_to_text_hits), len(row.motion_to_text_hits))
+            for row in rows
+        ),
+        start=Fraction(0, 1),
+    ) / count
+    return text, motion, (text + motion) / 2
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class BaseValidationScoreObservation:
+    """Immutable actual-score bytes plus checkpoint/runtime/query bindings."""
+
+    run_id: str
+    attempt_id: str
+    system_id: str
+    seed: int
+    terminal_sha256: str
+    selected_checkpoint_sha256: str
+    selected_checkpoint_state_digest: str
+    initialization_binding_sha256: str
+    runtime_config_sha256: str
+    factory_sha256: str
+    code_artifact_sha256: str
+    environment_sha256: str
+    behavior_sha256: str
+    source_manifest_sha256: str
+    admitted_val_census_sha256: str
+    query_census_sha256: str
+    parameter_census: BaseParameterCensus
+    scores_float64le: bytes = field(repr=False)
+    score_shape: tuple[int, int]
+    motion_commitments: tuple[bytes, ...] = field(repr=False)
+    caption_commitments: tuple[bytes, ...] = field(repr=False)
+    positive_motion_indices: tuple[tuple[int, ...], ...] = field(repr=False)
+    group_sizes: tuple[int, ...]
+    component_labels: tuple[str, ...]
+    text_to_motion_capture_r1: Fraction
+    motion_to_text_capture_r1: Fraction
+    primary_capture_r1: Fraction
+    device: str
+    precision_mode: str
+    encoded_bytes: int
+    authority: int = AUTHORITY
+    production: bool = PRODUCTION
+    result_claimed: bool = RESULT_CLAIMED
+    schema: str = OBSERVATION_SCHEMA
+    status: str = OBSERVATION_STATUS
+
+    def __post_init__(self) -> None:
+        role, seed, system_id = parse_run_id(self.run_id)
+        if (
+            role != "BASE_QUALIFICATION"
+            or self.seed != seed
+            or self.system_id != system_id
+        ):
+            raise BaseCohortValidationError("observation run identity is inconsistent")
+        if type(self.attempt_id) is not str or not self.attempt_id:
+            raise BaseCohortValidationError("observation attempt_id is invalid")
+        for label in (
+            "terminal_sha256",
+            "selected_checkpoint_sha256",
+            "selected_checkpoint_state_digest",
+            "initialization_binding_sha256",
+            "runtime_config_sha256",
+            "factory_sha256",
+            "code_artifact_sha256",
+            "environment_sha256",
+            "behavior_sha256",
+            "source_manifest_sha256",
+            "admitted_val_census_sha256",
+            "query_census_sha256",
+        ):
+            _lower_sha256(getattr(self, label), label)
+        if self.admitted_val_census_sha256 != self.query_census_sha256:
+            raise BaseCohortValidationError(
+                "checkpoint-admitted validation census differs from scored query census"
+            )
+        if (
+            type(self.parameter_census) is not BaseParameterCensus
+            or self.parameter_census.system_id != self.system_id
+        ):
+            raise BaseCohortValidationError("observation parameter census is incompatible")
+        motions = tuple(
+            _raw32(value, f"motion_commitments[{index}]")
+            for index, value in enumerate(self.motion_commitments)
+        )
+        captions = tuple(
+            _raw32(value, f"caption_commitments[{index}]")
+            for index, value in enumerate(self.caption_commitments)
+        )
+        if type(self.group_sizes) is not tuple or any(
+            type(value) is not int for value in self.group_sizes
+        ):
+            raise BaseCohortValidationError("group_sizes must be an exact int tuple")
+        if type(self.component_labels) is not tuple:
+            raise BaseCohortValidationError("component_labels must be an exact tuple")
+        dataset = _dataset_from_observation(
+            scores_float64le=self.scores_float64le,
+            score_shape=self.score_shape,
+            motion_commitments=motions,
+            caption_commitments=captions,
+            positive_motion_indices=self.positive_motion_indices,
+            group_sizes=self.group_sizes,
+            component_labels=self.component_labels,
+        )
+        expected_fractions = _capture_fractions(dataset)
+        observed_fractions = (
+            self.text_to_motion_capture_r1,
+            self.motion_to_text_capture_r1,
+            self.primary_capture_r1,
+        )
+        for value in observed_fractions:
+            _fraction_payload(value)
+        if observed_fractions != expected_fractions:
+            raise BaseCohortValidationError("observation capture R1 differs from score bytes")
+        if type(self.device) is not str or not self.device:
+            raise BaseCohortValidationError("observation device is invalid")
+        if self.precision_mode not in ("FP32", "BF16"):
+            raise BaseCohortValidationError("observation precision mode is invalid")
+        _positive_int(self.encoded_bytes, "encoded_bytes")
+        if (
+            type(self.authority) is not int
+            or self.authority != AUTHORITY
+            or self.production is not PRODUCTION
+            or self.result_claimed is not RESULT_CLAIMED
+            or self.schema != OBSERVATION_SCHEMA
+            or self.status != OBSERVATION_STATUS
+        ):
+            raise BaseCohortValidationError("observation result header is invalid")
+        object.__setattr__(self, "motion_commitments", motions)
+        object.__setattr__(self, "caption_commitments", captions)
+
+    @property
+    def scores_float64_sha256(self) -> str:
+        return _sha256(self.scores_float64le)
+
+    def canonical_bytes(self) -> bytes:
+        return _canonical_json_bytes(
+            {
+                "admitted_val_census_sha256": self.admitted_val_census_sha256,
+                "attempt_id": self.attempt_id,
+                "authority": self.authority,
+                "behavior_sha256": self.behavior_sha256,
+                "caption_commitments": [value.hex() for value in self.caption_commitments],
+                "code_artifact_sha256": self.code_artifact_sha256,
+                "component_labels": list(self.component_labels),
+                "device": self.device,
+                "encoded_bytes": self.encoded_bytes,
+                "environment_sha256": self.environment_sha256,
+                "factory_sha256": self.factory_sha256,
+                "group_sizes": list(self.group_sizes),
+                "initialization_binding_sha256": self.initialization_binding_sha256,
+                "motion_commitments": [value.hex() for value in self.motion_commitments],
+                "motion_to_text_capture_r1": _fraction_payload(
+                    self.motion_to_text_capture_r1
+                ),
+                "parameter_census_sha256": self.parameter_census.sha256,
+                "parameter_count": self.parameter_census.total,
+                "positive_motion_indices": [
+                    list(value) for value in self.positive_motion_indices
+                ],
+                "precision_mode": self.precision_mode,
+                "primary_capture_r1": _fraction_payload(self.primary_capture_r1),
+                "production": self.production,
+                "query_census_sha256": self.query_census_sha256,
+                "result_claimed": self.result_claimed,
+                "run_id": self.run_id,
+                "runtime_config_sha256": self.runtime_config_sha256,
+                "schema": self.schema,
+                "score_dtype": "<f8",
+                "score_shape": list(self.score_shape),
+                "scores_float64_sha256": self.scores_float64_sha256,
+                "seed": self.seed,
+                "selected_checkpoint_sha256": self.selected_checkpoint_sha256,
+                "selected_checkpoint_state_digest": (
+                    self.selected_checkpoint_state_digest
+                ),
+                "source_manifest_sha256": self.source_manifest_sha256,
+                "status": self.status,
+                "system_id": self.system_id,
+                "terminal_sha256": self.terminal_sha256,
+                "text_to_motion_capture_r1": _fraction_payload(
+                    self.text_to_motion_capture_r1
+                ),
+            }
+        )
+
+    @property
+    def score_artifact_sha256(self) -> str:
+        return _sha256(self.canonical_bytes())
+
+    def __repr__(self) -> str:
+        return (
+            f"BaseValidationScoreObservation(run_id={self.run_id!r}, "
+            f"primary={self.primary_capture_r1}, "
+            f"score_artifact_sha256={self.score_artifact_sha256!r})"
+        )
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class BaseCohortValidationResult:
+    """All nine score observations, still without latency or a winner."""
+
+    observations: tuple[BaseValidationScoreObservation, ...]
+    plan_sha256: str
+    matrix_sha256: str
+    training_config_sha256: str
+    source_tree_sha256: str
+    train_manifest_sha256: str
+    source_manifest_sha256: str
+    query_census_sha256: str
+    authority: int = AUTHORITY
+    production: bool = PRODUCTION
+    result_claimed: bool = RESULT_CLAIMED
+    schema: str = COHORT_SCHEMA
+    status: str = COHORT_STATUS
+
+    def __post_init__(self) -> None:
+        if type(self.observations) is not tuple or any(
+            type(value) is not BaseValidationScoreObservation
+            for value in self.observations
+        ):
+            raise BaseCohortValidationError("cohort observations have an invalid type")
+        if tuple(value.run_id for value in self.observations) != base_run_ids():
+            raise BaseCohortValidationError(
+                "cohort observations must cover the exact registered nine-row order"
+            )
+        for label in (
+            "plan_sha256",
+            "matrix_sha256",
+            "training_config_sha256",
+            "source_tree_sha256",
+            "train_manifest_sha256",
+            "source_manifest_sha256",
+            "query_census_sha256",
+        ):
+            _lower_sha256(getattr(self, label), label)
+        if any(
+            row.source_manifest_sha256 != self.source_manifest_sha256
+            or row.query_census_sha256 != self.query_census_sha256
+            for row in self.observations
+        ):
+            raise BaseCohortValidationError("cohort rows do not share one validation census")
+        if len({row.terminal_sha256 for row in self.observations}) != 9:
+            raise BaseCohortValidationError("cohort terminal digests must be unique")
+        if len({row.selected_checkpoint_sha256 for row in self.observations}) != 9:
+            raise BaseCohortValidationError("cohort selected checkpoints must be unique")
+        if len({row.score_artifact_sha256 for row in self.observations}) != 9:
+            raise BaseCohortValidationError("cohort score artifacts must be unique")
+        for system_id in training_module.REGISTERED_BASE_SYSTEM_IDS:
+            counts = {
+                row.parameter_census.total
+                for row in self.observations
+                if row.system_id == system_id
+            }
+            census_rows = {
+                tuple((item.name, item.shape, item.dtype, item.numel) for item in row.parameter_census.rows)
+                for row in self.observations
+                if row.system_id == system_id
+            }
+            if len(counts) != 1 or len(census_rows) != 1:
+                raise BaseCohortValidationError(
+                    "base parameter census must agree across each system's three seeds"
+                )
+        if (
+            type(self.authority) is not int
+            or self.authority != AUTHORITY
+            or self.production is not PRODUCTION
+            or self.result_claimed is not RESULT_CLAIMED
+            or self.schema != COHORT_SCHEMA
+            or self.status != COHORT_STATUS
+        ):
+            raise BaseCohortValidationError("cohort result header is invalid")
+
+    @property
+    def sha256(self) -> str:
+        return _sha256(
+            _canonical_json_bytes(
+                {
+                    "authority": self.authority,
+                    "matrix_sha256": self.matrix_sha256,
+                    "observation_sha256s": [
+                        value.score_artifact_sha256 for value in self.observations
+                    ],
+                    "plan_sha256": self.plan_sha256,
+                    "production": self.production,
+                    "query_census_sha256": self.query_census_sha256,
+                    "result_claimed": self.result_claimed,
+                    "schema": self.schema,
+                    "source_manifest_sha256": self.source_manifest_sha256,
+                    "source_tree_sha256": self.source_tree_sha256,
+                    "status": self.status,
+                    "training_config_sha256": self.training_config_sha256,
+                    "train_manifest_sha256": self.train_manifest_sha256,
+                }
+            )
+        )
+
+
+def _checked_admission(
+    value: object,
+) -> resolver_module.BaseCohortAdmission:
+    try:
+        return resolver_module._checked_admission(value)
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortValidationError("base cohort admission is invalid") from error
+
+
+def _checked_cohort(
+    value: object,
+    admission: resolver_module.BaseCohortAdmission,
+) -> resolver_module.ResolvedBaseCohort:
+    if type(value) is not resolver_module.ResolvedBaseCohort:
+        raise BaseCohortValidationError("cohort must be exact ResolvedBaseCohort")
+    if (
+        type(value.authority) is not int
+        or value.authority != resolver_module.AUTHORITY
+        or value.production is not resolver_module.PRODUCTION
+        or value.result_claimed is not resolver_module.RESULT_CLAIMED
+        or value.status != resolver_module.STATUS
+    ):
+        raise BaseCohortValidationError("resolved cohort header is invalid")
+    expected_bindings = {
+        "plan_sha256": admission.plan_sha256,
+        "matrix_sha256": admission.matrix_sha256,
+        "training_config_sha256": admission.training_config_sha256,
+        "source_tree_sha256": admission.source_tree_sha256,
+        "train_manifest_sha256": admission.train_manifest_sha256,
+        "val_manifest_sha256": admission.val_manifest_sha256,
+    }
+    for name, expected in expected_bindings.items():
+        if getattr(value, name) != expected:
+            raise BaseCohortValidationError(f"resolved cohort {name} binding mismatch")
+    if type(value.rows) is not tuple or tuple(row.run_id for row in value.rows) != base_run_ids():
+        raise BaseCohortValidationError("resolved cohort lost the registered nine-row order")
+    terminal_digests: set[str] = set()
+    checkpoint_digests: set[str] = set()
+    selected_bytes = 0
+    for index, row in enumerate(value.rows):
+        if type(row) is not resolver_module.ResolvedBaseRun:
+            raise BaseCohortValidationError(f"resolved cohort row {index} type is invalid")
+        role, seed, system_id = parse_run_id(row.run_id)
+        if role != "BASE_QUALIFICATION" or row.seed != seed or row.system_id != system_id:
+            raise BaseCohortValidationError("resolved row identity is inconsistent")
+        if type(row.attempt_id) is not str or not row.attempt_id:
+            raise BaseCohortValidationError("resolved row attempt_id is invalid")
+        terminal = _lower_sha256(row.terminal_sha256, "terminal_sha256")
+        _lower_sha256(row.latest_checkpoint_sha256, "latest_checkpoint_sha256")
+        if type(row.latest_checkpoint_name) is not str or not row.latest_checkpoint_name:
+            raise BaseCohortValidationError("latest checkpoint name is invalid")
+        selected = row.selected_checkpoint
+        if type(selected) is not resolver_module.SelectedBaseCheckpoint:
+            raise BaseCohortValidationError("selected checkpoint type is invalid")
+        if type(selected.raw) is not bytes or not selected.raw:
+            raise BaseCohortValidationError("selected checkpoint bytes are invalid")
+        selected_digest = _lower_sha256(selected.sha256, "selected checkpoint SHA-256")
+        if _sha256(selected.raw) != selected_digest:
+            raise BaseCohortValidationError("selected owned checkpoint digest mismatch")
+        if type(selected.name) is not str or not selected.name:
+            raise BaseCohortValidationError("selected checkpoint name is invalid")
+        _positive_int(selected.epoch, "selected checkpoint epoch")
+        _positive_int(selected.global_step, "selected checkpoint global_step")
+        _lower_sha256(selected.state_digest, "selected checkpoint state_digest")
+        if type(row.failed_predecessors) is not tuple or any(
+            type(item) is not resolver_module.FailedBaseAttempt
+            for item in row.failed_predecessors
+        ):
+            raise BaseCohortValidationError("failed predecessor census is invalid")
+        terminal_digests.add(terminal)
+        checkpoint_digests.add(selected_digest)
+        selected_bytes += len(selected.raw)
+        if selected_bytes > resolver_module.MAX_SELECTED_COHORT_BYTES:
+            raise BaseCohortValidationError("selected cohort exceeds its owned-byte bound")
+    if len(terminal_digests) != 9 or len(checkpoint_digests) != 9:
+        raise BaseCohortValidationError(
+            "resolved cohort terminals and selected checkpoints must be unique"
+        )
+    return value
+
+
+def _snapshot_validation_source(value: object) -> capture_module.CaptureValidationSource:
+    try:
+        source_type, _runner = capture_module._verified_capture_validation_bindings()
+        if type(value) is not source_type:
+            raise BaseCohortValidationError(
+                "validation source must be exact CaptureValidationSource"
+            )
+        return source_type(value.split, value.manifest_sha256, value.captures)
+    except BaseCohortValidationError:
+        raise
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortValidationError("capture validation source is invalid") from error
+
+
+def _parameter_census(
+    system: training_module.BaseRetrievalSystem,
+) -> BaseParameterCensus:
+    rows: list[BaseParameterRow] = []
+    for name, parameter in sorted(system.named_parameters()):
+        if (
+            type(parameter) is not nn.Parameter
+            or not parameter.requires_grad
+            or parameter.dtype != torch.float32
+            or parameter.numel() < 1
+            or not bool(torch.isfinite(parameter.detach()).all().item())
+        ):
+            raise BaseCohortValidationError(
+                "loaded registered base parameter census is invalid"
+            )
+        rows.append(
+            BaseParameterRow(
+                name=name,
+                shape=tuple(int(axis) for axis in parameter.shape),
+                dtype=str(parameter.dtype),
+                numel=int(parameter.numel()),
+            )
+        )
+    state_sha256 = training_module.training_system_state_sha256(system)
+    return BaseParameterCensus(
+        system_id=system.system_id,
+        rows=tuple(rows),
+        total=sum(row.numel for row in rows),
+        loaded_state_sha256=state_sha256,
+    )
+
+
+def _validate_selected_payload(
+    row: resolver_module.ResolvedBaseRun,
+    admission: resolver_module.BaseCohortAdmission,
+) -> dict[str, object]:
+    selected = row.selected_checkpoint
+    try:
+        checked = resolver_module._validate_checkpoint_payload(
+            selected.raw,
+            sha256=selected.sha256,
+            name=selected.name,
+            run_id=row.run_id,
+            admission=admission,
+            ledger_record=None,
+            require_completed_latest=False,
+            require_selected_best=True,
+        )
+        if (
+            checked.raw != selected.raw
+            or checked.sha256 != selected.sha256
+            or checked.name != selected.name
+            or checked.epoch != selected.epoch
+            or checked.global_step != selected.global_step
+            or checked.state_digest != selected.state_digest
+        ):
+            raise BaseCohortValidationError(
+                "revalidated selected checkpoint differs from resolved owned bytes"
+            )
+        payload = resolver_module._decode_checkpoint(selected.raw, selected.name)
+    except BaseCohortValidationError:
+        raise
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortValidationError(
+            "selected checkpoint failed scoring-time revalidation"
+        ) from error
+    return payload
+
+
+def _expected_dataset_identity(
+    source: capture_module.CaptureValidationSource,
+) -> tuple[
+    tuple[bytes, ...],
+    tuple[bytes, ...],
+    tuple[tuple[int, ...], ...],
+    tuple[int, ...],
+    tuple[str, ...],
+]:
+    motions = tuple(capture.capture_commitment for capture in source.captures)
+    captions: list[bytes] = []
+    positives: list[tuple[int, ...]] = []
+    for motion_index, capture in enumerate(source.captures):
+        commitments = capture.holistic_text.caption_commitments
+        order = sorted(range(len(commitments)), key=lambda index: commitments[index])
+        captions.extend(commitments[index] for index in order)
+        positives.extend((motion_index,) for _ in order)
+    return (
+        motions,
+        tuple(captions),
+        tuple(positives),
+        tuple(capture.group_size for capture in source.captures),
+        tuple(capture.component_label for capture in source.captures),
+    )
+
+
+def _preflight_score_matrix(source: capture_module.CaptureValidationSource) -> int:
+    motion_count = len(source.captures)
+    caption_count = sum(
+        len(capture.holistic_text.caption_commitments)
+        for capture in source.captures
+    )
+    expected = motion_count * caption_count * np.dtype("<f8").itemsize
+    if expected > MAX_SCORE_MATRIX_BYTES:
+        raise BaseCohortValidationError(
+            "complete score matrix exceeds its fixed observation byte bound"
+        )
+    return expected
+
+
+def _validated_capture_result(
+    result: object,
+    *,
+    row: resolver_module.ResolvedBaseRun,
+    source: capture_module.CaptureValidationSource,
+    config: training_module.TrainingConfig,
+) -> tuple[
+    evaluation_module.RetrievalDataset,
+    tuple[Fraction, Fraction, Fraction],
+    bytes,
+]:
+    if type(result) is not capture_module.CaptureValidationResult:
+        raise BaseCohortValidationError(
+            "capture scorer did not return exact CaptureValidationResult"
+        )
+    try:
+        dataset = evaluation_module.validate_retrieval_dataset(result.dataset)
+    except (TypeError, ValueError) as error:
+        raise BaseCohortValidationError("capture scorer dataset is invalid") from error
+    expected_identity = _expected_dataset_identity(source)
+    observed_identity = (
+        dataset.motion_commitments,
+        dataset.caption_commitments,
+        dataset.positive_motion_indices,
+        tuple(int(value) for value in dataset.group_sizes),
+        dataset.component_labels,
+    )
+    if observed_identity != expected_identity:
+        raise BaseCohortValidationError("scored gallery identity differs from frozen source")
+    public_score_sha256 = _sha256(dataset.scores.tobytes(order="C"))
+    scores = np.ascontiguousarray(dataset.scores, dtype="<f8")
+    score_bytes = scores.tobytes(order="C")
+    fractions = _capture_fractions(dataset)
+    configured_device = torch.device(config.device)
+    if configured_device.type == "cuda" and configured_device.index is None:
+        configured_device = torch.device("cuda", torch.cuda.current_device())
+    expected_encoded_bytes = (
+        sum(len(capture.windows) for capture in source.captures)
+        * training_module.REGISTERED_EMBEDDING_DIM
+        * np.dtype(np.float32).itemsize
+    )
+    if (
+        result.source_census_sha256 != source.census_sha256
+        or result.scores_float64_sha256 != public_score_sha256
+        or (
+            result.text_to_motion_capture_r1,
+            result.motion_to_text_capture_r1,
+            result.primary_capture_r1,
+        )
+        != fractions
+        or result.system_id != row.system_id
+        or result.stage != "base"
+        or result.device != str(configured_device)
+        or result.precision_mode != training_module.resolve_precision(config).mode
+        or result.encoded_bytes != expected_encoded_bytes
+        or result.status != capture_module.STATUS
+    ):
+        raise BaseCohortValidationError(
+            "capture scorer metadata or metric differs from recomputed gallery"
+        )
+    return dataset, fractions, score_bytes
+
+
+def _score_resolved_run(
+    row: resolver_module.ResolvedBaseRun,
+    *,
+    admission: resolver_module.BaseCohortAdmission,
+    source: capture_module.CaptureValidationSource,
+) -> BaseValidationScoreObservation:
+    before_census = source.census_sha256
+    if before_census != admission.val_manifest_sha256:
+        raise BaseCohortValidationError(
+            "validation source differs from checkpoint-admitted val census"
+        )
+    payload = _validate_selected_payload(row, admission)
+    config = training_module.TrainingConfig(
+        stage="base",
+        seed=row.seed,
+        device=admission.device,
+        request_bf16=admission.request_bf16,
+        bf16_runtime_qualified=admission.bf16_runtime_qualified,
+        edge_budget=admission.edge_budget,
+        checkpoint_every_updates=admission.checkpoint_every_updates,
+        synthetic_contract=False,
+    )
+    try:
+        system, binding = training_module.construct_registered_base_seed_bound_system(
+            row.system_id,
+            config,
+        )
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortValidationError("registered base reconstruction failed") from error
+    expected_bindings = {
+        "behavior_sha256": binding.behavior_sha256,
+        "code_artifact_sha256": binding.code_artifact_sha256,
+        "environment_sha256": binding.environment_sha256,
+        "factory_sha256": binding.factory_sha256,
+        "initial_optimizable_state_sha256": binding.initial_optimizable_state_sha256,
+        "initialization_binding_sha256": binding.sha256,
+    }
+    if any(payload.get(name) != value for name, value in expected_bindings.items()):
+        raise BaseCohortValidationError(
+            "selected checkpoint differs from live seed-bound initialization"
+        )
+    model = payload.get("model")
+    if not isinstance(model, Mapping):
+        raise BaseCohortValidationError("selected checkpoint model state is malformed")
+    try:
+        system.load_state_dict(model, strict=True)
+    except (KeyError, TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortValidationError("selected model state could not be loaded") from error
+    loaded_state_sha256 = training_module.training_system_state_sha256(system)
+    if (
+        loaded_state_sha256 != training_module._stable_hash(model)
+        or training_module.training_system_behavior_sha256(system)
+        != binding.behavior_sha256
+    ):
+        raise BaseCohortValidationError(
+            "loaded selected model differs from its checkpoint/runtime binding"
+        )
+    parameter_census = _parameter_census(system)
+    if parameter_census.loaded_state_sha256 != loaded_state_sha256:
+        raise AssertionError("parameter census lost the selected model state")
+    try:
+        system.to(torch.device(config.device))
+        source_type, runner = capture_module._verified_capture_validation_bindings()
+        if type(source) is not source_type:
+            raise BaseCohortValidationError("capture source type changed before scoring")
+        result = runner(system, config, source)
+    except BaseCohortValidationError:
+        raise
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortValidationError("complete capture gallery scoring failed") from error
+    dataset, fractions, score_bytes = _validated_capture_result(
+        result,
+        row=row,
+        source=source,
+        config=config,
+    )
+    after_census = source.census_sha256
+    if after_census != before_census or result.source_census_sha256 != before_census:
+        raise BaseCohortValidationError("validation census changed during scoring")
+    if _sha256(row.selected_checkpoint.raw) != row.selected_checkpoint.sha256:
+        raise BaseCohortValidationError("selected checkpoint bytes changed during scoring")
+    return BaseValidationScoreObservation(
+        run_id=row.run_id,
+        attempt_id=row.attempt_id,
+        system_id=row.system_id,
+        seed=row.seed,
+        terminal_sha256=row.terminal_sha256,
+        selected_checkpoint_sha256=row.selected_checkpoint.sha256,
+        selected_checkpoint_state_digest=row.selected_checkpoint.state_digest,
+        initialization_binding_sha256=binding.sha256,
+        runtime_config_sha256=config.sha256,
+        factory_sha256=binding.factory_sha256,
+        code_artifact_sha256=binding.code_artifact_sha256,
+        environment_sha256=binding.environment_sha256,
+        behavior_sha256=binding.behavior_sha256,
+        source_manifest_sha256=source.manifest_sha256,
+        admitted_val_census_sha256=admission.val_manifest_sha256,
+        query_census_sha256=result.source_census_sha256,
+        parameter_census=parameter_census,
+        scores_float64le=score_bytes,
+        score_shape=tuple(int(axis) for axis in dataset.scores.shape),
+        motion_commitments=dataset.motion_commitments,
+        caption_commitments=dataset.caption_commitments,
+        positive_motion_indices=dataset.positive_motion_indices,
+        group_sizes=tuple(int(value) for value in dataset.group_sizes),
+        component_labels=dataset.component_labels,
+        text_to_motion_capture_r1=fractions[0],
+        motion_to_text_capture_r1=fractions[1],
+        primary_capture_r1=fractions[2],
+        device=result.device,
+        precision_mode=result.precision_mode,
+        encoded_bytes=result.encoded_bytes,
+    )
+
+
+def score_resolved_base_cohort(
+    cohort: resolver_module.ResolvedBaseCohort,
+    *,
+    admission: resolver_module.BaseCohortAdmission,
+    validation_source: capture_module.CaptureValidationSource,
+) -> BaseCohortValidationResult:
+    """Score all nine selected-best bases without latency or winner selection."""
+
+    checked_admission = _checked_admission(admission)
+    checked_cohort = _checked_cohort(cohort, checked_admission)
+    source = _snapshot_validation_source(validation_source)
+    expected_census = source.census_sha256
+    if expected_census != checked_admission.val_manifest_sha256:
+        raise BaseCohortValidationError(
+            "capture census differs from the cohort validation binding"
+        )
+    _preflight_score_matrix(source)
+    observations: list[BaseValidationScoreObservation] = []
+    for row in checked_cohort.rows:
+        if source.census_sha256 != expected_census:
+            raise BaseCohortValidationError("validation source changed between cohort rows")
+        observations.append(
+            _score_resolved_run(
+                row,
+                admission=checked_admission,
+                source=source,
+            )
+        )
+    if source.census_sha256 != expected_census:
+        raise BaseCohortValidationError("validation source changed after cohort scoring")
+    return BaseCohortValidationResult(
+        observations=tuple(observations),
+        plan_sha256=checked_cohort.plan_sha256,
+        matrix_sha256=checked_cohort.matrix_sha256,
+        training_config_sha256=checked_cohort.training_config_sha256,
+        source_tree_sha256=checked_cohort.source_tree_sha256,
+        train_manifest_sha256=checked_cohort.train_manifest_sha256,
+        source_manifest_sha256=source.manifest_sha256,
+        query_census_sha256=expected_census,
+    )
+
+
+__all__ = [
+    "AUTHORITY",
+    "BaseCohortValidationError",
+    "BaseCohortValidationResult",
+    "BaseParameterCensus",
+    "BaseParameterRow",
+    "BaseValidationScoreObservation",
+    "COHORT_SCHEMA",
+    "COHORT_STATUS",
+    "MAX_SCORE_MATRIX_BYTES",
+    "OBSERVATION_SCHEMA",
+    "OBSERVATION_STATUS",
+    "PRODUCTION",
+    "RESULT_CLAIMED",
+    "score_resolved_base_cohort",
+]
diff --git a/src/phaseset_core/execution.py b/src/phaseset_core/execution.py
index 500c230..70226c4 100644
--- a/src/phaseset_core/execution.py
+++ b/src/phaseset_core/execution.py
@@ -40,6 +40,7 @@
 TRAINING_CONFIG_SCHEMA = "phaseset-training-config-v1"
 RUNTIME_ADMISSION_SCHEMA = "phaseset-runtime-admission-v1"
 COMMAND_RESULT_SCHEMA = "phaseset-command-result-v2"
+MAX_VERIFIED_ATTEMPT_CHAIN_LENGTH = 64
 
 COMMANDS = (
     "preflight",
@@ -1032,6 +1033,89 @@ class _VerifiedAttemptLedger:
     resume_raw: bytes | None
 
 
+@dataclass(frozen=True, slots=True, repr=False)
+class CompletedAttemptEvidence:
+    """Owned canonical evidence for one fully verified successful attempt.
+
+    The records and byte strings come from the same complete attempt-chain
+    verification pass. ``latest_checkpoint`` is the receipt bound by the
+    terminal; it is not a validation-best selection. A consumer that needs the
+    validation-best payload must load the payload identified by
+    ``latest_checkpoint.checkpoint_payload_sha256``, validate that payload's
+    best-checkpoint name/digest pointer, and authenticate the separately
+    materialized best payload against that digest.
+    """
+
+    attempt: AttemptRecord
+    attempt_raw: bytes
+    latest_checkpoint: CheckpointRecord
+    latest_checkpoint_raw: bytes
+    terminal: TerminalRecord
+    terminal_raw: bytes
+    resume: ResumeRecord | None
+    resume_raw: bytes | None
+
+    def __post_init__(self) -> None:
+        if (
+            type(self.attempt) is not AttemptRecord
+            or type(self.latest_checkpoint) is not CheckpointRecord
+            or type(self.terminal) is not TerminalRecord
+            or type(self.attempt_raw) is not bytes
+            or type(self.latest_checkpoint_raw) is not bytes
+            or type(self.terminal_raw) is not bytes
+        ):
+            raise TypeError("completed attempt evidence fields have the wrong exact type")
+        if (self.resume is None) != (self.resume_raw is None):
+            raise ExecutionContractError("completed attempt resume record/bytes are incomplete")
+        if self.resume is not None and (
+            type(self.resume) is not ResumeRecord or type(self.resume_raw) is not bytes
+        ):
+            raise TypeError("completed attempt resume fields have the wrong exact type")
+        if (
+            parse_attempt_bytes(self.attempt_raw) != self.attempt
+            or parse_checkpoint_bytes(self.latest_checkpoint_raw)
+            != self.latest_checkpoint
+            or parse_terminal_bytes(self.terminal_raw) != self.terminal
+            or (
+                self.resume is not None
+                and parse_resume_bytes(self.resume_raw) != self.resume
+            )
+        ):
+            raise ExecutionContractError("completed attempt records differ from owned bytes")
+        identity = (self.attempt.attempt_id, self.attempt.run_id)
+        if (
+            (self.latest_checkpoint.attempt_id, self.latest_checkpoint.run_id)
+            != identity
+            or (self.terminal.attempt_id, self.terminal.run_id) != identity
+            or self.terminal.attempt_receipt_sha256
+            != artifact_sha256(self.attempt_raw)
+            or self.terminal.outcome != "SUCCEEDED"
+            or self.terminal.failure_code is not None
+            or self.terminal.latest_checkpoint_receipt_sha256
+            != artifact_sha256(self.latest_checkpoint_raw)
+            or (
+                self.resume is not None
+                and (
+                    (self.resume.new_attempt_id, self.resume.run_id) != identity
+                    or self.resume.created_at_utc != self.attempt.created_at_utc
+                )
+            )
+        ):
+            raise ExecutionContractError("completed attempt evidence identity is inconsistent")
+
+    @property
+    def terminal_sha256(self) -> str:
+        return artifact_sha256(self.terminal_raw)
+
+    @property
+    def latest_checkpoint_receipt_sha256(self) -> str:
+        return artifact_sha256(self.latest_checkpoint_raw)
+
+    @property
+    def latest_checkpoint_payload_sha256(self) -> str:
+        return self.latest_checkpoint.checkpoint_payload_sha256
+
+
 def _attempt_root(root: str | Path) -> Path:
     checked = Path(root)
     if checked.is_symlink() or not checked.is_dir():
@@ -1129,7 +1213,15 @@ def _verify_attempt_chain(
     root: Path,
     attempt_id: str,
     visited: frozenset[str] = frozenset(),
+    *,
+    remaining_attempts: int = MAX_VERIFIED_ATTEMPT_CHAIN_LENGTH,
 ) -> _VerifiedAttemptLedger:
+    if (
+        type(remaining_attempts) is not int
+        or remaining_attempts < 1
+        or remaining_attempts > MAX_VERIFIED_ATTEMPT_CHAIN_LENGTH
+    ):
+        raise ExecutionContractError("resume predecessor chain exceeds its closed attempt bound")
     if attempt_id in visited:
         raise ExecutionContractError("resume predecessor chain contains a cycle")
     ledger = _verify_local_attempt(root / attempt_id, attempt_id)
@@ -1138,6 +1230,7 @@ def _verify_attempt_chain(
             root,
             ledger.resume.predecessor_attempt_id,
             visited | {attempt_id},
+            remaining_attempts=remaining_attempts - 1,
         )
         _validate_resume_source(ledger.resume, predecessor)
         if (
@@ -1150,6 +1243,44 @@ def _verify_attempt_chain(
     return ledger
 
 
+def verified_completed_attempt_evidence(
+    root: str | Path,
+    attempt_id: object,
+) -> CompletedAttemptEvidence:
+    """Return owned records/bytes after one complete successful-chain check.
+
+    No ledger path is reread after ``_verify_attempt_chain`` returns. The
+    returned latest checkpoint is only the terminal-bound resume/progress
+    checkpoint. Runtime-specific best-checkpoint pointers live inside that
+    checkpoint payload and remain the responsibility of the consumer.
+    """
+
+    checked_id = _attempt_id(attempt_id)
+    checked_root = _attempt_root(root)
+    ledger = _verify_attempt_chain(checked_root, checked_id)
+    terminal = ledger.terminal
+    terminal_raw = ledger.terminal_raw
+    if terminal is None or terminal_raw is None:
+        raise ExecutionContractError("completed attempt has no immutable terminal")
+    if terminal.outcome != "SUCCEEDED":
+        raise ExecutionContractError("completed attempt terminal is not SUCCEEDED")
+    if terminal.failure_code is not None:
+        raise ExecutionContractError("successful completed attempt cannot carry a failure code")
+    if not ledger.checkpoints:
+        raise ExecutionContractError("successful completed attempt has no checkpoint receipt")
+    latest_checkpoint, _, latest_checkpoint_raw = ledger.checkpoints[-1]
+    return CompletedAttemptEvidence(
+        attempt=ledger.attempt,
+        attempt_raw=ledger.attempt_raw,
+        latest_checkpoint=latest_checkpoint,
+        latest_checkpoint_raw=latest_checkpoint_raw,
+        terminal=terminal,
+        terminal_raw=terminal_raw,
+        resume=ledger.resume,
+        resume_raw=ledger.resume_raw,
+    )
+
+
 def verified_resume_checkpoint_payload_sha256(
     root: str | Path,
     resume: object,
@@ -1860,10 +1991,12 @@ def execute_command(
     "CheckpointRecord",
     "CommandIntent",
     "CommandResult",
+    "CompletedAttemptEvidence",
     "DataFreeRunner",
     "ExecutionContractError",
     "ExecutionHold",
     "HEARTBEAT_SCHEMA",
+    "MAX_VERIFIED_ATTEMPT_CHAIN_LENGTH",
     "HeartbeatRecord",
     "PERIODIC_CACHE_SCHEMA",
     "PeriodicCacheRecord",
@@ -1904,5 +2037,6 @@ def execute_command(
     "runtime_admission_sha256",
     "runtime_handler_manifest_sha256",
     "validate_training_config_bytes",
+    "verified_completed_attempt_evidence",
     "verified_resume_checkpoint_payload_sha256",
 ]
diff --git a/tests/test_phaseset_base_cohort_checkpoint_state.py b/tests/test_phaseset_base_cohort_checkpoint_state.py
new file mode 100644
index 0000000..b1e18eb
--- /dev/null
+++ b/tests/test_phaseset_base_cohort_checkpoint_state.py
@@ -0,0 +1,260 @@
+from __future__ import annotations
+
+import copy
+import hashlib
+import random
+from types import SimpleNamespace
+
+import pytest
+import torch
+from torch import nn
+
+from phaseset_core import base_cohort_resolver as resolver
+from phaseset_core import training
+
+
+def _sha(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+class _TinyBase(nn.Module):
+    """Small engineering fixture; never a registered or qualified PhaseSet base."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.tensor([0.25, -0.5], dtype=torch.float32))
+        self.bias = nn.Parameter(torch.tensor([0.125], dtype=torch.float32))
+
+
+class _ValueDriftTinyBase(_TinyBase):
+    def load_state_dict(self, *args: object, **kwargs: object) -> object:
+        result = super().load_state_dict(*args, **kwargs)
+        with torch.no_grad():
+            self.weight.add_(1.0)
+        return result
+
+
+def _config() -> training.TrainingConfig:
+    return training.TrainingConfig(
+        stage="base",
+        seed=1729,
+        device="cpu",
+        request_bf16=False,
+        bf16_runtime_qualified=False,
+        edge_budget=32_768,
+        checkpoint_every_updates=1,
+        synthetic_contract=False,
+    )
+
+
+def _binding() -> SimpleNamespace:
+    return SimpleNamespace(
+        behavior_sha256=_sha("tiny-behavior"),
+        code_artifact_sha256=_sha("tiny-code"),
+        environment_sha256=_sha("tiny-environment"),
+        factory_sha256=_sha("tiny-factory"),
+        initial_optimizable_state_sha256=_sha("tiny-initial-state"),
+        sha256=_sha("tiny-initialization-binding"),
+    )
+
+
+def _payload(
+    config: training.TrainingConfig,
+    *,
+    global_step: int,
+    total_steps: int,
+) -> dict[str, object]:
+    source = _TinyBase()
+    optimizer = torch.optim.AdamW(
+        source.parameters(),
+        lr=config.learning_rate,
+        weight_decay=training.WEIGHT_DECAY,
+    )
+
+    def multiplier(step: int) -> float:
+        return training._learning_rate_multiplier(step, total_steps)
+
+    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, multiplier)
+    for parameter in source.parameters():
+        parameter.grad = torch.full_like(parameter, 0.5)
+    optimizer.step()
+    scheduler.step()
+    optimizer.zero_grad(set_to_none=True)
+    binding = _binding()
+    return {
+        "behavior_sha256": binding.behavior_sha256,
+        "code_artifact_sha256": binding.code_artifact_sha256,
+        "environment_sha256": binding.environment_sha256,
+        "factory_sha256": binding.factory_sha256,
+        "initial_optimizable_state_sha256": binding.initial_optimizable_state_sha256,
+        "initialization_binding_sha256": binding.sha256,
+        "model": copy.deepcopy(source.state_dict()),
+        "optimizer": copy.deepcopy(optimizer.state_dict()),
+        "rng": copy.deepcopy(training._capture_rng()),
+        "scheduler": copy.deepcopy(scheduler.state_dict()),
+    }
+
+
+@pytest.fixture
+def installed_tiny_builder(monkeypatch: pytest.MonkeyPatch) -> SimpleNamespace:
+    binding = _binding()
+
+    def construct(
+        system_id: str,
+        _config_value: training.TrainingConfig,
+    ) -> tuple[nn.Module, SimpleNamespace]:
+        assert system_id == "B0"
+        return _TinyBase(), binding
+
+    def state_sha256(system: nn.Module, **_kwargs: object) -> str:
+        return training._stable_hash(system.state_dict())
+
+    monkeypatch.setattr(
+        resolver.training_module,
+        "construct_registered_base_seed_bound_system",
+        construct,
+    )
+    monkeypatch.setattr(
+        resolver.training_module,
+        "training_system_state_sha256",
+        state_sha256,
+    )
+    monkeypatch.setattr(
+        resolver.training_module,
+        "training_system_behavior_sha256",
+        lambda _system: binding.behavior_sha256,
+    )
+    return binding
+
+
+def test_real_model_optimizer_scheduler_and_rng_state_validate_without_rng_drift(
+    installed_tiny_builder: SimpleNamespace,
+) -> None:
+    del installed_tiny_builder
+    config = _config()
+    payload = _payload(config, global_step=1, total_steps=8)
+    before = training._stable_hash(training._capture_rng())
+    resolver._validate_model_and_optimizer(
+        payload,
+        config=config,
+        system_id="B0",
+        global_step=1,
+        total_steps=8,
+    )
+    assert training._stable_hash(training._capture_rng()) == before
+
+
+@pytest.mark.parametrize("mutation", ["missing_key", "wrong_shape"])
+def test_real_strict_model_restore_rejects_key_and_shape_corruption(
+    installed_tiny_builder: SimpleNamespace,
+    mutation: str,
+) -> None:
+    del installed_tiny_builder
+    config = _config()
+    payload = _payload(config, global_step=1, total_steps=8)
+    model = payload["model"]
+    assert isinstance(model, dict)
+    if mutation == "missing_key":
+        model.pop("bias")
+    else:
+        model["weight"] = torch.zeros((1, 2), dtype=torch.float32)
+    with pytest.raises(resolver.BaseCohortResolutionError, match="model/optimizer state"):
+        resolver._validate_model_and_optimizer(
+            payload,
+            config=config,
+            system_id="B0",
+            global_step=1,
+            total_steps=8,
+        )
+
+
+def test_loaded_model_value_digest_mismatch_is_rejected(
+    monkeypatch: pytest.MonkeyPatch,
+    installed_tiny_builder: SimpleNamespace,
+) -> None:
+    config = _config()
+    payload = _payload(config, global_step=1, total_steps=8)
+    monkeypatch.setattr(
+        resolver.training_module,
+        "construct_registered_base_seed_bound_system",
+        lambda _system_id, _config_value: (_ValueDriftTinyBase(), installed_tiny_builder),
+    )
+    with pytest.raises(resolver.BaseCohortResolutionError, match="differs from checkpoint bytes"):
+        resolver._validate_model_and_optimizer(
+            payload,
+            config=config,
+            system_id="B0",
+            global_step=1,
+            total_steps=8,
+        )
+
+
+@pytest.mark.parametrize("mutation", ["optimizer_step", "scheduler_cursor"])
+def test_real_optimizer_scheduler_cursor_corruption_is_rejected(
+    installed_tiny_builder: SimpleNamespace,
+    mutation: str,
+) -> None:
+    del installed_tiny_builder
+    config = _config()
+    payload = _payload(config, global_step=1, total_steps=8)
+    if mutation == "optimizer_step":
+        optimizer = payload["optimizer"]
+        assert isinstance(optimizer, dict)
+        states = optimizer["state"]
+        assert isinstance(states, dict)
+        first = next(iter(states.values()))
+        assert isinstance(first, dict)
+        first["step"] = torch.tensor(2.0)
+    else:
+        scheduler = payload["scheduler"]
+        assert isinstance(scheduler, dict)
+        scheduler["last_epoch"] = 2
+    with pytest.raises(resolver.BaseCohortResolutionError, match="model/optimizer state"):
+        resolver._validate_model_and_optimizer(
+            payload,
+            config=config,
+            system_id="B0",
+            global_step=1,
+            total_steps=8,
+        )
+
+
+def test_rng_partial_restore_failure_restores_ambient_state(
+    installed_tiny_builder: SimpleNamespace,
+) -> None:
+    del installed_tiny_builder
+    config = _config()
+    payload = _payload(config, global_step=1, total_steps=8)
+    rng = payload["rng"]
+    assert isinstance(rng, dict)
+    rng["python"] = random.Random(991).getstate()
+    rng.pop("numpy_keys")
+    before = training._stable_hash(training._capture_rng())
+    with pytest.raises(resolver.BaseCohortResolutionError, match="RNG state"):
+        resolver._validate_model_and_optimizer(
+            payload,
+            config=config,
+            system_id="B0",
+            global_step=1,
+            total_steps=8,
+        )
+    assert training._stable_hash(training._capture_rng()) == before
+
+
+def test_registered_builder_identity_mismatch_is_rejected_before_state_restore(
+    installed_tiny_builder: SimpleNamespace,
+) -> None:
+    del installed_tiny_builder
+    config = _config()
+    payload = _payload(config, global_step=1, total_steps=8)
+    payload["factory_sha256"] = _sha("substituted-factory")
+    before = training._stable_hash(training._capture_rng())
+    with pytest.raises(resolver.BaseCohortResolutionError, match="factory_sha256"):
+        resolver._validate_model_and_optimizer(
+            payload,
+            config=config,
+            system_id="B0",
+            global_step=1,
+            total_steps=8,
+        )
+    assert training._stable_hash(training._capture_rng()) == before
diff --git a/tests/test_phaseset_base_cohort_resolver.py b/tests/test_phaseset_base_cohort_resolver.py
new file mode 100644
index 0000000..02bbd1e
--- /dev/null
+++ b/tests/test_phaseset_base_cohort_resolver.py
@@ -0,0 +1,646 @@
+from __future__ import annotations
+
+from dataclasses import replace
+import hashlib
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from phaseset_core import base_cohort_resolver as resolver
+from phaseset_core import execution
+from phaseset_core import training
+from phaseset_core.experiments import (
+    base_run_ids,
+    build_experiment_plan,
+    experiment_plan_sha256,
+)
+
+
+def _sha(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+def _admission() -> resolver.BaseCohortAdmission:
+    plan = build_experiment_plan()
+    return resolver.BaseCohortAdmission(
+        plan_sha256=experiment_plan_sha256(plan),
+        matrix_sha256=plan.matrix_sha256,
+        training_config_sha256=_sha("host-config"),
+        source_tree_sha256=_sha("source"),
+        train_manifest_sha256=_sha("train"),
+        val_manifest_sha256=_sha("val"),
+        device="cpu",
+        request_bf16=False,
+        bf16_runtime_qualified=False,
+        edge_budget=32_768,
+        checkpoint_every_updates=1,
+    )
+
+
+def _registry() -> tuple[resolver.BaseAttemptRegistryRow, ...]:
+    return tuple(
+        resolver.BaseAttemptRegistryRow(
+            attempt_id=f"attempt-{index}",
+            run_id=run_id,
+            terminal_outcome="SUCCEEDED",
+            terminal_sha256=_sha(f"terminal-{index}"),
+        )
+        for index, run_id in enumerate(base_run_ids())
+    )
+
+
+def _dummy_resolved(run_id: str, row: resolver.BaseAttemptRegistryRow) -> resolver.ResolvedBaseRun:
+    _role, seed, system_id = run_id.split("/")[1:]
+    return resolver.ResolvedBaseRun(
+        run_id=run_id,
+        attempt_id=row.attempt_id,
+        system_id=system_id,
+        seed=int(seed),
+        terminal_sha256=row.terminal_sha256,
+        latest_checkpoint_sha256=_sha(f"latest-{run_id}"),
+        latest_checkpoint_name="checkpoint-000000000075-000150-validation.pt",
+        selected_checkpoint=resolver.SelectedBaseCheckpoint(
+            name="checkpoint-000000000001-000002-validation.pt",
+            sha256=_sha(f"best-{run_id}"),
+            raw=f"best-{run_id}".encode("ascii"),
+            epoch=1,
+            global_step=1,
+            state_digest=_sha(f"state-{run_id}"),
+        ),
+        failed_predecessors=(),
+    )
+
+
+def test_resolver_restores_exact_registered_order_without_claiming_result(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+) -> None:
+    registry = tuple(reversed(_registry()))
+
+    def fake_resolve(
+        _root: Path,
+        run_id: str,
+        history: tuple[resolver.BaseAttemptRegistryRow, ...],
+        _admitted: resolver.BaseCohortAdmission,
+        *,
+        selected_maximum_bytes: int,
+    ) -> resolver.ResolvedBaseRun:
+        assert len(history) == 1
+        assert selected_maximum_bytes > 0
+        return _dummy_resolved(run_id, history[0])
+
+    monkeypatch.setattr(resolver, "_resolve_registered_run", fake_resolve)
+    cohort = resolver.resolve_completed_base_cohort(
+        tmp_path,
+        registry_rows=registry,
+        admission=_admission(),
+    )
+    assert tuple(row.run_id for row in cohort.rows) == base_run_ids()
+    assert cohort.authority == 0
+    assert cohort.production is False
+    assert cohort.result_claimed is False
+    assert "NO_SCORE_NO_QUALIFICATION" in cohort.status
+
+
+@pytest.mark.parametrize("outcome", ["FAILED", "HELD"])
+def test_registry_requires_one_success_per_registered_row(outcome: str) -> None:
+    rows = list(_registry())
+    rows[0] = replace(rows[0], terminal_outcome=outcome)
+    with pytest.raises(resolver.BaseCohortResolutionError, match="exactly nine"):
+        resolver._checked_registry_rows(tuple(rows))
+
+
+def test_registry_rejects_duplicate_success_and_unregistered_rows() -> None:
+    rows = _registry()
+    duplicate = replace(rows[0], attempt_id="another-attempt")
+    with pytest.raises(resolver.BaseCohortResolutionError, match="exactly nine"):
+        resolver._checked_registry_rows(rows + (duplicate,))
+    foreign = replace(
+        rows[0],
+        run_id="phaseset-run-v1/BASE_QUALIFICATION/1729/B9",
+        terminal_outcome="FAILED",
+    )
+    with pytest.raises(resolver.BaseCohortResolutionError, match="unregistered"):
+        resolver._checked_registry_rows((foreign,) + rows[1:])
+
+
+def test_registry_rejects_history_before_chain_verification_can_exceed_bound() -> None:
+    successes = _registry()
+    run_id = successes[0].run_id
+    failed = tuple(
+        resolver.BaseAttemptRegistryRow(
+            f"failed-{index}",
+            run_id,
+            "FAILED",
+            _sha(f"failed-terminal-{index}"),
+        )
+        for index in range(resolver.MAX_ATTEMPTS_PER_RUN)
+    )
+    with pytest.raises(resolver.BaseCohortResolutionError, match="closed attempt bound"):
+        resolver._checked_registry_rows(failed + successes)
+
+
+def test_success_must_be_last_registry_history(tmp_path: Path) -> None:
+    run_id = base_run_ids()[0]
+    rows = (
+        resolver.BaseAttemptRegistryRow("success", run_id, "SUCCEEDED", _sha("success")),
+        resolver.BaseAttemptRegistryRow("later-failure", run_id, "FAILED", _sha("failure")),
+    )
+    with pytest.raises(resolver.BaseCohortResolutionError, match="close its run history"):
+        resolver._resolve_registered_run(tmp_path, run_id, rows, _admission())
+
+
+def _checkpoint_record(
+    attempt_id: str,
+    run_id: str,
+    *,
+    payload_sha256: str,
+    parent: str | None = None,
+) -> execution.CheckpointRecord:
+    return execution.CheckpointRecord(
+        attempt_id=attempt_id,
+        run_id=run_id,
+        epoch_index=1,
+        global_step=1,
+        written_at_utc="2026-09-08T12:00:01Z",
+        model_state_sha256=_sha(f"{attempt_id}-model"),
+        optimizer_state_sha256=_sha(f"{attempt_id}-optimizer"),
+        rng_state_sha256=_sha(f"{attempt_id}-rng"),
+        sampler_state_sha256=_sha(f"{attempt_id}-sampler"),
+        dataloader_state_sha256=_sha(f"{attempt_id}-loader"),
+        dropout_state_sha256=_sha(f"{attempt_id}-dropout"),
+        validation_state_sha256=_sha(f"{attempt_id}-validation"),
+        checkpoint_payload_sha256=payload_sha256,
+        parent_checkpoint_receipt_sha256=parent,
+    )
+
+
+def _attempt(
+    attempt_id: str,
+    run_id: str,
+    admission: resolver.BaseCohortAdmission,
+    *,
+    created_at: str,
+) -> execution.AttemptRecord:
+    return execution.AttemptRecord(
+        attempt_id=attempt_id,
+        run_id=run_id,
+        created_at_utc=created_at,
+        plan_sha256=admission.plan_sha256,
+        matrix_sha256=admission.matrix_sha256,
+        training_config_sha256=admission.training_config_sha256,
+        source_tree_sha256=admission.source_tree_sha256,
+    )
+
+
+def test_failed_and_held_predecessors_are_retained_in_resume_order(tmp_path: Path) -> None:
+    admission = _admission()
+    run_id = base_run_ids()[0]
+    failed_id = "attempt-failed"
+    held_id = "attempt-held"
+    success_id = "attempt-success"
+
+    failed_attempt = _attempt(
+        failed_id, run_id, admission, created_at="2026-09-08T12:00:00Z"
+    )
+    failed_attempt_raw = execution.canonical_attempt_bytes(failed_attempt)
+    failed_checkpoint_sha = _sha("failed-checkpoint-receipt")
+    failed_terminal = execution.TerminalRecord(
+        attempt_id=failed_id,
+        run_id=run_id,
+        completed_at_utc="2026-09-08T12:01:00Z",
+        outcome="FAILED",
+        attempt_receipt_sha256=hashlib.sha256(failed_attempt_raw).hexdigest(),
+        latest_heartbeat_sha256=None,
+        latest_checkpoint_receipt_sha256=failed_checkpoint_sha,
+        failure_code="HOST_EXECUTION_FAILED",
+    )
+    failed_terminal_raw = execution.canonical_terminal_bytes(failed_terminal)
+
+    held_attempt = _attempt(
+        held_id, run_id, admission, created_at="2026-09-08T12:02:00Z"
+    )
+    held_attempt_raw = execution.canonical_attempt_bytes(held_attempt)
+    held_checkpoint_sha = _sha("held-checkpoint-receipt")
+    held_resume = execution.ResumeRecord(
+        new_attempt_id=held_id,
+        predecessor_attempt_id=failed_id,
+        run_id=run_id,
+        created_at_utc=held_attempt.created_at_utc,
+        predecessor_terminal_sha256=hashlib.sha256(failed_terminal_raw).hexdigest(),
+        checkpoint_receipt_sha256=failed_checkpoint_sha,
+        corrective_change_sha256=_sha("first-correction"),
+        retry_class="IMPLEMENTATION",
+    )
+    held_terminal = execution.TerminalRecord(
+        attempt_id=held_id,
+        run_id=run_id,
+        completed_at_utc="2026-09-08T12:03:00Z",
+        outcome="HELD",
+        attempt_receipt_sha256=hashlib.sha256(held_attempt_raw).hexdigest(),
+        latest_heartbeat_sha256=None,
+        latest_checkpoint_receipt_sha256=held_checkpoint_sha,
+        failure_code="RESOURCE_UNAVAILABLE",
+    )
+    held_terminal_raw = execution.canonical_terminal_bytes(held_terminal)
+
+    for attempt_id, attempt_raw, terminal_raw, resume in (
+        (failed_id, failed_attempt_raw, failed_terminal_raw, None),
+        (held_id, held_attempt_raw, held_terminal_raw, held_resume),
+    ):
+        directory = tmp_path / attempt_id
+        directory.mkdir()
+        (directory / "attempt.json").write_bytes(attempt_raw)
+        (directory / "terminal.json").write_bytes(terminal_raw)
+        if resume is not None:
+            (directory / "resume.json").write_bytes(execution.canonical_resume_bytes(resume))
+
+    success_attempt = _attempt(
+        success_id, run_id, admission, created_at="2026-09-08T12:04:00Z"
+    )
+    success_attempt_raw = execution.canonical_attempt_bytes(success_attempt)
+    success_resume = execution.ResumeRecord(
+        new_attempt_id=success_id,
+        predecessor_attempt_id=held_id,
+        run_id=run_id,
+        created_at_utc=success_attempt.created_at_utc,
+        predecessor_terminal_sha256=hashlib.sha256(held_terminal_raw).hexdigest(),
+        checkpoint_receipt_sha256=held_checkpoint_sha,
+        corrective_change_sha256=_sha("second-correction"),
+        retry_class="RESOURCE",
+    )
+    latest = _checkpoint_record(
+        success_id,
+        run_id,
+        payload_sha256=_sha("success-payload"),
+    )
+    latest_raw = execution.canonical_checkpoint_bytes(latest)
+    success_terminal = execution.TerminalRecord(
+        attempt_id=success_id,
+        run_id=run_id,
+        completed_at_utc="2026-09-08T12:05:00Z",
+        outcome="SUCCEEDED",
+        attempt_receipt_sha256=hashlib.sha256(success_attempt_raw).hexdigest(),
+        latest_heartbeat_sha256=None,
+        latest_checkpoint_receipt_sha256=hashlib.sha256(latest_raw).hexdigest(),
+        failure_code=None,
+    )
+    evidence = execution.CompletedAttemptEvidence(
+        attempt=success_attempt,
+        attempt_raw=success_attempt_raw,
+        latest_checkpoint=latest,
+        latest_checkpoint_raw=latest_raw,
+        terminal=success_terminal,
+        terminal_raw=execution.canonical_terminal_bytes(success_terminal),
+        resume=success_resume,
+        resume_raw=execution.canonical_resume_bytes(success_resume),
+    )
+
+    observed = resolver._owned_failed_predecessors(
+        tmp_path,
+        evidence,
+        admission=admission,
+    )
+    assert tuple(row.attempt_id for row in observed) == (failed_id, held_id)
+    assert tuple(row.terminal_outcome for row in observed) == ("FAILED", "HELD")
+    assert tuple(row.failure_code for row in observed) == (
+        "HOST_EXECUTION_FAILED",
+        "RESOURCE_UNAVAILABLE",
+    )
+
+
+def test_owned_reader_rejects_symlink_bound_and_digest_mismatch(tmp_path: Path) -> None:
+    artifact = tmp_path / "artifact.pt"
+    artifact.write_bytes(b"payload")
+    with pytest.raises(resolver.BaseCohortResolutionError, match="byte bound"):
+        resolver._read_owned_file(
+            artifact,
+            label="artifact",
+            maximum_bytes=2,
+        )
+    with pytest.raises(resolver.BaseCohortResolutionError, match="digest mismatch"):
+        resolver._read_owned_file(
+            artifact,
+            label="artifact",
+            maximum_bytes=16,
+            expected_sha256=_sha("wrong"),
+        )
+    link = tmp_path / "link.pt"
+    try:
+        link.symlink_to(artifact)
+    except OSError:
+        pytest.skip("test account cannot create a symlink")
+    with pytest.raises(resolver.BaseCohortResolutionError, match="safely|symlink"):
+        resolver._read_owned_file(link, label="artifact", maximum_bytes=16)
+
+
+def test_checkpoint_directory_census_rejects_before_unbounded_materialization(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+) -> None:
+    observed: list[str] = []
+
+    class FakeEntries:
+        def __enter__(self) -> FakeEntries:
+            return self
+
+        def __exit__(self, *_args: object) -> None:
+            return None
+
+        def __iter__(self) -> object:
+            for index in range(4):
+                name = f"unrelated-{index}"
+                observed.append(name)
+                yield SimpleNamespace(name=name)
+
+    monkeypatch.setattr(resolver, "MAX_CHECKPOINT_DIRECTORY_ENTRIES", 2)
+    monkeypatch.setattr(resolver.os, "scandir", lambda _path: FakeEntries())
+    with pytest.raises(resolver.BaseCohortResolutionError, match="closed entry bound"):
+        resolver._latest_checkpoint_file(tmp_path, global_step=75)
+    assert observed == ["unrelated-0", "unrelated-1", "unrelated-2"]
+
+
+def test_checkpoint_decoder_uses_restricted_owned_bytes(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    observed: dict[str, object] = {}
+
+    def fake_load(stream: object, **kwargs: object) -> dict[str, object]:
+        observed.update(kwargs)
+        assert hasattr(stream, "read")
+        return {}
+
+    monkeypatch.setattr(resolver.torch, "load", fake_load)
+    with pytest.raises(resolver.BaseCohortResolutionError, match="keys are not closed"):
+        resolver._decode_checkpoint(b"not-a-real-checkpoint", "checkpoint")
+    assert observed == {"map_location": "cpu", "weights_only": True}
+
+
+def _checkpoint_payload(
+    *,
+    admission: resolver.BaseCohortAdmission,
+    run_id: str,
+    name: str,
+    epoch: int,
+    global_step: int,
+    sequence: int,
+    best_name: str,
+    best_sha256: str | None,
+) -> dict[str, object]:
+    _role, seed_text, system_id = run_id.split("/")[1:]
+    config = training.TrainingConfig(
+        stage="base",
+        seed=int(seed_text),
+        device=admission.device,
+        request_bf16=admission.request_bf16,
+        bf16_runtime_qualified=admission.bf16_runtime_qualified,
+        edge_budget=admission.edge_budget,
+        checkpoint_every_updates=admission.checkpoint_every_updates,
+        synthetic_contract=False,
+    )
+    payload: dict[str, object] = {
+        "authority": 0,
+        "behavior_sha256": _sha("behavior"),
+        "best_checkpoint_name": best_name,
+        "best_checkpoint_sha256": best_sha256,
+        "best_validation_metric": 0.5,
+        "checkpoint_sequence": sequence,
+        "code_artifact_sha256": _sha("code"),
+        "config_sha256": config.sha256,
+        "environment_sha256": _sha("environment"),
+        "epoch": epoch,
+        "external_receipt_verified": False,
+        "factory_sha256": _sha("factory"),
+        "frozen_base_checkpoint_sha256": None,
+        "frozen_base_state_sha256": None,
+        "global_step": global_step,
+        "initial_optimizable_state_sha256": _sha("initial"),
+        "initialization_binding_sha256": _sha("binding"),
+        "last_train_loss": 0.25,
+        "max_microbatch_edges": 1,
+        "model": {"weight": torch.zeros(1)},
+        "optimizer": {
+            "state": {0: {"step": global_step}},
+            "param_groups": [],
+        },
+        "precision_mode": training.resolve_precision(config).mode,
+        "production": False,
+        "qualified_base_selection_sha256": None,
+        "residual_capacity_audit_sha256": None,
+        "result_claimed": False,
+        "rng": {},
+        "scheduler": {"last_epoch": global_step},
+        "schema": training.CHECKPOINT_SCHEMA,
+        "seed": int(seed_text),
+        "stage": "base",
+        "system_id": system_id,
+        "total_steps": training.BASE_EPOCHS,
+        "train_edges_seen": max(1, global_step),
+        "train_manifest_sha256": admission.train_manifest_sha256,
+        "update_index": 0,
+        "updates_per_epoch": 1,
+        "val_manifest_sha256": admission.val_manifest_sha256,
+        "validation_edges_seen": max(1, epoch),
+    }
+    payload["state_digest"] = training._stable_hash(payload)
+    assert resolver._CHECKPOINT_NAME.fullmatch(name) is not None
+    return payload
+
+
+def test_latest_and_selected_checkpoint_semantics_remain_distinct(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    admission = _admission()
+    run_id = base_run_ids()[0]
+    latest_name = f"checkpoint-{training.BASE_EPOCHS:012d}-000150-validation.pt"
+    best_name = "checkpoint-000000000001-000002-validation.pt"
+    latest_raw = b"latest-owned-bytes"
+    best_raw = b"selected-owned-bytes"
+    latest_sha = hashlib.sha256(latest_raw).hexdigest()
+    best_sha = hashlib.sha256(best_raw).hexdigest()
+    latest_payload = _checkpoint_payload(
+        admission=admission,
+        run_id=run_id,
+        name=latest_name,
+        epoch=training.BASE_EPOCHS,
+        global_step=training.BASE_EPOCHS,
+        sequence=150,
+        best_name=best_name,
+        best_sha256=best_sha,
+    )
+    selected_payload = _checkpoint_payload(
+        admission=admission,
+        run_id=run_id,
+        name=best_name,
+        epoch=1,
+        global_step=1,
+        sequence=2,
+        best_name=best_name,
+        best_sha256=None,
+    )
+    payloads = {latest_raw: latest_payload, best_raw: selected_payload}
+    monkeypatch.setattr(resolver, "_decode_checkpoint", lambda raw, _label: payloads[raw])
+    monkeypatch.setattr(resolver, "_validate_model_and_optimizer", lambda *_args, **_kwargs: None)
+
+    latest = resolver._validate_checkpoint_payload(
+        latest_raw,
+        sha256=latest_sha,
+        name=latest_name,
+        run_id=run_id,
+        admission=admission,
+        ledger_record=None,
+        require_completed_latest=True,
+        require_selected_best=False,
+    )
+    selected = resolver._validate_checkpoint_payload(
+        best_raw,
+        sha256=best_sha,
+        name=best_name,
+        run_id=run_id,
+        admission=admission,
+        ledger_record=None,
+        require_completed_latest=False,
+        require_selected_best=True,
+    )
+    assert latest.sha256 != selected.sha256
+    assert latest.best_name == selected.name
+    assert latest.best_sha256 == selected.sha256
+    assert selected.best_sha256 == selected.sha256
+    resolver._validate_latest_selected_link(latest, selected)
+
+    with pytest.raises(resolver.BaseCohortResolutionError, match="metric mismatch"):
+        resolver._validate_latest_selected_link(
+            latest,
+            replace(selected, best_validation_metric=0.25),
+        )
+    with pytest.raises(resolver.BaseCohortResolutionError, match="schedule mismatch"):
+        resolver._validate_latest_selected_link(
+            latest,
+            replace(
+                selected,
+                updates_per_epoch=2,
+                total_steps=2 * training.BASE_EPOCHS,
+                global_step=2,
+            ),
+        )
+    with pytest.raises(resolver.BaseCohortResolutionError, match="counters exceed"):
+        resolver._validate_latest_selected_link(
+            latest,
+            replace(selected, validation_edges_seen=latest.validation_edges_seen + 1),
+        )
+
+
+def test_selected_validation_checkpoint_must_be_at_epoch_boundary(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    admission = _admission()
+    run_id = base_run_ids()[0]
+    name = "checkpoint-000000000002-000002-validation.pt"
+    raw = b"selected-with-update-cursor"
+    digest = hashlib.sha256(raw).hexdigest()
+    payload = _checkpoint_payload(
+        admission=admission,
+        run_id=run_id,
+        name=name,
+        epoch=1,
+        global_step=2,
+        sequence=2,
+        best_name=name,
+        best_sha256=None,
+    )
+    payload["update_index"] = 1
+    without_digest = dict(payload)
+    without_digest.pop("state_digest")
+    payload["state_digest"] = training._stable_hash(without_digest)
+    monkeypatch.setattr(resolver, "_decode_checkpoint", lambda *_args: payload)
+    monkeypatch.setattr(
+        resolver,
+        "_validate_model_and_optimizer",
+        lambda *_args, **_kwargs: None,
+    )
+    with pytest.raises(resolver.BaseCohortResolutionError, match="best-at-write"):
+        resolver._validate_checkpoint_payload(
+            raw,
+            sha256=digest,
+            name=name,
+            run_id=run_id,
+            admission=admission,
+            ledger_record=None,
+            require_completed_latest=False,
+            require_selected_best=True,
+        )
+
+
+def test_checkpoint_manifest_and_exact_header_cannot_drift(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    admission = _admission()
+    run_id = base_run_ids()[0]
+    name = "checkpoint-000000000075-000150-validation.pt"
+    raw = b"latest"
+    digest = hashlib.sha256(raw).hexdigest()
+    payload = _checkpoint_payload(
+        admission=admission,
+        run_id=run_id,
+        name=name,
+        epoch=training.BASE_EPOCHS,
+        global_step=training.BASE_EPOCHS,
+        sequence=150,
+        best_name=name,
+        best_sha256=None,
+    )
+    payload["train_manifest_sha256"] = _sha("substituted-train")
+    without_digest = dict(payload)
+    without_digest.pop("state_digest")
+    payload["state_digest"] = training._stable_hash(without_digest)
+    monkeypatch.setattr(resolver, "_decode_checkpoint", lambda *_args: payload)
+    monkeypatch.setattr(resolver, "_validate_model_and_optimizer", lambda *_args, **_kwargs: None)
+    with pytest.raises(resolver.BaseCohortResolutionError, match="train_manifest"):
+        resolver._validate_checkpoint_payload(
+            raw,
+            sha256=digest,
+            name=name,
+            run_id=run_id,
+            admission=admission,
+            ledger_record=None,
+            require_completed_latest=True,
+            require_selected_best=False,
+        )
+    payload["train_manifest_sha256"] = admission.train_manifest_sha256
+    payload["authority"] = False
+    without_digest = dict(payload)
+    without_digest.pop("state_digest")
+    payload["state_digest"] = training._stable_hash(without_digest)
+    with pytest.raises(resolver.BaseCohortResolutionError, match="authority"):
+        resolver._validate_checkpoint_payload(
+            raw,
+            sha256=digest,
+            name=name,
+            run_id=run_id,
+            admission=admission,
+            ledger_record=None,
+            require_completed_latest=True,
+            require_selected_best=False,
+        )
+
+
+def test_ledger_state_digests_are_recomputed_from_runtime_payload() -> None:
+    run_id = base_run_ids()[0]
+    payload = {
+        "epoch": 1,
+        "global_step": 1,
+        "update_index": 0,
+        "train_manifest_sha256": _sha("train"),
+        "val_manifest_sha256": _sha("val"),
+        "best_validation_metric": 0.5,
+        "best_checkpoint_name": "checkpoint-000000000001-000002-validation.pt",
+        "best_checkpoint_sha256": None,
+        "model": {"weight": torch.zeros(1)},
+        "optimizer": {"state": {}},
+        "rng": {"torch_cpu": torch.zeros(1, dtype=torch.uint8)},
+    }
+    record = _checkpoint_record("attempt-ledger", run_id, payload_sha256=_sha("payload"))
+    with pytest.raises(resolver.BaseCohortResolutionError, match="model_state"):
+        resolver._validate_ledger_payload_binding(payload, record)
diff --git a/tests/test_phaseset_base_cohort_score_witness.py b/tests/test_phaseset_base_cohort_score_witness.py
new file mode 100644
index 0000000..c25dc4c
--- /dev/null
+++ b/tests/test_phaseset_base_cohort_score_witness.py
@@ -0,0 +1,398 @@
+"""Server-only real-model witness from selected state to full capture gallery.
+
+The attempt registry and completed-ledger cohort are intentionally outside this
+bounded integration test.  Everything after one parsed selected checkpoint row
+uses the real registered implementation and is not monkeypatched.
+"""
+
+from __future__ import annotations
+
+from fractions import Fraction
+import gc
+import hashlib
+import io
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import base_cohort_resolver as resolver
+from phaseset_core import base_cohort_validation as scoring
+from phaseset_core import capture_validation as capture
+from phaseset_core import evaluation
+from phaseset_core import frozen_clip_text as clip
+from phaseset_core import training
+from phaseset_core.capture_pooling import CaptureWindowPlan
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+from phaseset_core.experiments import (
+    base_run_ids,
+    build_experiment_plan,
+    experiment_plan_sha256,
+    parse_run_id,
+)
+
+
+_SEED = 1729
+
+
+def _sha(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+def _identity(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().contiguous().view(torch.uint8).numpy().tobytes(order="C")
+
+
+def _analytic_groups(label: str, k: int) -> PreparedGroupBatch:
+    actor = np.arange(k, dtype=np.int64).reshape(1, k, 1, 1, 1)
+    frame = np.arange(200, dtype=np.int64).reshape(1, 1, 200, 1, 1)
+    joint = np.arange(22, dtype=np.int64).reshape(1, 1, 1, 22, 1)
+    coordinate = np.arange(3, dtype=np.int64).reshape(1, 1, 1, 1, 3)
+    integer = ((actor * 131 + frame * 17 + joint * 7 + coordinate * 3) % 2048) - 1024
+    skeletons = np.ascontiguousarray(
+        integer.astype(np.float32) * np.float32(2**-10),
+        dtype=np.float32,
+    )
+    actors = tuple(_identity(f"{label}/actor/{index}") for index in range(k))
+    return PreparedGroupBatch(
+        skeletons,
+        np.ones((1, k), dtype=np.bool_),
+        np.ones((1, 200), dtype=np.bool_),
+        np.ones((1, k, 200, 22), dtype=np.bool_),
+        (actors,),
+        (group_commitment(actors),),
+    )
+
+
+def _analytic_text(label: str, coordinate: int) -> clip.FrozenClipTextBatch:
+    embeddings = torch.zeros((1, training.REGISTERED_EMBEDDING_DIM), dtype=torch.float32)
+    embeddings[0, coordinate] = 1.0
+    commitment = _identity(f"{label}/caption")
+    raw = _tensor_bytes(embeddings)
+    receipt = clip.FrozenClipTextReceipt(
+        batch_size=1,
+        caption_rows=(
+            (
+                0,
+                commitment.hex(),
+                _identity(f"{label}/analytic-text-not-a-caption").hex(),
+                3,
+                3,
+                False,
+                _identity(f"{label}/analytic-token-row").hex(),
+                _identity(f"{label}/analytic-mask-row").hex(),
+                hashlib.sha256(raw).hexdigest(),
+            ),
+        ),
+        chunk_ranges=((0, 1),),
+        frozen_embedding_cache_key_sha256=_sha(f"{label}/analytic-cache"),
+        live_model_manifest_sha256=_sha(f"{label}/no-live-model"),
+        output_bytes=len(raw),
+        output_sha256=hashlib.sha256(raw).hexdigest(),
+        output_shape=tuple(embeddings.shape),
+        output_stride=tuple(embeddings.stride()),
+        runtime_identity=(("fixture", "server-only-no-clip"),),
+        runtime_manifest_sha256=_sha(f"{label}/analytic-runtime"),
+        snapshot_files=(),
+        snapshot_manifest_sha256=_sha(f"{label}/no-snapshot"),
+        source_files=(),
+        source_manifest_sha256=_sha(f"{label}/analytic-source"),
+    )
+    return clip.FrozenClipTextBatch(
+        embeddings,
+        (commitment,),
+        receipt,
+        _seal=clip._CONSTRUCTION_SEAL,
+    )
+
+
+def _analytic_capture(
+    label: str,
+    *,
+    k: int,
+    start_frame: int,
+    text_coordinate: int,
+    component: str,
+) -> capture.CaptureValidationCapture:
+    capture_id = _identity(f"{label}/capture")
+    window_id = _identity(f"{label}/window")
+    return capture.CaptureValidationCapture(
+        CaptureWindowPlan(capture_id, (window_id,), (start_frame,)),
+        (
+            capture.CaptureValidationWindow(
+                window_id,
+                _analytic_groups(label, k),
+            ),
+        ),
+        _analytic_text(label, text_coordinate),
+        component,
+    )
+
+
+def _analytic_source() -> capture.CaptureValidationSource:
+    return capture.CaptureValidationSource(
+        "val",
+        _sha("analytic-two-capture-manifest-no-rights-claim"),
+        (
+            _analytic_capture(
+                "capture-k2",
+                k=2,
+                start_frame=0,
+                text_coordinate=0,
+                component="C00",
+            ),
+            _analytic_capture(
+                "capture-k3",
+                k=3,
+                start_frame=300,
+                text_coordinate=1,
+                component="C01",
+            ),
+        ),
+    )
+
+
+def _admission(source: capture.CaptureValidationSource) -> resolver.BaseCohortAdmission:
+    plan = build_experiment_plan()
+    return resolver.BaseCohortAdmission(
+        plan_sha256=experiment_plan_sha256(plan),
+        matrix_sha256=plan.matrix_sha256,
+        training_config_sha256=_sha("analytic-host-training-config-no-receipt"),
+        source_tree_sha256=_sha("analytic-source-tree-comparison-only"),
+        train_manifest_sha256=_sha("analytic-train-manifest-no-training-run"),
+        val_manifest_sha256=source.census_sha256,
+        device="cpu",
+        request_bf16=False,
+        bf16_runtime_qualified=False,
+        edge_budget=32_768,
+        checkpoint_every_updates=1,
+    )
+
+
+def _run_id(system_id: str) -> str:
+    matches = tuple(
+        run_id
+        for run_id in base_run_ids()
+        if parse_run_id(run_id)[1:] == (_SEED, system_id)
+    )
+    assert len(matches) == 1
+    return matches[0]
+
+
+def _analytic_selected_row(
+    system_id: str,
+    admission: resolver.BaseCohortAdmission,
+) -> tuple[resolver.ResolvedBaseRun, int, tuple[tuple[str, tuple[int, ...], int], ...], str]:
+    """Build one real closed checkpoint payload without claiming a training attempt."""
+
+    run_id = _run_id(system_id)
+    config = training.TrainingConfig(
+        stage="base",
+        seed=_SEED,
+        device=admission.device,
+        request_bf16=admission.request_bf16,
+        bf16_runtime_qualified=admission.bf16_runtime_qualified,
+        edge_budget=admission.edge_budget,
+        checkpoint_every_updates=admission.checkpoint_every_updates,
+        synthetic_contract=False,
+    )
+    assert config.epochs == training.BASE_EPOCHS
+    system, binding = training.construct_registered_base_seed_bound_system(system_id, config)
+    initial_state_sha256 = training.training_system_state_sha256(system)
+    expected_rows = tuple(
+        (name, tuple(parameter.shape), int(parameter.numel()))
+        for name, parameter in sorted(system.named_parameters())
+    )
+    expected_parameter_count = sum(row[2] for row in expected_rows)
+    assert expected_parameter_count == sum(
+        int(parameter.numel()) for parameter in system.parameters()
+    )
+    assert all(parameter.requires_grad for parameter in system.parameters())
+
+    parameters = [parameter for parameter in system.parameters() if parameter.requires_grad]
+    optimizer = torch.optim.AdamW(
+        parameters,
+        lr=config.learning_rate,
+        weight_decay=training.WEIGHT_DECAY,
+    )
+    total_steps = training.BASE_EPOCHS
+
+    def multiplier(step: int) -> float:
+        return training._learning_rate_multiplier(step, total_steps)
+
+    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, multiplier)
+    optimizer.zero_grad(set_to_none=True)
+    system.logit_scale.grad = torch.zeros_like(system.logit_scale)
+    optimizer.step()
+    scheduler.step()
+    selected_state_sha256 = training.training_system_state_sha256(system)
+    assert selected_state_sha256 != initial_state_sha256
+
+    checkpoint_name = "checkpoint-000000000001-000001-validation.pt"
+    payload: dict[str, object] = {
+        "schema": training.CHECKPOINT_SCHEMA,
+        "authority": 0,
+        "production": False,
+        "result_claimed": False,
+        "external_receipt_verified": False,
+        "stage": "base",
+        "seed": _SEED,
+        "system_id": system_id,
+        "config_sha256": config.sha256,
+        "train_manifest_sha256": admission.train_manifest_sha256,
+        "val_manifest_sha256": admission.val_manifest_sha256,
+        "updates_per_epoch": 1,
+        "total_steps": total_steps,
+        "precision_mode": training.resolve_precision(config).mode,
+        "initialization_binding_sha256": binding.sha256,
+        "behavior_sha256": binding.behavior_sha256,
+        "initial_optimizable_state_sha256": binding.initial_optimizable_state_sha256,
+        "factory_sha256": binding.factory_sha256,
+        "code_artifact_sha256": binding.code_artifact_sha256,
+        "environment_sha256": binding.environment_sha256,
+        "frozen_base_checkpoint_sha256": None,
+        "frozen_base_state_sha256": None,
+        "qualified_base_selection_sha256": None,
+        "residual_capacity_audit_sha256": None,
+        "epoch": 1,
+        "update_index": 0,
+        "global_step": 1,
+        "checkpoint_sequence": 1,
+        "train_edges_seen": 1,
+        "validation_edges_seen": 1,
+        "max_microbatch_edges": 1,
+        "best_validation_metric": 0.5,
+        "best_checkpoint_name": checkpoint_name,
+        "best_checkpoint_sha256": None,
+        "last_train_loss": 0.0,
+        "model": {
+            name: value.detach().cpu().clone()
+            for name, value in system.state_dict().items()
+        },
+        "optimizer": optimizer.state_dict(),
+        "scheduler": scheduler.state_dict(),
+        "rng": training._capture_rng(),
+    }
+    payload["state_digest"] = training._stable_hash(payload)
+    assert frozenset(payload) == resolver._CHECKPOINT_KEYS
+    selected_model_sha256 = training._stable_hash(payload["model"])
+    assert selected_model_sha256 == selected_state_sha256
+    stream = io.BytesIO()
+    torch.save(payload, stream)
+    raw = stream.getvalue()
+    raw_sha256 = hashlib.sha256(raw).hexdigest()
+    row = resolver.ResolvedBaseRun(
+        run_id=run_id,
+        attempt_id=f"analytic-no-ledger-{system_id.lower()}",
+        system_id=system_id,
+        seed=_SEED,
+        terminal_sha256=_sha(f"{system_id}/no-terminal-evidence"),
+        latest_checkpoint_sha256=raw_sha256,
+        latest_checkpoint_name=checkpoint_name,
+        selected_checkpoint=resolver.SelectedBaseCheckpoint(
+            name=checkpoint_name,
+            sha256=raw_sha256,
+            raw=raw,
+            epoch=1,
+            global_step=1,
+            state_digest=str(payload["state_digest"]),
+        ),
+        failed_predecessors=(),
+    )
+    del payload, scheduler, optimizer, system
+    gc.collect()
+    return row, expected_parameter_count, expected_rows, selected_model_sha256
+
+
+def _independent_capture_fractions(
+    observation: scoring.BaseValidationScoreObservation,
+) -> tuple[Fraction, Fraction, Fraction]:
+    scores = (
+        np.frombuffer(observation.scores_float64le, dtype="<f8")
+        .reshape(observation.score_shape)
+        .astype(np.float64, copy=True, order="C")
+    )
+    dataset = evaluation.validate_retrieval_dataset(
+        evaluation.RetrievalDataset(
+            scores=scores,
+            motion_commitments=observation.motion_commitments,
+            caption_commitments=observation.caption_commitments,
+            positive_motion_indices=observation.positive_motion_indices,
+            group_sizes=np.ascontiguousarray(observation.group_sizes, dtype=np.int64),
+            component_labels=observation.component_labels,
+        )
+    )
+    rows = evaluation.capture_r1_contributions(dataset)
+    assert len(rows) == 2
+    count = len(rows)
+    text = sum(
+        (
+            Fraction(sum(row.text_to_motion_hits), len(row.text_to_motion_hits))
+            for row in rows
+        ),
+        start=Fraction(0, 1),
+    ) / count
+    motion = sum(
+        (
+            Fraction(sum(row.motion_to_text_hits), len(row.motion_to_text_hits))
+            for row in rows
+        ),
+        start=Fraction(0, 1),
+    ) / count
+    return text, motion, (text + motion) / 2
+
+
+@pytest.mark.parametrize("system_id", ("B0", "B1", "B2"))
+def test_real_registered_selected_state_scores_complete_analytic_gallery(
+    system_id: str,
+) -> None:
+    source = _analytic_source()
+    source_census_before = source.census_sha256
+    admission = resolver._checked_admission(_admission(source))
+    row, expected_count, expected_rows, selected_model_sha256 = _analytic_selected_row(
+        system_id,
+        admission,
+    )
+    raw_before = hashlib.sha256(row.selected_checkpoint.raw).hexdigest()
+    source_type, runner = capture._verified_capture_validation_bindings()
+    assert source_type is capture.CaptureValidationSource
+    assert runner is capture.run_capture_validation
+
+    observation = scoring._score_resolved_run(
+        row,
+        admission=admission,
+        source=source,
+    )
+
+    assert observation.authority == 0
+    assert observation.production is False
+    assert observation.result_claimed is False
+    assert observation.system_id == system_id
+    assert observation.score_shape == (2, 2)
+    assert np.isfinite(
+        np.frombuffer(observation.scores_float64le, dtype="<f8")
+    ).all()
+    assert hashlib.sha256(observation.scores_float64le).hexdigest() == (
+        observation.scores_float64_sha256
+    )
+    assert _independent_capture_fractions(observation) == (
+        observation.text_to_motion_capture_r1,
+        observation.motion_to_text_capture_r1,
+        observation.primary_capture_r1,
+    )
+    assert set(observation.group_sizes) == {2, 3}
+    assert observation.positive_motion_indices == ((0,), (1,))
+    assert observation.parameter_census.total == expected_count
+    assert tuple(
+        (item.name, item.shape, item.numel)
+        for item in observation.parameter_census.rows
+    ) == expected_rows
+    assert observation.parameter_census.loaded_state_sha256 == selected_model_sha256
+    assert observation.selected_checkpoint_sha256 == raw_before
+    assert hashlib.sha256(row.selected_checkpoint.raw).hexdigest() == raw_before
+    assert observation.query_census_sha256 == source_census_before
+    assert source.census_sha256 == source_census_before
diff --git a/tests/test_phaseset_base_cohort_validation.py b/tests/test_phaseset_base_cohort_validation.py
new file mode 100644
index 0000000..ef17d31
--- /dev/null
+++ b/tests/test_phaseset_base_cohort_validation.py
@@ -0,0 +1,522 @@
+"""Server-only contracts for selected-best base cohort capture scoring."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+from fractions import Fraction
+import hashlib
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+from torch import nn
+
+from phaseset_core import base_cohort_resolver as resolver
+from phaseset_core import base_cohort_validation as scoring
+from phaseset_core import capture_validation as capture
+from phaseset_core import frozen_clip_text as clip
+from phaseset_core import training
+from phaseset_core.capture_pooling import CaptureWindowPlan
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+from phaseset_core.evaluation import RetrievalDataset
+from phaseset_core.experiments import (
+    base_run_ids,
+    build_experiment_plan,
+    experiment_plan_sha256,
+)
+
+
+def _sha(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+def _id(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().contiguous().view(torch.uint8).numpy().tobytes()
+
+
+def _groups(label: str) -> PreparedGroupBatch:
+    actors = (_id(f"{label}/actor/0"), _id(f"{label}/actor/1"))
+    skeletons = np.zeros((1, 2, 200, 22, 3), dtype=np.float32)
+    skeletons[0, :, :, :, 0] = 1.0
+    return PreparedGroupBatch(
+        skeletons,
+        np.ones((1, 2), dtype=np.bool_),
+        np.ones((1, 200), dtype=np.bool_),
+        np.ones((1, 2, 200, 22), dtype=np.bool_),
+        (actors,),
+        (group_commitment(actors),),
+    )
+
+
+def _text(label: str) -> clip.FrozenClipTextBatch:
+    values = torch.zeros((1, 512), dtype=torch.float32)
+    values[0, 0] = 1.0
+    commitment = _id(f"{label}/caption")
+    raw = _tensor_bytes(values)
+    receipt = clip.FrozenClipTextReceipt(
+        batch_size=1,
+        caption_rows=(
+            (
+                0,
+                commitment.hex(),
+                _id(f"{label}/text").hex(),
+                3,
+                3,
+                False,
+                _id(f"{label}/tokens").hex(),
+                _id(f"{label}/mask").hex(),
+                hashlib.sha256(raw).hexdigest(),
+            ),
+        ),
+        chunk_ranges=((0, 1),),
+        frozen_embedding_cache_key_sha256=_sha(f"{label}/cache"),
+        live_model_manifest_sha256=_sha(f"{label}/model"),
+        output_bytes=len(raw),
+        output_sha256=hashlib.sha256(raw).hexdigest(),
+        output_shape=tuple(values.shape),
+        output_stride=tuple(values.stride()),
+        runtime_identity=(("fixture", "server-only"),),
+        runtime_manifest_sha256=_sha(f"{label}/runtime"),
+        snapshot_files=(),
+        snapshot_manifest_sha256=_sha(f"{label}/snapshot"),
+        source_files=(),
+        source_manifest_sha256=_sha(f"{label}/source"),
+    )
+    return clip.FrozenClipTextBatch(
+        values,
+        (commitment,),
+        receipt,
+        _seal=clip._CONSTRUCTION_SEAL,
+    )
+
+
+def _capture(label: str, frame: int) -> capture.CaptureValidationCapture:
+    window = _id(f"{label}/window")
+    return capture.CaptureValidationCapture(
+        CaptureWindowPlan(_id(label), (window,), (frame,)),
+        (capture.CaptureValidationWindow(window, _groups(label)),),
+        _text(label),
+        "C00",
+    )
+
+
+def _source() -> capture.CaptureValidationSource:
+    return capture.CaptureValidationSource(
+        "val",
+        _sha("upstream-validation-manifest"),
+        (_capture("capture-a", 0), _capture("capture-b", 300)),
+    )
+
+
+def _admission(source: capture.CaptureValidationSource) -> resolver.BaseCohortAdmission:
+    plan = build_experiment_plan()
+    return resolver.BaseCohortAdmission(
+        plan_sha256=experiment_plan_sha256(plan),
+        matrix_sha256=plan.matrix_sha256,
+        training_config_sha256=_sha("host-training-config"),
+        source_tree_sha256=_sha("source-tree"),
+        train_manifest_sha256=_sha("train-manifest"),
+        val_manifest_sha256=source.census_sha256,
+        device="cpu",
+        request_bf16=False,
+        bf16_runtime_qualified=False,
+        edge_budget=32_768,
+        checkpoint_every_updates=1,
+    )
+
+
+def _resolved_row(run_id: str, index: int) -> resolver.ResolvedBaseRun:
+    _role, seed_text, system_id = run_id.split("/")[1:]
+    raw = f"selected-best-{index}".encode("ascii")
+    return resolver.ResolvedBaseRun(
+        run_id=run_id,
+        attempt_id=f"attempt-{index}",
+        system_id=system_id,
+        seed=int(seed_text),
+        terminal_sha256=_sha(f"terminal-{index}"),
+        latest_checkpoint_sha256=_sha(f"latest-{index}"),
+        latest_checkpoint_name="checkpoint-000000000030-000060-validation.pt",
+        selected_checkpoint=resolver.SelectedBaseCheckpoint(
+            name="checkpoint-000000000001-000002-validation.pt",
+            sha256=hashlib.sha256(raw).hexdigest(),
+            raw=raw,
+            epoch=1,
+            global_step=1,
+            state_digest=_sha(f"checkpoint-state-{index}"),
+        ),
+        failed_predecessors=(),
+    )
+
+
+def _cohort(source: capture.CaptureValidationSource) -> resolver.ResolvedBaseCohort:
+    admission = _admission(source)
+    return resolver.ResolvedBaseCohort(
+        rows=tuple(
+            _resolved_row(run_id, index)
+            for index, run_id in enumerate(base_run_ids())
+        ),
+        plan_sha256=admission.plan_sha256,
+        matrix_sha256=admission.matrix_sha256,
+        training_config_sha256=admission.training_config_sha256,
+        source_tree_sha256=admission.source_tree_sha256,
+        train_manifest_sha256=admission.train_manifest_sha256,
+        val_manifest_sha256=admission.val_manifest_sha256,
+    )
+
+
+def _score_dataset(source: capture.CaptureValidationSource) -> RetrievalDataset:
+    motions, captions, positives, sizes, labels = scoring._expected_dataset_identity(source)
+    return RetrievalDataset(
+        scores=np.ascontiguousarray(((2.0, 0.0), (0.0, 2.0)), dtype=np.float64),
+        motion_commitments=motions,
+        caption_commitments=captions,
+        positive_motion_indices=positives,
+        group_sizes=np.ascontiguousarray(sizes, dtype=np.int64),
+        component_labels=labels,
+    )
+
+
+def _observation(
+    row: resolver.ResolvedBaseRun,
+    source: capture.CaptureValidationSource,
+) -> scoring.BaseValidationScoreObservation:
+    dataset = _score_dataset(source)
+    score_bytes = np.ascontiguousarray(dataset.scores, dtype="<f8").tobytes()
+    width = {"B0": 3, "B1": 5, "B2": 7}[row.system_id]
+    parameters = scoring.BaseParameterCensus(
+        row.system_id,
+        (
+            scoring.BaseParameterRow(
+                "weight",
+                (width,),
+                "torch.float32",
+                width,
+            ),
+        ),
+        width,
+        _sha(f"loaded-{row.run_id}"),
+    )
+    return scoring.BaseValidationScoreObservation(
+        run_id=row.run_id,
+        attempt_id=row.attempt_id,
+        system_id=row.system_id,
+        seed=row.seed,
+        terminal_sha256=row.terminal_sha256,
+        selected_checkpoint_sha256=row.selected_checkpoint.sha256,
+        selected_checkpoint_state_digest=row.selected_checkpoint.state_digest,
+        initialization_binding_sha256=_sha(f"binding-{row.run_id}"),
+        runtime_config_sha256=_sha(f"runtime-config-{row.run_id}"),
+        factory_sha256=_sha(f"factory-{row.system_id}"),
+        code_artifact_sha256=_sha("code"),
+        environment_sha256=_sha("environment"),
+        behavior_sha256=_sha(f"behavior-{row.system_id}"),
+        source_manifest_sha256=source.manifest_sha256,
+        admitted_val_census_sha256=source.census_sha256,
+        query_census_sha256=source.census_sha256,
+        parameter_census=parameters,
+        scores_float64le=score_bytes,
+        score_shape=(2, 2),
+        motion_commitments=dataset.motion_commitments,
+        caption_commitments=dataset.caption_commitments,
+        positive_motion_indices=dataset.positive_motion_indices,
+        group_sizes=tuple(int(value) for value in dataset.group_sizes),
+        component_labels=dataset.component_labels,
+        text_to_motion_capture_r1=Fraction(1, 1),
+        motion_to_text_capture_r1=Fraction(1, 1),
+        primary_capture_r1=Fraction(1, 1),
+        device="cpu",
+        precision_mode="FP32",
+        encoded_bytes=4096,
+    )
+
+
+def test_cohort_orchestration_preserves_nine_rows_without_selecting_winner(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    source = _source()
+    cohort = _cohort(source)
+    admission = _admission(source)
+    monkeypatch.setattr(
+        scoring,
+        "_score_resolved_run",
+        lambda row, *, admission, source: _observation(row, source),
+    )
+
+    result = scoring.score_resolved_base_cohort(
+        cohort,
+        admission=admission,
+        validation_source=source,
+    )
+
+    assert tuple(row.run_id for row in result.observations) == base_run_ids()
+    assert len({row.score_artifact_sha256 for row in result.observations}) == 9
+    assert result.authority == 0
+    assert result.production is False
+    assert result.result_claimed is False
+    assert result.training_config_sha256 == admission.training_config_sha256
+    assert all(row.runtime_config_sha256 for row in result.observations)
+    assert "NO_WINNER_NO_QUALIFICATION" in result.status
+    assert not hasattr(result, "winner_system_id")
+
+
+def test_resolved_owned_checkpoint_digest_is_rechecked_before_scoring() -> None:
+    source = _source()
+    cohort = _cohort(source)
+    first = cohort.rows[0]
+    changed_selected = replace(first.selected_checkpoint, raw=b"substituted")
+    changed = replace(cohort, rows=(replace(first, selected_checkpoint=changed_selected),) + cohort.rows[1:])
+
+    with pytest.raises(scoring.BaseCohortValidationError, match="owned checkpoint digest"):
+        scoring._checked_cohort(changed, _admission(source))
+
+
+def test_checkpoint_admitted_capture_census_cannot_be_relabelled(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    source = _source()
+    admission = replace(_admission(source), val_manifest_sha256=_sha("different-gallery"))
+    cohort = replace(_cohort(source), val_manifest_sha256=admission.val_manifest_sha256)
+    called = False
+
+    def forbidden(*_args, **_kwargs):
+        nonlocal called
+        called = True
+        raise AssertionError
+
+    monkeypatch.setattr(scoring, "_score_resolved_run", forbidden)
+    with pytest.raises(scoring.BaseCohortValidationError, match="capture census"):
+        scoring.score_resolved_base_cohort(
+            cohort,
+            admission=admission,
+            validation_source=source,
+        )
+    assert called is False
+
+
+def test_score_observation_recomputes_metric_from_actual_score_bytes() -> None:
+    source = _source()
+    observation = _observation(_cohort(source).rows[0], source)
+
+    with pytest.raises(scoring.BaseCohortValidationError, match="differs from score bytes"):
+        replace(observation, primary_capture_r1=Fraction(0, 1))
+    changed = np.ascontiguousarray(((0.0, 2.0), (2.0, 0.0)), dtype="<f8")
+    with pytest.raises(scoring.BaseCohortValidationError, match="differs from score bytes"):
+        replace(observation, scores_float64le=changed.tobytes(order="C"))
+
+
+def test_changed_scores_with_same_ranking_remain_valid_without_external_hash() -> None:
+    source = _source()
+    observation = _observation(_cohort(source).rows[0], source)
+    changed = np.ascontiguousarray(((3.0, 0.0), (0.0, 3.0)), dtype="<f8")
+    updated = replace(observation, scores_float64le=changed.tobytes(order="C"))
+    assert updated.primary_capture_r1 == observation.primary_capture_r1
+    assert updated.scores_float64_sha256 != observation.scores_float64_sha256
+    assert updated.score_artifact_sha256 != observation.score_artifact_sha256
+
+
+def test_resolved_cohort_rejects_reordered_registered_rows_before_scoring() -> None:
+    source = _source()
+    cohort = _cohort(source)
+    changed = replace(cohort, rows=tuple(reversed(cohort.rows)))
+    with pytest.raises(scoring.BaseCohortValidationError, match="nine-row order"):
+        scoring._checked_cohort(changed, _admission(source))
+
+
+def test_capture_result_metric_and_gallery_identity_are_not_trusted() -> None:
+    source = _source()
+    row = _cohort(source).rows[0]
+    config = training.TrainingConfig(stage="base", seed=row.seed)
+    dataset = _score_dataset(source)
+    result = capture.CaptureValidationResult(
+        dataset=dataset,
+        text_to_motion_capture_r1=Fraction(1, 1),
+        motion_to_text_capture_r1=Fraction(1, 1),
+        primary_capture_r1=Fraction(0, 1),
+        loss=0.0,
+        source_census_sha256=source.census_sha256,
+        scores_float64_sha256=hashlib.sha256(dataset.scores.tobytes()).hexdigest(),
+        system_id=row.system_id,
+        stage="base",
+        device="cpu",
+        precision_mode="FP32",
+        encoded_bytes=4096,
+        cuda_static_tensor_bytes=None,
+    )
+
+    with pytest.raises(scoring.BaseCohortValidationError, match="recomputed gallery"):
+        scoring._validated_capture_result(result, row=row, source=source, config=config)
+    changed_dataset = replace(
+        dataset,
+        motion_commitments=tuple(reversed(dataset.motion_commitments)),
+    )
+    changed = replace(
+        result,
+        dataset=changed_dataset,
+        primary_capture_r1=Fraction(1, 1),
+    )
+    with pytest.raises(scoring.BaseCohortValidationError, match="frozen source"):
+        scoring._validated_capture_result(changed, row=row, source=source, config=config)
+
+
+class _ParameterOnlyBase(nn.Module):
+    system_id = "B0"
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.zeros(2, dtype=torch.float32))
+
+    def forward(self, _groups):
+        raise AssertionError("fake capture runner must own this test's scoring result")
+
+
+def _selected_score_fixture(
+    source: capture.CaptureValidationSource,
+    row: resolver.ResolvedBaseRun,
+):
+    system = training.BaseRetrievalSystem(_ParameterOnlyBase(), embedding_dim=512)
+    initial_state = training.training_system_state_sha256(system)
+    behavior = training.training_system_behavior_sha256(system)
+    model = {
+        name: torch.ones_like(value)
+        for name, value in system.state_dict().items()
+    }
+    binding = SimpleNamespace(
+        behavior_sha256=behavior,
+        code_artifact_sha256=_sha("code"),
+        environment_sha256=_sha("environment"),
+        factory_sha256=_sha("factory"),
+        initial_optimizable_state_sha256=initial_state,
+        sha256=_sha("initialization-binding"),
+    )
+    payload = {
+        "behavior_sha256": binding.behavior_sha256,
+        "code_artifact_sha256": binding.code_artifact_sha256,
+        "environment_sha256": binding.environment_sha256,
+        "factory_sha256": binding.factory_sha256,
+        "initial_optimizable_state_sha256": binding.initial_optimizable_state_sha256,
+        "initialization_binding_sha256": binding.sha256,
+        "model": model,
+    }
+    dataset = _score_dataset(source)
+    result = capture.CaptureValidationResult(
+        dataset=dataset,
+        text_to_motion_capture_r1=Fraction(1, 1),
+        motion_to_text_capture_r1=Fraction(1, 1),
+        primary_capture_r1=Fraction(1, 1),
+        loss=0.0,
+        source_census_sha256=source.census_sha256,
+        scores_float64_sha256=hashlib.sha256(dataset.scores.tobytes()).hexdigest(),
+        system_id=row.system_id,
+        stage="base",
+        device="cpu",
+        precision_mode="FP32",
+        encoded_bytes=4096,
+        cuda_static_tensor_bytes=None,
+    )
+    return system, binding, payload, result
+
+
+def test_selected_model_state_is_strictly_loaded_and_parameters_are_derived(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    source = _source()
+    row = _cohort(source).rows[0]
+    admission = _admission(source)
+    system, binding, payload, result = _selected_score_fixture(source, row)
+    monkeypatch.setattr(scoring, "_validate_selected_payload", lambda *_args: payload)
+    monkeypatch.setattr(
+        training,
+        "construct_registered_base_seed_bound_system",
+        lambda *_args: (system, binding),
+    )
+
+    def runner(live, _config, _source):
+        assert all(torch.equal(value, torch.ones_like(value)) for value in live.state_dict().values())
+        return result
+
+    monkeypatch.setattr(
+        capture,
+        "_verified_capture_validation_bindings",
+        lambda: (capture.CaptureValidationSource, runner),
+    )
+    observation = scoring._score_resolved_run(
+        row,
+        admission=admission,
+        source=source,
+    )
+    assert observation.runtime_config_sha256 == training.TrainingConfig(
+        stage="base",
+        seed=row.seed,
+        device=admission.device,
+        request_bf16=admission.request_bf16,
+        bf16_runtime_qualified=admission.bf16_runtime_qualified,
+        edge_budget=admission.edge_budget,
+        checkpoint_every_updates=admission.checkpoint_every_updates,
+        synthetic_contract=False,
+    ).sha256
+    assert observation.parameter_census.total == 3
+    assert tuple(item.name for item in observation.parameter_census.rows) == (
+        "group_base.weight",
+        "logit_scale",
+    )
+    assert observation.parameter_census.total != 0
+
+
+def test_incomplete_selected_state_has_no_random_checkpoint_fallback(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    source = _source()
+    row = _cohort(source).rows[0]
+    admission = _admission(source)
+    system, binding, payload, _result = _selected_score_fixture(source, row)
+    payload["model"] = {"group_base.weight": torch.ones(2)}
+    monkeypatch.setattr(scoring, "_validate_selected_payload", lambda *_args: payload)
+    monkeypatch.setattr(
+        training,
+        "construct_registered_base_seed_bound_system",
+        lambda *_args: (system, binding),
+    )
+    with pytest.raises(scoring.BaseCohortValidationError, match="could not be loaded"):
+        scoring._score_resolved_run(row, admission=admission, source=source)
+
+
+def test_owned_validation_snapshot_detects_between_row_mutation(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    source = _source()
+    cohort = _cohort(source)
+    calls = 0
+
+    def mutate(row, *, admission, source):
+        nonlocal calls
+        calls += 1
+        result = _observation(row, source)
+        if calls == 1:
+            object.__setattr__(source, "manifest_sha256", _sha("mutated-source"))
+        return result
+
+    monkeypatch.setattr(scoring, "_score_resolved_run", mutate)
+    with pytest.raises(scoring.BaseCohortValidationError, match="between cohort rows"):
+        scoring.score_resolved_base_cohort(
+            cohort,
+            admission=_admission(source),
+            validation_source=source,
+        )
+    assert calls == 1
+
+
+@pytest.mark.parametrize("system_id", ("B0", "B1", "B2"))
+def test_registered_factory_is_actual_width_and_fully_trainable(system_id: str) -> None:
+    config = training.TrainingConfig(stage="base", seed=1729, synthetic_contract=False)
+    system, binding = training.construct_registered_base_seed_bound_system(system_id, config)
+    assert system.system_id == system_id
+    assert system.embedding_dim == 512
+    assert binding.system_id == system_id
+    assert all(parameter.requires_grad for parameter in system.parameters())
+    census = scoring._parameter_census(system)
+    assert census.total == sum(parameter.numel() for parameter in system.parameters())
diff --git a/tests/test_phaseset_execution.py b/tests/test_phaseset_execution.py
index 30ef0ae..908b780 100644
--- a/tests/test_phaseset_execution.py
+++ b/tests/test_phaseset_execution.py
@@ -164,6 +164,186 @@ def test_open_revalidates_existing_heartbeat_chain(tmp_path: Path) -> None:
         execution.AttemptStore.open(tmp_path, store.attempt.attempt_id)
 
 
+def test_completed_attempt_evidence_returns_owned_bytes_without_second_path_read(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    store = execution.AttemptStore.create(tmp_path, _attempt())
+    checkpoint = _checkpoint()
+    checkpoint_raw = execution.canonical_checkpoint_bytes(checkpoint)
+    checkpoint_sha = store.write_checkpoint(checkpoint)
+    terminal = execution.TerminalRecord(
+        attempt_id=store.attempt.attempt_id,
+        run_id=store.attempt.run_id,
+        completed_at_utc="2026-08-25T20:03:00Z",
+        outcome="SUCCEEDED",
+        attempt_receipt_sha256=execution.artifact_sha256(
+            (store.path / "attempt.json").read_bytes()
+        ),
+        latest_heartbeat_sha256=None,
+        latest_checkpoint_receipt_sha256=checkpoint_sha,
+        failure_code=None,
+    )
+    terminal_raw = execution.canonical_terminal_bytes(terminal)
+    store.write_terminal(terminal)
+
+    reads: dict[Path, int] = {}
+    original_read_regular = execution._read_regular
+
+    def counted_read(path: Path, label: str) -> bytes:
+        reads[path] = reads.get(path, 0) + 1
+        return original_read_regular(path, label)
+
+    monkeypatch.setattr(execution, "_read_regular", counted_read)
+    evidence = execution.verified_completed_attempt_evidence(
+        tmp_path,
+        store.attempt.attempt_id,
+    )
+    assert evidence.attempt == store.attempt
+    assert evidence.latest_checkpoint == checkpoint
+    assert evidence.latest_checkpoint_raw == checkpoint_raw
+    assert evidence.terminal == terminal
+    assert evidence.terminal_raw == terminal_raw
+    assert evidence.resume is None
+    assert evidence.resume_raw is None
+    assert evidence.terminal_sha256 == execution.artifact_sha256(terminal_raw)
+    assert evidence.latest_checkpoint_receipt_sha256 == checkpoint_sha
+    assert evidence.latest_checkpoint_payload_sha256 == checkpoint.checkpoint_payload_sha256
+    assert reads == {
+        store.path / "attempt.json": 1,
+        store.path / "checkpoints" / "checkpoint-000000000010.json": 1,
+        store.path / "terminal.json": 1,
+    }
+
+    (store.path / "terminal.json").write_bytes(b"changed after verified return")
+    (store.path / "checkpoints" / "checkpoint-000000000010.json").write_bytes(
+        b"changed after verified return"
+    )
+    assert evidence.terminal_raw == terminal_raw
+    assert evidence.latest_checkpoint_raw == checkpoint_raw
+    with pytest.raises(FrozenInstanceError):
+        evidence.terminal = terminal  # type: ignore[misc]
+
+    rebound_terminal = replace(terminal, attempt_receipt_sha256=DIGESTS[15])
+    with pytest.raises(execution.ExecutionContractError, match="identity is inconsistent"):
+        execution.CompletedAttemptEvidence(
+            attempt=evidence.attempt,
+            attempt_raw=evidence.attempt_raw,
+            latest_checkpoint=evidence.latest_checkpoint,
+            latest_checkpoint_raw=evidence.latest_checkpoint_raw,
+            terminal=rebound_terminal,
+            terminal_raw=execution.canonical_terminal_bytes(rebound_terminal),
+            resume=evidence.resume,
+            resume_raw=evidence.resume_raw,
+        )
+
+
+def test_completed_attempt_evidence_rejects_open_and_failed_attempts(
+    tmp_path: Path,
+) -> None:
+    failed = execution.AttemptStore.create(tmp_path, _attempt("attempt-failed"))
+    with pytest.raises(execution.ExecutionContractError, match="no immutable terminal"):
+        execution.verified_completed_attempt_evidence(tmp_path, "attempt-failed")
+    failed_checkpoint = replace(_checkpoint(), attempt_id="attempt-failed")
+    failed_checkpoint_sha = failed.write_checkpoint(failed_checkpoint)
+    failed.write_terminal(
+        execution.TerminalRecord(
+            attempt_id=failed.attempt.attempt_id,
+            run_id=failed.attempt.run_id,
+            completed_at_utc="2026-08-25T20:03:00Z",
+            outcome="FAILED",
+            attempt_receipt_sha256=execution.artifact_sha256(
+                (failed.path / "attempt.json").read_bytes()
+            ),
+            latest_heartbeat_sha256=None,
+            latest_checkpoint_receipt_sha256=failed_checkpoint_sha,
+            failure_code="INFRA_TRANSIENT",
+        )
+    )
+    with pytest.raises(execution.ExecutionContractError, match="not SUCCEEDED"):
+        execution.verified_completed_attempt_evidence(tmp_path, "attempt-failed")
+
+
+def test_completed_attempt_evidence_revalidates_resume_predecessor_chain(
+    tmp_path: Path,
+) -> None:
+    predecessor = execution.AttemptStore.create(tmp_path, _attempt())
+    predecessor_checkpoint = _checkpoint()
+    predecessor_checkpoint_sha = predecessor.write_checkpoint(predecessor_checkpoint)
+    predecessor_terminal_sha = predecessor.write_terminal(
+        execution.TerminalRecord(
+            attempt_id=predecessor.attempt.attempt_id,
+            run_id=predecessor.attempt.run_id,
+            completed_at_utc="2026-08-25T20:03:00Z",
+            outcome="FAILED",
+            attempt_receipt_sha256=execution.artifact_sha256(
+                (predecessor.path / "attempt.json").read_bytes()
+            ),
+            latest_heartbeat_sha256=None,
+            latest_checkpoint_receipt_sha256=predecessor_checkpoint_sha,
+            failure_code="INFRA_TRANSIENT",
+        )
+    )
+    resumed_attempt = replace(
+        predecessor.attempt,
+        attempt_id="attempt-0002",
+        created_at_utc="2026-08-25T20:04:00Z",
+    )
+    resume = execution.ResumeRecord(
+        new_attempt_id=resumed_attempt.attempt_id,
+        predecessor_attempt_id=predecessor.attempt.attempt_id,
+        run_id=resumed_attempt.run_id,
+        created_at_utc=resumed_attempt.created_at_utc,
+        predecessor_terminal_sha256=predecessor_terminal_sha,
+        checkpoint_receipt_sha256=predecessor_checkpoint_sha,
+        corrective_change_sha256=DIGESTS[13],
+        retry_class="INFRA_TRANSIENT",
+    )
+    resumed = execution.AttemptStore.create_resumed(
+        tmp_path,
+        resumed_attempt,
+        resume,
+    )
+    resumed_checkpoint = replace(
+        _checkpoint(step=20),
+        attempt_id=resumed_attempt.attempt_id,
+        checkpoint_payload_sha256=DIGESTS[12],
+    )
+    resumed_checkpoint_sha = resumed.write_checkpoint(resumed_checkpoint)
+    resumed.write_terminal(
+        execution.TerminalRecord(
+            attempt_id=resumed.attempt.attempt_id,
+            run_id=resumed.attempt.run_id,
+            completed_at_utc="2026-08-25T20:05:00Z",
+            outcome="SUCCEEDED",
+            attempt_receipt_sha256=execution.artifact_sha256(
+                (resumed.path / "attempt.json").read_bytes()
+            ),
+            latest_heartbeat_sha256=None,
+            latest_checkpoint_receipt_sha256=resumed_checkpoint_sha,
+            failure_code=None,
+        )
+    )
+    evidence = execution.verified_completed_attempt_evidence(
+        tmp_path,
+        resumed_attempt.attempt_id,
+    )
+    assert evidence.resume == resume
+    assert evidence.resume_raw == execution.canonical_resume_bytes(resume)
+    assert evidence.latest_checkpoint == resumed_checkpoint
+
+    (predecessor.path / "checkpoints" / "checkpoint-000000000010.json").write_bytes(
+        execution.canonical_checkpoint_bytes(
+            replace(predecessor_checkpoint, checkpoint_payload_sha256=DIGESTS[14])
+        )
+    )
+    with pytest.raises(execution.ExecutionContractError):
+        execution.verified_completed_attempt_evidence(
+            tmp_path,
+            resumed_attempt.attempt_id,
+        )
+
+
 def test_resume_payload_binding_revalidates_recursive_predecessor_chain(
     tmp_path: Path,
 ) -> None:
diff --git a/tests/test_phaseset_execution_chain_bound.py b/tests/test_phaseset_execution_chain_bound.py
new file mode 100644
index 0000000..7389156
--- /dev/null
+++ b/tests/test_phaseset_execution_chain_bound.py
@@ -0,0 +1,41 @@
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+from phaseset_core import execution
+
+
+def test_attempt_chain_depth_is_rejected_before_opening_past_budget(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+) -> None:
+    opened: list[str] = []
+
+    def fake_verify(_path: Path, attempt_id: str) -> object:
+        opened.append(attempt_id)
+        ordinal = int(attempt_id.removeprefix("attempt-"))
+        return SimpleNamespace(
+            resume=SimpleNamespace(predecessor_attempt_id=f"attempt-{ordinal + 1}"),
+        )
+
+    monkeypatch.setattr(execution, "_verify_local_attempt", fake_verify)
+    with pytest.raises(execution.ExecutionContractError, match="closed attempt bound"):
+        execution._verify_attempt_chain(
+            tmp_path,
+            "attempt-0",
+            remaining_attempts=2,
+        )
+    assert opened == ["attempt-0", "attempt-1"]
+
+
+@pytest.mark.parametrize("value", [True, 0, 65])
+def test_attempt_chain_depth_budget_is_closed(value: object, tmp_path: Path) -> None:
+    with pytest.raises(execution.ExecutionContractError, match="closed attempt bound"):
+        execution._verify_attempt_chain(
+            tmp_path,
+            "attempt-0",
+            remaining_attempts=value,  # type: ignore[arg-type]
+        )

From 363422ae63f7988e3bd69b2e4f28c235d11a5fcf Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 12:04:44 -0500
Subject: [PATCH 15/95] Bind window caption provenance and fix CLIP chunk
 coverage

---
 RELEASE_FILES.sha256                         |  11 +-
 STATUS.md                                    |  15 +
 docs/CAPTION_ROLE_PROVENANCE.md              |  72 ++
 docs/PREPARED_DATA_V2.md                     |   9 +-
 src/phaseset_core/caption_role_provenance.py | 871 +++++++++++++++++++
 src/phaseset_core/prepared_data_v2.py        |  28 +-
 tests/test_caption_role_provenance.py        | 463 ++++++++++
 tests/test_phaseset_prepared_data_v2.py      |  44 +
 8 files changed, 1504 insertions(+), 9 deletions(-)
 create mode 100644 docs/CAPTION_ROLE_PROVENANCE.md
 create mode 100644 src/phaseset_core/caption_role_provenance.py
 create mode 100644 tests/test_caption_role_provenance.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 2d5d4ed..137f7b7 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,11 +11,12 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-fbd12b9f5c12d46e4d536b7e67953fad6612d92a9692196b327b408505d1fc28	21749	STATUS.md
+798ea6c4ff3a0c19f9792713f38f836df3baf563a9b37bb97298713110ed66f4	22814	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
+81ecc1cc742604f47833a0aa5b4c567165aa01e9c500d05157e325bd3b4ccdb3	4281	docs/CAPTION_ROLE_PROVENANCE.md
 a9d6a4875760c58a0075e9e4e6b7a5f147da9762410fde953d648895ec159a76	3398	docs/CAPTURE_PREPARED_STORAGE.md
 6d562907df5ae5a598401eb6ba622775ae03d4b81795671fe0c86942174e90e8	6781	docs/CAPTURE_VALIDATION.md
 e9331b64297d6c4586ccb22022c972fad657ac2b3f9202203be62b067a38ca82	3686	docs/CUDA_LIFECYCLE_OBSERVATION.md
@@ -56,7 +57,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 ecb85faa3b442450f6ecc8d6f2fff77baf19e833254fa8a715127d9127742f63	13825	docs/PHASESET_RESEARCH_CONTRACT.md
-d45d19f444c7d90a820695f5e8315b4485d660f965c13ebeab5d2b5a31d7e265	5453	docs/PREPARED_DATA_V2.md
+ddb29ac72e4f11809bb2d689ddb898a3ef23f830eda14e04cea054f79c7e4c13	5806	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
 8d689f49b2704ff7c5988eff8a7e3e062f209b85d18425ccdba1e1099581079b	10658	docs/phasepair_research_contract_20260824_165840.md
@@ -127,6 +128,7 @@ dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phase
 d2d71766c1c6af95480d507960d7186bbec9ff8a424f6e79ce6611fd0aa2e611	38885	src/phaseset_core/base_cohort_resolver.py
 3d4f31b6a773081af41f1d9d45ee98986ea33ef6f954070564373f995af1f2e9	40593	src/phaseset_core/base_cohort_validation.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
+121dcb6b948b9a3a4878ed376b3692dcbbd4b19f7ea147a7d28341c64b77d296	36926	src/phaseset_core/caption_role_provenance.py
 d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
 3a60f7a68d9de9b0ded88274a631e1e4195b4d12689a60a7f9ae93401a36af16	44353	src/phaseset_core/capture_prepared_storage.py
 85e5e80bc3b9d21b5fb8865923d27cb6254c077ac2319afe38dad8b79d785bd2	34366	src/phaseset_core/capture_validation.py
@@ -145,7 +147,7 @@ e0242fc212e83f2d3b694734fadb62ab76c4ef90c3c5b04df503f5a7c907a3e0	68520	src/phase
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phaseset_core/pipeline.py
-c675143d6ba9bc98976041ff02c0fa6ae6ef7bfd122122d0063eeb85f62c8c88	43972	src/phaseset_core/prepared_data_v2.py
+2bee9279fa4098dc83729c934aa4d3d07f3f2c4090c1734f9a316462d17159ef	44879	src/phaseset_core/prepared_data_v2.py
 273225e6696351ccb16a3a9e7a653b55af18c64b316a4e5d80c7b265625cc5c1	17929	src/phaseset_core/preprocessing.py
 044b381ba73e73f476918d353f29c7cbee38cdca32ee612826549fef60671bdc	73146	src/phaseset_core/production.py
 6f60feada83ebf1a55635bb657a5a3998e9fc77fa85012aad46e4f8d90d344db	37785	src/phaseset_core/publication.py
@@ -155,6 +157,7 @@ eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phase
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 9f6c21e354a17facda89a542d53e39013477c5e1b1a259781e2446cd394f6d06	157160	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
+4488b894e36be70f0efef9fc509088ab5a79de4e1fba54446a78492897bce784	18579	tests/test_caption_role_provenance.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
@@ -212,7 +215,7 @@ e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test
 6d7005585882e25be9479542aac96a5b2aeb1bddc5619a0f37c57f61f2cbe6c3	6348	tests/test_phaseset_objectives.py
 52ccd9d4ea8351d5aaaa82eb99d84c6bd240c8b6147a506fd65dbca3d1394331	11547	tests/test_phaseset_periodic.py
 6ebbf2ece33ff2390036e5ce55dcd34a0459211b79922219971c27bee37844a0	10278	tests/test_phaseset_pipeline.py
-2cb88d371c1dbd2f52d37c4b68f4715fb40ca4deec2e23215300bc18efa6e0b0	15678	tests/test_phaseset_prepared_data_v2.py
+82c15cd86cd1ac918d4cec678814ad61509ae7405173a73871c2f2172e82faeb	17328	tests/test_phaseset_prepared_data_v2.py
 aae917cd95bde322f4ec7cea693fd5d0c926d51f0a366e7be160426472ad7973	15770	tests/test_phaseset_prepared_host_integration.py
 f6837c3ac23f4209cb3ec3eadb899d0bdcd4b4259c901b021254ffe7b3688492	8482	tests/test_phaseset_preprocessing.py
 64016f9369540dae8b87654f43d09df9b9c07a3c4af06fa45567008b57140193	45251	tests/test_phaseset_production.py
diff --git a/STATUS.md b/STATUS.md
index 89fd9a8..b62f18d 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -13,6 +13,21 @@ evidence, not training results or scientific claims.
 
 ## Server continuation
 
+- The window-caption provenance component now checks the complete chain from
+  same-window fusion inputs/outputs through ordered frozen text rows to the
+  materialized training census. An actual official-CLIP probe exposed and fixed
+  a prepared-data bug: inference chunk size had been mistaken for total caption
+  count. Exact chunk coverage and all existing row/tensor/digest checks are now
+  retained together. The unchanged probe subsequently passed three actual
+  encoding chunks, prepared-tree reload and exact repeated provenance checks,
+  without re-encoding or observed CPU Torch RNG change. The original failure
+  remains retained. The source passed 53 focused / 1155 complete Linux server
+  tests, two existing skips and 39 subtests; complete regression took 316.45 s
+  with source unchanged. Fusion receipts, captions and motion in this probe
+  were explicit software fixtures. No fusion backend, licensed data, motion
+  training or formal supervision selection is claimed. See
+  [caption-role and text-chunk binding](docs/CAPTION_ROLE_PROVENANCE.md).
+
 - The completed-base resolver now reconstructs the exact nine registered
   successful chains, preserves failed predecessors, authenticates terminal-bound
   latest and distinct validation-best checkpoint bytes, and revalidates the
diff --git a/docs/CAPTION_ROLE_PROVENANCE.md b/docs/CAPTION_ROLE_PROVENANCE.md
new file mode 100644
index 0000000..0d2bd28
--- /dev/null
+++ b/docs/CAPTION_ROLE_PROVENANCE.md
@@ -0,0 +1,72 @@
+# Window-caption provenance and frozen-text chunk coverage
+
+`phaseset_core.caption_role_provenance` joins the content records that already
+exist during private preparation: same-window human actor descriptions,
+machine-fused description/paraphrase outputs, ordered frozen CLIP receipt rows,
+and the materialized prepared-data v2 training census. It does not run a fusion
+model, select a scientific supervision policy, authorize data, or train motion
+models.
+
+## Content binding
+
+`validate_candidate_machine_fused_window_training(...)` checks the proposed
+window-supervision mapping while original text is available. Each admitted
+window has its own positive family, with the fused description followed by
+its paraphrase. Human input text/source digests, actor membership, group/source
+lineage, window start, fusion provenance, exact UTF-8 output digests, CLIP row
+order and embedding bytes must agree. Canonical actor ordering after storage
+is accepted without changing membership. The complete reloaded train census
+must contain each bound motion and caption exactly once.
+
+The returned private digest-only receipt retains machine-fused/non-human roles.
+It is not a claim that a language model preserved semantics, nor a data-rights
+certificate. Commitments can remain linkable and must stay private. A trusted
+preparation process supplies already verified source summaries; caller-supplied
+digests alone are not authentication. Runtime source bytes must be independently
+verified before import, not inferred from a static review record.
+
+The candidate is bounded to 16,384 windows, 32,768 caption rows, and 64 MiB of
+inspected float32 text embeddings per representation. This is accounting after
+objects or batches are materialized, not an aggregate allocator cap. The
+validator never samples the census to fit a bound.
+
+## Actual CLIP integration correction
+
+The frozen adapter's `receipt.batch_size` is its inference chunk size, not its
+total caption count. Prepared-data v2 now checks an exact integer chunk size
+within the existing adapter bound and reconstructs the exact contiguous
+`chunk_ranges` over every caption row. A chunk size larger than the census is
+valid and yields one shorter chunk. Missing, reordered, overlapping or oversized
+ranges and Boolean/malformed sizes are rejected. Existing row, shape, stride,
+byte, full/individual embedding digest and positive-family checks remain.
+
+A registered-server probe using the actual official CLIP weights first exposed
+the old false equality after encoding four texts in two chunks. Its failure
+was retained. After the correction, the unchanged probe encoded four training
+fixture texts and two separate auxiliary-validation fixture texts in three
+actual chunks, wrote and reloaded the prepared tree, and reproduced the exact
+provenance receipt without another encode call. Observed CPU Torch RNG was
+unchanged. All 53 focused server tests passed with source bytes unchanged.
+A subsequent complete Linux server regression passed 1,155 tests, two existing
+skips and 39 subtests in 316.45 seconds; its source census was also unchanged.
+
+The texts, fusion receipts and analytic motion in this probe were explicit
+software fixtures. No fusion backend, licensed dataset, motion-model training,
+real cohort, retrieval-quality metric or formal supervision choice was involved.
+
+## Scientific boundary
+
+This validator implements a candidate hybrid: machine-fused same-window
+description/paraphrase supervision for window InfoNCE, with separate official
+human holistic capture validation/test. The candidate has not amended the
+frozen experiment contract. It must not silently turn auxiliary captions into
+human primary labels or copy whole-capture descriptions onto individual windows.
+All systems/seeds must share whichever policy is explicitly preregistered
+before real training. Primary capture pooling and checkpoint selection remain
+the existing [capture-validation contract](CAPTURE_VALIDATION.md).
+
+This is a preparation component, not the complete `prepare-data` command.
+Licensed native inputs, participant-disjoint source preparation, actual fusion
+execution and full private command composition remain required. See
+[prepared-data v2](PREPARED_DATA_V2.md) and
+[the frozen text adapter](FROZEN_CLIP_TEXT_ADAPTER.md).
diff --git a/docs/PREPARED_DATA_V2.md b/docs/PREPARED_DATA_V2.md
index 4a4002d..ca8fb09 100644
--- a/docs/PREPARED_DATA_V2.md
+++ b/docs/PREPARED_DATA_V2.md
@@ -12,6 +12,10 @@ one `FrozenClipTextBatch`, variable per-motion `text_counts`, and independent
 uint64 `window_ordinals`. Every motion receives at least one text row; no fixed
 three-caption or two-caption assumption is made. The supplied frozen text
 receipt must bind the actual embedding bytes, row order, and commitments.
+Its `batch_size` denotes the CLIP inference chunk size, not total text rows.
+The exact contiguous `chunk_ranges` must cover the complete row census; all
+existing tensor and individual-row checks remain. See the
+[actual CLIP composition correction](CAPTION_ROLE_PROVENANCE.md).
 
 `write_prepared_training_tree_v2(...)` takes separate train/val blocks, an edge
 budget, optional microbatch-size bound, and explicit artifact-size bounds. It
@@ -82,8 +86,9 @@ Actor-set group commitments are lineage, not unique capture/window row IDs.
 Main validation must separately encode every admitted window in a capture,
 aggregate embeddings with the fixed capture mean, and score one capture gallery
 against official holistic text. The [capture validation API](CAPTURE_VALIDATION.md)
-now implements that operation and training checkpoint selection, but the
-private-host capture-source disk route is not yet connected to this storage.
+now implements that operation and training checkpoint selection. The separate
+capture-storage route is connected through the private host's combined index;
+it does not relabel this auxiliary window source as the primary task.
 
 This module alone is not the complete `prepare-data` command. Native capture
 loading, licensed body-model conversion, preprocessing rejection/tail census,
diff --git a/src/phaseset_core/caption_role_provenance.py b/src/phaseset_core/caption_role_provenance.py
new file mode 100644
index 0000000..905e8a1
--- /dev/null
+++ b/src/phaseset_core/caption_role_provenance.py
@@ -0,0 +1,871 @@
+"""Authority-zero binding for a candidate machine-fused window training policy.
+
+This private candidate is a consistency validator, not a policy selection,
+rights authenticator, semantic-equivalence review, or training authorization.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+import hashlib
+import json
+from typing import Final
+
+import torch
+
+from phaseset_core.caption_fusion import (
+    ANNOTATION_KIND,
+    TASK_SCOPE,
+    CaptionFusionInput,
+    CaptionFusionReceipt,
+    MachineFusedCaption,
+    _input_digest as _caption_input_digest,
+    _output_digest as _caption_output_digest,
+    canonical_caption_fusion_receipt_bytes,
+)
+from phaseset_core.contracts import group_commitment
+from phaseset_core.prepared_data_v2 import (
+    PreparedMotionTextBlock,
+    PreparedTrainingDataSourceV2,
+)
+from phaseset_core.pipeline import PreparedGroupSample
+from phaseset_core.training import RetrievalTrainingBatch
+
+
+SCHEMA: Final = "phaseset-private-caption-role-validation-v2"
+WINDOW_SUMMARY_SCHEMA: Final = "phaseset-private-window-manifest-content-summary-v1"
+CANDIDATE_POLICY_ID: Final = "candidate-machine-fused-window-supervision-v1"
+STATUS: Final = "VALIDATED_CANDIDATE_NOT_SELECTED_AUTHORITY0"
+CAPTION_ROLES: Final = ("group_description", "semantic_paraphrase")
+INSPECTION_SEED: Final = 1729
+MAX_WINDOWS: Final = 16_384
+MAX_CAPTIONS: Final = 32_768
+MAX_MICROBATCHES: Final = 16_384
+MAX_EMBEDDING_BYTES: Final = 64 * 1024 * 1024
+
+
+class CaptionRoleProvenanceError(ValueError):
+    """The proposed private supervision binding is inconsistent."""
+
+
+def _sha256_bytes(value: bytes) -> str:
+    return hashlib.sha256(value).hexdigest()
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise CaptionRoleProvenanceError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _raw32(value: object, label: str) -> bytes:
+    if type(value) is not bytes or len(value) != 32:
+        raise CaptionRoleProvenanceError(f"{label} must be exact bytes[32]")
+    return value
+
+
+def _canonical_json(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _manifest_digest(domain: bytes, values: tuple[str, ...]) -> str:
+    digest = hashlib.sha256(domain)
+    digest.update(len(values).to_bytes(8, "big"))
+    for value in values:
+        raw = value.encode("ascii")
+        digest.update(len(raw).to_bytes(8, "big"))
+        digest.update(raw)
+    return digest.hexdigest()
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().cpu().contiguous().view(torch.uint8).numpy().tobytes()
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class ActorCaptionContentSummary:
+    """Verified-manifest content digest for one same-window actor caption."""
+
+    actor_commitment: bytes = field(repr=False)
+    source_record_sha256: str = field(repr=False)
+    text_utf8_sha256: str = field(repr=False)
+
+    def __post_init__(self) -> None:
+        object.__setattr__(
+            self,
+            "actor_commitment",
+            _raw32(self.actor_commitment, "actor caption commitment"),
+        )
+        _lower_sha256(self.source_record_sha256, "actor caption source record sha256")
+        _lower_sha256(self.text_utf8_sha256, "actor caption text sha256")
+
+    def _private_dict(self) -> dict[str, str]:
+        return {
+            "actor_commitment": self.actor_commitment.hex(),
+            "source_record_sha256": self.source_record_sha256,
+            "text_utf8_sha256": self.text_utf8_sha256,
+        }
+
+    def __repr__(self) -> str:
+        return "ActorCaptionContentSummary(<private lineage>)"
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class HolisticContextContentSummary:
+    """Verified-manifest content digest for optional same-window context."""
+
+    source_record_sha256: str = field(repr=False)
+    text_utf8_sha256: str = field(repr=False)
+
+    def __post_init__(self) -> None:
+        _lower_sha256(self.source_record_sha256, "holistic source record sha256")
+        _lower_sha256(self.text_utf8_sha256, "holistic text sha256")
+
+    def _private_dict(self) -> dict[str, str]:
+        return {
+            "source_record_sha256": self.source_record_sha256,
+            "text_utf8_sha256": self.text_utf8_sha256,
+        }
+
+    def __repr__(self) -> str:
+        return "HolisticContextContentSummary(<private lineage>)"
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class WindowManifestRow:
+    """Private content summary of one already-verified train-window row."""
+
+    window_sha256: str
+    window_commitment: bytes = field(repr=False)
+    actor_commitments: tuple[bytes, ...] = field(repr=False)
+    group_commitment: bytes = field(repr=False)
+    source_sha256: str = field(repr=False)
+    source_start_frame: int
+    actor_caption_summaries: tuple[ActorCaptionContentSummary, ...] = field(repr=False)
+    holistic_context_summary: HolisticContextContentSummary | None = field(
+        default=None,
+        repr=False,
+    )
+
+    def __post_init__(self) -> None:
+        _lower_sha256(self.window_sha256, "window_sha256")
+        window = _raw32(self.window_commitment, "window_commitment")
+        if type(self.actor_commitments) is not tuple or len(self.actor_commitments) < 3:
+            raise CaptionRoleProvenanceError(
+                "window summary requires an exact tuple of at least three actors"
+            )
+        actors = tuple(
+            _raw32(value, f"actor_commitments[{index}]")
+            for index, value in enumerate(self.actor_commitments)
+        )
+        if len(set(actors)) != len(actors):
+            raise CaptionRoleProvenanceError("window summary repeats an actor commitment")
+        if (
+            type(self.actor_caption_summaries) is not tuple
+            or len(self.actor_caption_summaries) != len(actors)
+            or any(
+                type(value) is not ActorCaptionContentSummary
+                for value in self.actor_caption_summaries
+            )
+        ):
+            raise CaptionRoleProvenanceError(
+                "window summary actor-caption census is inconsistent"
+            )
+        actor_caption_summaries = tuple(
+            sorted(self.actor_caption_summaries, key=lambda value: value.actor_commitment)
+        )
+        if tuple(value.actor_commitment for value in actor_caption_summaries) != tuple(
+            sorted(actors)
+        ):
+            raise CaptionRoleProvenanceError(
+                "window summary actor-caption lineage is inconsistent"
+            )
+        if self.holistic_context_summary is not None and type(
+            self.holistic_context_summary
+        ) is not HolisticContextContentSummary:
+            raise CaptionRoleProvenanceError(
+                "holistic_context_summary must be exact or absent"
+            )
+        group = _raw32(self.group_commitment, "group_commitment")
+        if group_commitment(actors) != group:
+            raise CaptionRoleProvenanceError("window summary group commitment is inconsistent")
+        _lower_sha256(self.source_sha256, "source_sha256")
+        if type(self.source_start_frame) is not int or self.source_start_frame < 0:
+            raise CaptionRoleProvenanceError(
+                "source_start_frame must be an exact nonnegative int"
+            )
+        object.__setattr__(self, "window_commitment", window)
+        object.__setattr__(self, "actor_commitments", actors)
+        object.__setattr__(self, "group_commitment", group)
+        object.__setattr__(self, "actor_caption_summaries", actor_caption_summaries)
+
+    def _private_dict(self) -> dict[str, object]:
+        return {
+            "actor_commitments": [value.hex() for value in self.actor_commitments],
+            "actor_caption_summaries": [
+                value._private_dict() for value in self.actor_caption_summaries
+            ],
+            "group_commitment": self.group_commitment.hex(),
+            "source_sha256": self.source_sha256,
+            "source_start_frame": self.source_start_frame,
+            "holistic_context_summary": (
+                None
+                if self.holistic_context_summary is None
+                else self.holistic_context_summary._private_dict()
+            ),
+            "window_commitment": self.window_commitment.hex(),
+            "window_sha256": self.window_sha256,
+        }
+
+    def __repr__(self) -> str:
+        return "WindowManifestRow(<private lineage>)"
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class WindowManifestContentSummary:
+    """Content extracted from one verified private train-window manifest."""
+
+    manifest_sha256: str
+    rows: tuple[WindowManifestRow, ...] = field(repr=False)
+    split: str = "train"
+    schema: str = WINDOW_SUMMARY_SCHEMA
+
+    def __post_init__(self) -> None:
+        _lower_sha256(self.manifest_sha256, "window manifest sha256")
+        if self.split != "train" or self.schema != WINDOW_SUMMARY_SCHEMA:
+            raise CaptionRoleProvenanceError("window summary schema or split is not registered")
+        if (
+            type(self.rows) is not tuple
+            or not self.rows
+            or len(self.rows) > MAX_WINDOWS
+            or any(type(row) is not WindowManifestRow for row in self.rows)
+        ):
+            raise CaptionRoleProvenanceError("window summary rows are invalid or exceed the bound")
+        windows = tuple(row.window_sha256 for row in self.rows)
+        commitments = tuple(row.window_commitment for row in self.rows)
+        if len(set(windows)) != len(windows) or len(set(commitments)) != len(commitments):
+            raise CaptionRoleProvenanceError("window summary repeats a window identity")
+
+    @property
+    def content_sha256(self) -> str:
+        return _sha256_bytes(
+            _canonical_json(
+                {
+                    "manifest_sha256": self.manifest_sha256,
+                    "rows": [row._private_dict() for row in self.rows],
+                    "schema": self.schema,
+                    "split": self.split,
+                }
+            )
+        )
+
+    def __repr__(self) -> str:
+        return (
+            f"WindowManifestContentSummary(windows={len(self.rows)}, "
+            "<private lineage>)"
+        )
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class WindowFusionHandoff:
+    """Ephemeral fusion-to-CLIP handoff retained for one private validation."""
+
+    fusion_input: CaptionFusionInput = field(repr=False)
+    fused_caption: MachineFusedCaption = field(repr=False)
+    clip_caption_commitments: tuple[bytes, bytes] = field(repr=False)
+
+    def __post_init__(self) -> None:
+        if type(self.fusion_input) is not CaptionFusionInput:
+            raise CaptionRoleProvenanceError("fusion_input must be exact CaptionFusionInput")
+        if type(self.fused_caption) is not MachineFusedCaption:
+            raise CaptionRoleProvenanceError("fused_caption must be exact MachineFusedCaption")
+        if (
+            type(self.clip_caption_commitments) is not tuple
+            or len(self.clip_caption_commitments) != 2
+        ):
+            raise CaptionRoleProvenanceError(
+                "clip_caption_commitments must contain exactly two rows"
+            )
+        checked = tuple(
+            _raw32(value, f"clip_caption_commitments[{index}]")
+            for index, value in enumerate(self.clip_caption_commitments)
+        )
+        if len(set(checked)) != 2:
+            raise CaptionRoleProvenanceError("fusion handoff repeats a caption commitment")
+        object.__setattr__(self, "clip_caption_commitments", checked)
+
+    def __repr__(self) -> str:
+        return "WindowFusionHandoff(<private text and lineage>)"
+
+
+@dataclass(frozen=True, slots=True)
+class CaptionRoleValidationReceipt:
+    """Canonical private digest receipt with no authority or policy selection."""
+
+    window_manifest_sha256: str
+    window_manifest_content_sha256: str
+    prepared_train_manifest_sha256: str
+    dataset_manifest_sha256: str
+    caption_manifest_sha256: str
+    human_text_rights_assertion_sha256: str
+    row_binding_manifest_sha256: str
+    fusion_receipt_manifest_sha256: str
+    clip_receipt_manifest_sha256: str
+    positive_family_manifest_sha256: str
+    window_count: int
+    caption_count: int
+    microbatch_count: int
+    inspection_seed: int = INSPECTION_SEED
+    candidate_policy_id: str = CANDIDATE_POLICY_ID
+    formal_supervision_policy_selected: bool = False
+    rights_authenticated_by_this_receipt: bool = False
+    semantic_equivalence_proved: bool = False
+    training_authorized: bool = False
+    scientific_result_claimed: bool = False
+    authority: int = 0
+    status: str = STATUS
+    schema: str = SCHEMA
+
+    def canonical_json_bytes(self) -> bytes:
+        for name in (
+            "window_manifest_sha256",
+            "window_manifest_content_sha256",
+            "prepared_train_manifest_sha256",
+            "dataset_manifest_sha256",
+            "caption_manifest_sha256",
+            "human_text_rights_assertion_sha256",
+            "row_binding_manifest_sha256",
+            "fusion_receipt_manifest_sha256",
+            "clip_receipt_manifest_sha256",
+            "positive_family_manifest_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        if (
+            type(self.window_count) is not int
+            or not 1 <= self.window_count <= MAX_WINDOWS
+            or type(self.caption_count) is not int
+            or self.caption_count != 2 * self.window_count
+            or type(self.microbatch_count) is not int
+            or not 1 <= self.microbatch_count <= MAX_MICROBATCHES
+            or type(self.inspection_seed) is not int
+            or self.inspection_seed != INSPECTION_SEED
+            or self.formal_supervision_policy_selected is not False
+            or self.rights_authenticated_by_this_receipt is not False
+            or self.semantic_equivalence_proved is not False
+            or self.training_authorized is not False
+            or self.scientific_result_claimed is not False
+            or type(self.authority) is not int
+            or self.authority != 0
+        ):
+            raise CaptionRoleProvenanceError(
+                "validation receipt census or frozen field type is invalid"
+            )
+        frozen = {
+            "authority": 0,
+            "candidate_policy_id": CANDIDATE_POLICY_ID,
+            "formal_supervision_policy_selected": False,
+            "rights_authenticated_by_this_receipt": False,
+            "schema": SCHEMA,
+            "scientific_result_claimed": False,
+            "semantic_equivalence_proved": False,
+            "status": STATUS,
+            "training_authorized": False,
+        }
+        for name, expected in frozen.items():
+            if getattr(self, name) != expected:
+                raise CaptionRoleProvenanceError(f"receipt frozen field {name} changed")
+        return _canonical_json(
+            {
+                **frozen,
+                "caption_count": self.caption_count,
+                "caption_manifest_sha256": self.caption_manifest_sha256,
+                "clip_receipt_manifest_sha256": self.clip_receipt_manifest_sha256,
+                "dataset_manifest_sha256": self.dataset_manifest_sha256,
+                "fusion_receipt_manifest_sha256": self.fusion_receipt_manifest_sha256,
+                "human_text_rights_assertion_sha256": (
+                    self.human_text_rights_assertion_sha256
+                ),
+                "microbatch_count": self.microbatch_count,
+                "positive_family_manifest_sha256": self.positive_family_manifest_sha256,
+                "prepared_train_manifest_sha256": self.prepared_train_manifest_sha256,
+                "row_binding_manifest_sha256": self.row_binding_manifest_sha256,
+                "inspection_seed": self.inspection_seed,
+                "window_count": self.window_count,
+                "window_manifest_content_sha256": self.window_manifest_content_sha256,
+                "window_manifest_sha256": self.window_manifest_sha256,
+            }
+        )
+
+    @property
+    def sha256(self) -> str:
+        return _sha256_bytes(self.canonical_json_bytes())
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class _BoundCaption:
+    role: str
+    commitment: bytes
+    text_sha256: str
+    embedding_sha256: str
+    embedding: torch.Tensor = field(repr=False)
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class _BoundWindow:
+    family: bytes
+    row: WindowManifestRow
+    captions: tuple[_BoundCaption, _BoundCaption]
+    fusion_receipt_sha256: str
+    clip_receipt_sha256: str
+    row_binding_sha256: str
+
+
+def _validate_handoff(
+    *,
+    sample: PreparedGroupSample,
+    row: WindowManifestRow,
+    handoff: WindowFusionHandoff,
+    clip_commitments: tuple[bytes, bytes],
+    clip_text_digests: tuple[str, str],
+    clip_embedding_digests: tuple[str, str],
+    clip_embeddings: torch.Tensor,
+    clip_receipt_sha256: str,
+    expected_dataset_manifest_sha256: str,
+    expected_caption_manifest_sha256: str,
+    expected_human_text_rights_assertion_sha256: str,
+) -> _BoundWindow:
+    # PreparedMotionTextBlock has already reconstructed exact PreparedGroupSample
+    # instances; avoid importing its implementation-only row helper.
+    if sample.window_sha256 != row.window_sha256:
+        raise CaptionRoleProvenanceError("prepared and summarized window identity differ")
+    if (
+        sample.source_sha256 != row.source_sha256
+        or sample.source_start_frame != row.source_start_frame
+        or sample.actor_commitments != row.actor_commitments
+        or sample.group_commitment != row.group_commitment
+    ):
+        raise CaptionRoleProvenanceError("prepared and summarized window lineage differ")
+
+    fusion_input = handoff.fusion_input
+    fused = handoff.fused_caption
+    receipt = fused.receipt
+    if fusion_input.window_commitment != row.window_commitment:
+        raise CaptionRoleProvenanceError("fusion and summarized window commitments differ")
+    fusion_actors = tuple(sorted(item.actor_commitment for item in fusion_input.actor_captions))
+    if fusion_actors != tuple(sorted(row.actor_commitments)):
+        raise CaptionRoleProvenanceError("fusion and summarized actor lineages differ")
+    fusion_actor_content = tuple(
+        sorted(
+            (
+                item.actor_commitment,
+                item.source_record_sha256,
+                _sha256_bytes(item.text.encode("utf-8")),
+            )
+            for item in fusion_input.actor_captions
+        )
+    )
+    summarized_actor_content = tuple(
+        (
+            item.actor_commitment,
+            item.source_record_sha256,
+            item.text_utf8_sha256,
+        )
+        for item in row.actor_caption_summaries
+    )
+    if fusion_actor_content != summarized_actor_content:
+        raise CaptionRoleProvenanceError(
+            "fusion actor-caption content differs from verified window summary"
+        )
+    holistic = fusion_input.holistic_context
+    holistic_summary = row.holistic_context_summary
+    if (holistic is None) != (holistic_summary is None):
+        raise CaptionRoleProvenanceError(
+            "fusion holistic-context presence differs from verified window summary"
+        )
+    if holistic is not None and holistic_summary is not None and (
+        holistic.window_commitment != row.window_commitment
+        or holistic.source_record_sha256 != holistic_summary.source_record_sha256
+        or _sha256_bytes(holistic.text.encode("utf-8"))
+        != holistic_summary.text_utf8_sha256
+    ):
+        raise CaptionRoleProvenanceError(
+            "fusion holistic content differs from verified window summary"
+        )
+    provenance = fusion_input.provenance
+    if (
+        provenance.split != "train"
+        or provenance.dataset_manifest_sha256 != expected_dataset_manifest_sha256
+        or provenance.caption_manifest_sha256 != expected_caption_manifest_sha256
+        or provenance.human_text_rights_assertion_sha256
+        != expected_human_text_rights_assertion_sha256
+        or provenance.sealed_test_manifest_sha256 is not None
+        or provenance.sealed_test_caption_sha256 is not None
+    ):
+        raise CaptionRoleProvenanceError("fusion provenance differs from admitted train inputs")
+
+    try:
+        fusion_receipt_raw = canonical_caption_fusion_receipt_bytes(receipt)
+    except Exception:
+        raise CaptionRoleProvenanceError("caption-fusion receipt is invalid") from None
+    expected_admission = _sha256_bytes(
+        b"phaseset-caption-fusion-local-admission-v1\x00"
+        + provenance.digest().encode("ascii")
+    )
+    if (
+        type(receipt) is not CaptionFusionReceipt
+        or receipt.input_sha256 != _caption_input_digest(fusion_input)
+        or receipt.output_sha256
+        != _caption_output_digest(fused.group_description, fused.semantic_paraphrase)
+        or receipt.provenance_sha256 != provenance.digest()
+        or receipt.admission_sha256 != expected_admission
+        or receipt.actor_count != len(row.actor_commitments)
+        or receipt.annotation_kind != ANNOTATION_KIND
+        or receipt.task_scope != TASK_SCOPE
+        or receipt.machine_fused is not True
+        or receipt.human_annotation is not False
+        or receipt.public_verification_performed is not False
+        or type(receipt.authority) is not int
+        or receipt.authority != 0
+        or receipt.scientific_result_claimed is not False
+        or receipt.result_claimed is not False
+    ):
+        raise CaptionRoleProvenanceError("caption-fusion receipt does not bind this handoff")
+
+    texts = (fused.group_description, fused.semantic_paraphrase)
+    text_digests = tuple(_sha256_bytes(value.encode("utf-8")) for value in texts)
+    if (
+        handoff.clip_caption_commitments != clip_commitments
+        or text_digests != clip_text_digests
+    ):
+        raise CaptionRoleProvenanceError("fusion output differs from ordered frozen-CLIP rows")
+    caption_values = tuple(
+        _BoundCaption(
+            role=role,
+            commitment=commitment,
+            text_sha256=text_digest,
+            embedding_sha256=embedding_digest,
+            embedding=clip_embeddings[index : index + 1].detach().clone().contiguous(),
+        )
+        for index, (role, commitment, text_digest, embedding_digest) in enumerate(
+            zip(
+                CAPTION_ROLES,
+                clip_commitments,
+                clip_text_digests,
+                clip_embedding_digests,
+                strict=True,
+            )
+        )
+    )
+    family = bytes.fromhex(row.window_sha256)
+    fusion_receipt_sha256 = _sha256_bytes(fusion_receipt_raw)
+    row_binding = {
+        "actor_commitments": [value.hex() for value in row.actor_commitments],
+        "captions": [
+            {
+                "clip_caption_commitment": caption.commitment.hex(),
+                "embedding_sha256": caption.embedding_sha256,
+                "role": caption.role,
+                "text_utf8_sha256": caption.text_sha256,
+            }
+            for caption in caption_values
+        ],
+        "clip_receipt_sha256": clip_receipt_sha256,
+        "fusion_input_sha256": receipt.input_sha256,
+        "fusion_output_sha256": receipt.output_sha256,
+        "fusion_provenance_sha256": receipt.provenance_sha256,
+        "fusion_receipt_sha256": fusion_receipt_sha256,
+        "group_commitment": row.group_commitment.hex(),
+        "positive_family": family.hex(),
+        "source_sha256": row.source_sha256,
+        "source_start_frame": row.source_start_frame,
+        "window_commitment": row.window_commitment.hex(),
+        "window_sha256": row.window_sha256,
+    }
+    return _BoundWindow(
+        family=family,
+        row=row,
+        captions=(caption_values[0], caption_values[1]),
+        fusion_receipt_sha256=fusion_receipt_sha256,
+        clip_receipt_sha256=clip_receipt_sha256,
+        row_binding_sha256=_sha256_bytes(_canonical_json(row_binding)),
+    )
+
+
+def validate_candidate_machine_fused_window_training(
+    *,
+    blocks: tuple[PreparedMotionTextBlock, ...],
+    handoffs: tuple[WindowFusionHandoff, ...],
+    window_summary: WindowManifestContentSummary,
+    train_source: PreparedTrainingDataSourceV2,
+    expected_window_manifest_sha256: str,
+    expected_prepared_train_manifest_sha256: str,
+    expected_dataset_manifest_sha256: str,
+    expected_caption_manifest_sha256: str,
+    expected_human_text_rights_assertion_sha256: str,
+) -> CaptionRoleValidationReceipt:
+    """Validate one complete candidate auxiliary-window supervision census."""
+
+    expected_window = _lower_sha256(
+        expected_window_manifest_sha256, "expected window manifest sha256"
+    )
+    expected_prepared = _lower_sha256(
+        expected_prepared_train_manifest_sha256,
+        "expected prepared train manifest sha256",
+    )
+    expected_dataset = _lower_sha256(
+        expected_dataset_manifest_sha256, "expected dataset manifest sha256"
+    )
+    expected_caption = _lower_sha256(
+        expected_caption_manifest_sha256, "expected caption manifest sha256"
+    )
+    expected_rights = _lower_sha256(
+        expected_human_text_rights_assertion_sha256,
+        "expected human text rights assertion sha256",
+    )
+    if type(window_summary) is not WindowManifestContentSummary:
+        raise CaptionRoleProvenanceError(
+            "window_summary must be exact WindowManifestContentSummary"
+        )
+    if window_summary.manifest_sha256 != expected_window:
+        raise CaptionRoleProvenanceError("window summary differs from expected manifest digest")
+    if (
+        type(blocks) is not tuple
+        or not blocks
+        or any(type(block) is not PreparedMotionTextBlock for block in blocks)
+    ):
+        raise CaptionRoleProvenanceError("blocks must be a nonempty exact prepared-v2 tuple")
+    if type(handoffs) is not tuple or any(
+        type(value) is not WindowFusionHandoff for value in handoffs
+    ):
+        raise CaptionRoleProvenanceError("handoffs must be an exact handoff tuple")
+    if type(train_source) is not PreparedTrainingDataSourceV2:
+        raise CaptionRoleProvenanceError("train_source must be exact prepared-data-v2 source")
+    if train_source.split != "train" or train_source.manifest_sha256 != expected_prepared:
+        raise CaptionRoleProvenanceError("prepared train source identity differs from expected")
+
+    prepared_rows: list[tuple[PreparedGroupSample, int, int]] = []
+    caption_offset = 0
+    embedding_bytes = 0
+    clip_receipt_digests: list[str] = []
+    block_values: list[tuple[torch.Tensor, tuple[tuple[object, ...], ...], tuple[bytes, ...]]] = []
+    for block_index, block in enumerate(blocks):
+        if len(block.samples) + len(prepared_rows) > MAX_WINDOWS:
+            raise CaptionRoleProvenanceError("prepared window census exceeds the bound")
+        if any(count != 2 for count in block.text_counts):
+            raise CaptionRoleProvenanceError(
+                "candidate policy requires exactly two text rows per motion window"
+            )
+        embeddings = block.text_batch.embeddings
+        embedding_bytes += embeddings.numel() * embeddings.element_size()
+        if embedding_bytes > MAX_EMBEDDING_BYTES:
+            raise CaptionRoleProvenanceError("prepared frozen embeddings exceed the byte bound")
+        receipt = block.text_batch.receipt
+        try:
+            clip_receipt_sha256 = receipt.sha256
+        except Exception:
+            raise CaptionRoleProvenanceError("frozen-CLIP receipt is invalid") from None
+        clip_receipt_digests.append(clip_receipt_sha256)
+        rows = receipt.caption_rows
+        commitments = block.text_batch.caption_commitments
+        local_offset = 0
+        for sample, count in zip(block.samples, block.text_counts, strict=True):
+            prepared_rows.append((sample, block_index, local_offset))
+            local_offset += count
+            caption_offset += count
+        if local_offset != len(commitments) or len(rows) != len(commitments):
+            raise CaptionRoleProvenanceError("prepared block text census is inconsistent")
+        block_values.append((embeddings, rows, commitments))
+    if (
+        not prepared_rows
+        or len(prepared_rows) != len(handoffs)
+        or len(prepared_rows) != len(window_summary.rows)
+        or caption_offset != 2 * len(prepared_rows)
+        or caption_offset > MAX_CAPTIONS
+    ):
+        raise CaptionRoleProvenanceError("prepared, fusion, and window censuses differ")
+
+    bound: list[_BoundWindow] = []
+    all_caption_commitments: set[bytes] = set()
+    all_families: set[bytes] = set()
+    all_fusion_receipts: set[str] = set()
+    for index, ((sample, block_index, local_offset), row, handoff) in enumerate(
+        zip(prepared_rows, window_summary.rows, handoffs, strict=True)
+    ):
+        embeddings, receipt_rows, block_commitments = block_values[block_index]
+        row_slice = receipt_rows[local_offset : local_offset + 2]
+        commitment_slice = block_commitments[local_offset : local_offset + 2]
+        if (
+            len(row_slice) != 2
+            or len(commitment_slice) != 2
+            or tuple(item[0] for item in row_slice) != (local_offset, local_offset + 1)
+            or tuple(item[1] for item in row_slice)
+            != tuple(value.hex() for value in commitment_slice)
+        ):
+            raise CaptionRoleProvenanceError(
+                f"frozen-CLIP ordered rows differ at prepared window {index}"
+            )
+        checked = _validate_handoff(
+            sample=sample,
+            row=row,
+            handoff=handoff,
+            clip_commitments=(commitment_slice[0], commitment_slice[1]),
+            clip_text_digests=(row_slice[0][2], row_slice[1][2]),
+            clip_embedding_digests=(row_slice[0][8], row_slice[1][8]),
+            clip_embeddings=embeddings[local_offset : local_offset + 2],
+            clip_receipt_sha256=clip_receipt_digests[block_index],
+            expected_dataset_manifest_sha256=expected_dataset,
+            expected_caption_manifest_sha256=expected_caption,
+            expected_human_text_rights_assertion_sha256=expected_rights,
+        )
+        commitments = {caption.commitment for caption in checked.captions}
+        if (
+            checked.family in all_families
+            or all_caption_commitments.intersection(commitments)
+            or checked.fusion_receipt_sha256 in all_fusion_receipts
+        ):
+            raise CaptionRoleProvenanceError("candidate binding repeats a row identity")
+        all_families.add(checked.family)
+        all_caption_commitments.update(commitments)
+        all_fusion_receipts.add(checked.fusion_receipt_sha256)
+        bound.append(checked)
+
+    by_family = {value.family: value for value in bound}
+    by_caption = {
+        caption.commitment: (value.family, caption)
+        for value in bound
+        for caption in value.captions
+    }
+    seen_families: set[bytes] = set()
+    seen_captions: set[bytes] = set()
+    microbatch_count = 0
+    observed_embedding_bytes = 0
+    try:
+        iterator = train_source.iter_epoch(epoch=0, seed=INSPECTION_SEED)
+        for batch_index, batch in enumerate(iterator):
+            microbatch_count += 1
+            if microbatch_count > MAX_MICROBATCHES:
+                raise CaptionRoleProvenanceError("prepared source exceeds the microbatch bound")
+            if type(batch) is not RetrievalTrainingBatch or batch.split != "train":
+                raise CaptionRoleProvenanceError("prepared source emitted a non-train batch")
+            observed_embedding_bytes += (
+                batch.text_embeddings.numel() * batch.text_embeddings.element_size()
+            )
+            if observed_embedding_bytes > MAX_EMBEDDING_BYTES:
+                raise CaptionRoleProvenanceError("materialized embeddings exceed the byte bound")
+            if len(seen_families) + batch.motion_count > MAX_WINDOWS:
+                raise CaptionRoleProvenanceError("materialized window census exceeds the bound")
+            if len(seen_captions) + len(batch.text_commitments) > MAX_CAPTIONS:
+                raise CaptionRoleProvenanceError("materialized caption census exceeds the bound")
+            for motion_index, family in enumerate(batch.motion_positive_ids):
+                expected = by_family.get(family)
+                if expected is None or family in seen_families:
+                    raise CaptionRoleProvenanceError(
+                        f"materialized motion family differs at batch {batch_index}"
+                    )
+                group_row = batch.groups.group_commitments[motion_index]
+                actor_row = tuple(
+                    value
+                    for value in batch.groups.actor_commitments[motion_index]
+                    if value is not None
+                )
+                if (
+                    group_row != expected.row.group_commitment
+                    or actor_row != tuple(sorted(expected.row.actor_commitments))
+                ):
+                    raise CaptionRoleProvenanceError(
+                        f"materialized motion lineage differs at batch {batch_index}"
+                    )
+                seen_families.add(family)
+            for text_index, (family, commitment) in enumerate(
+                zip(batch.text_positive_ids, batch.text_commitments, strict=True)
+            ):
+                expected_pair = by_caption.get(commitment)
+                if expected_pair is None or commitment in seen_captions:
+                    raise CaptionRoleProvenanceError(
+                        f"materialized caption lineage differs at batch {batch_index}"
+                    )
+                expected_family, expected_caption_row = expected_pair
+                if family != expected_family or not torch.equal(
+                    batch.text_embeddings[text_index : text_index + 1],
+                    expected_caption_row.embedding,
+                ):
+                    raise CaptionRoleProvenanceError(
+                        f"materialized caption value or family differs at batch {batch_index}"
+                    )
+                if (
+                    _sha256_bytes(_tensor_bytes(batch.text_embeddings[text_index : text_index + 1]))
+                    != expected_caption_row.embedding_sha256
+                ):
+                    raise CaptionRoleProvenanceError(
+                        f"materialized caption digest differs at batch {batch_index}"
+                    )
+                seen_captions.add(commitment)
+    except CaptionRoleProvenanceError:
+        raise
+    except Exception:
+        raise CaptionRoleProvenanceError("prepared train source iteration failed") from None
+
+    if not microbatch_count:
+        raise CaptionRoleProvenanceError("prepared train source emitted no microbatches")
+    if seen_families != all_families or seen_captions != all_caption_commitments:
+        raise CaptionRoleProvenanceError("materialized prepared source has an incomplete census")
+
+    result = CaptionRoleValidationReceipt(
+        window_manifest_sha256=expected_window,
+        window_manifest_content_sha256=window_summary.content_sha256,
+        prepared_train_manifest_sha256=expected_prepared,
+        dataset_manifest_sha256=expected_dataset,
+        caption_manifest_sha256=expected_caption,
+        human_text_rights_assertion_sha256=expected_rights,
+        row_binding_manifest_sha256=_manifest_digest(
+            b"phaseset-caption-role-row-binding-manifest-v1\x00",
+            tuple(value.row_binding_sha256 for value in bound),
+        ),
+        fusion_receipt_manifest_sha256=_manifest_digest(
+            b"phaseset-caption-role-fusion-receipt-manifest-v1\x00",
+            tuple(value.fusion_receipt_sha256 for value in bound),
+        ),
+        clip_receipt_manifest_sha256=_manifest_digest(
+            b"phaseset-caption-role-clip-receipt-manifest-v1\x00",
+            tuple(value.clip_receipt_sha256 for value in bound),
+        ),
+        positive_family_manifest_sha256=_manifest_digest(
+            b"phaseset-caption-role-positive-family-manifest-v1\x00",
+            tuple(value.family.hex() for value in bound),
+        ),
+        window_count=len(bound),
+        caption_count=len(all_caption_commitments),
+        microbatch_count=microbatch_count,
+    )
+    result.canonical_json_bytes()
+    return result
+
+
+__all__ = [
+    "ActorCaptionContentSummary",
+    "CANDIDATE_POLICY_ID",
+    "CAPTION_ROLES",
+    "CaptionRoleProvenanceError",
+    "CaptionRoleValidationReceipt",
+    "HolisticContextContentSummary",
+    "INSPECTION_SEED",
+    "STATUS",
+    "WindowFusionHandoff",
+    "WindowManifestContentSummary",
+    "WindowManifestRow",
+    "validate_candidate_machine_fused_window_training",
+]
diff --git a/src/phaseset_core/prepared_data_v2.py b/src/phaseset_core/prepared_data_v2.py
index 1f0af66..65a2dd3 100644
--- a/src/phaseset_core/prepared_data_v2.py
+++ b/src/phaseset_core/prepared_data_v2.py
@@ -28,7 +28,11 @@
 import torch
 
 from phaseset_core.contracts import PreparedGroupBatch
-from phaseset_core.frozen_clip_text import FrozenClipTextBatch, FrozenClipTextReceipt
+from phaseset_core.frozen_clip_text import (
+    MAX_BATCH_SIZE as CLIP_MAX_BATCH_SIZE,
+    FrozenClipTextBatch,
+    FrozenClipTextReceipt,
+)
 from phaseset_core.pipeline import (
     PreparedGroupSample,
     collate_group_samples,
@@ -291,9 +295,27 @@ def __post_init__(self) -> None:
             _sha256_bytes(_tensor_bytes(embeddings[index : index + 1]))
             for index in range(len(lineage))
         )
+        # The receipt records configured inference chunk size, not total rows.
+        if (
+            type(receipt.batch_size) is not int
+            or not 1 <= receipt.batch_size <= CLIP_MAX_BATCH_SIZE
+            or type(receipt.chunk_ranges) is not tuple
+            or any(
+                type(chunk) is not tuple
+                or len(chunk) != 2
+                or any(type(index) is not int for index in chunk)
+                for chunk in receipt.chunk_ranges
+            )
+        ):
+            raise PreparedDataV2Error("frozen text receipt chunk census is invalid")
+        expected_chunks = tuple(
+            (start, min(start + receipt.batch_size, len(lineage)))
+            for start in range(0, len(lineage), receipt.batch_size)
+        )
+        if receipt.chunk_ranges != expected_chunks:
+            raise PreparedDataV2Error("frozen text receipt chunk census is incomplete")
         if (
-            receipt.batch_size != len(lineage)
-            or tuple(row[0] for row in rows) != tuple(range(len(lineage)))
+            tuple(row[0] for row in rows) != tuple(range(len(lineage)))
             or receipt.output_shape != tuple(embeddings.shape)
             or receipt.output_stride != tuple(embeddings.stride())
             or receipt.output_bytes != len(output)
diff --git a/tests/test_caption_role_provenance.py b/tests/test_caption_role_provenance.py
new file mode 100644
index 0000000..6997a3e
--- /dev/null
+++ b/tests/test_caption_role_provenance.py
@@ -0,0 +1,463 @@
+from __future__ import annotations
+
+from dataclasses import replace
+import hashlib
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import caption_fusion, frozen_clip_text
+from phaseset_core.caption_role_provenance import (
+    ActorCaptionContentSummary,
+    CaptionRoleProvenanceError,
+    HolisticContextContentSummary,
+    WindowFusionHandoff,
+    WindowManifestContentSummary,
+    WindowManifestRow,
+    validate_candidate_machine_fused_window_training,
+)
+from phaseset_core.contracts import group_commitment
+from phaseset_core.pipeline import PreparedGroupSample
+from phaseset_core.prepared_data_v2 import (
+    PreparedMotionTextBlock,
+    PreparedTrainingDataSourceV2,
+    load_prepared_training_sources_v2,
+    write_prepared_training_tree_v2,
+)
+from phaseset_core.preprocessing import WindowDecision
+
+
+DATASET_SHA = hashlib.sha256(b"dataset-manifest").hexdigest()
+CAPTION_SHA = hashlib.sha256(b"caption-manifest").hexdigest()
+RIGHTS_SHA = hashlib.sha256(b"human-text-rights").hexdigest()
+WINDOW_MANIFEST_SHA = hashlib.sha256(b"window-manifest").hexdigest()
+
+
+def _sha(label: str) -> str:
+    return hashlib.sha256(label.encode("utf-8")).hexdigest()
+
+
+def _raw(label: str) -> bytes:
+    return hashlib.sha256(label.encode("utf-8")).digest()
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().cpu().contiguous().view(torch.uint8).numpy().tobytes()
+
+
+def _sample(label: str, *, actor_prefix: str, ordinal: int) -> PreparedGroupSample:
+    actors = tuple(_raw(f"{actor_prefix}-actor-{index}") for index in range(3))
+    skeletons = np.zeros((3, 200, 22, 3), dtype=np.float32)
+    mask = np.ones((3, 200, 22), dtype=np.bool_)
+    return PreparedGroupSample(
+        skeletons=skeletons,
+        track_mask=mask,
+        actor_commitments=actors,
+        group_commitment=group_commitment(actors),
+        source_sha256=_sha(f"{label}-source"),
+        window_sha256=_sha(f"{label}-window"),
+        source_start_frame=ordinal * 300,
+        augmentation_yaw=0.0,
+        decision=WindowDecision(True, "accepted", 300, 3, 0, 1.0, 0, False),
+    )
+
+
+def _fusion(
+    sample: PreparedGroupSample,
+    label: str,
+) -> tuple[WindowFusionHandoff, WindowManifestRow, tuple[str, str]]:
+    window_commitment = _raw(f"{label}-private-window-commitment")
+    provenance = caption_fusion.CaptionFusionProvenance(
+        split="train",
+        dataset_manifest_sha256=DATASET_SHA,
+        caption_manifest_sha256=CAPTION_SHA,
+        human_text_rights_assertion_sha256=RIGHTS_SHA,
+    )
+    actors = tuple(
+        caption_fusion.HumanActorCaption(
+            text=f"A person performs action {label}-{index}.",
+            actor_commitment=commitment,
+            window_commitment=window_commitment,
+            source_record_sha256=_sha(f"{label}-actor-source-{index}"),
+        )
+        for index, commitment in enumerate(reversed(sample.actor_commitments))
+    )
+    holistic = caption_fusion.HumanHolisticContext(
+        text=f"The group jointly performs the {label} activity.",
+        window_commitment=window_commitment,
+        source_record_sha256=_sha(f"{label}-holistic-source"),
+    )
+    value = caption_fusion.CaptionFusionInput(actors, provenance, holistic)
+    group = f"Three people coordinate the {label} movement."
+    paraphrase = f"A trio jointly performs the {label} motion."
+    output_sha256 = caption_fusion._output_digest(group, paraphrase)
+    admission_sha256 = hashlib.sha256(
+        b"phaseset-caption-fusion-local-admission-v1\x00"
+        + provenance.digest().encode("ascii")
+    ).hexdigest()
+    receipt = caption_fusion.CaptionFusionReceipt(
+        input_sha256=caption_fusion._input_digest(value),
+        output_sha256=output_sha256,
+        provenance_sha256=provenance.digest(),
+        admission_sha256=admission_sha256,
+        backend_manifest_sha256=_sha("frozen-backend"),
+        actor_count=3,
+        attempts_per_audit_run=(1, 1, 1),
+        attempt_count=3,
+        retry_count=0,
+    )
+    fused = caption_fusion.MachineFusedCaption(group, paraphrase, receipt)
+    commitments = (_raw(f"{label}-group-text"), _raw(f"{label}-paraphrase-text"))
+    handoff = WindowFusionHandoff(value, fused, commitments)
+    row = WindowManifestRow(
+        window_sha256=sample.window_sha256,
+        window_commitment=window_commitment,
+        actor_commitments=sample.actor_commitments,
+        group_commitment=sample.group_commitment,
+        source_sha256=sample.source_sha256,
+        source_start_frame=sample.source_start_frame,
+        actor_caption_summaries=tuple(
+            ActorCaptionContentSummary(
+                actor_commitment=item.actor_commitment,
+                source_record_sha256=item.source_record_sha256,
+                text_utf8_sha256=hashlib.sha256(item.text.encode("utf-8")).hexdigest(),
+            )
+            for item in actors
+        ),
+        holistic_context_summary=HolisticContextContentSummary(
+            source_record_sha256=holistic.source_record_sha256,
+            text_utf8_sha256=hashlib.sha256(holistic.text.encode("utf-8")).hexdigest(),
+        ),
+    )
+    return handoff, row, (group, paraphrase)
+
+
+def _clip_batch(
+    captions: tuple[str, ...],
+    commitments: tuple[bytes, ...],
+) -> frozen_clip_text.FrozenClipTextBatch:
+    embeddings = torch.arange(len(captions) * 512, dtype=torch.float32).reshape(
+        len(captions), 512
+    )
+    output = _tensor_bytes(embeddings)
+    caption_rows = tuple(
+        (
+            index,
+            commitment.hex(),
+            hashlib.sha256(caption.encode("utf-8")).hexdigest(),
+            7,
+            7,
+            False,
+            _sha(f"input-ids-{index}"),
+            _sha(f"attention-mask-{index}"),
+            hashlib.sha256(_tensor_bytes(embeddings[index : index + 1])).hexdigest(),
+        )
+        for index, (caption, commitment) in enumerate(
+            zip(captions, commitments, strict=True)
+        )
+    )
+    receipt = frozen_clip_text.FrozenClipTextReceipt(
+        batch_size=len(captions),
+        caption_rows=caption_rows,
+        chunk_ranges=((0, len(captions)),),
+        frozen_embedding_cache_key_sha256=_sha("cache-key"),
+        live_model_manifest_sha256=_sha("model"),
+        output_bytes=len(output),
+        output_sha256=hashlib.sha256(output).hexdigest(),
+        output_shape=tuple(embeddings.shape),
+        output_stride=tuple(embeddings.stride()),
+        runtime_identity=(("python", "test"),),
+        runtime_manifest_sha256=_sha("runtime"),
+        snapshot_files=(("config.json", 1, _sha("snapshot-file")),),
+        snapshot_manifest_sha256=_sha("snapshot"),
+        source_files=(("adapter", 1, _sha("source-file")),),
+        source_manifest_sha256=_sha("source"),
+    )
+    return frozen_clip_text.FrozenClipTextBatch(
+        embeddings,
+        commitments,
+        receipt,
+        _seal=frozen_clip_text._CONSTRUCTION_SEAL,
+    )
+
+
+def _block_and_evidence(
+    labels: tuple[str, ...],
+    *,
+    actor_prefix: str,
+    ordinal_start: int,
+) -> tuple[
+    PreparedMotionTextBlock,
+    tuple[WindowFusionHandoff, ...],
+    tuple[WindowManifestRow, ...],
+]:
+    samples = tuple(
+        _sample(label, actor_prefix=f"{actor_prefix}-{index}", ordinal=ordinal_start + index)
+        for index, label in enumerate(labels)
+    )
+    built = tuple(_fusion(sample, label) for sample, label in zip(samples, labels, strict=True))
+    handoffs = tuple(value[0] for value in built)
+    rows = tuple(value[1] for value in built)
+    captions = tuple(text for value in built for text in value[2])
+    commitments = tuple(
+        commitment for value in handoffs for commitment in value.clip_caption_commitments
+    )
+    block = PreparedMotionTextBlock(
+        samples=samples,
+        text_batch=_clip_batch(captions, commitments),
+        text_counts=tuple(2 for _ in samples),
+        window_ordinals=tuple(ordinal_start + index for index in range(len(samples))),
+    )
+    return block, handoffs, rows
+
+
+def _case(tmp_path):
+    tmp_path.mkdir(parents=True, exist_ok=True)
+    train_block, handoffs, rows = _block_and_evidence(
+        ("train-a", "train-b"), actor_prefix="train", ordinal_start=10
+    )
+    val_block, _, _ = _block_and_evidence(
+        ("val-a",), actor_prefix="val", ordinal_start=100
+    )
+    result = write_prepared_training_tree_v2(
+        (tmp_path / "prepared").resolve(),
+        train_blocks=(train_block,),
+        val_blocks=(val_block,),
+        max_total_edges=32,
+    )
+    train_source, _ = load_prepared_training_sources_v2(
+        result.index_path,
+        expected_index_sha256=result.index_sha256,
+    )
+    summary = WindowManifestContentSummary(WINDOW_MANIFEST_SHA, rows)
+    kwargs = {
+        "blocks": (train_block,),
+        "handoffs": handoffs,
+        "window_summary": summary,
+        "train_source": train_source,
+        "expected_window_manifest_sha256": WINDOW_MANIFEST_SHA,
+        "expected_prepared_train_manifest_sha256": train_source.manifest_sha256,
+        "expected_dataset_manifest_sha256": DATASET_SHA,
+        "expected_caption_manifest_sha256": CAPTION_SHA,
+        "expected_human_text_rights_assertion_sha256": RIGHTS_SHA,
+    }
+    return kwargs
+
+
+def test_validates_complete_candidate_chain_without_selecting_or_authorizing_policy(
+    tmp_path,
+) -> None:
+    receipt = validate_candidate_machine_fused_window_training(**_case(tmp_path))
+    assert receipt.window_count == 2
+    assert receipt.caption_count == 4
+    assert receipt.microbatch_count >= 1
+    assert receipt.formal_supervision_policy_selected is False
+    assert receipt.rights_authenticated_by_this_receipt is False
+    assert receipt.semantic_equivalence_proved is False
+    assert receipt.training_authorized is False
+    assert receipt.scientific_result_claimed is False
+    assert receipt.authority == 0
+    assert receipt.schema == "phaseset-private-caption-role-validation-v2"
+    assert receipt.caption_manifest_sha256 == CAPTION_SHA
+    assert len(receipt.canonical_json_bytes()) > 1
+    assert len(receipt.sha256) == 64
+
+
+def test_rejects_fusion_window_or_actor_lineage_drift(tmp_path) -> None:
+    kwargs = _case(tmp_path)
+    summary = kwargs["window_summary"]
+    first = summary.rows[0]
+    changed = replace(first, window_commitment=_raw("wrong-window"))
+    kwargs["window_summary"] = replace(summary, rows=(changed, *summary.rows[1:]))
+    with pytest.raises(CaptionRoleProvenanceError, match="window commitments differ"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+
+def test_rejects_human_caption_content_summary_drift(tmp_path) -> None:
+    kwargs = _case(tmp_path)
+    summary = kwargs["window_summary"]
+    first = summary.rows[0]
+    actor_rows = first.actor_caption_summaries
+    changed_actor = replace(actor_rows[0], text_utf8_sha256=_sha("wrong-human-text"))
+    changed = replace(
+        first,
+        actor_caption_summaries=(changed_actor, *actor_rows[1:]),
+    )
+    kwargs["window_summary"] = replace(summary, rows=(changed, *summary.rows[1:]))
+    with pytest.raises(CaptionRoleProvenanceError, match="actor-caption content differs"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+    kwargs = _case(tmp_path / "second")
+    summary = kwargs["window_summary"]
+    first = summary.rows[0]
+    kwargs["window_summary"] = replace(
+        summary,
+        rows=(replace(first, holistic_context_summary=None), *summary.rows[1:]),
+    )
+    with pytest.raises(CaptionRoleProvenanceError, match="holistic-context presence"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+
+def test_rejects_swapped_description_and_paraphrase_clip_rows(tmp_path) -> None:
+    kwargs = _case(tmp_path)
+    first = kwargs["handoffs"][0]
+    swapped = replace(
+        first,
+        clip_caption_commitments=tuple(reversed(first.clip_caption_commitments)),
+    )
+    kwargs["handoffs"] = (swapped, *kwargs["handoffs"][1:])
+    with pytest.raises(CaptionRoleProvenanceError, match="ordered frozen-CLIP rows"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+
+@pytest.mark.parametrize(
+    ("field", "value"),
+    (
+        ("machine_fused", 1),
+        ("human_annotation", 0),
+        ("public_verification_performed", 0),
+        ("authority", False),
+        ("scientific_result_claimed", 0),
+        ("result_claimed", 0),
+    ),
+)
+def test_rejects_fusion_receipt_bool_int_aliases(tmp_path, field, value) -> None:
+    kwargs = _case(tmp_path)
+    first = kwargs["handoffs"][0]
+    changed_receipt = replace(first.fused_caption.receipt, **{field: value})
+    changed_caption = replace(first.fused_caption, receipt=changed_receipt)
+    kwargs["handoffs"] = (replace(first, fused_caption=changed_caption), *kwargs["handoffs"][1:])
+    with pytest.raises(CaptionRoleProvenanceError, match="caption-fusion receipt"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+
+def test_rejects_fusion_receipt_live_input_mismatch(tmp_path) -> None:
+    kwargs = _case(tmp_path)
+    first = kwargs["handoffs"][0]
+    changed_receipt = replace(first.fused_caption.receipt, input_sha256=_sha("wrong-input"))
+    changed_caption = replace(first.fused_caption, receipt=changed_receipt)
+    kwargs["handoffs"] = (replace(first, fused_caption=changed_caption), *kwargs["handoffs"][1:])
+    with pytest.raises(CaptionRoleProvenanceError, match="does not bind this handoff"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+
+def test_rejects_wrong_caption_provenance_or_non_two_caption_policy(tmp_path) -> None:
+    kwargs = _case(tmp_path)
+    kwargs["expected_caption_manifest_sha256"] = _sha("different-caption-manifest")
+    with pytest.raises(CaptionRoleProvenanceError, match="fusion provenance"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+    kwargs = _case(tmp_path / "second")
+    block = kwargs["blocks"][0]
+    object.__setattr__(block, "text_counts", (1, 3))
+    with pytest.raises(CaptionRoleProvenanceError, match="exactly two"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+
+def test_materialized_actor_lineage_accepts_canonical_reordering(tmp_path) -> None:
+    kwargs = _case(tmp_path)
+    original_row = kwargs["window_summary"].rows[0]
+    assert original_row.actor_commitments != tuple(sorted(original_row.actor_commitments))
+    batches = tuple(kwargs["train_source"].iter_epoch(epoch=0, seed=1729))
+    assert batches[0].groups.actor_commitments[0] == tuple(
+        sorted(original_row.actor_commitments)
+    )
+    receipt = validate_candidate_machine_fused_window_training(**kwargs)
+    assert receipt.window_count == 2
+
+
+def test_rejects_materialized_changed_actor_membership(tmp_path, monkeypatch) -> None:
+    kwargs = _case(tmp_path)
+    original = PreparedTrainingDataSourceV2.iter_epoch
+
+    def mutant(self, *, epoch: int, seed: int):
+        batches = tuple(original(self, epoch=epoch, seed=seed))
+        first = batches[0]
+        actor_rows = list(first.groups.actor_commitments)
+        actor_rows[0] = (_raw("changed-materialized-actor"), *actor_rows[0][1:])
+        group_rows = list(first.groups.group_commitments)
+        group_rows[0] = group_commitment(actor_rows[0])
+        changed_groups = replace(
+            first.groups,
+            actor_commitments=tuple(actor_rows),
+            group_commitments=tuple(group_rows),
+        )
+        yield replace(first, groups=changed_groups)
+        yield from batches[1:]
+
+    monkeypatch.setattr(PreparedTrainingDataSourceV2, "iter_epoch", mutant)
+    with pytest.raises(CaptionRoleProvenanceError, match="motion lineage differs"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+
+def test_rejects_materialized_positive_family_mutant(tmp_path, monkeypatch) -> None:
+    kwargs = _case(tmp_path)
+    original = PreparedTrainingDataSourceV2.iter_epoch
+
+    def mutant(self, *, epoch: int, seed: int):
+        batches = tuple(original(self, epoch=epoch, seed=seed))
+        first = batches[0]
+        old = first.motion_positive_ids[0]
+        wrong = _raw("wrong-positive-family")
+        motion_ids = (wrong, *first.motion_positive_ids[1:])
+        text_ids = tuple(wrong if value == old else value for value in first.text_positive_ids)
+        yield replace(
+            first,
+            motion_positive_ids=motion_ids,
+            text_positive_ids=text_ids,
+        )
+        yield from batches[1:]
+
+    monkeypatch.setattr(PreparedTrainingDataSourceV2, "iter_epoch", mutant)
+    with pytest.raises(CaptionRoleProvenanceError, match="motion family differs"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+
+def test_rejects_materialized_text_value_or_incomplete_census(tmp_path, monkeypatch) -> None:
+    kwargs = _case(tmp_path)
+    original = PreparedTrainingDataSourceV2.iter_epoch
+
+    def changed_text(self, *, epoch: int, seed: int):
+        batches = tuple(original(self, epoch=epoch, seed=seed))
+        first = batches[0]
+        values = first.text_embeddings
+        values[0, 0] += 1.0
+        yield replace(first, text_embeddings=values)
+        yield from batches[1:]
+
+    monkeypatch.setattr(PreparedTrainingDataSourceV2, "iter_epoch", changed_text)
+    with pytest.raises(CaptionRoleProvenanceError, match="caption value or family"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+    kwargs = _case(tmp_path / "second")
+
+    def incomplete(self, *, epoch: int, seed: int):
+        if False:
+            yield from original(self, epoch=epoch, seed=seed)
+
+    monkeypatch.setattr(PreparedTrainingDataSourceV2, "iter_epoch", incomplete)
+    with pytest.raises(CaptionRoleProvenanceError, match="emitted no microbatches"):
+        validate_candidate_machine_fused_window_training(**kwargs)
+
+
+def test_receipt_canonicalization_rejects_claim_escalation(tmp_path) -> None:
+    receipt = validate_candidate_machine_fused_window_training(**_case(tmp_path))
+    for field, value in (
+        ("formal_supervision_policy_selected", True),
+        ("rights_authenticated_by_this_receipt", True),
+        ("training_authorized", True),
+        ("scientific_result_claimed", True),
+        ("authority", 1),
+        ("formal_supervision_policy_selected", 0),
+        ("rights_authenticated_by_this_receipt", 0),
+        ("semantic_equivalence_proved", 0),
+        ("training_authorized", 0),
+        ("scientific_result_claimed", 0),
+        ("authority", False),
+    ):
+        with pytest.raises(
+            CaptionRoleProvenanceError,
+            match="frozen field|frozen field type",
+        ):
+            replace(receipt, **{field: value}).canonical_json_bytes()
diff --git a/tests/test_phaseset_prepared_data_v2.py b/tests/test_phaseset_prepared_data_v2.py
index 150d805..f06a02b 100644
--- a/tests/test_phaseset_prepared_data_v2.py
+++ b/tests/test_phaseset_prepared_data_v2.py
@@ -142,6 +142,50 @@ def _canonical_json(value: object) -> bytes:
     )
 
 
+@pytest.mark.parametrize("chunk_size", (1, 2, 3, 4, 8))
+def test_text_receipt_chunk_size_is_not_the_total_caption_census(chunk_size: int) -> None:
+    block = _block("chunked", (1, 3), (10, 11))
+    original = block.text_batch
+    chunk_ranges = tuple((start, min(start + chunk_size, 4)) for start in range(0, 4, chunk_size))
+    changed = clip.FrozenClipTextBatch(
+        original.embeddings,
+        original.caption_commitments,
+        replace(original.receipt, batch_size=chunk_size, chunk_ranges=chunk_ranges),
+        _seal=clip._CONSTRUCTION_SEAL,
+    )
+    rebuilt = replace(block, text_batch=changed)
+    assert rebuilt.text_counts == (1, 3)
+    assert len(rebuilt.text_batch.receipt.caption_rows) == 4
+    assert torch.equal(rebuilt.text_batch.embeddings, original.embeddings)
+
+
+@pytest.mark.parametrize(
+    ("chunk_size", "chunks"),
+    (
+        (False, ((0, 4),)),
+        (0, ((0, 4),)),
+        (257, ((0, 4),)),
+        (2, ((0, 2),)),
+        (2, ((2, 4), (0, 2))),
+        (2, ((0, 2), (1, 4))),
+        (2, ((0, 2), (2, 5))),
+        (2, ((False, 2), (2, 4))),
+        (2, [(0, 2), (2, 4)]),
+    ),
+)
+def test_text_receipt_rejects_invalid_or_incomplete_chunks(chunk_size, chunks) -> None:
+    block = _block("invalid-chunks", (1, 3), (10, 11))
+    original = block.text_batch
+    changed = clip.FrozenClipTextBatch(
+        original.embeddings,
+        original.caption_commitments,
+        replace(original.receipt, batch_size=chunk_size, chunk_ranges=chunks),
+        _seal=clip._CONSTRUCTION_SEAL,
+    )
+    with pytest.raises(prepared.PreparedDataV2Error, match="chunk census"):
+        replace(block, text_batch=changed)
+
+
 def test_write_load_variable_q_and_exact_ten_key_npz(tmp_path: Path) -> None:
     result = _write_tree(tmp_path / "prepared")
     assert result.authority == 0

From 16f4b51363a696412a1a053b72756b267539ba64 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 12:49:07 -0500
Subject: [PATCH 16/95] Add verified cohort qualification and bounded
 descriptor cache

---
 RELEASE_FILES.sha256                          |   14 +-
 STATUS.md                                     |   20 +-
 docs/BASE_COHORT_QUALIFICATION.md             |   81 +
 docs/LATENCY_PROGRESS_JOURNAL.md              |   67 +
 docs/PERIODIC_DESCRIPTOR_CACHE_V2.md          |   75 +
 src/phaseset_core/base_cohort_latency.py      | 1673 +++++++++++++++++
 .../base_cohort_qualification.py              |  666 +++++++
 src/phaseset_core/latency_progress_journal.py | 1194 ++++++++++++
 .../periodic_descriptor_cache_v2.py           | 1483 +++++++++++++++
 tests/test_periodic_descriptor_cache_v2.py    |  702 +++++++
 tests/test_phaseset_base_cohort_latency.py    |  431 +++++
 ...test_phaseset_base_cohort_qualification.py |  335 ++++
 ...aseset_base_latency_journal_integration.py |  481 +++++
 .../test_phaseset_latency_progress_journal.py |  515 +++++
 14 files changed, 7734 insertions(+), 3 deletions(-)
 create mode 100644 docs/BASE_COHORT_QUALIFICATION.md
 create mode 100644 docs/LATENCY_PROGRESS_JOURNAL.md
 create mode 100644 docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
 create mode 100644 src/phaseset_core/base_cohort_latency.py
 create mode 100644 src/phaseset_core/base_cohort_qualification.py
 create mode 100644 src/phaseset_core/latency_progress_journal.py
 create mode 100644 src/phaseset_core/periodic_descriptor_cache_v2.py
 create mode 100644 tests/test_periodic_descriptor_cache_v2.py
 create mode 100644 tests/test_phaseset_base_cohort_latency.py
 create mode 100644 tests/test_phaseset_base_cohort_qualification.py
 create mode 100644 tests/test_phaseset_base_latency_journal_integration.py
 create mode 100644 tests/test_phaseset_latency_progress_journal.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 137f7b7..22c3c46 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,10 +11,11 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-798ea6c4ff3a0c19f9792713f38f836df3baf563a9b37bb97298713110ed66f4	22814	STATUS.md
+0941ddad002fba0fd0841e94735a8132dbc0821ba3a2b0ffed0dc2ae703c6b37	23908	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
+69a97cdc4cb297dead25cdb49da911d0a5b8470e9b3033e37e079c0bd48367de	4573	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
 81ecc1cc742604f47833a0aa5b4c567165aa01e9c500d05157e325bd3b4ccdb3	4281	docs/CAPTION_ROLE_PROVENANCE.md
 a9d6a4875760c58a0075e9e4e6b7a5f147da9762410fde953d648895ec159a76	3398	docs/CAPTURE_PREPARED_STORAGE.md
@@ -28,8 +29,10 @@ dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPER
 97b7b7e68b7896220b38661b094505b47c711a431f546e6da9772cfedcf6beaa	14238	docs/FROZEN_CLIP_TEXT_ADAPTER.md
 f2c59540bfc4dcdbb5013b76eb20090a7dd61c805523121995ed9206c843f9c8	3711	docs/FROZEN_MHA_RUNTIME.md
 fad4d9e8d1a51bf443af94b536b7aee0b8031cf42edab58190db7b174a768e0e	15565	docs/HOST_RUNTIME_INTEGRATION.md
+5dd898347d3646cde4a74e10afa8ba1d12703e3d0c6df51e5113659a31808086	3606	docs/LATENCY_PROGRESS_JOURNAL.md
 f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
+dfb98b3f8e83619b208a82a416ac57517666fab9ae540221d9cea685200aa3bd	4416	docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
 69b5e67cbb59c9c658e5feb11d3f6c01afbc5e2d26a1acd2d6dba8f81f9e67ff	4296	docs/PHASEPAIR_B0_ANNOTATION_CAPTION_CLOSURE_SUCCESSOR_PUBLIC_V4_20260825_145013.json
@@ -125,6 +128,8 @@ fa68bcab33d09588a8653151ad8e5741d5e6530263bcb633d871d7744598b194	10364	src/phase
 dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phasepair_core/validation_gallery.py
 2e5fb5280ed27a46c587d07a60ce663fce676cbdd30b059ec34882b4507d157f	604	src/phaseset_core/__init__.py
 9668a89535264a3ed6b58ffc7ac31b4904a4d8f68636d405e15cbf7368096716	8437	src/phaseset_core/_morlet_oracle_bytes.py
+51cf33c84883ca6965306eac30bfad26d06efb4de623773bc8512ceac6a18eb1	71215	src/phaseset_core/base_cohort_latency.py
+ecda879d3f531ebb747b561612fa34df37f1ecbd8e1977d5089d38b01ec63131	29076	src/phaseset_core/base_cohort_qualification.py
 d2d71766c1c6af95480d507960d7186bbec9ff8a424f6e79ce6611fd0aa2e611	38885	src/phaseset_core/base_cohort_resolver.py
 3d4f31b6a773081af41f1d9d45ee98986ea33ef6f954070564373f995af1f2e9	40593	src/phaseset_core/base_cohort_validation.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
@@ -142,10 +147,12 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
 e0242fc212e83f2d3b694734fadb62ab76c4ef90c3c5b04df503f5a7c907a3e0	68520	src/phaseset_core/frozen_clip_text.py
 7fcc111d269ed22db55ba2c3a5bee8d6c79f3c850c42eca5ff8aaa08b6e809e1	90636	src/phaseset_core/host.py
+3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
 47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
+c227060960bee56d8ec47edfac38284d176e51bbe6e75450de6a1e1faa74be87	55317	src/phaseset_core/periodic_descriptor_cache_v2.py
 a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phaseset_core/pipeline.py
 2bee9279fa4098dc83729c934aa4d3d07f3f2c4090c1734f9a316462d17159ef	44879	src/phaseset_core/prepared_data_v2.py
 273225e6696351ccb16a3a9e7a653b55af18c64b316a4e5d80c7b265625cc5c1	17929	src/phaseset_core/preprocessing.py
@@ -161,6 +168,7 @@ eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phase
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
+9c8ff961404bb8d5da86a30c3f533fe8d1909cdfbd76ebb496262ff222121d51	24769	tests/test_periodic_descriptor_cache_v2.py
 af9b5adc42d82b9a0aa4ed744bb069e36b7dd041c1957fdc97d17eb814a0fc0c	17284	tests/test_phasepair_core_base_qualification.py
 ad630675cc642350a988d92de6176053d1bfab5f81cbc49548cb0b5dbeb679d9	23596	tests/test_phasepair_core_batching_readiness.py
 3b4b3654d38d9b99983e924d838b7236b6d2e8b27554298762be1e8c4fcd00c4	10736	tests/test_phasepair_core_caption_processing.py
@@ -188,9 +196,12 @@ b801d46fb184d99c65825d7f9bf91cbced022418600a95d421c5d9244dbded13	19373	tests/tes
 8f31d46a9ff2d4ca96fcd34dc6fd617933f2e604c0dd6e95e0d10ec9db487074	10255	tests/test_phasepair_core_validation_gallery.py
 4da1d6c1b05bc485b9afb78776264ae7757744f104c37b947cdce0280cdfb172	2347	tests/test_phasepair_posix_checkpoint_ownership.py
 6462129abd78cce74c4a2b21c50cefc7220b9092511dab13f5abb9b6277ea621	8467	tests/test_phaseset_base_cohort_checkpoint_state.py
+69a7e034f81c381d5eb4ce6caa02f98950975ebaaeed6ff3e212e4d4fe80d03f	15361	tests/test_phaseset_base_cohort_latency.py
+1379f5e8d8bb11443615d14e6a14e4091319563cc4bfe67de0c4621f1e9476cb	13454	tests/test_phaseset_base_cohort_qualification.py
 41535795e088b5374bf59b95491afa4d1aac08622cddf1aa7d76c5fb0af990fa	23471	tests/test_phaseset_base_cohort_resolver.py
 2cd1a40ebf6c10c83dae4f72bf41821d25ee153c8b3f05819ac664eb0a8f611e	14477	tests/test_phaseset_base_cohort_score_witness.py
 e4b3ebf50a8f0971431905cebd12829b5799f677333ec573bf5809b90209018a	19405	tests/test_phaseset_base_cohort_validation.py
+1fd40ded07adcd93a3eb0bc85d3167991702a39b5b3fe72f9291a46fe35e234f	17163	tests/test_phaseset_base_latency_journal_integration.py
 fa34950fb26f63d5d1ba169499e6e4af49c9b691f33c5cb3aac6e961bdac9e55	18384	tests/test_phaseset_caption_fusion.py
 c88e5b91823b22ebb7a4019acb328040c6b42be7acc03738949439e4efbe443f	10126	tests/test_phaseset_capture_pooling.py
 ec0609bf54766ffec53a1d7e4af7e778e2c2efbd9f4d2c4c06984814a066ca7f	21782	tests/test_phaseset_capture_prepared_storage.py
@@ -210,6 +221,7 @@ dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/tes
 937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
 4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
 14f6d4b54d78876822e6f96440a32360ea4d4654c1d18d8d8662fe0f4dc42bf8	23309	tests/test_phaseset_host_capture_storage.py
+d133ca3a4df71baaea1cc40f68d049b56a7fcfc0d0861a87e5916c9637bdadb2	16345	tests/test_phaseset_latency_progress_journal.py
 a75f583fd0a233760b1a421ca51680afe4195c33f9ef4bce6e9a1737828f207e	14907	tests/test_phaseset_models.py
 e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test_phaseset_morlet.py
 6d7005585882e25be9479542aac96a5b2aeb1bddc5619a0f37c57f61f2cbe6c3	6348	tests/test_phaseset_objectives.py
diff --git a/STATUS.md b/STATUS.md
index b62f18d..c903027 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -13,6 +13,21 @@ evidence, not training results or scientific claims.
 
 ## Server continuation
 
+- The complete frozen base-latency runner, exact qualification assembler,
+  typed progress observer and durable POSIX journal are implemented. Separately,
+  a weight-independent three-stream periodic descriptor cache now preserves
+  exact canonical batches and bounded immutable shards. The merged Linux
+  server source passed 1211 tests, 2 existing skips and 39 subtests in 362.75 s
+  with source unchanged. Journal integration passed 73 combined tests;
+  descriptor storage/streaming then passed 19 focused tests after a test-only
+  hardlink/oversize case split. These are explicit
+  software fixtures, not a real nine-run cohort, formal CUDA latency session,
+  winner or licensed-data cache. Production cache-model/training/resume and
+  host qualification command assembly remain unfinished. See
+  [base qualification](docs/BASE_COHORT_QUALIFICATION.md),
+  [progress journal](docs/LATENCY_PROGRESS_JOURNAL.md) and
+  [descriptor cache](docs/PERIODIC_DESCRIPTOR_CACHE_V2.md).
+
 - The window-caption provenance component now checks the complete chain from
   same-window fusion inputs/outputs through ordered frozen text rows to the
   materialized training census. An actual official-CLIP probe exposed and fixed
@@ -38,8 +53,9 @@ evidence, not training results or scientific claims.
   A separate unmocked full-width B0/B1/B2 selected-state-to-gallery witness
   passed all three lanes in 19.36 s on analytic K2/K3, T200 captures. It uses
   explicit checkpoint-cursor and text fixtures, not completed formal epochs,
-  real captions or an actual nine-run cohort. Latency, qualification selection
-  and host command assembly remain separate unfinished steps. The merged source
+  real captions or an actual nine-run cohort. At that earlier snapshot, latency and qualification assembly were
+  unfinished; their newer software verification is reported above. Actual
+  qualification execution and host command assembly remain unfinished. The merged source
   then passed 1124 complete Linux server tests, 2 existing skips and 39 subtests
   in 315.67 s with the source census unchanged. See
   [completed-base capture scoring](docs/BASE_COHORT_VALIDATION.md).
diff --git a/docs/BASE_COHORT_QUALIFICATION.md b/docs/BASE_COHORT_QUALIFICATION.md
new file mode 100644
index 0000000..796155a
--- /dev/null
+++ b/docs/BASE_COHORT_QUALIFICATION.md
@@ -0,0 +1,81 @@
+# Base cohort latency and qualification
+
+The latency runner and deterministic assembler extend the
+[completed-base resolver and capture scorer](BASE_COHORT_VALIDATION.md).
+They implement a software path from nine completed training chains to the
+existing qualification selector. No real nine-run cohort, winner or latency
+benchmark has yet been observed.
+
+## One complete latency session
+
+`run_base_cohort_latency_session` reuses the admitted, validation-selected
+checkpoint bytes for all three bases and all three seeds. It checks the
+installed source, selected state, runtime, validation census and live parameter
+census against the resolver and scorer. It does not accept supplied timings
+or parameter counts.
+
+The frozen workload is an analytic K=32, T=200, body-22 group at the registered
+512-dimensional model width. It is an engineering timing workload, not a
+replacement dataset. Nine cyclic rounds visit all nine rows: 81 visits in
+total. Each visit has five warmups and eleven synchronized timed forwards,
+giving 99 samples per row. Output copying and finite-value checking are inside
+the defined forward interval; progress-journal I/O is outside it. The statistic
+is the exact integer median of all 99 positive nanosecond samples.
+
+The runner records the actual CUDA UUID, resolved device, runtime and precision.
+Its shared-device policy requires at least 8 GiB free and a 2 GiB allocator cap.
+An admission, resource, identity or execution failure holds the entire session.
+It does not discard slow rows, lower K, remove layers, switch checkpoints or
+select a quiet subset of visits. The controller must predeclare and freeze an
+appropriate outer wall timeout before the actual session. A short functionality
+probe's timeout is not the formal session timeout. Existing unrelated device
+workloads are not interrupted.
+
+The injected runtime seam is explicitly nonformal and exists for contract
+tests. It cannot produce an accepted actual-runtime qualification merely by
+finishing the same number of loops.
+
+## Qualification assembly
+
+`assemble_base_cohort_qualification` accepts the resolved cohort, admission,
+complete capture-scoring result and complete actual latency session. It accepts
+no submitted score rows, winner, timing samples or evaluator digest. It checks
+the complete row order and binds each terminal, selected checkpoint, selected
+state, query census, precision, runtime and installed-source identity across
+the three observations.
+
+Each `BaseScore` obtains its exact `Fraction` metric from the capture scorer,
+its parameter count from matching scorer/latency live censuses, and its latency
+from recomputing the retained 99-sample median. Its combined artifact digest
+binds both the score and latency observations. The evaluator identity is the
+installed production qualification artifact, not an unrelated module digest.
+
+The assembler calls the existing `experiments.qualify_base` and production
+canonical verifier. Selection remains: highest exact three-seed mean primary
+score; then fewer parameters, lower three-seed median latency, and smaller base
+ID. It does not implement a second competing selection rule. Selected bytes
+and scoped source dependencies are checked for stability around assembly.
+
+The result remains a software artifact with `authority=0`, `production=False`
+and `result_claimed=False`. The trusted private controller must use actual
+independently retained execution evidence through the existing backend flow.
+This does not introduce an extra user-signature requirement, turn hashes into
+data rights, or authorize access to sealed test data.
+
+## Progress, verification and remaining work
+
+The optional typed observer connects to the
+[durable progress journal](LATENCY_PROGRESS_JOURNAL.md). A journal terminal is
+not a substitute for the actual latency call outcome and its receipt.
+
+The latency contract passed 45 focused server tests; qualification assembly
+passed a 53-test combined suite. The final observer/journal integration passed
+73 tests in 22.63 s. The merged source, also containing the descriptor cache,
+passed 1211 tests, two existing skips and 39 subtests in 362.75 s on the Linux
+experiment server, with its complete source census unchanged. These counts
+overlap and must not be added as independent experiments.
+
+Tests use explicit software fixtures and injected observations. No actual
+81-visit CUDA timing session, real completed training cohort, qualified winner
+or host `qualify-base` command completion is claimed. Private host command
+composition and all data-dependent execution remain unfinished.
diff --git a/docs/LATENCY_PROGRESS_JOURNAL.md b/docs/LATENCY_PROGRESS_JOURNAL.md
new file mode 100644
index 0000000..7674610
--- /dev/null
+++ b/docs/LATENCY_PROGRESS_JOURNAL.md
@@ -0,0 +1,67 @@
+# Durable latency progress journal
+
+This optional journal preserves a diagnostic prefix when a latency session
+fails or stops. It is POSIX-only; importing the module is portable, while
+creating or reading a journal on an unsupported runtime fails explicitly.
+Journal integration tests are correspondingly POSIX-scoped.
+
+## Files and ordering
+
+The controller exclusively creates a fresh session below trusted, stable
+private ancestors. The writer uses mode 0700 for the session directory and
+0600, exclusive no-follow creation for canonical event files. Events and
+sequence markers are flushed and synchronized in a fixed order. Every event
+binds its sequence, predecessor digest and the external cohort, scorer,
+installed-source, protocol, wrapper and registered-device identities.
+
+The typed interface allows only session start, context ready, visit start,
+warmup completion, timed-sample completion, visit end and terminal events. It
+does not offer arbitrary metric insertion. A successful fixed session has
+1461 committed events. Enumeration stops at fixed bounds before collecting an
+unbounded directory; event count, leaf count and individual byte sizes are
+separately bounded. Reads require stable, single-link regular files.
+
+These checks assume trusted stable ancestors and are not a sandbox against
+hostile concurrent filesystem writers. Read-only flags and digests are not
+operating-system immutability or external authorization.
+
+## Failure semantics
+
+The latency runner owns all actual visit/sample observations. Observer calls
+occur after the corresponding operation; timed-sample callbacks run after the
+timer has stopped. One callback failure latches observation failure, holds the
+latency session and prevents later callbacks. Previously committed files are
+never erased or rewritten to make the attempt appear successful.
+
+A callback can finish durable terminal commit and then throw. The committed
+terminal cannot be rolled back:
+
+- After a COMPLETED terminal, the API can conservatively hold the session even
+  though the reader sees a structurally complete, nonformal terminal. The
+  complete-session digest differs from the subsequently held-session digest.
+- After a HELD terminal, the API retains the original held receipt and chains
+  the observer exception. The reader can retain that matching nonformal HELD
+  terminal.
+
+The controller must cross-check the actual API outcome, session receipt and
+terminal digest. Neither terminal grants formal status on its own. An
+incomplete committed prefix or one physically uncommitted tail is preserved;
+the reader does not infer whether a hard kill, interruption or I/O failure
+caused it. `KeyboardInterrupt`, `SystemExit` and hard kills are not recast as
+successful sessions.
+
+## Observed scope
+
+The journal dependency passed a 68-test Linux suite in 14.13 s with source
+unchanged. A previous attempt passed its software assertions but failed its
+source-stability check because an isolated SIGKILL child generated bytecode.
+That attempt remains failed. Adding explicit `-B` to that test child fixed the
+harness without deleting the original files or weakening the comparison.
+
+The final latency-to-journal integration passed 73 combined tests in 22.63 s.
+It includes all 81 fake-runtime visits, real durable filesystem writes,
+admission failure, active-visit failure, post-sample callback failure, and a
+post-commit COMPLETED-terminal exception. Those fixtures establish software
+mechanics, not real GPU timings or formal training. The merged 1211-test
+server regression is reported in
+[base qualification](BASE_COHORT_QUALIFICATION.md).
diff --git a/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md b/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
new file mode 100644
index 0000000..0ea66bc
--- /dev/null
+++ b/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
@@ -0,0 +1,75 @@
+# Immutable periodic descriptor cache
+
+`periodic_descriptor_cache_v2` stores weight-independent physical pair
+descriptors, not learned tokens or activations. The existing small periodic
+cache stores energy-floor metadata; this separate format stores all three
+descriptor streams: full relations, marginal power, and mean/difference DCT.
+No licensed motion payload or private cache artifact is included here.
+
+## Exact cache identity
+
+A cache key binds the complete canonical numeric group batch, masks, padding,
+actor/group lineage commitments, fixed Morlet oracle and energy floors. It
+also binds explicit source/environment, manifest, batch, split, window ordinal,
+seed, epoch and exact yaw identities. Commitments determine canonical reduction
+order but do not become neural-network inputs.
+
+These caller-supplied digests provide integrity and equality binding only;
+cache construction does not authenticate their origin or grant dataset access,
+consent, split, training or execution authority.
+
+Reordering actors preserves canonical identity; changing dynamic batch packing
+is a distinct key, not an inferred cache hit. Shared yaw includes floating-point
+rotation and rounding, so another epoch's apparently rotation-invariant motion
+is not assumed bitwise interchangeable. There is no approximate cross-epoch
+reuse or silent uncached fallback for a configured wrong cache.
+
+The bounded format preserves directed descriptor arrays, validity and endpoint
+indices in canonical order. Re-iteration can yield requested runtime chunks
+without changing the logical stream, including chunks spanning batch groups.
+The same physical full-relation stream can underlie the controls that transform
+it; marginal and DCT require their separately identified streams.
+
+## Storage and resource boundaries
+
+The writer checks expected pair count and decoded byte requirements from actor
+counts before materializing a stream. The fixed decoded census is 1914 bytes
+per edge for all stored arrays. Per-shard, decoded, total-byte and edge bounds
+are explicit; an additional exact serialized-size check follows encoding.
+Resource rejection does not drop actors, sample edges or truncate an artifact.
+
+Artifacts are exclusively created and the index is published last. The reader
+performs bounded owned reads, raw digest checks, strict schema checks and exact
+dtype/shape/offset/payload digest validation. It checks static symlinks,
+single-link payloads and byte bounds; loaded arrays are non-writeable. On
+platforms providing `O_NOFOLLOW`, the owned read also requests no-follow
+opening. On Windows the module is import-safe, but cache use requires a trusted,
+stable, non-concurrently-mutated tree and does not claim POSIX-equivalent
+no-follow or junction protection. Static symlink checks, single-link checks,
+hashes and read-only flags are integrity hardening, not filesystem isolation
+or data authorization.
+
+## Verified boundary and unfinished integration
+
+Nineteen server tests passed in 36.21 s with source unchanged. They cover all
+three streams, a 186-edge K12/K16 mixed batch, chunks 64/128/256 including a
+cross-group boundary, malformed authenticated artifacts, bounds, and unchanged
+CPU Torch RNG. A small test-only encoder seam also checks exact cached versus
+uncached parameter gradients through the existing custom backward replay.
+The hardlink and oversize checks are independent: only an explicit hardlink
+creation capability error can skip that case; unknown I/O errors fail, and the
+oversize check still runs. All nineteen ran successfully in this Linux
+observation. Its private receipt digest is
+`8edbb675a64773fe104a1eae7b4bf398262b61827199152833da1e0765237f41`;
+the receipt itself and its private paths are not published.
+
+That test seam is not a production cache provider: it deliberately supplies
+fixture chunks. Production model entry points, training initialization,
+checkpoint/resume bindings and complete epoch-cache census still require
+explicit integration. No real-data cache, training acceleration, measured
+throughput or model-quality result is claimed. The unchanged-source 1211-test
+full Linux server regression described in
+[base qualification](BASE_COHORT_QUALIFICATION.md) used this exact production
+module and the preceding eighteen-test file. The only subsequent test change
+split hardlink and oversize checks; those newer bytes were separately verified
+by the nineteen-test observation above.
diff --git a/src/phaseset_core/base_cohort_latency.py b/src/phaseset_core/base_cohort_latency.py
new file mode 100644
index 0000000..4b92402
--- /dev/null
+++ b/src/phaseset_core/base_cohort_latency.py
@@ -0,0 +1,1673 @@
+"""Run the frozen nine-row PhaseSet base latency session on one CUDA device.
+
+The public entry point derives every timed sample itself.  Resolver-owned
+selected-best checkpoint bytes and the separately recomputed capture-score
+cohort are comparison inputs; neither a latency nor a parameter count can be
+submitted by a caller.  Results remain authority zero and stop before
+``BaseScore`` construction, winner selection, or production authorization.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+import hashlib
+import json
+import math
+import struct
+import subprocess
+import time
+from typing import Final, Protocol, runtime_checkable
+
+import numpy as np
+import torch
+
+from . import base_cohort_resolver as resolver_module
+from . import base_cohort_validation as scoring_module
+from . import training as training_module
+from .contracts import PreparedGroupBatch, group_commitment
+from .experiments import base_run_ids, parse_run_id
+
+
+AUTHORITY: Final = 0
+PRODUCTION: Final = False
+RESULT_CLAIMED: Final = False
+SESSION_SCHEMA: Final = "phaseset-base-cohort-latency-session-v1"
+VISIT_SCHEMA: Final = "phaseset-base-cohort-latency-visit-v1"
+ROW_SCHEMA: Final = "phaseset-base-cohort-latency-row-v1"
+WORKLOAD_SCHEMA: Final = "phaseset-base-cohort-latency-workload-v1"
+RUNTIME_SCHEMA: Final = "phaseset-base-cohort-latency-runtime-v1"
+SNAPSHOT_SCHEMA: Final = "phaseset-shared-cuda-observation-v1"
+ACTUAL_COMPLETE_STATUS: Final = "ACTUAL_NINE_ROW_LATENCY_SESSION_COMPLETE_NO_QUALIFICATION"
+FAKE_COMPLETE_STATUS: Final = "ENGINEERING_FAKE_RUNTIME_COMPLETE_NOT_FORMAL"
+HOLD_STATUS: Final = "LATENCY_SESSION_HOLD_NO_FORMAL_ROW_LATENCY"
+
+ROUND_COUNT: Final = 9
+ROWS_PER_ROUND: Final = 9
+WARMUPS_PER_VISIT: Final = 5
+TIMED_PER_VISIT: Final = 11
+SAMPLES_PER_ROW: Final = ROUND_COUNT * TIMED_PER_VISIT
+FIXTURE_BATCH: Final = 1
+FIXTURE_ACTORS: Final = 32
+FIXTURE_FRAMES: Final = 200
+FIXTURE_JOINTS: Final = 22
+FIXTURE_COORDINATES: Final = 3
+FIXTURE_EMBEDDING_DIM: Final = 512
+MINIMUM_FREE_MIB: Final = 8192
+CUDA_ALLOCATOR_LIMIT_BYTES: Final = 2 * 1024**3
+_ACTOR_DOMAIN: Final = b"phaseset-base-latency-actor-v1\n"
+
+
+class BaseCohortLatencyError(ValueError):
+    """The cohort, score binding, runtime, or measured sample is invalid."""
+
+
+class BaseCohortLatencyHold(RuntimeError):
+    """A started session failed; ``receipt`` retains every observed sample."""
+
+    def __init__(self, failure_code: str, receipt: BaseCohortLatencySession) -> None:
+        super().__init__(failure_code)
+        self.failure_code = failure_code
+        self.receipt = receipt
+
+
+def _sha256(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise BaseCohortLatencyError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _positive_int(value: object, label: str) -> int:
+    if type(value) is not int or not 1 <= value <= (1 << 63) - 1:
+        raise BaseCohortLatencyError(f"{label} must be an exact positive int")
+    return value
+
+
+def _nonnegative_int(value: object, label: str) -> int:
+    if type(value) is not int or not 0 <= value <= (1 << 63) - 1:
+        raise BaseCohortLatencyError(f"{label} must be an exact nonnegative int")
+    return value
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _canonical_digest(value: object) -> str:
+    return _sha256(_canonical_json_bytes(value))
+
+
+@dataclass(frozen=True, slots=True)
+class SharedCudaObservation:
+    captured_unix_ns: int
+    device_uuid: str
+    memory_total_mib: int
+    memory_free_mib: int
+    memory_used_mib: int
+    gpu_utilization_percent: int
+    memory_utilization_percent: int
+    compute_mode: str
+    driver_version: str
+    schema: str = SNAPSHOT_SCHEMA
+
+    def __post_init__(self) -> None:
+        _positive_int(self.captured_unix_ns, "captured_unix_ns")
+        if type(self.device_uuid) is not str or not self.device_uuid.startswith("GPU-"):
+            raise BaseCohortLatencyError("CUDA observation UUID must be an exact GPU UUID")
+        _positive_int(self.memory_total_mib, "memory_total_mib")
+        _nonnegative_int(self.memory_free_mib, "memory_free_mib")
+        _nonnegative_int(self.memory_used_mib, "memory_used_mib")
+        if (
+            self.memory_free_mib > self.memory_total_mib
+            or self.memory_used_mib > self.memory_total_mib
+        ):
+            raise BaseCohortLatencyError("CUDA memory observation exceeds physical total")
+        for name in ("gpu_utilization_percent", "memory_utilization_percent"):
+            value = getattr(self, name)
+            if type(value) is not int or not 0 <= value <= 100:
+                raise BaseCohortLatencyError(f"{name} must be an exact percent")
+        if type(self.compute_mode) is not str or not self.compute_mode:
+            raise BaseCohortLatencyError("CUDA compute mode is absent")
+        if type(self.driver_version) is not str or not self.driver_version:
+            raise BaseCohortLatencyError("CUDA driver version is absent")
+        if self.schema != SNAPSHOT_SCHEMA:
+            raise BaseCohortLatencyError("CUDA observation schema changed")
+
+    def payload(self) -> dict[str, object]:
+        return {
+            "captured_unix_ns": self.captured_unix_ns,
+            "compute_mode": self.compute_mode,
+            "device_uuid": self.device_uuid,
+            "driver_version": self.driver_version,
+            "gpu_utilization_percent": self.gpu_utilization_percent,
+            "memory_free_mib": self.memory_free_mib,
+            "memory_total_mib": self.memory_total_mib,
+            "memory_used_mib": self.memory_used_mib,
+            "memory_utilization_percent": self.memory_utilization_percent,
+            "schema": self.schema,
+        }
+
+
+@dataclass(frozen=True, slots=True)
+class BaseLatencyRuntimeIdentity:
+    device: str
+    device_uuid: str
+    device_name: str
+    torch_version: str
+    cuda_runtime_version: str
+    cudnn_version: int
+    driver_version: str
+    nvidia_total_memory_mib: int
+    compute_mode: str
+    torch_total_memory_bytes: int
+    initial_allocated_bytes: int
+    initial_reserved_bytes: int
+    environment_sha256: str
+    precision_mode: str
+    allocator_limit_bytes: int = CUDA_ALLOCATOR_LIMIT_BYTES
+    schema: str = RUNTIME_SCHEMA
+
+    def __post_init__(self) -> None:
+        try:
+            device = torch.device(self.device)
+        except (RuntimeError, ValueError) as error:
+            raise BaseCohortLatencyError("latency runtime device is invalid") from error
+        if device.type != "cuda":
+            raise BaseCohortLatencyError("formal latency runtime must be CUDA")
+        if type(self.device_uuid) is not str or not self.device_uuid.startswith("GPU-"):
+            raise BaseCohortLatencyError("latency runtime UUID is invalid")
+        for name in (
+            "device_name",
+            "torch_version",
+            "cuda_runtime_version",
+            "driver_version",
+        ):
+            if type(getattr(self, name)) is not str or not getattr(self, name):
+                raise BaseCohortLatencyError(f"latency runtime {name} is absent")
+        _positive_int(self.cudnn_version, "cudnn_version")
+        _positive_int(self.nvidia_total_memory_mib, "nvidia_total_memory_mib")
+        if type(self.compute_mode) is not str or not self.compute_mode:
+            raise BaseCohortLatencyError("latency runtime compute mode is absent")
+        _positive_int(self.torch_total_memory_bytes, "torch_total_memory_bytes")
+        _nonnegative_int(self.initial_allocated_bytes, "initial_allocated_bytes")
+        _nonnegative_int(self.initial_reserved_bytes, "initial_reserved_bytes")
+        if self.initial_allocated_bytes != 0 or self.initial_reserved_bytes != 0:
+            raise BaseCohortLatencyError("latency process has preexisting CUDA allocations")
+        _lower_sha256(self.environment_sha256, "environment_sha256")
+        if self.precision_mode not in ("FP32", "BF16"):
+            raise BaseCohortLatencyError("latency precision mode is invalid")
+        if self.allocator_limit_bytes != CUDA_ALLOCATOR_LIMIT_BYTES:
+            raise BaseCohortLatencyError("CUDA allocator limit must remain exact 2 GiB")
+        if self.schema != RUNTIME_SCHEMA:
+            raise BaseCohortLatencyError("latency runtime schema changed")
+
+    @property
+    def sha256(self) -> str:
+        return _canonical_digest(
+            {
+                "allocator_limit_bytes": self.allocator_limit_bytes,
+                "cuda_runtime_version": self.cuda_runtime_version,
+                "cudnn_version": self.cudnn_version,
+                "compute_mode": self.compute_mode,
+                "device": self.device,
+                "device_name": self.device_name,
+                "device_uuid": self.device_uuid,
+                "driver_version": self.driver_version,
+                "environment_sha256": self.environment_sha256,
+                "initial_allocated_bytes": self.initial_allocated_bytes,
+                "initial_reserved_bytes": self.initial_reserved_bytes,
+                "nvidia_total_memory_mib": self.nvidia_total_memory_mib,
+                "precision_mode": self.precision_mode,
+                "schema": self.schema,
+                "torch_total_memory_bytes": self.torch_total_memory_bytes,
+                "torch_version": self.torch_version,
+            }
+        )
+
+
+@dataclass(frozen=True, slots=True)
+class BaseLatencyWorkloadReceipt:
+    array_rows: tuple[tuple[str, str, tuple[int, ...], str], ...]
+    raw_skeletons_sha256: str
+    actor_commitments: tuple[bytes, ...] = field(repr=False)
+    group_commitment: bytes = field(repr=False)
+    schema: str = WORKLOAD_SCHEMA
+
+    def __post_init__(self) -> None:
+        expected_rows = (
+            (
+                "skeletons",
+                (
+                    FIXTURE_BATCH,
+                    FIXTURE_ACTORS,
+                    FIXTURE_FRAMES,
+                    FIXTURE_JOINTS,
+                    FIXTURE_COORDINATES,
+                ),
+                "<f4",
+            ),
+            ("actor_mask", (FIXTURE_BATCH, FIXTURE_ACTORS), "|b1"),
+            ("frame_mask", (FIXTURE_BATCH, FIXTURE_FRAMES), "|b1"),
+            ("track_mask", (FIXTURE_BATCH, FIXTURE_ACTORS, FIXTURE_FRAMES, FIXTURE_JOINTS), "|b1"),
+        )
+        if tuple((row[0], row[2], row[3]) for row in self.array_rows) != expected_rows:
+            raise BaseCohortLatencyError("latency workload array census changed")
+        for name, digest, shape, dtype in self.array_rows:
+            if type(name) is not str or type(shape) is not tuple or type(dtype) is not str:
+                raise BaseCohortLatencyError("latency workload array row is malformed")
+            _lower_sha256(digest, f"{name} SHA-256")
+        _lower_sha256(self.raw_skeletons_sha256, "raw_skeletons_sha256")
+        if (
+            type(self.actor_commitments) is not tuple
+            or len(self.actor_commitments) != FIXTURE_ACTORS
+        ):
+            raise BaseCohortLatencyError("latency actor census must contain exact K=32")
+        if any(type(value) is not bytes or len(value) != 32 for value in self.actor_commitments):
+            raise BaseCohortLatencyError("latency actor commitments are malformed")
+        if len(set(self.actor_commitments)) != FIXTURE_ACTORS:
+            raise BaseCohortLatencyError("latency actor commitments must be unique")
+        if type(self.group_commitment) is not bytes or len(self.group_commitment) != 32:
+            raise BaseCohortLatencyError("latency group commitment is malformed")
+        if group_commitment(self.actor_commitments) != self.group_commitment:
+            raise BaseCohortLatencyError("latency group commitment differs from actors")
+        if self.schema != WORKLOAD_SCHEMA:
+            raise BaseCohortLatencyError("latency workload schema changed")
+
+    @property
+    def sha256(self) -> str:
+        return _canonical_digest(
+            {
+                "actor_commitments": [value.hex() for value in self.actor_commitments],
+                "array_rows": [
+                    {
+                        "dtype": dtype,
+                        "name": name,
+                        "sha256": digest,
+                        "shape": list(shape),
+                    }
+                    for name, digest, shape, dtype in self.array_rows
+                ],
+                "group_commitment": self.group_commitment.hex(),
+                "raw_skeletons_sha256": self.raw_skeletons_sha256,
+                "schema": self.schema,
+            }
+        )
+
+
+@dataclass(frozen=True, slots=True)
+class BaseLatencyVisit:
+    round_index: int
+    ordinal_in_round: int
+    run_id: str
+    before: SharedCudaObservation
+    after: SharedCudaObservation | None
+    samples_ns: tuple[int, ...]
+    peak_allocated_bytes: int | None
+    peak_reserved_bytes: int | None
+    completed: bool
+    failure_code: str | None
+    warmups_completed: int = WARMUPS_PER_VISIT
+    schema: str = VISIT_SCHEMA
+
+    def __post_init__(self) -> None:
+        if type(self.round_index) is not int or not 0 <= self.round_index < ROUND_COUNT:
+            raise BaseCohortLatencyError("visit round index is invalid")
+        if (
+            type(self.ordinal_in_round) is not int
+            or not 0 <= self.ordinal_in_round < ROWS_PER_ROUND
+        ):
+            raise BaseCohortLatencyError("visit ordinal is invalid")
+        if self.run_id not in base_run_ids():
+            raise BaseCohortLatencyError("visit run ID is unregistered")
+        if type(self.before) is not SharedCudaObservation:
+            raise BaseCohortLatencyError("visit before observation is missing")
+        if type(self.samples_ns) is not tuple or len(self.samples_ns) > TIMED_PER_VISIT:
+            raise BaseCohortLatencyError("visit sample census exceeds eleven")
+        for value in self.samples_ns:
+            _positive_int(value, "latency sample")
+        if (
+            type(self.warmups_completed) is not int
+            or not 0 <= self.warmups_completed <= WARMUPS_PER_VISIT
+            or self.schema != VISIT_SCHEMA
+        ):
+            raise BaseCohortLatencyError("visit protocol header changed")
+        if self.completed:
+            if (
+                type(self.after) is not SharedCudaObservation
+                or len(self.samples_ns) != TIMED_PER_VISIT
+                or type(self.peak_allocated_bytes) is not int
+                or type(self.peak_reserved_bytes) is not int
+                or self.warmups_completed != WARMUPS_PER_VISIT
+                or self.failure_code is not None
+            ):
+                raise BaseCohortLatencyError("completed visit evidence is incomplete")
+        elif self.failure_code is None:
+            raise BaseCohortLatencyError("incomplete visit requires a failure code")
+        for name in ("peak_allocated_bytes", "peak_reserved_bytes"):
+            value = getattr(self, name)
+            if value is not None:
+                _nonnegative_int(value, name)
+
+    @property
+    def sha256(self) -> str:
+        return _canonical_digest(
+            {
+                "after": None if self.after is None else self.after.payload(),
+                "before": self.before.payload(),
+                "completed": self.completed,
+                "failure_code": self.failure_code,
+                "ordinal_in_round": self.ordinal_in_round,
+                "peak_allocated_bytes": self.peak_allocated_bytes,
+                "peak_reserved_bytes": self.peak_reserved_bytes,
+                "round_index": self.round_index,
+                "run_id": self.run_id,
+                "samples_ns": list(self.samples_ns),
+                "schema": self.schema,
+                "warmups_completed": self.warmups_completed,
+            }
+        )
+
+
+def _nearest_rank(sorted_values: tuple[int, ...], percentile: int) -> int:
+    rank = math.ceil(percentile * len(sorted_values) / 100)
+    return sorted_values[max(1, rank) - 1]
+
+
+@dataclass(frozen=True, slots=True)
+class BaseLatencyRowObservation:
+    run_id: str
+    system_id: str
+    seed: int
+    selected_checkpoint_sha256: str
+    selected_checkpoint_state_digest: str
+    parameter_census_sha256: str
+    parameter_count: int
+    runtime_sha256: str
+    workload_sha256: str
+    samples_ns: tuple[int, ...]
+    median_ns: int
+    median_absolute_deviation_ns: int
+    minimum_ns: int
+    maximum_ns: int
+    p05_nearest_rank_ns: int
+    p95_nearest_rank_ns: int
+    schema: str = ROW_SCHEMA
+
+    def __post_init__(self) -> None:
+        role, seed, system_id = parse_run_id(self.run_id)
+        if role != "BASE_QUALIFICATION" or self.seed != seed or self.system_id != system_id:
+            raise BaseCohortLatencyError("latency row identity is inconsistent")
+        for name in (
+            "selected_checkpoint_sha256",
+            "selected_checkpoint_state_digest",
+            "parameter_census_sha256",
+            "runtime_sha256",
+            "workload_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        _positive_int(self.parameter_count, "parameter_count")
+        if type(self.samples_ns) is not tuple or len(self.samples_ns) != SAMPLES_PER_ROW:
+            raise BaseCohortLatencyError("latency row must retain exact 99 samples")
+        for value in self.samples_ns:
+            _positive_int(value, "latency sample")
+        sorted_values = tuple(sorted(self.samples_ns))
+        median = sorted_values[len(sorted_values) // 2]
+        deviations = tuple(sorted(abs(value - median) for value in self.samples_ns))
+        expected = (
+            median,
+            deviations[len(deviations) // 2],
+            sorted_values[0],
+            sorted_values[-1],
+            _nearest_rank(sorted_values, 5),
+            _nearest_rank(sorted_values, 95),
+        )
+        observed = (
+            self.median_ns,
+            self.median_absolute_deviation_ns,
+            self.minimum_ns,
+            self.maximum_ns,
+            self.p05_nearest_rank_ns,
+            self.p95_nearest_rank_ns,
+        )
+        if observed != expected or self.schema != ROW_SCHEMA:
+            raise BaseCohortLatencyError("latency row statistics differ from raw samples")
+
+    @property
+    def frozen_runtime_latency_ns(self) -> int:
+        return self.median_ns
+
+    @property
+    def sha256(self) -> str:
+        return _canonical_digest(
+            {
+                "maximum_ns": self.maximum_ns,
+                "median_absolute_deviation_ns": self.median_absolute_deviation_ns,
+                "median_ns": self.median_ns,
+                "minimum_ns": self.minimum_ns,
+                "p05_nearest_rank_ns": self.p05_nearest_rank_ns,
+                "p95_nearest_rank_ns": self.p95_nearest_rank_ns,
+                "parameter_census_sha256": self.parameter_census_sha256,
+                "parameter_count": self.parameter_count,
+                "run_id": self.run_id,
+                "runtime_sha256": self.runtime_sha256,
+                "samples_ns": list(self.samples_ns),
+                "schema": self.schema,
+                "seed": self.seed,
+                "selected_checkpoint_sha256": self.selected_checkpoint_sha256,
+                "selected_checkpoint_state_digest": self.selected_checkpoint_state_digest,
+                "system_id": self.system_id,
+                "workload_sha256": self.workload_sha256,
+            }
+        )
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class BaseCohortLatencySession:
+    registered_cuda_uuid: str
+    resolved_cohort_sha256: str
+    scored_cohort_sha256: str
+    workload_receipt: BaseLatencyWorkloadReceipt
+    admission_observation: SharedCudaObservation
+    post_context_observation: SharedCudaObservation | None
+    runtime_identity: BaseLatencyRuntimeIdentity | None
+    visits: tuple[BaseLatencyVisit, ...]
+    rows: tuple[BaseLatencyRowObservation, ...]
+    actual_runtime: bool
+    complete: bool
+    formal_samples_complete: bool
+    failure_code: str | None
+    authority: int = AUTHORITY
+    production: bool = PRODUCTION
+    result_claimed: bool = RESULT_CLAIMED
+    schema: str = SESSION_SCHEMA
+    status: str = HOLD_STATUS
+
+    def __post_init__(self) -> None:
+        if type(self.registered_cuda_uuid) is not str or not self.registered_cuda_uuid.startswith(
+            "GPU-"
+        ):
+            raise BaseCohortLatencyError("session CUDA UUID is invalid")
+        _lower_sha256(self.resolved_cohort_sha256, "resolved_cohort_sha256")
+        _lower_sha256(self.scored_cohort_sha256, "scored_cohort_sha256")
+        if type(self.workload_receipt) is not BaseLatencyWorkloadReceipt:
+            raise BaseCohortLatencyError("session workload receipt is invalid")
+        if type(self.admission_observation) is not SharedCudaObservation:
+            raise BaseCohortLatencyError("session admission observation is invalid")
+        if type(self.visits) is not tuple or any(
+            type(value) is not BaseLatencyVisit for value in self.visits
+        ):
+            raise BaseCohortLatencyError("session visit census is malformed")
+        if type(self.rows) is not tuple or any(
+            type(value) is not BaseLatencyRowObservation for value in self.rows
+        ):
+            raise BaseCohortLatencyError("session row census is malformed")
+        if type(self.actual_runtime) is not bool or type(self.complete) is not bool:
+            raise BaseCohortLatencyError("session runtime/completion flags are malformed")
+        if type(self.formal_samples_complete) is not bool:
+            raise BaseCohortLatencyError("session formal sample flag is malformed")
+        if self.complete:
+            expected_visits = tuple(
+                (round_index, ordinal, run_id)
+                for round_index, round_rows in enumerate(cyclic_visit_schedule())
+                for ordinal, run_id in enumerate(round_rows)
+            )
+            observed_visits = tuple(
+                (visit.round_index, visit.ordinal_in_round, visit.run_id) for visit in self.visits
+            )
+            if (
+                observed_visits != expected_visits
+                or any(not visit.completed for visit in self.visits)
+                or tuple(row.run_id for row in self.rows) != base_run_ids()
+                or type(self.runtime_identity) is not BaseLatencyRuntimeIdentity
+                or type(self.post_context_observation) is not SharedCudaObservation
+                or self.failure_code is not None
+            ):
+                raise BaseCohortLatencyError("completed latency session census is incomplete")
+            if any(
+                visit.before.device_uuid != self.registered_cuda_uuid
+                or visit.after is None
+                or visit.after.device_uuid != self.registered_cuda_uuid
+                for visit in self.visits
+            ):
+                raise BaseCohortLatencyError("completed session visit changed CUDA UUID")
+            if any(
+                row.runtime_sha256 != self.runtime_identity.sha256
+                or row.workload_sha256 != self.workload_receipt.sha256
+                for row in self.rows
+            ):
+                raise BaseCohortLatencyError("completed latency rows lost runtime/workload binding")
+            expected_formal = self.actual_runtime
+            expected_status = (
+                ACTUAL_COMPLETE_STATUS if self.actual_runtime else FAKE_COMPLETE_STATUS
+            )
+            if (
+                self.formal_samples_complete is not expected_formal
+                or self.status != expected_status
+            ):
+                raise BaseCohortLatencyError("completed latency session status is inconsistent")
+        elif (
+            self.rows
+            or self.formal_samples_complete
+            or self.failure_code is None
+            or self.status != HOLD_STATUS
+        ):
+            raise BaseCohortLatencyError("held latency session must not expose formal rows")
+        if (
+            type(self.authority) is not int
+            or self.authority != AUTHORITY
+            or self.production is not PRODUCTION
+            or self.result_claimed is not RESULT_CLAIMED
+            or self.schema != SESSION_SCHEMA
+        ):
+            raise BaseCohortLatencyError("latency session result header is invalid")
+
+    @property
+    def sha256(self) -> str:
+        return _canonical_digest(
+            {
+                "actual_runtime": self.actual_runtime,
+                "admission_observation": self.admission_observation.payload(),
+                "authority": self.authority,
+                "complete": self.complete,
+                "failure_code": self.failure_code,
+                "formal_samples_complete": self.formal_samples_complete,
+                "post_context_observation": (
+                    None
+                    if self.post_context_observation is None
+                    else self.post_context_observation.payload()
+                ),
+                "production": self.production,
+                "registered_cuda_uuid": self.registered_cuda_uuid,
+                "resolved_cohort_sha256": self.resolved_cohort_sha256,
+                "result_claimed": self.result_claimed,
+                "row_sha256s": [row.sha256 for row in self.rows],
+                "runtime_sha256": None
+                if self.runtime_identity is None
+                else self.runtime_identity.sha256,
+                "schema": self.schema,
+                "scored_cohort_sha256": self.scored_cohort_sha256,
+                "status": self.status,
+                "visit_sha256s": [visit.sha256 for visit in self.visits],
+                "workload_sha256": self.workload_receipt.sha256,
+            }
+        )
+
+    def __repr__(self) -> str:
+        return (
+            "BaseCohortLatencySession("
+            f"complete={self.complete}, actual_runtime={self.actual_runtime}, "
+            f"visits={len(self.visits)}, sha256={self.sha256!r})"
+        )
+
+
+class _LatencyRuntime(Protocol):
+    def snapshot(self) -> SharedCudaObservation: ...
+
+    def initialize(
+        self,
+        *,
+        device: torch.device,
+        precision_mode: str,
+        admission_observation: SharedCudaObservation,
+    ) -> BaseLatencyRuntimeIdentity: ...
+
+    def synchronize(self, device: torch.device) -> None: ...
+
+    def perf_counter_ns(self) -> int: ...
+
+    def reset_peak_memory(self, device: torch.device) -> None: ...
+
+    def peak_memory(self, device: torch.device) -> tuple[int, int]: ...
+
+    def place(self, system: training_module.BaseRetrievalSystem, device: torch.device) -> None: ...
+
+    def encode(
+        self,
+        system: training_module.BaseRetrievalSystem,
+        groups: PreparedGroupBatch,
+    ) -> object: ...
+
+    def validate_output(self, output: object, device: torch.device) -> None: ...
+
+    def release(
+        self, system: training_module.BaseRetrievalSystem, device: torch.device
+    ) -> None: ...
+
+
+@runtime_checkable
+class LatencySessionObserver(Protocol):
+    """Typed post-operation observer for the private latency controller.
+
+    Implementations receive only values already derived by this module.  They
+    cannot submit samples, metrics, checkpoints, or completion state.
+    """
+
+    def session_start(
+        self,
+        *,
+        admission_observation_sha256: str,
+        started_unix_ns: int,
+    ) -> object: ...
+
+    def context_ready(
+        self,
+        *,
+        runtime_sha256: str,
+        post_context_observation_sha256: str,
+    ) -> object: ...
+
+    def visit_started(
+        self,
+        *,
+        round_index: int,
+        ordinal_in_round: int,
+        run_id: str,
+        before_observation_sha256: str,
+    ) -> object: ...
+
+    def warmup_completed(
+        self,
+        *,
+        round_index: int,
+        ordinal_in_round: int,
+        run_id: str,
+        warmup_index: int,
+    ) -> object: ...
+
+    def timed_sample_completed(
+        self,
+        *,
+        round_index: int,
+        ordinal_in_round: int,
+        run_id: str,
+        sample_index: int,
+        sample_ns: int,
+    ) -> object: ...
+
+    def visit_ended(
+        self,
+        *,
+        round_index: int,
+        ordinal_in_round: int,
+        run_id: str,
+        outcome: str,
+        after_observation_sha256: str | None,
+        peak_allocated_bytes: int | None,
+        peak_reserved_bytes: int | None,
+        failure_code: str | None,
+    ) -> object: ...
+
+    def terminal(
+        self,
+        *,
+        outcome: str,
+        latency_session_sha256: str,
+        failure_code: str | None,
+    ) -> object: ...
+
+
+class _LatencyObserverFailure(RuntimeError):
+    """An observer callback failed; no later callback may be attempted."""
+
+
+class _NvidiaSmiTorchRuntime:
+    __slots__ = ("_uuid",)
+
+    def __init__(self, registered_cuda_uuid: str) -> None:
+        self._uuid = registered_cuda_uuid
+
+    def snapshot(self) -> SharedCudaObservation:
+        query = (
+            "uuid,memory.total,memory.free,memory.used,utilization.gpu,"
+            "utilization.memory,compute_mode,driver_version"
+        )
+        try:
+            completed = subprocess.run(
+                [
+                    "nvidia-smi",
+                    f"--id={self._uuid}",
+                    f"--query-gpu={query}",
+                    "--format=csv,noheader,nounits",
+                ],
+                check=True,
+                capture_output=True,
+                text=True,
+                timeout=10,
+            )
+        except (OSError, subprocess.SubprocessError) as error:
+            raise BaseCohortLatencyError("NVIDIA device observation failed") from error
+        rows = tuple(line.strip() for line in completed.stdout.splitlines() if line.strip())
+        if len(rows) != 1:
+            raise BaseCohortLatencyError("NVIDIA device observation is not one exact row")
+        fields = tuple(value.strip() for value in rows[0].split(","))
+        if len(fields) != 8:
+            raise BaseCohortLatencyError("NVIDIA device observation field count changed")
+        try:
+            return SharedCudaObservation(
+                captured_unix_ns=time.time_ns(),
+                device_uuid=fields[0],
+                memory_total_mib=int(fields[1]),
+                memory_free_mib=int(fields[2]),
+                memory_used_mib=int(fields[3]),
+                gpu_utilization_percent=int(fields[4]),
+                memory_utilization_percent=int(fields[5]),
+                compute_mode=fields[6],
+                driver_version=fields[7],
+            )
+        except (TypeError, ValueError) as error:
+            raise BaseCohortLatencyError("NVIDIA device observation is malformed") from error
+
+    def initialize(
+        self,
+        *,
+        device: torch.device,
+        precision_mode: str,
+        admission_observation: SharedCudaObservation,
+    ) -> BaseLatencyRuntimeIdentity:
+        if device.type != "cuda" or not torch.cuda.is_available():
+            raise BaseCohortLatencyError("formal latency CUDA device is unavailable")
+        index = torch.cuda.current_device() if device.index is None else device.index
+        properties = torch.cuda.get_device_properties(index)
+        observed_uuid = str(getattr(properties, "uuid", ""))
+        if observed_uuid != self._uuid or admission_observation.device_uuid != self._uuid:
+            raise BaseCohortLatencyError("Torch and NVIDIA device UUIDs differ")
+        fraction = CUDA_ALLOCATOR_LIMIT_BYTES / int(properties.total_memory)
+        if not 0.0 < fraction <= 1.0:
+            raise BaseCohortLatencyError("CUDA allocator limit is incompatible with device")
+        torch.cuda.set_per_process_memory_fraction(fraction, device=index)
+        probe = torch.empty((1,), dtype=torch.float32, device=device)
+        del probe
+        torch.cuda.synchronize(device)
+        torch.cuda.empty_cache()
+        initial_allocated = int(torch.cuda.memory_allocated(index))
+        initial_reserved = int(torch.cuda.memory_reserved(index))
+        if initial_allocated != 0 or initial_reserved != 0:
+            raise BaseCohortLatencyError("latency process has preexisting CUDA allocations")
+        cudnn = torch.backends.cudnn.version()
+        if type(cudnn) is not int or cudnn < 1 or type(torch.version.cuda) is not str:
+            raise BaseCohortLatencyError("Torch CUDA runtime identity is incomplete")
+        return BaseLatencyRuntimeIdentity(
+            device=str(torch.device("cuda", index)),
+            device_uuid=self._uuid,
+            device_name=str(properties.name),
+            torch_version=str(torch.__version__),
+            cuda_runtime_version=torch.version.cuda,
+            cudnn_version=cudnn,
+            driver_version=admission_observation.driver_version,
+            nvidia_total_memory_mib=admission_observation.memory_total_mib,
+            compute_mode=admission_observation.compute_mode,
+            torch_total_memory_bytes=int(properties.total_memory),
+            initial_allocated_bytes=initial_allocated,
+            initial_reserved_bytes=initial_reserved,
+            environment_sha256=training_module.training_environment_sha256(),
+            precision_mode=precision_mode,
+        )
+
+    def synchronize(self, device: torch.device) -> None:
+        torch.cuda.synchronize(device)
+
+    def perf_counter_ns(self) -> int:
+        return time.perf_counter_ns()
+
+    def reset_peak_memory(self, device: torch.device) -> None:
+        torch.cuda.reset_peak_memory_stats(device)
+
+    def peak_memory(self, device: torch.device) -> tuple[int, int]:
+        return (
+            int(torch.cuda.max_memory_allocated(device)),
+            int(torch.cuda.max_memory_reserved(device)),
+        )
+
+    def place(self, system: training_module.BaseRetrievalSystem, device: torch.device) -> None:
+        system.to(device)
+
+    def encode(
+        self,
+        system: training_module.BaseRetrievalSystem,
+        groups: PreparedGroupBatch,
+    ) -> object:
+        return system.encode_trainable(groups)
+
+    def validate_output(self, output: object, device: torch.device) -> None:
+        _validate_output(output, device)
+
+    def release(self, system: training_module.BaseRetrievalSystem, device: torch.device) -> None:
+        system.to(torch.device("cpu"))
+        torch.cuda.synchronize(device)
+        torch.cuda.empty_cache()
+        if torch.cuda.memory_allocated(device) != 0 or torch.cuda.memory_reserved(device) != 0:
+            raise BaseCohortLatencyError(
+                "measured model CUDA allocation remained after visit release"
+            )
+
+
+def cyclic_visit_schedule() -> tuple[tuple[str, ...], ...]:
+    rows = base_run_ids()
+    if len(rows) != ROWS_PER_ROUND:
+        raise AssertionError("registered base row count changed")
+    return tuple(
+        tuple(rows[(round_index + ordinal) % len(rows)] for ordinal in range(len(rows)))
+        for round_index in range(ROUND_COUNT)
+    )
+
+
+def _array_row(name: str, value: np.ndarray) -> tuple[str, str, tuple[int, ...], str]:
+    return (
+        name,
+        _sha256(value.tobytes(order="C")),
+        tuple(int(axis) for axis in value.shape),
+        value.dtype.str,
+    )
+
+
+def _source_actor_commitments() -> tuple[bytes, ...]:
+    return tuple(
+        hashlib.sha256(_ACTOR_DOMAIN + struct.pack(">I", actor)).digest()
+        for actor in range(FIXTURE_ACTORS)
+    )
+
+
+def build_frozen_latency_workload() -> tuple[PreparedGroupBatch, BaseLatencyWorkloadReceipt]:
+    actors = np.arange(FIXTURE_ACTORS, dtype=np.int64).reshape(1, FIXTURE_ACTORS, 1, 1, 1)
+    frames = np.arange(FIXTURE_FRAMES, dtype=np.int64).reshape(1, 1, FIXTURE_FRAMES, 1, 1)
+    joints = np.arange(FIXTURE_JOINTS, dtype=np.int64).reshape(1, 1, 1, FIXTURE_JOINTS, 1)
+    coordinates = np.arange(FIXTURE_COORDINATES, dtype=np.int64).reshape(
+        1, 1, 1, 1, FIXTURE_COORDINATES
+    )
+    raw = ((actors * 131 + frames * 17 + joints * 7 + coordinates * 3) % 2048) - 1024
+    skeletons = np.ascontiguousarray(raw.astype(np.float32) * np.float32(2**-10))
+    actor_mask = np.ones((FIXTURE_BATCH, FIXTURE_ACTORS), dtype=np.bool_)
+    frame_mask = np.ones((FIXTURE_BATCH, FIXTURE_FRAMES), dtype=np.bool_)
+    track_mask = np.ones(
+        (FIXTURE_BATCH, FIXTURE_ACTORS, FIXTURE_FRAMES, FIXTURE_JOINTS),
+        dtype=np.bool_,
+    )
+    commitments = _source_actor_commitments()
+    group = group_commitment(commitments)
+    batch = PreparedGroupBatch(
+        skeletons,
+        actor_mask,
+        frame_mask,
+        track_mask,
+        (commitments,),
+        (group,),
+    )
+    receipt = BaseLatencyWorkloadReceipt(
+        array_rows=tuple(
+            _array_row(name, value)
+            for name, value in (
+                ("skeletons", batch.skeletons),
+                ("actor_mask", batch.actor_mask),
+                ("frame_mask", batch.frame_mask),
+                ("track_mask", batch.track_mask),
+            )
+        ),
+        raw_skeletons_sha256=_sha256(skeletons.tobytes(order="C")),
+        actor_commitments=tuple(value for value in batch.actor_commitments[0] if value is not None),
+        group_commitment=batch.group_commitments[0],
+    )
+    return batch, receipt
+
+
+def _workload_receipt(groups: PreparedGroupBatch) -> BaseLatencyWorkloadReceipt:
+    commitments = tuple(value for value in groups.actor_commitments[0] if value is not None)
+    source_commitments = _source_actor_commitments()
+    if set(commitments) != set(source_commitments):
+        raise BaseCohortLatencyError("latency workload actor census changed")
+    source_order = tuple(commitments.index(value) for value in source_commitments)
+    raw_skeletons = np.ascontiguousarray(groups.skeletons[:, source_order])
+    return BaseLatencyWorkloadReceipt(
+        array_rows=tuple(
+            _array_row(name, value)
+            for name, value in (
+                ("skeletons", groups.skeletons),
+                ("actor_mask", groups.actor_mask),
+                ("frame_mask", groups.frame_mask),
+                ("track_mask", groups.track_mask),
+            )
+        ),
+        raw_skeletons_sha256=_sha256(raw_skeletons.tobytes(order="C")),
+        actor_commitments=commitments,
+        group_commitment=groups.group_commitments[0],
+    )
+
+
+def _resolved_cohort_sha256(cohort: resolver_module.ResolvedBaseCohort) -> str:
+    return _canonical_digest(
+        {
+            "matrix_sha256": cohort.matrix_sha256,
+            "plan_sha256": cohort.plan_sha256,
+            "rows": [
+                {
+                    "attempt_id": row.attempt_id,
+                    "failed_predecessors": [
+                        {
+                            "attempt_id": failed.attempt_id,
+                            "failure_code": failed.failure_code,
+                            "terminal_outcome": failed.terminal_outcome,
+                            "terminal_sha256": failed.terminal_sha256,
+                        }
+                        for failed in row.failed_predecessors
+                    ],
+                    "latest_checkpoint_name": row.latest_checkpoint_name,
+                    "latest_checkpoint_sha256": row.latest_checkpoint_sha256,
+                    "run_id": row.run_id,
+                    "selected_checkpoint_epoch": row.selected_checkpoint.epoch,
+                    "selected_checkpoint_global_step": row.selected_checkpoint.global_step,
+                    "selected_checkpoint_name": row.selected_checkpoint.name,
+                    "selected_checkpoint_sha256": row.selected_checkpoint.sha256,
+                    "selected_checkpoint_state_digest": row.selected_checkpoint.state_digest,
+                    "terminal_sha256": row.terminal_sha256,
+                }
+                for row in cohort.rows
+            ],
+            "source_tree_sha256": cohort.source_tree_sha256,
+            "training_config_sha256": cohort.training_config_sha256,
+            "train_manifest_sha256": cohort.train_manifest_sha256,
+            "val_manifest_sha256": cohort.val_manifest_sha256,
+        }
+    )
+
+
+def _checked_inputs(
+    cohort: object,
+    admission: object,
+    scored_cohort: object,
+) -> tuple[
+    resolver_module.ResolvedBaseCohort,
+    resolver_module.BaseCohortAdmission,
+    scoring_module.BaseCohortValidationResult,
+]:
+    try:
+        checked_admission = scoring_module._checked_admission(admission)
+        checked_cohort = scoring_module._checked_cohort(cohort, checked_admission)
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortLatencyError("resolved base cohort admission is invalid") from error
+    if type(scored_cohort) is not scoring_module.BaseCohortValidationResult:
+        raise BaseCohortLatencyError("score input must be exact BaseCohortValidationResult")
+    if (
+        type(scored_cohort.authority) is not int
+        or scored_cohort.authority != scoring_module.AUTHORITY
+        or scored_cohort.production is not scoring_module.PRODUCTION
+        or scored_cohort.result_claimed is not scoring_module.RESULT_CLAIMED
+        or scored_cohort.status != scoring_module.COHORT_STATUS
+    ):
+        raise BaseCohortLatencyError("score cohort header is invalid")
+    expected = {
+        "plan_sha256": checked_cohort.plan_sha256,
+        "matrix_sha256": checked_cohort.matrix_sha256,
+        "training_config_sha256": checked_cohort.training_config_sha256,
+        "source_tree_sha256": checked_cohort.source_tree_sha256,
+        "train_manifest_sha256": checked_cohort.train_manifest_sha256,
+        "query_census_sha256": checked_cohort.val_manifest_sha256,
+    }
+    if any(getattr(scored_cohort, name) != value for name, value in expected.items()):
+        raise BaseCohortLatencyError("score cohort differs from resolved cohort admission")
+    if tuple(row.run_id for row in scored_cohort.observations) != base_run_ids():
+        raise BaseCohortLatencyError("score cohort lost the registered nine-row order")
+    for resolved, scored in zip(checked_cohort.rows, scored_cohort.observations, strict=True):
+        if (
+            scored.run_id != resolved.run_id
+            or scored.attempt_id != resolved.attempt_id
+            or scored.system_id != resolved.system_id
+            or scored.seed != resolved.seed
+            or scored.terminal_sha256 != resolved.terminal_sha256
+            or scored.selected_checkpoint_sha256 != resolved.selected_checkpoint.sha256
+            or scored.selected_checkpoint_state_digest != resolved.selected_checkpoint.state_digest
+        ):
+            raise BaseCohortLatencyError("score row differs from resolved selected-best row")
+    return checked_cohort, checked_admission, scored_cohort
+
+
+def _checked_scorer_runtime_device(
+    *,
+    config_device: str,
+    score_device: str,
+    runtime_device: torch.device,
+) -> None:
+    try:
+        configured_device = torch.device(config_device)
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortLatencyError("configured latency device is invalid") from error
+    if configured_device.type == "cuda" and configured_device.index is None:
+        configured_device = torch.device("cuda", torch.cuda.current_device())
+    if (
+        configured_device.type != "cuda"
+        or runtime_device.type != "cuda"
+        or configured_device != runtime_device
+        or type(score_device) is not str
+        or score_device != str(configured_device)
+    ):
+        raise BaseCohortLatencyError(
+            "score device differs from the resolved physical latency device"
+        )
+
+
+def _load_selected_system(
+    row: resolver_module.ResolvedBaseRun,
+    score: scoring_module.BaseValidationScoreObservation,
+    admission: resolver_module.BaseCohortAdmission,
+    *,
+    runtime_device: torch.device,
+) -> tuple[
+    training_module.BaseRetrievalSystem,
+    training_module.TrainingConfig,
+    training_module.PrecisionDecision,
+    scoring_module.BaseParameterCensus,
+]:
+    payload = scoring_module._validate_selected_payload(row, admission)
+    config = training_module.TrainingConfig(
+        stage="base",
+        seed=row.seed,
+        device=admission.device,
+        request_bf16=admission.request_bf16,
+        bf16_runtime_qualified=admission.bf16_runtime_qualified,
+        edge_budget=admission.edge_budget,
+        checkpoint_every_updates=admission.checkpoint_every_updates,
+        synthetic_contract=False,
+    )
+    try:
+        system, binding = training_module.construct_registered_base_seed_bound_system(
+            row.system_id,
+            config,
+        )
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortLatencyError("registered base reconstruction failed") from error
+    expected_bindings = {
+        "behavior_sha256": binding.behavior_sha256,
+        "code_artifact_sha256": binding.code_artifact_sha256,
+        "environment_sha256": binding.environment_sha256,
+        "factory_sha256": binding.factory_sha256,
+        "initial_optimizable_state_sha256": binding.initial_optimizable_state_sha256,
+        "initialization_binding_sha256": binding.sha256,
+    }
+    precision = training_module.resolve_precision(config)
+    _checked_scorer_runtime_device(
+        config_device=config.device,
+        score_device=score.device,
+        runtime_device=runtime_device,
+    )
+    if any(payload.get(name) != expected for name, expected in expected_bindings.items()):
+        raise BaseCohortLatencyError("selected checkpoint initialization binding changed")
+    if (
+        score.initialization_binding_sha256 != binding.sha256
+        or score.factory_sha256 != binding.factory_sha256
+        or score.code_artifact_sha256 != binding.code_artifact_sha256
+        or score.environment_sha256 != binding.environment_sha256
+        or score.behavior_sha256 != binding.behavior_sha256
+        or score.runtime_config_sha256 != config.sha256
+        or score.precision_mode != precision.mode
+    ):
+        raise BaseCohortLatencyError("score runtime binding differs from latency reconstruction")
+    model = payload.get("model")
+    if not isinstance(model, Mapping):
+        raise BaseCohortLatencyError("selected checkpoint model is malformed")
+    try:
+        system.load_state_dict(model, strict=True)
+    except (KeyError, TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortLatencyError("selected model state could not be loaded") from error
+    if (
+        training_module.training_system_state_sha256(system) != training_module._stable_hash(model)
+        or training_module.training_system_behavior_sha256(system) != binding.behavior_sha256
+    ):
+        raise BaseCohortLatencyError("loaded selected model state or behavior changed")
+    census = scoring_module._parameter_census(system)
+    if census != score.parameter_census:
+        raise BaseCohortLatencyError("latency live parameter census differs from score census")
+    return system, config, precision, census
+
+
+def _validate_snapshot(
+    snapshot: SharedCudaObservation,
+    *,
+    registered_uuid: str,
+    runtime: BaseLatencyRuntimeIdentity | None,
+) -> None:
+    if type(snapshot) is not SharedCudaObservation or snapshot.device_uuid != registered_uuid:
+        raise BaseCohortLatencyError("shared CUDA observation changed device UUID")
+    if runtime is not None and (
+        snapshot.driver_version != runtime.driver_version
+        or snapshot.memory_total_mib != runtime.nvidia_total_memory_mib
+        or snapshot.compute_mode != runtime.compute_mode
+    ):
+        raise BaseCohortLatencyError("shared CUDA runtime identity changed")
+
+
+def _validate_output(output: object, device: torch.device) -> torch.Tensor:
+    if (
+        type(output) is not torch.Tensor
+        or output.shape != (FIXTURE_BATCH, FIXTURE_EMBEDDING_DIM)
+        or output.dtype != torch.float32
+        or output.device != device
+        or not output.is_contiguous()
+        or output.requires_grad
+        or output.grad_fn is not None
+        or not bool(torch.isfinite(output).all().item())
+    ):
+        raise BaseCohortLatencyError("timed base embedding is malformed or nonfinite")
+    return output
+
+
+def _forward_once(
+    system: training_module.BaseRetrievalSystem,
+    groups: PreparedGroupBatch,
+    *,
+    precision: training_module.PrecisionDecision,
+    device: torch.device,
+    runtime: _LatencyRuntime,
+    timed: bool,
+) -> int | None:
+    runtime.synchronize(device)
+    start = runtime.perf_counter_ns() if timed else None
+    with training_module._autocast_context(precision, device):
+        output = runtime.encode(system, groups)
+        runtime.validate_output(output, device)
+    runtime.synchronize(device)
+    stop = runtime.perf_counter_ns() if timed else None
+    del output
+    if start is None or stop is None:
+        return None
+    if type(start) is not int or type(stop) is not int or stop <= start:
+        raise BaseCohortLatencyError("host monotonic latency timer is invalid")
+    return stop - start
+
+
+def _row_observations(
+    visits: tuple[BaseLatencyVisit, ...],
+    cohort: resolver_module.ResolvedBaseCohort,
+    scored: scoring_module.BaseCohortValidationResult,
+    runtime: BaseLatencyRuntimeIdentity,
+    workload: BaseLatencyWorkloadReceipt,
+) -> tuple[BaseLatencyRowObservation, ...]:
+    rows: list[BaseLatencyRowObservation] = []
+    for resolved, score in zip(cohort.rows, scored.observations, strict=True):
+        samples = tuple(
+            sample
+            for visit in visits
+            if visit.run_id == resolved.run_id
+            for sample in visit.samples_ns
+        )
+        ordered = tuple(sorted(samples))
+        median = ordered[len(ordered) // 2]
+        deviations = tuple(sorted(abs(value - median) for value in samples))
+        rows.append(
+            BaseLatencyRowObservation(
+                run_id=resolved.run_id,
+                system_id=resolved.system_id,
+                seed=resolved.seed,
+                selected_checkpoint_sha256=resolved.selected_checkpoint.sha256,
+                selected_checkpoint_state_digest=resolved.selected_checkpoint.state_digest,
+                parameter_census_sha256=score.parameter_census.sha256,
+                parameter_count=score.parameter_census.total,
+                runtime_sha256=runtime.sha256,
+                workload_sha256=workload.sha256,
+                samples_ns=samples,
+                median_ns=median,
+                median_absolute_deviation_ns=deviations[len(deviations) // 2],
+                minimum_ns=ordered[0],
+                maximum_ns=ordered[-1],
+                p05_nearest_rank_ns=_nearest_rank(ordered, 5),
+                p95_nearest_rank_ns=_nearest_rank(ordered, 95),
+            )
+        )
+    return tuple(rows)
+
+
+def _failure_code(error: BaseException) -> str:
+    chain: list[BaseException] = []
+    seen: set[int] = set()
+    current: BaseException | None = error
+    while current is not None and id(current) not in seen:
+        seen.add(id(current))
+        chain.append(current)
+        current = current.__cause__
+    if any(isinstance(item, torch.cuda.OutOfMemoryError) for item in chain):
+        return "CUDA_OUT_OF_MEMORY"
+    if any(isinstance(item, (TimeoutError, subprocess.TimeoutExpired)) for item in chain):
+        return "SESSION_TIMEOUT"
+    if isinstance(error, BaseCohortLatencyError):
+        message = str(error)
+        if "UUID" in message or "runtime identity" in message or "precision" in message:
+            return "RUNTIME_DRIFT"
+        if "allocator" in message or "memory" in message or "free" in message:
+            return "RESOURCE_LIMIT"
+        if "checkpoint" in message or "selected" in message or "score" in message:
+            return "CHECKPOINT_OR_SCORE_DRIFT"
+        if "embedding" in message or "timer" in message:
+            return "MALFORMED_MEASUREMENT"
+    return "UNEXPECTED_SESSION_FAILURE"
+
+
+def run_base_cohort_latency_session(
+    cohort: resolver_module.ResolvedBaseCohort,
+    *,
+    admission: resolver_module.BaseCohortAdmission,
+    scored_cohort: scoring_module.BaseCohortValidationResult,
+    registered_cuda_uuid: str,
+    observer: LatencySessionObserver | None = None,
+    _runtime: _LatencyRuntime | None = None,
+) -> BaseCohortLatencySession:
+    """Measure all nine selected-best rows; no latency value is caller supplied.
+
+    ``_runtime`` exists solely for deterministic software contract tests.  Any
+    injected runtime is permanently marked non-formal in the returned object.
+    """
+
+    checked_cohort, checked_admission, checked_scores = _checked_inputs(
+        cohort,
+        admission,
+        scored_cohort,
+    )
+    if type(registered_cuda_uuid) is not str or not registered_cuda_uuid.startswith("GPU-"):
+        raise BaseCohortLatencyError("registered_cuda_uuid must be an exact GPU UUID")
+    if observer is not None and not isinstance(observer, LatencySessionObserver):
+        raise BaseCohortLatencyError("latency observer does not implement the typed interface")
+    device = torch.device(checked_admission.device)
+    if device.type != "cuda":
+        raise BaseCohortLatencyError("formal base latency requires the admitted CUDA device")
+    decisions = tuple(
+        training_module.resolve_precision(
+            training_module.TrainingConfig(
+                stage="base",
+                seed=row.seed,
+                device=checked_admission.device,
+                request_bf16=checked_admission.request_bf16,
+                bf16_runtime_qualified=checked_admission.bf16_runtime_qualified,
+                edge_budget=checked_admission.edge_budget,
+                checkpoint_every_updates=checked_admission.checkpoint_every_updates,
+                synthetic_contract=False,
+            )
+        )
+        for row in checked_cohort.rows
+    )
+    if len({decision.mode for decision in decisions}) != 1:
+        raise BaseCohortLatencyError("latency precision must be session-wide")
+    precision_mode = decisions[0].mode
+    groups, workload = build_frozen_latency_workload()
+    if checked_admission.edge_budget < FIXTURE_ACTORS * (FIXTURE_ACTORS - 1) // 2:
+        raise BaseCohortLatencyError("admitted edge budget cannot contain the K=32 workload")
+    actual_runtime = _runtime is None
+    runtime: _LatencyRuntime = (
+        _NvidiaSmiTorchRuntime(registered_cuda_uuid) if _runtime is None else _runtime
+    )
+    resolved_sha = _resolved_cohort_sha256(checked_cohort)
+    scored_sha = checked_scores.sha256
+    admission_observation = runtime.snapshot()
+    if type(admission_observation) is not SharedCudaObservation:
+        raise BaseCohortLatencyError("pre-session CUDA observation is malformed")
+    visits: list[BaseLatencyVisit] = []
+    runtime_identity: BaseLatencyRuntimeIdentity | None = None
+    post_context: SharedCudaObservation | None = None
+    observer_failed = False
+
+    def observe(method_name: str, **payload: object) -> None:
+        nonlocal observer_failed
+        if observer is None:
+            return
+        try:
+            getattr(observer, method_name)(**payload)
+        except Exception as error:
+            observer_failed = True
+            raise _LatencyObserverFailure(
+                f"latency observer failed during {method_name}"
+            ) from error
+
+    def held(error: BaseException) -> BaseCohortLatencyHold:
+        code = _failure_code(error)
+        receipt = BaseCohortLatencySession(
+            registered_cuda_uuid=registered_cuda_uuid,
+            resolved_cohort_sha256=resolved_sha,
+            scored_cohort_sha256=scored_sha,
+            workload_receipt=workload,
+            admission_observation=admission_observation,
+            post_context_observation=post_context,
+            runtime_identity=runtime_identity,
+            visits=tuple(visits),
+            rows=(),
+            actual_runtime=actual_runtime,
+            complete=False,
+            formal_samples_complete=False,
+            failure_code=code,
+            status=HOLD_STATUS,
+        )
+        return BaseCohortLatencyHold(code, receipt)
+
+    def observed_hold(error: BaseException) -> BaseCohortLatencyHold:
+        session_hold = held(error)
+        if not observer_failed:
+            try:
+                observe(
+                    "terminal",
+                    outcome="HELD",
+                    latency_session_sha256=session_hold.receipt.sha256,
+                    failure_code=session_hold.failure_code,
+                )
+            except _LatencyObserverFailure as observer_error:
+                raise session_hold from observer_error
+        return session_hold
+
+    try:
+        observe(
+            "session_start",
+            admission_observation_sha256=_canonical_digest(admission_observation.payload()),
+            started_unix_ns=time.time_ns(),
+        )
+        _validate_snapshot(
+            admission_observation,
+            registered_uuid=registered_cuda_uuid,
+            runtime=None,
+        )
+        if admission_observation.memory_free_mib < MINIMUM_FREE_MIB:
+            raise BaseCohortLatencyError("shared CUDA admission has less than 8 GiB free")
+        runtime_identity = runtime.initialize(
+            device=device,
+            precision_mode=precision_mode,
+            admission_observation=admission_observation,
+        )
+        if type(runtime_identity) is not BaseLatencyRuntimeIdentity:
+            raise BaseCohortLatencyError("latency runtime initialization receipt is invalid")
+        device = torch.device(runtime_identity.device)
+        post_context = runtime.snapshot()
+        _validate_snapshot(
+            post_context,
+            registered_uuid=registered_cuda_uuid,
+            runtime=runtime_identity,
+        )
+        if post_context.memory_free_mib < MINIMUM_FREE_MIB:
+            raise BaseCohortLatencyError("shared CUDA context has less than 8 GiB free")
+        if runtime_identity.precision_mode != precision_mode:
+            raise BaseCohortLatencyError("latency runtime precision changed")
+        score_environments = {score.environment_sha256 for score in checked_scores.observations}
+        if score_environments != {runtime_identity.environment_sha256}:
+            raise BaseCohortLatencyError("score cohort and latency runtime environment differ")
+        observe(
+            "context_ready",
+            runtime_sha256=runtime_identity.sha256,
+            post_context_observation_sha256=_canonical_digest(post_context.payload()),
+        )
+        with training_module._frozen_numerical_runtime(device), torch.no_grad():
+            for round_index, round_rows in enumerate(cyclic_visit_schedule()):
+                for ordinal, run_id in enumerate(round_rows):
+                    row_index = base_run_ids().index(run_id)
+                    row = checked_cohort.rows[row_index]
+                    score = checked_scores.observations[row_index]
+                    before = runtime.snapshot()
+                    _validate_snapshot(
+                        before,
+                        registered_uuid=registered_cuda_uuid,
+                        runtime=runtime_identity,
+                    )
+                    observe(
+                        "visit_started",
+                        round_index=round_index,
+                        ordinal_in_round=ordinal,
+                        run_id=run_id,
+                        before_observation_sha256=_canonical_digest(before.payload()),
+                    )
+                    samples: list[int] = []
+                    warmups_completed = 0
+                    peaks: tuple[int, int] | None = None
+                    after: SharedCudaObservation | None = None
+                    system: training_module.BaseRetrievalSystem | None = None
+                    visit_error: BaseException | None = None
+                    try:
+                        if (
+                            _resolved_cohort_sha256(checked_cohort) != resolved_sha
+                            or checked_scores.sha256 != scored_sha
+                            or _workload_receipt(groups) != workload
+                            or _sha256(row.selected_checkpoint.raw)
+                            != row.selected_checkpoint.sha256
+                        ):
+                            raise BaseCohortLatencyError("session input changed between visits")
+                        system, _config, precision, census = _load_selected_system(
+                            row,
+                            score,
+                            checked_admission,
+                            runtime_device=device,
+                        )
+                        if precision.mode != precision_mode or census != score.parameter_census:
+                            raise BaseCohortLatencyError(
+                                "visit precision or parameter census changed"
+                            )
+                        system.eval()
+                        for parameter in system.parameters():
+                            parameter.grad = None
+                            parameter.requires_grad_(False)
+                        if any(parameter.requires_grad for parameter in system.parameters()):
+                            raise BaseCohortLatencyError("measured model was not completely frozen")
+                        runtime.reset_peak_memory(device)
+                        runtime.place(system, device)
+                        for warmup_index in range(WARMUPS_PER_VISIT):
+                            _forward_once(
+                                system,
+                                groups,
+                                precision=precision,
+                                device=device,
+                                runtime=runtime,
+                                timed=False,
+                            )
+                            warmups_completed += 1
+                            observe(
+                                "warmup_completed",
+                                round_index=round_index,
+                                ordinal_in_round=ordinal,
+                                run_id=run_id,
+                                warmup_index=warmup_index,
+                            )
+                        for sample_index in range(TIMED_PER_VISIT):
+                            sample = _forward_once(
+                                system,
+                                groups,
+                                precision=precision,
+                                device=device,
+                                runtime=runtime,
+                                timed=True,
+                            )
+                            if type(sample) is not int:
+                                raise AssertionError("timed forward did not produce an integer")
+                            samples.append(sample)
+                            observe(
+                                "timed_sample_completed",
+                                round_index=round_index,
+                                ordinal_in_round=ordinal,
+                                run_id=run_id,
+                                sample_index=sample_index,
+                                sample_ns=sample,
+                            )
+                        peaks = runtime.peak_memory(device)
+                        if (
+                            type(peaks) is not tuple
+                            or len(peaks) != 2
+                            or any(type(value) is not int or value < 0 for value in peaks)
+                            or max(peaks) > CUDA_ALLOCATOR_LIMIT_BYTES
+                        ):
+                            raise BaseCohortLatencyError("CUDA allocator peak exceeded 2 GiB")
+                        if any(parameter.grad is not None for parameter in system.parameters()):
+                            raise BaseCohortLatencyError("latency measurement created a gradient")
+                        if _sha256(row.selected_checkpoint.raw) != row.selected_checkpoint.sha256:
+                            raise BaseCohortLatencyError("selected checkpoint changed during visit")
+                    except Exception as error:
+                        visit_error = error
+                    finally:
+                        if system is not None:
+                            if peaks is None:
+                                try:
+                                    peaks = runtime.peak_memory(device)
+                                except Exception as error:
+                                    if visit_error is None:
+                                        visit_error = error
+                            try:
+                                runtime.release(system, device)
+                            except Exception as error:
+                                if visit_error is None:
+                                    visit_error = error
+                        try:
+                            after = runtime.snapshot()
+                        except Exception as error:
+                            if visit_error is None:
+                                visit_error = error
+                        else:
+                            try:
+                                _validate_snapshot(
+                                    after,
+                                    registered_uuid=registered_cuda_uuid,
+                                    runtime=runtime_identity,
+                                )
+                            except Exception as error:
+                                if visit_error is None:
+                                    visit_error = error
+                    if visit_error is not None:
+                        code = _failure_code(visit_error)
+                        valid_peaks = (
+                            type(peaks) is tuple
+                            and len(peaks) == 2
+                            and all(type(value) is int and value >= 0 for value in peaks)
+                        )
+                        failed_visit = BaseLatencyVisit(
+                            round_index=round_index,
+                            ordinal_in_round=ordinal,
+                            run_id=run_id,
+                            before=before,
+                            after=after,
+                            samples_ns=tuple(samples),
+                            peak_allocated_bytes=peaks[0] if valid_peaks else None,
+                            peak_reserved_bytes=peaks[1] if valid_peaks else None,
+                            completed=False,
+                            failure_code=code,
+                            warmups_completed=warmups_completed,
+                        )
+                        visits.append(failed_visit)
+                        if not observer_failed:
+                            observe(
+                                "visit_ended",
+                                round_index=round_index,
+                                ordinal_in_round=ordinal,
+                                run_id=run_id,
+                                outcome="HELD",
+                                after_observation_sha256=(
+                                    None if after is None else _canonical_digest(after.payload())
+                                ),
+                                peak_allocated_bytes=failed_visit.peak_allocated_bytes,
+                                peak_reserved_bytes=failed_visit.peak_reserved_bytes,
+                                failure_code=code,
+                            )
+                        raise visit_error
+                    completed_visit = BaseLatencyVisit(
+                        round_index=round_index,
+                        ordinal_in_round=ordinal,
+                        run_id=run_id,
+                        before=before,
+                        after=after,
+                        samples_ns=tuple(samples),
+                        peak_allocated_bytes=peaks[0],
+                        peak_reserved_bytes=peaks[1],
+                        completed=True,
+                        failure_code=None,
+                        warmups_completed=warmups_completed,
+                    )
+                    visits.append(completed_visit)
+                    observe(
+                        "visit_ended",
+                        round_index=round_index,
+                        ordinal_in_round=ordinal,
+                        run_id=run_id,
+                        outcome="COMPLETED",
+                        after_observation_sha256=_canonical_digest(after.payload()),
+                        peak_allocated_bytes=completed_visit.peak_allocated_bytes,
+                        peak_reserved_bytes=completed_visit.peak_reserved_bytes,
+                        failure_code=None,
+                    )
+    except BaseCohortLatencyHold:
+        raise
+    except Exception as error:
+        raise observed_hold(error) from error
+    if runtime_identity is None or post_context is None:
+        raise AssertionError("completed session lost its runtime admission")
+    visit_tuple = tuple(visits)
+    try:
+        rows = _row_observations(
+            visit_tuple,
+            checked_cohort,
+            checked_scores,
+            runtime_identity,
+            workload,
+        )
+        if (
+            _resolved_cohort_sha256(checked_cohort) != resolved_sha
+            or checked_scores.sha256 != scored_sha
+            or _workload_receipt(groups) != workload
+        ):
+            raise BaseCohortLatencyError("session input changed after measurement")
+    except Exception as error:
+        raise observed_hold(error) from error
+    status = ACTUAL_COMPLETE_STATUS if actual_runtime else FAKE_COMPLETE_STATUS
+    result = BaseCohortLatencySession(
+        registered_cuda_uuid=registered_cuda_uuid,
+        resolved_cohort_sha256=resolved_sha,
+        scored_cohort_sha256=scored_sha,
+        workload_receipt=workload,
+        admission_observation=admission_observation,
+        post_context_observation=post_context,
+        runtime_identity=runtime_identity,
+        visits=visit_tuple,
+        rows=rows,
+        actual_runtime=actual_runtime,
+        complete=True,
+        formal_samples_complete=actual_runtime,
+        failure_code=None,
+        status=status,
+    )
+    try:
+        observe(
+            "terminal",
+            outcome="COMPLETED",
+            latency_session_sha256=result.sha256,
+            failure_code=None,
+        )
+    except _LatencyObserverFailure as error:
+        raise held(error) from error
+    return result
+
+
+__all__ = [
+    "ACTUAL_COMPLETE_STATUS",
+    "AUTHORITY",
+    "BaseCohortLatencyError",
+    "BaseCohortLatencyHold",
+    "BaseCohortLatencySession",
+    "BaseLatencyRowObservation",
+    "BaseLatencyRuntimeIdentity",
+    "BaseLatencyVisit",
+    "BaseLatencyWorkloadReceipt",
+    "CUDA_ALLOCATOR_LIMIT_BYTES",
+    "FAKE_COMPLETE_STATUS",
+    "HOLD_STATUS",
+    "MINIMUM_FREE_MIB",
+    "LatencySessionObserver",
+    "ROUND_COUNT",
+    "SAMPLES_PER_ROW",
+    "SharedCudaObservation",
+    "TIMED_PER_VISIT",
+    "WARMUPS_PER_VISIT",
+    "build_frozen_latency_workload",
+    "cyclic_visit_schedule",
+    "run_base_cohort_latency_session",
+]
diff --git a/src/phaseset_core/base_cohort_qualification.py b/src/phaseset_core/base_cohort_qualification.py
new file mode 100644
index 0000000..7e35aed
--- /dev/null
+++ b/src/phaseset_core/base_cohort_qualification.py
@@ -0,0 +1,666 @@
+"""Assemble one verified nine-row base cohort into the existing selector.
+
+This module performs no scoring, latency measurement, training, winner override,
+or authorization.  It accepts only the resolver, scorer, and formal-latency
+objects, closes their shared bindings, derives every :class:`BaseScore` field,
+and invokes the already installed deterministic qualification path.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from fractions import Fraction
+import hashlib
+import json
+from pathlib import Path
+from types import ModuleType
+from typing import Final
+
+from . import base_cohort_latency as latency_module
+from . import base_cohort_resolver as resolver_module
+from . import base_cohort_validation as scoring_module
+from . import experiments as experiments_module
+from . import production as production_module
+
+
+AUTHORITY: Final = 0
+PRODUCTION: Final = False
+RESULT_CLAIMED: Final = False
+ROW_BINDING_SCHEMA: Final = "phaseset-base-qualification-combined-row-v1"
+DEPENDENCY_SCHEMA: Final = "phaseset-base-qualification-dependencies-v1"
+ASSEMBLY_SCHEMA: Final = "phaseset-base-cohort-qualification-assembly-v1"
+STATUS: Final = "BASE_QUALIFICATION_ASSEMBLED_NO_EXTERNAL_AUTHORIZATION"
+
+
+class BaseCohortQualificationError(ValueError):
+    """The three input evidence families cannot form one qualification."""
+
+
+def _sha256(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise BaseCohortQualificationError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _positive_int(value: object, label: str) -> int:
+    if type(value) is not int or not 1 <= value <= (1 << 63) - 1:
+        raise BaseCohortQualificationError(f"{label} must be an exact positive int")
+    return value
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _canonical_digest(value: object) -> str:
+    return _sha256(_canonical_json_bytes(value))
+
+
+def _fraction_payload(value: Fraction) -> dict[str, int]:
+    if type(value) is not Fraction or not Fraction(0, 1) <= value <= Fraction(1, 1):
+        raise BaseCohortQualificationError("base validation score must be an exact fraction")
+    return {"denominator": value.denominator, "numerator": value.numerator}
+
+
+def _source_bytes(module: ModuleType, label: str) -> bytes:
+    module_file = getattr(module, "__file__", None)
+    if type(module_file) is not str:
+        raise BaseCohortQualificationError(f"installed {label} source path is unavailable")
+    path = Path(module_file)
+    if path.suffix in {".pyc", ".pyo"}:
+        path = path.with_suffix(".py")
+    if path.is_symlink() or not path.is_file():
+        raise BaseCohortQualificationError(f"installed {label} source is irregular")
+    raw = path.read_bytes()
+    if not raw:
+        raise BaseCohortQualificationError(f"installed {label} source is empty")
+    return raw
+
+
+def _admission_sha256(value: resolver_module.BaseCohortAdmission) -> str:
+    return _canonical_digest(
+        {
+            "bf16_runtime_qualified": value.bf16_runtime_qualified,
+            "checkpoint_every_updates": value.checkpoint_every_updates,
+            "device": value.device,
+            "edge_budget": value.edge_budget,
+            "matrix_sha256": value.matrix_sha256,
+            "plan_sha256": value.plan_sha256,
+            "request_bf16": value.request_bf16,
+            "source_tree_sha256": value.source_tree_sha256,
+            "train_manifest_sha256": value.train_manifest_sha256,
+            "training_config_sha256": value.training_config_sha256,
+            "val_manifest_sha256": value.val_manifest_sha256,
+        }
+    )
+
+
+def _samples_sha256(samples: tuple[int, ...]) -> str:
+    if type(samples) is not tuple or len(samples) != latency_module.SAMPLES_PER_ROW:
+        raise BaseCohortQualificationError("latency row must contain exact 99 samples")
+    for value in samples:
+        _positive_int(value, "latency sample")
+    return _canonical_digest(
+        {
+            "samples_ns": list(samples),
+            "schema": "phaseset-base-latency-raw-samples-v1",
+        }
+    )
+
+
+def _median_from_samples(samples: tuple[int, ...]) -> int:
+    _samples_sha256(samples)
+    ordered = tuple(sorted(samples))
+    return ordered[len(ordered) // 2]
+
+
+def _visit_samples_for_run(
+    latency: latency_module.BaseCohortLatencySession,
+    run_id: str,
+) -> tuple[int, ...]:
+    matching = tuple(visit for visit in latency.visits if visit.run_id == run_id)
+    if len(matching) != latency_module.ROUND_COUNT or any(
+        visit.completed is not True or len(visit.samples_ns) != latency_module.TIMED_PER_VISIT
+        for visit in matching
+    ):
+        raise BaseCohortQualificationError(
+            "latency visit census is incomplete for a qualification row"
+        )
+    samples = tuple(sample for visit in matching for sample in visit.samples_ns)
+    _samples_sha256(samples)
+    return samples
+
+
+@dataclass(frozen=True, slots=True)
+class BaseQualificationDependencyBinding:
+    admission_sha256: str
+    resolved_cohort_sha256: str
+    scored_cohort_sha256: str
+    latency_session_sha256: str
+    plan_sha256: str
+    matrix_sha256: str
+    training_config_sha256: str
+    source_tree_sha256: str
+    train_manifest_sha256: str
+    admitted_val_census_sha256: str
+    validation_source_manifest_sha256: str
+    query_census_sha256: str
+    resolver_source_sha256: str
+    scorer_source_sha256: str
+    latency_source_sha256: str
+    assembler_source_sha256: str
+    production_source_sha256: str
+    selector_artifact_sha256: str
+    runtime_sha256: str
+    environment_sha256: str
+    precision_mode: str
+    schema: str = DEPENDENCY_SCHEMA
+
+    def __post_init__(self) -> None:
+        for name in (
+            "admission_sha256",
+            "resolved_cohort_sha256",
+            "scored_cohort_sha256",
+            "latency_session_sha256",
+            "plan_sha256",
+            "matrix_sha256",
+            "training_config_sha256",
+            "source_tree_sha256",
+            "train_manifest_sha256",
+            "admitted_val_census_sha256",
+            "validation_source_manifest_sha256",
+            "query_census_sha256",
+            "resolver_source_sha256",
+            "scorer_source_sha256",
+            "latency_source_sha256",
+            "assembler_source_sha256",
+            "production_source_sha256",
+            "selector_artifact_sha256",
+            "runtime_sha256",
+            "environment_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        if self.admitted_val_census_sha256 != self.query_census_sha256:
+            raise BaseCohortQualificationError(
+                "admitted validation census differs from scored query census"
+            )
+        if self.precision_mode not in ("FP32", "BF16"):
+            raise BaseCohortQualificationError("qualification precision mode is invalid")
+        if self.schema != DEPENDENCY_SCHEMA:
+            raise BaseCohortQualificationError("qualification dependency schema changed")
+
+    @property
+    def sha256(self) -> str:
+        return _canonical_digest({name: getattr(self, name) for name in self.__dataclass_fields__})
+
+
+@dataclass(frozen=True, slots=True)
+class CombinedBaseScoreBinding:
+    run_id: str
+    attempt_id: str
+    system_id: str
+    seed: int
+    terminal_sha256: str
+    selected_checkpoint_sha256: str
+    selected_checkpoint_state_digest: str
+    validation_score_artifact_sha256: str
+    latency_row_sha256: str
+    latency_samples_sha256: str
+    parameter_census_sha256: str
+    parameter_count: int
+    frozen_runtime_latency_ns: int
+    bidirectional_capture_r1: Fraction
+    validation_manifest_sha256: str
+    validation_source_manifest_sha256: str
+    query_census_sha256: str
+    evaluator_sha256: str
+    runtime_sha256: str
+    environment_sha256: str
+    precision_mode: str
+    schema: str = ROW_BINDING_SCHEMA
+
+    def __post_init__(self) -> None:
+        role, seed, system_id = experiments_module.parse_run_id(self.run_id)
+        if role != "BASE_QUALIFICATION" or seed != self.seed or system_id != self.system_id:
+            raise BaseCohortQualificationError("combined base row identity is inconsistent")
+        if type(self.attempt_id) is not str or not self.attempt_id:
+            raise BaseCohortQualificationError("combined base attempt ID is invalid")
+        for name in (
+            "terminal_sha256",
+            "selected_checkpoint_sha256",
+            "selected_checkpoint_state_digest",
+            "validation_score_artifact_sha256",
+            "latency_row_sha256",
+            "latency_samples_sha256",
+            "parameter_census_sha256",
+            "validation_manifest_sha256",
+            "validation_source_manifest_sha256",
+            "query_census_sha256",
+            "evaluator_sha256",
+            "runtime_sha256",
+            "environment_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        _positive_int(self.parameter_count, "parameter_count")
+        _positive_int(self.frozen_runtime_latency_ns, "frozen_runtime_latency_ns")
+        _fraction_payload(self.bidirectional_capture_r1)
+        if self.precision_mode not in ("FP32", "BF16"):
+            raise BaseCohortQualificationError("combined row precision mode is invalid")
+        if self.schema != ROW_BINDING_SCHEMA:
+            raise BaseCohortQualificationError("combined row schema changed")
+
+    def canonical_bytes(self) -> bytes:
+        return _canonical_json_bytes(
+            {
+                "attempt_id": self.attempt_id,
+                "bidirectional_capture_r1": _fraction_payload(self.bidirectional_capture_r1),
+                "environment_sha256": self.environment_sha256,
+                "evaluator_sha256": self.evaluator_sha256,
+                "frozen_runtime_latency_ns": self.frozen_runtime_latency_ns,
+                "latency_row_sha256": self.latency_row_sha256,
+                "latency_samples_sha256": self.latency_samples_sha256,
+                "parameter_census_sha256": self.parameter_census_sha256,
+                "parameter_count": self.parameter_count,
+                "precision_mode": self.precision_mode,
+                "query_census_sha256": self.query_census_sha256,
+                "run_id": self.run_id,
+                "runtime_sha256": self.runtime_sha256,
+                "schema": self.schema,
+                "seed": self.seed,
+                "selected_checkpoint_sha256": self.selected_checkpoint_sha256,
+                "selected_checkpoint_state_digest": (self.selected_checkpoint_state_digest),
+                "system_id": self.system_id,
+                "terminal_sha256": self.terminal_sha256,
+                "validation_manifest_sha256": self.validation_manifest_sha256,
+                "validation_score_artifact_sha256": (self.validation_score_artifact_sha256),
+                "validation_source_manifest_sha256": (self.validation_source_manifest_sha256),
+            }
+        )
+
+    @property
+    def sha256(self) -> str:
+        return _sha256(self.canonical_bytes())
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class BaseCohortQualificationAssembly:
+    qualification: experiments_module.BaseQualification
+    execution_evidence: production_module.BaseQualificationExecutionEvidence
+    artifact: bytes = field(repr=False)
+    row_bindings: tuple[CombinedBaseScoreBinding, ...]
+    dependency_binding: BaseQualificationDependencyBinding
+    authority: int = AUTHORITY
+    production: bool = PRODUCTION
+    result_claimed: bool = RESULT_CLAIMED
+    schema: str = ASSEMBLY_SCHEMA
+    status: str = STATUS
+
+    def __post_init__(self) -> None:
+        if type(self.qualification) is not experiments_module.BaseQualification:
+            raise BaseCohortQualificationError("assembly qualification type is invalid")
+        if (
+            type(self.execution_evidence)
+            is not production_module.BaseQualificationExecutionEvidence
+        ):
+            raise BaseCohortQualificationError("assembly execution evidence type is invalid")
+        if type(self.artifact) is not bytes or not self.artifact:
+            raise BaseCohortQualificationError("assembly artifact must be nonempty bytes")
+        if (
+            type(self.row_bindings) is not tuple
+            or len(self.row_bindings) != len(experiments_module.base_run_ids())
+            or any(type(row) is not CombinedBaseScoreBinding for row in self.row_bindings)
+        ):
+            raise BaseCohortQualificationError("assembly combined row census is invalid")
+        if type(self.dependency_binding) is not BaseQualificationDependencyBinding:
+            raise BaseCohortQualificationError("assembly dependency binding is invalid")
+        if tuple(row.run_id for row in self.row_bindings) != experiments_module.base_run_ids():
+            raise BaseCohortQualificationError("assembly combined rows changed order")
+        if self.execution_evidence.score_rows != self.qualification.score_rows:
+            raise BaseCohortQualificationError("execution evidence differs from qualification rows")
+        if tuple(row.sha256 for row in self.row_bindings) != tuple(
+            row.score_artifact_sha256 for row in self.qualification.score_rows
+        ):
+            raise BaseCohortQualificationError("combined evidence differs from BaseScore rows")
+        if _sha256(self.execution_evidence.selector_artifact) != (
+            self.dependency_binding.selector_artifact_sha256
+        ):
+            raise BaseCohortQualificationError("execution selector differs from dependencies")
+        try:
+            canonical = experiments_module.canonical_base_qualification_bytes(self.qualification)
+        except (TypeError, ValueError, RuntimeError) as error:
+            raise BaseCohortQualificationError("assembled qualification is invalid") from error
+        if self.artifact != canonical:
+            raise BaseCohortQualificationError("assembly artifact is not canonical qualification")
+        if (
+            type(self.authority) is not int
+            or self.authority != AUTHORITY
+            or self.production is not PRODUCTION
+            or self.result_claimed is not RESULT_CLAIMED
+            or self.schema != ASSEMBLY_SCHEMA
+            or self.status != STATUS
+        ):
+            raise BaseCohortQualificationError("assembly result header is invalid")
+
+    @property
+    def sha256(self) -> str:
+        return _canonical_digest(
+            {
+                "artifact_sha256": _sha256(self.artifact),
+                "authority": self.authority,
+                "dependency_binding_sha256": self.dependency_binding.sha256,
+                "production": self.production,
+                "result_claimed": self.result_claimed,
+                "row_binding_sha256s": [row.sha256 for row in self.row_bindings],
+                "schema": self.schema,
+                "status": self.status,
+            }
+        )
+
+    def __repr__(self) -> str:
+        return (
+            "BaseCohortQualificationAssembly("
+            f"winner={self.qualification.winner_system_id!r}, "
+            f"sha256={self.sha256!r})"
+        )
+
+
+def _installed_code_snapshot() -> tuple[dict[str, str], bytes]:
+    selector_artifact = production_module.current_base_qualification_artifact_bytes()
+    if type(selector_artifact) is not bytes or not selector_artifact:
+        raise BaseCohortQualificationError("installed selector artifact is invalid")
+    sources = {
+        "resolver_source_sha256": _sha256(_source_bytes(resolver_module, "base cohort resolver")),
+        "scorer_source_sha256": _sha256(_source_bytes(scoring_module, "base cohort validation")),
+        "latency_source_sha256": _sha256(_source_bytes(latency_module, "base cohort latency")),
+        "assembler_source_sha256": _sha256(
+            _source_bytes(
+                __import__(__name__, fromlist=["*"]),
+                "base cohort qualification",
+            )
+        ),
+        "production_source_sha256": _sha256(_source_bytes(production_module, "production adapter")),
+        "selector_artifact_sha256": _sha256(selector_artifact),
+    }
+    return sources, selector_artifact
+
+
+def _checked_inputs(
+    cohort: object,
+    admission: object,
+    scored_cohort: object,
+    latency_session: object,
+) -> tuple[
+    resolver_module.ResolvedBaseCohort,
+    resolver_module.BaseCohortAdmission,
+    scoring_module.BaseCohortValidationResult,
+    latency_module.BaseCohortLatencySession,
+]:
+    try:
+        checked_cohort, checked_admission, checked_scores = latency_module._checked_inputs(
+            cohort,
+            admission,
+            scored_cohort,
+        )
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortQualificationError(
+            "resolver and scorer inputs do not form one admitted cohort"
+        ) from error
+    if type(latency_session) is not latency_module.BaseCohortLatencySession:
+        raise BaseCohortQualificationError("latency input must be exact formal session")
+    if (
+        latency_session.authority != latency_module.AUTHORITY
+        or latency_session.production is not latency_module.PRODUCTION
+        or latency_session.result_claimed is not latency_module.RESULT_CLAIMED
+        or latency_session.status != latency_module.ACTUAL_COMPLETE_STATUS
+        or latency_session.actual_runtime is not True
+        or latency_session.complete is not True
+        or latency_session.formal_samples_complete is not True
+        or latency_session.failure_code is not None
+    ):
+        raise BaseCohortQualificationError("latency session is not one actual complete session")
+    if (
+        latency_session.resolved_cohort_sha256
+        != latency_module._resolved_cohort_sha256(checked_cohort)
+        or latency_session.scored_cohort_sha256 != checked_scores.sha256
+    ):
+        raise BaseCohortQualificationError("latency session differs from resolver or scorer")
+    runtime = latency_session.runtime_identity
+    if type(runtime) is not latency_module.BaseLatencyRuntimeIdentity:
+        raise BaseCohortQualificationError("latency session runtime identity is absent")
+    if (
+        runtime.device_uuid != latency_session.registered_cuda_uuid
+        or latency_session.admission_observation.device_uuid != runtime.device_uuid
+        or latency_session.post_context_observation is None
+        or latency_session.post_context_observation.device_uuid != runtime.device_uuid
+    ):
+        raise BaseCohortQualificationError("latency session CUDA identity is inconsistent")
+    expected_runs = experiments_module.base_run_ids()
+    if tuple(row.run_id for row in latency_session.rows) != expected_runs:
+        raise BaseCohortQualificationError("latency session row order is incomplete")
+    return checked_cohort, checked_admission, checked_scores, latency_session
+
+
+def _build_row_bindings(
+    cohort: resolver_module.ResolvedBaseCohort,
+    scored: scoring_module.BaseCohortValidationResult,
+    latency: latency_module.BaseCohortLatencySession,
+    evaluator_sha256: str,
+) -> tuple[CombinedBaseScoreBinding, ...]:
+    runtime = latency.runtime_identity
+    if type(runtime) is not latency_module.BaseLatencyRuntimeIdentity:
+        raise BaseCohortQualificationError("formal latency runtime is absent")
+    rows: list[CombinedBaseScoreBinding] = []
+    for resolved, score, measured in zip(
+        cohort.rows,
+        scored.observations,
+        latency.rows,
+        strict=True,
+    ):
+        visit_samples = _visit_samples_for_run(latency, resolved.run_id)
+        samples_sha256 = _samples_sha256(visit_samples)
+        measured_median = _median_from_samples(visit_samples)
+        if (
+            resolved.run_id != score.run_id
+            or resolved.run_id != measured.run_id
+            or resolved.attempt_id != score.attempt_id
+            or resolved.system_id != score.system_id
+            or resolved.system_id != measured.system_id
+            or resolved.seed != score.seed
+            or resolved.seed != measured.seed
+            or resolved.terminal_sha256 != score.terminal_sha256
+            or resolved.selected_checkpoint.sha256 != score.selected_checkpoint_sha256
+            or resolved.selected_checkpoint.sha256 != measured.selected_checkpoint_sha256
+            or resolved.selected_checkpoint.state_digest != score.selected_checkpoint_state_digest
+            or resolved.selected_checkpoint.state_digest
+            != measured.selected_checkpoint_state_digest
+        ):
+            raise BaseCohortQualificationError("qualification row identity or checkpoint changed")
+        if _sha256(resolved.selected_checkpoint.raw) != resolved.selected_checkpoint.sha256:
+            raise BaseCohortQualificationError("selected-best checkpoint bytes changed")
+        if (
+            measured.parameter_census_sha256 != score.parameter_census.sha256
+            or measured.parameter_count != score.parameter_census.total
+            or measured.runtime_sha256 != runtime.sha256
+            or measured.samples_ns != visit_samples
+            or measured_median != measured.median_ns
+            or score.environment_sha256 != runtime.environment_sha256
+            or score.precision_mode != runtime.precision_mode
+            or score.device != runtime.device
+            or score.admitted_val_census_sha256 != cohort.val_manifest_sha256
+            or score.query_census_sha256 != cohort.val_manifest_sha256
+            or score.source_manifest_sha256 != scored.source_manifest_sha256
+        ):
+            raise BaseCohortQualificationError(
+                "qualification row score, parameter, runtime, or census changed"
+            )
+        rows.append(
+            CombinedBaseScoreBinding(
+                run_id=resolved.run_id,
+                attempt_id=resolved.attempt_id,
+                system_id=resolved.system_id,
+                seed=resolved.seed,
+                terminal_sha256=resolved.terminal_sha256,
+                selected_checkpoint_sha256=resolved.selected_checkpoint.sha256,
+                selected_checkpoint_state_digest=(resolved.selected_checkpoint.state_digest),
+                validation_score_artifact_sha256=score.score_artifact_sha256,
+                latency_row_sha256=measured.sha256,
+                latency_samples_sha256=samples_sha256,
+                parameter_census_sha256=score.parameter_census.sha256,
+                parameter_count=score.parameter_census.total,
+                frozen_runtime_latency_ns=measured_median,
+                bidirectional_capture_r1=score.primary_capture_r1,
+                validation_manifest_sha256=cohort.val_manifest_sha256,
+                validation_source_manifest_sha256=score.source_manifest_sha256,
+                query_census_sha256=score.query_census_sha256,
+                evaluator_sha256=evaluator_sha256,
+                runtime_sha256=runtime.sha256,
+                environment_sha256=runtime.environment_sha256,
+                precision_mode=runtime.precision_mode,
+            )
+        )
+    if tuple(row.run_id for row in rows) != experiments_module.base_run_ids():
+        raise BaseCohortQualificationError("combined qualification rows are incomplete")
+    return tuple(rows)
+
+
+def _base_scores(
+    row_bindings: tuple[CombinedBaseScoreBinding, ...],
+) -> tuple[experiments_module.BaseScore, ...]:
+    return tuple(
+        experiments_module.BaseScore(
+            run_id=row.run_id,
+            bidirectional_r1_numerator=row.bidirectional_capture_r1.numerator,
+            bidirectional_r1_denominator=row.bidirectional_capture_r1.denominator,
+            parameter_count=row.parameter_count,
+            frozen_runtime_latency_ns=row.frozen_runtime_latency_ns,
+            terminal_sha256=row.terminal_sha256,
+            selected_checkpoint_sha256=row.selected_checkpoint_sha256,
+            split="validation",
+            validation_manifest_sha256=row.validation_manifest_sha256,
+            query_census_sha256=row.query_census_sha256,
+            evaluator_sha256=row.evaluator_sha256,
+            score_artifact_sha256=row.sha256,
+        )
+        for row in row_bindings
+    )
+
+
+def assemble_base_cohort_qualification(
+    cohort: resolver_module.ResolvedBaseCohort,
+    *,
+    admission: resolver_module.BaseCohortAdmission,
+    scored_cohort: scoring_module.BaseCohortValidationResult,
+    latency_session: latency_module.BaseCohortLatencySession,
+) -> BaseCohortQualificationAssembly:
+    """Derive all nine BaseScore rows and invoke the existing selector once."""
+
+    checked_cohort, checked_admission, checked_scores, checked_latency = _checked_inputs(
+        cohort,
+        admission,
+        scored_cohort,
+        latency_session,
+    )
+    before_sources, selector_artifact = _installed_code_snapshot()
+    evaluator_sha256 = before_sources["selector_artifact_sha256"]
+    row_bindings = _build_row_bindings(
+        checked_cohort,
+        checked_scores,
+        checked_latency,
+        evaluator_sha256,
+    )
+    score_rows = _base_scores(row_bindings)
+    try:
+        qualification = experiments_module.qualify_base(score_rows)
+        evidence = production_module.BaseQualificationExecutionEvidence(
+            score_rows=score_rows,
+            selector_artifact=selector_artifact,
+        )
+        artifact = production_module.build_base_qualification_execution_artifact(evidence)
+        canonical = experiments_module.canonical_base_qualification_bytes(qualification)
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise BaseCohortQualificationError(
+            "installed base qualification recomputation failed"
+        ) from error
+    if artifact != canonical:
+        raise BaseCohortQualificationError(
+            "production evidence and deterministic selector artifacts differ"
+        )
+    runtime = checked_latency.runtime_identity
+    if type(runtime) is not latency_module.BaseLatencyRuntimeIdentity:
+        raise AssertionError("checked formal latency lost runtime identity")
+    dependency = BaseQualificationDependencyBinding(
+        admission_sha256=_admission_sha256(checked_admission),
+        resolved_cohort_sha256=latency_module._resolved_cohort_sha256(checked_cohort),
+        scored_cohort_sha256=checked_scores.sha256,
+        latency_session_sha256=checked_latency.sha256,
+        plan_sha256=checked_cohort.plan_sha256,
+        matrix_sha256=checked_cohort.matrix_sha256,
+        training_config_sha256=checked_cohort.training_config_sha256,
+        source_tree_sha256=checked_cohort.source_tree_sha256,
+        train_manifest_sha256=checked_cohort.train_manifest_sha256,
+        admitted_val_census_sha256=checked_cohort.val_manifest_sha256,
+        validation_source_manifest_sha256=checked_scores.source_manifest_sha256,
+        query_census_sha256=checked_scores.query_census_sha256,
+        resolver_source_sha256=before_sources["resolver_source_sha256"],
+        scorer_source_sha256=before_sources["scorer_source_sha256"],
+        latency_source_sha256=before_sources["latency_source_sha256"],
+        assembler_source_sha256=before_sources["assembler_source_sha256"],
+        production_source_sha256=before_sources["production_source_sha256"],
+        selector_artifact_sha256=evaluator_sha256,
+        runtime_sha256=runtime.sha256,
+        environment_sha256=runtime.environment_sha256,
+        precision_mode=runtime.precision_mode,
+    )
+    after_sources, after_selector = _installed_code_snapshot()
+    if after_sources != before_sources or after_selector != selector_artifact:
+        raise BaseCohortQualificationError("installed dependency bytes changed during assembly")
+    if (
+        latency_module._resolved_cohort_sha256(checked_cohort) != dependency.resolved_cohort_sha256
+        or checked_scores.sha256 != dependency.scored_cohort_sha256
+        or checked_latency.sha256 != dependency.latency_session_sha256
+        or _admission_sha256(checked_admission) != dependency.admission_sha256
+        or any(
+            _sha256(row.selected_checkpoint.raw) != row.selected_checkpoint.sha256
+            for row in checked_cohort.rows
+        )
+    ):
+        raise BaseCohortQualificationError("qualification inputs changed during assembly")
+    return BaseCohortQualificationAssembly(
+        qualification=qualification,
+        execution_evidence=evidence,
+        artifact=artifact,
+        row_bindings=row_bindings,
+        dependency_binding=dependency,
+    )
+
+
+__all__ = [
+    "ASSEMBLY_SCHEMA",
+    "AUTHORITY",
+    "BaseCohortQualificationAssembly",
+    "BaseCohortQualificationError",
+    "BaseQualificationDependencyBinding",
+    "CombinedBaseScoreBinding",
+    "PRODUCTION",
+    "RESULT_CLAIMED",
+    "STATUS",
+    "assemble_base_cohort_qualification",
+]
diff --git a/src/phaseset_core/latency_progress_journal.py b/src/phaseset_core/latency_progress_journal.py
new file mode 100644
index 0000000..efeae98
--- /dev/null
+++ b/src/phaseset_core/latency_progress_journal.py
@@ -0,0 +1,1194 @@
+"""POSIX write-once progress journal for the formal base-latency controller.
+
+The journal preserves a durable event prefix across ordinary failures or a
+hard process kill.  It is diagnostic evidence only: neither a terminal event
+nor a complete prefix is accepted as formal latency or qualification evidence.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, replace
+import errno
+import hashlib
+import json
+import os
+from pathlib import Path
+import re
+import stat
+import time
+from typing import Callable, Final
+
+from . import base_cohort_latency as latency_module
+
+
+AUTHORITY: Final = 0
+PRODUCTION: Final = False
+RESULT_CLAIMED: Final = False
+EVENT_SCHEMA: Final = "phaseset-latency-progress-event-v1"
+MARKER_SCHEMA: Final = "phaseset-latency-progress-completion-marker-v1"
+BINDING_SCHEMA: Final = "phaseset-latency-progress-binding-v1"
+READ_SCHEMA: Final = "phaseset-latency-progress-read-v1"
+INCOMPLETE_STATUS: Final = "INCOMPLETE_PREFIX_NOT_FORMAL"
+COMPLETED_STATUS: Final = "TERMINAL_COMPLETED_PREFIX_NOT_FORMAL"
+HELD_STATUS: Final = "TERMINAL_HELD_PREFIX_NOT_FORMAL"
+MAX_EVENTS: Final = 4096
+MAX_EVENT_BYTES: Final = 64 * 1024
+MAX_MARKER_BYTES: Final = 1024
+ZERO_SHA256: Final = "0" * 64
+
+_SESSION_ID = re.compile(r"[A-Za-z0-9][A-Za-z0-9._-]{0,63}")
+_EVENT_NAME = re.compile(r"event-([0-9]{8})\.json")
+_MARKER_NAME = re.compile(r"event-([0-9]{8})\.complete")
+_EVENT_TYPES = frozenset(
+    {
+        "SESSION_START",
+        "CONTEXT_READY",
+        "VISIT_START",
+        "WARMUP_COMPLETED",
+        "TIMED_SAMPLE_COMPLETED",
+        "VISIT_END",
+        "TERMINAL",
+    }
+)
+_FAILURE_CODES = frozenset(
+    {
+        "CUDA_OUT_OF_MEMORY",
+        "SESSION_TIMEOUT",
+        "RUNTIME_DRIFT",
+        "RESOURCE_LIMIT",
+        "CHECKPOINT_OR_SCORE_DRIFT",
+        "MALFORMED_MEASUREMENT",
+        "UNEXPECTED_SESSION_FAILURE",
+    }
+)
+
+
+class LatencyJournalError(ValueError):
+    """The journal request, durable record, or on-disk tree is invalid."""
+
+
+class LatencyJournalWriteError(RuntimeError):
+    """A write failed; the session directory retains its physical prefix."""
+
+
+def _sha256(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise LatencyJournalError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _positive_int(value: object, label: str) -> int:
+    if type(value) is not int or not 1 <= value <= (1 << 63) - 1:
+        raise LatencyJournalError(f"{label} must be an exact positive int")
+    return value
+
+
+def _nonnegative_int(value: object, label: str) -> int:
+    if type(value) is not int or not 0 <= value <= (1 << 63) - 1:
+        raise LatencyJournalError(f"{label} must be an exact nonnegative int")
+    return value
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _reject_float(value: str) -> object:
+    raise LatencyJournalError(f"floating-point JSON is forbidden: {value}")
+
+
+def _reject_constant(value: str) -> object:
+    raise LatencyJournalError(f"non-finite JSON is forbidden: {value}")
+
+
+def _object_without_duplicates(pairs: list[tuple[str, object]]) -> dict[str, object]:
+    result: dict[str, object] = {}
+    for key, value in pairs:
+        if key in result:
+            raise LatencyJournalError(f"duplicate JSON key: {key}")
+        result[key] = value
+    return result
+
+
+def _parse_canonical_object(raw: bytes, label: str) -> dict[str, object]:
+    if type(raw) is not bytes:
+        raise LatencyJournalError(f"{label} must be exact bytes")
+    try:
+        value = json.loads(
+            raw.decode("ascii"),
+            object_pairs_hook=_object_without_duplicates,
+            parse_float=_reject_float,
+            parse_constant=_reject_constant,
+        )
+    except (UnicodeDecodeError, RecursionError, ValueError) as error:
+        raise LatencyJournalError(f"{label} is not strict canonical JSON") from error
+    if type(value) is not dict or _canonical_json_bytes(value) != raw:
+        raise LatencyJournalError(f"{label} differs from canonical JSON bytes")
+    return value
+
+
+def _exact_keys(value: dict[str, object], expected: set[str], label: str) -> None:
+    if set(value) != expected:
+        raise LatencyJournalError(f"{label} keys changed")
+
+
+def _safe_session_id(value: object) -> str:
+    if type(value) is not str or _SESSION_ID.fullmatch(value) is None or value in {".", ".."}:
+        raise LatencyJournalError("session_id must be one safe ASCII component")
+    return value
+
+
+def _require_posix() -> None:
+    if os.name != "posix" or not all(
+        hasattr(os, name) for name in ("O_DIRECTORY", "O_NOFOLLOW", "O_CLOEXEC")
+    ):
+        raise LatencyJournalError("latency journal v1 requires POSIX nofollow support")
+
+
+@dataclass(frozen=True, slots=True)
+class LatencyJournalBindings:
+    resolved_cohort_sha256: str
+    scored_cohort_sha256: str
+    source_tree_sha256: str
+    latency_protocol_sha256: str
+    wrapper_sha256: str
+    registered_cuda_uuid: str
+    schema: str = BINDING_SCHEMA
+
+    def __post_init__(self) -> None:
+        for name in (
+            "resolved_cohort_sha256",
+            "scored_cohort_sha256",
+            "source_tree_sha256",
+            "latency_protocol_sha256",
+            "wrapper_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        if (
+            type(self.registered_cuda_uuid) is not str
+            or not self.registered_cuda_uuid.startswith("GPU-")
+            or len(self.registered_cuda_uuid) > 80
+        ):
+            raise LatencyJournalError("registered CUDA UUID is invalid")
+        if self.schema != BINDING_SCHEMA:
+            raise LatencyJournalError("latency journal binding schema changed")
+
+    def payload(self) -> dict[str, object]:
+        return {
+            "latency_protocol_sha256": self.latency_protocol_sha256,
+            "registered_cuda_uuid": self.registered_cuda_uuid,
+            "resolved_cohort_sha256": self.resolved_cohort_sha256,
+            "schema": self.schema,
+            "scored_cohort_sha256": self.scored_cohort_sha256,
+            "source_tree_sha256": self.source_tree_sha256,
+            "wrapper_sha256": self.wrapper_sha256,
+        }
+
+    @property
+    def sha256(self) -> str:
+        return _sha256(_canonical_json_bytes(self.payload()))
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class LatencyJournalRecord:
+    sequence: int
+    event_type: str
+    previous_event_sha256: str
+    binding_sha256: str
+    raw: bytes
+    marker_raw: bytes
+
+    def __post_init__(self) -> None:
+        _nonnegative_int(self.sequence, "event sequence")
+        if self.event_type not in _EVENT_TYPES:
+            raise LatencyJournalError("event type is outside the closed vocabulary")
+        _lower_sha256(self.previous_event_sha256, "previous_event_sha256")
+        _lower_sha256(self.binding_sha256, "binding_sha256")
+        if type(self.raw) is not bytes or not self.raw or len(self.raw) > MAX_EVENT_BYTES:
+            raise LatencyJournalError("journal record raw bytes are invalid")
+        if (
+            type(self.marker_raw) is not bytes
+            or not self.marker_raw
+            or len(self.marker_raw) > MAX_MARKER_BYTES
+        ):
+            raise LatencyJournalError("journal marker raw bytes are invalid")
+
+    @property
+    def sha256(self) -> str:
+        return _sha256(self.raw)
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class LatencyJournalReadResult:
+    session_id: str
+    bindings: LatencyJournalBindings
+    records: tuple[LatencyJournalRecord, ...]
+    committed_event_raw: tuple[bytes, ...]
+    committed_marker_raw: tuple[bytes, ...]
+    uncommitted_tail_files: tuple[tuple[str, bytes], ...]
+    status: str
+    terminal_outcome: str | None
+    formal_latency: bool = False
+    authority: int = AUTHORITY
+    production: bool = PRODUCTION
+    result_claimed: bool = RESULT_CLAIMED
+    schema: str = READ_SCHEMA
+
+    def __post_init__(self) -> None:
+        _safe_session_id(self.session_id)
+        if type(self.bindings) is not LatencyJournalBindings:
+            raise LatencyJournalError("journal read bindings are invalid")
+        if type(self.records) is not tuple or any(
+            type(record) is not LatencyJournalRecord for record in self.records
+        ):
+            raise LatencyJournalError("journal record census is invalid")
+        if self.committed_event_raw != tuple(record.raw for record in self.records):
+            raise LatencyJournalError("journal committed event bytes changed")
+        if self.committed_marker_raw != tuple(record.marker_raw for record in self.records):
+            raise LatencyJournalError("journal committed marker bytes changed")
+        if type(self.uncommitted_tail_files) is not tuple or len(self.uncommitted_tail_files) > 2:
+            raise LatencyJournalError("journal uncommitted tail census is invalid")
+        for name, raw in self.uncommitted_tail_files:
+            if type(name) is not str or type(raw) is not bytes:
+                raise LatencyJournalError("journal uncommitted tail is malformed")
+            limit = MAX_MARKER_BYTES if name.endswith(".complete") else MAX_EVENT_BYTES
+            if len(raw) > limit:
+                raise LatencyJournalError("journal uncommitted tail exceeds its byte bound")
+        expected = {
+            INCOMPLETE_STATUS: None,
+            COMPLETED_STATUS: "COMPLETED",
+            HELD_STATUS: "HELD",
+        }
+        if self.status not in expected or self.terminal_outcome != expected[self.status]:
+            raise LatencyJournalError("journal read terminal status is inconsistent")
+        if self.status != INCOMPLETE_STATUS and self.uncommitted_tail_files:
+            raise LatencyJournalError("terminal journal cannot contain an extra tail")
+        if (
+            self.formal_latency is not False
+            or type(self.authority) is not int
+            or self.authority != AUTHORITY
+            or self.production is not PRODUCTION
+            or self.result_claimed is not RESULT_CLAIMED
+            or self.schema != READ_SCHEMA
+        ):
+            raise LatencyJournalError("journal read result cannot claim formal authority")
+
+    @property
+    def sha256(self) -> str:
+        return _sha256(
+            _canonical_json_bytes(
+                {
+                    "authority": self.authority,
+                    "binding_sha256": self.bindings.sha256,
+                    "event_sha256s": [record.sha256 for record in self.records],
+                    "formal_latency": self.formal_latency,
+                    "production": self.production,
+                    "result_claimed": self.result_claimed,
+                    "schema": self.schema,
+                    "session_id": self.session_id,
+                    "status": self.status,
+                    "tail_files": [
+                        {"name": name, "sha256": _sha256(raw)}
+                        for name, raw in self.uncommitted_tail_files
+                    ],
+                    "terminal_outcome": self.terminal_outcome,
+                }
+            )
+        )
+
+
+@dataclass(frozen=True, slots=True)
+class _JournalState:
+    stage: str = "EMPTY"
+    visit_index: int = 0
+    warmups_completed: int = 0
+    timed_completed: int = 0
+    held_failure_code: str | None = None
+    terminal_outcome: str | None = None
+
+    def transition(self, event_type: str, payload: dict[str, object]) -> _JournalState:
+        if self.terminal_outcome is not None:
+            raise LatencyJournalError("journal contains an event after terminal")
+        if event_type == "SESSION_START":
+            _exact_keys(
+                payload,
+                {"admission_observation_sha256", "bindings", "started_unix_ns"},
+                "SESSION_START payload",
+            )
+            _lower_sha256(
+                payload["admission_observation_sha256"],
+                "admission_observation_sha256",
+            )
+            _positive_int(payload["started_unix_ns"], "started_unix_ns")
+            if self.stage != "EMPTY":
+                raise LatencyJournalError("SESSION_START must be the first event")
+            return replace(self, stage="STARTED")
+        if event_type == "CONTEXT_READY":
+            _exact_keys(
+                payload,
+                {"post_context_observation_sha256", "runtime_sha256"},
+                "CONTEXT_READY payload",
+            )
+            _lower_sha256(payload["runtime_sha256"], "runtime_sha256")
+            _lower_sha256(
+                payload["post_context_observation_sha256"],
+                "post_context_observation_sha256",
+            )
+            if self.stage != "STARTED":
+                raise LatencyJournalError("CONTEXT_READY is out of order")
+            return replace(self, stage="BETWEEN_VISITS")
+        if event_type == "VISIT_START":
+            self._validate_visit_identity(payload, {"before_observation_sha256"})
+            _lower_sha256(payload["before_observation_sha256"], "before_observation_sha256")
+            if self.stage != "BETWEEN_VISITS" or self.visit_index >= 81:
+                raise LatencyJournalError("VISIT_START is out of order")
+            return replace(
+                self,
+                stage="ACTIVE_VISIT",
+                warmups_completed=0,
+                timed_completed=0,
+            )
+        if event_type == "WARMUP_COMPLETED":
+            self._validate_visit_identity(payload, {"warmup_index"})
+            index = _nonnegative_int(payload["warmup_index"], "warmup_index")
+            if (
+                self.stage != "ACTIVE_VISIT"
+                or self.timed_completed != 0
+                or index != self.warmups_completed
+                or index >= latency_module.WARMUPS_PER_VISIT
+            ):
+                raise LatencyJournalError("WARMUP_COMPLETED is out of order")
+            return replace(self, warmups_completed=self.warmups_completed + 1)
+        if event_type == "TIMED_SAMPLE_COMPLETED":
+            self._validate_visit_identity(payload, {"sample_index", "sample_ns"})
+            index = _nonnegative_int(payload["sample_index"], "sample_index")
+            _positive_int(payload["sample_ns"], "sample_ns")
+            if (
+                self.stage != "ACTIVE_VISIT"
+                or self.warmups_completed != latency_module.WARMUPS_PER_VISIT
+                or index != self.timed_completed
+                or index >= latency_module.TIMED_PER_VISIT
+            ):
+                raise LatencyJournalError("TIMED_SAMPLE_COMPLETED is out of order")
+            return replace(self, timed_completed=self.timed_completed + 1)
+        if event_type == "VISIT_END":
+            self._validate_visit_identity(
+                payload,
+                {
+                    "after_observation_sha256",
+                    "failure_code",
+                    "outcome",
+                    "peak_allocated_bytes",
+                    "peak_reserved_bytes",
+                },
+            )
+            if self.stage != "ACTIVE_VISIT":
+                raise LatencyJournalError("VISIT_END is out of order")
+            outcome = payload["outcome"]
+            if outcome == "COMPLETED":
+                if (
+                    self.warmups_completed != latency_module.WARMUPS_PER_VISIT
+                    or self.timed_completed != latency_module.TIMED_PER_VISIT
+                    or payload["failure_code"] is not None
+                ):
+                    raise LatencyJournalError("completed visit has an incomplete event census")
+                _lower_sha256(
+                    payload["after_observation_sha256"],
+                    "after_observation_sha256",
+                )
+                _nonnegative_int(payload["peak_allocated_bytes"], "peak_allocated_bytes")
+                _nonnegative_int(payload["peak_reserved_bytes"], "peak_reserved_bytes")
+                return replace(
+                    self,
+                    stage="BETWEEN_VISITS",
+                    visit_index=self.visit_index + 1,
+                    warmups_completed=0,
+                    timed_completed=0,
+                )
+            if outcome != "HELD":
+                raise LatencyJournalError("visit outcome is outside the closed vocabulary")
+            failure = payload["failure_code"]
+            if type(failure) is not str or failure not in _FAILURE_CODES:
+                raise LatencyJournalError("held visit failure_code is invalid")
+            for name in (
+                "after_observation_sha256",
+                "peak_allocated_bytes",
+                "peak_reserved_bytes",
+            ):
+                value = payload[name]
+                if value is not None:
+                    if name.endswith("sha256"):
+                        _lower_sha256(value, name)
+                    else:
+                        _nonnegative_int(value, name)
+            if (payload["peak_allocated_bytes"] is None) != (
+                payload["peak_reserved_bytes"] is None
+            ):
+                raise LatencyJournalError("held visit peak pair is incomplete")
+            return replace(self, stage="HELD_VISIT", held_failure_code=failure)
+        if event_type == "TERMINAL":
+            _exact_keys(
+                payload,
+                {"failure_code", "latency_session_sha256", "outcome"},
+                "TERMINAL payload",
+            )
+            _lower_sha256(payload["latency_session_sha256"], "latency_session_sha256")
+            outcome = payload["outcome"]
+            if outcome == "COMPLETED":
+                if (
+                    self.stage != "BETWEEN_VISITS"
+                    or self.visit_index != 81
+                    or payload["failure_code"] is not None
+                ):
+                    raise LatencyJournalError("completed terminal has an incomplete visit census")
+            elif outcome == "HELD":
+                failure = payload["failure_code"]
+                if type(failure) is not str or failure not in _FAILURE_CODES:
+                    raise LatencyJournalError("held terminal failure_code is invalid")
+                if self.stage not in {"STARTED", "BETWEEN_VISITS", "HELD_VISIT"}:
+                    raise LatencyJournalError("held terminal cannot skip an active visit end")
+                if self.stage == "HELD_VISIT" and failure != self.held_failure_code:
+                    raise LatencyJournalError(
+                        "held terminal failure_code differs from held visit"
+                    )
+            else:
+                raise LatencyJournalError("terminal outcome is outside the closed vocabulary")
+            return replace(self, stage="TERMINAL", terminal_outcome=outcome)
+        raise LatencyJournalError("event type is outside the closed vocabulary")
+
+    def _validate_visit_identity(
+        self,
+        payload: dict[str, object],
+        additional_keys: set[str],
+    ) -> None:
+        _exact_keys(
+            payload,
+            {"ordinal_in_round", "round_index", "run_id"} | additional_keys,
+            "visit event payload",
+        )
+        round_index = _nonnegative_int(payload["round_index"], "round_index")
+        ordinal = _nonnegative_int(payload["ordinal_in_round"], "ordinal_in_round")
+        if round_index >= latency_module.ROUND_COUNT or ordinal >= 9:
+            raise LatencyJournalError("visit coordinate is outside the schedule")
+        expected_index = round_index * 9 + ordinal
+        schedule = latency_module.cyclic_visit_schedule()
+        if (
+            expected_index != self.visit_index
+            or type(payload["run_id"]) is not str
+            or payload["run_id"] != schedule[round_index][ordinal]
+        ):
+            raise LatencyJournalError("visit identity differs from the cyclic schedule")
+
+
+def _event_payload(
+    *,
+    sequence: int,
+    session_id: str,
+    event_type: str,
+    previous_event_sha256: str,
+    binding_sha256: str,
+    recorded_unix_ns: int,
+    payload: dict[str, object],
+) -> dict[str, object]:
+    if event_type not in _EVENT_TYPES:
+        raise LatencyJournalError("event type is outside the closed vocabulary")
+    return {
+        "binding_sha256": binding_sha256,
+        "event_type": event_type,
+        "payload": payload,
+        "previous_event_sha256": previous_event_sha256,
+        "recorded_unix_ns": recorded_unix_ns,
+        "schema": EVENT_SCHEMA,
+        "sequence": sequence,
+        "session_id": session_id,
+    }
+
+
+def _event_names(sequence: int) -> tuple[str, str]:
+    if type(sequence) is not int or not 0 <= sequence < MAX_EVENTS:
+        raise LatencyJournalError("event sequence exceeds the fixed journal bound")
+    return f"event-{sequence:08d}.json", f"event-{sequence:08d}.complete"
+
+
+def _completion_marker_raw(sequence: int, event_sha256: str) -> bytes:
+    _lower_sha256(event_sha256, "completion event_sha256")
+    return _canonical_json_bytes(
+        {
+            "event_sha256": event_sha256,
+            "schema": MARKER_SCHEMA,
+            "sequence": sequence,
+        }
+    )
+
+
+def _stat_identity(value: os.stat_result) -> tuple[int, int, int, int, int, int, int]:
+    return (
+        value.st_dev,
+        value.st_ino,
+        value.st_mode,
+        value.st_nlink,
+        value.st_size,
+        value.st_mtime_ns,
+        value.st_ctime_ns,
+    )
+
+
+def _directory_identity(value: os.stat_result) -> tuple[int, int, int]:
+    return value.st_dev, value.st_ino, value.st_mode
+
+
+def _open_directory(path: Path, *, private: bool) -> tuple[int, os.stat_result]:
+    _require_posix()
+    try:
+        before = path.lstat()
+        fd = os.open(path, os.O_RDONLY | os.O_DIRECTORY | os.O_NOFOLLOW | os.O_CLOEXEC)
+    except OSError as error:
+        raise LatencyJournalError("journal directory could not be opened safely") from error
+    try:
+        opened = os.fstat(fd)
+        if (
+            not stat.S_ISDIR(before.st_mode)
+            or not stat.S_ISDIR(opened.st_mode)
+            or _stat_identity(before) != _stat_identity(opened)
+            or (private and stat.S_IMODE(opened.st_mode) & 0o077)
+        ):
+            raise LatencyJournalError("journal directory identity or mode is unsafe")
+    except Exception:
+        os.close(fd)
+        raise
+    return fd, opened
+
+
+def _check_directory_stable(path: Path, fd: int, opened: os.stat_result) -> None:
+    try:
+        current_fd = os.fstat(fd)
+        current_path = path.lstat()
+    except OSError as error:
+        raise LatencyJournalError("journal directory disappeared") from error
+    if _directory_identity(current_fd) != _directory_identity(opened) or _directory_identity(
+        current_path
+    ) != _directory_identity(opened):
+        raise LatencyJournalError("journal directory identity changed")
+
+
+def _check_directory_unchanged(path: Path, fd: int, expected: os.stat_result) -> None:
+    try:
+        current_fd = os.fstat(fd)
+        current_path = path.lstat()
+    except OSError as error:
+        raise LatencyJournalError("journal directory disappeared during read") from error
+    if _stat_identity(current_fd) != _stat_identity(expected) or _stat_identity(
+        current_path
+    ) != _stat_identity(expected):
+        raise LatencyJournalError("journal directory changed during read")
+
+
+def _write_exclusive_at(directory_fd: int, name: str, raw: bytes, limit: int) -> None:
+    if type(raw) is not bytes or not raw or len(raw) > limit:
+        raise LatencyJournalError("journal file exceeds its fixed byte bound")
+    flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_NOFOLLOW | os.O_CLOEXEC
+    fd: int | None = None
+    try:
+        fd = os.open(name, flags, 0o600, dir_fd=directory_fd)
+        opened = os.fstat(fd)
+        if not stat.S_ISREG(opened.st_mode) or opened.st_nlink != 1:
+            raise LatencyJournalError("new journal file is not one regular link")
+        view = memoryview(raw)
+        written = 0
+        while written < len(view):
+            count = os.write(fd, view[written:])
+            if count <= 0:
+                raise OSError(errno.ENOSPC, "journal write made no progress")
+            written += count
+        os.fsync(fd)
+    finally:
+        if fd is not None:
+            os.close(fd)
+
+
+def _read_regular_at(directory_fd: int, name: str, limit: int) -> bytes:
+    try:
+        before = os.stat(name, dir_fd=directory_fd, follow_symlinks=False)
+        fd = os.open(
+            name,
+            os.O_RDONLY | os.O_NOFOLLOW | os.O_CLOEXEC,
+            dir_fd=directory_fd,
+        )
+    except OSError as error:
+        raise LatencyJournalError("journal leaf could not be opened safely") from error
+    try:
+        opened = os.fstat(fd)
+        if (
+            not stat.S_ISREG(before.st_mode)
+            or not stat.S_ISREG(opened.st_mode)
+            or before.st_nlink != 1
+            or opened.st_nlink != 1
+            or stat.S_IMODE(opened.st_mode) & 0o077
+            or _stat_identity(before) != _stat_identity(opened)
+            or opened.st_size > limit
+        ):
+            raise LatencyJournalError("journal leaf identity, type, or size is invalid")
+        chunks: list[bytes] = []
+        remaining = limit + 1
+        while remaining:
+            chunk = os.read(fd, min(remaining, 8192))
+            if not chunk:
+                break
+            chunks.append(chunk)
+            remaining -= len(chunk)
+        raw = b"".join(chunks)
+        after_fd = os.fstat(fd)
+        after_path = os.stat(name, dir_fd=directory_fd, follow_symlinks=False)
+        if (
+            len(raw) > limit
+            or len(raw) != opened.st_size
+            or _stat_identity(after_fd) != _stat_identity(opened)
+            or _stat_identity(after_path) != _stat_identity(opened)
+        ):
+            raise LatencyJournalError("journal leaf changed during its bounded read")
+        return raw
+    finally:
+        os.close(fd)
+
+
+def _bounded_directory_names(directory_fd: int) -> tuple[str, ...]:
+    names: list[str] = []
+    try:
+        with os.scandir(directory_fd) as entries:
+            for entry in entries:
+                if len(names) >= 2 * MAX_EVENTS:
+                    raise LatencyJournalError(
+                        "journal directory exceeds its fixed event bound"
+                    )
+                names.append(entry.name)
+    except LatencyJournalError:
+        raise
+    except OSError as error:
+        raise LatencyJournalError("journal directory could not be enumerated") from error
+    return tuple(names)
+
+
+class LatencyProgressJournalWriter:
+    """Typed, single-process writer for one freshly created journal directory."""
+
+    __slots__ = (
+        "_bindings",
+        "_clock_ns",
+        "_closed",
+        "_directory_fd",
+        "_directory_stat",
+        "_failed",
+        "_next_sequence",
+        "_path",
+        "_previous_sha256",
+        "_session_id",
+        "_state",
+    )
+
+    def __init__(
+        self,
+        path: Path,
+        directory_fd: int,
+        directory_stat: os.stat_result,
+        session_id: str,
+        bindings: LatencyJournalBindings,
+        clock_ns: Callable[[], int],
+    ) -> None:
+        self._path = path
+        self._directory_fd = directory_fd
+        self._directory_stat = directory_stat
+        self._session_id = session_id
+        self._bindings = bindings
+        self._clock_ns = clock_ns
+        self._next_sequence = 0
+        self._previous_sha256 = ZERO_SHA256
+        self._state = _JournalState()
+        self._failed = False
+        self._closed = False
+
+    @property
+    def path(self) -> Path:
+        return self._path
+
+    def _append(self, event_type: str, payload: dict[str, object]) -> LatencyJournalRecord:
+        if self._closed or self._failed:
+            raise LatencyJournalWriteError("latency journal writer is closed or failed")
+        if self._next_sequence >= MAX_EVENTS:
+            self._failed = True
+            raise LatencyJournalWriteError("latency journal event bound is exhausted")
+        recorded = _positive_int(self._clock_ns(), "recorded_unix_ns")
+        if event_type == "SESSION_START":
+            bindings_payload = payload.get("bindings")
+            if bindings_payload != self._bindings.payload():
+                raise LatencyJournalError("SESSION_START bindings differ from writer binding")
+        next_state = self._state.transition(event_type, payload)
+        event = _event_payload(
+            sequence=self._next_sequence,
+            session_id=self._session_id,
+            event_type=event_type,
+            previous_event_sha256=self._previous_sha256,
+            binding_sha256=self._bindings.sha256,
+            recorded_unix_ns=recorded,
+            payload=payload,
+        )
+        raw = _canonical_json_bytes(event)
+        if len(raw) > MAX_EVENT_BYTES:
+            raise LatencyJournalError("journal event exceeds 64 KiB")
+        event_sha256 = _sha256(raw)
+        marker_raw = _completion_marker_raw(self._next_sequence, event_sha256)
+        event_name, marker_name = _event_names(self._next_sequence)
+        try:
+            _check_directory_stable(self._path, self._directory_fd, self._directory_stat)
+            _write_exclusive_at(
+                self._directory_fd,
+                event_name,
+                raw,
+                MAX_EVENT_BYTES,
+            )
+            _write_exclusive_at(
+                self._directory_fd,
+                marker_name,
+                marker_raw,
+                MAX_MARKER_BYTES,
+            )
+            os.fsync(self._directory_fd)
+            _check_directory_stable(self._path, self._directory_fd, self._directory_stat)
+        except (OSError, LatencyJournalError) as error:
+            self._failed = True
+            raise LatencyJournalWriteError(
+                f"latency journal failed while writing sequence {self._next_sequence}"
+            ) from error
+        record = LatencyJournalRecord(
+            sequence=self._next_sequence,
+            event_type=event_type,
+            previous_event_sha256=self._previous_sha256,
+            binding_sha256=self._bindings.sha256,
+            raw=raw,
+            marker_raw=marker_raw,
+        )
+        self._state = next_state
+        self._previous_sha256 = event_sha256
+        self._next_sequence += 1
+        return record
+
+    def session_start(
+        self,
+        *,
+        admission_observation_sha256: str,
+        started_unix_ns: int,
+    ) -> LatencyJournalRecord:
+        return self._append(
+            "SESSION_START",
+            {
+                "admission_observation_sha256": _lower_sha256(
+                    admission_observation_sha256,
+                    "admission_observation_sha256",
+                ),
+                "bindings": self._bindings.payload(),
+                "started_unix_ns": _positive_int(started_unix_ns, "started_unix_ns"),
+            },
+        )
+
+    def context_ready(
+        self,
+        *,
+        runtime_sha256: str,
+        post_context_observation_sha256: str,
+    ) -> LatencyJournalRecord:
+        return self._append(
+            "CONTEXT_READY",
+            {
+                "post_context_observation_sha256": _lower_sha256(
+                    post_context_observation_sha256,
+                    "post_context_observation_sha256",
+                ),
+                "runtime_sha256": _lower_sha256(runtime_sha256, "runtime_sha256"),
+            },
+        )
+
+    def visit_started(
+        self,
+        *,
+        round_index: int,
+        ordinal_in_round: int,
+        run_id: str,
+        before_observation_sha256: str,
+    ) -> LatencyJournalRecord:
+        return self._append(
+            "VISIT_START",
+            {
+                "before_observation_sha256": _lower_sha256(
+                    before_observation_sha256,
+                    "before_observation_sha256",
+                ),
+                "ordinal_in_round": ordinal_in_round,
+                "round_index": round_index,
+                "run_id": run_id,
+            },
+        )
+
+    def warmup_completed(
+        self,
+        *,
+        round_index: int,
+        ordinal_in_round: int,
+        run_id: str,
+        warmup_index: int,
+    ) -> LatencyJournalRecord:
+        return self._append(
+            "WARMUP_COMPLETED",
+            {
+                "ordinal_in_round": ordinal_in_round,
+                "round_index": round_index,
+                "run_id": run_id,
+                "warmup_index": warmup_index,
+            },
+        )
+
+    def timed_sample_completed(
+        self,
+        *,
+        round_index: int,
+        ordinal_in_round: int,
+        run_id: str,
+        sample_index: int,
+        sample_ns: int,
+    ) -> LatencyJournalRecord:
+        """Persist one sample only after the latency function returned its stop delta."""
+
+        return self._append(
+            "TIMED_SAMPLE_COMPLETED",
+            {
+                "ordinal_in_round": ordinal_in_round,
+                "round_index": round_index,
+                "run_id": run_id,
+                "sample_index": sample_index,
+                "sample_ns": sample_ns,
+            },
+        )
+
+    def visit_ended(
+        self,
+        *,
+        round_index: int,
+        ordinal_in_round: int,
+        run_id: str,
+        outcome: str,
+        after_observation_sha256: str | None,
+        peak_allocated_bytes: int | None,
+        peak_reserved_bytes: int | None,
+        failure_code: str | None,
+    ) -> LatencyJournalRecord:
+        return self._append(
+            "VISIT_END",
+            {
+                "after_observation_sha256": after_observation_sha256,
+                "failure_code": failure_code,
+                "ordinal_in_round": ordinal_in_round,
+                "outcome": outcome,
+                "peak_allocated_bytes": peak_allocated_bytes,
+                "peak_reserved_bytes": peak_reserved_bytes,
+                "round_index": round_index,
+                "run_id": run_id,
+            },
+        )
+
+    def terminal(
+        self,
+        *,
+        outcome: str,
+        latency_session_sha256: str,
+        failure_code: str | None,
+    ) -> LatencyJournalRecord:
+        return self._append(
+            "TERMINAL",
+            {
+                "failure_code": failure_code,
+                "latency_session_sha256": _lower_sha256(
+                    latency_session_sha256,
+                    "latency_session_sha256",
+                ),
+                "outcome": outcome,
+            },
+        )
+
+    def close(self) -> None:
+        if not self._closed:
+            os.close(self._directory_fd)
+            self._closed = True
+
+    def __enter__(self) -> LatencyProgressJournalWriter:
+        return self
+
+    def __exit__(self, _type: object, _value: object, _traceback: object) -> None:
+        self.close()
+
+
+def create_latency_progress_journal(
+    parent: Path,
+    *,
+    session_id: str,
+    bindings: LatencyJournalBindings,
+    _clock_ns: Callable[[], int] = time.time_ns,
+) -> LatencyProgressJournalWriter:
+    """Create one new private session directory; existing paths are rejected."""
+
+    _require_posix()
+    if not isinstance(parent, Path):
+        raise TypeError("parent must be pathlib.Path")
+    checked_session_id = _safe_session_id(session_id)
+    if type(bindings) is not LatencyJournalBindings:
+        raise TypeError("bindings must be exact LatencyJournalBindings")
+    if not callable(_clock_ns):
+        raise TypeError("_clock_ns must be callable")
+    parent_fd, parent_stat = _open_directory(parent, private=False)
+    try:
+        try:
+            os.mkdir(checked_session_id, mode=0o700, dir_fd=parent_fd)
+            os.fsync(parent_fd)
+        except OSError as error:
+            raise LatencyJournalWriteError(
+                "latency journal session directory already exists or cannot be created"
+            ) from error
+        _check_directory_stable(parent, parent_fd, parent_stat)
+    finally:
+        os.close(parent_fd)
+    path = parent / checked_session_id
+    directory_fd, directory_stat = _open_directory(path, private=True)
+    return LatencyProgressJournalWriter(
+        path,
+        directory_fd,
+        directory_stat,
+        checked_session_id,
+        bindings,
+        _clock_ns,
+    )
+
+
+def _parse_event(
+    raw: bytes,
+    *,
+    expected_sequence: int,
+    expected_session_id: str,
+    expected_previous_sha256: str,
+    expected_bindings: LatencyJournalBindings,
+) -> tuple[str, dict[str, object]]:
+    value = _parse_canonical_object(raw, "journal event")
+    _exact_keys(
+        value,
+        {
+            "binding_sha256",
+            "event_type",
+            "payload",
+            "previous_event_sha256",
+            "recorded_unix_ns",
+            "schema",
+            "sequence",
+            "session_id",
+        },
+        "journal event",
+    )
+    if (
+        value["schema"] != EVENT_SCHEMA
+        or value["sequence"] != expected_sequence
+        or value["session_id"] != expected_session_id
+        or value["previous_event_sha256"] != expected_previous_sha256
+        or value["binding_sha256"] != expected_bindings.sha256
+        or type(value["event_type"]) is not str
+        or value["event_type"] not in _EVENT_TYPES
+        or type(value["payload"]) is not dict
+    ):
+        raise LatencyJournalError("journal event header or chain is invalid")
+    _positive_int(value["recorded_unix_ns"], "recorded_unix_ns")
+    return value["event_type"], value["payload"]
+
+
+def _read_result(
+    *,
+    session_id: str,
+    bindings: LatencyJournalBindings,
+    records: list[LatencyJournalRecord],
+    tail: tuple[tuple[str, bytes], ...],
+    terminal_outcome: str | None,
+) -> LatencyJournalReadResult:
+    status = {
+        None: INCOMPLETE_STATUS,
+        "COMPLETED": COMPLETED_STATUS,
+        "HELD": HELD_STATUS,
+    }[terminal_outcome]
+    return LatencyJournalReadResult(
+        session_id=session_id,
+        bindings=bindings,
+        records=tuple(records),
+        committed_event_raw=tuple(record.raw for record in records),
+        committed_marker_raw=tuple(record.marker_raw for record in records),
+        uncommitted_tail_files=tail,
+        status=status,
+        terminal_outcome=terminal_outcome,
+    )
+
+
+def read_latency_progress_journal(
+    path: Path,
+    *,
+    expected_bindings: LatencyJournalBindings,
+) -> LatencyJournalReadResult:
+    """Read one stable committed prefix; never upgrade it to formal latency."""
+
+    _require_posix()
+    if not isinstance(path, Path):
+        raise TypeError("path must be pathlib.Path")
+    if type(expected_bindings) is not LatencyJournalBindings:
+        raise TypeError("expected_bindings must be exact LatencyJournalBindings")
+    session_id = _safe_session_id(path.name)
+    directory_fd, opened = _open_directory(path, private=True)
+    try:
+        try:
+            before_list = os.fstat(directory_fd)
+            names = _bounded_directory_names(directory_fd)
+            after_list = os.fstat(directory_fd)
+        except OSError as error:
+            raise LatencyJournalError("journal directory could not be enumerated") from error
+        if _stat_identity(before_list) != _stat_identity(after_list):
+            raise LatencyJournalError("journal directory changed during enumeration")
+        event_names: dict[int, str] = {}
+        marker_names: dict[int, str] = {}
+        for name in names:
+            if type(name) is not str:
+                raise LatencyJournalError("journal leaf name is not text")
+            event_match = _EVENT_NAME.fullmatch(name)
+            marker_match = _MARKER_NAME.fullmatch(name)
+            if event_match is None and marker_match is None:
+                raise LatencyJournalError("journal directory contains an unexpected leaf")
+            sequence = int((event_match or marker_match).group(1))
+            if sequence >= MAX_EVENTS:
+                raise LatencyJournalError("journal leaf sequence exceeds the fixed bound")
+            target = event_names if event_match is not None else marker_names
+            if sequence in target:
+                raise LatencyJournalError("journal directory repeats a sequence leaf")
+            target[sequence] = name
+        sequences = sorted(set(event_names) | set(marker_names))
+        if sequences and sequences != list(range(sequences[-1] + 1)):
+            raise LatencyJournalError("journal sequence has a gap")
+        state = _JournalState()
+        records: list[LatencyJournalRecord] = []
+        previous_sha256 = ZERO_SHA256
+        highest = sequences[-1] if sequences else None
+        for sequence in sequences:
+            if sequence not in event_names:
+                raise LatencyJournalError("completion marker has no event file")
+            event_name = event_names[sequence]
+            event_raw = _read_regular_at(
+                directory_fd,
+                event_name,
+                MAX_EVENT_BYTES,
+            )
+            if sequence not in marker_names:
+                if sequence != highest or state.terminal_outcome is not None:
+                    raise LatencyJournalError("uncommitted event is not the sole final tail")
+                _check_directory_unchanged(path, directory_fd, after_list)
+                return _read_result(
+                    session_id=session_id,
+                    bindings=expected_bindings,
+                    records=records,
+                    tail=((event_name, event_raw),),
+                    terminal_outcome=None,
+                )
+            marker_name = marker_names[sequence]
+            marker_raw = _read_regular_at(
+                directory_fd,
+                marker_name,
+                MAX_MARKER_BYTES,
+            )
+            expected_marker_raw = _completion_marker_raw(
+                sequence,
+                _sha256(event_raw),
+            )
+            if marker_raw != expected_marker_raw:
+                is_truncated = len(marker_raw) < len(
+                    expected_marker_raw
+                ) and expected_marker_raw.startswith(marker_raw)
+                if not is_truncated:
+                    raise LatencyJournalError("completion marker differs from event bytes")
+                if sequence != highest or state.terminal_outcome is not None:
+                    raise LatencyJournalError(
+                        "truncated completion marker is not the sole final tail"
+                    )
+                _check_directory_unchanged(path, directory_fd, after_list)
+                return _read_result(
+                    session_id=session_id,
+                    bindings=expected_bindings,
+                    records=records,
+                    tail=((event_name, event_raw), (marker_name, marker_raw)),
+                    terminal_outcome=None,
+                )
+            event_type, payload = _parse_event(
+                event_raw,
+                expected_sequence=sequence,
+                expected_session_id=session_id,
+                expected_previous_sha256=previous_sha256,
+                expected_bindings=expected_bindings,
+            )
+            if sequence == 0 and payload.get("bindings") != expected_bindings.payload():
+                raise LatencyJournalError("SESSION_START does not contain expected bindings")
+            state = state.transition(event_type, payload)
+            records.append(
+                LatencyJournalRecord(
+                    sequence=sequence,
+                    event_type=event_type,
+                    previous_event_sha256=previous_sha256,
+                    binding_sha256=expected_bindings.sha256,
+                    raw=event_raw,
+                    marker_raw=marker_raw,
+                )
+            )
+            previous_sha256 = _sha256(event_raw)
+        _check_directory_stable(path, directory_fd, opened)
+        _check_directory_unchanged(path, directory_fd, after_list)
+        return _read_result(
+            session_id=session_id,
+            bindings=expected_bindings,
+            records=records,
+            tail=(),
+            terminal_outcome=state.terminal_outcome,
+        )
+    finally:
+        os.close(directory_fd)
+
+
+__all__ = [
+    "AUTHORITY",
+    "COMPLETED_STATUS",
+    "HELD_STATUS",
+    "INCOMPLETE_STATUS",
+    "LatencyJournalBindings",
+    "LatencyJournalError",
+    "LatencyJournalReadResult",
+    "LatencyJournalRecord",
+    "LatencyJournalWriteError",
+    "LatencyProgressJournalWriter",
+    "MAX_EVENT_BYTES",
+    "MAX_EVENTS",
+    "PRODUCTION",
+    "RESULT_CLAIMED",
+    "create_latency_progress_journal",
+    "read_latency_progress_journal",
+]
diff --git a/src/phaseset_core/periodic_descriptor_cache_v2.py b/src/phaseset_core/periodic_descriptor_cache_v2.py
new file mode 100644
index 0000000..0d69e71
--- /dev/null
+++ b/src/phaseset_core/periodic_descriptor_cache_v2.py
@@ -0,0 +1,1483 @@
+"""Private candidate for immutable PhaseSet periodic descriptor shards.
+
+This module has no execution or scientific authority.  It freezes only the
+weight-independent ``PairChunk`` inputs produced by the existing public
+descriptor streams.  It is intentionally not wired into the host, registered
+constructors, training runtime, checkpoints, or resume path.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterator
+from dataclasses import dataclass, field
+import hashlib
+import json
+import math
+import os
+from pathlib import Path
+import stat
+import struct
+import sys
+from typing import Final, Literal
+
+import numpy as np
+
+from .contracts import PreparedGroupBatch, validate_prepared_group_batch
+from .controls import iter_mean_difference_dct_chunks
+from .morlet import MORLET_ORACLE_SHA256
+from .periodic import (
+    BAND_COUNT,
+    CANONICAL_MICROBLOCK_SIZE,
+    TOKEN_WIDTH,
+    PairChunk,
+    iter_marginal_power_pair_chunks,
+    iter_unordered_pair_chunks,
+    validate_edge_chunk_size,
+    validate_energy_floors,
+)
+from .pipeline import deterministic_group_yaw
+
+
+AUTHORITY: Final = 0
+PRODUCTION: Final = False
+RESULT_CLAIMED: Final = False
+STATUS: Final = "PRIVATE_CANDIDATE_NO_EXECUTION_AUTHORITY"
+
+INDEX_SCHEMA: Final = "phaseset-periodic-descriptor-cache-index-v2"
+ENTRY_SCHEMA: Final = "phaseset-periodic-descriptor-cache-entry-v2"
+SHARD_SCHEMA: Final = "phaseset-periodic-descriptor-cache-shard-v2"
+MAGIC: Final = b"PHASESET-PERIODIC-DESCRIPTOR-CACHE-V2\x00"
+
+DescriptorStreamKind = Literal[
+    "FULL_RELATION",
+    "MARGINAL_POWER",
+    "MEAN_DIFFERENCE_DCT",
+]
+STREAM_KINDS: Final[tuple[DescriptorStreamKind, ...]] = (
+    "FULL_RELATION",
+    "MARGINAL_POWER",
+    "MEAN_DIFFERENCE_DCT",
+)
+
+_SHARED_ARRAYS: Final = ("batch_indices", "actor_i", "actor_j")
+_STREAM_SUFFIXES: Final = ("tokens_ij", "tokens_ji", "support_mask")
+_ARRAY_NAMES: Final = _SHARED_ARRAYS + tuple(
+    f"{kind}__{suffix}" for kind in STREAM_KINDS for suffix in _STREAM_SUFFIXES
+)
+_EXPECTED_DTYPES: Final = {
+    "batch_indices": "<i8",
+    "actor_i": "<i8",
+    "actor_j": "<i8",
+    **{
+        f"{kind}__{suffix}": "|b1" if suffix == "support_mask" else "<f4"
+        for kind in STREAM_KINDS
+        for suffix in _STREAM_SUFFIXES
+    },
+}
+_DECODED_BYTES_PER_EDGE: Final = len(_SHARED_ARRAYS) * np.dtype("<i8").itemsize + len(
+    STREAM_KINDS
+) * (
+    2 * BAND_COUNT * TOKEN_WIDTH * np.dtype("<f4").itemsize
+    + BAND_COUNT * np.dtype("|b1").itemsize
+)
+_CACHE_BATCH_TOKEN: Final = object()
+
+
+class DescriptorCacheV2Error(ValueError):
+    """The private descriptor-cache contract is invalid."""
+
+
+class DescriptorCacheV2Miss(DescriptorCacheV2Error):
+    """The exact explicit descriptor entry is absent; no fallback is allowed."""
+
+
+@dataclass(frozen=True, slots=True)
+class DescriptorCacheBounds:
+    """Caller-visible resource bounds for index, shards, and decoded arrays."""
+
+    max_index_bytes: int = 64 * 1024 * 1024
+    max_shard_bytes: int = 512 * 1024 * 1024
+    max_decoded_shard_bytes: int = 256 * 1024 * 1024
+    max_shards: int = 262_144
+    max_total_shard_bytes: int = 1 << 40
+    max_edges_per_shard: int = 32_768
+
+    def __post_init__(self) -> None:
+        for name in (
+            "max_index_bytes",
+            "max_shard_bytes",
+            "max_decoded_shard_bytes",
+            "max_shards",
+            "max_total_shard_bytes",
+            "max_edges_per_shard",
+        ):
+            value = getattr(self, name)
+            if type(value) is not int or value < 1:
+                raise TypeError(f"{name} must be an exact positive int")
+        if self.max_decoded_shard_bytes > self.max_shard_bytes:
+            raise DescriptorCacheV2Error(
+                "decoded shard bound exceeds stored shard bound"
+            )
+
+
+@dataclass(frozen=True, slots=True)
+class DescriptorCacheSourceBinding:
+    """Exact upstream identities supplied by a trusted caller, not authority."""
+
+    prepared_manifest_sha256: str
+    source_tree_sha256: str
+    environment_sha256: str
+
+    def __post_init__(self) -> None:
+        for name in (
+            "prepared_manifest_sha256",
+            "source_tree_sha256",
+            "environment_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+
+    def _value(self) -> dict[str, str]:
+        return {
+            "environment_sha256": self.environment_sha256,
+            "prepared_manifest_sha256": self.prepared_manifest_sha256,
+            "source_tree_sha256": self.source_tree_sha256,
+        }
+
+
+@dataclass(frozen=True, slots=True)
+class DescriptorWindowContext:
+    """One explicit prepared-window identity and augmentation context."""
+
+    prepared_manifest_sha256: str
+    source_batch_sha256: str
+    window_sha256: str
+    window_ordinal: int
+    split: Literal["train", "val"]
+    seed: int
+    epoch: int
+    augmentation_yaw_be_hex: str
+
+    def __post_init__(self) -> None:
+        _lower_sha256(self.prepared_manifest_sha256, "prepared_manifest_sha256")
+        _lower_sha256(self.source_batch_sha256, "source_batch_sha256")
+        _lower_sha256(self.window_sha256, "window_sha256")
+        _uint64(self.window_ordinal, "window_ordinal")
+        _uint64(self.seed, "seed")
+        _uint64(self.epoch, "epoch")
+        if self.split not in ("train", "val"):
+            raise DescriptorCacheV2Error("split must be exactly train or val")
+        yaw = _yaw_from_hex(self.augmentation_yaw_be_hex)
+        if self.split == "train":
+            expected = deterministic_group_yaw(
+                seed=self.seed,
+                epoch=self.epoch,
+                window_ordinal=self.window_ordinal,
+            )
+            if struct.pack(">d", expected).hex() != self.augmentation_yaw_be_hex:
+                raise DescriptorCacheV2Error(
+                    "train yaw differs from deterministic_group_yaw"
+                )
+        elif self.epoch != 0 or struct.pack(">d", yaw) != b"\x00" * 8:
+            raise DescriptorCacheV2Error(
+                "validation context requires epoch zero and +0 yaw"
+            )
+
+    @classmethod
+    def from_yaw(
+        cls,
+        *,
+        prepared_manifest_sha256: str,
+        source_batch_sha256: str,
+        window_sha256: str,
+        window_ordinal: int,
+        split: Literal["train", "val"],
+        seed: int,
+        epoch: int,
+        augmentation_yaw: float,
+    ) -> DescriptorWindowContext:
+        if type(augmentation_yaw) is not float or not math.isfinite(augmentation_yaw):
+            raise TypeError("augmentation_yaw must be an exact finite float")
+        return cls(
+            prepared_manifest_sha256=prepared_manifest_sha256,
+            source_batch_sha256=source_batch_sha256,
+            window_sha256=window_sha256,
+            window_ordinal=window_ordinal,
+            split=split,
+            seed=seed,
+            epoch=epoch,
+            augmentation_yaw_be_hex=struct.pack(">d", augmentation_yaw).hex(),
+        )
+
+    def _value(self) -> dict[str, object]:
+        return {
+            "augmentation_yaw_be_hex": self.augmentation_yaw_be_hex,
+            "epoch": self.epoch,
+            "prepared_manifest_sha256": self.prepared_manifest_sha256,
+            "seed": self.seed,
+            "source_batch_sha256": self.source_batch_sha256,
+            "split": self.split,
+            "window_ordinal": self.window_ordinal,
+            "window_sha256": self.window_sha256,
+        }
+
+
+@dataclass(frozen=True, slots=True)
+class DescriptorCacheArtifact:
+    root: Path
+    index_path: Path
+    index_sha256: str
+    shard_count: int
+    total_shard_bytes: int
+
+
+@dataclass(frozen=True, slots=True)
+class _IndexRow:
+    batch_input_sha256: str
+    bytes: int
+    cache_key_sha256: str
+    edge_count: int
+    epoch: int
+    path: str
+    seed: int
+    sha256: str
+    source_batch_sha256: str
+    split: str
+    window_contexts_sha256: str
+    window_count: int
+
+    def _value(self) -> dict[str, object]:
+        return {
+            "batch_input_sha256": self.batch_input_sha256,
+            "bytes": self.bytes,
+            "cache_key_sha256": self.cache_key_sha256,
+            "edge_count": self.edge_count,
+            "epoch": self.epoch,
+            "path": self.path,
+            "seed": self.seed,
+            "sha256": self.sha256,
+            "source_batch_sha256": self.source_batch_sha256,
+            "split": self.split,
+            "window_contexts_sha256": self.window_contexts_sha256,
+            "window_count": self.window_count,
+        }
+
+
+def _lower_sha256(value: object, name: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise TypeError(f"{name} must be a lowercase SHA-256")
+    return value
+
+
+def _uint64(value: object, name: str) -> int:
+    if type(value) is not int or not 0 <= value < 2**64:
+        raise TypeError(f"{name} must be an exact uint64")
+    return value
+
+
+def _yaw_from_hex(value: object) -> float:
+    if (
+        type(value) is not str
+        or len(value) != 16
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise TypeError("augmentation_yaw_be_hex must be 16 lowercase hex characters")
+    yaw = struct.unpack(">d", bytes.fromhex(value))[0]
+    if not math.isfinite(yaw) or not -math.pi <= yaw < math.pi:
+        raise DescriptorCacheV2Error("augmentation yaw must be finite in [-pi,pi)")
+    return yaw
+
+
+def _sha256(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _canonical_json(value: object) -> bytes:
+    try:
+        return (
+            json.dumps(
+                value,
+                ensure_ascii=True,
+                allow_nan=False,
+                sort_keys=True,
+                separators=(",", ":"),
+            ).encode("ascii")
+            + b"\n"
+        )
+    except (TypeError, ValueError, UnicodeEncodeError) as error:
+        raise DescriptorCacheV2Error(
+            "value cannot be encoded as canonical ASCII JSON"
+        ) from error
+
+
+def _pairs_no_duplicates(pairs: list[tuple[str, object]]) -> dict[str, object]:
+    value: dict[str, object] = {}
+    for key, item in pairs:
+        if key in value:
+            raise DescriptorCacheV2Error(f"duplicate JSON key: {key}")
+        value[key] = item
+    return value
+
+
+def _reject_float(value: str) -> object:
+    raise DescriptorCacheV2Error(f"floating-point JSON is forbidden: {value}")
+
+
+def _parse_canonical_json(raw: bytes, label: str) -> dict[str, object]:
+    if type(raw) is not bytes or not raw.endswith(b"\n") or b"\r" in raw:
+        raise DescriptorCacheV2Error(f"{label} is not LF-terminated canonical JSON")
+    try:
+        value = json.loads(
+            raw.decode("ascii", "strict"),
+            object_pairs_hook=_pairs_no_duplicates,
+            parse_float=_reject_float,
+            parse_constant=_reject_float,
+        )
+    except (UnicodeDecodeError, json.JSONDecodeError) as error:
+        raise DescriptorCacheV2Error(f"{label} is not strict ASCII JSON") from error
+    if type(value) is not dict or _canonical_json(value) != raw:
+        raise DescriptorCacheV2Error(f"{label} bytes are not canonical")
+    return value
+
+
+def _closed(value: object, keys: set[str], label: str) -> dict[str, object]:
+    if type(value) is not dict or set(value) != keys:
+        raise DescriptorCacheV2Error(f"{label} keys are not closed")
+    return value
+
+
+def _require_little_endian() -> None:
+    if sys.byteorder != "little":
+        raise DescriptorCacheV2Error(
+            "descriptor cache v2 requires a little-endian runtime"
+        )
+
+
+def prepared_group_batch_sha256(value: PreparedGroupBatch) -> str:
+    """Hash the complete canonical numeric, mask, padding, and commitment input."""
+
+    checked = validate_prepared_group_batch(value)
+    digest = hashlib.sha256(b"phaseset-prepared-group-batch-cache-v2\x00")
+    for name, array in (
+        ("skeletons", checked.skeletons),
+        ("actor_mask", checked.actor_mask),
+        ("frame_mask", checked.frame_mask),
+        ("track_mask", checked.track_mask),
+    ):
+        encoded_name = name.encode("ascii")
+        encoded_dtype = array.dtype.str.encode("ascii")
+        digest.update(struct.pack(">H", len(encoded_name)))
+        digest.update(encoded_name)
+        digest.update(struct.pack(">H", len(encoded_dtype)))
+        digest.update(encoded_dtype)
+        digest.update(struct.pack(">H", array.ndim))
+        for extent in array.shape:
+            digest.update(struct.pack(">Q", extent))
+        raw = array.tobytes(order="C")
+        digest.update(struct.pack(">Q", len(raw)))
+        digest.update(raw)
+    digest.update(struct.pack(">Q", len(checked.actor_commitments)))
+    for row in checked.actor_commitments:
+        digest.update(struct.pack(">Q", len(row)))
+        for commitment in row:
+            if commitment is None:
+                digest.update(b"\x00")
+            else:
+                digest.update(b"\x01" + commitment)
+    digest.update(struct.pack(">Q", len(checked.group_commitments)))
+    for commitment in checked.group_commitments:
+        digest.update(commitment)
+    return digest.hexdigest()
+
+
+def energy_floors_sha256(value: np.ndarray) -> str:
+    _require_little_endian()
+    floors = validate_energy_floors(value)
+    return _sha256(np.asarray(floors, dtype="<f8", order="C").tobytes(order="C"))
+
+
+def _validate_contexts(
+    contexts: object,
+    *,
+    batch_size: int,
+    binding: DescriptorCacheSourceBinding,
+) -> tuple[DescriptorWindowContext, ...]:
+    if type(contexts) is not tuple or len(contexts) != batch_size:
+        raise DescriptorCacheV2Error(
+            "contexts must be an exact tuple covering every batch row"
+        )
+    if any(type(context) is not DescriptorWindowContext for context in contexts):
+        raise TypeError("contexts must contain exact DescriptorWindowContext values")
+    checked = contexts
+    if any(
+        context.prepared_manifest_sha256 != binding.prepared_manifest_sha256
+        for context in checked
+    ):
+        raise DescriptorCacheV2Error(
+            "window context prepared manifest differs from binding"
+        )
+    first = checked[0]
+    for context in checked[1:]:
+        if (
+            context.source_batch_sha256 != first.source_batch_sha256
+            or context.split != first.split
+            or context.seed != first.seed
+            or context.epoch != first.epoch
+        ):
+            raise DescriptorCacheV2Error(
+                "one shard cannot mix batch, split, seed, or epoch"
+            )
+    if len({context.window_sha256 for context in checked}) != len(checked):
+        raise DescriptorCacheV2Error("one shard repeats a window identity")
+    if len({context.window_ordinal for context in checked}) != len(checked):
+        raise DescriptorCacheV2Error("one shard repeats a window ordinal")
+    return checked
+
+
+def _contexts_value(
+    contexts: tuple[DescriptorWindowContext, ...],
+) -> list[dict[str, object]]:
+    return [context._value() for context in contexts]
+
+
+def _contexts_sha256(contexts: tuple[DescriptorWindowContext, ...]) -> str:
+    return _sha256(_canonical_json({"windows": _contexts_value(contexts)}))
+
+
+def _entry_value(
+    *,
+    binding: DescriptorCacheSourceBinding,
+    contexts: tuple[DescriptorWindowContext, ...],
+    batch_input_sha256: str,
+    floors_sha256: str,
+) -> dict[str, object]:
+    return {
+        "batch_input_sha256": batch_input_sha256,
+        "energy_floors_sha256": floors_sha256,
+        "morlet_oracle_sha256": MORLET_ORACLE_SHA256,
+        "schema": ENTRY_SCHEMA,
+        "source_binding": binding._value(),
+        "streams": list(STREAM_KINDS),
+        "windows": _contexts_value(contexts),
+    }
+
+
+def _entry_key(
+    *,
+    binding: DescriptorCacheSourceBinding,
+    contexts: tuple[DescriptorWindowContext, ...],
+    batch_input_sha256: str,
+    floors_sha256: str,
+) -> str:
+    return _sha256(
+        _canonical_json(
+            _entry_value(
+                binding=binding,
+                contexts=contexts,
+                batch_input_sha256=batch_input_sha256,
+                floors_sha256=floors_sha256,
+            )
+        )
+    )
+
+
+def iter_uncached_pair_chunks(
+    batch: PreparedGroupBatch,
+    stream_kind: DescriptorStreamKind,
+    *,
+    energy_floors: np.ndarray,
+    edge_chunk_size: int = CANONICAL_MICROBLOCK_SIZE,
+) -> Iterator[PairChunk]:
+    """Return the existing public stream as the uncached comparison oracle."""
+
+    from .contracts import skeleton_to_activity
+
+    checked = validate_prepared_group_batch(batch)
+    floors = validate_energy_floors(energy_floors)
+    chunk_size = validate_edge_chunk_size(edge_chunk_size)
+    activity = skeleton_to_activity(checked)
+    edge_budget = sum(count * (count - 1) // 2 for count in checked.actor_counts)
+    if stream_kind == "FULL_RELATION":
+        return iter_unordered_pair_chunks(
+            activity,
+            energy_floors=floors,
+            edge_chunk_size=chunk_size,
+            edge_budget=edge_budget,
+        )
+    if stream_kind == "MARGINAL_POWER":
+        return iter_marginal_power_pair_chunks(
+            activity,
+            edge_chunk_size=chunk_size,
+            edge_budget=edge_budget,
+        )
+    if stream_kind == "MEAN_DIFFERENCE_DCT":
+        return iter_mean_difference_dct_chunks(
+            activity,
+            energy_floors=floors,
+            edge_chunk_size=chunk_size,
+            edge_budget=edge_budget,
+        )
+    raise DescriptorCacheV2Error("unknown descriptor stream kind")
+
+
+def _expected_edge_census(
+    batch: PreparedGroupBatch,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    groups: list[int] = []
+    left: list[int] = []
+    right: list[int] = []
+    for group, actor_count in enumerate(batch.actor_counts):
+        for actor_i in range(actor_count):
+            for actor_j in range(actor_i + 1, actor_count):
+                groups.append(group)
+                left.append(actor_i)
+                right.append(actor_j)
+    if not groups:
+        raise DescriptorCacheV2Error("descriptor batch contains no unordered edges")
+    return (
+        np.ascontiguousarray(groups, dtype=np.int64),
+        np.ascontiguousarray(left, dtype=np.int64),
+        np.ascontiguousarray(right, dtype=np.int64),
+    )
+
+
+def _collect_stream(
+    batch: PreparedGroupBatch,
+    stream_kind: DescriptorStreamKind,
+    floors: np.ndarray,
+) -> dict[str, np.ndarray]:
+    chunks = tuple(
+        iter_uncached_pair_chunks(
+            batch,
+            stream_kind,
+            energy_floors=floors,
+            edge_chunk_size=CANONICAL_MICROBLOCK_SIZE,
+        )
+    )
+    if not chunks:
+        raise DescriptorCacheV2Error("uncached stream emitted no PairChunk")
+    return {
+        "batch_indices": np.ascontiguousarray(
+            np.concatenate([chunk.batch_indices for chunk in chunks]), dtype=np.int64
+        ),
+        "actor_i": np.ascontiguousarray(
+            np.concatenate([chunk.actor_i for chunk in chunks]), dtype=np.int64
+        ),
+        "actor_j": np.ascontiguousarray(
+            np.concatenate([chunk.actor_j for chunk in chunks]), dtype=np.int64
+        ),
+        "tokens_ij": np.ascontiguousarray(
+            np.concatenate([chunk.tokens_ij for chunk in chunks]), dtype=np.float32
+        ),
+        "tokens_ji": np.ascontiguousarray(
+            np.concatenate([chunk.tokens_ji for chunk in chunks]), dtype=np.float32
+        ),
+        "support_mask": np.ascontiguousarray(
+            np.concatenate([chunk.support_mask for chunk in chunks]), dtype=np.bool_
+        ),
+    }
+
+
+def _collect_all_arrays(
+    batch: PreparedGroupBatch, floors: np.ndarray
+) -> dict[str, np.ndarray]:
+    expected = _expected_edge_census(batch)
+    arrays: dict[str, np.ndarray] = {
+        "batch_indices": expected[0],
+        "actor_i": expected[1],
+        "actor_j": expected[2],
+    }
+    for kind in STREAM_KINDS:
+        stream = _collect_stream(batch, kind, floors)
+        for name, expected_values in zip(_SHARED_ARRAYS, expected, strict=True):
+            if stream[name].tobytes(order="C") != expected_values.tobytes(order="C"):
+                raise DescriptorCacheV2Error(
+                    f"{kind} changed the canonical edge census"
+                )
+        for suffix in _STREAM_SUFFIXES:
+            arrays[f"{kind}__{suffix}"] = stream[suffix]
+    if tuple(arrays) != _ARRAY_NAMES:
+        raise AssertionError("descriptor array insertion order drifted")
+    return arrays
+
+
+def _preflight_descriptor_resources(
+    batch: PreparedGroupBatch,
+    *,
+    bounds: DescriptorCacheBounds,
+    stored_bytes_before: int,
+) -> tuple[int, int]:
+    edge_count = sum(count * (count - 1) // 2 for count in batch.actor_counts)
+    if edge_count < 1:
+        raise DescriptorCacheV2Error("descriptor batch contains no unordered edges")
+    if edge_count > bounds.max_edges_per_shard:
+        raise DescriptorCacheV2Error("descriptor shard exceeds its edge-count bound")
+    decoded_nbytes = edge_count * _DECODED_BYTES_PER_EDGE
+    if decoded_nbytes > bounds.max_decoded_shard_bytes:
+        raise DescriptorCacheV2Error("decoded descriptor shard exceeds its byte bound")
+    minimum_stored_nbytes = len(MAGIC) + 8 + decoded_nbytes
+    if minimum_stored_nbytes > bounds.max_shard_bytes:
+        raise DescriptorCacheV2Error("stored descriptor shard exceeds its byte bound")
+    if stored_bytes_before + minimum_stored_nbytes > bounds.max_total_shard_bytes:
+        raise DescriptorCacheV2Error("descriptor cache exceeds its total byte bound")
+    return edge_count, decoded_nbytes
+
+
+def _storage_array(name: str, value: np.ndarray) -> np.ndarray:
+    dtype = np.dtype(_EXPECTED_DTYPES[name])
+    stored = np.asarray(value, dtype=dtype, order="C")
+    if not stored.flags.c_contiguous:
+        stored = np.ascontiguousarray(stored, dtype=dtype)
+    return stored
+
+
+def _serialize_shard(
+    *,
+    binding: DescriptorCacheSourceBinding,
+    contexts: tuple[DescriptorWindowContext, ...],
+    batch_input_sha256: str,
+    floors_sha256: str,
+    cache_key_sha256: str,
+    arrays: dict[str, np.ndarray],
+    bounds: DescriptorCacheBounds,
+) -> bytes:
+    metadata: list[dict[str, object]] = []
+    payload = bytearray()
+    for name in _ARRAY_NAMES:
+        stored = _storage_array(name, arrays[name])
+        raw = stored.tobytes(order="C")
+        metadata.append(
+            {
+                "dtype": _EXPECTED_DTYPES[name],
+                "name": name,
+                "nbytes": len(raw),
+                "offset": len(payload),
+                "sha256": _sha256(raw),
+                "shape": list(stored.shape),
+            }
+        )
+        payload.extend(raw)
+    edge_count = int(arrays["batch_indices"].shape[0])
+    expected_decoded_nbytes = edge_count * _DECODED_BYTES_PER_EDGE
+    if len(payload) != expected_decoded_nbytes:
+        raise DescriptorCacheV2Error("descriptor shard array byte census changed")
+    if len(payload) > bounds.max_decoded_shard_bytes:
+        raise DescriptorCacheV2Error("decoded descriptor shard exceeds its byte bound")
+    header = _canonical_json(
+        {
+            "arrays": metadata,
+            "batch_input_sha256": batch_input_sha256,
+            "cache_key_sha256": cache_key_sha256,
+            "decoded_nbytes": len(payload),
+            "edge_count": edge_count,
+            "energy_floors_sha256": floors_sha256,
+            "morlet_oracle_sha256": MORLET_ORACLE_SHA256,
+            "schema": SHARD_SCHEMA,
+            "source_binding": binding._value(),
+            "streams": list(STREAM_KINDS),
+            "windows": _contexts_value(contexts),
+        }
+    )
+    raw = MAGIC + struct.pack(">Q", len(header)) + header + bytes(payload)
+    if len(raw) > bounds.max_shard_bytes:
+        raise DescriptorCacheV2Error("stored descriptor shard exceeds its byte bound")
+    return raw
+
+
+def _reject_symlink_components(path: Path, label: str) -> None:
+    for component in (path, *path.parents):
+        if component.is_symlink():
+            raise DescriptorCacheV2Error(f"{label} cannot traverse a symlink")
+
+
+def _read_owned(path: Path, *, maximum: int, label: str) -> bytes:
+    _reject_symlink_components(path, label)
+    flags = os.O_RDONLY | getattr(os, "O_BINARY", 0) | getattr(os, "O_NOFOLLOW", 0)
+    try:
+        descriptor = os.open(path, flags)
+    except OSError as error:
+        raise DescriptorCacheV2Error(f"{label} cannot be opened safely") from error
+    try:
+        try:
+            before = os.fstat(descriptor)
+            if not stat.S_ISREG(before.st_mode) or before.st_nlink != 1:
+                raise DescriptorCacheV2Error(
+                    f"{label} must be a single-link regular file"
+                )
+            if before.st_size < 1 or before.st_size > maximum:
+                raise DescriptorCacheV2Error(f"{label} exceeds its stored byte bound")
+            chunks: list[bytes] = []
+            remaining = before.st_size
+            while remaining:
+                chunk = os.read(descriptor, min(1024 * 1024, remaining))
+                if not chunk:
+                    raise DescriptorCacheV2Error(
+                        f"{label} ended before its declared size"
+                    )
+                chunks.append(chunk)
+                remaining -= len(chunk)
+            if os.read(descriptor, 1):
+                raise DescriptorCacheV2Error(f"{label} grew during its bounded read")
+            after = os.fstat(descriptor)
+            if (
+                before.st_dev,
+                before.st_ino,
+                before.st_size,
+                before.st_mtime_ns,
+                before.st_ctime_ns,
+            ) != (
+                after.st_dev,
+                after.st_ino,
+                after.st_size,
+                after.st_mtime_ns,
+                after.st_ctime_ns,
+            ):
+                raise DescriptorCacheV2Error(f"{label} changed during its owned read")
+        finally:
+            os.close(descriptor)
+    except DescriptorCacheV2Error:
+        raise
+    except OSError as error:
+        raise DescriptorCacheV2Error(f"{label} owned read failed") from error
+    raw = b"".join(chunks)
+    _reject_symlink_components(path, label)
+    try:
+        current = path.stat()
+    except OSError as error:
+        raise DescriptorCacheV2Error(
+            f"{label} disappeared after its owned read"
+        ) from error
+    if (
+        not stat.S_ISREG(current.st_mode)
+        or current.st_nlink != 1
+        or (
+            current.st_dev,
+            current.st_ino,
+            current.st_size,
+            current.st_mtime_ns,
+        )
+        != (before.st_dev, before.st_ino, before.st_size, before.st_mtime_ns)
+    ):
+        raise DescriptorCacheV2Error(
+            f"{label} path identity changed during its owned read"
+        )
+    return raw
+
+
+def _write_exclusive(path: Path, raw: bytes) -> None:
+    try:
+        with path.open("xb") as stream:
+            stream.write(raw)
+            stream.flush()
+            os.fsync(stream.fileno())
+        os.chmod(path, stat.S_IREAD)
+    except OSError as error:
+        raise DescriptorCacheV2Error(
+            "descriptor artifact exclusive write failed"
+        ) from error
+
+
+def _row_from_value(value: object, *, bounds: DescriptorCacheBounds) -> _IndexRow:
+    row = _closed(
+        value,
+        {
+            "batch_input_sha256",
+            "bytes",
+            "cache_key_sha256",
+            "edge_count",
+            "epoch",
+            "path",
+            "seed",
+            "sha256",
+            "source_batch_sha256",
+            "split",
+            "window_contexts_sha256",
+            "window_count",
+        },
+        "descriptor index row",
+    )
+    for name in (
+        "batch_input_sha256",
+        "cache_key_sha256",
+        "sha256",
+        "source_batch_sha256",
+        "window_contexts_sha256",
+    ):
+        _lower_sha256(row[name], name)
+    for name, maximum in (
+        ("bytes", bounds.max_shard_bytes),
+        ("edge_count", bounds.max_edges_per_shard),
+        ("window_count", bounds.max_edges_per_shard),
+    ):
+        if type(row[name]) is not int or not 1 <= row[name] <= maximum:
+            raise DescriptorCacheV2Error(
+                f"descriptor index row {name} is out of bounds"
+            )
+    _uint64(row["seed"], "seed")
+    _uint64(row["epoch"], "epoch")
+    if row["split"] not in ("train", "val"):
+        raise DescriptorCacheV2Error("descriptor index row split is invalid")
+    expected_path = f"shards/{row['cache_key_sha256']}.pdc2"
+    if row["path"] != expected_path:
+        raise DescriptorCacheV2Error("descriptor shard path is not canonical")
+    return _IndexRow(**row)  # type: ignore[arg-type]
+
+
+def _shape_tuple(value: object, name: str) -> tuple[int, ...]:
+    if type(value) is not list or not value:
+        raise DescriptorCacheV2Error(f"{name} shape must be a nonempty list")
+    if any(type(extent) is not int or extent < 0 for extent in value):
+        raise DescriptorCacheV2Error(f"{name} shape contains an invalid extent")
+    return tuple(value)
+
+
+def _contexts_from_values(
+    value: object,
+    *,
+    expected_count: int,
+) -> tuple[DescriptorWindowContext, ...]:
+    if type(value) is not list or len(value) != expected_count:
+        raise DescriptorCacheV2Error("descriptor shard windows must be a nonempty list")
+    contexts: list[DescriptorWindowContext] = []
+    for index, item in enumerate(value):
+        row = _closed(
+            item,
+            {
+                "augmentation_yaw_be_hex",
+                "epoch",
+                "prepared_manifest_sha256",
+                "seed",
+                "source_batch_sha256",
+                "split",
+                "window_ordinal",
+                "window_sha256",
+            },
+            f"descriptor window {index}",
+        )
+        try:
+            contexts.append(DescriptorWindowContext(**row))  # type: ignore[arg-type]
+        except (TypeError, DescriptorCacheV2Error) as error:
+            raise DescriptorCacheV2Error(
+                f"descriptor window {index} is malformed"
+            ) from error
+    return tuple(contexts)
+
+
+def _decode_shard(
+    raw: bytes,
+    *,
+    row: _IndexRow,
+    binding: DescriptorCacheSourceBinding,
+    contexts: tuple[DescriptorWindowContext, ...],
+    batch_input_sha256: str,
+    floors_sha256: str,
+    bounds: DescriptorCacheBounds,
+) -> dict[str, np.ndarray]:
+    prefix_size = len(MAGIC) + 8
+    if len(raw) < prefix_size or not raw.startswith(MAGIC):
+        raise DescriptorCacheV2Error("descriptor shard magic is invalid")
+    header_size = struct.unpack(">Q", raw[len(MAGIC) : prefix_size])[0]
+    if header_size < 2 or prefix_size + header_size > len(raw):
+        raise DescriptorCacheV2Error("descriptor shard header length is invalid")
+    header_raw = raw[prefix_size : prefix_size + header_size]
+    payload = memoryview(raw)[prefix_size + header_size :]
+    header = _closed(
+        _parse_canonical_json(header_raw, "descriptor shard header"),
+        {
+            "arrays",
+            "batch_input_sha256",
+            "cache_key_sha256",
+            "decoded_nbytes",
+            "edge_count",
+            "energy_floors_sha256",
+            "morlet_oracle_sha256",
+            "schema",
+            "source_binding",
+            "streams",
+            "windows",
+        },
+        "descriptor shard header",
+    )
+    decoded_nbytes = header["decoded_nbytes"]
+    if type(decoded_nbytes) is not int or decoded_nbytes < 0:
+        raise DescriptorCacheV2Error("descriptor shard decoded byte census is invalid")
+    decoded_contexts = _contexts_from_values(
+        header["windows"],
+        expected_count=row.window_count,
+    )
+    if (
+        header["schema"] != SHARD_SCHEMA
+        or header["source_binding"] != binding._value()
+        or header["streams"] != list(STREAM_KINDS)
+        or header["batch_input_sha256"] != batch_input_sha256
+        or header["cache_key_sha256"] != row.cache_key_sha256
+        or header["energy_floors_sha256"] != floors_sha256
+        or header["morlet_oracle_sha256"] != MORLET_ORACLE_SHA256
+        or decoded_contexts != contexts
+        or header["edge_count"] != row.edge_count
+        or decoded_nbytes != len(payload)
+        or decoded_nbytes > bounds.max_decoded_shard_bytes
+    ):
+        raise DescriptorCacheV2Error("descriptor shard binding or census mismatch")
+    items = header["arrays"]
+    if type(items) is not list or len(items) != len(_ARRAY_NAMES):
+        raise DescriptorCacheV2Error("descriptor shard array census is invalid")
+    arrays: dict[str, np.ndarray] = {}
+    cursor = 0
+    for expected_name, item in zip(_ARRAY_NAMES, items, strict=True):
+        metadata = _closed(
+            item,
+            {"dtype", "name", "nbytes", "offset", "sha256", "shape"},
+            f"descriptor array {expected_name}",
+        )
+        if (
+            metadata["name"] != expected_name
+            or metadata["dtype"] != _EXPECTED_DTYPES[expected_name]
+        ):
+            raise DescriptorCacheV2Error("descriptor array name or dtype changed")
+        shape = _shape_tuple(metadata["shape"], expected_name)
+        dtype = np.dtype(_EXPECTED_DTYPES[expected_name])
+        element_count = math.prod(shape)
+        nbytes = element_count * dtype.itemsize
+        if (
+            type(metadata["offset"]) is not int
+            or metadata["offset"] != cursor
+            or type(metadata["nbytes"]) is not int
+            or metadata["nbytes"] != nbytes
+            or cursor + nbytes > len(payload)
+        ):
+            raise DescriptorCacheV2Error("descriptor array offsets are not gap-free")
+        _lower_sha256(metadata["sha256"], f"{expected_name} sha256")
+        selected = payload[cursor : cursor + nbytes]
+        if _sha256(selected.tobytes()) != metadata["sha256"]:
+            raise DescriptorCacheV2Error(
+                f"descriptor array {expected_name} digest mismatch"
+            )
+        array = np.frombuffer(selected, dtype=dtype).reshape(shape)
+        if array.flags.writeable:
+            raise DescriptorCacheV2Error(
+                "decoded descriptor array unexpectedly writeable"
+            )
+        arrays[expected_name] = array
+        cursor += nbytes
+    if cursor != len(payload):
+        raise DescriptorCacheV2Error("descriptor shard contains trailing payload bytes")
+    return arrays
+
+
+def _validate_decoded_arrays(
+    batch: PreparedGroupBatch,
+    arrays: dict[str, np.ndarray],
+    *,
+    edge_count: int,
+) -> None:
+    expected = _expected_edge_census(batch)
+    for name, expected_values in zip(_SHARED_ARRAYS, expected, strict=True):
+        value = arrays[name]
+        if (
+            value.dtype != np.dtype(np.int64)
+            or value.shape != (edge_count,)
+            or not value.flags.c_contiguous
+            or value.tobytes(order="C") != expected_values.tobytes(order="C")
+        ):
+            raise DescriptorCacheV2Error(
+                "cached canonical edge census differs from batch"
+            )
+    for kind in STREAM_KINDS:
+        for suffix in ("tokens_ij", "tokens_ji"):
+            value = arrays[f"{kind}__{suffix}"]
+            if (
+                value.dtype != np.dtype(np.float32)
+                or value.shape != (edge_count, BAND_COUNT, TOKEN_WIDTH)
+                or not value.flags.c_contiguous
+                or not bool(np.isfinite(value).all())
+            ):
+                raise DescriptorCacheV2Error(f"cached {kind} token tensor is invalid")
+        support = arrays[f"{kind}__support_mask"]
+        if (
+            support.dtype != np.dtype(np.bool_)
+            or support.shape != (edge_count, BAND_COUNT)
+            or not support.flags.c_contiguous
+        ):
+            raise DescriptorCacheV2Error(f"cached {kind} support tensor is invalid")
+
+
+@dataclass(frozen=True, slots=True)
+class CachedDescriptorBatch:
+    """One verified immutable shard supporting arbitrarily many stream replays."""
+
+    cache_key_sha256: str
+    batch_input_sha256: str
+    contexts: tuple[DescriptorWindowContext, ...]
+    edge_count: int
+    _arrays: tuple[tuple[str, np.ndarray], ...] = field(repr=False)
+    _reader_token: object = field(repr=False, compare=False)
+
+    def __post_init__(self) -> None:
+        if self._reader_token is not _CACHE_BATCH_TOKEN:
+            raise DescriptorCacheV2Error(
+                "cached descriptor batches must come from the verified reader"
+            )
+        _lower_sha256(self.cache_key_sha256, "cache_key_sha256")
+        _lower_sha256(self.batch_input_sha256, "batch_input_sha256")
+        if (
+            type(self.contexts) is not tuple
+            or not self.contexts
+            or any(
+                type(context) is not DescriptorWindowContext
+                for context in self.contexts
+            )
+        ):
+            raise DescriptorCacheV2Error("cached descriptor contexts are invalid")
+        if type(self.edge_count) is not int or self.edge_count < 1:
+            raise DescriptorCacheV2Error("cached descriptor edge count is invalid")
+        if (
+            type(self._arrays) is not tuple
+            or tuple(name for name, _ in self._arrays) != _ARRAY_NAMES
+        ):
+            raise DescriptorCacheV2Error("cached descriptor array census is invalid")
+        for name, array in self._arrays:
+            if (
+                type(array) is not np.ndarray
+                or array.dtype != np.dtype(_EXPECTED_DTYPES[name])
+                or not array.flags.c_contiguous
+                or array.flags.writeable
+            ):
+                raise DescriptorCacheV2Error(
+                    "cached descriptor array storage is mutable"
+                )
+
+    def _mapping(self) -> dict[str, np.ndarray]:
+        return dict(self._arrays)
+
+    def stream(self, stream_kind: DescriptorStreamKind) -> CachedPairChunkStream:
+        if stream_kind not in STREAM_KINDS:
+            raise DescriptorCacheV2Error("unknown descriptor stream kind")
+        return CachedPairChunkStream(self, stream_kind)
+
+    def iter_chunks(
+        self,
+        stream_kind: DescriptorStreamKind,
+        *,
+        edge_chunk_size: int,
+    ) -> Iterator[PairChunk]:
+        return self.stream(stream_kind).iter_chunks(edge_chunk_size=edge_chunk_size)
+
+
+@dataclass(frozen=True, slots=True)
+class CachedPairChunkStream:
+    """A re-iterable exact ``PairChunk`` source for the future model seam."""
+
+    batch: CachedDescriptorBatch
+    stream_kind: DescriptorStreamKind
+
+    def __post_init__(self) -> None:
+        if (
+            type(self.batch) is not CachedDescriptorBatch
+            or self.stream_kind not in STREAM_KINDS
+        ):
+            raise DescriptorCacheV2Error("cached PairChunk stream identity is invalid")
+
+    def iter_chunks(self, *, edge_chunk_size: int) -> Iterator[PairChunk]:
+        chunk_size = validate_edge_chunk_size(edge_chunk_size)
+        arrays = self.batch._mapping()
+        prefix = f"{self.stream_kind}__"
+        for start in range(0, self.batch.edge_count, chunk_size):
+            stop = min(self.batch.edge_count, start + chunk_size)
+            yield PairChunk(
+                np.ascontiguousarray(arrays["batch_indices"][start:stop]),
+                np.ascontiguousarray(arrays["actor_i"][start:stop]),
+                np.ascontiguousarray(arrays["actor_j"][start:stop]),
+                np.ascontiguousarray(arrays[f"{prefix}tokens_ij"][start:stop]),
+                np.ascontiguousarray(arrays[f"{prefix}tokens_ji"][start:stop]),
+                np.ascontiguousarray(arrays[f"{prefix}support_mask"][start:stop]),
+            )
+
+
+class PeriodicDescriptorCacheWriterV2:
+    """Write a fresh bounded private cache tree and publish its index last."""
+
+    def __init__(
+        self,
+        root: str | Path,
+        *,
+        source_binding: DescriptorCacheSourceBinding,
+        energy_floors: np.ndarray,
+        bounds: DescriptorCacheBounds = DescriptorCacheBounds(),
+    ) -> None:
+        _require_little_endian()
+        if type(source_binding) is not DescriptorCacheSourceBinding:
+            raise TypeError("source_binding must be exact DescriptorCacheSourceBinding")
+        if type(bounds) is not DescriptorCacheBounds:
+            raise TypeError("bounds must be exact DescriptorCacheBounds")
+        unresolved = Path(root)
+        _reject_symlink_components(unresolved, "descriptor cache root")
+        try:
+            unresolved.mkdir(parents=False, exist_ok=False)
+            shards = unresolved / "shards"
+            shards.mkdir(exist_ok=False)
+        except OSError as error:
+            raise DescriptorCacheV2Error("descriptor cache root must be new") from error
+        self.root = unresolved.resolve()
+        self.shards = (self.root / "shards").resolve()
+        self.source_binding = source_binding
+        self.energy_floors = validate_energy_floors(energy_floors)
+        self.energy_floors_sha256 = energy_floors_sha256(self.energy_floors)
+        self.bounds = bounds
+        self._rows: list[_IndexRow] = []
+        self._keys: set[str] = set()
+        self._total_bytes = 0
+        self._finalized = False
+
+    def add_batch(
+        self,
+        batch: PreparedGroupBatch,
+        contexts: tuple[DescriptorWindowContext, ...],
+    ) -> str:
+        if self._finalized:
+            raise DescriptorCacheV2Error("descriptor cache index is already finalized")
+        checked = validate_prepared_group_batch(batch)
+        checked_contexts = _validate_contexts(
+            contexts,
+            batch_size=checked.batch_size,
+            binding=self.source_binding,
+        )
+        batch_digest = prepared_group_batch_sha256(checked)
+        cache_key = _entry_key(
+            binding=self.source_binding,
+            contexts=checked_contexts,
+            batch_input_sha256=batch_digest,
+            floors_sha256=self.energy_floors_sha256,
+        )
+        if cache_key in self._keys:
+            raise DescriptorCacheV2Error("descriptor cache repeats an exact entry key")
+        if len(self._rows) >= self.bounds.max_shards:
+            raise DescriptorCacheV2Error(
+                "descriptor cache exceeds its shard-count bound"
+            )
+        expected_edge_count, _ = _preflight_descriptor_resources(
+            checked,
+            bounds=self.bounds,
+            stored_bytes_before=self._total_bytes,
+        )
+        arrays = _collect_all_arrays(checked, self.energy_floors)
+        edge_count = int(arrays["batch_indices"].shape[0])
+        if edge_count != expected_edge_count:
+            raise DescriptorCacheV2Error("descriptor shard preflight census changed")
+        raw = _serialize_shard(
+            binding=self.source_binding,
+            contexts=checked_contexts,
+            batch_input_sha256=batch_digest,
+            floors_sha256=self.energy_floors_sha256,
+            cache_key_sha256=cache_key,
+            arrays=arrays,
+            bounds=self.bounds,
+        )
+        if self._total_bytes + len(raw) > self.bounds.max_total_shard_bytes:
+            raise DescriptorCacheV2Error(
+                "descriptor cache exceeds its total byte bound"
+            )
+        relative = f"shards/{cache_key}.pdc2"
+        _write_exclusive(self.root / relative, raw)
+        first = checked_contexts[0]
+        row = _IndexRow(
+            batch_input_sha256=batch_digest,
+            bytes=len(raw),
+            cache_key_sha256=cache_key,
+            edge_count=edge_count,
+            epoch=first.epoch,
+            path=relative,
+            seed=first.seed,
+            sha256=_sha256(raw),
+            source_batch_sha256=first.source_batch_sha256,
+            split=first.split,
+            window_contexts_sha256=_contexts_sha256(checked_contexts),
+            window_count=len(checked_contexts),
+        )
+        self._rows.append(row)
+        self._keys.add(cache_key)
+        self._total_bytes += len(raw)
+        return cache_key
+
+    def finalize(self) -> DescriptorCacheArtifact:
+        if self._finalized:
+            raise DescriptorCacheV2Error("descriptor cache index is already finalized")
+        if not self._rows:
+            raise DescriptorCacheV2Error(
+                "descriptor cache cannot finalize without shards"
+            )
+        rows = sorted(self._rows, key=lambda row: row.cache_key_sha256)
+        raw = _canonical_json(
+            {
+                "authority": AUTHORITY,
+                "energy_floors_sha256": self.energy_floors_sha256,
+                "environment_sha256": self.source_binding.environment_sha256,
+                "external_authentication_asserted": False,
+                "morlet_oracle_sha256": MORLET_ORACLE_SHA256,
+                "prepared_manifest_sha256": (
+                    self.source_binding.prepared_manifest_sha256
+                ),
+                "production": PRODUCTION,
+                "result_claimed": RESULT_CLAIMED,
+                "schema": INDEX_SCHEMA,
+                "shards": [row._value() for row in rows],
+                "source_tree_sha256": self.source_binding.source_tree_sha256,
+                "streams": list(STREAM_KINDS),
+                "total_shard_bytes": self._total_bytes,
+            }
+        )
+        if len(raw) > self.bounds.max_index_bytes:
+            raise DescriptorCacheV2Error(
+                "descriptor cache index exceeds its byte bound"
+            )
+        index = self.root / "index.json"
+        _write_exclusive(index, raw)
+        self._finalized = True
+        return DescriptorCacheArtifact(
+            root=self.root,
+            index_path=index,
+            index_sha256=_sha256(raw),
+            shard_count=len(rows),
+            total_shard_bytes=self._total_bytes,
+        )
+
+
+class PeriodicDescriptorCacheV2:
+    """Read and resolve exact immutable descriptor shards without fallback."""
+
+    def __init__(
+        self,
+        root: str | Path,
+        *,
+        expected_index_sha256: str,
+        expected_source_binding: DescriptorCacheSourceBinding,
+        energy_floors: np.ndarray,
+        bounds: DescriptorCacheBounds = DescriptorCacheBounds(),
+    ) -> None:
+        _require_little_endian()
+        _lower_sha256(expected_index_sha256, "expected_index_sha256")
+        if type(expected_source_binding) is not DescriptorCacheSourceBinding:
+            raise TypeError(
+                "expected_source_binding must be exact DescriptorCacheSourceBinding"
+            )
+        if type(bounds) is not DescriptorCacheBounds:
+            raise TypeError("bounds must be exact DescriptorCacheBounds")
+        unresolved = Path(root)
+        _reject_symlink_components(unresolved, "descriptor cache root")
+        try:
+            directory = unresolved.resolve(strict=True)
+        except OSError as error:
+            raise DescriptorCacheV2Error("descriptor cache root is absent") from error
+        if not directory.is_dir() or directory.is_symlink():
+            raise DescriptorCacheV2Error(
+                "descriptor cache root must be a non-symlink directory"
+            )
+        self.root = directory
+        self.source_binding = expected_source_binding
+        self.energy_floors = validate_energy_floors(energy_floors)
+        self.energy_floors_sha256 = energy_floors_sha256(self.energy_floors)
+        self.bounds = bounds
+        index_raw = _read_owned(
+            self.root / "index.json",
+            maximum=bounds.max_index_bytes,
+            label="descriptor cache index",
+        )
+        if _sha256(index_raw) != expected_index_sha256:
+            raise DescriptorCacheV2Error("descriptor cache index digest mismatch")
+        value = _closed(
+            _parse_canonical_json(index_raw, "descriptor cache index"),
+            {
+                "authority",
+                "energy_floors_sha256",
+                "environment_sha256",
+                "external_authentication_asserted",
+                "morlet_oracle_sha256",
+                "prepared_manifest_sha256",
+                "production",
+                "result_claimed",
+                "schema",
+                "shards",
+                "source_tree_sha256",
+                "streams",
+                "total_shard_bytes",
+            },
+            "descriptor cache index",
+        )
+        if (
+            type(value["authority"]) is not int
+            or value["authority"] != AUTHORITY
+            or value["production"] is not PRODUCTION
+            or value["result_claimed"] is not RESULT_CLAIMED
+            or value["external_authentication_asserted"] is not False
+            or value["schema"] != INDEX_SCHEMA
+            or value["streams"] != list(STREAM_KINDS)
+            or value["energy_floors_sha256"] != self.energy_floors_sha256
+            or value["morlet_oracle_sha256"] != MORLET_ORACLE_SHA256
+            or value["prepared_manifest_sha256"]
+            != expected_source_binding.prepared_manifest_sha256
+            or value["source_tree_sha256"] != expected_source_binding.source_tree_sha256
+            or value["environment_sha256"] != expected_source_binding.environment_sha256
+        ):
+            raise DescriptorCacheV2Error("descriptor cache index binding mismatch")
+        rows_value = value["shards"]
+        if (
+            type(rows_value) is not list
+            or not rows_value
+            or len(rows_value) > bounds.max_shards
+        ):
+            raise DescriptorCacheV2Error("descriptor cache shard census is invalid")
+        rows = tuple(_row_from_value(row, bounds=bounds) for row in rows_value)
+        if tuple(row.cache_key_sha256 for row in rows) != tuple(
+            sorted(row.cache_key_sha256 for row in rows)
+        ):
+            raise DescriptorCacheV2Error("descriptor cache index rows are not sorted")
+        if len({row.cache_key_sha256 for row in rows}) != len(rows):
+            raise DescriptorCacheV2Error("descriptor cache index repeats a key")
+        total = sum(row.bytes for row in rows)
+        if (
+            type(value["total_shard_bytes"]) is not int
+            or value["total_shard_bytes"] != total
+            or total > bounds.max_total_shard_bytes
+        ):
+            raise DescriptorCacheV2Error(
+                "descriptor cache total byte census is invalid"
+            )
+        self._rows = {row.cache_key_sha256: row for row in rows}
+        self.index_sha256 = expected_index_sha256
+        self._validate_tree_census()
+
+    def _validate_tree_census(self) -> None:
+        shards_path = self.root / "shards"
+        _reject_symlink_components(shards_path, "descriptor shard directory")
+        if not shards_path.is_dir() or shards_path.is_symlink():
+            raise DescriptorCacheV2Error(
+                "descriptor shard directory must be a non-symlink directory"
+            )
+        try:
+            root_names: set[str] = set()
+            for entry in os.scandir(self.root):
+                root_names.add(entry.name)
+                if len(root_names) > 2:
+                    raise DescriptorCacheV2Error(
+                        "descriptor cache root entries are not closed"
+                    )
+            shard_entries: list[os.DirEntry[str]] = []
+            for entry in os.scandir(shards_path):
+                shard_entries.append(entry)
+                if len(shard_entries) > self.bounds.max_shards:
+                    raise DescriptorCacheV2Error(
+                        "descriptor shard directory exceeds its entry bound"
+                    )
+        except OSError as error:
+            raise DescriptorCacheV2Error(
+                "descriptor cache tree cannot be enumerated"
+            ) from error
+        if root_names != {"index.json", "shards"}:
+            raise DescriptorCacheV2Error("descriptor cache root entries are not closed")
+        if (
+            len(shard_entries) != len(self._rows)
+            or len(shard_entries) > self.bounds.max_shards
+        ):
+            raise DescriptorCacheV2Error("descriptor shard directory census mismatch")
+        expected = {Path(row.path).name for row in self._rows.values()}
+        observed = {entry.name for entry in shard_entries}
+        if observed != expected or any(
+            not entry.is_file(follow_symlinks=False) for entry in shard_entries
+        ):
+            raise DescriptorCacheV2Error(
+                "descriptor shard directory entries are not closed"
+            )
+
+    def open_batch(
+        self,
+        batch: PreparedGroupBatch,
+        contexts: tuple[DescriptorWindowContext, ...],
+    ) -> CachedDescriptorBatch:
+        self._validate_tree_census()
+        checked = validate_prepared_group_batch(batch)
+        checked_contexts = _validate_contexts(
+            contexts,
+            batch_size=checked.batch_size,
+            binding=self.source_binding,
+        )
+        batch_digest = prepared_group_batch_sha256(checked)
+        cache_key = _entry_key(
+            binding=self.source_binding,
+            contexts=checked_contexts,
+            batch_input_sha256=batch_digest,
+            floors_sha256=self.energy_floors_sha256,
+        )
+        row = self._rows.get(cache_key)
+        if row is None:
+            raise DescriptorCacheV2Miss("exact descriptor cache entry is absent")
+        first = checked_contexts[0]
+        if (
+            row.batch_input_sha256 != batch_digest
+            or row.source_batch_sha256 != first.source_batch_sha256
+            or row.split != first.split
+            or row.seed != first.seed
+            or row.epoch != first.epoch
+            or row.window_count != len(checked_contexts)
+            or row.window_contexts_sha256 != _contexts_sha256(checked_contexts)
+        ):
+            raise DescriptorCacheV2Error(
+                "descriptor index row differs from explicit request"
+            )
+        raw = _read_owned(
+            self.root / row.path,
+            maximum=self.bounds.max_shard_bytes,
+            label="descriptor cache shard",
+        )
+        if len(raw) != row.bytes or _sha256(raw) != row.sha256:
+            raise DescriptorCacheV2Error(
+                "descriptor cache shard bytes differ from index"
+            )
+        arrays = _decode_shard(
+            raw,
+            row=row,
+            binding=self.source_binding,
+            contexts=checked_contexts,
+            batch_input_sha256=batch_digest,
+            floors_sha256=self.energy_floors_sha256,
+            bounds=self.bounds,
+        )
+        _validate_decoded_arrays(checked, arrays, edge_count=row.edge_count)
+        return CachedDescriptorBatch(
+            cache_key_sha256=cache_key,
+            batch_input_sha256=batch_digest,
+            contexts=checked_contexts,
+            edge_count=row.edge_count,
+            _arrays=tuple((name, arrays[name]) for name in _ARRAY_NAMES),
+            _reader_token=_CACHE_BATCH_TOKEN,
+        )
+
+
+__all__ = [
+    "AUTHORITY",
+    "CachedDescriptorBatch",
+    "CachedPairChunkStream",
+    "DescriptorCacheArtifact",
+    "DescriptorCacheBounds",
+    "DescriptorCacheSourceBinding",
+    "DescriptorCacheV2Error",
+    "DescriptorCacheV2Miss",
+    "DescriptorStreamKind",
+    "DescriptorWindowContext",
+    "ENTRY_SCHEMA",
+    "INDEX_SCHEMA",
+    "PRODUCTION",
+    "PeriodicDescriptorCacheV2",
+    "PeriodicDescriptorCacheWriterV2",
+    "RESULT_CLAIMED",
+    "SHARD_SCHEMA",
+    "STATUS",
+    "STREAM_KINDS",
+    "energy_floors_sha256",
+    "iter_uncached_pair_chunks",
+    "prepared_group_batch_sha256",
+]
diff --git a/tests/test_periodic_descriptor_cache_v2.py b/tests/test_periodic_descriptor_cache_v2.py
new file mode 100644
index 0000000..a579321
--- /dev/null
+++ b/tests/test_periodic_descriptor_cache_v2.py
@@ -0,0 +1,702 @@
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import replace
+import errno
+import hashlib
+import json
+import os
+from pathlib import Path
+import random
+import stat
+import struct
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import periodic_descriptor_cache_v2 as cache_module
+from phaseset_core.contracts import (
+    PreparedGroupBatch,
+    PreparedActivityBatch,
+    group_commitment,
+)
+from phaseset_core.models import PhaseSetEncoder
+from phaseset_core.periodic import PairChunk
+from phaseset_core.periodic_descriptor_cache_v2 import (
+    STREAM_KINDS,
+    CachedDescriptorBatch,
+    DescriptorCacheBounds,
+    DescriptorCacheSourceBinding,
+    DescriptorCacheV2Error,
+    DescriptorCacheV2Miss,
+    DescriptorWindowContext,
+    PeriodicDescriptorCacheV2,
+    PeriodicDescriptorCacheWriterV2,
+    iter_uncached_pair_chunks,
+    prepared_group_batch_sha256,
+)
+from phaseset_core.pipeline import deterministic_group_yaw
+
+
+def _digest(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+MANIFEST_SHA = _digest("prepared-manifest")
+SOURCE_BATCH_SHA = _digest("prepared-source-batch")
+SOURCE_TREE_SHA = _digest("source-tree")
+ENVIRONMENT_SHA = _digest("environment")
+WINDOW_SHAS = (_digest("window-a"), _digest("window-b"))
+
+
+def _binding() -> DescriptorCacheSourceBinding:
+    return DescriptorCacheSourceBinding(
+        prepared_manifest_sha256=MANIFEST_SHA,
+        source_tree_sha256=SOURCE_TREE_SHA,
+        environment_sha256=ENVIRONMENT_SHA,
+    )
+
+
+def _floors(offset: float = 0.0) -> np.ndarray:
+    return np.ascontiguousarray(
+        np.asarray(
+            [offset + (index + 1) * 1.0e-12 for index in range(6)], dtype=np.float64
+        )
+    )
+
+
+def _actor_motion(actor_identity: int, valid_length: int) -> np.ndarray:
+    time = np.arange(valid_length, dtype=np.float64)[:, None]
+    joint = np.arange(22, dtype=np.float64)[None, :]
+    value = np.zeros((valid_length, 22, 3), dtype=np.float64)
+    value[..., 0] = 0.013 * actor_identity + 0.0011 * time * (joint + 1.0)
+    value[..., 1] = 0.007 * actor_identity + 0.0003 * time * (23.0 - joint)
+    value[..., 2] = -0.017 * actor_identity + np.sin((time + joint) / 31.0)
+    return np.ascontiguousarray(value, dtype=np.float32)
+
+
+def _batch(
+    *,
+    permuted: bool = False,
+    padded_actors: int = 4,
+    identities: tuple[tuple[int, ...], tuple[int, ...]] = ((1, 3), (2, 4, 6)),
+) -> PreparedGroupBatch:
+    if padded_actors < 3:
+        raise ValueError("fixture requires at least three padded actors")
+    if any(len(row) > padded_actors for row in identities):
+        raise ValueError("fixture identities exceed padded actor count")
+    batch_size = 2
+    time = 200
+    skeletons = np.zeros((batch_size, padded_actors, time, 22, 3), dtype=np.float32)
+    actor_mask = np.zeros((batch_size, padded_actors), dtype=np.bool_)
+    frame_mask = np.zeros((batch_size, time), dtype=np.bool_)
+    track_mask = np.zeros((batch_size, padded_actors, time, 22), dtype=np.bool_)
+    valid_lengths = (181, 200)
+    actor_rows: list[tuple[bytes | None, ...]] = []
+    groups: list[bytes] = []
+    for row, (row_ids, valid_length) in enumerate(
+        zip(identities, valid_lengths, strict=True)
+    ):
+        frame_mask[row, :valid_length] = True
+        if permuted:
+            positions = tuple(range(len(row_ids)))
+            source_ids = tuple(reversed(row_ids))
+        else:
+            positions = tuple(range(len(row_ids)))
+            source_ids = row_ids
+        commitments: list[bytes | None] = [None] * padded_actors
+        for position, identity in zip(positions, source_ids, strict=True):
+            commitment = bytes([identity]) * 32
+            commitments[position] = commitment
+            actor_mask[row, position] = True
+            track_mask[row, position, :valid_length] = True
+            skeletons[row, position, :valid_length] = _actor_motion(
+                identity, valid_length
+            )
+        valid_commitments = tuple(bytes([identity]) * 32 for identity in row_ids)
+        actor_rows.append(tuple(commitments))
+        groups.append(group_commitment(valid_commitments))
+    return PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(skeletons),
+        actor_mask=np.ascontiguousarray(actor_mask),
+        frame_mask=np.ascontiguousarray(frame_mask),
+        track_mask=np.ascontiguousarray(track_mask),
+        actor_commitments=tuple(actor_rows),
+        group_commitments=tuple(groups),
+    )
+
+
+def _boundary_batch() -> PreparedGroupBatch:
+    return _batch(
+        padded_actors=16,
+        identities=(tuple(range(1, 13)), tuple(range(33, 49))),
+    )
+
+
+def _numeric_mutant(batch: PreparedGroupBatch) -> PreparedGroupBatch:
+    skeletons = np.array(batch.skeletons, copy=True)
+    skeletons[0, 0, 17, 3, 0] += np.float32(0.125)
+    return PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(skeletons),
+        actor_mask=np.ascontiguousarray(batch.actor_mask),
+        frame_mask=np.ascontiguousarray(batch.frame_mask),
+        track_mask=np.ascontiguousarray(batch.track_mask),
+        actor_commitments=batch.actor_commitments,
+        group_commitments=batch.group_commitments,
+    )
+
+
+def _mask_mutant(batch: PreparedGroupBatch) -> PreparedGroupBatch:
+    skeletons = np.array(batch.skeletons, copy=True)
+    track_mask = np.array(batch.track_mask, copy=True)
+    track_mask[0, 0, 17, 3] = False
+    skeletons[0, 0, 17, 3] = np.float32(0.0)
+    return PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(skeletons),
+        actor_mask=np.ascontiguousarray(batch.actor_mask),
+        frame_mask=np.ascontiguousarray(batch.frame_mask),
+        track_mask=np.ascontiguousarray(track_mask),
+        actor_commitments=batch.actor_commitments,
+        group_commitments=batch.group_commitments,
+    )
+
+
+def _commitment_mutant(batch: PreparedGroupBatch) -> PreparedGroupBatch:
+    rows = [list(row) for row in batch.actor_commitments]
+    rows[0][0] = bytes([8]) * 32
+    commitments = tuple(tuple(row) for row in rows)
+    groups = tuple(
+        group_commitment(tuple(value for value in row if value is not None))
+        for row in commitments
+    )
+    return PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(batch.skeletons),
+        actor_mask=np.ascontiguousarray(batch.actor_mask),
+        frame_mask=np.ascontiguousarray(batch.frame_mask),
+        track_mask=np.ascontiguousarray(batch.track_mask),
+        actor_commitments=commitments,
+        group_commitments=groups,
+    )
+
+
+def _contexts(
+    *,
+    split: str = "val",
+    seed: int = 1729,
+    epoch: int = 0,
+    source_batch_sha256: str = SOURCE_BATCH_SHA,
+    window_shas: tuple[str, str] = WINDOW_SHAS,
+) -> tuple[DescriptorWindowContext, ...]:
+    values = []
+    for window_sha, ordinal in zip(window_shas, (101, 205), strict=True):
+        yaw = (
+            deterministic_group_yaw(seed=seed, epoch=epoch, window_ordinal=ordinal)
+            if split == "train"
+            else 0.0
+        )
+        values.append(
+            DescriptorWindowContext.from_yaw(
+                prepared_manifest_sha256=MANIFEST_SHA,
+                source_batch_sha256=source_batch_sha256,
+                window_sha256=window_sha,
+                window_ordinal=ordinal,
+                split=split,  # type: ignore[arg-type]
+                seed=seed,
+                epoch=epoch,
+                augmentation_yaw=yaw,
+            )
+        )
+    return tuple(values)
+
+
+def _create_cache(
+    root: Path,
+    batch: PreparedGroupBatch,
+    contexts: tuple[DescriptorWindowContext, ...],
+    floors: np.ndarray,
+) -> tuple[PeriodicDescriptorCacheV2, CachedDescriptorBatch]:
+    writer = PeriodicDescriptorCacheWriterV2(
+        root,
+        source_binding=_binding(),
+        energy_floors=floors,
+    )
+    writer.add_batch(batch, contexts)
+    artifact = writer.finalize()
+    cache = PeriodicDescriptorCacheV2(
+        root,
+        expected_index_sha256=artifact.index_sha256,
+        expected_source_binding=_binding(),
+        energy_floors=floors,
+    )
+    return cache, cache.open_batch(batch, contexts)
+
+
+def _flatten(chunks: object) -> dict[str, np.ndarray]:
+    selected = tuple(chunks)  # type: ignore[arg-type]
+    assert selected and all(type(chunk) is PairChunk for chunk in selected)
+    return {
+        "batch_indices": np.concatenate([chunk.batch_indices for chunk in selected]),
+        "actor_i": np.concatenate([chunk.actor_i for chunk in selected]),
+        "actor_j": np.concatenate([chunk.actor_j for chunk in selected]),
+        "tokens_ij": np.concatenate([chunk.tokens_ij for chunk in selected]),
+        "tokens_ji": np.concatenate([chunk.tokens_ji for chunk in selected]),
+        "support_mask": np.concatenate([chunk.support_mask for chunk in selected]),
+    }
+
+
+def _assert_exact_arrays(
+    left: dict[str, np.ndarray], right: dict[str, np.ndarray]
+) -> None:
+    assert left.keys() == right.keys()
+    for name in left:
+        assert left[name].dtype == right[name].dtype
+        assert left[name].shape == right[name].shape
+        assert left[name].tobytes(order="C") == right[name].tobytes(order="C")
+
+
+def _assert_numpy_rng_equal(
+    left: tuple[object, np.ndarray, int, int, float],
+    right: tuple[object, np.ndarray, int, int, float],
+) -> None:
+    assert left[0] == right[0]
+    assert np.array_equal(left[1], right[1])
+    assert left[2:] == right[2:]
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            ensure_ascii=True,
+            allow_nan=False,
+            sort_keys=True,
+            separators=(",", ":"),
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _reauthenticate_single_shard(
+    root: Path,
+    mutate: Callable[[bytes], bytes],
+) -> str:
+    index_path = root / "index.json"
+    value = json.loads(index_path.read_text(encoding="ascii"))
+    assert type(value) is dict and type(value["shards"]) is list
+    assert len(value["shards"]) == 1
+    row = value["shards"][0]
+    assert type(row) is dict and type(row["path"]) is str
+    shard_path = root / row["path"]
+    changed = mutate(shard_path.read_bytes())
+    os.chmod(shard_path, stat.S_IREAD | stat.S_IWRITE)
+    shard_path.write_bytes(changed)
+    row["bytes"] = len(changed)
+    row["sha256"] = hashlib.sha256(changed).hexdigest()
+    value["total_shard_bytes"] = sum(item["bytes"] for item in value["shards"])
+    index_raw = _canonical_json_bytes(value)
+    os.chmod(index_path, stat.S_IREAD | stat.S_IWRITE)
+    index_path.write_bytes(index_raw)
+    return hashlib.sha256(index_raw).hexdigest()
+
+
+def _bad_magic(raw: bytes) -> bytes:
+    return b"X" + raw[1:]
+
+
+def _bad_header_length(raw: bytes) -> bytes:
+    offset = len(cache_module.MAGIC)
+    return raw[:offset] + struct.pack(">Q", len(raw)) + raw[offset + 8 :]
+
+
+def _bad_array_digest(raw: bytes) -> bytes:
+    changed = bytearray(raw)
+    changed[-1] ^= 1
+    return bytes(changed)
+
+
+def test_cached_stream_is_exact_uncached_descriptor_and_reiterable(
+    tmp_path: Path,
+) -> None:
+    batch = _boundary_batch()
+    floors = _floors()
+    _, cached = _create_cache(tmp_path / "cache", batch, _contexts(), floors)
+
+    assert cached.edge_count == 66 + 120 == 186
+    for stream_kind in STREAM_KINDS:
+        expected = _flatten(
+            iter_uncached_pair_chunks(
+                batch,
+                stream_kind,
+                energy_floors=floors,
+                edge_chunk_size=64,
+            )
+        )
+        for chunk_size, expected_lengths in (
+            (64, (64, 64, 58)),
+            (128, (128, 58)),
+            (256, (186,)),
+        ):
+            chunks = tuple(
+                cached.stream(stream_kind).iter_chunks(edge_chunk_size=chunk_size)
+            )
+            assert (
+                tuple(len(chunk.batch_indices) for chunk in chunks) == expected_lengths
+            )
+            if chunk_size == 128:
+                assert set(chunks[0].batch_indices.tolist()) == {0, 1}
+            _assert_exact_arrays(_flatten(chunks), expected)
+    for _, array in cached._arrays:  # noqa: SLF001 - deliberate immutable-byte audit
+        assert not array.flags.writeable
+    with pytest.raises(ValueError):
+        cached._arrays[0][1][0] = 99  # noqa: SLF001 - deliberate mutation probe
+
+
+@pytest.mark.parametrize(
+    "bounds",
+    (
+        DescriptorCacheBounds(max_edges_per_shard=3),
+        DescriptorCacheBounds(max_decoded_shard_bytes=4 * 1914 - 1),
+        DescriptorCacheBounds(
+            max_decoded_shard_bytes=4 * 1914,
+            max_shard_bytes=len(cache_module.MAGIC) + 8 + 4 * 1914 - 1,
+        ),
+        DescriptorCacheBounds(
+            max_total_shard_bytes=len(cache_module.MAGIC) + 8 + 4 * 1914 - 1,
+        ),
+    ),
+)
+def test_writer_rejects_resource_bounds_before_descriptor_materialization(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    bounds: DescriptorCacheBounds,
+) -> None:
+    assert cache_module._DECODED_BYTES_PER_EDGE == 1914
+    calls = 0
+
+    def forbidden_collect(*_args: object, **_kwargs: object) -> object:
+        nonlocal calls
+        calls += 1
+        raise AssertionError("descriptor collection ran before its resource preflight")
+
+    monkeypatch.setattr(cache_module, "_collect_all_arrays", forbidden_collect)
+    writer = PeriodicDescriptorCacheWriterV2(
+        tmp_path / "cache",
+        source_binding=_binding(),
+        energy_floors=_floors(),
+        bounds=bounds,
+    )
+    with pytest.raises(DescriptorCacheV2Error, match="bound"):
+        writer.add_batch(_batch(), _contexts())
+    assert calls == 0
+
+
+def test_context_enforces_native_train_yaw_and_positive_zero_validation() -> None:
+    ordinal = 101
+    yaw = deterministic_group_yaw(seed=1729, epoch=7, window_ordinal=ordinal)
+    value = DescriptorWindowContext.from_yaw(
+        prepared_manifest_sha256=MANIFEST_SHA,
+        source_batch_sha256=SOURCE_BATCH_SHA,
+        window_sha256=WINDOW_SHAS[0],
+        window_ordinal=ordinal,
+        split="train",
+        seed=1729,
+        epoch=7,
+        augmentation_yaw=yaw,
+    )
+    assert value.augmentation_yaw_be_hex == struct.pack(">d", yaw).hex()
+    with pytest.raises(DescriptorCacheV2Error, match="deterministic_group_yaw"):
+        DescriptorWindowContext.from_yaw(
+            prepared_manifest_sha256=MANIFEST_SHA,
+            source_batch_sha256=SOURCE_BATCH_SHA,
+            window_sha256=WINDOW_SHAS[0],
+            window_ordinal=ordinal,
+            split="train",
+            seed=1729,
+            epoch=7,
+            augmentation_yaw=0.0,
+        )
+    with pytest.raises(DescriptorCacheV2Error, match="validation context"):
+        DescriptorWindowContext.from_yaw(
+            prepared_manifest_sha256=MANIFEST_SHA,
+            source_batch_sha256=SOURCE_BATCH_SHA,
+            window_sha256=WINDOW_SHAS[0],
+            window_ordinal=ordinal,
+            split="val",
+            seed=1729,
+            epoch=0,
+            augmentation_yaw=-0.0,
+        )
+
+
+def test_actor_permutation_canonicalizes_but_dynamic_repacking_is_rejected(
+    tmp_path: Path,
+) -> None:
+    canonical = _batch()
+    permuted = _batch(permuted=True)
+    repacked = _batch(padded_actors=5)
+    assert prepared_group_batch_sha256(canonical) == prepared_group_batch_sha256(
+        permuted
+    )
+    assert prepared_group_batch_sha256(canonical) != prepared_group_batch_sha256(
+        repacked
+    )
+    contexts = _contexts()
+    floors = _floors()
+    cache, expected = _create_cache(tmp_path / "cache", canonical, contexts, floors)
+
+    actual = cache.open_batch(permuted, contexts)
+    assert actual.cache_key_sha256 == expected.cache_key_sha256
+    with pytest.raises(DescriptorCacheV2Miss, match="absent"):
+        cache.open_batch(repacked, contexts)
+
+
+@pytest.mark.parametrize(
+    "mutant_factory",
+    (_numeric_mutant, _mask_mutant, _commitment_mutant),
+)
+def test_complete_numeric_mask_and_commitment_digest_rejects_mutants(
+    tmp_path: Path,
+    mutant_factory: Callable[[PreparedGroupBatch], PreparedGroupBatch],
+) -> None:
+    batch = _batch()
+    contexts = _contexts()
+    floors = _floors()
+    cache, _ = _create_cache(tmp_path / "cache", batch, contexts, floors)
+    mutant = mutant_factory(batch)
+    assert prepared_group_batch_sha256(mutant) != prepared_group_batch_sha256(batch)
+    with pytest.raises(DescriptorCacheV2Miss, match="absent"):
+        cache.open_batch(mutant, contexts)
+
+
+def test_wrong_source_floor_window_and_missing_entry_fail_closed(
+    tmp_path: Path,
+) -> None:
+    batch = _batch()
+    contexts = _contexts()
+    floors = _floors()
+    writer = PeriodicDescriptorCacheWriterV2(
+        tmp_path / "cache",
+        source_binding=_binding(),
+        energy_floors=floors,
+    )
+    writer.add_batch(batch, contexts)
+    artifact = writer.finalize()
+
+    wrong_binding = replace(_binding(), source_tree_sha256=_digest("wrong-source"))
+    with pytest.raises(DescriptorCacheV2Error, match="binding mismatch"):
+        PeriodicDescriptorCacheV2(
+            artifact.root,
+            expected_index_sha256=artifact.index_sha256,
+            expected_source_binding=wrong_binding,
+            energy_floors=floors,
+        )
+    with pytest.raises(DescriptorCacheV2Error, match="binding mismatch"):
+        PeriodicDescriptorCacheV2(
+            artifact.root,
+            expected_index_sha256=artifact.index_sha256,
+            expected_source_binding=_binding(),
+            energy_floors=_floors(1.0e-8),
+        )
+    cache = PeriodicDescriptorCacheV2(
+        artifact.root,
+        expected_index_sha256=artifact.index_sha256,
+        expected_source_binding=_binding(),
+        energy_floors=floors,
+    )
+    missing = (
+        replace(contexts[0], window_sha256=_digest("other-window")),
+        contexts[1],
+    )
+    with pytest.raises(DescriptorCacheV2Miss, match="absent"):
+        cache.open_batch(batch, missing)
+    wrong_batch = (
+        replace(contexts[0], source_batch_sha256=_digest("other-batch")),
+        replace(contexts[1], source_batch_sha256=_digest("other-batch")),
+    )
+    with pytest.raises(DescriptorCacheV2Miss, match="absent"):
+        cache.open_batch(batch, wrong_batch)
+
+
+def test_shard_tamper_and_extra_file_are_rejected_before_stream(tmp_path: Path) -> None:
+    batch = _batch()
+    contexts = _contexts()
+    floors = _floors()
+    cache, _ = _create_cache(tmp_path / "cache", batch, contexts, floors)
+    shard = next((cache.root / "shards").iterdir())
+    os.chmod(shard, stat.S_IREAD | stat.S_IWRITE)
+    raw = bytearray(shard.read_bytes())
+    raw[-1] ^= 1
+    shard.write_bytes(raw)
+    with pytest.raises(DescriptorCacheV2Error, match="bytes differ"):
+        cache.open_batch(batch, contexts)
+
+    other_root = tmp_path / "other-cache"
+    other_cache, _ = _create_cache(other_root, batch, contexts, floors)
+    (other_root / "shards" / "extra.pdc2").write_bytes(b"extra")
+    with pytest.raises(DescriptorCacheV2Error, match="census"):
+        other_cache.open_batch(batch, contexts)
+
+
+@pytest.mark.parametrize(
+    ("mutate", "message"),
+    (
+        (_bad_magic, "magic"),
+        (_bad_header_length, "header length"),
+        (_bad_array_digest, "digest mismatch"),
+    ),
+)
+def test_reauthenticated_malformed_shard_reaches_closed_decoder(
+    tmp_path: Path,
+    mutate: Callable[[bytes], bytes],
+    message: str,
+) -> None:
+    batch = _batch()
+    contexts = _contexts()
+    floors = _floors()
+    original, _ = _create_cache(tmp_path / "cache", batch, contexts, floors)
+    changed_index_sha256 = _reauthenticate_single_shard(original.root, mutate)
+    changed = PeriodicDescriptorCacheV2(
+        original.root,
+        expected_index_sha256=changed_index_sha256,
+        expected_source_binding=_binding(),
+        energy_floors=floors,
+    )
+
+    with pytest.raises(DescriptorCacheV2Error, match=message):
+        changed.open_batch(batch, contexts)
+
+
+def test_owned_shard_read_rejects_hardlink_before_payload_read(tmp_path: Path) -> None:
+    batch = _batch()
+    contexts = _contexts()
+    floors = _floors()
+    hardlink_cache, _ = _create_cache(
+        tmp_path / "hardlink-cache", batch, contexts, floors
+    )
+    hardlink_shard = next((hardlink_cache.root / "shards").iterdir())
+    try:
+        os.link(hardlink_shard, tmp_path / "outside-hardlink.pdc2")
+    except OSError as error:
+        capability_errnos = {
+            errno.EACCES,
+            errno.EPERM,
+            errno.ENOSYS,
+            getattr(errno, "ENOTSUP", -1),
+            getattr(errno, "EOPNOTSUPP", -1),
+        }
+        capability_winerrors = {1, 5, 50, 1314}
+        if (
+            error.errno in capability_errnos
+            or getattr(error, "winerror", None) in capability_winerrors
+        ):
+            pytest.skip("temporary filesystem does not permit hard-link creation")
+        raise
+    with pytest.raises(DescriptorCacheV2Error, match="single-link"):
+        hardlink_cache.open_batch(batch, contexts)
+
+
+def test_owned_shard_read_rejects_oversize_before_payload_read(tmp_path: Path) -> None:
+    batch = _batch()
+    contexts = _contexts()
+    floors = _floors()
+    oversize_cache, _ = _create_cache(
+        tmp_path / "oversize-cache", batch, contexts, floors
+    )
+    oversize_shard = next((oversize_cache.root / "shards").iterdir())
+    original_size = oversize_shard.stat().st_size
+    bounds = DescriptorCacheBounds(
+        max_shard_bytes=original_size,
+        max_decoded_shard_bytes=original_size,
+    )
+    os.chmod(oversize_shard, stat.S_IREAD | stat.S_IWRITE)
+    with oversize_shard.open("ab") as stream:
+        stream.write(b"x")
+    bounded = PeriodicDescriptorCacheV2(
+        oversize_cache.root,
+        expected_index_sha256=oversize_cache.index_sha256,
+        expected_source_binding=_binding(),
+        energy_floors=floors,
+        bounds=bounds,
+    )
+    with pytest.raises(DescriptorCacheV2Error, match="stored byte bound"):
+        bounded.open_batch(batch, contexts)
+
+
+def test_writer_and_reader_do_not_consume_any_rng_stream(tmp_path: Path) -> None:
+    batch = _batch()
+    contexts = _contexts()
+    floors = _floors()
+    python_before = random.getstate()
+    numpy_before = np.random.get_state()
+    torch_before = torch.random.get_rng_state().clone()
+
+    _, cached = _create_cache(tmp_path / "cache", batch, contexts, floors)
+    tuple(cached.stream("FULL_RELATION").iter_chunks(edge_chunk_size=64))
+
+    assert random.getstate() == python_before
+    _assert_numpy_rng_equal(np.random.get_state(), numpy_before)
+    assert torch.equal(torch.random.get_rng_state(), torch_before)
+
+
+class _CachedFullEncoder(PhaseSetEncoder):
+    def __init__(self, cached: CachedDescriptorBatch, **kwargs: object) -> None:
+        super().__init__(**kwargs)
+        self._cached_descriptor_batch = cached
+
+    def _iter_pair_chunks(
+        self,
+        batch: PreparedActivityBatch,
+        *,
+        edge_chunk_size: int,
+    ):
+        del batch
+        return self._cached_descriptor_batch.stream("FULL_RELATION").iter_chunks(
+            edge_chunk_size=edge_chunk_size
+        )
+
+
+def test_cached_stream_preserves_actual_edge_gradients(tmp_path: Path) -> None:
+    batch = _boundary_batch()
+    contexts = _contexts()
+    floors = _floors()
+    _, cached = _create_cache(tmp_path / "cache", batch, contexts, floors)
+    torch.manual_seed(81)
+    uncached = PhaseSetEncoder(
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+        edge_budget=32_768,
+    )
+    expected = uncached(batch, edge_chunk_size=64)
+    expected.tokens.square().sum().backward()
+    expected_parameters = dict(uncached.named_parameters())
+    for chunk_size in (64, 128, 256):
+        cached_encoder = _CachedFullEncoder(
+            cached,
+            embedding_dim=16,
+            hidden_dim=11,
+            energy_floors=floors,
+            edge_budget=32_768,
+        )
+        cached_encoder.load_state_dict(uncached.state_dict(), strict=True)
+        actual = cached_encoder(batch, edge_chunk_size=chunk_size)
+        for name in (
+            "tokens",
+            "band_mask",
+            "pair_component",
+            "topology_delta",
+            "valid_pair_count",
+            "topology_node_count",
+        ):
+            assert torch.equal(getattr(actual, name), getattr(expected, name))
+
+        actual.tokens.square().sum().backward()
+        actual_parameters = dict(cached_encoder.named_parameters())
+        assert expected_parameters.keys() == actual_parameters.keys()
+        for name in expected_parameters:
+            expected_gradient = expected_parameters[name].grad
+            actual_gradient = actual_parameters[name].grad
+            assert expected_gradient is not None
+            assert actual_gradient is not None
+            assert torch.equal(actual_gradient, expected_gradient), (chunk_size, name)
diff --git a/tests/test_phaseset_base_cohort_latency.py b/tests/test_phaseset_base_cohort_latency.py
new file mode 100644
index 0000000..71be843
--- /dev/null
+++ b/tests/test_phaseset_base_cohort_latency.py
@@ -0,0 +1,431 @@
+"""Server-only software contracts for the frozen base latency session."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+import hashlib
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+from torch import nn
+
+from phaseset_core import base_cohort_latency as latency
+from phaseset_core import base_cohort_validation as scoring
+from phaseset_core import training
+from phaseset_core.experiments import base_run_ids
+
+
+UUID = "GPU-11111111-2222-3333-4444-555555555555"
+
+
+def _sha(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+def _snapshot(ordinal: int, *, uuid: str = UUID) -> latency.SharedCudaObservation:
+    return latency.SharedCudaObservation(
+        captured_unix_ns=1_800_000_000_000_000_000 + ordinal,
+        device_uuid=uuid,
+        memory_total_mib=49_140,
+        memory_free_mib=12_000,
+        memory_used_mib=36_000,
+        gpu_utilization_percent=(ordinal * 17) % 101,
+        memory_utilization_percent=(ordinal * 11) % 101,
+        compute_mode="Default",
+        driver_version="580.95.05",
+    )
+
+
+def _parameter_census(system_id: str) -> scoring.BaseParameterCensus:
+    return scoring.BaseParameterCensus(
+        system_id=system_id,
+        rows=(scoring.BaseParameterRow("weight", (1,), "torch.float32", 1),),
+        total=1,
+        loaded_state_sha256=_sha(f"loaded/{system_id}"),
+    )
+
+
+def _analytic_inputs():
+    rows = []
+    scores = []
+    for index, run_id in enumerate(base_run_ids()):
+        _prefix, _role, seed_text, system_id = run_id.split("/")
+        raw = f"selected-{index}".encode("ascii")
+        selected = SimpleNamespace(
+            name="checkpoint-000000000001-000001-validation.pt",
+            sha256=hashlib.sha256(raw).hexdigest(),
+            raw=raw,
+            epoch=1,
+            global_step=1,
+            state_digest=_sha(f"state/{index}"),
+        )
+        rows.append(
+            SimpleNamespace(
+                run_id=run_id,
+                attempt_id=f"attempt-{index}",
+                system_id=system_id,
+                seed=int(seed_text),
+                terminal_sha256=_sha(f"terminal/{index}"),
+                latest_checkpoint_sha256=_sha(f"latest/{index}"),
+                latest_checkpoint_name="checkpoint-000000000030-000030-validation.pt",
+                selected_checkpoint=selected,
+                failed_predecessors=(),
+            )
+        )
+        scores.append(
+            SimpleNamespace(
+                run_id=run_id,
+                attempt_id=f"attempt-{index}",
+                system_id=system_id,
+                seed=int(seed_text),
+                terminal_sha256=_sha(f"terminal/{index}"),
+                selected_checkpoint_sha256=selected.sha256,
+                selected_checkpoint_state_digest=selected.state_digest,
+                environment_sha256=_sha("environment"),
+                parameter_census=_parameter_census(system_id),
+            )
+        )
+    cohort = SimpleNamespace(
+        rows=tuple(rows),
+        plan_sha256=_sha("plan"),
+        matrix_sha256=_sha("matrix"),
+        training_config_sha256=_sha("host-config"),
+        source_tree_sha256=_sha("source-tree"),
+        train_manifest_sha256=_sha("train"),
+        val_manifest_sha256=_sha("val"),
+    )
+    scored = SimpleNamespace(
+        observations=tuple(scores),
+        sha256=_sha("scored-cohort"),
+    )
+    admission = SimpleNamespace(
+        device="cuda",
+        request_bf16=False,
+        bf16_runtime_qualified=False,
+        edge_budget=32_768,
+        checkpoint_every_updates=1,
+    )
+    return cohort, admission, scored
+
+
+class _FakeSystem(nn.Module):
+    def __init__(self, system_id: str) -> None:
+        super().__init__()
+        self.system_id = system_id
+        self.weight = nn.Parameter(torch.ones((1,), dtype=torch.float32))
+
+
+class _FakeRuntime:
+    def __init__(self, *, fail_encode_at: int | None = None) -> None:
+        self.snapshot_count = 0
+        self.clock = 10_000
+        self.encode_count = 0
+        self.place_count = 0
+        self.release_count = 0
+        self.fail_encode_at = fail_encode_at
+
+    def snapshot(self) -> latency.SharedCudaObservation:
+        result = _snapshot(self.snapshot_count)
+        self.snapshot_count += 1
+        return result
+
+    def initialize(self, *, device, precision_mode, admission_observation):
+        assert str(device) == "cuda"
+        assert admission_observation.device_uuid == UUID
+        return latency.BaseLatencyRuntimeIdentity(
+            device="cuda:0",
+            device_uuid=UUID,
+            device_name="registered-fixture-device",
+            torch_version="2.12.0+cu126",
+            cuda_runtime_version="12.6",
+            cudnn_version=9_000,
+            driver_version="580.95.05",
+            nvidia_total_memory_mib=49_140,
+            compute_mode="Default",
+            torch_total_memory_bytes=48_453 * 1024**2,
+            initial_allocated_bytes=0,
+            initial_reserved_bytes=0,
+            environment_sha256=_sha("environment"),
+            precision_mode=precision_mode,
+        )
+
+    def synchronize(self, _device) -> None:
+        return None
+
+    def perf_counter_ns(self) -> int:
+        self.clock += 100
+        return self.clock
+
+    def reset_peak_memory(self, _device) -> None:
+        return None
+
+    def peak_memory(self, _device) -> tuple[int, int]:
+        return 256 * 1024**2, 512 * 1024**2
+
+    def place(self, _system, _device) -> None:
+        self.place_count += 1
+
+    def encode(self, _system, groups):
+        self.encode_count += 1
+        assert groups.skeletons.shape == (1, 32, 200, 22, 3)
+        if self.fail_encode_at == self.encode_count:
+            raise RuntimeError("injected visit failure")
+        return object()
+
+    def validate_output(self, output, _device) -> None:
+        assert type(output) is object
+
+    def release(self, _system, _device) -> None:
+        self.release_count += 1
+
+
+def _patch_analytic_session(monkeypatch: pytest.MonkeyPatch):
+    cohort, admission, scored = _analytic_inputs()
+    monkeypatch.setattr(
+        latency,
+        "_checked_inputs",
+        lambda *_args: (cohort, admission, scored),
+    )
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+
+    def load(row, score, _admission, *, runtime_device):
+        assert runtime_device == torch.device("cuda:0")
+        config = training.TrainingConfig(
+            stage="base",
+            seed=row.seed,
+            device="cuda",
+            synthetic_contract=False,
+        )
+        return (
+            _FakeSystem(row.system_id),
+            config,
+            training.PrecisionDecision("FP32", False, "FP32_REQUESTED"),
+            score.parameter_census,
+        )
+
+    monkeypatch.setattr(latency, "_load_selected_system", load)
+    monkeypatch.setenv("CUBLAS_WORKSPACE_CONFIG", ":4096:8")
+    return cohort, admission, scored
+
+
+def test_unindexed_cuda_device_matches_scorer_and_resolved_runtime(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setattr(torch.cuda, "current_device", lambda: 0)
+    latency._checked_scorer_runtime_device(
+        config_device="cuda",
+        score_device="cuda:0",
+        runtime_device=torch.device("cuda:0"),
+    )
+
+    with pytest.raises(latency.BaseCohortLatencyError, match="resolved physical"):
+        latency._checked_scorer_runtime_device(
+            config_device="cuda",
+            score_device="cuda",
+            runtime_device=torch.device("cuda:0"),
+        )
+    with pytest.raises(latency.BaseCohortLatencyError, match="resolved physical"):
+        latency._checked_scorer_runtime_device(
+            config_device="cuda",
+            score_device="cuda:0",
+            runtime_device=torch.device("cuda:1"),
+        )
+
+
+def test_cyclic_schedule_balances_every_row_and_ordinal() -> None:
+    schedule = latency.cyclic_visit_schedule()
+    rows = base_run_ids()
+
+    assert len(schedule) == 9
+    assert all(len(round_rows) == 9 and set(round_rows) == set(rows) for round_rows in schedule)
+    for row in rows:
+        assert sorted(round_rows.index(row) for round_rows in schedule) == list(range(9))
+    assert schedule[0] == rows
+    assert schedule[1] == rows[1:] + rows[:1]
+
+
+def test_frozen_workload_uses_literal_formula_and_complete_masks() -> None:
+    groups, receipt = latency.build_frozen_latency_workload()
+
+    assert groups.skeletons.shape == (1, 32, 200, 22, 3)
+    assert groups.skeletons.dtype == np.float32
+    assert groups.actor_mask.all() and groups.frame_mask.all() and groups.track_mask.all()
+    source_commitments = latency._source_actor_commitments()
+    for actor, frame, joint, coordinate in ((0, 0, 0, 0), (31, 199, 21, 2), (13, 79, 8, 1)):
+        raw = ((actor * 131 + frame * 17 + joint * 7 + coordinate * 3) % 2048) - 1024
+        expected = np.float32(raw) * np.float32(2**-10)
+        prepared_actor = receipt.actor_commitments.index(source_commitments[actor])
+        assert groups.skeletons[0, prepared_actor, frame, joint, coordinate] == expected
+    expected_actor0 = hashlib.sha256(latency._ACTOR_DOMAIN + (0).to_bytes(4, "big")).digest()
+    assert expected_actor0 in receipt.actor_commitments
+    assert tuple((name, shape, dtype) for name, _digest, shape, dtype in receipt.array_rows) == (
+        ("skeletons", (1, 32, 200, 22, 3), "<f4"),
+        ("actor_mask", (1, 32), "|b1"),
+        ("frame_mask", (1, 200), "|b1"),
+        ("track_mask", (1, 32, 200, 22), "|b1"),
+    )
+    assert receipt == latency._workload_receipt(groups)
+    bad_rows = list(receipt.array_rows)
+    name, digest, _shape, dtype = bad_rows[0]
+    bad_rows[0] = (name, digest, (1, 31, 200, 22, 3), dtype)
+    with pytest.raises(latency.BaseCohortLatencyError, match="array census"):
+        replace(receipt, array_rows=tuple(bad_rows))
+
+
+def test_row_statistics_are_exact_integer_median_mad_and_nearest_rank() -> None:
+    samples = tuple(range(1, 100))
+    row = latency.BaseLatencyRowObservation(
+        run_id=base_run_ids()[0],
+        system_id="B0",
+        seed=1729,
+        selected_checkpoint_sha256=_sha("selected"),
+        selected_checkpoint_state_digest=_sha("state"),
+        parameter_census_sha256=_sha("parameters"),
+        parameter_count=123,
+        runtime_sha256=_sha("runtime"),
+        workload_sha256=_sha("workload"),
+        samples_ns=samples,
+        median_ns=50,
+        median_absolute_deviation_ns=25,
+        minimum_ns=1,
+        maximum_ns=99,
+        p05_nearest_rank_ns=5,
+        p95_nearest_rank_ns=95,
+    )
+
+    assert row.frozen_runtime_latency_ns == 50
+    with pytest.raises(latency.BaseCohortLatencyError, match="statistics"):
+        replace(row, p95_nearest_rank_ns=94)
+
+
+def test_fake_runtime_executes_exact_visits_but_cannot_emit_formal_samples(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    cohort, admission, scored = _patch_analytic_session(monkeypatch)
+    runtime = _FakeRuntime()
+
+    result = latency.run_base_cohort_latency_session(
+        cohort,
+        admission=admission,
+        scored_cohort=scored,
+        registered_cuda_uuid=UUID,
+        _runtime=runtime,
+    )
+
+    assert result.complete is True
+    assert result.actual_runtime is False
+    assert result.formal_samples_complete is False
+    assert result.status == latency.FAKE_COMPLETE_STATUS
+    assert len(result.visits) == 81
+    assert all(visit.warmups_completed == 5 for visit in result.visits)
+    assert all(len(visit.samples_ns) == 11 for visit in result.visits)
+    assert tuple(row.run_id for row in result.rows) == base_run_ids()
+    assert all(len(row.samples_ns) == 99 and row.median_ns == 100 for row in result.rows)
+    assert runtime.encode_count == 81 * (5 + 11)
+    assert runtime.place_count == runtime.release_count == 81
+    with pytest.raises(latency.BaseCohortLatencyError, match="status"):
+        replace(result, actual_runtime=True, formal_samples_complete=True)
+
+
+def test_failed_visit_retains_completed_warmups_and_timed_samples(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    cohort, admission, scored = _patch_analytic_session(monkeypatch)
+    runtime = _FakeRuntime(fail_encode_at=9)
+
+    with pytest.raises(latency.BaseCohortLatencyHold) as caught:
+        latency.run_base_cohort_latency_session(
+            cohort,
+            admission=admission,
+            scored_cohort=scored,
+            registered_cuda_uuid=UUID,
+            _runtime=runtime,
+        )
+
+    receipt = caught.value.receipt
+    assert receipt.complete is False
+    assert receipt.rows == ()
+    assert receipt.formal_samples_complete is False
+    assert len(receipt.visits) == 1
+    failed = receipt.visits[0]
+    assert failed.completed is False
+    assert failed.warmups_completed == 5
+    assert failed.samples_ns == (100, 100, 100)
+    assert failed.peak_allocated_bytes == 256 * 1024**2
+    assert runtime.release_count == 1
+
+
+def test_runtime_drift_after_visit_is_a_whole_session_hold(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    cohort, admission, scored = _patch_analytic_session(monkeypatch)
+    runtime = _FakeRuntime()
+    original_snapshot = runtime.snapshot
+
+    def drifting_snapshot():
+        observed = original_snapshot()
+        if runtime.snapshot_count == 4:
+            return replace(observed, device_uuid="GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee")
+        return observed
+
+    runtime.snapshot = drifting_snapshot
+    with pytest.raises(latency.BaseCohortLatencyHold) as caught:
+        latency.run_base_cohort_latency_session(
+            cohort,
+            admission=admission,
+            scored_cohort=scored,
+            registered_cuda_uuid=UUID,
+            _runtime=runtime,
+        )
+    receipt = caught.value.receipt
+    assert receipt.failure_code == "RUNTIME_DRIFT"
+    assert len(receipt.visits) == 1
+    assert receipt.visits[0].after is not None
+    assert receipt.visits[0].after.device_uuid != UUID
+
+
+@pytest.mark.parametrize(
+    ("changed", "failure_code"),
+    (
+        ({"memory_free_mib": 8191}, "RESOURCE_LIMIT"),
+        (
+            {"device_uuid": "GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee"},
+            "RUNTIME_DRIFT",
+        ),
+    ),
+)
+def test_admission_failure_retains_zero_visit_observation_as_whole_session_hold(
+    monkeypatch: pytest.MonkeyPatch,
+    changed: dict[str, object],
+    failure_code: str,
+) -> None:
+    cohort, admission, scored = _patch_analytic_session(monkeypatch)
+    runtime = _FakeRuntime()
+    runtime.snapshot = lambda: replace(_snapshot(0), **changed)
+
+    with pytest.raises(latency.BaseCohortLatencyHold) as caught:
+        latency.run_base_cohort_latency_session(
+            cohort,
+            admission=admission,
+            scored_cohort=scored,
+            registered_cuda_uuid=UUID,
+            _runtime=runtime,
+        )
+
+    receipt = caught.value.receipt
+    assert receipt.failure_code == failure_code
+    assert receipt.complete is False
+    assert receipt.rows == ()
+    assert receipt.visits == ()
+    assert receipt.admission_observation == replace(_snapshot(0), **changed)
+    assert receipt.post_context_observation is None
+    assert receipt.runtime_identity is None
+
+
+def test_public_entry_has_no_submitted_latency_or_sample_parameter() -> None:
+    names = tuple(latency.run_base_cohort_latency_session.__annotations__)
+    assert "latency" not in names
+    assert "samples" not in names
+    assert "parameter_count" not in names
diff --git a/tests/test_phaseset_base_cohort_qualification.py b/tests/test_phaseset_base_cohort_qualification.py
new file mode 100644
index 0000000..7b3ccf7
--- /dev/null
+++ b/tests/test_phaseset_base_cohort_qualification.py
@@ -0,0 +1,335 @@
+from __future__ import annotations
+
+from dataclasses import replace
+from fractions import Fraction
+import hashlib
+import inspect
+from types import SimpleNamespace
+
+import pytest
+
+from phaseset_core import base_cohort_latency as latency
+from phaseset_core import base_cohort_qualification as assembly
+from phaseset_core import experiments, production
+
+
+def _sha(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+def _combined_rows() -> tuple[assembly.CombinedBaseScoreBinding, ...]:
+    counts = {"B0": 100, "B1": 200, "B2": 300}
+    fractions = {"B0": Fraction(1, 2), "B1": Fraction(3, 4), "B2": Fraction(3, 4)}
+    latencies = {"B0": 300, "B1": 200, "B2": 100}
+    rows = []
+    for ordinal, run_id in enumerate(experiments.base_run_ids()):
+        _role, seed, system_id = experiments.parse_run_id(run_id)
+        rows.append(
+            assembly.CombinedBaseScoreBinding(
+                run_id=run_id,
+                attempt_id=f"attempt-{ordinal}",
+                system_id=system_id,
+                seed=seed,
+                terminal_sha256=_sha(f"terminal-{ordinal}"),
+                selected_checkpoint_sha256=_sha(f"checkpoint-{ordinal}"),
+                selected_checkpoint_state_digest=_sha(f"state-{ordinal}"),
+                validation_score_artifact_sha256=_sha(f"score-{ordinal}"),
+                latency_row_sha256=_sha(f"latency-{ordinal}"),
+                latency_samples_sha256=_sha(f"samples-{ordinal}"),
+                parameter_census_sha256=_sha(f"parameters-{system_id}"),
+                parameter_count=counts[system_id],
+                frozen_runtime_latency_ns=latencies[system_id] + ordinal,
+                bidirectional_capture_r1=fractions[system_id],
+                validation_manifest_sha256=_sha("val-census"),
+                validation_source_manifest_sha256=_sha("val-source"),
+                query_census_sha256=_sha("val-census"),
+                evaluator_sha256=_sha("evaluator"),
+                runtime_sha256=_sha("runtime"),
+                environment_sha256=_sha("environment"),
+                precision_mode="FP32",
+            )
+        )
+    return tuple(rows)
+
+
+def _dependency(selector_artifact: bytes) -> assembly.BaseQualificationDependencyBinding:
+    return assembly.BaseQualificationDependencyBinding(
+        admission_sha256=_sha("admission"),
+        resolved_cohort_sha256=_sha("resolved"),
+        scored_cohort_sha256=_sha("scored"),
+        latency_session_sha256=_sha("latency-session"),
+        plan_sha256=_sha("plan"),
+        matrix_sha256=_sha("matrix"),
+        training_config_sha256=_sha("config"),
+        source_tree_sha256=_sha("source-tree"),
+        train_manifest_sha256=_sha("train"),
+        admitted_val_census_sha256=_sha("val-census"),
+        validation_source_manifest_sha256=_sha("val-source"),
+        query_census_sha256=_sha("val-census"),
+        resolver_source_sha256=_sha("resolver-source"),
+        scorer_source_sha256=_sha("scorer-source"),
+        latency_source_sha256=_sha("latency-source"),
+        assembler_source_sha256=_sha("assembler-source"),
+        production_source_sha256=_sha("production-source"),
+        selector_artifact_sha256=hashlib.sha256(selector_artifact).hexdigest(),
+        runtime_sha256=_sha("runtime"),
+        environment_sha256=_sha("environment"),
+        precision_mode="FP32",
+    )
+
+
+def _structural_result() -> assembly.BaseCohortQualificationAssembly:
+    rows = _combined_rows()
+    score_rows = assembly._base_scores(rows)
+    qualification = experiments.qualify_base(score_rows)
+    selector = b"analytic-selector-bytes"
+    dependency = _dependency(selector)
+    evaluator = dependency.selector_artifact_sha256
+    rebound = tuple(replace(row, evaluator_sha256=evaluator) for row in rows)
+    score_rows = assembly._base_scores(rebound)
+    qualification = experiments.qualify_base(score_rows)
+    evidence = production.BaseQualificationExecutionEvidence(
+        score_rows=score_rows,
+        selector_artifact=selector,
+    )
+    return assembly.BaseCohortQualificationAssembly(
+        qualification=qualification,
+        execution_evidence=evidence,
+        artifact=experiments.canonical_base_qualification_bytes(qualification),
+        row_bindings=rebound,
+        dependency_binding=dependency,
+    )
+
+
+def test_exact_99_sample_median_is_recomputed_from_raw_integers() -> None:
+    samples = tuple(range(1, 100))
+
+    assert assembly._median_from_samples(samples) == 50
+    assert len(assembly._samples_sha256(samples)) == 64
+    with pytest.raises(assembly.BaseCohortQualificationError, match="exact 99"):
+        assembly._median_from_samples(samples[:-1])
+    with pytest.raises(assembly.BaseCohortQualificationError, match="positive"):
+        assembly._median_from_samples(samples[:-1] + (0,))
+
+
+def test_combined_row_digest_changes_for_score_or_latency_evidence() -> None:
+    row = _combined_rows()[0]
+
+    assert replace(row, validation_score_artifact_sha256=_sha("other-score")).sha256 != row.sha256
+    assert replace(row, latency_row_sha256=_sha("other-latency")).sha256 != row.sha256
+    assert replace(row, latency_samples_sha256=_sha("other-samples")).sha256 != row.sha256
+
+
+def test_base_scores_use_fraction_live_count_median_and_combined_digest() -> None:
+    bindings = _combined_rows()
+    scores = assembly._base_scores(bindings)
+
+    assert tuple(row.run_id for row in scores) == experiments.base_run_ids()
+    for binding, score in zip(bindings, scores, strict=True):
+        assert (
+            Fraction(
+                score.bidirectional_r1_numerator,
+                score.bidirectional_r1_denominator,
+            )
+            == binding.bidirectional_capture_r1
+        )
+        assert score.parameter_count == binding.parameter_count
+        assert score.frozen_runtime_latency_ns == binding.frozen_runtime_latency_ns
+        assert score.score_artifact_sha256 == binding.sha256
+        assert score.evaluator_sha256 == binding.evaluator_sha256
+
+
+def test_existing_selector_and_canonical_evidence_are_retained() -> None:
+    result = _structural_result()
+
+    assert result.qualification.winner_system_id == "B1"
+    assert result.execution_evidence.score_rows == result.qualification.score_rows
+    assert result.artifact == experiments.canonical_base_qualification_bytes(result.qualification)
+    assert result.authority == 0
+    assert result.production is False
+    assert result.result_claimed is False
+    assert len(result.sha256) == 64
+
+
+def test_result_rejects_artifact_and_combined_row_drift() -> None:
+    result = _structural_result()
+
+    with pytest.raises(assembly.BaseCohortQualificationError, match="not canonical"):
+        replace(result, artifact=result.artifact + b"\n")
+    changed = list(result.row_bindings)
+    changed[0] = replace(
+        changed[0],
+        validation_score_artifact_sha256=_sha("mutated-score"),
+    )
+    with pytest.raises(assembly.BaseCohortQualificationError, match="combined evidence"):
+        replace(result, row_bindings=tuple(changed))
+
+
+def test_row_builder_closes_selected_state_runtime_census_median_and_device() -> None:
+    runtime = latency.BaseLatencyRuntimeIdentity(
+        device="cuda:1",
+        device_uuid="GPU-11111111-2222-3333-4444-555555555555",
+        device_name="registered-device",
+        torch_version="2.12.0+cu126",
+        cuda_runtime_version="12.6",
+        cudnn_version=90100,
+        driver_version="570.00",
+        nvidia_total_memory_mib=49140,
+        compute_mode="Default",
+        torch_total_memory_bytes=50_807_242_752,
+        initial_allocated_bytes=0,
+        initial_reserved_bytes=0,
+        environment_sha256=_sha("environment"),
+        precision_mode="FP32",
+    )
+    resolved_rows = []
+    score_rows = []
+    latency_rows = []
+    visits = []
+    val_census = _sha("val-census")
+    val_source = _sha("val-source")
+    for ordinal, run_id in enumerate(experiments.base_run_ids()):
+        _role, seed, system_id = experiments.parse_run_id(run_id)
+        raw = f"selected-{ordinal}".encode("ascii")
+        checkpoint_sha = hashlib.sha256(raw).hexdigest()
+        state = _sha(f"state-{ordinal}")
+        parameter_sha = _sha(f"parameter-{system_id}")
+        parameter_count = {"B0": 10, "B1": 20, "B2": 30}[system_id]
+        resolved_rows.append(
+            SimpleNamespace(
+                run_id=run_id,
+                attempt_id=f"attempt-{ordinal}",
+                system_id=system_id,
+                seed=seed,
+                terminal_sha256=_sha(f"terminal-{ordinal}"),
+                selected_checkpoint=SimpleNamespace(
+                    raw=raw,
+                    sha256=checkpoint_sha,
+                    state_digest=state,
+                ),
+            )
+        )
+        score_rows.append(
+            SimpleNamespace(
+                run_id=run_id,
+                attempt_id=f"attempt-{ordinal}",
+                system_id=system_id,
+                seed=seed,
+                terminal_sha256=_sha(f"terminal-{ordinal}"),
+                selected_checkpoint_sha256=checkpoint_sha,
+                selected_checkpoint_state_digest=state,
+                parameter_census=SimpleNamespace(
+                    sha256=parameter_sha,
+                    total=parameter_count,
+                ),
+                score_artifact_sha256=_sha(f"score-{ordinal}"),
+                primary_capture_r1=Fraction(ordinal, 8),
+                environment_sha256=runtime.environment_sha256,
+                precision_mode=runtime.precision_mode,
+                device=runtime.device,
+                admitted_val_census_sha256=val_census,
+                query_census_sha256=val_census,
+                source_manifest_sha256=val_source,
+            )
+        )
+        samples = tuple(1000 + ordinal + offset for offset in range(99))
+        for visit_index in range(9):
+            start = visit_index * 11
+            visits.append(
+                SimpleNamespace(
+                    run_id=run_id,
+                    completed=True,
+                    samples_ns=samples[start : start + 11],
+                )
+            )
+        latency_rows.append(
+            SimpleNamespace(
+                run_id=run_id,
+                system_id=system_id,
+                seed=seed,
+                selected_checkpoint_sha256=checkpoint_sha,
+                selected_checkpoint_state_digest=state,
+                parameter_census_sha256=parameter_sha,
+                parameter_count=parameter_count,
+                runtime_sha256=runtime.sha256,
+                samples_ns=samples,
+                median_ns=sorted(samples)[49],
+                sha256=_sha(f"latency-{ordinal}"),
+            )
+        )
+    cohort = SimpleNamespace(rows=tuple(resolved_rows), val_manifest_sha256=val_census)
+    scored = SimpleNamespace(
+        observations=tuple(score_rows),
+        source_manifest_sha256=val_source,
+    )
+    measured = SimpleNamespace(
+        runtime_identity=runtime,
+        rows=tuple(latency_rows),
+        visits=tuple(visits),
+    )
+
+    rows = assembly._build_row_bindings(
+        cohort,
+        scored,
+        measured,
+        _sha("evaluator"),
+    )
+    assert tuple(row.run_id for row in rows) == experiments.base_run_ids()
+    assert rows[0].frozen_runtime_latency_ns == 1049
+    assert rows[0].parameter_count == 10
+    assert score_rows[0].device == runtime.device == "cuda:1"
+    score_rows[0].device = "cuda"
+    with pytest.raises(assembly.BaseCohortQualificationError, match="runtime, or census"):
+        assembly._build_row_bindings(
+            cohort,
+            scored,
+            measured,
+            _sha("evaluator"),
+        )
+    score_rows[0].device = runtime.device
+    latency_rows[0].median_ns += 1
+    with pytest.raises(assembly.BaseCohortQualificationError, match="runtime, or census"):
+        assembly._build_row_bindings(
+            cohort,
+            scored,
+            measured,
+            _sha("evaluator"),
+        )
+
+
+def test_fake_or_held_latency_cannot_reach_assembly(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setattr(
+        latency,
+        "_checked_inputs",
+        lambda *_args: (SimpleNamespace(), SimpleNamespace(), SimpleNamespace()),
+    )
+    fake_session = object.__new__(latency.BaseCohortLatencySession)
+    object.__setattr__(fake_session, "authority", latency.AUTHORITY)
+    object.__setattr__(fake_session, "production", latency.PRODUCTION)
+    object.__setattr__(fake_session, "result_claimed", latency.RESULT_CLAIMED)
+    object.__setattr__(fake_session, "status", latency.FAKE_COMPLETE_STATUS)
+    object.__setattr__(fake_session, "actual_runtime", False)
+    object.__setattr__(fake_session, "complete", True)
+    object.__setattr__(fake_session, "formal_samples_complete", False)
+    object.__setattr__(fake_session, "failure_code", None)
+
+    with pytest.raises(assembly.BaseCohortQualificationError, match="not one actual"):
+        assembly._checked_inputs(object(), object(), object(), fake_session)
+
+
+def test_public_entry_accepts_no_submitted_metrics_or_dependencies() -> None:
+    names = tuple(inspect.signature(assembly.assemble_base_cohort_qualification).parameters)
+
+    assert names == ("cohort", "admission", "scored_cohort", "latency_session")
+    assert not {
+        "scores",
+        "score_rows",
+        "parameter_count",
+        "latency_ns",
+        "samples",
+        "evaluator_sha256",
+        "selector_artifact",
+        "winner",
+    }.intersection(names)
diff --git a/tests/test_phaseset_base_latency_journal_integration.py b/tests/test_phaseset_base_latency_journal_integration.py
new file mode 100644
index 0000000..b063e46
--- /dev/null
+++ b/tests/test_phaseset_base_latency_journal_integration.py
@@ -0,0 +1,481 @@
+"""Server-only integration contracts for latency observation journaling."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from types import SimpleNamespace
+
+import pytest
+import torch
+from torch import nn
+
+from phaseset_core import base_cohort_latency as latency
+from phaseset_core import base_cohort_validation as scoring
+from phaseset_core import latency_progress_journal as journal
+from phaseset_core import training
+from phaseset_core.experiments import base_run_ids
+
+
+pytestmark = pytest.mark.skipif(os.name != "posix", reason="journal is POSIX-only")
+UUID = "GPU-11111111-2222-3333-4444-555555555555"
+
+
+def _sha(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+def _snapshot(ordinal: int, *, free_mib: int = 12_000) -> latency.SharedCudaObservation:
+    return latency.SharedCudaObservation(
+        captured_unix_ns=1_800_000_000_000_000_000 + ordinal,
+        device_uuid=UUID,
+        memory_total_mib=49_140,
+        memory_free_mib=free_mib,
+        memory_used_mib=36_000,
+        gpu_utilization_percent=(ordinal * 17) % 101,
+        memory_utilization_percent=(ordinal * 11) % 101,
+        compute_mode="Default",
+        driver_version="580.95.05",
+    )
+
+
+def _parameter_census(system_id: str) -> scoring.BaseParameterCensus:
+    return scoring.BaseParameterCensus(
+        system_id=system_id,
+        rows=(scoring.BaseParameterRow("weight", (1,), "torch.float32", 1),),
+        total=1,
+        loaded_state_sha256=_sha(f"loaded/{system_id}"),
+    )
+
+
+def _analytic_inputs():
+    rows = []
+    scores = []
+    for index, run_id in enumerate(base_run_ids()):
+        _prefix, _role, seed_text, system_id = run_id.split("/")
+        raw = f"selected-{index}".encode("ascii")
+        selected = SimpleNamespace(
+            name="checkpoint-000000000001-000001-validation.pt",
+            sha256=hashlib.sha256(raw).hexdigest(),
+            raw=raw,
+            epoch=1,
+            global_step=1,
+            state_digest=_sha(f"state/{index}"),
+        )
+        rows.append(
+            SimpleNamespace(
+                run_id=run_id,
+                attempt_id=f"attempt-{index}",
+                system_id=system_id,
+                seed=int(seed_text),
+                terminal_sha256=_sha(f"terminal/{index}"),
+                latest_checkpoint_sha256=_sha(f"latest/{index}"),
+                latest_checkpoint_name="checkpoint-000000000030-000030-validation.pt",
+                selected_checkpoint=selected,
+                failed_predecessors=(),
+            )
+        )
+        scores.append(
+            SimpleNamespace(
+                run_id=run_id,
+                attempt_id=f"attempt-{index}",
+                system_id=system_id,
+                seed=int(seed_text),
+                terminal_sha256=_sha(f"terminal/{index}"),
+                selected_checkpoint_sha256=selected.sha256,
+                selected_checkpoint_state_digest=selected.state_digest,
+                environment_sha256=_sha("environment"),
+                parameter_census=_parameter_census(system_id),
+            )
+        )
+    cohort = SimpleNamespace(
+        rows=tuple(rows),
+        plan_sha256=_sha("plan"),
+        matrix_sha256=_sha("matrix"),
+        training_config_sha256=_sha("host-config"),
+        source_tree_sha256=_sha("source-tree"),
+        train_manifest_sha256=_sha("train"),
+        val_manifest_sha256=_sha("val"),
+    )
+    scored = SimpleNamespace(observations=tuple(scores), sha256=_sha("scored-cohort"))
+    admission = SimpleNamespace(
+        device="cuda",
+        request_bf16=False,
+        bf16_runtime_qualified=False,
+        edge_budget=32_768,
+        checkpoint_every_updates=1,
+    )
+    return cohort, admission, scored
+
+
+class _FakeSystem(nn.Module):
+    def __init__(self, system_id: str) -> None:
+        super().__init__()
+        self.system_id = system_id
+        self.weight = nn.Parameter(torch.ones((1,), dtype=torch.float32))
+
+
+class _FakeRuntime:
+    def __init__(self, *, fail_encode_at: int | None = None, free_mib: int = 12_000) -> None:
+        self.snapshot_count = 0
+        self.clock = 10_000
+        self.encode_count = 0
+        self.timer_open = False
+        self.fail_encode_at = fail_encode_at
+        self.free_mib = free_mib
+
+    def snapshot(self):
+        result = _snapshot(self.snapshot_count, free_mib=self.free_mib)
+        self.snapshot_count += 1
+        return result
+
+    def initialize(self, *, device, precision_mode, admission_observation):
+        assert str(device) == "cuda"
+        assert admission_observation.device_uuid == UUID
+        return latency.BaseLatencyRuntimeIdentity(
+            device="cuda:0",
+            device_uuid=UUID,
+            device_name="registered-fixture-device",
+            torch_version="2.12.0+cu126",
+            cuda_runtime_version="12.6",
+            cudnn_version=9_000,
+            driver_version="580.95.05",
+            nvidia_total_memory_mib=49_140,
+            compute_mode="Default",
+            torch_total_memory_bytes=48_453 * 1024**2,
+            initial_allocated_bytes=0,
+            initial_reserved_bytes=0,
+            environment_sha256=_sha("environment"),
+            precision_mode=precision_mode,
+        )
+
+    def synchronize(self, _device) -> None:
+        return None
+
+    def perf_counter_ns(self) -> int:
+        self.timer_open = not self.timer_open
+        self.clock += 100
+        return self.clock
+
+    def reset_peak_memory(self, _device) -> None:
+        return None
+
+    def peak_memory(self, _device):
+        return 256 * 1024**2, 512 * 1024**2
+
+    def place(self, _system, _device) -> None:
+        return None
+
+    def encode(self, _system, _groups):
+        self.encode_count += 1
+        if self.fail_encode_at == self.encode_count:
+            raise RuntimeError("injected visit failure")
+        return object()
+
+    def validate_output(self, output, _device) -> None:
+        assert type(output) is object
+
+    def release(self, _system, _device) -> None:
+        return None
+
+
+class _ObservedWriter:
+    def __init__(self, writer, runtime: _FakeRuntime) -> None:
+        self.writer = writer
+        self.runtime = runtime
+        self.calls: list[str] = []
+
+    def _call(self, name: str, **payload):
+        if name == "timed_sample_completed":
+            assert self.runtime.timer_open is False
+        self.calls.append(name)
+        return getattr(self.writer, name)(**payload)
+
+    def session_start(self, **payload):
+        return self._call("session_start", **payload)
+
+    def context_ready(self, **payload):
+        return self._call("context_ready", **payload)
+
+    def visit_started(self, **payload):
+        return self._call("visit_started", **payload)
+
+    def warmup_completed(self, **payload):
+        return self._call("warmup_completed", **payload)
+
+    def timed_sample_completed(self, **payload):
+        return self._call("timed_sample_completed", **payload)
+
+    def visit_ended(self, **payload):
+        return self._call("visit_ended", **payload)
+
+    def terminal(self, **payload):
+        return self._call("terminal", **payload)
+
+
+class _FailAfterFirstSample(_ObservedWriter):
+    def timed_sample_completed(self, **payload):
+        super().timed_sample_completed(**payload)
+        raise journal.LatencyJournalWriteError("injected post-commit write failure")
+
+
+class _FailAfterCompletedTerminal(_ObservedWriter):
+    def __init__(self, writer, runtime: _FakeRuntime) -> None:
+        super().__init__(writer, runtime)
+        self.committed_session_sha256: str | None = None
+
+    def terminal(self, **payload):
+        super().terminal(**payload)
+        if payload["outcome"] == "COMPLETED":
+            self.committed_session_sha256 = payload["latency_session_sha256"]
+            raise journal.LatencyJournalWriteError("injected error after completed terminal commit")
+
+
+def _patch_session(monkeypatch: pytest.MonkeyPatch):
+    cohort, admission, scored = _analytic_inputs()
+    monkeypatch.setattr(latency, "_checked_inputs", lambda *_args: (cohort, admission, scored))
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+
+    def load(row, score, _admission, *, runtime_device):
+        assert runtime_device == torch.device("cuda:0")
+        config = training.TrainingConfig(
+            stage="base",
+            seed=row.seed,
+            device="cuda",
+            synthetic_contract=False,
+        )
+        return (
+            _FakeSystem(row.system_id),
+            config,
+            training.PrecisionDecision("FP32", False, "FP32_REQUESTED"),
+            score.parameter_census,
+        )
+
+    monkeypatch.setattr(latency, "_load_selected_system", load)
+    monkeypatch.setenv("CUBLAS_WORKSPACE_CONFIG", ":4096:8")
+    return cohort, admission, scored
+
+
+def _bindings(cohort, scored) -> journal.LatencyJournalBindings:
+    return journal.LatencyJournalBindings(
+        resolved_cohort_sha256=latency._resolved_cohort_sha256(cohort),
+        scored_cohort_sha256=scored.sha256,
+        source_tree_sha256=cohort.source_tree_sha256,
+        latency_protocol_sha256=_sha("frozen-latency-protocol"),
+        wrapper_sha256=_sha("outer-timeout-wrapper"),
+        registered_cuda_uuid=UUID,
+    )
+
+
+def _payload(record) -> dict[str, object]:
+    return json.loads(record.raw.decode("ascii"))["payload"]
+
+
+def test_fake_entry_writes_exact_complete_prefix_after_every_timer_stop(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path,
+) -> None:
+    cohort, admission, scored = _patch_session(monkeypatch)
+    runtime = _FakeRuntime()
+    bindings = _bindings(cohort, scored)
+    with journal.create_latency_progress_journal(
+        tmp_path,
+        session_id="complete-session",
+        bindings=bindings,
+    ) as writer:
+        observer = _ObservedWriter(writer, runtime)
+        result = latency.run_base_cohort_latency_session(
+            cohort,
+            admission=admission,
+            scored_cohort=scored,
+            registered_cuda_uuid=UUID,
+            observer=observer,
+            _runtime=runtime,
+        )
+    read = journal.read_latency_progress_journal(
+        tmp_path / "complete-session",
+        expected_bindings=bindings,
+    )
+
+    assert read.status == journal.COMPLETED_STATUS
+    assert read.formal_latency is False
+    assert len(read.records) == 1461
+    expected_events = ["SESSION_START", "CONTEXT_READY"]
+    for _round_rows in latency.cyclic_visit_schedule():
+        for _run_id in _round_rows:
+            expected_events.extend(
+                ["VISIT_START"]
+                + ["WARMUP_COMPLETED"] * latency.WARMUPS_PER_VISIT
+                + ["TIMED_SAMPLE_COMPLETED"] * latency.TIMED_PER_VISIT
+                + ["VISIT_END"]
+            )
+    expected_events.append("TERMINAL")
+    assert tuple(record.event_type for record in read.records) == tuple(expected_events)
+    callback_name = {
+        "SESSION_START": "session_start",
+        "CONTEXT_READY": "context_ready",
+        "VISIT_START": "visit_started",
+        "WARMUP_COMPLETED": "warmup_completed",
+        "TIMED_SAMPLE_COMPLETED": "timed_sample_completed",
+        "VISIT_END": "visit_ended",
+        "TERMINAL": "terminal",
+    }
+    assert tuple(observer.calls) == tuple(callback_name[name] for name in expected_events)
+    assert _payload(read.records[-1]) == {
+        "failure_code": None,
+        "latency_session_sha256": result.sha256,
+        "outcome": "COMPLETED",
+    }
+    assert observer.calls.count("timed_sample_completed") == 81 * 11
+    assert runtime.timer_open is False
+    assert result.actual_runtime is False
+    assert result.formal_samples_complete is False
+
+
+def test_handled_visit_hold_writes_matching_visit_and_terminal_failure(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path,
+) -> None:
+    cohort, admission, scored = _patch_session(monkeypatch)
+    runtime = _FakeRuntime(fail_encode_at=3)
+    bindings = _bindings(cohort, scored)
+    with journal.create_latency_progress_journal(
+        tmp_path,
+        session_id="held-visit",
+        bindings=bindings,
+    ) as writer:
+        observer = _ObservedWriter(writer, runtime)
+        with pytest.raises(latency.BaseCohortLatencyHold) as caught:
+            latency.run_base_cohort_latency_session(
+                cohort,
+                admission=admission,
+                scored_cohort=scored,
+                registered_cuda_uuid=UUID,
+                observer=observer,
+                _runtime=runtime,
+            )
+    read = journal.read_latency_progress_journal(
+        tmp_path / "held-visit",
+        expected_bindings=bindings,
+    )
+
+    assert read.status == journal.HELD_STATUS
+    assert tuple(record.event_type for record in read.records[-2:]) == (
+        "VISIT_END",
+        "TERMINAL",
+    )
+    visit_payload, terminal_payload = map(_payload, read.records[-2:])
+    assert visit_payload["failure_code"] == terminal_payload["failure_code"]
+    assert terminal_payload["failure_code"] == caught.value.failure_code
+    assert terminal_payload["latency_session_sha256"] == caught.value.receipt.sha256
+    assert caught.value.receipt.visits[0].warmups_completed == 2
+
+
+def test_admission_hold_has_session_start_and_bound_terminal_only(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path,
+) -> None:
+    cohort, admission, scored = _patch_session(monkeypatch)
+    runtime = _FakeRuntime(free_mib=8191)
+    bindings = _bindings(cohort, scored)
+    with journal.create_latency_progress_journal(
+        tmp_path,
+        session_id="admission-hold",
+        bindings=bindings,
+    ) as writer:
+        observer = _ObservedWriter(writer, runtime)
+        with pytest.raises(latency.BaseCohortLatencyHold) as caught:
+            latency.run_base_cohort_latency_session(
+                cohort,
+                admission=admission,
+                scored_cohort=scored,
+                registered_cuda_uuid=UUID,
+                observer=observer,
+                _runtime=runtime,
+            )
+    read = journal.read_latency_progress_journal(
+        tmp_path / "admission-hold",
+        expected_bindings=bindings,
+    )
+
+    assert tuple(record.event_type for record in read.records) == (
+        "SESSION_START",
+        "TERMINAL",
+    )
+    assert read.status == journal.HELD_STATUS
+    assert _payload(read.records[-1])["latency_session_sha256"] == caught.value.receipt.sha256
+    assert caught.value.receipt.visits == ()
+
+
+def test_observer_failure_holds_session_without_fabricating_terminal(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path,
+) -> None:
+    cohort, admission, scored = _patch_session(monkeypatch)
+    runtime = _FakeRuntime()
+    bindings = _bindings(cohort, scored)
+    with journal.create_latency_progress_journal(
+        tmp_path,
+        session_id="observer-failure",
+        bindings=bindings,
+    ) as writer:
+        observer = _FailAfterFirstSample(writer, runtime)
+        with pytest.raises(latency.BaseCohortLatencyHold) as caught:
+            latency.run_base_cohort_latency_session(
+                cohort,
+                admission=admission,
+                scored_cohort=scored,
+                registered_cuda_uuid=UUID,
+                observer=observer,
+                _runtime=runtime,
+            )
+    read = journal.read_latency_progress_journal(
+        tmp_path / "observer-failure",
+        expected_bindings=bindings,
+    )
+
+    assert read.status == journal.INCOMPLETE_STATUS
+    assert read.records[-1].event_type == "TIMED_SAMPLE_COMPLETED"
+    assert all(record.event_type not in {"VISIT_END", "TERMINAL"} for record in read.records)
+    assert caught.value.receipt.complete is False
+    assert caught.value.receipt.rows == ()
+    assert caught.value.receipt.visits[0].samples_ns == (100,)
+
+
+def test_postcommit_completed_terminal_stays_nonformal_when_api_holds(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path,
+) -> None:
+    cohort, admission, scored = _patch_session(monkeypatch)
+    runtime = _FakeRuntime()
+    bindings = _bindings(cohort, scored)
+    with journal.create_latency_progress_journal(
+        tmp_path,
+        session_id="postcommit-completed-terminal",
+        bindings=bindings,
+    ) as writer:
+        observer = _FailAfterCompletedTerminal(writer, runtime)
+        with pytest.raises(latency.BaseCohortLatencyHold) as caught:
+            latency.run_base_cohort_latency_session(
+                cohort,
+                admission=admission,
+                scored_cohort=scored,
+                registered_cuda_uuid=UUID,
+                observer=observer,
+                _runtime=runtime,
+            )
+    read = journal.read_latency_progress_journal(
+        tmp_path / "postcommit-completed-terminal",
+        expected_bindings=bindings,
+    )
+
+    terminal_payload = _payload(read.records[-1])
+    assert read.status == journal.COMPLETED_STATUS
+    assert read.formal_latency is False
+    assert terminal_payload["outcome"] == "COMPLETED"
+    assert terminal_payload["latency_session_sha256"] == observer.committed_session_sha256
+    assert terminal_payload["latency_session_sha256"] != caught.value.receipt.sha256
+    assert caught.value.receipt.complete is False
+    assert caught.value.receipt.rows == ()
+    assert len(caught.value.receipt.visits) == 81
diff --git a/tests/test_phaseset_latency_progress_journal.py b/tests/test_phaseset_latency_progress_journal.py
new file mode 100644
index 0000000..059b8be
--- /dev/null
+++ b/tests/test_phaseset_latency_progress_journal.py
@@ -0,0 +1,515 @@
+from __future__ import annotations
+
+from dataclasses import replace
+import errno
+import hashlib
+import json
+import os
+from pathlib import Path
+import subprocess
+import sys
+import textwrap
+
+import pytest
+
+from phaseset_core import base_cohort_latency as latency
+from phaseset_core import latency_progress_journal as journal
+
+
+pytestmark = pytest.mark.skipif(os.name != "posix", reason="POSIX journal contract")
+
+
+def _sha(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+def _bindings() -> journal.LatencyJournalBindings:
+    return journal.LatencyJournalBindings(
+        resolved_cohort_sha256=_sha("resolved"),
+        scored_cohort_sha256=_sha("scored"),
+        source_tree_sha256=_sha("source-tree"),
+        latency_protocol_sha256=_sha("protocol"),
+        wrapper_sha256=_sha("wrapper"),
+        registered_cuda_uuid="GPU-11111111-2222-3333-4444-555555555555",
+    )
+
+
+def _new_writer(tmp_path: Path, session_id: str = "session-001"):
+    return journal.create_latency_progress_journal(
+        tmp_path,
+        session_id=session_id,
+        bindings=_bindings(),
+        _clock_ns=lambda: 1_800_000_000_000_000_000,
+    )
+
+
+def _start(writer: journal.LatencyProgressJournalWriter) -> None:
+    writer.session_start(
+        admission_observation_sha256=_sha("admission-observation"),
+        started_unix_ns=1_800_000_000_000_000_000,
+    )
+
+
+def _context(writer: journal.LatencyProgressJournalWriter) -> None:
+    writer.context_ready(
+        runtime_sha256=_sha("runtime"),
+        post_context_observation_sha256=_sha("post-context"),
+    )
+
+
+def _write_complete_visit(
+    writer: journal.LatencyProgressJournalWriter,
+    *,
+    round_index: int,
+    ordinal: int,
+    run_id: str,
+) -> None:
+    writer.visit_started(
+        round_index=round_index,
+        ordinal_in_round=ordinal,
+        run_id=run_id,
+        before_observation_sha256=_sha(f"before-{round_index}-{ordinal}"),
+    )
+    for warmup in range(5):
+        writer.warmup_completed(
+            round_index=round_index,
+            ordinal_in_round=ordinal,
+            run_id=run_id,
+            warmup_index=warmup,
+        )
+    for sample in range(11):
+        writer.timed_sample_completed(
+            round_index=round_index,
+            ordinal_in_round=ordinal,
+            run_id=run_id,
+            sample_index=sample,
+            sample_ns=100_000 + round_index * 100 + ordinal * 11 + sample,
+        )
+    writer.visit_ended(
+        round_index=round_index,
+        ordinal_in_round=ordinal,
+        run_id=run_id,
+        outcome="COMPLETED",
+        after_observation_sha256=_sha(f"after-{round_index}-{ordinal}"),
+        peak_allocated_bytes=512 * 1024**2,
+        peak_reserved_bytes=768 * 1024**2,
+        failure_code=None,
+    )
+
+
+def test_complete_exact_session_is_terminal_but_never_formal(tmp_path: Path) -> None:
+    writer = _new_writer(tmp_path)
+    _start(writer)
+    _context(writer)
+    for round_index, round_rows in enumerate(latency.cyclic_visit_schedule()):
+        for ordinal, run_id in enumerate(round_rows):
+            _write_complete_visit(
+                writer,
+                round_index=round_index,
+                ordinal=ordinal,
+                run_id=run_id,
+            )
+    writer.terminal(
+        outcome="COMPLETED",
+        latency_session_sha256=_sha("actual-latency-session"),
+        failure_code=None,
+    )
+    path = writer.path
+    writer.close()
+
+    result = journal.read_latency_progress_journal(
+        path,
+        expected_bindings=_bindings(),
+    )
+    assert result.status == journal.COMPLETED_STATUS
+    assert result.terminal_outcome == "COMPLETED"
+    assert len(result.records) == 2 + 81 * 18 + 1 == 1461
+    assert result.records[-1].event_type == "TERMINAL"
+    assert result.uncommitted_tail_files == ()
+    assert result.formal_latency is False
+    assert result.authority == 0 and result.production is False
+
+
+def test_handled_failure_closes_partial_visit_and_terminal_hold(tmp_path: Path) -> None:
+    writer = _new_writer(tmp_path)
+    _start(writer)
+    _context(writer)
+    run_id = latency.cyclic_visit_schedule()[0][0]
+    writer.visit_started(
+        round_index=0,
+        ordinal_in_round=0,
+        run_id=run_id,
+        before_observation_sha256=_sha("before"),
+    )
+    writer.warmup_completed(
+        round_index=0,
+        ordinal_in_round=0,
+        run_id=run_id,
+        warmup_index=0,
+    )
+    writer.visit_ended(
+        round_index=0,
+        ordinal_in_round=0,
+        run_id=run_id,
+        outcome="HELD",
+        after_observation_sha256=None,
+        peak_allocated_bytes=32,
+        peak_reserved_bytes=64,
+        failure_code="CUDA_OUT_OF_MEMORY",
+    )
+    writer.terminal(
+        outcome="HELD",
+        latency_session_sha256=_sha("held-session"),
+        failure_code="CUDA_OUT_OF_MEMORY",
+    )
+    path = writer.path
+    writer.close()
+
+    result = journal.read_latency_progress_journal(
+        path,
+        expected_bindings=_bindings(),
+    )
+    assert result.status == journal.HELD_STATUS
+    assert result.terminal_outcome == "HELD"
+    assert tuple(record.event_type for record in result.records[-2:]) == (
+        "VISIT_END",
+        "TERMINAL",
+    )
+    assert result.formal_latency is False
+
+
+def test_held_terminal_failure_must_match_active_held_visit(tmp_path: Path) -> None:
+    writer = _new_writer(tmp_path)
+    _start(writer)
+    _context(writer)
+    run_id = latency.cyclic_visit_schedule()[0][0]
+    writer.visit_started(
+        round_index=0,
+        ordinal_in_round=0,
+        run_id=run_id,
+        before_observation_sha256=_sha("before"),
+    )
+    writer.visit_ended(
+        round_index=0,
+        ordinal_in_round=0,
+        run_id=run_id,
+        outcome="HELD",
+        after_observation_sha256=None,
+        peak_allocated_bytes=None,
+        peak_reserved_bytes=None,
+        failure_code="CUDA_OUT_OF_MEMORY",
+    )
+
+    with pytest.raises(journal.LatencyJournalError, match="differs from held visit"):
+        writer.terminal(
+            outcome="HELD",
+            latency_session_sha256=_sha("held-session"),
+            failure_code="RESOURCE_LIMIT",
+        )
+    writer.close()
+
+
+def test_directory_enumeration_stops_at_fixed_entry_bound(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    yielded = 0
+
+    class Entry:
+        name = "event-00000000.json"
+
+    class Entries:
+        def __enter__(self):
+            return self
+
+        def __exit__(self, *_args) -> None:
+            return None
+
+        def __iter__(self):
+            nonlocal yielded
+            for _index in range(2 * journal.MAX_EVENTS + 1):
+                yielded += 1
+                yield Entry()
+            raise AssertionError("bounded reader requested an entry beyond its limit")
+
+    monkeypatch.setattr(journal.os, "scandir", lambda _fd: Entries())
+    with pytest.raises(journal.LatencyJournalError, match="fixed event bound"):
+        journal._bounded_directory_names(123)
+    assert yielded == 2 * journal.MAX_EVENTS + 1
+
+
+def test_sample_cannot_be_written_before_five_completed_warmups(tmp_path: Path) -> None:
+    writer = _new_writer(tmp_path)
+    _start(writer)
+    _context(writer)
+    run_id = latency.cyclic_visit_schedule()[0][0]
+    writer.visit_started(
+        round_index=0,
+        ordinal_in_round=0,
+        run_id=run_id,
+        before_observation_sha256=_sha("before"),
+    )
+    with pytest.raises(journal.LatencyJournalError, match="out of order"):
+        writer.timed_sample_completed(
+            round_index=0,
+            ordinal_in_round=0,
+            run_id=run_id,
+            sample_index=0,
+            sample_ns=123,
+        )
+    writer.close()
+
+
+def test_tail_event_without_marker_is_preserved_as_incomplete(tmp_path: Path) -> None:
+    writer = _new_writer(tmp_path)
+    _start(writer)
+    path = writer.path
+    writer.close()
+    tail = path / "event-00000001.json"
+    tail.write_bytes(b'{"binding_sha256":"truncated')
+    tail.chmod(0o600)
+
+    result = journal.read_latency_progress_journal(
+        path,
+        expected_bindings=_bindings(),
+    )
+    assert result.status == journal.INCOMPLETE_STATUS
+    assert len(result.records) == 1
+    assert result.uncommitted_tail_files == ((tail.name, tail.read_bytes()),)
+    assert result.terminal_outcome is None
+    assert result.formal_latency is False
+
+
+def test_truncated_final_marker_does_not_commit_its_event(tmp_path: Path) -> None:
+    writer = _new_writer(tmp_path)
+    _start(writer)
+    path = writer.path
+    writer.close()
+    marker = path / "event-00000000.complete"
+    marker.write_bytes(marker.read_bytes()[:13])
+    marker.chmod(0o600)
+
+    result = journal.read_latency_progress_journal(
+        path,
+        expected_bindings=_bindings(),
+    )
+    assert result.status == journal.INCOMPLETE_STATUS
+    assert result.records == ()
+    assert tuple(name for name, _raw in result.uncommitted_tail_files) == (
+        "event-00000000.json",
+        "event-00000000.complete",
+    )
+
+
+def test_committed_event_tamper_is_rejected_even_at_tail(tmp_path: Path) -> None:
+    writer = _new_writer(tmp_path)
+    _start(writer)
+    path = writer.path
+    writer.close()
+    event = path / "event-00000000.json"
+    raw = event.read_bytes()
+    event.write_bytes(raw.replace(b"SESSION_START", b"SESSION_STARK", 1))
+    event.chmod(0o600)
+
+    with pytest.raises(journal.LatencyJournalError, match="differs from event bytes"):
+        journal.read_latency_progress_journal(
+            path,
+            expected_bindings=_bindings(),
+        )
+
+
+def test_appended_marker_bytes_and_nonfinal_truncation_are_rejected(
+    tmp_path: Path,
+) -> None:
+    appended_writer = _new_writer(tmp_path, "appended-marker")
+    _start(appended_writer)
+    appended_path = appended_writer.path
+    appended_writer.close()
+    appended_marker = appended_path / "event-00000000.complete"
+    appended_marker.write_bytes(appended_marker.read_bytes() + b"x")
+    appended_marker.chmod(0o600)
+    with pytest.raises(journal.LatencyJournalError, match="differs from event bytes"):
+        journal.read_latency_progress_journal(
+            appended_path,
+            expected_bindings=_bindings(),
+        )
+
+    nonfinal_writer = _new_writer(tmp_path, "nonfinal-truncation")
+    _start(nonfinal_writer)
+    _context(nonfinal_writer)
+    nonfinal_path = nonfinal_writer.path
+    nonfinal_writer.close()
+    nonfinal_marker = nonfinal_path / "event-00000000.complete"
+    nonfinal_marker.write_bytes(nonfinal_marker.read_bytes()[:7])
+    nonfinal_marker.chmod(0o600)
+    with pytest.raises(journal.LatencyJournalError, match="not the sole final tail"):
+        journal.read_latency_progress_journal(
+            nonfinal_path,
+            expected_bindings=_bindings(),
+        )
+
+
+def test_gap_nonfinal_damage_and_external_binding_drift_are_rejected(
+    tmp_path: Path,
+) -> None:
+    writer = _new_writer(tmp_path, "gap-session")
+    _start(writer)
+    path = writer.path
+    writer.close()
+    gap = path / "event-00000002.json"
+    gap.write_bytes(b"partial")
+    gap.chmod(0o600)
+    with pytest.raises(journal.LatencyJournalError, match="gap"):
+        journal.read_latency_progress_journal(path, expected_bindings=_bindings())
+
+    other = _new_writer(tmp_path, "binding-session")
+    _start(other)
+    other_path = other.path
+    other.close()
+    drifted = replace(_bindings(), source_tree_sha256=_sha("other-source"))
+    with pytest.raises(journal.LatencyJournalError, match="header or chain"):
+        journal.read_latency_progress_journal(
+            other_path,
+            expected_bindings=drifted,
+        )
+
+
+def test_added_tail_after_terminal_is_rejected(tmp_path: Path) -> None:
+    writer = _new_writer(tmp_path)
+    _start(writer)
+    writer.terminal(
+        outcome="HELD",
+        latency_session_sha256=_sha("held"),
+        failure_code="RESOURCE_LIMIT",
+    )
+    path = writer.path
+    writer.close()
+    added = path / "event-00000002.json"
+    added.write_bytes(b"uncommitted-added-tail")
+    added.chmod(0o600)
+
+    with pytest.raises(journal.LatencyJournalError, match="sole final tail"):
+        journal.read_latency_progress_journal(path, expected_bindings=_bindings())
+
+
+def test_existing_next_event_causes_exclusive_writer_failure(tmp_path: Path) -> None:
+    writer = _new_writer(tmp_path)
+    _start(writer)
+    occupied = writer.path / "event-00000001.json"
+    occupied.write_bytes(b"occupied")
+    occupied.chmod(0o600)
+
+    with pytest.raises(journal.LatencyJournalWriteError, match="sequence 1"):
+        _context(writer)
+    with pytest.raises(journal.LatencyJournalWriteError, match="closed or failed"):
+        _context(writer)
+    writer.close()
+
+
+def test_reader_rejects_symlinked_event_leaf(tmp_path: Path) -> None:
+    writer = _new_writer(tmp_path)
+    _start(writer)
+    path = writer.path
+    writer.close()
+    outside = tmp_path / "outside.json"
+    outside.write_bytes((path / "event-00000000.json").read_bytes())
+    outside.chmod(0o600)
+    event = path / "event-00000000.json"
+    event.unlink()
+    event.symlink_to(outside)
+
+    with pytest.raises(journal.LatencyJournalError, match="opened safely"):
+        journal.read_latency_progress_journal(path, expected_bindings=_bindings())
+
+
+def test_disk_full_write_retains_only_uncommitted_physical_tail(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    writer = _new_writer(tmp_path)
+    real_write = os.write
+    calls = 0
+
+    def fail_after_prefix(fd: int, raw) -> int:
+        nonlocal calls
+        calls += 1
+        if calls == 1:
+            return real_write(fd, raw[:9])
+        raise OSError(errno.ENOSPC, "synthetic disk full")
+
+    monkeypatch.setattr(os, "write", fail_after_prefix)
+    with pytest.raises(journal.LatencyJournalWriteError, match="sequence 0"):
+        _start(writer)
+    path = writer.path
+    writer.close()
+    monkeypatch.setattr(os, "write", real_write)
+
+    result = journal.read_latency_progress_journal(
+        path,
+        expected_bindings=_bindings(),
+    )
+    assert result.status == journal.INCOMPLETE_STATUS
+    assert result.records == ()
+    assert result.uncommitted_tail_files[0][0] == "event-00000000.json"
+    assert len(result.uncommitted_tail_files[0][1]) == 9
+
+
+def test_real_subprocess_hard_kill_leaves_readable_incomplete_prefix(
+    tmp_path: Path,
+) -> None:
+    package_src = str(Path(journal.__file__).resolve().parents[1])
+    payload = json.dumps(
+        {
+            "package_src": package_src,
+            "parent": str(tmp_path),
+            "bindings": _bindings().payload(),
+        }
+    )
+    script = textwrap.dedent(
+        """
+        import hashlib, json, pathlib, sys, time
+        cfg = json.loads(sys.argv[1])
+        sys.path.insert(0, cfg['package_src'])
+        from phaseset_core import latency_progress_journal as journal
+        bindings = journal.LatencyJournalBindings(**{
+            key: value for key, value in cfg['bindings'].items() if key != 'schema'
+        })
+        writer = journal.create_latency_progress_journal(
+            pathlib.Path(cfg['parent']), session_id='killed-session', bindings=bindings
+        )
+        writer.session_start(
+            admission_observation_sha256=hashlib.sha256(b'admission').hexdigest(),
+            started_unix_ns=time.time_ns(),
+        )
+        writer.context_ready(
+            runtime_sha256=hashlib.sha256(b'runtime').hexdigest(),
+            post_context_observation_sha256=hashlib.sha256(b'context').hexdigest(),
+        )
+        print('READY', flush=True)
+        time.sleep(300)
+        """
+    )
+    process = subprocess.Popen(
+        [sys.executable, "-I", "-B", "-c", script, payload],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        text=True,
+    )
+    try:
+        assert process.stdout is not None
+        assert process.stdout.readline().strip() == "READY"
+    finally:
+        if process.poll() is None:
+            process.kill()
+        process.wait(timeout=10)
+
+    result = journal.read_latency_progress_journal(
+        tmp_path / "killed-session",
+        expected_bindings=_bindings(),
+    )
+    assert result.status == journal.INCOMPLETE_STATUS
+    assert tuple(record.event_type for record in result.records) == (
+        "SESSION_START",
+        "CONTEXT_READY",
+    )
+    assert result.uncommitted_tail_files == ()
+    assert result.formal_latency is False

From b04a3a45acc1927c0be67a653ef6c152589efaf2 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 13:23:53 -0500
Subject: [PATCH 17/95] Integrate verified periodic descriptor caches into
 model execution

---
 RELEASE_FILES.sha256                          |  13 +-
 STATUS.md                                     |  21 +-
 docs/PERIODIC_DESCRIPTOR_CACHE_V2.md          |  71 ++-
 src/phaseset_core/controls.py                 |  36 +-
 src/phaseset_core/models.py                   | 100 +++-
 .../periodic_descriptor_cache_v2.py           |  10 +-
 src/phaseset_core/streaming_autograd.py       |  39 +-
 .../test_periodic_cache_model_integration.py  | 534 ++++++++++++++++++
 8 files changed, 794 insertions(+), 30 deletions(-)
 create mode 100644 tests/test_periodic_cache_model_integration.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 22c3c46..3e16cab 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,7 +11,7 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-0941ddad002fba0fd0841e94735a8132dbc0821ba3a2b0ffed0dc2ae703c6b37	23908	STATUS.md
+3cc295f3691425582a6281e432ca9a9f81ac3a25d2024033b39f0d0f4539b01a	25026	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
@@ -32,7 +32,7 @@ fad4d9e8d1a51bf443af94b536b7aee0b8031cf42edab58190db7b174a768e0e	15565	docs/HOST
 5dd898347d3646cde4a74e10afa8ba1d12703e3d0c6df51e5113659a31808086	3606	docs/LATENCY_PROGRESS_JOURNAL.md
 f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
-dfb98b3f8e83619b208a82a416ac57517666fab9ae540221d9cea685200aa3bd	4416	docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
+be65fdd1fd914467f28487c30a81a07b9429db8552166c28064f829a48f593c4	6923	docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
 69b5e67cbb59c9c658e5feb11d3f6c01afbc5e2d26a1acd2d6dba8f81f9e67ff	4296	docs/PHASEPAIR_B0_ANNOTATION_CAPTION_CLOSURE_SUCCESSOR_PUBLIC_V4_20260825_145013.json
@@ -139,7 +139,7 @@ d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phases
 85e5e80bc3b9d21b5fb8865923d27cb6254c077ac2319afe38dad8b79d785bd2	34366	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
-9636324cff46e2a89bb93b71f8c3a4cc07544f8dd0e10e2da60d5a2ffbc7b356	25892	src/phaseset_core/controls.py
+bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
@@ -148,11 +148,11 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 e0242fc212e83f2d3b694734fadb62ab76c4ef90c3c5b04df503f5a7c907a3e0	68520	src/phaseset_core/frozen_clip_text.py
 7fcc111d269ed22db55ba2c3a5bee8d6c79f3c850c42eca5ff8aaa08b6e809e1	90636	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
-47cff521b4f43ce185bb99326fe4b579dd630fc493daaeb85768e9a5475d7a62	33917	src/phaseset_core/models.py
+9e7fa84554d5676f8ef75049b17b53580395c3c60be0f75fcb55996ccc8ef36d	37720	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
-c227060960bee56d8ec47edfac38284d176e51bbe6e75450de6a1e1faa74be87	55317	src/phaseset_core/periodic_descriptor_cache_v2.py
+03fdc10250fd11ae111fc1813eb3200575f08ea301091963f3ee14425b51bd8d	55520	src/phaseset_core/periodic_descriptor_cache_v2.py
 a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phaseset_core/pipeline.py
 2bee9279fa4098dc83729c934aa4d3d07f3f2c4090c1734f9a316462d17159ef	44879	src/phaseset_core/prepared_data_v2.py
 273225e6696351ccb16a3a9e7a653b55af18c64b316a4e5d80c7b265625cc5c1	17929	src/phaseset_core/preprocessing.py
@@ -160,7 +160,7 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 6f60feada83ebf1a55635bb657a5a3998e9fc77fa85012aad46e4f8d90d344db	37785	src/phaseset_core/publication.py
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
-0c8f52f8822f4e3822e5124ce794d482bb09d0545c73f5c6b341c1bbd7bc86f2	35557	src/phaseset_core/streaming_autograd.py
+a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 9f6c21e354a17facda89a542d53e39013477c5e1b1a259781e2446cd394f6d06	157160	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
@@ -168,6 +168,7 @@ eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phase
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
+deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
 9c8ff961404bb8d5da86a30c3f533fe8d1909cdfbd76ebb496262ff222121d51	24769	tests/test_periodic_descriptor_cache_v2.py
 af9b5adc42d82b9a0aa4ed744bb069e36b7dd041c1957fdc97d17eb814a0fc0c	17284	tests/test_phasepair_core_base_qualification.py
 ad630675cc642350a988d92de6176053d1bfab5f81cbc49548cb0b5dbeb679d9	23596	tests/test_phasepair_core_batching_readiness.py
diff --git a/STATUS.md b/STATUS.md
index c903027..516731f 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -13,6 +13,22 @@ evidence, not training results or scientific claims.
 
 ## Server continuation
 
+- Explicit reader-backed cached model execution binds the actual complete
+  batch, energy floors and stream family, and enforces the live encoder edge
+  budget before consuming descriptors. The same stream is
+  replayed through forward incidence, backward moments and parameter VJP,
+  without an uncached fallback. The model/cache/control suite passed 98 Linux
+  server tests; full regression passed 1228 tests, 2 existing skips and
+  39 subtests in 401.07 s, source unchanged. These are software fixtures,
+  not real data or measured acceleration. Training-row selection, complete
+  epoch census and checkpoint/resume cache consumption remain unfinished.
+  See [descriptor model integration](docs/PERIODIC_DESCRIPTOR_CACHE_V2.md).
+  A separate static audit also identified a host dispatch defect: new base
+  and residual attempts read two fields absent from the actual CommandIntent.
+  That repair and a real CLI-to-backend regression are in progress; existing
+  synthetic runtime checks must not be treated as proof that this host path
+  already works end to end.
+
 - The complete frozen base-latency runner, exact qualification assembler,
   typed progress observer and durable POSIX journal are implemented. Separately,
   a weight-independent three-stream periodic descriptor cache now preserves
@@ -22,8 +38,9 @@ evidence, not training results or scientific claims.
   descriptor storage/streaming then passed 19 focused tests after a test-only
   hardlink/oversize case split. These are explicit
   software fixtures, not a real nine-run cohort, formal CUDA latency session,
-  winner or licensed-data cache. Production cache-model/training/resume and
-  host qualification command assembly remain unfinished. See
+  winner or licensed-data cache. The later explicit cache-model API is reported
+  above; training/resume cache consumption and host qualification command
+  assembly remain unfinished. See
   [base qualification](docs/BASE_COHORT_QUALIFICATION.md),
   [progress journal](docs/LATENCY_PROGRESS_JOURNAL.md) and
   [descriptor cache](docs/PERIODIC_DESCRIPTOR_CACHE_V2.md).
diff --git a/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md b/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
index 0ea66bc..14e24d3 100644
--- a/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
+++ b/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
@@ -49,7 +49,7 @@ no-follow or junction protection. Static symlink checks, single-link checks,
 hashes and read-only flags are integrity hardening, not filesystem isolation
 or data authorization.
 
-## Verified boundary and unfinished integration
+## Descriptor storage verification
 
 Nineteen server tests passed in 36.21 s with source unchanged. They cover all
 three streams, a 186-edge K12/K16 mixed batch, chunks 64/128/256 including a
@@ -63,13 +63,62 @@ observation. Its private receipt digest is
 `8edbb675a64773fe104a1eae7b4bf398262b61827199152833da1e0765237f41`;
 the receipt itself and its private paths are not published.
 
-That test seam is not a production cache provider: it deliberately supplies
-fixture chunks. Production model entry points, training initialization,
-checkpoint/resume bindings and complete epoch-cache census still require
-explicit integration. No real-data cache, training acceleration, measured
-throughput or model-quality result is claimed. The unchanged-source 1211-test
-full Linux server regression described in
-[base qualification](BASE_COHORT_QUALIFICATION.md) used this exact production
-module and the preceding eighteen-test file. The only subsequent test change
-split hardlink and oversize checks; those newer bytes were separately verified
-by the nineteen-test observation above.
+That earlier test seam deliberately supplied fixture chunks and was not a
+production cache provider. The unchanged-source 1211-test regression used the
+preceding production module and eighteen-test file; the test-only case split
+was separately verified by the nineteen-test observation above.
+
+## Explicit model entry and backward replay
+
+`PhaseSetEncoder.forward_cached` and `PhaseSetSystem.forward_cached` now
+accept an exact reader-backed `CachedPairChunkStream`. They revalidate the
+actual complete `PreparedGroupBatch`, its canonical numeric digest, the
+reader-bound six-band energy-floor digest, the expected stream family and the
+complete unordered-edge census. Configured cached execution never silently
+falls back to a physical recomputation.
+
+| Consumer | Required cached stream |
+| --- | --- |
+| Plain encoder; systems 04, 06, 07, 08 | FULL_RELATION |
+| System 02 | MARGINAL_POWER |
+| System 03 | MEAN_DIFFERENCE_DCT |
+| Systems 00, 01, 05 | Cached execution rejected |
+
+Systems 06 and 07 still apply the registered incidence and phase-stripping
+transformations. System 04 retains the shared postprocess with topology
+disabled. No learned token, intermediate activation or gradient is stored in
+the descriptor cache.
+
+The same re-iterable stream is explicitly passed through forward reduction,
+system-06 incidence census, no-grad backward moment reconstruction, and
+differentiable microblock VJP replay. It is retained on that autograd call's
+context, not installed globally or as mutable model state. Legacy `forward`
+and `forward_activity` remain uncached.
+
+Cached execution preserves the encoder's exact edge-budget contract. It
+rejects E greater than the budget with the existing `ResourceLimitError` and
+`RESOURCE_LIMIT` fields before consuming descriptors; equality is accepted.
+This checks a real early integration defect: bypassing the uncached iterator
+must not also bypass its resource admission.
+
+## Observed model integration and remaining work
+
+The combined descriptor/model/control/custom-autograd suite passed 98 tests
+in 90.90 s on the Linux experiment server, with source unchanged. It includes
+the 186-edge two-group stream at chunks 64/128/256, exact output and parameter
+gradient comparisons, all six registered cached consumers, actor-storage
+permutation, wrong batch/floors/family, caller RNG preservation, and cached
+forward/backward with an instance-local forbidden uncached iterator, including
+system 06. The 185/186 edge-budget boundary is independently exercised.
+
+The complete integrated source then passed 1228 tests, two existing skips and
+39 subtests in 401.07 s with source unchanged. These are CPU software fixtures,
+not licensed-data training, CUDA performance measurements or retrieval-quality
+evidence.
+
+Training initialization, the actual prepared-row context selector, complete
+epoch-cache census, and checkpoint/resume consumption still require explicit
+integration. The model checks actual batch bytes but does not invent source
+window ordinals or silently select a different epoch/yaw context. Host
+preparation must supply the correct already verified reader handle. No real-data
+cache, acceleration, measured throughput or model-quality result is claimed.
diff --git a/src/phaseset_core/controls.py b/src/phaseset_core/controls.py
index 6f010f4..b7a893a 100644
--- a/src/phaseset_core/controls.py
+++ b/src/phaseset_core/controls.py
@@ -15,7 +15,7 @@
 from fractions import Fraction
 import hashlib
 import math
-from typing import Final
+from typing import TYPE_CHECKING, Final
 
 import numpy as np
 import torch
@@ -45,6 +45,9 @@
     validate_energy_floors,
 )
 
+if TYPE_CHECKING:
+    from .periodic_descriptor_cache_v2 import CachedPairChunkStream
+
 
 STATUS: Final = "DATA_FREE_EXECUTABLE_PHASESET_CONTROLS_NONPRODUCTION_AUTHORITY0"
 SYSTEM_IDS: Final = tuple(f"{index:02d}" for index in range(9))
@@ -507,6 +510,16 @@ def _iter_pair_chunks(
             )
         return super()._iter_pair_chunks(batch, edge_chunk_size=edge_chunk_size)
 
+    def _expected_descriptor_stream_kind(self) -> str | None:
+        policy = self.control_spec.feature_policy
+        if policy == "MARGINAL_POWER":
+            return "MARGINAL_POWER"
+        if policy == "MEAN_DIFFERENCE_DCT":
+            return "MEAN_DIFFERENCE_DCT"
+        if policy in ("FULL_RELATION", "SHUFFLED_INCIDENCE", "PHASE_STRIPPED"):
+            return "FULL_RELATION"
+        return None
+
     def _transform_edge_outputs(
         self,
         half_ij: Tensor,
@@ -590,6 +603,27 @@ def forward(
             edge_chunk_size=edge_chunk_size,
         )
 
+    def forward_cached(
+        self,
+        batch: PreparedGroupBatch,
+        *,
+        descriptor_stream: CachedPairChunkStream,
+        edge_chunk_size: int = DEFAULT_EDGE_CHUNK_SIZE,
+    ) -> GroupTokenOutput:
+        """Run an enabled system from its exact registered cached input family."""
+
+        checked = validate_prepared_group_batch(batch)
+        if self.encoder is None:
+            raise PhaseSetControlError(
+                "system 00 does not admit cached descriptor execution"
+            )
+        return self.encoder.forward_cached(
+            checked,
+            descriptor_stream=descriptor_stream,
+            edge_chunk_size=edge_chunk_size,
+            include_topology=self.spec.include_topology,
+        )
+
     def forward_activity(
         self,
         batch: PreparedActivityBatch,
diff --git a/src/phaseset_core/models.py b/src/phaseset_core/models.py
index ace4491..10d1465 100644
--- a/src/phaseset_core/models.py
+++ b/src/phaseset_core/models.py
@@ -5,7 +5,7 @@
 from collections.abc import Iterator
 from dataclasses import dataclass
 import math
-from typing import Final
+from typing import TYPE_CHECKING, Final
 
 import numpy as np
 import torch
@@ -29,11 +29,15 @@
     ResourceLimitError,
     TOKEN_WIDTH,
     iter_unordered_pair_chunks,
+    validate_edge_budget,
     validate_energy_floors,
 )
 from .streaming_autograd import StreamingEdgeSummaries, streaming_edge_summaries
 from .torch_periodic import differentiable_edge_summaries
 
+if TYPE_CHECKING:
+    from .periodic_descriptor_cache_v2 import CachedPairChunkStream
+
 
 STATUS: Final = "DATA_FREE_PHASESET_MODEL_NONPRODUCTION_AUTHORITY0"
 DEFAULT_EDGE_BUDGET: Final = 32_768
@@ -767,6 +771,61 @@ def _iter_pair_chunks(
             edge_budget=self.edge_budget,
         )
 
+    def _expected_descriptor_stream_kind(self) -> str | None:
+        """Return the only persisted descriptor family this encoder accepts."""
+
+        return "FULL_RELATION"
+
+    def _validate_cached_descriptor_stream(
+        self,
+        batch: PreparedGroupBatch,
+        descriptor_stream: CachedPairChunkStream,
+    ) -> CachedPairChunkStream:
+        """Bind a verified descriptor stream to this exact batch and model."""
+
+        from .periodic_descriptor_cache_v2 import (
+            CachedPairChunkStream,
+            energy_floors_sha256,
+            prepared_group_batch_sha256,
+        )
+
+        if type(descriptor_stream) is not CachedPairChunkStream:
+            raise PhaseSetModelError(
+                "cached execution requires an exact verified descriptor stream"
+            )
+        expected_kind = self._expected_descriptor_stream_kind()
+        if expected_kind is None:
+            raise PhaseSetModelError(
+                "this registered system does not admit cached descriptor execution"
+            )
+        if descriptor_stream.stream_kind != expected_kind:
+            raise PhaseSetModelError(
+                "cached descriptor stream kind does not match the registered system"
+            )
+        budget = validate_edge_budget(self.edge_budget)
+        expected_edges = sum(count * (count - 1) // 2 for count in batch.actor_counts)
+        if expected_edges > budget:
+            raise ResourceLimitError(
+                required_edges=expected_edges,
+                edge_budget=budget,
+            )
+        cached_batch = descriptor_stream.batch
+        if cached_batch.batch_input_sha256 != prepared_group_batch_sha256(batch):
+            raise PhaseSetModelError(
+                "cached descriptor stream does not bind the actual model batch"
+            )
+        if cached_batch.energy_floors_sha256 != energy_floors_sha256(
+            self._energy_floors
+        ):
+            raise PhaseSetModelError(
+                "cached descriptor stream does not bind the model energy floors"
+            )
+        if cached_batch.edge_count != expected_edges:
+            raise PhaseSetModelError(
+                "cached descriptor stream does not contain the complete batch edge set"
+            )
+        return descriptor_stream
+
     def _transform_edge_outputs(
         self,
         half_ij: Tensor,
@@ -799,6 +858,27 @@ def forward(
             include_topology=include_topology,
         )
 
+    def forward_cached(
+        self,
+        batch: PreparedGroupBatch,
+        *,
+        descriptor_stream: CachedPairChunkStream,
+        edge_chunk_size: int = DEFAULT_EDGE_CHUNK_SIZE,
+        include_topology: bool = True,
+    ) -> GroupTokenOutput:
+        """Encode with an explicitly verified, batch-bound descriptor stream."""
+
+        if type(include_topology) is not bool:
+            raise TypeError("include_topology must be an exact built-in bool")
+        checked = validate_prepared_group_batch(batch)
+        stream = self._validate_cached_descriptor_stream(checked, descriptor_stream)
+        return self._forward_activity(
+            skeleton_to_activity(checked),
+            edge_chunk_size=edge_chunk_size,
+            include_topology=include_topology,
+            descriptor_stream=stream,
+        )
+
     def forward_activity(
         self,
         batch: PreparedActivityBatch,
@@ -811,10 +891,28 @@ def forward_activity(
         if type(include_topology) is not bool:
             raise TypeError("include_topology must be an exact built-in bool")
         checked = validate_prepared_activity_batch(batch)
+        return self._forward_activity(
+            checked,
+            edge_chunk_size=edge_chunk_size,
+            include_topology=include_topology,
+            descriptor_stream=None,
+        )
+
+    def _forward_activity(
+        self,
+        checked: PreparedActivityBatch,
+        *,
+        edge_chunk_size: int,
+        include_topology: bool,
+        descriptor_stream: CachedPairChunkStream | None,
+    ) -> GroupTokenOutput:
+        """Shared reduction path for uncached and explicitly cached execution."""
+
         summaries = streaming_edge_summaries(
             self,
             checked,
             edge_chunk_size=edge_chunk_size,
+            pair_chunk_stream=descriptor_stream,
         )
         return self._finish_summaries(
             summaries,
diff --git a/src/phaseset_core/periodic_descriptor_cache_v2.py b/src/phaseset_core/periodic_descriptor_cache_v2.py
index 0d69e71..a57c315 100644
--- a/src/phaseset_core/periodic_descriptor_cache_v2.py
+++ b/src/phaseset_core/periodic_descriptor_cache_v2.py
@@ -2,8 +2,9 @@
 
 This module has no execution or scientific authority.  It freezes only the
 weight-independent ``PairChunk`` inputs produced by the existing public
-descriptor streams.  It is intentionally not wired into the host, registered
-constructors, training runtime, checkpoints, or resume path.
+descriptor streams.  The sibling private model candidate consumes its sealed
+reader handle explicitly; the host, training runtime, checkpoints, and resume
+path remain unwired.
 """
 
 from __future__ import annotations
@@ -1013,6 +1014,7 @@ class CachedDescriptorBatch:
 
     cache_key_sha256: str
     batch_input_sha256: str
+    energy_floors_sha256: str
     contexts: tuple[DescriptorWindowContext, ...]
     edge_count: int
     _arrays: tuple[tuple[str, np.ndarray], ...] = field(repr=False)
@@ -1025,6 +1027,7 @@ def __post_init__(self) -> None:
             )
         _lower_sha256(self.cache_key_sha256, "cache_key_sha256")
         _lower_sha256(self.batch_input_sha256, "batch_input_sha256")
+        _lower_sha256(self.energy_floors_sha256, "energy_floors_sha256")
         if (
             type(self.contexts) is not tuple
             or not self.contexts
@@ -1071,7 +1074,7 @@ def iter_chunks(
 
 @dataclass(frozen=True, slots=True)
 class CachedPairChunkStream:
-    """A re-iterable exact ``PairChunk`` source for the future model seam."""
+    """A re-iterable exact ``PairChunk`` source for the explicit model seam."""
 
     batch: CachedDescriptorBatch
     stream_kind: DescriptorStreamKind
@@ -1450,6 +1453,7 @@ def open_batch(
         return CachedDescriptorBatch(
             cache_key_sha256=cache_key,
             batch_input_sha256=batch_digest,
+            energy_floors_sha256=self.energy_floors_sha256,
             contexts=checked_contexts,
             edge_count=row.edge_count,
             _arrays=tuple((name, arrays[name]) for name in _ARRAY_NAMES),
diff --git a/src/phaseset_core/streaming_autograd.py b/src/phaseset_core/streaming_autograd.py
index ecd64be..4342992 100644
--- a/src/phaseset_core/streaming_autograd.py
+++ b/src/phaseset_core/streaming_autograd.py
@@ -66,6 +66,12 @@ def _transform_edge_outputs(
     ) -> tuple[Tensor, Tensor, Tensor]: ...
 
 
+class _PairChunkStream(Protocol):
+    """Re-iterable descriptor input retained by the custom autograd context."""
+
+    def iter_chunks(self, *, edge_chunk_size: int) -> Iterator[PairChunk]: ...
+
+
 @dataclass(frozen=True, slots=True)
 class StreamingEdgeSummaries:
     """Differentiable summaries plus structural, non-differentiable masks."""
@@ -208,6 +214,7 @@ def _iter_group_microblocks(
     batch: PreparedActivityBatch,
     *,
     edge_chunk_size: int,
+    pair_chunk_stream: _PairChunkStream | None = None,
 ) -> Iterator[PairChunk]:
     """Reblock a runtime stream by group, independent of batch/chunk packing.
 
@@ -232,10 +239,12 @@ def clear() -> None:
         reverse_tokens.clear()
         supports.clear()
 
-    for runtime_chunk in model._iter_pair_chunks(
-        batch,
-        edge_chunk_size=edge_chunk_size,
-    ):
+    runtime_chunks = (
+        model._iter_pair_chunks(batch, edge_chunk_size=edge_chunk_size)
+        if pair_chunk_stream is None
+        else pair_chunk_stream.iter_chunks(edge_chunk_size=edge_chunk_size)
+    )
+    for runtime_chunk in runtime_chunks:
         for edge in range(int(runtime_chunk.batch_indices.shape[0])):
             group = int(runtime_chunk.batch_indices[edge])
             if pending_group is not None and group != pending_group:
@@ -329,6 +338,7 @@ def _build_global_incidence_routing_plan(
     batch: PreparedActivityBatch,
     *,
     edge_chunk_size: int,
+    pair_chunk_stream: _PairChunkStream | None = None,
 ) -> _GlobalIncidenceRoutingPlan | None:
     """Census all valid endpoint slots without retaining any width-D activation."""
 
@@ -343,6 +353,7 @@ def _build_global_incidence_routing_plan(
         model,
         checked,
         edge_chunk_size=edge_chunk_size,
+        pair_chunk_stream=pair_chunk_stream,
     ):
         for edge in range(int(chunk.batch_indices.shape[0])):
             group = int(chunk.batch_indices[edge])
@@ -431,6 +442,7 @@ def _compute_edge_summaries(
     batch: PreparedActivityBatch,
     *,
     edge_chunk_size: int,
+    pair_chunk_stream: _PairChunkStream | None = None,
 ) -> tuple[StreamingEdgeSummaries, _MomentState]:
     """Run the exact canonical forward reductions under the caller's grad mode."""
 
@@ -442,6 +454,7 @@ def _compute_edge_summaries(
         model,
         checked,
         edge_chunk_size=edge_chunk_size,
+        pair_chunk_stream=pair_chunk_stream,
     )
     incidence_cursors = _incidence_cursors(checked)
 
@@ -489,6 +502,7 @@ def _compute_edge_summaries(
         model,
         checked,
         edge_chunk_size=edge_chunk_size,
+        pair_chunk_stream=pair_chunk_stream,
     ):
         edge_count = int(chunk.batch_indices.shape[0])
         for micro_start in range(0, edge_count, CANONICAL_MICROBLOCK_SIZE):
@@ -687,6 +701,7 @@ def eager_edge_summaries(
     batch: PreparedActivityBatch,
     *,
     edge_chunk_size: int,
+    pair_chunk_stream: _PairChunkStream | None = None,
 ) -> StreamingEdgeSummaries:
     """Autograd-heavy reference path used to audit the analytical VJP."""
 
@@ -694,6 +709,7 @@ def eager_edge_summaries(
         model,
         batch,
         edge_chunk_size=edge_chunk_size,
+        pair_chunk_stream=pair_chunk_stream,
     )
     return summaries
 
@@ -703,6 +719,7 @@ def _stream_parameter_vjp(
     batch: PreparedActivityBatch,
     *,
     edge_chunk_size: int,
+    pair_chunk_stream: _PairChunkStream | None,
     state: _MomentState,
     grad_pair_component: Tensor | None,
     grad_node_statistics: Tensor | None,
@@ -717,6 +734,7 @@ def _stream_parameter_vjp(
         model,
         checked,
         edge_chunk_size=edge_chunk_size,
+        pair_chunk_stream=pair_chunk_stream,
     )
     incidence_cursors = _incidence_cursors(checked)
     if grad_pair_component is None:
@@ -769,6 +787,7 @@ def _stream_parameter_vjp(
         model,
         checked,
         edge_chunk_size=edge_chunk_size,
+        pair_chunk_stream=pair_chunk_stream,
     ):
         edge_count = int(chunk.batch_indices.shape[0])
         for micro_start in range(0, edge_count, CANONICAL_MICROBLOCK_SIZE):
@@ -882,12 +901,14 @@ def forward(
         model: _StreamingEdgeModel,
         batch: PreparedActivityBatch,
         edge_chunk_size: int,
+        pair_chunk_stream: _PairChunkStream | None,
         *parameters: Tensor,
     ) -> tuple[Tensor, Tensor, Tensor, Tensor]:
         ctx.set_materialize_grads(False)
         ctx.model = model
         ctx.batch = batch
         ctx.edge_chunk_size = edge_chunk_size
+        ctx.pair_chunk_stream = pair_chunk_stream
         device_type = next(model.parameters()).device.type
         ctx.autocast_device_type = device_type
         ctx.autocast_enabled = torch.is_autocast_enabled(device_type)
@@ -898,6 +919,7 @@ def forward(
             model,
             batch,
             edge_chunk_size=edge_chunk_size,
+            pair_chunk_stream=pair_chunk_stream,
         )
         ctx.mark_non_differentiable(
             summaries.valid_pair_count,
@@ -918,7 +940,7 @@ def backward(
         grad_node_statistics: Tensor | None,
         grad_valid_pair_count: Tensor | None,
         grad_topology_node_mask: Tensor | None,
-    ) -> tuple[None, None, None, *tuple[Tensor, ...]]:
+    ) -> tuple[None, None, None, None, *tuple[Tensor, ...]]:
         del grad_valid_pair_count, grad_topology_node_mask
         parameters = cast(tuple[Tensor, ...], ctx.saved_tensors)
         with torch.autocast(
@@ -932,6 +954,7 @@ def backward(
                     ctx.model,
                     ctx.batch,
                     edge_chunk_size=ctx.edge_chunk_size,
+                    pair_chunk_stream=ctx.pair_chunk_stream,
                 )
         # Exit and re-enter autocast so a backend cannot reuse a no-grad cached
         # low-precision parameter view during the differentiable replay.
@@ -945,12 +968,13 @@ def backward(
                 ctx.model,
                 ctx.batch,
                 edge_chunk_size=ctx.edge_chunk_size,
+                pair_chunk_stream=ctx.pair_chunk_stream,
                 state=state,
                 grad_pair_component=grad_pair_component,
                 grad_node_statistics=grad_node_statistics,
                 parameters=parameters,
             )
-        return (None, None, None, *gradients)
+        return (None, None, None, None, *gradients)
 
 
 def streaming_edge_summaries(
@@ -958,6 +982,7 @@ def streaming_edge_summaries(
     batch: PreparedActivityBatch,
     *,
     edge_chunk_size: int,
+    pair_chunk_stream: _PairChunkStream | None = None,
 ) -> StreamingEdgeSummaries:
     """Return exact summaries with chunk-recomputed edge-parameter gradients."""
 
@@ -971,12 +996,14 @@ def streaming_edge_summaries(
             model,
             checked,
             edge_chunk_size=edge_chunk_size,
+            pair_chunk_stream=pair_chunk_stream,
         )
         return summaries
     outputs = _StreamingEdgeSummaryFunction.apply(
         model,
         checked,
         edge_chunk_size,
+        pair_chunk_stream,
         *parameters,
     )
     return StreamingEdgeSummaries(*outputs)
diff --git a/tests/test_periodic_cache_model_integration.py b/tests/test_periodic_cache_model_integration.py
new file mode 100644
index 0000000..829d86b
--- /dev/null
+++ b/tests/test_periodic_cache_model_integration.py
@@ -0,0 +1,534 @@
+from __future__ import annotations
+
+import hashlib
+from pathlib import Path
+import random
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+from phaseset_core.controls import PhaseSetControlError, build_phaseset_system
+from phaseset_core.models import PhaseSetEncoder, PhaseSetModelError
+from phaseset_core.periodic import ResourceLimitError
+from phaseset_core.periodic_descriptor_cache_v2 import (
+    CachedDescriptorBatch,
+    DescriptorCacheSourceBinding,
+    DescriptorWindowContext,
+    PeriodicDescriptorCacheV2,
+    PeriodicDescriptorCacheWriterV2,
+    energy_floors_sha256,
+)
+
+
+def _digest(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+MANIFEST_SHA = _digest("prepared-manifest")
+SOURCE_BATCH_SHA = _digest("prepared-source-batch")
+SOURCE_TREE_SHA = _digest("source-tree")
+ENVIRONMENT_SHA = _digest("environment")
+
+
+def _binding() -> DescriptorCacheSourceBinding:
+    return DescriptorCacheSourceBinding(
+        prepared_manifest_sha256=MANIFEST_SHA,
+        source_tree_sha256=SOURCE_TREE_SHA,
+        environment_sha256=ENVIRONMENT_SHA,
+    )
+
+
+def _floors(offset: float = 0.0) -> np.ndarray:
+    return np.ascontiguousarray(
+        np.asarray(
+            [offset + (index + 1) * 1.0e-12 for index in range(6)],
+            dtype=np.float64,
+        )
+    )
+
+
+def _actor_motion(actor_identity: int, valid_length: int) -> np.ndarray:
+    time = np.arange(valid_length, dtype=np.float64)[:, None]
+    joint = np.arange(22, dtype=np.float64)[None, :]
+    value = np.zeros((valid_length, 22, 3), dtype=np.float64)
+    value[..., 0] = 0.013 * actor_identity + 0.0011 * time * (joint + 1.0)
+    value[..., 1] = 0.007 * actor_identity + 0.0003 * time * (23.0 - joint)
+    value[..., 2] = -0.017 * actor_identity + np.sin((time + joint) / 31.0)
+    return np.ascontiguousarray(value, dtype=np.float32)
+
+
+def _batch(
+    *,
+    padded_actors: int = 4,
+    identities: tuple[tuple[int, ...], tuple[int, ...]] = ((1, 3), (2, 4, 6)),
+    permuted_storage: bool = False,
+) -> PreparedGroupBatch:
+    batch_size = 2
+    time_steps = 200
+    skeletons = np.zeros((batch_size, padded_actors, time_steps, 22, 3), dtype=np.float32)
+    actor_mask = np.zeros((batch_size, padded_actors), dtype=np.bool_)
+    frame_mask = np.zeros((batch_size, time_steps), dtype=np.bool_)
+    track_mask = np.zeros((batch_size, padded_actors, time_steps, 22), dtype=np.bool_)
+    commitments: list[tuple[bytes | None, ...]] = []
+    groups: list[bytes] = []
+    for row, (actor_ids, valid_length) in enumerate(zip(identities, (181, 200), strict=True)):
+        frame_mask[row, :valid_length] = True
+        actor_commitments: list[bytes | None] = [None] * padded_actors
+        stored_actor_ids = tuple(reversed(actor_ids)) if permuted_storage else actor_ids
+        for position, actor_identity in enumerate(stored_actor_ids):
+            actor_mask[row, position] = True
+            track_mask[row, position, :valid_length] = True
+            skeletons[row, position, :valid_length] = _actor_motion(actor_identity, valid_length)
+            actor_commitments[position] = bytes([actor_identity]) * 32
+        valid_commitments = tuple(bytes([value]) * 32 for value in actor_ids)
+        commitments.append(tuple(actor_commitments))
+        groups.append(group_commitment(valid_commitments))
+    return PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(skeletons),
+        actor_mask=np.ascontiguousarray(actor_mask),
+        frame_mask=np.ascontiguousarray(frame_mask),
+        track_mask=np.ascontiguousarray(track_mask),
+        actor_commitments=tuple(commitments),
+        group_commitments=tuple(groups),
+    )
+
+
+def _boundary_batch() -> PreparedGroupBatch:
+    return _batch(
+        padded_actors=16,
+        identities=(tuple(range(1, 13)), tuple(range(33, 49))),
+    )
+
+
+def _numeric_mutant(batch: PreparedGroupBatch) -> PreparedGroupBatch:
+    skeletons = np.array(batch.skeletons, copy=True)
+    skeletons[0, 0, 17, 3, 0] += np.float32(0.125)
+    return PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(skeletons),
+        actor_mask=np.ascontiguousarray(batch.actor_mask),
+        frame_mask=np.ascontiguousarray(batch.frame_mask),
+        track_mask=np.ascontiguousarray(batch.track_mask),
+        actor_commitments=batch.actor_commitments,
+        group_commitments=batch.group_commitments,
+    )
+
+
+def _contexts() -> tuple[DescriptorWindowContext, ...]:
+    return tuple(
+        DescriptorWindowContext.from_yaw(
+            prepared_manifest_sha256=MANIFEST_SHA,
+            source_batch_sha256=SOURCE_BATCH_SHA,
+            window_sha256=_digest(f"window-{row}"),
+            window_ordinal=ordinal,
+            split="val",
+            seed=1729,
+            epoch=0,
+            augmentation_yaw=0.0,
+        )
+        for row, ordinal in enumerate((101, 205))
+    )
+
+
+def _create_cached_batch(
+    root: Path,
+    batch: PreparedGroupBatch,
+    floors: np.ndarray,
+) -> CachedDescriptorBatch:
+    contexts = _contexts()
+    writer = PeriodicDescriptorCacheWriterV2(
+        root,
+        source_binding=_binding(),
+        energy_floors=floors,
+    )
+    writer.add_batch(batch, contexts)
+    artifact = writer.finalize()
+    cache = PeriodicDescriptorCacheV2(
+        root,
+        expected_index_sha256=artifact.index_sha256,
+        expected_source_binding=_binding(),
+        energy_floors=floors,
+    )
+    return cache.open_batch(batch, contexts)
+
+
+def _assert_outputs_equal(left: object, right: object) -> None:
+    for name in (
+        "tokens",
+        "band_mask",
+        "pair_component",
+        "topology_delta",
+        "valid_pair_count",
+        "topology_node_count",
+    ):
+        assert torch.equal(getattr(left, name), getattr(right, name)), name
+
+
+def _parameter_gradients(
+    model: torch.nn.Module,
+) -> dict[str, torch.Tensor | None]:
+    values: dict[str, torch.Tensor | None] = {}
+    for name, parameter in model.named_parameters():
+        values[name] = None if parameter.grad is None else parameter.grad.detach().clone()
+    return values
+
+
+def _assert_numpy_rng_equal(
+    left: tuple[object, np.ndarray, int, int, float],
+    right: tuple[object, np.ndarray, int, int, float],
+) -> None:
+    assert left[0] == right[0]
+    assert np.array_equal(left[1], right[1])
+    assert left[2:] == right[2:]
+
+
+def test_production_cached_entry_replays_full_stream_for_exact_vjp(
+    tmp_path: Path,
+) -> None:
+    batch = _boundary_batch()
+    floors = _floors()
+    cached = _create_cached_batch(tmp_path / "cache", batch, floors)
+    assert cached.edge_count == 66 + 120 == 186
+    assert cached.energy_floors_sha256 == energy_floors_sha256(floors)
+    assert cached.contexts == _contexts()
+
+    torch.manual_seed(81)
+    reference = PhaseSetEncoder(
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+        edge_budget=32_768,
+    )
+    expected = reference(batch, edge_chunk_size=64)
+    expected.tokens.square().sum().backward()
+    expected_gradients = _parameter_gradients(reference)
+
+    for chunk_size in (64, 128, 256):
+        candidate = PhaseSetEncoder(
+            embedding_dim=16,
+            hidden_dim=11,
+            energy_floors=floors,
+            edge_budget=32_768,
+        )
+        candidate.load_state_dict(reference.state_dict(), strict=True)
+        python_before = random.getstate()
+        numpy_before = np.random.get_state()
+        torch_before = torch.random.get_rng_state().clone()
+
+        actual = candidate.forward_cached(
+            batch,
+            descriptor_stream=cached.stream("FULL_RELATION"),
+            edge_chunk_size=chunk_size,
+        )
+        _assert_outputs_equal(actual, expected)
+        actual.tokens.square().sum().backward()
+        actual_gradients = _parameter_gradients(candidate)
+        assert actual_gradients.keys() == expected_gradients.keys()
+        for name in expected_gradients:
+            expected_gradient = expected_gradients[name]
+            actual_gradient = actual_gradients[name]
+            assert expected_gradient is not None, name
+            assert actual_gradient is not None, name
+            assert torch.equal(actual_gradient, expected_gradient), (chunk_size, name)
+
+        assert random.getstate() == python_before
+        _assert_numpy_rng_equal(np.random.get_state(), numpy_before)
+        assert torch.equal(torch.random.get_rng_state(), torch_before)
+
+
+def test_cached_production_path_never_calls_uncached_descriptor_iterator(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    batch = _batch()
+    floors = _floors()
+    cached = _create_cached_batch(tmp_path / "cache", batch, floors)
+    encoder = PhaseSetEncoder(
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+    )
+    calls = 0
+
+    def forbidden_uncached(*_args: object, **_kwargs: object) -> object:
+        nonlocal calls
+        calls += 1
+        raise AssertionError("configured cached execution used the uncached iterator")
+
+    monkeypatch.setattr(encoder, "_iter_pair_chunks", forbidden_uncached)
+    output = encoder.forward_cached(
+        batch,
+        descriptor_stream=cached.stream("FULL_RELATION"),
+        edge_chunk_size=128,
+    )
+    output.tokens.square().sum().backward()
+    assert calls == 0
+
+
+def test_system06_cached_incidence_and_vjp_never_call_uncached_iterator(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    batch = _batch()
+    floors = _floors()
+    cached = _create_cached_batch(tmp_path / "cache", batch, floors)
+    system = build_phaseset_system(
+        "06",
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+        edge_budget=32_768,
+    )
+    assert system.encoder is not None
+    calls = 0
+
+    def forbidden_uncached(*_args: object, **_kwargs: object) -> object:
+        nonlocal calls
+        calls += 1
+        raise AssertionError("system 06 cached incidence used the uncached iterator")
+
+    monkeypatch.setattr(system.encoder, "_iter_pair_chunks", forbidden_uncached)
+    output = system.forward_cached(
+        batch,
+        descriptor_stream=cached.stream("FULL_RELATION"),
+        edge_chunk_size=128,
+    )
+    output.tokens.square().sum().backward()
+    assert calls == 0
+
+
+def test_equivalent_actor_storage_permutation_preserves_cached_output_and_gradients(
+    tmp_path: Path,
+) -> None:
+    canonical = _batch()
+    permuted = _batch(permuted_storage=True)
+    floors = _floors()
+    cached = _create_cached_batch(tmp_path / "cache", canonical, floors)
+    torch.manual_seed(211)
+    reference = PhaseSetEncoder(
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+        edge_budget=32_768,
+    )
+    expected = reference(canonical, edge_chunk_size=64)
+    expected.tokens.square().sum().backward()
+    expected_gradients = _parameter_gradients(reference)
+
+    candidate = PhaseSetEncoder(
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+        edge_budget=32_768,
+    )
+    candidate.load_state_dict(reference.state_dict(), strict=True)
+    actual = candidate.forward_cached(
+        permuted,
+        descriptor_stream=cached.stream("FULL_RELATION"),
+        edge_chunk_size=128,
+    )
+    _assert_outputs_equal(actual, expected)
+    actual.tokens.square().sum().backward()
+    actual_gradients = _parameter_gradients(candidate)
+    assert actual_gradients.keys() == expected_gradients.keys()
+    for name in expected_gradients:
+        expected_gradient = expected_gradients[name]
+        actual_gradient = actual_gradients[name]
+        assert expected_gradient is not None, name
+        assert actual_gradient is not None, name
+        assert torch.equal(actual_gradient, expected_gradient), name
+
+
+def test_cached_and_uncached_paths_share_resource_limit_before_stream_consumption(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    batch = _boundary_batch()
+    floors = _floors()
+    cached = _create_cached_batch(tmp_path / "cache", batch, floors)
+    stream = cached.stream("FULL_RELATION")
+    calls = 0
+
+    def forbidden_cached_stream(*_args: object, **_kwargs: object) -> object:
+        nonlocal calls
+        calls += 1
+        raise AssertionError("over-budget cached execution consumed descriptors")
+
+    monkeypatch.setattr(type(stream), "iter_chunks", forbidden_cached_stream)
+    encoder = PhaseSetEncoder(
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+        edge_budget=185,
+    )
+    for execute in (
+        lambda: encoder(batch, edge_chunk_size=128),
+        lambda: encoder.forward_cached(
+            batch,
+            descriptor_stream=stream,
+            edge_chunk_size=128,
+        ),
+    ):
+        with pytest.raises(ResourceLimitError) as caught:
+            execute()
+        assert caught.value.code == "RESOURCE_LIMIT"
+        assert caught.value.required_edges == 186
+        assert caught.value.edge_budget == 185
+    assert calls == 0
+
+
+def test_cached_and_uncached_paths_accept_exact_edge_budget_boundary(
+    tmp_path: Path,
+) -> None:
+    batch = _boundary_batch()
+    floors = _floors()
+    cached = _create_cached_batch(tmp_path / "cache", batch, floors)
+    encoder = PhaseSetEncoder(
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+        edge_budget=186,
+    )
+    with torch.no_grad():
+        expected = encoder(batch, edge_chunk_size=128)
+        actual = encoder.forward_cached(
+            batch,
+            descriptor_stream=cached.stream("FULL_RELATION"),
+            edge_chunk_size=128,
+        )
+    _assert_outputs_equal(actual, expected)
+
+
+@pytest.mark.parametrize(
+    ("system_id", "stream_kind"),
+    (
+        ("02", "MARGINAL_POWER"),
+        ("03", "MEAN_DIFFERENCE_DCT"),
+        ("04", "FULL_RELATION"),
+        ("06", "FULL_RELATION"),
+        ("07", "FULL_RELATION"),
+        ("08", "FULL_RELATION"),
+    ),
+)
+def test_registered_systems_consume_only_their_cached_descriptor_family(
+    tmp_path: Path,
+    system_id: str,
+    stream_kind: str,
+) -> None:
+    batch = _batch()
+    floors = _floors()
+    cached = _create_cached_batch(tmp_path / system_id, batch, floors)
+    torch.manual_seed(109)
+    system = build_phaseset_system(
+        system_id,
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+        edge_budget=32_768,
+    )
+
+    expected = system(batch, edge_chunk_size=64)
+    expected.tokens.square().sum().backward()
+    expected_gradients = _parameter_gradients(system)
+    system.zero_grad(set_to_none=True)
+
+    actual = system.forward_cached(
+        batch,
+        descriptor_stream=cached.stream(stream_kind),  # type: ignore[arg-type]
+        edge_chunk_size=128,
+    )
+    _assert_outputs_equal(actual, expected)
+    actual.tokens.square().sum().backward()
+    actual_gradients = _parameter_gradients(system)
+    for name in expected_gradients:
+        expected_gradient = expected_gradients[name]
+        actual_gradient = actual_gradients[name]
+        if expected_gradient is None:
+            assert actual_gradient is None, name
+        else:
+            assert actual_gradient is not None, name
+            assert torch.equal(actual_gradient, expected_gradient), name
+
+
+def test_cached_entry_rejects_wrong_batch_floors_stream_and_missing_verified_stream(
+    tmp_path: Path,
+) -> None:
+    batch = _batch()
+    floors = _floors()
+    cached = _create_cached_batch(tmp_path / "cache", batch, floors)
+    encoder = PhaseSetEncoder(
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+    )
+
+    with pytest.raises(PhaseSetModelError, match="actual model batch"):
+        encoder.forward_cached(
+            _numeric_mutant(batch),
+            descriptor_stream=cached.stream("FULL_RELATION"),
+        )
+    wrong_floors = PhaseSetEncoder(
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=_floors(1.0e-8),
+    )
+    with pytest.raises(PhaseSetModelError, match="energy floors"):
+        wrong_floors.forward_cached(
+            batch,
+            descriptor_stream=cached.stream("FULL_RELATION"),
+        )
+    with pytest.raises(PhaseSetModelError, match="stream kind"):
+        encoder.forward_cached(
+            batch,
+            descriptor_stream=cached.stream("MARGINAL_POWER"),
+        )
+    with pytest.raises(PhaseSetModelError, match="exact verified"):
+        encoder.forward_cached(batch, descriptor_stream=None)  # type: ignore[arg-type]
+
+    marginal = build_phaseset_system(
+        "02",
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+    )
+    with pytest.raises(PhaseSetModelError, match="stream kind"):
+        marginal.forward_cached(
+            batch,
+            descriptor_stream=cached.stream("FULL_RELATION"),
+        )
+
+
+@pytest.mark.parametrize("system_id", ("01", "05"))
+def test_uncacheable_registered_controls_fail_closed_but_legacy_forward_remains(
+    tmp_path: Path,
+    system_id: str,
+) -> None:
+    batch = _batch()
+    floors = _floors()
+    cached = _create_cached_batch(tmp_path / system_id, batch, floors)
+    system = build_phaseset_system(
+        system_id,
+        embedding_dim=16,
+        hidden_dim=11,
+        energy_floors=floors,
+    )
+    assert system(batch).tokens.shape == (2, 6, 16)
+    with pytest.raises(PhaseSetModelError, match="does not admit"):
+        system.forward_cached(
+            batch,
+            descriptor_stream=cached.stream("FULL_RELATION"),
+        )
+
+
+def test_zero_residual_system_rejects_cached_execution(tmp_path: Path) -> None:
+    batch = _batch()
+    floors = _floors()
+    cached = _create_cached_batch(tmp_path / "cache", batch, floors)
+    system = build_phaseset_system("00", embedding_dim=16, energy_floors=floors)
+    assert not bool(system(batch).band_mask.any().item())
+    with pytest.raises(PhaseSetControlError, match="system 00"):
+        system.forward_cached(
+            batch,
+            descriptor_stream=cached.stream("FULL_RELATION"),
+        )

From 14eca09967654f17b7053e01f9493006e1ca6223 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 13:42:36 -0500
Subject: [PATCH 18/95] Fix canonical host admission and real command-intent
 bindings

---
 RELEASE_FILES.sha256                       |   7 +-
 STATUS.md                                  |  13 +-
 docs/HOST_RUNTIME_INTEGRATION.md           |  24 ++
 src/phaseset_core/host.py                  |  60 +++-
 tests/test_phaseset_host_intent_binding.py | 302 +++++++++++++++++++++
 5 files changed, 389 insertions(+), 17 deletions(-)
 create mode 100644 tests/test_phaseset_host_intent_binding.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 3e16cab..7fabbbc 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,7 +11,7 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-3cc295f3691425582a6281e432ca9a9f81ac3a25d2024033b39f0d0f4539b01a	25026	STATUS.md
+96bf21381f6708fdd6c054daeb75fbf720dc8d0941c6bf071d1e7247b924c452	25273	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
@@ -28,7 +28,7 @@ e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
 97b7b7e68b7896220b38661b094505b47c711a431f546e6da9772cfedcf6beaa	14238	docs/FROZEN_CLIP_TEXT_ADAPTER.md
 f2c59540bfc4dcdbb5013b76eb20090a7dd61c805523121995ed9206c843f9c8	3711	docs/FROZEN_MHA_RUNTIME.md
-fad4d9e8d1a51bf443af94b536b7aee0b8031cf42edab58190db7b174a768e0e	15565	docs/HOST_RUNTIME_INTEGRATION.md
+2643d17f18c64105ea4c4ef7873167fc3abf2169efdf4b2e946f20b9081da855	17150	docs/HOST_RUNTIME_INTEGRATION.md
 5dd898347d3646cde4a74e10afa8ba1d12703e3d0c6df51e5113659a31808086	3606	docs/LATENCY_PROGRESS_JOURNAL.md
 f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
@@ -146,7 +146,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
 e0242fc212e83f2d3b694734fadb62ab76c4ef90c3c5b04df503f5a7c907a3e0	68520	src/phaseset_core/frozen_clip_text.py
-7fcc111d269ed22db55ba2c3a5bee8d6c79f3c850c42eca5ff8aaa08b6e809e1	90636	src/phaseset_core/host.py
+e76f255c27c310682ac363e3d0f4fa223b07b85522e607ab3a4ef111cd246332	92373	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
 9e7fa84554d5676f8ef75049b17b53580395c3c60be0f75fcb55996ccc8ef36d	37720	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
@@ -222,6 +222,7 @@ dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/tes
 937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
 4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
 14f6d4b54d78876822e6f96440a32360ea4d4654c1d18d8d8662fe0f4dc42bf8	23309	tests/test_phaseset_host_capture_storage.py
+6eb892149a9b3dee02f48e816acfe0026a0efaed8985a32eedf412bad1edd141	11008	tests/test_phaseset_host_intent_binding.py
 d133ca3a4df71baaea1cc40f68d049b56a7fcfc0d0861a87e5916c9637bdadb2	16345	tests/test_phaseset_latency_progress_journal.py
 a75f583fd0a233760b1a421ca51680afe4195c33f9ef4bce6e9a1737828f207e	14907	tests/test_phaseset_models.py
 e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test_phaseset_morlet.py
diff --git a/STATUS.md b/STATUS.md
index 516731f..8be92a0 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -23,11 +23,14 @@ evidence, not training results or scientific claims.
   not real data or measured acceleration. Training-row selection, complete
   epoch census and checkpoint/resume cache consumption remain unfinished.
   See [descriptor model integration](docs/PERIODIC_DESCRIPTOR_CACHE_V2.md).
-  A separate static audit also identified a host dispatch defect: new base
-  and residual attempts read two fields absent from the actual CommandIntent.
-  That repair and a real CLI-to-backend regression are in progress; existing
-  synthetic runtime checks must not be treated as proof that this host path
-  already works end to end.
+  The host now retains the canonical admitted plan/matrix/config instead of
+  reading fields absent from the actual command intent. Commands and handler
+  identity are checked before retaining the admission snapshot. Real
+  CLI-to-backend fixture regression passed 94 focused tests; full regression
+  passed 1232 tests, 2 existing skips and 39 subtests in 401.58 s, source
+  unchanged. The new CLI fixtures intentionally stop at the model-runtime
+  seam and retain FAILED terminals; they are not completed training runs.
+  See [host integration](docs/HOST_RUNTIME_INTEGRATION.md).
 
 - The complete frozen base-latency runner, exact qualification assembler,
   typed progress observer and durable POSIX journal are implemented. Separately,
diff --git a/docs/HOST_RUNTIME_INTEGRATION.md b/docs/HOST_RUNTIME_INTEGRATION.md
index b38873b..47d13c4 100644
--- a/docs/HOST_RUNTIME_INTEGRATION.md
+++ b/docs/HOST_RUNTIME_INTEGRATION.md
@@ -20,6 +20,18 @@ It never creates authority, rights, runtime, or execution receipts.  If an
 artifact is absent or changed, the factory raises the existing
 `ExecutionHold` codes and the public CLI remains authority zero.
 
+Admission and command execution have distinct typed contracts.
+`RuntimeAdmissionRequest` carries the frozen plan, matrix, training-config,
+command census and handler digest; it has no per-command `command` field.
+`CommandIntent` carries the selected command and run, not matrix/config fields.
+The backend checks all five canonical admission bindings before retaining its
+one-shot snapshot. New base/residual attempt records obtain their three
+experiment digests from that retained admission, while dispatch checks the
+actual intent against the parsed request. A rejected admission cannot poison
+the snapshot and prevent a later canonical admission. Resume continues to
+inherit its verified predecessor binding. No extra operator hash approval is
+introduced.
+
 The factory dispatches v1 and v2 prepared indexes using the actual index bytes
 bound to the private prepared-data receipt. The v2 loader also verifies each
 consumed split manifest before decoding its numeric batches. Malformed batch
@@ -270,3 +282,15 @@ before/after identity check including `ctime`. Content digests, bounded reads,
 regular-file and symlink checks remain mandatory. Tests simulate the stable
 cross-interface difference and reject drift within either interface. Windows
 CI for the correction is a separate external acceptance check.
+
+The later admission/intent correction passed 94 focused Linux server tests
+and 1232 full tests, two existing skips and 39 subtests in 401.58 seconds.
+Source censuses remained unchanged. New tests enter the real public CLI,
+request-aware factory, production admission and backend with real typed
+requests. They create actual attempt ledgers, then deliberately raise at the
+typed base/residual model-runtime seam. Correct experiment bindings and
+durable FAILED terminals must exist; no runtime checkpoint is manufactured.
+Negative admissions with changed matrix, command census or handler digest
+leave the snapshot unbound and still allow the subsequent canonical request.
+These tests close the concrete missing-field/poisoning defects, not a complete
+licensed-data CLI training run or the unimplemented commands listed above.
diff --git a/src/phaseset_core/host.py b/src/phaseset_core/host.py
index 27642a8..3c89b52 100644
--- a/src/phaseset_core/host.py
+++ b/src/phaseset_core/host.py
@@ -51,6 +51,7 @@
     TerminalRecord,
     artifact_sha256,
     canonical_attempt_bytes,
+    canonical_public_training_config_bytes,
     parse_attempt_bytes,
     parse_checkpoint_bytes,
     parse_periodic_cache_bytes,
@@ -60,7 +61,9 @@
     SEEDS,
     BaseQualification,
     BaseScore,
+    build_experiment_plan,
     canonical_base_qualification_bytes,
+    experiment_plan_sha256,
     parse_run_id,
 )
 from phaseset_core.prepared_data_v2 import (
@@ -1597,6 +1600,7 @@ class PhaseSetHostBackend:
     def __init__(self, config: HostConfig, request: cli.CLICommandRequest) -> None:
         self._config = config
         self._request = request
+        self._admission_request: RuntimeAdmissionRequest | None = None
         self.backend_sha256 = _sha256_bytes(Path(__file__).read_bytes())
 
     def authenticate(
@@ -1608,11 +1612,43 @@ def authenticate(
         del adapter_sha256
         if receipts != self._config.load_receipts():
             return False
-        # Receipt fields are checked against their exact artifacts above.  The
-        # public ProductionRuntimeAdapter binds plan/matrix/config/handler hashes.
-        return request.command == self._request.command
+        plan = build_experiment_plan()
+        if (
+            request.plan_sha256 != experiment_plan_sha256(plan)
+            or request.matrix_sha256 != plan.matrix_sha256
+            or request.training_config_sha256
+            != artifact_sha256(canonical_public_training_config_bytes())
+            or request.commands != ProductionRuntimeAdapter.commands
+            or request.handler_manifest_sha256
+            != ProductionRuntimeAdapter.handler_manifest_sha256
+        ):
+            return False
+        # RuntimeAdmissionRequest deliberately has no command field.  The backend
+        # is request-specific and execute() binds the later CommandIntent to that
+        # parsed CLI command.  Retain the exact runner-created admission request so
+        # attempt ledgers never read fields that CommandIntent does not define.
+        if self._admission_request is not None:
+            return request == self._admission_request
+        self._admission_request = request
+        return True
+
+    def _attempt_plan_bindings(self) -> tuple[str, str, str]:
+        request = self._admission_request
+        if request is None:
+            raise HostConfigurationError(
+                "runtime admission must complete before an attempt is created"
+            )
+        return (
+            request.plan_sha256,
+            request.matrix_sha256,
+            request.training_config_sha256,
+        )
 
     def execute(self, intent: CommandIntent) -> BackendExecution:
+        if intent.command != self._request.command:
+            raise HostConfigurationError(
+                "command intent differs from the request-specific host backend"
+            )
         if intent.command == "run-base":
             return self._run_base(intent)
         if intent.command == "run-residual":
@@ -1757,14 +1793,17 @@ def _run_base(self, intent: CommandIntent) -> BackendExecution:
         attempt_directory = checkpoint_directory.parent
         if attempt_directory.exists() or attempt_directory.is_symlink():
             raise HostConfigurationError("new attempt directory already exists")
+        plan_sha256, matrix_sha256, training_config_sha256 = (
+            self._attempt_plan_bindings()
+        )
         created_at = _utc_now()
         attempt = AttemptRecord(
             attempt_id=attempt_directory.name,
             run_id=request.run_id,
             created_at_utc=created_at,
-            plan_sha256=intent.plan_sha256,
-            matrix_sha256=intent.matrix_sha256,
-            training_config_sha256=intent.training_config_sha256,
+            plan_sha256=plan_sha256,
+            matrix_sha256=matrix_sha256,
+            training_config_sha256=training_config_sha256,
             source_tree_sha256=self._config.source_tree_sha256,
         )
         store = AttemptStore.create(attempt_directory.parent, attempt)
@@ -1825,6 +1864,9 @@ def _run_residual(self, intent: CommandIntent) -> BackendExecution:
         attempt_directory = checkpoint_directory.parent
         if attempt_directory.exists() or attempt_directory.is_symlink():
             raise HostConfigurationError("new attempt directory already exists")
+        plan_sha256, matrix_sha256, training_config_sha256 = (
+            self._attempt_plan_bindings()
+        )
         _reject_symlink_components(request.base_checkpoint, "qualified base checkpoint")
         base_checkpoint = request.base_checkpoint.resolve()
         if not base_checkpoint.is_file():
@@ -1836,9 +1878,9 @@ def _run_residual(self, intent: CommandIntent) -> BackendExecution:
             attempt_id=attempt_directory.name,
             run_id=request.run_id,
             created_at_utc=created_at,
-            plan_sha256=intent.plan_sha256,
-            matrix_sha256=intent.matrix_sha256,
-            training_config_sha256=intent.training_config_sha256,
+            plan_sha256=plan_sha256,
+            matrix_sha256=matrix_sha256,
+            training_config_sha256=training_config_sha256,
             source_tree_sha256=self._config.source_tree_sha256,
         )
         store = AttemptStore.create(attempt_directory.parent, attempt)
diff --git a/tests/test_phaseset_host_intent_binding.py b/tests/test_phaseset_host_intent_binding.py
new file mode 100644
index 0000000..7c23b21
--- /dev/null
+++ b/tests/test_phaseset_host_intent_binding.py
@@ -0,0 +1,302 @@
+from __future__ import annotations
+
+import hashlib
+import json
+from io import StringIO
+from pathlib import Path
+
+import numpy as np
+import pytest
+from phaseset_core import cli, execution, experiments, host
+from phaseset_core.contracts import group_commitment
+
+
+# The focused test must be copied into ``<public checkout>/tests`` and run with
+# the candidate host overlaid at ``src/phaseset_core/host.py``.  Do not infer a
+# repository root from an installed wheel's package location: wheels need not
+# contain the repository-level frozen config fixture.
+PUBLIC_ROOT = Path(__file__).resolve().parents[1]
+PUBLIC_CONFIG = PUBLIC_ROOT / "configs" / "phaseset" / "training.json"
+
+
+class _StoppedBeforeModel(RuntimeError):
+    pass
+
+
+def _sha(path: Path) -> str:
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def _commitment(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _write_json(path: Path, value: object) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        )
+        + "\n",
+        encoding="ascii",
+    )
+
+
+def _write_batch(path: Path, label: str) -> None:
+    actors = (_commitment(f"{label}-actor-0"), _commitment(f"{label}-actor-1"))
+    family = _commitment(f"{label}-family")
+    np.savez(
+        path,
+        skeletons=np.zeros((1, 2, 200, 22, 3), dtype=np.float32),
+        actor_mask=np.ones((1, 2), dtype=np.bool_),
+        frame_mask=np.ones((1, 200), dtype=np.bool_),
+        track_mask=np.ones((1, 2, 200, 22), dtype=np.bool_),
+        actor_commitments=np.asarray([[list(actors[0]), list(actors[1])]], dtype=np.uint8),
+        group_commitments=np.asarray([list(group_commitment(actors))], dtype=np.uint8),
+        text_embeddings=np.asarray([[1.0, 0.0]], dtype=np.float32),
+        motion_positive_ids=np.asarray([list(family)], dtype=np.uint8),
+        text_positive_ids=np.asarray([list(family)], dtype=np.uint8),
+        text_commitments=np.asarray([list(_commitment(f"{label}-text"))], dtype=np.uint8),
+    )
+
+
+def _valid_host_config(root: Path) -> Path:
+    split_rows: dict[str, dict[str, str]] = {}
+    for split in ("train", "val"):
+        batch = root / f"{split}.npz"
+        _write_batch(batch, split)
+        manifest = root / f"{split}.json"
+        _write_json(
+            manifest,
+            {
+                "batches": [{"path": batch.name, "sha256": _sha(batch)}],
+                "schema": host.PREPARED_SPLIT_SCHEMA,
+                "split": split,
+            },
+        )
+        split_rows[split] = {"path": manifest.name, "sha256": _sha(manifest)}
+    index = root / "prepared-index.json"
+    _write_json(index, {"schema": host.PREPARED_INDEX_SCHEMA, **split_rows})
+
+    artifact_paths: dict[str, str] = {}
+    artifact_digests: dict[str, str] = {}
+    for field in host.RECEIPT_DIGEST_FIELDS:
+        artifact = index if field == "prepared_data_manifest_sha256" else root / field
+        if artifact != index:
+            artifact.write_text(f"{field}\n", encoding="ascii")
+        artifact_paths[field] = artifact.name
+        artifact_digests[field] = _sha(artifact)
+    receipts = root / "receipts.json"
+    _write_json(
+        receipts,
+        {
+            "admitted_at_utc": "2026-09-08T12:00:00Z",
+            "authority": 7,
+            **artifact_digests,
+        },
+    )
+    config = root / "host.json"
+    _write_json(
+        config,
+        {
+            "prepared_index": index.name,
+            "receipts": {"artifacts": artifact_paths, "record": receipts.name},
+            "runtime": {
+                "bf16_runtime_qualified": False,
+                "checkpoint_every_updates": 1,
+                "corrective_change_sha256": hashlib.sha256(b"none").hexdigest(),
+                "device": "cpu",
+                "edge_budget": 32768,
+                "max_batch_bytes": 10_000_000,
+                "request_bf16": False,
+                "resume_retry_class": "INFRA_TRANSIENT",
+            },
+            "schema": host.HOST_CONFIG_SCHEMA,
+            "source_tree_sha256": hashlib.sha256(b"source-tree").hexdigest(),
+        },
+    )
+    return config
+
+
+def _invoke(arguments: list[str], config: Path) -> tuple[int, dict[str, object], str]:
+    stdout = StringIO()
+    stderr = StringIO()
+    code = cli.main(
+        arguments,
+        stdout=stdout,
+        stderr=stderr,
+        runtime_adapter_factory=host.RuntimeAdapterFactory(config),
+    )
+    raw = stdout.getvalue() or stderr.getvalue()
+    return code, json.loads(raw), stderr.getvalue()
+
+
+@pytest.mark.parametrize("command", ["run-base", "run-residual"])
+def test_real_cli_intent_uses_authenticated_admission_bindings_before_model(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    command: str,
+) -> None:
+    config = _valid_host_config(tmp_path)
+    attempts = tmp_path / "attempts"
+    attempts.mkdir()
+    attempt = attempts / command
+    checkpoint_directory = attempt / "model-checkpoints"
+    captured_intents: list[execution.CommandIntent] = []
+    original_execute = host.PhaseSetHostBackend.execute
+
+    def capture_execute(
+        self: host.PhaseSetHostBackend,
+        intent: execution.CommandIntent,
+    ) -> object:
+        assert type(intent) is execution.CommandIntent
+        captured_intents.append(intent)
+        return original_execute(self, intent)
+
+    def stop_before_base_model(self: object, **kwargs: object) -> object:
+        del self, kwargs
+        raise _StoppedBeforeModel("test stopped before base model construction")
+
+    def stop_before_residual_model(self: object, **kwargs: object) -> object:
+        del self, kwargs
+        raise _StoppedBeforeModel("test stopped before residual model construction")
+
+    monkeypatch.setattr(host.PhaseSetHostBackend, "execute", capture_execute)
+    monkeypatch.setattr(
+        host.PhaseSetHostBackend,
+        "_execute_base_runtime",
+        stop_before_base_model,
+    )
+    monkeypatch.setattr(
+        host.PhaseSetHostBackend,
+        "_execute_residual_runtime",
+        stop_before_residual_model,
+    )
+
+    if command == "run-base":
+        run_id = "phaseset-run-v1/BASE_QUALIFICATION/1729/B0"
+        arguments = [
+            "run-base",
+            "--run-id",
+            run_id,
+            "--checkpoint-dir",
+            str(checkpoint_directory),
+            "--config",
+            str(PUBLIC_CONFIG),
+        ]
+    else:
+        run_id = "phaseset-run-v1/RESIDUAL_TRAIN/1729/01"
+        base_checkpoint = tmp_path / "base-selected.pt"
+        base_checkpoint.write_bytes(b"test stops before decoding this checkpoint\n")
+        periodic_cache = tmp_path / "periodic-cache"
+        periodic_cache.mkdir()
+        arguments = [
+            "run-residual",
+            "--run-id",
+            run_id,
+            "--checkpoint-dir",
+            str(checkpoint_directory),
+            "--base-checkpoint",
+            str(base_checkpoint),
+            "--periodic-cache",
+            str(periodic_cache),
+            "--config",
+            str(PUBLIC_CONFIG),
+        ]
+
+    code, payload, stderr = _invoke(arguments, config)
+    assert code == cli.EXIT_EXECUTION_FAILED
+    assert stderr == ""
+    assert payload["command"] == command
+    assert payload["outcome"] == "FAILED"
+    assert len(captured_intents) == 1
+    intent = captured_intents[0]
+    assert not hasattr(intent, "matrix_sha256")
+    assert not hasattr(intent, "training_config_sha256")
+
+    record = execution.parse_attempt_bytes((attempt / "attempt.json").read_bytes())
+    plan = experiments.build_experiment_plan()
+    assert record.run_id == run_id
+    assert record.plan_sha256 == experiments.experiment_plan_sha256(plan)
+    assert record.matrix_sha256 == plan.matrix_sha256
+    assert record.training_config_sha256 == execution.artifact_sha256(
+        execution.canonical_public_training_config_bytes()
+    )
+    terminal = execution.parse_terminal_bytes((attempt / "terminal.json").read_bytes())
+    assert terminal.outcome == "FAILED"
+    assert terminal.failure_code == "HOST_EXECUTION_FAILED"
+    assert not checkpoint_directory.exists()
+
+
+def test_backend_rejects_noncanonical_admissions_without_poisoning_snapshot(
+    tmp_path: Path,
+) -> None:
+    config_path = _valid_host_config(tmp_path)
+    config = host.HostConfig.load(config_path)
+    request = cli.CLICommandRequest(command="run-base")
+    backend = host.PhaseSetHostBackend(config, request)
+    receipts = config.load_receipts()
+    plan = experiments.build_experiment_plan()
+    canonical = execution.RuntimeAdmissionRequest(
+        plan_sha256=experiments.experiment_plan_sha256(plan),
+        matrix_sha256=plan.matrix_sha256,
+        training_config_sha256=execution.artifact_sha256(
+            execution.canonical_public_training_config_bytes()
+        ),
+        handler_manifest_sha256=execution.runtime_handler_manifest_sha256(
+            execution.COMMANDS
+        ),
+    )
+    changed_matrix = execution.RuntimeAdmissionRequest(
+        plan_sha256=canonical.plan_sha256,
+        matrix_sha256="f" * 64,
+        training_config_sha256=canonical.training_config_sha256,
+        handler_manifest_sha256=canonical.handler_manifest_sha256,
+    )
+    changed_commands = execution.RuntimeAdmissionRequest(
+        plan_sha256=canonical.plan_sha256,
+        matrix_sha256=canonical.matrix_sha256,
+        training_config_sha256=canonical.training_config_sha256,
+        handler_manifest_sha256=canonical.handler_manifest_sha256,
+        commands=tuple(reversed(execution.COMMANDS)),
+    )
+    changed_handler = execution.RuntimeAdmissionRequest(
+        plan_sha256=canonical.plan_sha256,
+        matrix_sha256=canonical.matrix_sha256,
+        training_config_sha256=canonical.training_config_sha256,
+        handler_manifest_sha256="e" * 64,
+    )
+    for changed in (changed_matrix, changed_commands, changed_handler):
+        assert backend.authenticate(changed, receipts, "0" * 64) is False
+        with pytest.raises(host.HostConfigurationError, match="admission"):
+            backend._attempt_plan_bindings()
+    assert backend.authenticate(canonical, receipts, "0" * 64) is True
+    assert backend.authenticate(canonical, receipts, "0" * 64) is True
+    assert backend._attempt_plan_bindings() == (
+        canonical.plan_sha256,
+        canonical.matrix_sha256,
+        canonical.training_config_sha256,
+    )
+
+
+def test_request_specific_backend_rejects_other_real_intent_command(
+    tmp_path: Path,
+) -> None:
+    config = host.HostConfig.load(_valid_host_config(tmp_path))
+    backend = host.PhaseSetHostBackend(
+        config,
+        cli.CLICommandRequest(command="run-base"),
+    )
+    intent = execution.CommandIntent(
+        command="resume",
+        run_id=None,
+        seed=None,
+        split=None,
+        plan_sha256="0" * 64,
+    )
+    with pytest.raises(host.HostConfigurationError, match="request-specific"):
+        backend.execute(intent)

From 05f93724f3672688f4154fec0207479a361b86a2 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 14:26:04 -0500
Subject: [PATCH 19/95] Bind prepared descriptor lineage and strict CUDA UUID
 identity

---
 RELEASE_FILES.sha256                          |  20 +-
 STATUS.md                                     |  13 +
 docs/BASE_COHORT_QUALIFICATION.md             |  22 ++
 docs/PERIODIC_DESCRIPTOR_CACHE_V2.md          |  17 +-
 docs/PREPARED_DATA_V2.md                      |  40 ++
 src/phaseset_core/base_cohort_latency.py      |  53 ++-
 src/phaseset_core/frozen_clip_text.py         |   4 +-
 src/phaseset_core/prepared_data_v2.py         |  79 +++-
 src/phaseset_core/training.py                 |  40 +-
 tests/test_caption_role_provenance.py         |   6 +
 tests/test_periodic_cache_source_lineage.py   | 366 ++++++++++++++++++
 .../test_phaseset_base_cohort_latency_uuid.py | 205 ++++++++++
 12 files changed, 831 insertions(+), 34 deletions(-)
 create mode 100644 tests/test_periodic_cache_source_lineage.py
 create mode 100644 tests/test_phaseset_base_cohort_latency_uuid.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 7fabbbc..6e26022 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,11 +11,11 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-96bf21381f6708fdd6c054daeb75fbf720dc8d0941c6bf071d1e7247b924c452	25273	STATUS.md
+62b6437093a41d3225a665a37c4ddb6cbd0541ea4ba9da5feb358f530e1643d7	26144	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
-69a97cdc4cb297dead25cdb49da911d0a5b8470e9b3033e37e079c0bd48367de	4573	docs/BASE_COHORT_QUALIFICATION.md
+2a3fe097bbda8e7bd3434546e025446f963d4e369aed0ea8222bc2de1d562eec	6012	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
 81ecc1cc742604f47833a0aa5b4c567165aa01e9c500d05157e325bd3b4ccdb3	4281	docs/CAPTION_ROLE_PROVENANCE.md
 a9d6a4875760c58a0075e9e4e6b7a5f147da9762410fde953d648895ec159a76	3398	docs/CAPTURE_PREPARED_STORAGE.md
@@ -32,7 +32,7 @@ f2c59540bfc4dcdbb5013b76eb20090a7dd61c805523121995ed9206c843f9c8	3711	docs/FROZE
 5dd898347d3646cde4a74e10afa8ba1d12703e3d0c6df51e5113659a31808086	3606	docs/LATENCY_PROGRESS_JOURNAL.md
 f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
-be65fdd1fd914467f28487c30a81a07b9429db8552166c28064f829a48f593c4	6923	docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
+d36816ee6efeb999a909ef4f94b1173cbfe188428c37b814563f1445808850e8	7213	docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
 69b5e67cbb59c9c658e5feb11d3f6c01afbc5e2d26a1acd2d6dba8f81f9e67ff	4296	docs/PHASEPAIR_B0_ANNOTATION_CAPTION_CLOSURE_SUCCESSOR_PUBLIC_V4_20260825_145013.json
@@ -60,7 +60,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 ecb85faa3b442450f6ecc8d6f2fff77baf19e833254fa8a715127d9127742f63	13825	docs/PHASESET_RESEARCH_CONTRACT.md
-ddb29ac72e4f11809bb2d689ddb898a3ef23f830eda14e04cea054f79c7e4c13	5806	docs/PREPARED_DATA_V2.md
+a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
 8d689f49b2704ff7c5988eff8a7e3e062f209b85d18425ccdba1e1099581079b	10658	docs/phasepair_research_contract_20260824_165840.md
@@ -128,7 +128,7 @@ fa68bcab33d09588a8653151ad8e5741d5e6530263bcb633d871d7744598b194	10364	src/phase
 dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phasepair_core/validation_gallery.py
 2e5fb5280ed27a46c587d07a60ce663fce676cbdd30b059ec34882b4507d157f	604	src/phaseset_core/__init__.py
 9668a89535264a3ed6b58ffc7ac31b4904a4d8f68636d405e15cbf7368096716	8437	src/phaseset_core/_morlet_oracle_bytes.py
-51cf33c84883ca6965306eac30bfad26d06efb4de623773bc8512ceac6a18eb1	71215	src/phaseset_core/base_cohort_latency.py
+64596c44649acda8f86b70ad43fcb26aa9a80accd962e2e9ef74277883c1ae29	71946	src/phaseset_core/base_cohort_latency.py
 ecda879d3f531ebb747b561612fa34df37f1ecbd8e1977d5089d38b01ec63131	29076	src/phaseset_core/base_cohort_qualification.py
 d2d71766c1c6af95480d507960d7186bbec9ff8a424f6e79ce6611fd0aa2e611	38885	src/phaseset_core/base_cohort_resolver.py
 3d4f31b6a773081af41f1d9d45ee98986ea33ef6f954070564373f995af1f2e9	40593	src/phaseset_core/base_cohort_validation.py
@@ -145,7 +145,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
-e0242fc212e83f2d3b694734fadb62ab76c4ef90c3c5b04df503f5a7c907a3e0	68520	src/phaseset_core/frozen_clip_text.py
+566a4e1c0c8ec4ab26dc1a26950134762f653006e39a4ca8e5d17813ed6dca14	68520	src/phaseset_core/frozen_clip_text.py
 e76f255c27c310682ac363e3d0f4fa223b07b85522e607ab3a4ef111cd246332	92373	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
 9e7fa84554d5676f8ef75049b17b53580395c3c60be0f75fcb55996ccc8ef36d	37720	src/phaseset_core/models.py
@@ -154,7 +154,7 @@ e76f255c27c310682ac363e3d0f4fa223b07b85522e607ab3a4ef111cd246332	92373	src/phase
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 03fdc10250fd11ae111fc1813eb3200575f08ea301091963f3ee14425b51bd8d	55520	src/phaseset_core/periodic_descriptor_cache_v2.py
 a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phaseset_core/pipeline.py
-2bee9279fa4098dc83729c934aa4d3d07f3f2c4090c1734f9a316462d17159ef	44879	src/phaseset_core/prepared_data_v2.py
+5bd46d884f0fa3b8f022d1684ab5ffa1799ded3b970649753b0dd0510350216d	47614	src/phaseset_core/prepared_data_v2.py
 273225e6696351ccb16a3a9e7a653b55af18c64b316a4e5d80c7b265625cc5c1	17929	src/phaseset_core/preprocessing.py
 044b381ba73e73f476918d353f29c7cbee38cdca32ee612826549fef60671bdc	73146	src/phaseset_core/production.py
 6f60feada83ebf1a55635bb657a5a3998e9fc77fa85012aad46e4f8d90d344db	37785	src/phaseset_core/publication.py
@@ -162,13 +162,14 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
-9f6c21e354a17facda89a542d53e39013477c5e1b1a259781e2446cd394f6d06	157160	src/phaseset_core/training.py
+0b91f3a5f1f4eea3d95c98bf899e12fb7de28394046468a05a24eaf2b6c55491	159407	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
-4488b894e36be70f0efef9fc509088ab5a79de4e1fba54446a78492897bce784	18579	tests/test_caption_role_provenance.py
+d5d69092cc271059400773ef68447e06068aa802d5e9c82193704c6b65c71f59	18827	tests/test_caption_role_provenance.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
+9a05b2a025a694f94e158d2de74242d2a820efbc8973920001895114e7a894fb	14880	tests/test_periodic_cache_source_lineage.py
 9c8ff961404bb8d5da86a30c3f533fe8d1909cdfbd76ebb496262ff222121d51	24769	tests/test_periodic_descriptor_cache_v2.py
 af9b5adc42d82b9a0aa4ed744bb069e36b7dd041c1957fdc97d17eb814a0fc0c	17284	tests/test_phasepair_core_base_qualification.py
 ad630675cc642350a988d92de6176053d1bfab5f81cbc49548cb0b5dbeb679d9	23596	tests/test_phasepair_core_batching_readiness.py
@@ -198,6 +199,7 @@ b801d46fb184d99c65825d7f9bf91cbced022418600a95d421c5d9244dbded13	19373	tests/tes
 4da1d6c1b05bc485b9afb78776264ae7757744f104c37b947cdce0280cdfb172	2347	tests/test_phasepair_posix_checkpoint_ownership.py
 6462129abd78cce74c4a2b21c50cefc7220b9092511dab13f5abb9b6277ea621	8467	tests/test_phaseset_base_cohort_checkpoint_state.py
 69a7e034f81c381d5eb4ce6caa02f98950975ebaaeed6ff3e212e4d4fe80d03f	15361	tests/test_phaseset_base_cohort_latency.py
+c34ac09fa80b4809db91f383b4f1ea79a609aa75e852aba91298fa2ca97e618c	6277	tests/test_phaseset_base_cohort_latency_uuid.py
 1379f5e8d8bb11443615d14e6a14e4091319563cc4bfe67de0c4621f1e9476cb	13454	tests/test_phaseset_base_cohort_qualification.py
 41535795e088b5374bf59b95491afa4d1aac08622cddf1aa7d76c5fb0af990fa	23471	tests/test_phaseset_base_cohort_resolver.py
 2cd1a40ebf6c10c83dae4f72bf41821d25ee153c8b3f05819ac664eb0a8f611e	14477	tests/test_phaseset_base_cohort_score_witness.py
diff --git a/STATUS.md b/STATUS.md
index 8be92a0..0e91451 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -13,6 +13,19 @@ evidence, not training results or scientific claims.
 
 ## Server continuation
 
+- Prepared-v2 batches now retain their exact descriptor source/window/epoch/yaw
+  contexts. Repeated rotation of an already contextualized batch is rejected.
+  The live CLIP training-source pin was updated and actual official text
+  embedding bytes remained unchanged. A separate actual diagnostic localized
+  a Torch/NVIDIA UUID-prefix mismatch; strict full-UUID matching now supports
+  the observed representation without bypassing device or resource checks.
+  The combined Linux server suite passed 136 focused tests and 1257 full
+  tests, 2 existing skips and 39 subtests in 413.42 s, source unchanged.
+  Complete training cache-plan/checkpoint/resume integration is still pending;
+  these observations are not real-data training or a K32 performance result.
+  See [source lineage](docs/PREPARED_DATA_V2.md) and
+  [CUDA identity](docs/BASE_COHORT_QUALIFICATION.md).
+
 - Explicit reader-backed cached model execution binds the actual complete
   batch, energy floors and stream family, and enforces the live encoder edge
   budget before consuming descriptors. The same stream is
diff --git a/docs/BASE_COHORT_QUALIFICATION.md b/docs/BASE_COHORT_QUALIFICATION.md
index 796155a..8473446 100644
--- a/docs/BASE_COHORT_QUALIFICATION.md
+++ b/docs/BASE_COHORT_QUALIFICATION.md
@@ -35,6 +35,28 @@ The injected runtime seam is explicitly nonformal and exists for contract
 tests. It cannot produce an accepted actual-runtime qualification merely by
 finishing the same number of loops.
 
+An actual registered-server diagnostic found that NVIDIA returned the complete
+`GPU-`-prefixed UUID while Torch's CUDA UUID object rendered the same payload
+without that prefix. The runtime now accepts only those two complete canonical
+lowercase representations from Torch and compares the resulting full UUID
+exactly against both registration and the NVIDIA observation. Short forms,
+whitespace, uppercase payloads, repeated prefixes, MIG identifiers and changed
+payloads are rejected; this conversion cannot select or change the device.
+Tests prove invalid identities fail before allocator setup or tensor creation,
+and accepted representations preserve the original allocator cap. The original
+failed K32 observations remain retained; this identity correction alone is not
+a completed K32 model run or a formal latency session.
+
+The following actual K32 retry passed device initialization but failed the
+strict zero-allocation check when releasing B0. No completed model row was
+returned. Its failure and unchanged-source receipts are retained; allocator
+cleanup diagnosis is ongoing without changing the workload or resource limit.
+
+The integrated UUID/source-lineage/text-boundary CPU regression passed 136
+focused tests and 1257 full tests, two existing skips and 39 subtests in
+413.42 seconds with source unchanged. Its CUDA API stand-ins are explicitly
+software tests, not a substitute for the separate actual device observation.
+
 ## Qualification assembly
 
 `assemble_base_cohort_qualification` accepts the resolved cohort, admission,
diff --git a/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md b/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
index 14e24d3..fa4d7bf 100644
--- a/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
+++ b/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
@@ -116,9 +116,14 @@ The complete integrated source then passed 1228 tests, two existing skips and
 not licensed-data training, CUDA performance measurements or retrieval-quality
 evidence.
 
-Training initialization, the actual prepared-row context selector, complete
-epoch-cache census, and checkpoint/resume consumption still require explicit
-integration. The model checks actual batch bytes but does not invent source
-window ordinals or silently select a different epoch/yaw context. Host
-preparation must supply the correct already verified reader handle. No real-data
-cache, acceleration, measured throughput or model-quality result is claimed.
+The [prepared-v2 source](PREPARED_DATA_V2.md) now derives immutable descriptor
+contexts from the actual consumed NPZ, split manifest, global window ordinal,
+seed, epoch and applied yaw. Legacy batches retain `None`, and a contextualized
+batch cannot be rotated again while keeping stale yaw metadata.
+
+Training initialization, admitted cache-row selection, complete epoch-cache
+census, and checkpoint/resume consumption still require explicit integration.
+The model checks actual batch bytes but does not invent source window ordinals
+or silently select a different epoch/yaw context. Host preparation must supply
+the correct already verified reader handle. No real-data cache, acceleration,
+measured throughput or model-quality result is claimed.
diff --git a/docs/PREPARED_DATA_V2.md b/docs/PREPARED_DATA_V2.md
index ca8fb09..7270b6a 100644
--- a/docs/PREPARED_DATA_V2.md
+++ b/docs/PREPARED_DATA_V2.md
@@ -67,6 +67,27 @@ Validation is unrotated. Masks, lineage, positive families, caption identity,
 and frozen text values do not change. This storage-based path is not claimed
 bitwise equal to rotating earlier, before preprocessing's final float32 cast.
 
+## Descriptor source context
+
+Actual returned `RetrievalTrainingBatch` values now carry an optional immutable
+`descriptor_contexts` tuple, one exact context per motion row. Prepared v2
+derives it from the same verified split-manifest bytes, consumed NPZ digest,
+global ordinal and motion-window identity used to load that row. Train contexts
+record the actual seed, epoch and exact float64 yaw bits. Validation keeps its
+historical unrotated iteration behavior while contexts use epoch zero and
+exact positive-zero yaw. Legacy batches default to `None`.
+
+Batch validation requires matching row identities/split, one common source,
+seed and epoch, and unique window identities/ordinals. The raw rotation helper
+rejects an already contextualized batch before converting arrays, preventing
+second rotation with stale first-rotation lineage. Numeric or padding changes
+remain distinct complete-batch cache digests even when lineage is unchanged.
+
+These contexts are integrity metadata, not cache admission, data rights or
+proof of execution. Epoch-complete cache lookup, training replay and checkpoint
+binding are separate unfinished layers; main holistic capture validation has
+its own source lineage and is not replaced by this window source.
+
 ## Provenance and integration boundary
 
 The private build census binds index/batch/window/source/text digests and the
@@ -103,3 +124,22 @@ creation, and invalid/replaced manifests and ZIP payloads. They are regression
 evidence, not real-data experiments or scientific results. The final combined
 host integration passed 94 focused tests and a separate complete Linux server
 suite of 964 tests, 2 skipped, and 39 subtests, with unchanged source bytes.
+
+Later source-lineage tests cover all three official seeds and all 20 residual
+epochs against the actual returned rotated values, canonical validation,
+legacy behavior and altered lineage/padding. The 89-test focused integration
+passed on the Linux server. A full integration exposed one old negative
+fixture whose deliberately changed family was now rejected earlier by the
+new batch invariant. Its context family was changed consistently to exercise
+the original caption-role rejection; the production validator and expected
+error were unchanged. The subsequent combined suite, including text-boundary
+and CUDA-identity contracts, passed 136 focused tests, then 1257 full tests,
+two existing skips and 39 subtests in 413.42 seconds, source unchanged.
+
+The live official CLIP adapter's imported training-boundary source pin was
+updated to the reviewed source bytes. An actual server call reproduced the
+earlier embedding bytes exactly, using four/two text rows in three chunks;
+CPU Torch RNG was unchanged and repeated provenance validation made no new
+encode call. Receipts retain new source identities without relabeling old
+receipts. Analytic motion and explicitly fabricated fusion fixtures in that
+check do not establish semantic caption quality or licensed main data.
diff --git a/src/phaseset_core/base_cohort_latency.py b/src/phaseset_core/base_cohort_latency.py
index 4b92402..8a06b4c 100644
--- a/src/phaseset_core/base_cohort_latency.py
+++ b/src/phaseset_core/base_cohort_latency.py
@@ -14,6 +14,7 @@
 import hashlib
 import json
 import math
+import re
 import struct
 import subprocess
 import time
@@ -56,6 +57,9 @@
 MINIMUM_FREE_MIB: Final = 8192
 CUDA_ALLOCATOR_LIMIT_BYTES: Final = 2 * 1024**3
 _ACTOR_DOMAIN: Final = b"phaseset-base-latency-actor-v1\n"
+_CUDA_UUID_PAYLOAD: Final = re.compile(
+    r"[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}"
+)
 
 
 class BaseCohortLatencyError(ValueError):
@@ -86,6 +90,33 @@ def _lower_sha256(value: object, label: str) -> str:
     return value
 
 
+def _prefixed_cuda_uuid(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or not value.startswith("GPU-")
+        or _CUDA_UUID_PAYLOAD.fullmatch(value[4:]) is None
+    ):
+        raise BaseCohortLatencyError(
+            f"{label} must be GPU- plus one canonical lowercase CUDA UUID"
+        )
+    return value
+
+
+def _torch_cuda_uuid(value: object) -> str:
+    try:
+        rendered = str(value)
+    except Exception as error:
+        raise BaseCohortLatencyError(
+            "Torch device UUID cannot be rendered as a canonical CUDA UUID"
+        ) from error
+    payload = rendered[4:] if rendered.startswith("GPU-") else rendered
+    if _CUDA_UUID_PAYLOAD.fullmatch(payload) is None:
+        raise BaseCohortLatencyError(
+            "Torch device UUID is not a canonical lowercase CUDA UUID"
+        )
+    return f"GPU-{payload}"
+
+
 def _positive_int(value: object, label: str) -> int:
     if type(value) is not int or not 1 <= value <= (1 << 63) - 1:
         raise BaseCohortLatencyError(f"{label} must be an exact positive int")
@@ -130,8 +161,7 @@ class SharedCudaObservation:
 
     def __post_init__(self) -> None:
         _positive_int(self.captured_unix_ns, "captured_unix_ns")
-        if type(self.device_uuid) is not str or not self.device_uuid.startswith("GPU-"):
-            raise BaseCohortLatencyError("CUDA observation UUID must be an exact GPU UUID")
+        _prefixed_cuda_uuid(self.device_uuid, "CUDA observation UUID")
         _positive_int(self.memory_total_mib, "memory_total_mib")
         _nonnegative_int(self.memory_free_mib, "memory_free_mib")
         _nonnegative_int(self.memory_used_mib, "memory_used_mib")
@@ -192,8 +222,7 @@ def __post_init__(self) -> None:
             raise BaseCohortLatencyError("latency runtime device is invalid") from error
         if device.type != "cuda":
             raise BaseCohortLatencyError("formal latency runtime must be CUDA")
-        if type(self.device_uuid) is not str or not self.device_uuid.startswith("GPU-"):
-            raise BaseCohortLatencyError("latency runtime UUID is invalid")
+        _prefixed_cuda_uuid(self.device_uuid, "latency runtime UUID")
         for name in (
             "device_name",
             "torch_version",
@@ -501,10 +530,7 @@ class BaseCohortLatencySession:
     status: str = HOLD_STATUS
 
     def __post_init__(self) -> None:
-        if type(self.registered_cuda_uuid) is not str or not self.registered_cuda_uuid.startswith(
-            "GPU-"
-        ):
-            raise BaseCohortLatencyError("session CUDA UUID is invalid")
+        _prefixed_cuda_uuid(self.registered_cuda_uuid, "session CUDA UUID")
         _lower_sha256(self.resolved_cohort_sha256, "resolved_cohort_sha256")
         _lower_sha256(self.scored_cohort_sha256, "scored_cohort_sha256")
         if type(self.workload_receipt) is not BaseLatencyWorkloadReceipt:
@@ -732,7 +758,10 @@ class _NvidiaSmiTorchRuntime:
     __slots__ = ("_uuid",)
 
     def __init__(self, registered_cuda_uuid: str) -> None:
-        self._uuid = registered_cuda_uuid
+        self._uuid = _prefixed_cuda_uuid(
+            registered_cuda_uuid,
+            "registered CUDA UUID",
+        )
 
     def snapshot(self) -> SharedCudaObservation:
         query = (
@@ -786,7 +815,8 @@ def initialize(
             raise BaseCohortLatencyError("formal latency CUDA device is unavailable")
         index = torch.cuda.current_device() if device.index is None else device.index
         properties = torch.cuda.get_device_properties(index)
-        observed_uuid = str(getattr(properties, "uuid", ""))
+        observed_uuid = _torch_cuda_uuid(getattr(properties, "uuid", None))
+        _prefixed_cuda_uuid(admission_observation.device_uuid, "admission CUDA UUID")
         if observed_uuid != self._uuid or admission_observation.device_uuid != self._uuid:
             raise BaseCohortLatencyError("Torch and NVIDIA device UUIDs differ")
         fraction = CUDA_ALLOCATOR_LIMIT_BYTES / int(properties.total_memory)
@@ -1276,8 +1306,7 @@ def run_base_cohort_latency_session(
         admission,
         scored_cohort,
     )
-    if type(registered_cuda_uuid) is not str or not registered_cuda_uuid.startswith("GPU-"):
-        raise BaseCohortLatencyError("registered_cuda_uuid must be an exact GPU UUID")
+    _prefixed_cuda_uuid(registered_cuda_uuid, "registered_cuda_uuid")
     if observer is not None and not isinstance(observer, LatencySessionObserver):
         raise BaseCohortLatencyError("latency observer does not implement the typed interface")
     device = torch.device(checked_admission.device)
diff --git a/src/phaseset_core/frozen_clip_text.py b/src/phaseset_core/frozen_clip_text.py
index 1a140cd..f1d41ba 100644
--- a/src/phaseset_core/frozen_clip_text.py
+++ b/src/phaseset_core/frozen_clip_text.py
@@ -157,8 +157,8 @@
     ),
     (
         "phaseset_core.training",
-        157_160,
-        "9f6c21e354a17facda89a542d53e39013477c5e1b1a259781e2446cd394f6d06",
+        159_407,
+        "0b91f3a5f1f4eea3d95c98bf899e12fb7de28394046468a05a24eaf2b6c55491",
     ),
 )
 
diff --git a/src/phaseset_core/prepared_data_v2.py b/src/phaseset_core/prepared_data_v2.py
index 65a2dd3..1b64cae 100644
--- a/src/phaseset_core/prepared_data_v2.py
+++ b/src/phaseset_core/prepared_data_v2.py
@@ -7,8 +7,9 @@
 The ten-array NPZ payload is an unaugmented canonical float32 window.  A v2
 split manifest binds one independent global uint64 window ordinal per motion.
 The training source rereads the authenticated NPZ for every epoch and applies
-the existing group_yaw_v2 angle once from (seed, epoch, ordinal).  Validation
-is never augmented.
+the existing group_yaw_v2 angle once from (seed, epoch, ordinal), then attaches
+the matching immutable descriptor contexts to that exact returned batch.
+Validation is never augmented and uses canonical epoch zero/+0 yaw contexts.
 """
 from __future__ import annotations
 
@@ -39,6 +40,7 @@
     deterministic_group_yaw,
     edge_budget_batches,
 )
+from phaseset_core.periodic_descriptor_cache_v2 import DescriptorWindowContext
 from phaseset_core.training import RetrievalTrainingBatch
 
 
@@ -861,6 +863,61 @@ def _load_npz_training_batch(
     return _arrays_to_training_batch(arrays, split=split)
 
 
+def _descriptor_contexts(
+    batch: RetrievalTrainingBatch,
+    *,
+    prepared_manifest_sha256: str,
+    source_batch_sha256: str,
+    window_ordinals: tuple[int, ...],
+    seed: int,
+    epoch: int,
+) -> tuple[DescriptorWindowContext, ...]:
+    if len(window_ordinals) != batch.motion_count:
+        raise PreparedDataV2Error("window ordinal count differs from motion count")
+    context_epoch = epoch if batch.split == "train" else 0
+    contexts = []
+    for row, (positive_id, ordinal) in enumerate(
+        zip(batch.motion_positive_ids, window_ordinals, strict=True)
+    ):
+        yaw = (
+            deterministic_group_yaw(seed=seed, epoch=epoch, window_ordinal=ordinal)
+            if batch.split == "train"
+            else 0.0
+        )
+        try:
+            context = DescriptorWindowContext.from_yaw(
+                prepared_manifest_sha256=prepared_manifest_sha256,
+                source_batch_sha256=source_batch_sha256,
+                window_sha256=positive_id.hex(),
+                window_ordinal=ordinal,
+                split=batch.split,
+                seed=seed,
+                epoch=context_epoch,
+                augmentation_yaw=yaw,
+            )
+        except (TypeError, ValueError) as error:
+            raise PreparedDataV2Error(
+                f"prepared descriptor context {row} is invalid"
+            ) from error
+        contexts.append(context)
+    return tuple(contexts)
+
+
+def _with_descriptor_contexts(
+    batch: RetrievalTrainingBatch,
+    contexts: tuple[DescriptorWindowContext, ...],
+) -> RetrievalTrainingBatch:
+    return RetrievalTrainingBatch(
+        groups=batch.groups,
+        text_embeddings=batch.text_embeddings,
+        motion_positive_ids=batch.motion_positive_ids,
+        text_positive_ids=batch.text_positive_ids,
+        text_commitments=batch.text_commitments,
+        split=batch.split,
+        descriptor_contexts=contexts,
+    )
+
+
 def _rotate_training_batch(
     batch: RetrievalTrainingBatch,
     *,
@@ -868,6 +925,10 @@ def _rotate_training_batch(
     epoch: int,
     ordinals: tuple[int, ...],
 ) -> RetrievalTrainingBatch:
+    if batch.descriptor_contexts is not None:
+        raise PreparedDataV2Error(
+            "a batch with descriptor contexts cannot be rotated again"
+        )
     groups = batch.groups
     if len(ordinals) != groups.batch_size:
         raise PreparedDataV2Error("window ordinal count differs from motion count")
@@ -904,6 +965,7 @@ def _rotate_training_batch(
         text_positive_ids=batch.text_positive_ids,
         text_commitments=batch.text_commitments,
         split=batch.split,
+        descriptor_contexts=batch.descriptor_contexts,
     )
 
 
@@ -1031,14 +1093,23 @@ def iter_epoch(self, *, epoch: int, seed: int):
                 max_decoded_bytes=self._max_decoded_bytes,
             )
             if self.split == "train":
-                yield _rotate_training_batch(
+                prepared = _rotate_training_batch(
                     original,
                     seed=checked_seed,
                     epoch=checked_epoch,
                     ordinals=entry.window_ordinals,
                 )
             else:
-                yield original
+                prepared = original
+            contexts = _descriptor_contexts(
+                prepared,
+                prepared_manifest_sha256=self.manifest_sha256,
+                source_batch_sha256=entry.sha256,
+                window_ordinals=entry.window_ordinals,
+                seed=checked_seed,
+                epoch=checked_epoch,
+            )
+            yield _with_descriptor_contexts(prepared, contexts)
 
 
 def load_prepared_training_sources_v2(
diff --git a/src/phaseset_core/training.py b/src/phaseset_core/training.py
index 6465ff4..b09813b 100644
--- a/src/phaseset_core/training.py
+++ b/src/phaseset_core/training.py
@@ -59,6 +59,7 @@
     build_group_base,
 )
 from .objectives import PhaseSetRetrievalHead, variable_positive_symmetric_infonce
+from .periodic_descriptor_cache_v2 import DescriptorWindowContext
 
 
 STATUS: Final = "DATA_FREE_EXECUTABLE_TRAINING_RUNTIME_NONPRODUCTION_AUTHORITY0"
@@ -137,7 +138,9 @@ class RetrievalTrainingBatch:
     """One edge-budget microbatch with frozen text semantics.
 
     Positive-family identifiers are opaque commitments used only to construct
-    the variable-positive mask.  They never enter either neural tower.
+    the variable-positive mask.  They never enter either neural tower.  An
+    optional descriptor context tuple preserves authenticated prepared-source
+    lineage; its presence alone grants no cache, data, or execution authority.
     """
 
     groups: PreparedGroupBatch
@@ -146,6 +149,7 @@ class RetrievalTrainingBatch:
     text_positive_ids: tuple[bytes, ...]
     text_commitments: tuple[bytes, ...]
     split: Split
+    descriptor_contexts: tuple[DescriptorWindowContext, ...] | None = None
 
     def __post_init__(self) -> None:
         checked = validate_prepared_group_batch(self.groups)
@@ -199,6 +203,39 @@ def __post_init__(self) -> None:
             raise TrainingRuntimeError("every motion must have a positive text in its microbatch")
         if any(value not in motions for value in texts):
             raise TrainingRuntimeError("every text must have a positive motion in its microbatch")
+        contexts = self.descriptor_contexts
+        if contexts is not None:
+            if type(contexts) is not tuple or len(contexts) != checked.batch_size:
+                raise TypeError(
+                    "descriptor_contexts must be None or an exact tuple covering every motion"
+                )
+            if any(type(context) is not DescriptorWindowContext for context in contexts):
+                raise TypeError(
+                    "descriptor_contexts must contain exact DescriptorWindowContext values"
+                )
+            first = contexts[0]
+            for index, context in enumerate(contexts):
+                if context.window_sha256 != motions[index].hex():
+                    raise TrainingRuntimeError(
+                        "descriptor window identity differs from its motion positive family"
+                    )
+                if context.split != self.split:
+                    raise TrainingRuntimeError(
+                        "descriptor context split differs from its training batch"
+                    )
+                if (
+                    context.prepared_manifest_sha256 != first.prepared_manifest_sha256
+                    or context.source_batch_sha256 != first.source_batch_sha256
+                    or context.seed != first.seed
+                    or context.epoch != first.epoch
+                ):
+                    raise TrainingRuntimeError(
+                        "one training batch cannot mix descriptor source, seed, or epoch"
+                    )
+            if len({context.window_sha256 for context in contexts}) != len(contexts):
+                raise TrainingRuntimeError("descriptor contexts repeat a window identity")
+            if len({context.window_ordinal for context in contexts}) != len(contexts):
+                raise TrainingRuntimeError("descriptor contexts repeat a window ordinal")
         # Snapshot text values so a host iterator cannot mutate an in-flight epoch.
         frozen_text = self.text_embeddings.detach().clone(memory_format=torch.contiguous_format)
         object.__setattr__(self, "groups", checked)
@@ -206,6 +243,7 @@ def __post_init__(self) -> None:
         object.__setattr__(self, "motion_positive_ids", motions)
         object.__setattr__(self, "text_positive_ids", texts)
         object.__setattr__(self, "text_commitments", text_commitments)
+        object.__setattr__(self, "descriptor_contexts", contexts)
 
     @property
     def motion_count(self) -> int:
diff --git a/tests/test_caption_role_provenance.py b/tests/test_caption_role_provenance.py
index 6997a3e..8588e67 100644
--- a/tests/test_caption_role_provenance.py
+++ b/tests/test_caption_role_provenance.py
@@ -402,10 +402,16 @@ def mutant(self, *, epoch: int, seed: int):
         wrong = _raw("wrong-positive-family")
         motion_ids = (wrong, *first.motion_positive_ids[1:])
         text_ids = tuple(wrong if value == old else value for value in first.text_positive_ids)
+        assert first.descriptor_contexts is not None
+        contexts = (
+            replace(first.descriptor_contexts[0], window_sha256=wrong.hex()),
+            *first.descriptor_contexts[1:],
+        )
         yield replace(
             first,
             motion_positive_ids=motion_ids,
             text_positive_ids=text_ids,
+            descriptor_contexts=contexts,
         )
         yield from batches[1:]
 
diff --git a/tests/test_periodic_cache_source_lineage.py b/tests/test_periodic_cache_source_lineage.py
new file mode 100644
index 0000000..6b92d31
--- /dev/null
+++ b/tests/test_periodic_cache_source_lineage.py
@@ -0,0 +1,366 @@
+"""Static-candidate tests for prepared-data descriptor source lineage.
+
+The fixtures are analytic arrays and sealed synthetic text embeddings.  They
+do not execute CLIP, SMPL-X, a training model, or a descriptor encoder.
+"""
+
+from __future__ import annotations
+
+from dataclasses import replace
+import hashlib
+import json
+import math
+from pathlib import Path
+import struct
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import frozen_clip_text as clip
+from phaseset_core import prepared_data_v2 as prepared
+from phaseset_core import training
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+from phaseset_core.periodic_descriptor_cache_v2 import (
+    DescriptorWindowContext,
+    prepared_group_batch_sha256,
+)
+from phaseset_core.pipeline import PreparedGroupSample, deterministic_group_yaw
+from phaseset_core.preprocessing import WindowDecision
+
+
+_MAX_BYTES = 32 * 1024 * 1024
+
+
+def _digest(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _sample(label: str, *, actors: int) -> PreparedGroupSample:
+    commitments = tuple(sorted(_digest(f"{label}/actor/{index}") for index in range(actors)))
+    skeletons = np.zeros((actors, 200, 22, 3), dtype=np.float32)
+    grid = np.arange(200, dtype=np.float32)
+    for actor in range(actors):
+        for joint in range(22):
+            skeletons[actor, :, joint, 0] = grid * np.float32(0.01) + actor
+            skeletons[actor, :, joint, 1] = np.float32(joint * 0.03)
+            skeletons[actor, :, joint, 2] = grid * np.float32(-0.004) + actor * 2
+    track = np.ones((actors, 200, 22), dtype=np.bool_)
+    return PreparedGroupSample(
+        skeletons=np.ascontiguousarray(skeletons),
+        track_mask=track,
+        actor_commitments=commitments,
+        group_commitment=group_commitment(commitments),
+        source_sha256=hashlib.sha256((label + "/source").encode("ascii")).hexdigest(),
+        window_sha256=hashlib.sha256((label + "/window").encode("ascii")).hexdigest(),
+        source_start_frame=0,
+        augmentation_yaw=0.0,
+        decision=WindowDecision(True, "ACCEPT", 300, actors, 0, 1.0, 0, False),
+    )
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().contiguous().view(torch.uint8).numpy().tobytes()
+
+
+def _text_batch(label: str, count: int) -> clip.FrozenClipTextBatch:
+    values = torch.arange(count * 512, dtype=torch.float32).reshape(count, 512)
+    embeddings = (torch.sin(values * 0.001) + len(label)).contiguous()
+    commitments = tuple(_digest(f"{label}/caption/{index}") for index in range(count))
+    rows = tuple(
+        (
+            index,
+            commitment.hex(),
+            hashlib.sha256(f"{label}/text/{index}".encode("ascii")).hexdigest(),
+            3,
+            3,
+            False,
+            hashlib.sha256(f"{label}/tokens/{index}".encode("ascii")).hexdigest(),
+            hashlib.sha256(f"{label}/mask/{index}".encode("ascii")).hexdigest(),
+            hashlib.sha256(_tensor_bytes(embeddings[index : index + 1])).hexdigest(),
+        )
+        for index, commitment in enumerate(commitments)
+    )
+    output = _tensor_bytes(embeddings)
+    receipt = clip.FrozenClipTextReceipt(
+        batch_size=count,
+        caption_rows=rows,
+        chunk_ranges=((0, count),),
+        frozen_embedding_cache_key_sha256="1" * 64,
+        live_model_manifest_sha256="2" * 64,
+        output_bytes=len(output),
+        output_sha256=hashlib.sha256(output).hexdigest(),
+        output_shape=tuple(embeddings.shape),
+        output_stride=tuple(embeddings.stride()),
+        runtime_identity=(("fixture", "server-only"),),
+        runtime_manifest_sha256="3" * 64,
+        snapshot_files=(),
+        snapshot_manifest_sha256="4" * 64,
+        source_files=(),
+        source_manifest_sha256="5" * 64,
+    )
+    return clip.FrozenClipTextBatch(
+        embeddings,
+        commitments,
+        receipt,
+        _seal=clip._CONSTRUCTION_SEAL,
+    )
+
+
+def _block(
+    label: str,
+    counts: tuple[int, ...],
+    ordinals: tuple[int, ...],
+    *,
+    actors: tuple[int, ...],
+) -> prepared.PreparedMotionTextBlock:
+    samples = tuple(
+        _sample(f"{label}/{index}", actors=actor_count) for index, actor_count in enumerate(actors)
+    )
+    return prepared.PreparedMotionTextBlock(
+        samples=samples,
+        text_batch=_text_batch(label, sum(counts)),
+        text_counts=counts,
+        window_ordinals=ordinals,
+    )
+
+
+def _write_tree(root: Path) -> prepared.PreparedDataBuildResult:
+    return prepared.write_prepared_training_tree_v2(
+        root,
+        train_blocks=(_block("train", (1, 3), (10, 11), actors=(2, 3)),),
+        val_blocks=(_block("val", (2,), (20,), actors=(2,)),),
+        max_total_edges=4,
+        max_batch_size=2,
+        max_batch_bytes=_MAX_BYTES,
+        max_decoded_batch_bytes=_MAX_BYTES,
+    )
+
+
+def _load_sources(root: Path):
+    result = _write_tree(root)
+    sources = prepared.load_prepared_training_sources_v2(
+        result.index_path,
+        expected_index_sha256=result.index_sha256,
+        max_batch_bytes=_MAX_BYTES,
+        max_decoded_batch_bytes=_MAX_BYTES,
+    )
+    return result, *sources
+
+
+def _expected_rotation(source: np.ndarray, yaws: tuple[float, ...], arrays) -> np.ndarray:
+    expected = np.empty(source.shape, dtype=np.float64)
+    for row, yaw in enumerate(yaws):
+        cosine = math.cos(yaw)
+        sine = math.sin(yaw)
+        expected[row, ..., 0] = cosine * source[row, ..., 0] + sine * source[row, ..., 2]
+        expected[row, ..., 1] = source[row, ..., 1]
+        expected[row, ..., 2] = -sine * source[row, ..., 0] + cosine * source[row, ..., 2]
+    result = np.ascontiguousarray(expected, dtype=np.float32)
+    valid = (
+        arrays["actor_mask"][:, :, None, None]
+        & arrays["frame_mask"][:, None, :, None]
+        & arrays["track_mask"]
+    )
+    result[~np.broadcast_to(valid[..., None], result.shape)] = np.float32(0.0)
+    return result
+
+
+def test_all_formal_train_epochs_bind_actual_source_and_returned_yaw(tmp_path: Path) -> None:
+    result, train_source, _ = _load_sources(tmp_path / "prepared")
+    manifest = json.loads((result.root / "train.json").read_text(encoding="ascii"))
+    assert len(manifest["batches"]) == 1
+    row = manifest["batches"][0]
+    source_path = result.root / row["path"]
+    assert hashlib.sha256(source_path.read_bytes()).hexdigest() == row["sha256"]
+    with np.load(source_path, allow_pickle=False) as archive:
+        arrays = {name: np.array(archive[name], copy=True) for name in archive.files}
+    source = np.asarray(arrays["skeletons"], dtype=np.float64)
+
+    for seed in training.OFFICIAL_SEEDS:
+        for epoch in range(training.RESIDUAL_EPOCHS):
+            (batch,) = tuple(train_source.iter_epoch(epoch=epoch, seed=seed))
+            contexts = batch.descriptor_contexts
+            assert contexts is not None
+            assert len(contexts) == batch.motion_count == 2
+            assert tuple(context.window_ordinal for context in contexts) == (10, 11)
+            yaws = tuple(
+                deterministic_group_yaw(
+                    seed=seed,
+                    epoch=epoch,
+                    window_ordinal=context.window_ordinal,
+                )
+                for context in contexts
+            )
+            for index, (context, yaw) in enumerate(zip(contexts, yaws, strict=True)):
+                assert type(context) is DescriptorWindowContext
+                assert context.prepared_manifest_sha256 == train_source.manifest_sha256
+                assert context.source_batch_sha256 == row["sha256"]
+                assert context.window_sha256 == batch.motion_positive_ids[index].hex()
+                assert (context.split, context.seed, context.epoch) == (
+                    "train",
+                    seed,
+                    epoch,
+                )
+                assert context.augmentation_yaw_be_hex == struct.pack(">d", yaw).hex()
+            assert np.array_equal(
+                batch.groups.skeletons,
+                _expected_rotation(source, yaws, arrays),
+            )
+
+
+def test_validation_is_unaugmented_with_canonical_epoch_zero_context(tmp_path: Path) -> None:
+    result, _, val_source = _load_sources(tmp_path / "prepared")
+    manifest = json.loads((result.root / "val.json").read_text(encoding="ascii"))
+    row = manifest["batches"][0]
+    first = tuple(val_source.iter_epoch(epoch=0, seed=1729))[0]
+    historical_call = tuple(val_source.iter_epoch(epoch=29, seed=31415))[0]
+    with np.load(result.root / row["path"], allow_pickle=False) as archive:
+        assert np.array_equal(first.groups.skeletons, archive["skeletons"])
+        assert np.array_equal(historical_call.groups.skeletons, archive["skeletons"])
+    for batch, seed in ((first, 1729), (historical_call, 31415)):
+        assert batch.descriptor_contexts is not None
+        (context,) = batch.descriptor_contexts
+        assert context.prepared_manifest_sha256 == val_source.manifest_sha256
+        assert context.source_batch_sha256 == row["sha256"]
+        assert context.window_sha256 == batch.motion_positive_ids[0].hex()
+        assert (context.window_ordinal, context.split, context.seed, context.epoch) == (
+            20,
+            "val",
+            seed,
+            0,
+        )
+        assert context.augmentation_yaw_be_hex == "0000000000000000"
+
+
+def test_batch_rejects_mixed_or_inconsistent_descriptor_contexts(tmp_path: Path) -> None:
+    _, train_source, _ = _load_sources(tmp_path / "prepared")
+    batch = tuple(train_source.iter_epoch(epoch=0, seed=1729))[0]
+    assert batch.descriptor_contexts is not None
+    first, second = batch.descriptor_contexts
+
+    with pytest.raises(TypeError, match="exact tuple"):
+        replace(batch, descriptor_contexts=list(batch.descriptor_contexts))
+    with pytest.raises(TypeError, match="exact DescriptorWindowContext"):
+        replace(batch, descriptor_contexts=(first, object()))
+    with pytest.raises(training.TrainingRuntimeError, match="window identity"):
+        replace(
+            batch,
+            descriptor_contexts=(first, replace(second, window_sha256="f" * 64)),
+        )
+    with pytest.raises(training.TrainingRuntimeError, match="source, seed, or epoch"):
+        replace(
+            batch,
+            descriptor_contexts=(first, replace(second, source_batch_sha256="e" * 64)),
+        )
+    duplicate_ordinal = DescriptorWindowContext.from_yaw(
+        prepared_manifest_sha256=second.prepared_manifest_sha256,
+        source_batch_sha256=second.source_batch_sha256,
+        window_sha256=second.window_sha256,
+        window_ordinal=first.window_ordinal,
+        split="train",
+        seed=second.seed,
+        epoch=second.epoch,
+        augmentation_yaw=deterministic_group_yaw(
+            seed=second.seed,
+            epoch=second.epoch,
+            window_ordinal=first.window_ordinal,
+        ),
+    )
+    with pytest.raises(training.TrainingRuntimeError, match="repeat.*ordinal"):
+        replace(batch, descriptor_contexts=(first, duplicate_ordinal))
+    val_context = DescriptorWindowContext.from_yaw(
+        prepared_manifest_sha256=first.prepared_manifest_sha256,
+        source_batch_sha256=first.source_batch_sha256,
+        window_sha256=first.window_sha256,
+        window_ordinal=first.window_ordinal,
+        split="val",
+        seed=first.seed,
+        epoch=0,
+        augmentation_yaw=0.0,
+    )
+    with pytest.raises(training.TrainingRuntimeError, match="split differs"):
+        replace(batch, descriptor_contexts=(val_context, second))
+
+
+def _changed_numeric(groups: PreparedGroupBatch) -> PreparedGroupBatch:
+    skeletons = np.array(groups.skeletons, copy=True, order="C")
+    skeletons[0, 0, 0, 0, 0] += np.float32(0.125)
+    return PreparedGroupBatch(
+        skeletons=skeletons,
+        actor_mask=np.array(groups.actor_mask, copy=True, order="C"),
+        frame_mask=np.array(groups.frame_mask, copy=True, order="C"),
+        track_mask=np.array(groups.track_mask, copy=True, order="C"),
+        actor_commitments=groups.actor_commitments,
+        group_commitments=groups.group_commitments,
+    )
+
+
+def _extra_padding(groups: PreparedGroupBatch) -> PreparedGroupBatch:
+    batch_size, padded_actors, time, joints, coordinates = groups.skeletons.shape
+    skeletons = np.zeros(
+        (batch_size, padded_actors + 1, time, joints, coordinates),
+        dtype=np.float32,
+    )
+    skeletons[:, :padded_actors] = groups.skeletons
+    actor_mask = np.zeros((batch_size, padded_actors + 1), dtype=np.bool_)
+    actor_mask[:, :padded_actors] = groups.actor_mask
+    track_mask = np.zeros(
+        (batch_size, padded_actors + 1, time, joints),
+        dtype=np.bool_,
+    )
+    track_mask[:, :padded_actors] = groups.track_mask
+    return PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(skeletons),
+        actor_mask=np.ascontiguousarray(actor_mask),
+        frame_mask=np.array(groups.frame_mask, copy=True, order="C"),
+        track_mask=np.ascontiguousarray(track_mask),
+        actor_commitments=tuple(row + (None,) for row in groups.actor_commitments),
+        group_commitments=groups.group_commitments,
+    )
+
+
+def test_numeric_and_padding_changes_preserve_lineage_but_change_complete_batch_key(
+    tmp_path: Path,
+) -> None:
+    _, train_source, _ = _load_sources(tmp_path / "prepared")
+    batch = tuple(train_source.iter_epoch(epoch=3, seed=2718))[0]
+    original_digest = prepared_group_batch_sha256(batch.groups)
+    numeric = replace(batch, groups=_changed_numeric(batch.groups))
+    padded = replace(batch, groups=_extra_padding(batch.groups))
+    assert numeric.descriptor_contexts == batch.descriptor_contexts
+    assert padded.descriptor_contexts == batch.descriptor_contexts
+    assert prepared_group_batch_sha256(numeric.groups) != original_digest
+    assert prepared_group_batch_sha256(padded.groups) != original_digest
+
+
+def test_legacy_batch_defaults_to_none_and_contextualized_batch_cannot_rotate_again(
+    tmp_path: Path,
+) -> None:
+    _, train_source, _ = _load_sources(tmp_path / "prepared")
+    batch = tuple(train_source.iter_epoch(epoch=2, seed=31415))[0]
+    legacy = training.RetrievalTrainingBatch(
+        batch.groups,
+        batch.text_embeddings,
+        batch.motion_positive_ids,
+        batch.text_positive_ids,
+        batch.text_commitments,
+        batch.split,
+    )
+    assert legacy.descriptor_contexts is None
+    legacy_rotated = prepared._rotate_training_batch(
+        legacy,
+        seed=31415,
+        epoch=3,
+        ordinals=(10, 11),
+    )
+    assert legacy_rotated.descriptor_contexts is None
+
+    for seed, epoch in ((31415, 2), (1729, 19)):
+        with pytest.raises(prepared.PreparedDataV2Error, match="cannot be rotated again"):
+            prepared._rotate_training_batch(
+                batch,
+                seed=seed,
+                epoch=epoch,
+                ordinals=(10, 11),
+            )
diff --git a/tests/test_phaseset_base_cohort_latency_uuid.py b/tests/test_phaseset_base_cohort_latency_uuid.py
new file mode 100644
index 0000000..55e2890
--- /dev/null
+++ b/tests/test_phaseset_base_cohort_latency_uuid.py
@@ -0,0 +1,205 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import pytest
+import torch
+from phaseset_core import base_cohort_latency as latency
+
+
+REGISTERED_UUID = "GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee"
+BARE_UUID = REGISTERED_UUID[4:]
+CHANGED_UUID = "GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeef"
+TOTAL_BYTES = 50_807_570_432
+
+
+@dataclass
+class _Properties:
+    uuid: object
+    name: str = "NVIDIA RTX A6000"
+    total_memory: int = TOTAL_BYTES
+
+
+class _CudaUuid:
+    """CPU-only stand-in for the observed torch._C._CUuuid string surface."""
+
+    def __init__(self, rendered: str) -> None:
+        self._rendered = rendered
+
+    def __str__(self) -> str:
+        return self._rendered
+
+
+def _admission(uuid: str = REGISTERED_UUID) -> latency.SharedCudaObservation:
+    return latency.SharedCudaObservation(
+        captured_unix_ns=1,
+        device_uuid=uuid,
+        memory_total_mib=49_140,
+        memory_free_mib=48_429,
+        memory_used_mib=23,
+        gpu_utilization_percent=0,
+        memory_utilization_percent=0,
+        compute_mode="Default",
+        driver_version="550.54.14",
+    )
+
+
+def _install_preallocation_cuda_surface(
+    monkeypatch: pytest.MonkeyPatch,
+    observed_uuid: object,
+) -> list[str]:
+    forbidden_calls: list[str] = []
+
+    def forbidden(label: str):
+        def call(*args: object, **kwargs: object) -> object:
+            del args, kwargs
+            forbidden_calls.append(label)
+            raise AssertionError(f"{label} ran before UUID rejection")
+
+        return call
+
+    monkeypatch.setattr(latency.torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(latency.torch.cuda, "current_device", lambda: 0)
+    monkeypatch.setattr(
+        latency.torch.cuda,
+        "get_device_properties",
+        lambda _index: _Properties(observed_uuid),
+    )
+    monkeypatch.setattr(
+        latency.torch.cuda,
+        "set_per_process_memory_fraction",
+        forbidden("allocator cap"),
+    )
+    monkeypatch.setattr(latency.torch, "empty", forbidden("tensor allocation"))
+    return forbidden_calls
+
+
+@pytest.mark.parametrize(
+    "observed_uuid",
+    [
+        None,
+        "",
+        "aaaaaaaa",
+        f" {BARE_UUID}",
+        f"{BARE_UUID} ",
+        f"MIG-{BARE_UUID}",
+        CHANGED_UUID[4:],
+        BARE_UUID.upper(),
+        f"GPU-{BARE_UUID.upper()}",
+        f"GPU-GPU-{BARE_UUID}",
+    ],
+)
+def test_observed_uuid_mutants_fail_before_allocator_or_tensor(
+    monkeypatch: pytest.MonkeyPatch,
+    observed_uuid: object,
+) -> None:
+    forbidden_calls = _install_preallocation_cuda_surface(monkeypatch, observed_uuid)
+    runtime = latency._NvidiaSmiTorchRuntime(REGISTERED_UUID)
+    with pytest.raises(latency.BaseCohortLatencyError, match="UUID"):
+        runtime.initialize(
+            device=torch.device("cuda"),
+            precision_mode="FP32",
+            admission_observation=_admission(),
+        )
+    assert forbidden_calls == []
+
+
+def test_changed_valid_admission_uuid_fails_before_allocator_or_tensor(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    forbidden_calls = _install_preallocation_cuda_surface(
+        monkeypatch,
+        _CudaUuid(BARE_UUID),
+    )
+    changed = CHANGED_UUID
+    runtime = latency._NvidiaSmiTorchRuntime(REGISTERED_UUID)
+    with pytest.raises(latency.BaseCohortLatencyError, match="differ"):
+        runtime.initialize(
+            device=torch.device("cuda"),
+            precision_mode="FP32",
+            admission_observation=_admission(changed),
+        )
+    assert forbidden_calls == []
+
+
+@pytest.mark.parametrize(
+    "observed_uuid",
+    [_CudaUuid(BARE_UUID), BARE_UUID, REGISTERED_UUID],
+)
+def test_canonical_bare_or_prefixed_uuid_preserves_allocator_cap_and_identity(
+    monkeypatch: pytest.MonkeyPatch,
+    observed_uuid: object,
+) -> None:
+    calls: list[tuple[str, object]] = []
+    monkeypatch.setattr(latency.torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(latency.torch.cuda, "current_device", lambda: 0)
+    monkeypatch.setattr(
+        latency.torch.cuda,
+        "get_device_properties",
+        lambda _index: _Properties(observed_uuid),
+    )
+
+    def set_fraction(value: float, *, device: int) -> None:
+        calls.append(("allocator", (value, device)))
+
+    def empty(
+        shape: tuple[int, ...],
+        *,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> object:
+        calls.append(("empty", (shape, dtype, device)))
+        return object()
+
+    monkeypatch.setattr(
+        latency.torch.cuda,
+        "set_per_process_memory_fraction",
+        set_fraction,
+    )
+    monkeypatch.setattr(latency.torch, "empty", empty)
+    monkeypatch.setattr(latency.torch.cuda, "synchronize", lambda device: None)
+    monkeypatch.setattr(latency.torch.cuda, "empty_cache", lambda: None)
+    monkeypatch.setattr(latency.torch.cuda, "memory_allocated", lambda index: 0)
+    monkeypatch.setattr(latency.torch.cuda, "memory_reserved", lambda index: 0)
+    monkeypatch.setattr(latency.torch.backends.cudnn, "version", lambda: 90_100)
+    monkeypatch.setattr(latency.torch.version, "cuda", "12.6")
+    monkeypatch.setattr(
+        latency.training_module,
+        "training_environment_sha256",
+        lambda: "a" * 64,
+    )
+
+    runtime = latency._NvidiaSmiTorchRuntime(REGISTERED_UUID).initialize(
+        device=torch.device("cuda"),
+        precision_mode="FP32",
+        admission_observation=_admission(),
+    )
+    assert runtime.device == "cuda:0"
+    assert runtime.device_uuid == REGISTERED_UUID
+    assert runtime.allocator_limit_bytes == latency.CUDA_ALLOCATOR_LIMIT_BYTES
+    assert runtime.torch_total_memory_bytes == TOTAL_BYTES
+    assert calls == [
+        (
+            "allocator",
+            (latency.CUDA_ALLOCATOR_LIMIT_BYTES / TOTAL_BYTES, 0),
+        ),
+        ("empty", ((1,), torch.float32, torch.device("cuda"))),
+    ]
+
+
+@pytest.mark.parametrize(
+    "registered_uuid",
+    [
+        BARE_UUID,
+        "",
+        f" {REGISTERED_UUID}",
+        f"{REGISTERED_UUID} ",
+        f"MIG-{BARE_UUID}",
+        CHANGED_UUID.upper(),
+    ],
+)
+def test_runtime_constructor_requires_full_registered_uuid(
+    registered_uuid: str,
+) -> None:
+    with pytest.raises(latency.BaseCohortLatencyError, match="UUID"):
+        latency._NvidiaSmiTorchRuntime(registered_uuid)

From 9a072e5663c23b6fbfa56658052c1150a4e957a8 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 15:05:07 -0500
Subject: [PATCH 20/95] Integrate capture cache lineage and verified CUDA
 workspace release

---
 RELEASE_FILES.sha256                          |  23 +-
 STATUS.md                                     |  23 +
 docs/BASE_COHORT_QUALIFICATION.md             |  38 +-
 docs/CAPTURE_PREPARED_STORAGE.md              |  24 +
 docs/CAPTURE_VALIDATION.md                    |   7 +
 docs/PERIODIC_DESCRIPTOR_CACHE_V2.md          |   9 +-
 docs/PERIODIC_TRAINING_CACHE_PLAN.md          |  58 ++
 src/phaseset_core/base_cohort_latency.py      |  17 +
 src/phaseset_core/capture_prepared_storage.py | 137 ++-
 src/phaseset_core/capture_validation.py       | 101 +-
 .../periodic_descriptor_cache_v2.py           |   6 +
 src/phaseset_core/periodic_training_cache.py  | 946 ++++++++++++++++++
 ...st_capture_validation_source_lineage_v3.py | 226 +++++
 tests/test_periodic_training_cache.py         | 611 +++++++++++
 ..._phaseset_base_cohort_latency_workspace.py | 191 ++++
 15 files changed, 2374 insertions(+), 43 deletions(-)
 create mode 100644 docs/PERIODIC_TRAINING_CACHE_PLAN.md
 create mode 100644 src/phaseset_core/periodic_training_cache.py
 create mode 100644 tests/test_capture_validation_source_lineage_v3.py
 create mode 100644 tests/test_periodic_training_cache.py
 create mode 100644 tests/test_phaseset_base_cohort_latency_workspace.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 6e26022..dd0505d 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,15 +11,15 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-62b6437093a41d3225a665a37c4ddb6cbd0541ea4ba9da5feb358f530e1643d7	26144	STATUS.md
+2c8e488dc4cc70f115641167dd6fc5e55b024a187fbbbb961139a98b57654c23	27678	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
-2a3fe097bbda8e7bd3434546e025446f963d4e369aed0ea8222bc2de1d562eec	6012	docs/BASE_COHORT_QUALIFICATION.md
+a269e19f1b058d560967befa723f34281728f28caa0ddc470c8c1612b48cb006	7991	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
 81ecc1cc742604f47833a0aa5b4c567165aa01e9c500d05157e325bd3b4ccdb3	4281	docs/CAPTION_ROLE_PROVENANCE.md
-a9d6a4875760c58a0075e9e4e6b7a5f147da9762410fde953d648895ec159a76	3398	docs/CAPTURE_PREPARED_STORAGE.md
-6d562907df5ae5a598401eb6ba622775ae03d4b81795671fe0c86942174e90e8	6781	docs/CAPTURE_VALIDATION.md
+62f6077a4d1cf0142e85ac28455a090d732b550874e47ea5644403566500f784	4644	docs/CAPTURE_PREPARED_STORAGE.md
+9906509270b1f307fadf0253f9ece494a9e4bd20b3c50d9769ace3e40771f4c9	7237	docs/CAPTURE_VALIDATION.md
 e9331b64297d6c4586ccb22022c972fad657ac2b3f9202203be62b067a38ca82	3686	docs/CUDA_LIFECYCLE_OBSERVATION.md
 e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA_PROTOCOL.md
 2a18716b57d21362672faaaf76944821764302064b7d7d931f9ec6a0d3f4c598	5564	docs/EMBODY_ADAPTER_INTEGRATION.md
@@ -32,7 +32,8 @@ f2c59540bfc4dcdbb5013b76eb20090a7dd61c805523121995ed9206c843f9c8	3711	docs/FROZE
 5dd898347d3646cde4a74e10afa8ba1d12703e3d0c6df51e5113659a31808086	3606	docs/LATENCY_PROGRESS_JOURNAL.md
 f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
-d36816ee6efeb999a909ef4f94b1173cbfe188428c37b814563f1445808850e8	7213	docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
+aaed799e60152b2ee9e30d76f8eaa62587c97b133ba57a0a5ea36117029e35cb	7570	docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
+75d5f35864a5884f3dd548652fcde47f48b6da11c6d8430049f142954ea71756	3042	docs/PERIODIC_TRAINING_CACHE_PLAN.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
 69b5e67cbb59c9c658e5feb11d3f6c01afbc5e2d26a1acd2d6dba8f81f9e67ff	4296	docs/PHASEPAIR_B0_ANNOTATION_CAPTION_CLOSURE_SUCCESSOR_PUBLIC_V4_20260825_145013.json
@@ -128,15 +129,15 @@ fa68bcab33d09588a8653151ad8e5741d5e6530263bcb633d871d7744598b194	10364	src/phase
 dbdb210202b1e2f5ade401f9fea488d948cc0f3d861fb8ab94a6fb3b50f68e33	20534	src/phasepair_core/validation_gallery.py
 2e5fb5280ed27a46c587d07a60ce663fce676cbdd30b059ec34882b4507d157f	604	src/phaseset_core/__init__.py
 9668a89535264a3ed6b58ffc7ac31b4904a4d8f68636d405e15cbf7368096716	8437	src/phaseset_core/_morlet_oracle_bytes.py
-64596c44649acda8f86b70ad43fcb26aa9a80accd962e2e9ef74277883c1ae29	71946	src/phaseset_core/base_cohort_latency.py
+d1aa8d6837a40ed062dc941a5052c25c6304b2f60e97782af306a83ef4078842	72640	src/phaseset_core/base_cohort_latency.py
 ecda879d3f531ebb747b561612fa34df37f1ecbd8e1977d5089d38b01ec63131	29076	src/phaseset_core/base_cohort_qualification.py
 d2d71766c1c6af95480d507960d7186bbec9ff8a424f6e79ce6611fd0aa2e611	38885	src/phaseset_core/base_cohort_resolver.py
 3d4f31b6a773081af41f1d9d45ee98986ea33ef6f954070564373f995af1f2e9	40593	src/phaseset_core/base_cohort_validation.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
 121dcb6b948b9a3a4878ed376b3692dcbbd4b19f7ea147a7d28341c64b77d296	36926	src/phaseset_core/caption_role_provenance.py
 d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
-3a60f7a68d9de9b0ded88274a631e1e4195b4d12689a60a7f9ae93401a36af16	44353	src/phaseset_core/capture_prepared_storage.py
-85e5e80bc3b9d21b5fb8865923d27cb6254c077ac2319afe38dad8b79d785bd2	34366	src/phaseset_core/capture_validation.py
+0eaf0057ccf6ab6a4b83c1fa6e649399c4f783a79fab301e0778bafb5699daa9	47086	src/phaseset_core/capture_prepared_storage.py
+3ef5fb44f1af4c9f1599178af3e5c3363091f4ec7838f03b189acf08dac00bce	38524	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
@@ -152,7 +153,8 @@ e76f255c27c310682ac363e3d0f4fa223b07b85522e607ab3a4ef111cd246332	92373	src/phase
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
-03fdc10250fd11ae111fc1813eb3200575f08ea301091963f3ee14425b51bd8d	55520	src/phaseset_core/periodic_descriptor_cache_v2.py
+10a3dcde2653e4ffa3eeaba06426736d47c9ebb2caa565cc0698df744658d249	55700	src/phaseset_core/periodic_descriptor_cache_v2.py
+8e36e1a62d55962e255d1d08d3608de0fcc31dbacc879051b8e1d88d8d3988c8	35602	src/phaseset_core/periodic_training_cache.py
 a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phaseset_core/pipeline.py
 5bd46d884f0fa3b8f022d1684ab5ffa1799ded3b970649753b0dd0510350216d	47614	src/phaseset_core/prepared_data_v2.py
 273225e6696351ccb16a3a9e7a653b55af18c64b316a4e5d80c7b265625cc5c1	17929	src/phaseset_core/preprocessing.py
@@ -165,12 +167,14 @@ a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phase
 0b91f3a5f1f4eea3d95c98bf899e12fb7de28394046468a05a24eaf2b6c55491	159407	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
 d5d69092cc271059400773ef68447e06068aa802d5e9c82193704c6b65c71f59	18827	tests/test_caption_role_provenance.py
+ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test_capture_validation_source_lineage_v3.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
 9a05b2a025a694f94e158d2de74242d2a820efbc8973920001895114e7a894fb	14880	tests/test_periodic_cache_source_lineage.py
 9c8ff961404bb8d5da86a30c3f533fe8d1909cdfbd76ebb496262ff222121d51	24769	tests/test_periodic_descriptor_cache_v2.py
+0397dbfdfc5f3cd356d437dfec762bcc498e9847eb1c30ae8f71f4139b4e9dd7	22302	tests/test_periodic_training_cache.py
 af9b5adc42d82b9a0aa4ed744bb069e36b7dd041c1957fdc97d17eb814a0fc0c	17284	tests/test_phasepair_core_base_qualification.py
 ad630675cc642350a988d92de6176053d1bfab5f81cbc49548cb0b5dbeb679d9	23596	tests/test_phasepair_core_batching_readiness.py
 3b4b3654d38d9b99983e924d838b7236b6d2e8b27554298762be1e8c4fcd00c4	10736	tests/test_phasepair_core_caption_processing.py
@@ -200,6 +204,7 @@ b801d46fb184d99c65825d7f9bf91cbced022418600a95d421c5d9244dbded13	19373	tests/tes
 6462129abd78cce74c4a2b21c50cefc7220b9092511dab13f5abb9b6277ea621	8467	tests/test_phaseset_base_cohort_checkpoint_state.py
 69a7e034f81c381d5eb4ce6caa02f98950975ebaaeed6ff3e212e4d4fe80d03f	15361	tests/test_phaseset_base_cohort_latency.py
 c34ac09fa80b4809db91f383b4f1ea79a609aa75e852aba91298fa2ca97e618c	6277	tests/test_phaseset_base_cohort_latency_uuid.py
+e1e944d235aa1e91965805edb522486edea8ce850bf89e6cb05d8678d62e5938	6119	tests/test_phaseset_base_cohort_latency_workspace.py
 1379f5e8d8bb11443615d14e6a14e4091319563cc4bfe67de0c4621f1e9476cb	13454	tests/test_phaseset_base_cohort_qualification.py
 41535795e088b5374bf59b95491afa4d1aac08622cddf1aa7d76c5fb0af990fa	23471	tests/test_phaseset_base_cohort_resolver.py
 2cd1a40ebf6c10c83dae4f72bf41821d25ee153c8b3f05819ac664eb0a8f611e	14477	tests/test_phaseset_base_cohort_score_witness.py
diff --git a/STATUS.md b/STATUS.md
index 0e91451..1523ab4 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -13,6 +13,29 @@ evidence, not training results or scientific claims.
 
 ## Server continuation
 
+- The combined capture-lineage, prepared-window cache-plan and CUDA-workspace
+  correction passed 157 focused and 1285 full Linux server tests, two existing
+  skips and 39 subtests, with source unchanged. A separate actual K=32 GPU
+  retry completed B0/B1/B2 at full registered width and T=200: valid outputs,
+  unchanged model state, and exact zero allocated/reserved memory after each
+  release. Peak allocations were 81,548,800 / 94,160,384 / 315,323,392 bytes,
+  below the unchanged 2 GiB cap. The probe used untrained models and one warmup
+  plus one observed forward per model; it is not the formal 81-visit latency
+  session, a trained cohort, retrieval evidence or a performance benchmark.
+  Earlier failed attempts remain retained. See
+  [CUDA lifecycle correction](docs/BASE_COHORT_QUALIFICATION.md).
+
+- Capture-storage v2 now derives every descriptor context from the consumed
+  manifest/NPZ and canonical global window ordinal while keeping v1 and full
+  holistic-gallery semantics intact. It passed 92 focused and 1265 full
+  server tests, two existing skips and 39 subtests, source unchanged.
+  A separate prepared-window periodic plan passed 76 combined tests, admitting
+  the exact twenty-epoch source/cache census for each registered seed and
+  reopening each selected shard without fallback. It is not the main-task
+  capture cache or an integrated training/checkpoint/resume implementation.
+  See [capture storage](docs/CAPTURE_PREPARED_STORAGE.md) and
+  [window cache plan](docs/PERIODIC_TRAINING_CACHE_PLAN.md).
+
 - Prepared-v2 batches now retain their exact descriptor source/window/epoch/yaw
   contexts. Repeated rotation of an already contextualized batch is rejected.
   The live CLIP training-source pin was updated and actual official text
diff --git a/docs/BASE_COHORT_QUALIFICATION.md b/docs/BASE_COHORT_QUALIFICATION.md
index 8473446..2ab0d8d 100644
--- a/docs/BASE_COHORT_QUALIFICATION.md
+++ b/docs/BASE_COHORT_QUALIFICATION.md
@@ -49,8 +49,42 @@ a completed K32 model run or a formal latency session.
 
 The following actual K32 retry passed device initialization but failed the
 strict zero-allocation check when releasing B0. No completed model row was
-returned. Its failure and unchanged-source receipts are retained; allocator
-cleanup diagnosis is ongoing without changing the workload or resource limit.
+returned. Its failure and unchanged-source receipts remain retained; the
+following diagnosis and correction did not change the workload or limit.
+
+A separate actual diagnostic then found that B0's warmup and observed forward
+returned without an earlier exception. All model parameters and buffers were
+on CPU, but one 32 MiB process-local cuBLAS workspace remained. Garbage
+collection did not remove it; the installed cuBLAS workspace-clear API reduced
+both allocated and reserved memory to zero. That diagnostic still rethrew the
+original failure and did not produce a completed latency row.
+
+The release path now explicitly synchronizes and clears this process's cuBLAS
+workspaces before emptying the allocator cache and applying the original exact
+zero guard. Missing, noncallable or failing cleanup APIs raise a typed error;
+real remaining allocations still reject release. Device selection, the 2 GiB
+cap, the 8 GiB threshold, warmups, timing boundaries and visit order are
+unchanged. This cleanup does not stop or alter any other process.
+
+The corrected release path, capture lineage and window cache plan passed 157
+focused and 1285 full server tests, two existing skips and 39 subtests in
+512.46 seconds, with source unchanged. A subsequent actual K32 GPU retry
+completed B0, B1 and B2 using untrained registered-width models, T=200, one
+warmup and one observed FP32 forward each. All outputs were finite CUDA
+`[1,512]` tensors, model state was unchanged and each production release passed
+the unchanged exact-zero allocated/reserved guard.
+
+| Model | Peak allocated bytes | Peak reserved bytes |
+| --- | ---: | ---: |
+| B0 ActorMean | 81,548,800 | 102,760,448 |
+| B1 SetPMA | 94,160,384 | 119,537,664 |
+| B2 SocialTemporal | 315,323,392 | 375,390,208 |
+
+These are bounded diagnostic resource observations under the original 2 GiB
+cap and 8 GiB free-memory admission. They do not constitute the formal
+81-visit/99-samples-per-row session, latency qualification rows, trained
+checkpoints or retrieval-quality results. No diagnostic duration is used as a
+qualification tie-break. Every earlier failed attempt remains retained.
 
 The integrated UUID/source-lineage/text-boundary CPU regression passed 136
 focused tests and 1257 full tests, two existing skips and 39 subtests in
diff --git a/docs/CAPTURE_PREPARED_STORAGE.md b/docs/CAPTURE_PREPARED_STORAGE.md
index 66f8212..61eed6f 100644
--- a/docs/CAPTURE_PREPARED_STORAGE.md
+++ b/docs/CAPTURE_PREPARED_STORAGE.md
@@ -25,6 +25,30 @@ authenticated preparation record. Reading a digest from the same untrusted
 artifact is not authentication. The writer requires a new root; existing
 artifacts are not overwritten.
 
+## Descriptor source lineage in v2
+
+`write_capture_validation_source_v2` writes the additive v2 schema. The same
+loader accepts both versions. V1 remains the default writer and restores no
+descriptor lineage; it rejects attempts to discard existing v2 lineage.
+
+For each v2 window, the loader derives `CaptureDescriptorWindowSource` from
+the exact consumed storage-manifest digest, the verified window NPZ digest,
+and its canonical global ordinal. Global ordinals cover all windows in sorted
+capture-commitment and capture-plan order. They are neither capture-local
+positions nor source-frame starts.
+
+`window.descriptor_context(seed=...)` produces that window's val/epoch-zero/
+positive-zero-yaw context. The source rejects mixed v1/v2 lineage, multiple
+storage manifests and any noncanonical ordinal census. The upstream scientific
+manifest and complete source census retain their existing meanings; the
+storage-manifest binding is separate to avoid a circular digest.
+
+The server integration passed 92 focused and 1265 full tests, two existing
+skips and 39 subtests, with source unchanged. These are storage/software
+fixtures, not access approval, human annotation verification or a real cache.
+
+## Existing storage and provenance guarantees
+
 The manifest binds exact capture/window/caption counts, complete window plans,
 the upstream manifest and source census, every numeric artifact, original text
 receipts, caption lineage, output feature hashes and embedding cache keys.
diff --git a/docs/CAPTURE_VALIDATION.md b/docs/CAPTURE_VALIDATION.md
index 5e367c8..2da6cc8 100644
--- a/docs/CAPTURE_VALIDATION.md
+++ b/docs/CAPTURE_VALIDATION.md
@@ -32,6 +32,13 @@ It canonicalizes capture and window order and checks duplicate/missing/foreign
 identities. Test cannot be relabeled through a split argument. Source plans,
 commitments, embeddings, captions, and component membership remain private.
 
+Optional [v2 storage lineage](CAPTURE_PREPARED_STORAGE.md) binds every window
+to its actual consumed manifest/NPZ and canonical global ordinal. A source
+must contain either no such lineage or a complete, single-manifest sequence.
+It does not change capture pooling, text order or gallery semantics, and it
+does not by itself connect descriptor caching to this evaluator. The separate
+prepared-window cache plan cannot replace holistic capture validation.
+
 ## Encoding and pooling
 
 The evaluator encodes every admitted window at B=1 with a registered retrieval
diff --git a/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md b/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
index fa4d7bf..cde5493 100644
--- a/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
+++ b/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
@@ -121,8 +121,13 @@ contexts from the actual consumed NPZ, split manifest, global window ordinal,
 seed, epoch and applied yaw. Legacy batches retain `None`, and a contextualized
 batch cannot be rotated again while keeping stale yaw metadata.
 
-Training initialization, admitted cache-row selection, complete epoch-cache
-census, and checkpoint/resume consumption still require explicit integration.
+The [prepared-window cache plan](PERIODIC_TRAINING_CACHE_PLAN.md) now admits
+an exact complete 20-epoch training census and one val/epoch-zero census for
+each official seed. Its selector reopens the authenticated reader on every
+request; the reader exposes only a read-only complete key-census tuple.
+This is an engineering window-validation seam, not holistic capture
+validation. Training initialization, complete-capture cache composition and
+checkpoint/resume consumption still require explicit integration.
 The model checks actual batch bytes but does not invent source window ordinals
 or silently select a different epoch/yaw context. Host preparation must supply
 the correct already verified reader handle. No real-data cache, acceleration,
diff --git a/docs/PERIODIC_TRAINING_CACHE_PLAN.md b/docs/PERIODIC_TRAINING_CACHE_PLAN.md
new file mode 100644
index 0000000..9d6532e
--- /dev/null
+++ b/docs/PERIODIC_TRAINING_CACHE_PLAN.md
@@ -0,0 +1,58 @@
+# Prepared-window periodic descriptor plans
+
+`admit_periodic_descriptor_training_plan` derives a sealed plan from exact
+formal-residual configuration, prepared-v2 train/val sources and authenticated
+`PeriodicDescriptorCacheV2` readers. It accepts no caller-authored plan digest,
+key subset or cache-miss fallback.
+
+This API is an engineering prepared-window seam. It does not implement the
+main task's holistic capture validation, start training, restore a checkpoint
+or establish data rights.
+
+## Complete requests, one seed at a time
+
+Admission enumerates the actual training source for epochs 0 through 19 and
+the actual prepared validation source once at epoch zero, using one of the
+three registered seeds. Every request binds its consumed manifest/NPZ,
+window identities and ordinals, exact yaw bits, full numeric batch including
+padding, edge count, energy floors and descriptor family.
+
+The keys actually required by those requests must exactly equal each reader's
+complete `cache_key_census`. Missing, additional, duplicate or corrupt rows
+reject admission. The edge budget is checked before shard opening, with the
+existing `ResourceLimitError`; no person or edge is silently removed.
+
+System 02 uses marginal-power descriptors, 03 uses mean/difference DCT, and
+04/06/07/08 use full relations. The registered model still applies incidence
+shuffling or phase stripping where required. Base systems and residual
+systems 01/05 reject this cache-plan API.
+
+## Stateless selection
+
+`plan.open_training_batch(batch)` and `plan.open_validation_batch(batch)`
+recompute the actual request identity, match one admitted row, revalidate the
+reader and complete key census, enforce the edge budget, and reopen the real
+shard before returning its sealed stream. Repeated selection is repeated
+verification, not an execution cursor or a cached learned output.
+
+The plan binds actual source contexts, not unobservable iterator call
+arguments. Prepared validation contexts are always epoch zero; a future
+training integration must explicitly request validation epoch zero as well.
+
+Admission preserves Python, NumPy, CPU Torch and already-initialized CUDA RNG
+states. It does not initialize CUDA to inspect its RNG. If an input source
+unexpectedly consumes randomness, the saved states are restored and admission
+fails. These checks are ordinary API integrity, not arbitrary-Python isolation.
+
+## Observed scope
+
+The combined plan/source/cache/model suite passed 76 tests in 184.61 seconds
+on the Linux experiment server, with source unchanged. Tests build actual
+descriptor artifacts from explicitly analytic prepared-data fixtures and
+exercise all three seeds, twenty epochs, family mapping, closed census,
+resource order, request mutants and repeated reader opens.
+
+No optimization, trained checkpoint, real-data cache, speedup, formal result or
+holistic validation is claimed by that observation. Complete-capture plans,
+the retrieval runtime's cached entry, initialization/report/checkpoint binding
+and resume remain distinct integration work.
diff --git a/src/phaseset_core/base_cohort_latency.py b/src/phaseset_core/base_cohort_latency.py
index 8a06b4c..3fcae0a 100644
--- a/src/phaseset_core/base_cohort_latency.py
+++ b/src/phaseset_core/base_cohort_latency.py
@@ -882,6 +882,23 @@ def validate_output(self, output: object, device: torch.device) -> None:
     def release(self, system: training_module.BaseRetrievalSystem, device: torch.device) -> None:
         system.to(torch.device("cpu"))
         torch.cuda.synchronize(device)
+        try:
+            clear_cublas_workspaces = torch._C._cuda_clearCublasWorkspaces
+        except AttributeError as error:
+            raise BaseCohortLatencyError(
+                "Torch CUDA cuBLAS workspace clear API is unavailable"
+            ) from error
+        if not callable(clear_cublas_workspaces):
+            raise BaseCohortLatencyError(
+                "Torch CUDA cuBLAS workspace clear API is unavailable"
+            )
+        try:
+            clear_cublas_workspaces()
+        except Exception as error:
+            raise BaseCohortLatencyError(
+                "Torch CUDA cuBLAS workspace clear failed"
+            ) from error
+        torch.cuda.synchronize(device)
         torch.cuda.empty_cache()
         if torch.cuda.memory_allocated(device) != 0 or torch.cuda.memory_reserved(device) != 0:
             raise BaseCohortLatencyError(
diff --git a/src/phaseset_core/capture_prepared_storage.py b/src/phaseset_core/capture_prepared_storage.py
index 08d2eb6..7152316 100644
--- a/src/phaseset_core/capture_prepared_storage.py
+++ b/src/phaseset_core/capture_prepared_storage.py
@@ -23,6 +23,7 @@
 
 from .capture_pooling import CaptureWindowPlan
 from .capture_validation import (
+    CaptureDescriptorWindowSource,
     CaptureValidationCapture,
     CaptureValidationSource,
     CaptureValidationWindow,
@@ -32,7 +33,9 @@
 
 
 AUTHORITY: Final = 0
-SCHEMA: Final = "phaseset-capture-prepared-storage-v1"
+SCHEMA_V1: Final = "phaseset-capture-prepared-storage-v1"
+SCHEMA_V2: Final = "phaseset-capture-prepared-storage-v2"
+SCHEMA: Final = SCHEMA_V1
 MANIFEST_NAME: Final = "capture-validation-manifest.json"
 WINDOW_KEYS: Final = (
     "skeletons",
@@ -582,16 +585,19 @@ def _add_materialized(total: int, amount: int, limits: CaptureStorageLimits) ->
     return result
 
 
-def write_capture_validation_source(
+def _write_capture_validation_source(
     root: Path,
     source: CaptureValidationSource,
     *,
-    limits: CaptureStorageLimits = CaptureStorageLimits(),
+    limits: CaptureStorageLimits,
+    schema: str,
 ) -> CaptureStorageBuildResult:
-    """Write one new immutable artifact tree and return its exact receipt."""
+    """Write one new immutable artifact tree under an exact storage schema."""
 
     if type(limits) is not CaptureStorageLimits:
         raise CapturePreparedStorageError("limits must be exact CaptureStorageLimits")
+    if schema not in (SCHEMA_V1, SCHEMA_V2):
+        raise CapturePreparedStorageError("storage writer schema is not registered")
     checked = _snapshot_source(source)
     counts = _count_source(checked)
     _check_counts(counts, limits)
@@ -610,6 +616,7 @@ def write_capture_validation_source(
 
     capture_rows: list[dict[str, object]] = []
     total = 0
+    next_window_ordinal = 0
     for capture_index, capture in enumerate(checked.captures):
         window_rows: list[dict[str, object]] = []
         for window_index, (window, source_start) in enumerate(
@@ -645,15 +652,17 @@ def write_capture_validation_source(
             _groups_from_arrays(rebuilt)
             if rebuilt_bytes != decoded:
                 raise CapturePreparedStorageError("window decoded-byte census changed")
-            window_rows.append(
-                {
-                    "decoded_bytes": decoded,
-                    "path": relative,
-                    "sha256": _sha256(consumed),
-                    "source_start_frame": source_start,
-                    "window_commitment": window.window_commitment.hex(),
-                }
-            )
+            window_row = {
+                "decoded_bytes": decoded,
+                "path": relative,
+                "sha256": _sha256(consumed),
+                "source_start_frame": source_start,
+                "window_commitment": window.window_commitment.hex(),
+            }
+            if schema == SCHEMA_V2:
+                window_row["window_ordinal"] = next_window_ordinal
+            window_rows.append(window_row)
+            next_window_ordinal += 1
 
         expected_text_decoded = capture.holistic_text.receipt.output_bytes
         if (
@@ -737,7 +746,7 @@ def write_capture_validation_source(
             "captures": counts[0],
             "windows": counts[1],
         },
-        "schema": SCHEMA,
+        "schema": schema,
         "source_census_sha256": checked.census_sha256,
         "split": "val",
         "upstream_manifest_sha256": checked.manifest_sha256,
@@ -766,6 +775,46 @@ def write_capture_validation_source(
     )
 
 
+def write_capture_validation_source(
+    root: Path,
+    source: CaptureValidationSource,
+    *,
+    limits: CaptureStorageLimits = CaptureStorageLimits(),
+) -> CaptureStorageBuildResult:
+    """Write the unchanged v1 storage format for uncached validation."""
+
+    if type(source) is CaptureValidationSource and any(
+        window.descriptor_source is not None
+        for capture in source.captures
+        for window in capture.windows
+    ):
+        raise CapturePreparedStorageError(
+            "v1 storage cannot discard v2 descriptor source lineage"
+        )
+    return _write_capture_validation_source(
+        root,
+        source,
+        limits=limits,
+        schema=SCHEMA_V1,
+    )
+
+
+def write_capture_validation_source_v2(
+    root: Path,
+    source: CaptureValidationSource,
+    *,
+    limits: CaptureStorageLimits = CaptureStorageLimits(),
+) -> CaptureStorageBuildResult:
+    """Write v2 with canonical global descriptor-window ordinals."""
+
+    return _write_capture_validation_source(
+        root,
+        source,
+        limits=limits,
+        schema=SCHEMA_V2,
+    )
+
+
 def _manifest_counts(value: object, limits: CaptureStorageLimits) -> tuple[int, int, int]:
     if type(value) is not dict:
         raise CapturePreparedStorageError("manifest counts must be an object")
@@ -805,7 +854,8 @@ def load_capture_validation_source(
         ),
         label="storage manifest",
     )
-    if _sha256(manifest_raw) != expected:
+    consumed_manifest_sha256 = _sha256(manifest_raw)
+    if consumed_manifest_sha256 != expected:
         raise CapturePreparedStorageError("consumed storage manifest SHA-256 mismatch")
     manifest = _parse_json(manifest_raw, "storage manifest")
     _closed(
@@ -821,10 +871,12 @@ def load_capture_validation_source(
         },
         "storage manifest",
     )
+    storage_schema = manifest["schema"]
     if (
         type(manifest["authority"]) is not int
         or manifest["authority"] != AUTHORITY
-        or manifest["schema"] != SCHEMA
+        or type(storage_schema) is not str
+        or storage_schema not in (SCHEMA_V1, SCHEMA_V2)
         or manifest["split"] != "val"
     ):
         raise CapturePreparedStorageError("storage manifest literals are invalid")
@@ -840,6 +892,7 @@ def load_capture_validation_source(
     observed_windows = 0
     observed_captions = 0
     prior_capture_id: bytes | None = None
+    next_window_ordinal = 0
     for capture_index, raw_capture in enumerate(rows):
         if type(raw_capture) is not dict:
             raise CapturePreparedStorageError("capture row must be an object")
@@ -879,17 +932,16 @@ def load_capture_validation_source(
         for window_index, raw_window in enumerate(window_rows):
             if type(raw_window) is not dict:
                 raise CapturePreparedStorageError("window row must be an object")
-            _closed(
-                raw_window,
-                {
-                    "decoded_bytes",
-                    "path",
-                    "sha256",
-                    "source_start_frame",
-                    "window_commitment",
-                },
-                "window row",
-            )
+            window_keys = {
+                "decoded_bytes",
+                "path",
+                "sha256",
+                "source_start_frame",
+                "window_commitment",
+            }
+            if storage_schema == SCHEMA_V2:
+                window_keys.add("window_ordinal")
+            _closed(raw_window, window_keys, "window row")
             decoded_expected = _positive_int(
                 raw_window["decoded_bytes"], "window decoded bytes"
             )
@@ -905,7 +957,11 @@ def load_capture_validation_source(
             seen_paths.add(expected_relative)
             path = _resolve_under(root, expected_relative, "window NPZ")
             raw = _read_bounded(path, maximum=limits.max_file_bytes, label="window NPZ")
-            if _sha256(raw) != _lower_sha256(raw_window["sha256"], "window SHA-256"):
+            source_window_npz_sha256 = _lower_sha256(
+                raw_window["sha256"],
+                "window SHA-256",
+            )
+            if _sha256(raw) != source_window_npz_sha256:
                 raise CapturePreparedStorageError("consumed window NPZ SHA-256 mismatch")
             arrays, decoded = _decode_npz(
                 raw,
@@ -926,7 +982,30 @@ def load_capture_validation_source(
             )
             window_ids.append(window_id)
             starts.append(_uint64(raw_window["source_start_frame"], "window start"))
-            windows.append(CaptureValidationWindow(window_id, _groups_from_arrays(arrays)))
+            if storage_schema == SCHEMA_V2:
+                window_ordinal = _uint64(
+                    raw_window["window_ordinal"],
+                    "window ordinal",
+                )
+                if window_ordinal != next_window_ordinal:
+                    raise CapturePreparedStorageError(
+                        "window ordinals must equal canonical global order"
+                    )
+                descriptor_source = CaptureDescriptorWindowSource(
+                    storage_manifest_sha256=consumed_manifest_sha256,
+                    source_window_npz_sha256=source_window_npz_sha256,
+                    window_ordinal=window_ordinal,
+                )
+            else:
+                descriptor_source = None
+            next_window_ordinal += 1
+            windows.append(
+                CaptureValidationWindow(
+                    window_id,
+                    _groups_from_arrays(arrays),
+                    descriptor_source,
+                )
+            )
 
         try:
             plan = CaptureWindowPlan(capture_id, tuple(window_ids), tuple(starts))
diff --git a/src/phaseset_core/capture_validation.py b/src/phaseset_core/capture_validation.py
index e4e0124..7b49aa0 100644
--- a/src/phaseset_core/capture_validation.py
+++ b/src/phaseset_core/capture_validation.py
@@ -36,6 +36,7 @@
 )
 from .frozen_clip_text import FrozenClipTextBatch, FrozenClipTextReceipt
 from .objectives import variable_positive_symmetric_infonce
+from .periodic_descriptor_cache_v2 import DescriptorWindowContext
 from .training import (
     BaseRetrievalSystem,
     ResidualRetrievalSystem,
@@ -203,12 +204,44 @@ def _validate_text_batch(
     return embeddings.detach().clone().contiguous(), commitments, receipt_sha256
 
 
+@dataclass(frozen=True, slots=True, repr=False)
+class CaptureDescriptorWindowSource:
+    """Authenticated storage identities needed to resolve one descriptor row.
+
+    These values are integrity observations, not dataset or execution
+    authority.  The v2 storage loader populates them from bytes admitted under
+    its externally expected manifest digest.
+    """
+
+    storage_manifest_sha256: str
+    source_window_npz_sha256: str
+    window_ordinal: int
+
+    def __post_init__(self) -> None:
+        storage = _lower_sha256(
+            self.storage_manifest_sha256,
+            "storage_manifest_sha256",
+        )
+        source = _lower_sha256(
+            self.source_window_npz_sha256,
+            "source_window_npz_sha256",
+        )
+        if type(self.window_ordinal) is not int or not 0 <= self.window_ordinal < 2**64:
+            raise CaptureValidationError("window_ordinal must be an exact uint64")
+        object.__setattr__(self, "storage_manifest_sha256", storage)
+        object.__setattr__(self, "source_window_npz_sha256", source)
+
+
 @dataclass(frozen=True, slots=True, repr=False)
 class CaptureValidationWindow:
     """One actual accepted-window identity and its B=1 prepared motion row."""
 
     window_commitment: bytes
     groups: PreparedGroupBatch = field(repr=False)
+    descriptor_source: CaptureDescriptorWindowSource | None = field(
+        default=None,
+        repr=False,
+    )
 
     def __post_init__(self) -> None:
         window = _raw32(self.window_commitment, "window_commitment")
@@ -220,8 +253,40 @@ def __post_init__(self) -> None:
             raise CaptureValidationError("capture validation encodes exactly B=1 per window")
         if groups.skeletons.shape[2] != 200 or not bool(groups.frame_mask.all()):
             raise CaptureValidationError("capture validation requires complete 200-frame windows")
+        descriptor_source = self.descriptor_source
+        if descriptor_source is not None and type(descriptor_source) is not CaptureDescriptorWindowSource:
+            raise CaptureValidationError(
+                "descriptor_source must be None or exact CaptureDescriptorWindowSource"
+            )
         object.__setattr__(self, "window_commitment", window)
         object.__setattr__(self, "groups", groups)
+        object.__setattr__(self, "descriptor_source", descriptor_source)
+
+    def descriptor_context(self, *, seed: int) -> DescriptorWindowContext:
+        """Return the formal val context for an authenticated v2 storage row."""
+
+        if type(seed) is not int or not 0 <= seed < 2**64:
+            raise CaptureValidationError("descriptor seed must be an exact uint64")
+        source = self.descriptor_source
+        if type(source) is not CaptureDescriptorWindowSource:
+            raise CaptureValidationError(
+                "capture window has no authenticated descriptor source lineage"
+            )
+        try:
+            return DescriptorWindowContext.from_yaw(
+                prepared_manifest_sha256=source.storage_manifest_sha256,
+                source_batch_sha256=source.source_window_npz_sha256,
+                window_sha256=self.window_commitment.hex(),
+                window_ordinal=source.window_ordinal,
+                split="val",
+                seed=seed,
+                epoch=0,
+                augmentation_yaw=0.0,
+            )
+        except (TypeError, ValueError) as error:
+            raise CaptureValidationError(
+                "capture descriptor context could not be constructed"
+            ) from error
 
     def __repr__(self) -> str:
         return "CaptureValidationWindow(B=1, <identity/features hidden>)"
@@ -259,7 +324,13 @@ def __post_init__(self) -> None:
         for value in self.windows:
             if type(value) is not CaptureValidationWindow:
                 raise CaptureValidationError("capture window type is invalid")
-            rebuilt.append(CaptureValidationWindow(value.window_commitment, value.groups))
+            rebuilt.append(
+                CaptureValidationWindow(
+                    value.window_commitment,
+                    value.groups,
+                    value.descriptor_source,
+                )
+            )
         by_identity = {item.window_commitment: item for item in rebuilt}
         if (
             len(by_identity) != len(rebuilt)
@@ -396,8 +467,16 @@ def __post_init__(self) -> None:
             raise CaptureValidationError("capture identities must be globally unique")
         seen_windows: set[bytes] = set()
         seen_captions: set[bytes] = set()
+        descriptor_rows: list[CaptureDescriptorWindowSource] = []
+        window_count = 0
         for capture in ordered:
             windows = set(capture.plan.window_commitments)
+            window_count += len(capture.windows)
+            descriptor_rows.extend(
+                window.descriptor_source
+                for window in capture.windows
+                if window.descriptor_source is not None
+            )
             _, captions, _ = _validate_text_batch(capture.holistic_text)
             caption_set = set(captions)
             if seen_windows.intersection(windows):
@@ -406,6 +485,25 @@ def __post_init__(self) -> None:
                 raise CaptureValidationError("caption identities must be globally unique")
             seen_windows.update(windows)
             seen_captions.update(caption_set)
+        if descriptor_rows:
+            if len(descriptor_rows) != window_count:
+                raise CaptureValidationError(
+                    "capture source cannot mix v1 and v2 window lineage"
+                )
+            descriptor_manifest = descriptor_rows[0].storage_manifest_sha256
+            if any(
+                row.storage_manifest_sha256 != descriptor_manifest
+                for row in descriptor_rows
+            ):
+                raise CaptureValidationError(
+                    "capture descriptor rows must share one storage manifest"
+                )
+            if tuple(row.window_ordinal for row in descriptor_rows) != tuple(
+                range(window_count)
+            ):
+                raise CaptureValidationError(
+                    "capture descriptor ordinals must equal canonical global order"
+                )
         object.__setattr__(self, "manifest_sha256", manifest)
         object.__setattr__(self, "captures", ordered)
 
@@ -815,6 +913,7 @@ def _verified_capture_validation_bindings():
 
 
 __all__ = [
+    "CaptureDescriptorWindowSource",
     "CaptureValidationCapture",
     "CaptureValidationError",
     "CaptureValidationResourceLimit",
diff --git a/src/phaseset_core/periodic_descriptor_cache_v2.py b/src/phaseset_core/periodic_descriptor_cache_v2.py
index a57c315..e19aca1 100644
--- a/src/phaseset_core/periodic_descriptor_cache_v2.py
+++ b/src/phaseset_core/periodic_descriptor_cache_v2.py
@@ -1354,6 +1354,12 @@ def __init__(
         self.index_sha256 = expected_index_sha256
         self._validate_tree_census()
 
+    @property
+    def cache_key_census(self) -> tuple[str, ...]:
+        """Return the closed sorted key census from the authenticated index."""
+
+        return tuple(self._rows)
+
     def _validate_tree_census(self) -> None:
         shards_path = self.root / "shards"
         _reject_symlink_components(shards_path, "descriptor shard directory")
diff --git a/src/phaseset_core/periodic_training_cache.py b/src/phaseset_core/periodic_training_cache.py
new file mode 100644
index 0000000..92f2f15
--- /dev/null
+++ b/src/phaseset_core/periodic_training_cache.py
@@ -0,0 +1,946 @@
+"""Sealed authority-zero cache plans for prepared-window residual training.
+
+This private candidate derives one exact plan from already-authenticated v2
+prepared sources and periodic descriptor readers.  It does not construct a
+model, authorize data, run training, or alter checkpoint/report schemas.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+import hashlib
+import json
+import random
+from types import MappingProxyType
+from typing import Final, Literal
+
+import numpy as np
+import torch
+
+from .controls import system_spec
+from .periodic import ResourceLimitError
+from .periodic_descriptor_cache_v2 import (
+    CachedDescriptorBatch,
+    CachedPairChunkStream,
+    DescriptorCacheSourceBinding,
+    DescriptorStreamKind,
+    DescriptorWindowContext,
+    PeriodicDescriptorCacheV2,
+    prepared_group_batch_sha256,
+)
+from .prepared_data_v2 import PreparedTrainingDataSourceV2
+from .training import (
+    OFFICIAL_SEEDS,
+    RESIDUAL_EPOCHS,
+    RetrievalTrainingBatch,
+    TrainingConfig,
+)
+
+
+AUTHORITY: Final = 0
+PRODUCTION: Final = False
+RESULT_CLAIMED: Final = False
+STATUS: Final = "PRIVATE_STATIC_CACHE_PLAN_CANDIDATE_UNEXECUTED"
+PLAN_SCHEMA: Final = "phaseset-periodic-training-cache-plan-v1"
+WINDOW_VALIDATION_SEMANTICS: Final = (
+    "prepared-window-engineering-only-not-holistic-capture-validation"
+)
+
+_PLAN_TOKEN: Final = object()
+_CACHEABLE_SYSTEMS: Final[Mapping[str, DescriptorStreamKind]] = MappingProxyType(
+    {
+        "02": "MARGINAL_POWER",
+        "03": "MEAN_DIFFERENCE_DCT",
+        "04": "FULL_RELATION",
+        "06": "FULL_RELATION",
+        "07": "FULL_RELATION",
+        "08": "FULL_RELATION",
+    }
+)
+_EXPECTED_POLICIES: Final[Mapping[str, str]] = MappingProxyType(
+    {
+        "02": "MARGINAL_POWER",
+        "03": "MEAN_DIFFERENCE_DCT",
+        "04": "FULL_RELATION",
+        "06": "SHUFFLED_INCIDENCE",
+        "07": "PHASE_STRIPPED",
+        "08": "FULL_RELATION",
+    }
+)
+
+Split = Literal["train", "val"]
+RequestIdentity = tuple[str, int, int, str, str, str, int, int]
+
+
+class PeriodicTrainingCacheError(ValueError):
+    """An exact training cache plan cannot be admitted or selected."""
+
+
+def _sha256(value: bytes) -> str:
+    return hashlib.sha256(value).hexdigest()
+
+
+def _canonical_json(value: object) -> bytes:
+    return (
+        json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True).encode("ascii")
+        + b"\n"
+    )
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise TypeError(f"{label} must be one lowercase SHA-256 digest")
+    return value
+
+
+def _source_binding_value(binding: DescriptorCacheSourceBinding) -> dict[str, str]:
+    return {
+        "environment_sha256": binding.environment_sha256,
+        "prepared_manifest_sha256": binding.prepared_manifest_sha256,
+        "source_tree_sha256": binding.source_tree_sha256,
+    }
+
+
+def _context_value(context: DescriptorWindowContext) -> dict[str, object]:
+    return {
+        "augmentation_yaw_be_hex": context.augmentation_yaw_be_hex,
+        "epoch": context.epoch,
+        "prepared_manifest_sha256": context.prepared_manifest_sha256,
+        "seed": context.seed,
+        "source_batch_sha256": context.source_batch_sha256,
+        "split": context.split,
+        "window_ordinal": context.window_ordinal,
+        "window_sha256": context.window_sha256,
+    }
+
+
+def _contexts_sha256(contexts: tuple[DescriptorWindowContext, ...]) -> str:
+    return _sha256(_canonical_json({"windows": [_context_value(row) for row in contexts]}))
+
+
+def _request_sha256(
+    *,
+    split: Split,
+    epoch: int,
+    seed: int,
+    source_batch_sha256: str,
+    contexts_sha256: str,
+    batch_input_sha256: str,
+    edge_count: int,
+    window_count: int,
+) -> str:
+    return _sha256(
+        _canonical_json(
+            {
+                "batch_input_sha256": batch_input_sha256,
+                "contexts_sha256": contexts_sha256,
+                "edge_count": edge_count,
+                "epoch": epoch,
+                "seed": seed,
+                "source_batch_sha256": source_batch_sha256,
+                "split": split,
+                "window_count": window_count,
+            }
+        )
+    )
+
+
+def _request_identity(
+    *,
+    split: Split,
+    epoch: int,
+    seed: int,
+    source_batch_sha256: str,
+    contexts_sha256: str,
+    batch_input_sha256: str,
+    edge_count: int,
+    window_count: int,
+) -> RequestIdentity:
+    return (
+        split,
+        epoch,
+        seed,
+        source_batch_sha256,
+        contexts_sha256,
+        batch_input_sha256,
+        edge_count,
+        window_count,
+    )
+
+
+@dataclass(frozen=True, slots=True)
+class PeriodicDescriptorPlanRow:
+    """One actual prepared-source request proven against one descriptor shard."""
+
+    split: Split
+    epoch: int
+    seed: int
+    execution_position: int
+    source_batch_sha256: str
+    contexts_sha256: str
+    batch_input_sha256: str
+    cache_key_sha256: str
+    request_sha256: str
+    edge_count: int
+    window_count: int
+
+    def __post_init__(self) -> None:
+        if self.split not in ("train", "val"):
+            raise PeriodicTrainingCacheError("plan row split must be train or val")
+        for name in ("epoch", "seed", "execution_position", "edge_count", "window_count"):
+            value = getattr(self, name)
+            if type(value) is not int or value < 0:
+                raise TypeError(f"{name} must be an exact nonnegative int")
+        if self.edge_count < 1 or self.window_count < 1:
+            raise PeriodicTrainingCacheError("plan rows require windows and edges")
+        for name in (
+            "source_batch_sha256",
+            "contexts_sha256",
+            "batch_input_sha256",
+            "cache_key_sha256",
+            "request_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        expected = _request_sha256(
+            split=self.split,
+            epoch=self.epoch,
+            seed=self.seed,
+            source_batch_sha256=self.source_batch_sha256,
+            contexts_sha256=self.contexts_sha256,
+            batch_input_sha256=self.batch_input_sha256,
+            edge_count=self.edge_count,
+            window_count=self.window_count,
+        )
+        if self.request_sha256 != expected:
+            raise PeriodicTrainingCacheError("plan row request digest is inconsistent")
+
+    def _value(self) -> dict[str, object]:
+        return {
+            "batch_input_sha256": self.batch_input_sha256,
+            "cache_key_sha256": self.cache_key_sha256,
+            "contexts_sha256": self.contexts_sha256,
+            "edge_count": self.edge_count,
+            "epoch": self.epoch,
+            "execution_position": self.execution_position,
+            "request_sha256": self.request_sha256,
+            "seed": self.seed,
+            "source_batch_sha256": self.source_batch_sha256,
+            "split": self.split,
+            "window_count": self.window_count,
+        }
+
+    def _identity(self) -> RequestIdentity:
+        return _request_identity(
+            split=self.split,
+            epoch=self.epoch,
+            seed=self.seed,
+            source_batch_sha256=self.source_batch_sha256,
+            contexts_sha256=self.contexts_sha256,
+            batch_input_sha256=self.batch_input_sha256,
+            edge_count=self.edge_count,
+            window_count=self.window_count,
+        )
+
+
+@dataclass(frozen=True, slots=True)
+class _RngSnapshot:
+    python_state: object
+    numpy_state: tuple[object, ...]
+    torch_cpu_state: torch.Tensor
+    cuda_initialized: bool
+    torch_cuda_states: tuple[torch.Tensor, ...]
+
+
+def _capture_rng() -> _RngSnapshot:
+    numpy_state = np.random.get_state()
+    frozen_numpy = (
+        numpy_state[0],
+        np.array(numpy_state[1], copy=True),
+        numpy_state[2],
+        numpy_state[3],
+        numpy_state[4],
+    )
+    cuda_initialized = torch.cuda.is_initialized()
+    cuda_states = (
+        tuple(state.clone() for state in torch.cuda.get_rng_state_all()) if cuda_initialized else ()
+    )
+    return _RngSnapshot(
+        python_state=random.getstate(),
+        numpy_state=frozen_numpy,
+        torch_cpu_state=torch.random.get_rng_state().clone(),
+        cuda_initialized=cuda_initialized,
+        torch_cuda_states=cuda_states,
+    )
+
+
+def _rng_matches(snapshot: _RngSnapshot) -> bool:
+    current_numpy = np.random.get_state()
+    if random.getstate() != snapshot.python_state:
+        return False
+    if (
+        current_numpy[0] != snapshot.numpy_state[0]
+        or not np.array_equal(current_numpy[1], snapshot.numpy_state[1])
+        or current_numpy[2:] != snapshot.numpy_state[2:]
+        or not torch.equal(torch.random.get_rng_state(), snapshot.torch_cpu_state)
+        or torch.cuda.is_initialized() is not snapshot.cuda_initialized
+    ):
+        return False
+    if not snapshot.cuda_initialized:
+        return True
+    current_cuda = tuple(torch.cuda.get_rng_state_all())
+    return len(current_cuda) == len(snapshot.torch_cuda_states) and all(
+        torch.equal(left, right)
+        for left, right in zip(current_cuda, snapshot.torch_cuda_states, strict=True)
+    )
+
+
+def _restore_rng(snapshot: _RngSnapshot) -> None:
+    random.setstate(snapshot.python_state)
+    np.random.set_state(snapshot.numpy_state)  # type: ignore[arg-type]
+    torch.random.set_rng_state(snapshot.torch_cpu_state)
+    if snapshot.cuda_initialized:
+        torch.cuda.set_rng_state_all(list(snapshot.torch_cuda_states))
+
+
+@dataclass(frozen=True, slots=True)
+class PeriodicDescriptorTrainingPlan:
+    """Sealed metadata plus readers for one exact formal residual seed."""
+
+    system_id: str
+    seed: int
+    epochs: int
+    stream_kind: DescriptorStreamKind
+    config_sha256: str
+    edge_budget: int
+    energy_floors_sha256: str
+    train_source_manifest_sha256: str
+    validation_source_manifest_sha256: str
+    train_cache_index_sha256: str
+    validation_cache_index_sha256: str
+    train_cache_source_binding: DescriptorCacheSourceBinding
+    validation_cache_source_binding: DescriptorCacheSourceBinding
+    train_cache_key_census: tuple[str, ...]
+    validation_cache_key_census: tuple[str, ...]
+    train_rows: tuple[PeriodicDescriptorPlanRow, ...]
+    validation_rows: tuple[PeriodicDescriptorPlanRow, ...]
+    _train_cache: PeriodicDescriptorCacheV2 = field(repr=False, compare=False)
+    _validation_cache: PeriodicDescriptorCacheV2 = field(repr=False, compare=False)
+    _train_lookup: Mapping[RequestIdentity, PeriodicDescriptorPlanRow] = field(
+        repr=False, compare=False
+    )
+    _validation_lookup: Mapping[RequestIdentity, PeriodicDescriptorPlanRow] = field(
+        repr=False, compare=False
+    )
+    _seal: object = field(repr=False, compare=False)
+
+    def __post_init__(self) -> None:
+        if self._seal is not _PLAN_TOKEN:
+            raise PeriodicTrainingCacheError(
+                "training cache plans must come from complete plan admission"
+            )
+        if self.system_id not in _CACHEABLE_SYSTEMS:
+            raise PeriodicTrainingCacheError("plan system is not cacheable")
+        if self.seed not in OFFICIAL_SEEDS or self.epochs != RESIDUAL_EPOCHS:
+            raise PeriodicTrainingCacheError("plan seed or epoch census changed")
+        if self.stream_kind != _CACHEABLE_SYSTEMS[self.system_id]:
+            raise PeriodicTrainingCacheError("plan stream differs from its system")
+        if type(self.edge_budget) is not int or self.edge_budget < 1:
+            raise TypeError("edge_budget must be an exact positive int")
+        for name in (
+            "config_sha256",
+            "energy_floors_sha256",
+            "train_source_manifest_sha256",
+            "validation_source_manifest_sha256",
+            "train_cache_index_sha256",
+            "validation_cache_index_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        for rows, split in ((self.train_rows, "train"), (self.validation_rows, "val")):
+            if (
+                type(rows) is not tuple
+                or not rows
+                or any(type(row) is not PeriodicDescriptorPlanRow for row in rows)
+                or any(row.split != split for row in rows)
+            ):
+                raise PeriodicTrainingCacheError("plan row census is invalid")
+        all_rows = self.train_rows + self.validation_rows
+        if tuple(row.execution_position for row in all_rows) != tuple(range(len(all_rows))):
+            raise PeriodicTrainingCacheError("plan execution positions are not canonical")
+        if len({row.cache_key_sha256 for row in self.train_rows}) != len(self.train_rows):
+            raise PeriodicTrainingCacheError("training plan repeats a cache key")
+        if len({row.cache_key_sha256 for row in self.validation_rows}) != len(self.validation_rows):
+            raise PeriodicTrainingCacheError("validation plan repeats a cache key")
+        if tuple(sorted(row.cache_key_sha256 for row in self.train_rows)) != (
+            self.train_cache_key_census
+        ):
+            raise PeriodicTrainingCacheError("training rows differ from cache key census")
+        if tuple(sorted(row.cache_key_sha256 for row in self.validation_rows)) != (
+            self.validation_cache_key_census
+        ):
+            raise PeriodicTrainingCacheError("validation rows differ from cache key census")
+        for census, label in (
+            (self.train_cache_key_census, "train"),
+            (self.validation_cache_key_census, "validation"),
+        ):
+            if (
+                type(census) is not tuple
+                or not census
+                or census != tuple(sorted(census))
+                or len(set(census)) != len(census)
+            ):
+                raise PeriodicTrainingCacheError(f"{label} cache key census is invalid")
+            for index, digest in enumerate(census):
+                _lower_sha256(digest, f"{label} cache key {index}")
+        if set(self._train_lookup) != {row._identity() for row in self.train_rows}:
+            raise PeriodicTrainingCacheError("training lookup differs from plan rows")
+        if set(self._validation_lookup) != {row._identity() for row in self.validation_rows}:
+            raise PeriodicTrainingCacheError("validation lookup differs from plan rows")
+        if any(self._train_lookup[row._identity()] != row for row in self.train_rows):
+            raise PeriodicTrainingCacheError("training lookup values differ from plan rows")
+        if any(self._validation_lookup[row._identity()] != row for row in self.validation_rows):
+            raise PeriodicTrainingCacheError("validation lookup values differ from plan rows")
+
+    @property
+    def authority(self) -> int:
+        return AUTHORITY
+
+    @property
+    def production(self) -> bool:
+        return PRODUCTION
+
+    @property
+    def result_claimed(self) -> bool:
+        return RESULT_CLAIMED
+
+    def canonical_bytes(self) -> bytes:
+        value = {
+            "authority": AUTHORITY,
+            "config_sha256": self.config_sha256,
+            "edge_budget": self.edge_budget,
+            "energy_floors_sha256": self.energy_floors_sha256,
+            "epochs": self.epochs,
+            "external_authentication_asserted": False,
+            "production": PRODUCTION,
+            "result_claimed": RESULT_CLAIMED,
+            "schema": PLAN_SCHEMA,
+            "seed": self.seed,
+            "stream_kind": self.stream_kind,
+            "system_id": self.system_id,
+            "train_cache_index_sha256": self.train_cache_index_sha256,
+            "train_cache_key_census": list(self.train_cache_key_census),
+            "train_cache_source_binding": _source_binding_value(self.train_cache_source_binding),
+            "train_rows": [row._value() for row in self.train_rows],
+            "train_source_manifest_sha256": self.train_source_manifest_sha256,
+            "validation_cache_index_sha256": self.validation_cache_index_sha256,
+            "validation_cache_key_census": list(self.validation_cache_key_census),
+            "validation_cache_source_binding": _source_binding_value(
+                self.validation_cache_source_binding
+            ),
+            "validation_rows": [row._value() for row in self.validation_rows],
+            "validation_source_manifest_sha256": (self.validation_source_manifest_sha256),
+            "window_validation_semantics": WINDOW_VALIDATION_SEMANTICS,
+        }
+        return _canonical_json(value)
+
+    @property
+    def sha256(self) -> str:
+        return _sha256(self.canonical_bytes())
+
+    def open_training_batch(self, batch: RetrievalTrainingBatch) -> CachedPairChunkStream:
+        """Reopen and verify one admitted training shard without fallback."""
+
+        return self._open_batch(
+            batch,
+            split="train",
+            cache=self._train_cache,
+            lookup=self._train_lookup,
+            manifest_sha256=self.train_source_manifest_sha256,
+            cache_index_sha256=self.train_cache_index_sha256,
+            source_binding=self.train_cache_source_binding,
+            cache_key_census=self.train_cache_key_census,
+        )
+
+    def open_validation_batch(self, batch: RetrievalTrainingBatch) -> CachedPairChunkStream:
+        """Reopen one admitted prepared-window validation shard without fallback."""
+
+        return self._open_batch(
+            batch,
+            split="val",
+            cache=self._validation_cache,
+            lookup=self._validation_lookup,
+            manifest_sha256=self.validation_source_manifest_sha256,
+            cache_index_sha256=self.validation_cache_index_sha256,
+            source_binding=self.validation_cache_source_binding,
+            cache_key_census=self.validation_cache_key_census,
+        )
+
+    def _open_batch(
+        self,
+        batch: RetrievalTrainingBatch,
+        *,
+        split: Split,
+        cache: PeriodicDescriptorCacheV2,
+        lookup: Mapping[RequestIdentity, PeriodicDescriptorPlanRow],
+        manifest_sha256: str,
+        cache_index_sha256: str,
+        source_binding: DescriptorCacheSourceBinding,
+        cache_key_census: tuple[str, ...],
+    ) -> CachedPairChunkStream:
+        checked, identity = _batch_identity(
+            batch,
+            split=split,
+            seed=self.seed,
+            expected_manifest_sha256=manifest_sha256,
+            epochs=self.epochs,
+        )
+        row = lookup.get(identity)
+        if row is None:
+            raise PeriodicTrainingCacheError("batch is not an exact admitted plan row")
+        _validate_reader_snapshot(
+            cache,
+            index_sha256=cache_index_sha256,
+            source_binding=source_binding,
+            floors_sha256=self.energy_floors_sha256,
+            cache_key_census=cache_key_census,
+        )
+        _enforce_edge_budget(row.edge_count, self.edge_budget)
+        contexts = checked.descriptor_contexts
+        assert contexts is not None
+        opened = cache.open_batch(checked.groups, contexts)
+        _validate_opened(
+            opened,
+            row=row,
+            contexts=contexts,
+            floors_sha256=self.energy_floors_sha256,
+        )
+        stream = opened.stream(self.stream_kind)
+        if type(stream) is not CachedPairChunkStream:
+            raise PeriodicTrainingCacheError("cache returned an unsealed descriptor stream")
+        return stream
+
+
+def _validate_reader_snapshot(
+    cache: PeriodicDescriptorCacheV2,
+    *,
+    index_sha256: str,
+    source_binding: DescriptorCacheSourceBinding,
+    floors_sha256: str,
+    cache_key_census: tuple[str, ...],
+) -> None:
+    if type(cache) is not PeriodicDescriptorCacheV2:
+        raise TypeError("cache must be an exact PeriodicDescriptorCacheV2")
+    if (
+        cache.index_sha256 != index_sha256
+        or type(cache.source_binding) is not DescriptorCacheSourceBinding
+        or cache.source_binding != source_binding
+        or cache.energy_floors_sha256 != floors_sha256
+        or cache.cache_key_census != cache_key_census
+    ):
+        raise PeriodicTrainingCacheError("cache reader metadata changed after admission")
+
+
+def _enforce_edge_budget(edge_count: int, edge_budget: int) -> None:
+    if edge_count > edge_budget:
+        raise ResourceLimitError(required_edges=edge_count, edge_budget=edge_budget)
+
+
+def _batch_identity(
+    batch: RetrievalTrainingBatch,
+    *,
+    split: Split,
+    seed: int,
+    expected_manifest_sha256: str,
+    epochs: int,
+) -> tuple[RetrievalTrainingBatch, RequestIdentity]:
+    if type(batch) is not RetrievalTrainingBatch:
+        raise TypeError("cache plan requires an exact RetrievalTrainingBatch")
+    if batch.split != split:
+        raise PeriodicTrainingCacheError("batch split differs from selector")
+    contexts = batch.descriptor_contexts
+    if (
+        type(contexts) is not tuple
+        or not contexts
+        or any(type(context) is not DescriptorWindowContext for context in contexts)
+    ):
+        raise PeriodicTrainingCacheError("batch lacks exact descriptor contexts")
+    first = contexts[0]
+    if (
+        first.prepared_manifest_sha256 != expected_manifest_sha256
+        or first.seed != seed
+        or first.split != split
+        or (split == "train" and not 0 <= first.epoch < epochs)
+        or (split == "val" and first.epoch != 0)
+    ):
+        raise PeriodicTrainingCacheError("batch descriptor context differs from the plan")
+    if any(
+        context.prepared_manifest_sha256 != expected_manifest_sha256
+        or context.seed != seed
+        or context.split != split
+        or context.epoch != first.epoch
+        or context.source_batch_sha256 != first.source_batch_sha256
+        for context in contexts
+    ):
+        raise PeriodicTrainingCacheError("batch mixes descriptor context identities")
+    edge_count = sum(count * (count - 1) // 2 for count in batch.groups.actor_counts)
+    if edge_count < 1:
+        raise PeriodicTrainingCacheError("descriptor cache rows require at least one edge")
+    contexts_digest = _contexts_sha256(contexts)
+    batch_digest = prepared_group_batch_sha256(batch.groups)
+    identity = _request_identity(
+        split=split,
+        epoch=first.epoch,
+        seed=seed,
+        source_batch_sha256=first.source_batch_sha256,
+        contexts_sha256=contexts_digest,
+        batch_input_sha256=batch_digest,
+        edge_count=edge_count,
+        window_count=len(contexts),
+    )
+    return batch, identity
+
+
+def _validate_opened(
+    opened: CachedDescriptorBatch,
+    *,
+    row: PeriodicDescriptorPlanRow,
+    contexts: tuple[DescriptorWindowContext, ...],
+    floors_sha256: str,
+) -> None:
+    if type(opened) is not CachedDescriptorBatch:
+        raise PeriodicTrainingCacheError("cache returned an unsealed descriptor batch")
+    if (
+        opened.cache_key_sha256 != row.cache_key_sha256
+        or opened.batch_input_sha256 != row.batch_input_sha256
+        or opened.energy_floors_sha256 != floors_sha256
+        or opened.contexts != contexts
+        or opened.edge_count != row.edge_count
+    ):
+        raise PeriodicTrainingCacheError("opened cache shard differs from its plan row")
+
+
+def _plan_row(
+    batch: RetrievalTrainingBatch,
+    opened: CachedDescriptorBatch,
+    *,
+    split: Split,
+    seed: int,
+    execution_position: int,
+) -> PeriodicDescriptorPlanRow:
+    contexts = batch.descriptor_contexts
+    assert contexts is not None
+    first = contexts[0]
+    contexts_digest = _contexts_sha256(contexts)
+    batch_digest = prepared_group_batch_sha256(batch.groups)
+    request_digest = _request_sha256(
+        split=split,
+        epoch=first.epoch,
+        seed=seed,
+        source_batch_sha256=first.source_batch_sha256,
+        contexts_sha256=contexts_digest,
+        batch_input_sha256=batch_digest,
+        edge_count=opened.edge_count,
+        window_count=len(contexts),
+    )
+    return PeriodicDescriptorPlanRow(
+        split=split,
+        epoch=first.epoch,
+        seed=seed,
+        execution_position=execution_position,
+        source_batch_sha256=first.source_batch_sha256,
+        contexts_sha256=contexts_digest,
+        batch_input_sha256=batch_digest,
+        cache_key_sha256=opened.cache_key_sha256,
+        request_sha256=request_digest,
+        edge_count=opened.edge_count,
+        window_count=len(contexts),
+    )
+
+
+def _enumerate_rows(
+    source: PreparedTrainingDataSourceV2,
+    cache: PeriodicDescriptorCacheV2,
+    *,
+    split: Split,
+    seed: int,
+    epochs: tuple[int, ...],
+    stream_kind: DescriptorStreamKind,
+    edge_budget: int,
+    start_position: int,
+    source_binding: DescriptorCacheSourceBinding,
+    floors_sha256: str,
+    index_sha256: str,
+    cache_key_census: tuple[str, ...],
+) -> tuple[
+    tuple[PeriodicDescriptorPlanRow, ...], Mapping[RequestIdentity, PeriodicDescriptorPlanRow]
+]:
+    rows: list[PeriodicDescriptorPlanRow] = []
+    lookup: dict[RequestIdentity, PeriodicDescriptorPlanRow] = {}
+    keys: set[str] = set()
+    for epoch in epochs:
+        for batch in source.iter_epoch(epoch=epoch, seed=seed):
+            if len(rows) >= len(cache_key_census):
+                raise PeriodicTrainingCacheError(
+                    f"{split} source row census exceeds its closed cache census"
+                )
+            checked, identity = _batch_identity(
+                batch,
+                split=split,
+                seed=seed,
+                expected_manifest_sha256=source.manifest_sha256,
+                epochs=RESIDUAL_EPOCHS,
+            )
+            edge_count = identity[-2]
+            _enforce_edge_budget(edge_count, edge_budget)
+            _validate_reader_snapshot(
+                cache,
+                index_sha256=index_sha256,
+                source_binding=source_binding,
+                floors_sha256=floors_sha256,
+                cache_key_census=cache_key_census,
+            )
+            contexts = checked.descriptor_contexts
+            assert contexts is not None
+            opened = cache.open_batch(checked.groups, contexts)
+            if opened.energy_floors_sha256 != floors_sha256:
+                raise PeriodicTrainingCacheError("opened shard energy floors changed")
+            stream = opened.stream(stream_kind)
+            if type(stream) is not CachedPairChunkStream:
+                raise PeriodicTrainingCacheError("cache returned an unsealed descriptor stream")
+            row = _plan_row(
+                checked,
+                opened,
+                split=split,
+                seed=seed,
+                execution_position=start_position + len(rows),
+            )
+            _validate_opened(
+                opened,
+                row=row,
+                contexts=contexts,
+                floors_sha256=floors_sha256,
+            )
+            if row._identity() != identity:
+                raise PeriodicTrainingCacheError(
+                    "opened cache shard request differs from the actual source batch"
+                )
+            if identity in lookup or row.cache_key_sha256 in keys:
+                raise PeriodicTrainingCacheError(
+                    f"{split} execution plan repeats an exact descriptor request"
+                )
+            rows.append(row)
+            lookup[identity] = row
+            keys.add(row.cache_key_sha256)
+    if not rows:
+        raise PeriodicTrainingCacheError(f"{split} source produced no cache plan rows")
+    if tuple(sorted(keys)) != cache_key_census:
+        raise PeriodicTrainingCacheError(
+            f"{split} required cache-key set differs from the closed reader census"
+        )
+    return tuple(rows), MappingProxyType(lookup)
+
+
+def _admit_periodic_descriptor_training_plan(
+    *,
+    system_id: str,
+    config: TrainingConfig,
+    train_source: PreparedTrainingDataSourceV2,
+    val_source: PreparedTrainingDataSourceV2,
+    train_cache: PeriodicDescriptorCacheV2,
+    val_cache: PeriodicDescriptorCacheV2,
+) -> PeriodicDescriptorTrainingPlan:
+    if type(config) is not TrainingConfig:
+        raise TypeError("config must be an exact TrainingConfig")
+    if config.stage != "residual" or config.synthetic_contract:
+        raise PeriodicTrainingCacheError(
+            "descriptor plans require an exact formal residual configuration"
+        )
+    if config.seed not in OFFICIAL_SEEDS or config.epochs != RESIDUAL_EPOCHS:
+        raise PeriodicTrainingCacheError("descriptor plan seed or epoch census changed")
+    if type(system_id) is not str or system_id not in _CACHEABLE_SYSTEMS:
+        raise PeriodicTrainingCacheError(
+            "descriptor plans support residual systems 02, 03, 04, 06, 07, and 08 only"
+        )
+    stream_kind = _CACHEABLE_SYSTEMS[system_id]
+    specification = system_spec(system_id)
+    if (
+        specification.feature_policy != _EXPECTED_POLICIES[system_id]
+        or specification.residual_enabled is not True
+    ):
+        raise PeriodicTrainingCacheError("registered system feature policy changed")
+    if (
+        type(train_source) is not PreparedTrainingDataSourceV2
+        or type(val_source) is not PreparedTrainingDataSourceV2
+    ):
+        raise TypeError("sources must be exact PreparedTrainingDataSourceV2 values")
+    if train_source is val_source or train_source.split != "train" or val_source.split != "val":
+        raise PeriodicTrainingCacheError("plan requires distinct train and validation sources")
+    if (
+        type(train_cache) is not PeriodicDescriptorCacheV2
+        or type(val_cache) is not PeriodicDescriptorCacheV2
+    ):
+        raise TypeError("caches must be exact PeriodicDescriptorCacheV2 readers")
+    if train_cache is val_cache:
+        raise PeriodicTrainingCacheError("train and validation require distinct cache readers")
+    train_binding = train_cache.source_binding
+    validation_binding = val_cache.source_binding
+    if (
+        type(train_binding) is not DescriptorCacheSourceBinding
+        or type(validation_binding) is not DescriptorCacheSourceBinding
+    ):
+        raise PeriodicTrainingCacheError("cache source binding type changed")
+    if (
+        train_binding.prepared_manifest_sha256 != train_source.manifest_sha256
+        or validation_binding.prepared_manifest_sha256 != val_source.manifest_sha256
+    ):
+        raise PeriodicTrainingCacheError("cache source binding differs from prepared source")
+    if train_cache.energy_floors_sha256 != val_cache.energy_floors_sha256:
+        raise PeriodicTrainingCacheError("train and validation cache energy floors differ")
+    if not np.array_equal(train_cache.energy_floors, val_cache.energy_floors):
+        raise PeriodicTrainingCacheError("train and validation energy-floor bytes differ")
+    train_census = train_cache.cache_key_census
+    validation_census = val_cache.cache_key_census
+    for census, label in ((train_census, "train"), (validation_census, "validation")):
+        if (
+            type(census) is not tuple
+            or not census
+            or census != tuple(sorted(census))
+            or len(set(census)) != len(census)
+            or any(type(value) is not str for value in census)
+        ):
+            raise PeriodicTrainingCacheError(f"{label} cache key census is invalid")
+    config_sha256 = config.sha256
+    train_manifest = train_source.manifest_sha256
+    validation_manifest = val_source.manifest_sha256
+    floors_sha256 = train_cache.energy_floors_sha256
+    train_index = train_cache.index_sha256
+    validation_index = val_cache.index_sha256
+    train_rows, train_lookup = _enumerate_rows(
+        train_source,
+        train_cache,
+        split="train",
+        seed=config.seed,
+        epochs=tuple(range(RESIDUAL_EPOCHS)),
+        stream_kind=stream_kind,
+        edge_budget=config.edge_budget,
+        start_position=0,
+        source_binding=train_binding,
+        floors_sha256=floors_sha256,
+        index_sha256=train_index,
+        cache_key_census=train_census,
+    )
+    validation_rows, validation_lookup = _enumerate_rows(
+        val_source,
+        val_cache,
+        split="val",
+        seed=config.seed,
+        epochs=(0,),
+        stream_kind=stream_kind,
+        edge_budget=config.edge_budget,
+        start_position=len(train_rows),
+        source_binding=validation_binding,
+        floors_sha256=floors_sha256,
+        index_sha256=validation_index,
+        cache_key_census=validation_census,
+    )
+    if (
+        config.sha256 != config_sha256
+        or train_source.manifest_sha256 != train_manifest
+        or val_source.manifest_sha256 != validation_manifest
+        or train_source.split != "train"
+        or val_source.split != "val"
+    ):
+        raise PeriodicTrainingCacheError(
+            "configuration or source identity changed during admission"
+        )
+    _validate_reader_snapshot(
+        train_cache,
+        index_sha256=train_index,
+        source_binding=train_binding,
+        floors_sha256=floors_sha256,
+        cache_key_census=train_census,
+    )
+    _validate_reader_snapshot(
+        val_cache,
+        index_sha256=validation_index,
+        source_binding=validation_binding,
+        floors_sha256=floors_sha256,
+        cache_key_census=validation_census,
+    )
+    return PeriodicDescriptorTrainingPlan(
+        system_id=system_id,
+        seed=config.seed,
+        epochs=RESIDUAL_EPOCHS,
+        stream_kind=stream_kind,
+        config_sha256=config_sha256,
+        edge_budget=config.edge_budget,
+        energy_floors_sha256=floors_sha256,
+        train_source_manifest_sha256=train_manifest,
+        validation_source_manifest_sha256=validation_manifest,
+        train_cache_index_sha256=train_index,
+        validation_cache_index_sha256=validation_index,
+        train_cache_source_binding=train_binding,
+        validation_cache_source_binding=validation_binding,
+        train_cache_key_census=train_census,
+        validation_cache_key_census=validation_census,
+        train_rows=train_rows,
+        validation_rows=validation_rows,
+        _train_cache=train_cache,
+        _validation_cache=val_cache,
+        _train_lookup=train_lookup,
+        _validation_lookup=validation_lookup,
+        _seal=_PLAN_TOKEN,
+    )
+
+
+def admit_periodic_descriptor_training_plan(
+    *,
+    system_id: str,
+    config: TrainingConfig,
+    train_source: PreparedTrainingDataSourceV2,
+    val_source: PreparedTrainingDataSourceV2,
+    train_cache: PeriodicDescriptorCacheV2,
+    val_cache: PeriodicDescriptorCacheV2,
+) -> PeriodicDescriptorTrainingPlan:
+    """Derive and preflight one exact authority-zero seed-specific cache plan."""
+
+    snapshot = _capture_rng()
+    try:
+        plan = _admit_periodic_descriptor_training_plan(
+            system_id=system_id,
+            config=config,
+            train_source=train_source,
+            val_source=val_source,
+            train_cache=train_cache,
+            val_cache=val_cache,
+        )
+    except Exception as error:
+        if not _rng_matches(snapshot):
+            _restore_rng(snapshot)
+            raise PeriodicTrainingCacheError(
+                "cache plan admission changed caller RNG state"
+            ) from error
+        raise
+    if not _rng_matches(snapshot):
+        _restore_rng(snapshot)
+        raise PeriodicTrainingCacheError("cache plan admission changed caller RNG state")
+    return plan
+
+
+__all__ = [
+    "AUTHORITY",
+    "PLAN_SCHEMA",
+    "PRODUCTION",
+    "PeriodicDescriptorPlanRow",
+    "PeriodicDescriptorTrainingPlan",
+    "PeriodicTrainingCacheError",
+    "RESULT_CLAIMED",
+    "STATUS",
+    "WINDOW_VALIDATION_SEMANTICS",
+    "admit_periodic_descriptor_training_plan",
+]
diff --git a/tests/test_capture_validation_source_lineage_v3.py b/tests/test_capture_validation_source_lineage_v3.py
new file mode 100644
index 0000000..a018079
--- /dev/null
+++ b/tests/test_capture_validation_source_lineage_v3.py
@@ -0,0 +1,226 @@
+"""Focused server-only contracts for capture descriptor source lineage v2."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from dataclasses import replace
+from pathlib import Path
+
+import pytest
+from phaseset_core import capture_prepared_storage as storage
+from phaseset_core import capture_validation
+from phaseset_core.capture_validation import (
+    CaptureDescriptorWindowSource,
+    CaptureValidationCapture,
+    CaptureValidationSource,
+)
+from test_phaseset_capture_prepared_storage import _source
+
+
+def _canonical(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _write_v1(tmp_path: Path) -> tuple[Path, storage.CaptureStorageBuildResult]:
+    root = (tmp_path / "capture-store-v1").resolve()
+    return root, storage.write_capture_validation_source(root, _source())
+
+
+def _write_v2(tmp_path: Path) -> tuple[Path, storage.CaptureStorageBuildResult]:
+    root = (tmp_path / "capture-store-v2").resolve()
+    return root, storage.write_capture_validation_source_v2(root, _source())
+
+
+def _rewrite_manifest(path: Path, mutate) -> str:
+    value = json.loads(path.read_text(encoding="ascii"))
+    mutate(value)
+    raw = _canonical(value)
+    os.chmod(path, 0o600)
+    path.write_bytes(raw)
+    os.chmod(path, 0o400)
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _replace_first_descriptor(
+    source: CaptureValidationSource,
+    descriptor: CaptureDescriptorWindowSource | None,
+) -> tuple[CaptureValidationCapture, ...]:
+    first = source.captures[0]
+    changed_window = replace(first.windows[0], descriptor_source=descriptor)
+    changed_capture = replace(first, windows=(changed_window, *first.windows[1:]))
+    return (changed_capture, *source.captures[1:])
+
+
+def test_v1_storage_remains_uncached_and_uses_the_original_closed_schema(
+    tmp_path: Path,
+) -> None:
+    _, result = _write_v1(tmp_path)
+    manifest = json.loads(result.manifest_path.read_text(encoding="ascii"))
+    assert manifest["schema"] == storage.SCHEMA == storage.SCHEMA_V1
+    assert all(
+        "window_ordinal" not in window
+        for capture in manifest["captures"]
+        for window in capture["windows"]
+    )
+    loaded = storage.load_capture_validation_source(
+        result.manifest_path,
+        expected_manifest_sha256=result.manifest_sha256,
+    )
+    for capture in loaded.captures:
+        for window in capture.windows:
+            assert window.descriptor_source is None
+            with pytest.raises(
+                capture_validation.CaptureValidationError,
+                match="no authenticated descriptor source lineage",
+            ):
+                window.descriptor_context(seed=1729)
+
+
+def test_v2_roundtrip_binds_consumed_manifest_npz_and_canonical_global_ordinal(
+    tmp_path: Path,
+) -> None:
+    root, result = _write_v2(tmp_path)
+    manifest_raw = result.manifest_path.read_bytes()
+    manifest = json.loads(manifest_raw.decode("ascii"))
+    assert manifest_raw == _canonical(manifest)
+    assert manifest["schema"] == storage.SCHEMA_V2
+    rows = [window for capture in manifest["captures"] for window in capture["windows"]]
+    assert [row["window_ordinal"] for row in rows] == [0, 1, 2]
+    assert sorted(row["source_start_frame"] for row in rows) == [0, 0, 300]
+    assert [row["window_ordinal"] for row in rows] != [row["source_start_frame"] for row in rows]
+
+    loaded = storage.load_capture_validation_source(
+        result.manifest_path,
+        expected_manifest_sha256=result.manifest_sha256,
+    )
+    assert loaded.manifest_sha256 == _source().manifest_sha256
+    assert loaded.census_sha256 == result.source_census_sha256
+    windows = [window for capture in loaded.captures for window in capture.windows]
+    assert len(windows) == len(rows)
+    for invalid_seed in (False, -1, 2**64):
+        with pytest.raises(
+            capture_validation.CaptureValidationError,
+            match="exact uint64",
+        ):
+            windows[0].descriptor_context(seed=invalid_seed)
+    for ordinal, (window, row) in enumerate(zip(windows, rows, strict=True)):
+        provenance = window.descriptor_source
+        assert type(provenance) is CaptureDescriptorWindowSource
+        assert provenance.storage_manifest_sha256 == result.manifest_sha256
+        assert provenance.source_window_npz_sha256 == row["sha256"]
+        assert provenance.window_ordinal == ordinal
+        path = root.joinpath(*row["path"].split("/"))
+        assert hashlib.sha256(path.read_bytes()).hexdigest() == row["sha256"]
+        context = window.descriptor_context(seed=2718)
+        assert context.prepared_manifest_sha256 == result.manifest_sha256
+        assert context.source_batch_sha256 == row["sha256"]
+        assert context.window_sha256 == window.window_commitment.hex()
+        assert (context.window_ordinal, context.split, context.seed, context.epoch) == (
+            ordinal,
+            "val",
+            2718,
+            0,
+        )
+        assert context.augmentation_yaw_be_hex == "0000000000000000"
+
+    rebuilt = capture_validation._snapshot_source(loaded)
+    assert tuple(
+        window.descriptor_source for capture in rebuilt.captures for window in capture.windows
+    ) == tuple(
+        window.descriptor_source for capture in loaded.captures for window in capture.windows
+    )
+
+
+@pytest.mark.parametrize("changed_ordinal", [False, 1, 4])
+def test_v2_loader_rejects_nonexact_or_noncanonical_global_ordinals(
+    tmp_path: Path,
+    changed_ordinal: object,
+) -> None:
+    _, result = _write_v2(tmp_path)
+
+    def mutate(value: dict[str, object]) -> None:
+        value["captures"][0]["windows"][0]["window_ordinal"] = changed_ordinal
+
+    rebound = _rewrite_manifest(result.manifest_path, mutate)
+    expected = "exact uint64" if changed_ordinal is False else "canonical global order"
+    with pytest.raises(storage.CapturePreparedStorageError, match=expected):
+        storage.load_capture_validation_source(
+            result.manifest_path,
+            expected_manifest_sha256=rebound,
+        )
+
+
+def test_source_rejects_mixed_v1_v2_lineage_and_v1_writer_cannot_discard_v2(
+    tmp_path: Path,
+) -> None:
+    _, result = _write_v2(tmp_path)
+    loaded = storage.load_capture_validation_source(
+        result.manifest_path,
+        expected_manifest_sha256=result.manifest_sha256,
+    )
+    with pytest.raises(storage.CapturePreparedStorageError, match="cannot discard"):
+        storage.write_capture_validation_source(
+            (tmp_path / "forbidden-downgrade").resolve(),
+            loaded,
+        )
+    with pytest.raises(
+        capture_validation.CaptureValidationError,
+        match="cannot mix v1 and v2",
+    ):
+        CaptureValidationSource(
+            loaded.split,
+            loaded.manifest_sha256,
+            _replace_first_descriptor(loaded, None),
+        )
+
+
+def test_source_directly_rejects_cross_manifest_descriptor_lineage(tmp_path: Path) -> None:
+    _, result = _write_v2(tmp_path)
+    loaded = storage.load_capture_validation_source(
+        result.manifest_path,
+        expected_manifest_sha256=result.manifest_sha256,
+    )
+    original = loaded.captures[0].windows[0].descriptor_source
+    assert type(original) is CaptureDescriptorWindowSource
+    changed_manifest = "f" * 64 if original.storage_manifest_sha256 != "f" * 64 else "e" * 64
+    changed = replace(original, storage_manifest_sha256=changed_manifest)
+    with pytest.raises(
+        capture_validation.CaptureValidationError,
+        match="share one storage manifest",
+    ):
+        CaptureValidationSource(
+            loaded.split,
+            loaded.manifest_sha256,
+            _replace_first_descriptor(loaded, changed),
+        )
+
+
+def test_source_directly_rejects_noncanonical_global_ordinal(tmp_path: Path) -> None:
+    _, result = _write_v2(tmp_path)
+    loaded = storage.load_capture_validation_source(
+        result.manifest_path,
+        expected_manifest_sha256=result.manifest_sha256,
+    )
+    original = loaded.captures[0].windows[0].descriptor_source
+    assert type(original) is CaptureDescriptorWindowSource
+    changed = replace(original, window_ordinal=original.window_ordinal + 1)
+    with pytest.raises(
+        capture_validation.CaptureValidationError,
+        match="ordinals must equal canonical global order",
+    ):
+        CaptureValidationSource(
+            loaded.split,
+            loaded.manifest_sha256,
+            _replace_first_descriptor(loaded, changed),
+        )
diff --git a/tests/test_periodic_training_cache.py b/tests/test_periodic_training_cache.py
new file mode 100644
index 0000000..58ad8d4
--- /dev/null
+++ b/tests/test_periodic_training_cache.py
@@ -0,0 +1,611 @@
+"""Focused tests for the private periodic training-cache plan candidate.
+
+All inputs are analytic fixtures.  These tests do not load CLIP, construct a
+training model, run optimization, or claim a formal dataset/cache result.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, replace
+import hashlib
+from pathlib import Path
+import random
+import struct
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import frozen_clip_text as clip
+from phaseset_core import prepared_data_v2 as prepared
+from phaseset_core import training
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+from phaseset_core.periodic import ResourceLimitError
+from phaseset_core.periodic_descriptor_cache_v2 import (
+    CachedPairChunkStream,
+    DescriptorCacheSourceBinding,
+    DescriptorWindowContext,
+    PeriodicDescriptorCacheV2,
+    PeriodicDescriptorCacheWriterV2,
+)
+from phaseset_core.periodic_training_cache import (
+    PLAN_SCHEMA,
+    PeriodicTrainingCacheError,
+    admit_periodic_descriptor_training_plan,
+)
+from phaseset_core.pipeline import PreparedGroupSample, deterministic_group_yaw
+from phaseset_core.preprocessing import WindowDecision
+
+
+_MAX_BYTES = 32 * 1024 * 1024
+_FLOORS = np.ascontiguousarray(
+    np.asarray([(index + 1) * 1.0e-12 for index in range(6)], dtype=np.float64)
+)
+
+
+def _digest(label: str) -> bytes:
+    return hashlib.sha256(label.encode("ascii")).digest()
+
+
+def _sample(label: str, *, actors: int) -> PreparedGroupSample:
+    commitments = tuple(sorted(_digest(f"{label}/actor/{index}") for index in range(actors)))
+    skeletons = np.zeros((actors, 200, 22, 3), dtype=np.float32)
+    frame = np.arange(200, dtype=np.float32)
+    for actor in range(actors):
+        for joint in range(22):
+            skeletons[actor, :, joint, 0] = frame * np.float32(0.001) + actor
+            skeletons[actor, :, joint, 1] = np.float32(joint * 0.002)
+            skeletons[actor, :, joint, 2] = frame * np.float32(-0.003) + joint
+    return PreparedGroupSample(
+        skeletons=np.ascontiguousarray(skeletons),
+        track_mask=np.ones((actors, 200, 22), dtype=np.bool_),
+        actor_commitments=commitments,
+        group_commitment=group_commitment(commitments),
+        source_sha256=hashlib.sha256(f"{label}/source".encode("ascii")).hexdigest(),
+        window_sha256=hashlib.sha256(f"{label}/window".encode("ascii")).hexdigest(),
+        source_start_frame=0,
+        augmentation_yaw=0.0,
+        decision=WindowDecision(True, "ACCEPT", 300, actors, 0, 1.0, 0, False),
+    )
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+    return value.detach().contiguous().view(torch.uint8).numpy().tobytes()
+
+
+def _text_batch(label: str) -> clip.FrozenClipTextBatch:
+    values = torch.arange(512, dtype=torch.float32).reshape(1, 512)
+    embeddings = (torch.sin(values * 0.001) + len(label)).contiguous()
+    commitment = _digest(f"{label}/caption")
+    output = _tensor_bytes(embeddings)
+    receipt = clip.FrozenClipTextReceipt(
+        batch_size=1,
+        caption_rows=(
+            (
+                0,
+                commitment.hex(),
+                hashlib.sha256(f"{label}/text".encode("ascii")).hexdigest(),
+                3,
+                3,
+                False,
+                hashlib.sha256(f"{label}/tokens".encode("ascii")).hexdigest(),
+                hashlib.sha256(f"{label}/mask".encode("ascii")).hexdigest(),
+                hashlib.sha256(output).hexdigest(),
+            ),
+        ),
+        chunk_ranges=((0, 1),),
+        frozen_embedding_cache_key_sha256="1" * 64,
+        live_model_manifest_sha256="2" * 64,
+        output_bytes=len(output),
+        output_sha256=hashlib.sha256(output).hexdigest(),
+        output_shape=tuple(embeddings.shape),
+        output_stride=tuple(embeddings.stride()),
+        runtime_identity=(("fixture", "server-only"),),
+        runtime_manifest_sha256="3" * 64,
+        snapshot_files=(),
+        snapshot_manifest_sha256="4" * 64,
+        source_files=(),
+        source_manifest_sha256="5" * 64,
+    )
+    return clip.FrozenClipTextBatch(
+        embeddings,
+        (commitment,),
+        receipt,
+        _seal=clip._CONSTRUCTION_SEAL,
+    )
+
+
+def _block(label: str, *, actors: int, ordinal: int) -> prepared.PreparedMotionTextBlock:
+    return prepared.PreparedMotionTextBlock(
+        samples=(_sample(label, actors=actors),),
+        text_batch=_text_batch(label),
+        text_counts=(1,),
+        window_ordinals=(ordinal,),
+    )
+
+
+def _write_sources(root: Path, label: str = "canonical"):
+    root.parent.mkdir(parents=True, exist_ok=True)
+    result = prepared.write_prepared_training_tree_v2(
+        root,
+        train_blocks=(_block(f"{label}/train", actors=3, ordinal=10),),
+        val_blocks=(_block(f"{label}/val", actors=2, ordinal=20),),
+        max_total_edges=3,
+        max_batch_size=1,
+        max_batch_bytes=_MAX_BYTES,
+        max_decoded_batch_bytes=_MAX_BYTES,
+    )
+    train_source, val_source = prepared.load_prepared_training_sources_v2(
+        result.index_path,
+        expected_index_sha256=result.index_sha256,
+        max_batch_bytes=_MAX_BYTES,
+        max_decoded_batch_bytes=_MAX_BYTES,
+    )
+    return train_source, val_source
+
+
+def _binding(source: prepared.PreparedTrainingDataSourceV2, label: str):
+    return DescriptorCacheSourceBinding(
+        prepared_manifest_sha256=source.manifest_sha256,
+        source_tree_sha256=hashlib.sha256(f"{label}/tree".encode("ascii")).hexdigest(),
+        environment_sha256=hashlib.sha256(f"{label}/env".encode("ascii")).hexdigest(),
+    )
+
+
+def _write_cache(
+    root: Path,
+    source: prepared.PreparedTrainingDataSourceV2,
+    *,
+    seed: int,
+    epochs: tuple[int, ...],
+    floors: np.ndarray = _FLOORS,
+    extras: tuple[tuple[int, int], ...] = (),
+) -> PeriodicDescriptorCacheV2:
+    binding = _binding(source, source.split)
+    writer = PeriodicDescriptorCacheWriterV2(
+        root,
+        source_binding=binding,
+        energy_floors=floors,
+    )
+    for epoch in epochs:
+        for batch in source.iter_epoch(epoch=epoch, seed=seed):
+            assert batch.descriptor_contexts is not None
+            writer.add_batch(batch.groups, batch.descriptor_contexts)
+    for extra_seed, extra_epoch in extras:
+        for batch in source.iter_epoch(epoch=extra_epoch, seed=extra_seed):
+            assert batch.descriptor_contexts is not None
+            writer.add_batch(batch.groups, batch.descriptor_contexts)
+    artifact = writer.finalize()
+    return PeriodicDescriptorCacheV2(
+        root,
+        expected_index_sha256=artifact.index_sha256,
+        expected_source_binding=binding,
+        energy_floors=floors,
+    )
+
+
+@dataclass(frozen=True)
+class _Bundle:
+    train_source: prepared.PreparedTrainingDataSourceV2
+    val_source: prepared.PreparedTrainingDataSourceV2
+    train_cache: PeriodicDescriptorCacheV2
+    val_cache: PeriodicDescriptorCacheV2
+    config: training.TrainingConfig
+
+
+def _bundle(tmp_path: Path, *, seed: int = 1729) -> _Bundle:
+    train_source, val_source = _write_sources(tmp_path / "prepared")
+    train_cache = _write_cache(
+        tmp_path / "train-cache",
+        train_source,
+        seed=seed,
+        epochs=tuple(range(training.RESIDUAL_EPOCHS)),
+    )
+    val_cache = _write_cache(
+        tmp_path / "val-cache",
+        val_source,
+        seed=seed,
+        epochs=(0,),
+    )
+    return _Bundle(
+        train_source=train_source,
+        val_source=val_source,
+        train_cache=train_cache,
+        val_cache=val_cache,
+        config=training.TrainingConfig(stage="residual", seed=seed),
+    )
+
+
+def _admit(bundle: _Bundle, *, system_id: str = "08"):
+    return admit_periodic_descriptor_training_plan(
+        system_id=system_id,
+        config=bundle.config,
+        train_source=bundle.train_source,
+        val_source=bundle.val_source,
+        train_cache=bundle.train_cache,
+        val_cache=bundle.val_cache,
+    )
+
+
+def _rng_snapshot():
+    state = np.random.get_state()
+    return (
+        random.getstate(),
+        (state[0], np.array(state[1], copy=True), *state[2:]),
+        torch.random.get_rng_state().clone(),
+    )
+
+
+def _assert_rng_equal(before) -> None:
+    python_before, numpy_before, torch_before = before
+    current = np.random.get_state()
+    assert random.getstate() == python_before
+    assert current[0] == numpy_before[0]
+    assert np.array_equal(current[1], numpy_before[1])
+    assert current[2:] == numpy_before[2:]
+    assert torch.equal(torch.random.get_rng_state(), torch_before)
+
+
+def test_complete_plan_is_seed_specific_exact_and_reopens_each_selection(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    bundle = _bundle(tmp_path)
+    original = PeriodicDescriptorCacheV2.open_batch
+    calls = 0
+
+    def counted(self, batch, contexts):
+        nonlocal calls
+        calls += 1
+        return original(self, batch, contexts)
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", counted)
+    before = _rng_snapshot()
+    plan = _admit(bundle, system_id="06")
+    _assert_rng_equal(before)
+    assert calls == 21
+    assert plan.authority == 0
+    assert plan.production is False
+    assert plan.result_claimed is False
+    assert plan.canonical_bytes().endswith(b"\n")
+    assert PLAN_SCHEMA.encode("ascii") in plan.canonical_bytes()
+    assert len(plan.sha256) == 64
+    assert plan.stream_kind == "FULL_RELATION"
+    assert len(plan.train_rows) == 20
+    assert {row.epoch for row in plan.train_rows} == set(range(20))
+    assert len(plan.validation_rows) == 1
+    assert plan.validation_rows[0].epoch == 0
+    assert tuple(row.execution_position for row in plan.train_rows) == tuple(range(20))
+    assert plan.validation_rows[0].execution_position == 20
+    assert tuple(sorted(row.cache_key_sha256 for row in plan.train_rows)) == (
+        bundle.train_cache.cache_key_census
+    )
+
+    train_batch = next(bundle.train_source.iter_epoch(epoch=7, seed=1729))
+    first = plan.open_training_batch(train_batch)
+    second = plan.open_training_batch(train_batch)
+    val_batch = next(bundle.val_source.iter_epoch(epoch=0, seed=1729))
+    validation = plan.open_validation_batch(val_batch)
+    assert calls == 24
+    assert type(first) is CachedPairChunkStream
+    assert first.stream_kind == second.stream_kind == validation.stream_kind == "FULL_RELATION"
+
+
+def test_registered_system_stream_mapping_and_uncached_system_rejection(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    bundle = _bundle(tmp_path)
+    expected = {
+        "02": "MARGINAL_POWER",
+        "03": "MEAN_DIFFERENCE_DCT",
+        "04": "FULL_RELATION",
+        "06": "FULL_RELATION",
+        "07": "FULL_RELATION",
+        "08": "FULL_RELATION",
+    }
+    for system_id, stream_kind in expected.items():
+        assert _admit(bundle, system_id=system_id).stream_kind == stream_kind
+
+    opened = 0
+    original = PeriodicDescriptorCacheV2.open_batch
+
+    def counted(self, batch, contexts):
+        nonlocal opened
+        opened += 1
+        return original(self, batch, contexts)
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", counted)
+    for system_id in ("01", "05", "B0", "00"):
+        with pytest.raises(PeriodicTrainingCacheError):
+            _admit(bundle, system_id=system_id)
+    base_config = replace(bundle.config, stage="base")
+    with pytest.raises(PeriodicTrainingCacheError):
+        admit_periodic_descriptor_training_plan(
+            system_id="08",
+            config=base_config,
+            train_source=bundle.train_source,
+            val_source=bundle.val_source,
+            train_cache=bundle.train_cache,
+            val_cache=bundle.val_cache,
+        )
+    assert opened == 0
+
+
+@pytest.mark.parametrize("seed", training.OFFICIAL_SEEDS)
+def test_each_official_seed_is_admitted_as_a_separate_twenty_epoch_plan(
+    tmp_path: Path, seed: int
+) -> None:
+    bundle = _bundle(tmp_path, seed=seed)
+    plan = _admit(bundle)
+    assert plan.seed == seed
+    assert len(plan.train_rows) == training.RESIDUAL_EPOCHS
+    assert {row.seed for row in plan.train_rows + plan.validation_rows} == {seed}
+    assert {row.epoch for row in plan.train_rows} == set(range(training.RESIDUAL_EPOCHS))
+
+
+def test_cache_key_census_is_a_read_only_closed_tuple(tmp_path: Path) -> None:
+    bundle = _bundle(tmp_path)
+    census = bundle.train_cache.cache_key_census
+    assert type(census) is tuple
+    assert census == tuple(sorted(census))
+    assert len(census) == 20
+    with pytest.raises(AttributeError):
+        bundle.train_cache.cache_key_census = ()  # type: ignore[misc]
+
+
+def test_missing_and_extra_cache_keys_never_admit_a_subset(tmp_path: Path) -> None:
+    train_source, val_source = _write_sources(tmp_path / "prepared")
+    val_cache = _write_cache(tmp_path / "val-cache", val_source, seed=1729, epochs=(0,))
+    config = training.TrainingConfig(stage="residual", seed=1729)
+    missing = _write_cache(
+        tmp_path / "missing-train-cache",
+        train_source,
+        seed=1729,
+        epochs=tuple(range(19)),
+    )
+    with pytest.raises(PeriodicTrainingCacheError, match="exceeds its closed"):
+        admit_periodic_descriptor_training_plan(
+            system_id="08",
+            config=config,
+            train_source=train_source,
+            val_source=val_source,
+            train_cache=missing,
+            val_cache=val_cache,
+        )
+    extra = _write_cache(
+        tmp_path / "extra-train-cache",
+        train_source,
+        seed=1729,
+        epochs=tuple(range(20)),
+        extras=((2718, 0),),
+    )
+    with pytest.raises(PeriodicTrainingCacheError, match="differs from the closed"):
+        admit_periodic_descriptor_training_plan(
+            system_id="08",
+            config=config,
+            train_source=train_source,
+            val_source=val_source,
+            train_cache=extra,
+            val_cache=val_cache,
+        )
+
+
+def test_source_binding_and_energy_floor_mismatch_fail_closed(tmp_path: Path) -> None:
+    bundle = _bundle(tmp_path / "one")
+    other_train, _ = _write_sources(tmp_path / "two" / "prepared", "other")
+    with pytest.raises(PeriodicTrainingCacheError, match="source binding"):
+        admit_periodic_descriptor_training_plan(
+            system_id="08",
+            config=bundle.config,
+            train_source=other_train,
+            val_source=bundle.val_source,
+            train_cache=bundle.train_cache,
+            val_cache=bundle.val_cache,
+        )
+
+    changed_floors = np.ascontiguousarray(_FLOORS + np.float64(1.0e-13))
+    wrong_val = _write_cache(
+        tmp_path / "wrong-val-cache",
+        bundle.val_source,
+        seed=1729,
+        epochs=(0,),
+        floors=changed_floors,
+    )
+    with pytest.raises(PeriodicTrainingCacheError, match="energy floors"):
+        admit_periodic_descriptor_training_plan(
+            system_id="08",
+            config=bundle.config,
+            train_source=bundle.train_source,
+            val_source=bundle.val_source,
+            train_cache=bundle.train_cache,
+            val_cache=wrong_val,
+        )
+
+
+def test_resource_limit_precedes_any_shard_open(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    bundle = _bundle(tmp_path)
+    opened = 0
+    original = PeriodicDescriptorCacheV2.open_batch
+
+    def counted(self, batch, contexts):
+        nonlocal opened
+        opened += 1
+        return original(self, batch, contexts)
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", counted)
+    with pytest.raises(ResourceLimitError) as captured:
+        admit_periodic_descriptor_training_plan(
+            system_id="08",
+            config=replace(bundle.config, edge_budget=2),
+            train_source=bundle.train_source,
+            val_source=bundle.val_source,
+            train_cache=bundle.train_cache,
+            val_cache=bundle.val_cache,
+        )
+    assert captured.value.required_edges == 3
+    assert captured.value.edge_budget == 2
+    assert opened == 0
+
+
+def _numeric_mutant(batch: training.RetrievalTrainingBatch):
+    groups = batch.groups
+    skeletons = np.array(groups.skeletons, copy=True)
+    skeletons[0, 0, 0, 0, 0] += np.float32(0.125)
+    changed = PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(skeletons),
+        actor_mask=np.ascontiguousarray(groups.actor_mask),
+        frame_mask=np.ascontiguousarray(groups.frame_mask),
+        track_mask=np.ascontiguousarray(groups.track_mask),
+        actor_commitments=groups.actor_commitments,
+        group_commitments=groups.group_commitments,
+    )
+    return replace(batch, groups=changed)
+
+
+def _padded_mutant(batch: training.RetrievalTrainingBatch):
+    groups = batch.groups
+    batch_size, actors, frames, joints, axes = groups.skeletons.shape
+    skeletons = np.zeros((batch_size, actors + 1, frames, joints, axes), dtype=np.float32)
+    skeletons[:, :actors] = groups.skeletons
+    actor_mask = np.zeros((batch_size, actors + 1), dtype=np.bool_)
+    actor_mask[:, :actors] = groups.actor_mask
+    track_mask = np.zeros((batch_size, actors + 1, frames, joints), dtype=np.bool_)
+    track_mask[:, :actors] = groups.track_mask
+    changed = PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(skeletons),
+        actor_mask=np.ascontiguousarray(actor_mask),
+        frame_mask=np.ascontiguousarray(groups.frame_mask),
+        track_mask=np.ascontiguousarray(track_mask),
+        actor_commitments=tuple(row + (None,) for row in groups.actor_commitments),
+        group_commitments=groups.group_commitments,
+    )
+    return replace(batch, groups=changed)
+
+
+def _source_digest_mutant(batch: training.RetrievalTrainingBatch):
+    assert batch.descriptor_contexts is not None
+    contexts = tuple(
+        replace(context, source_batch_sha256="f" * 64) for context in batch.descriptor_contexts
+    )
+    return replace(batch, descriptor_contexts=contexts)
+
+
+def _epoch_mutant(batch: training.RetrievalTrainingBatch):
+    assert batch.descriptor_contexts is not None
+    contexts = tuple(
+        DescriptorWindowContext.from_yaw(
+            prepared_manifest_sha256=context.prepared_manifest_sha256,
+            source_batch_sha256=context.source_batch_sha256,
+            window_sha256=context.window_sha256,
+            window_ordinal=context.window_ordinal,
+            split="train",
+            seed=context.seed,
+            epoch=context.epoch + 1,
+            augmentation_yaw=deterministic_group_yaw(
+                seed=context.seed,
+                epoch=context.epoch + 1,
+                window_ordinal=context.window_ordinal,
+            ),
+        )
+        for context in batch.descriptor_contexts
+    )
+    return replace(batch, descriptor_contexts=contexts)
+
+
+def test_selector_rejects_numeric_padding_source_and_context_mutants_before_open(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    bundle = _bundle(tmp_path)
+    plan = _admit(bundle)
+    batch = next(bundle.train_source.iter_epoch(epoch=2, seed=1729))
+    opened = 0
+    original = PeriodicDescriptorCacheV2.open_batch
+
+    def counted(self, groups, contexts):
+        nonlocal opened
+        opened += 1
+        return original(self, groups, contexts)
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", counted)
+    for mutant in (
+        _numeric_mutant(batch),
+        _padded_mutant(batch),
+        _source_digest_mutant(batch),
+        _epoch_mutant(batch),
+    ):
+        with pytest.raises(PeriodicTrainingCacheError, match="not an exact admitted"):
+            plan.open_training_batch(mutant)
+    assert opened == 0
+    bundle.train_cache.index_sha256 = "f" * 64
+    with pytest.raises(PeriodicTrainingCacheError, match="metadata changed"):
+        plan.open_training_batch(batch)
+    assert opened == 0
+
+
+def test_rng_consuming_source_is_detected_and_caller_states_are_restored(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    bundle = _bundle(tmp_path)
+    original = prepared.PreparedTrainingDataSourceV2.iter_epoch
+
+    def consuming(self, *, epoch: int, seed: int):
+        random.random()
+        np.random.random()
+        torch.rand(1)
+        yield from original(self, epoch=epoch, seed=seed)
+
+    monkeypatch.setattr(prepared.PreparedTrainingDataSourceV2, "iter_epoch", consuming)
+    before = _rng_snapshot()
+    with pytest.raises(PeriodicTrainingCacheError, match="changed caller RNG"):
+        _admit(bundle)
+    _assert_rng_equal(before)
+
+
+def test_validation_selector_requires_canonical_epoch_zero_context_and_same_seed(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    bundle = _bundle(tmp_path)
+    plan = _admit(bundle)
+    opened = 0
+    original = PeriodicDescriptorCacheV2.open_batch
+
+    def counted(self, groups, contexts):
+        nonlocal opened
+        opened += 1
+        return original(self, groups, contexts)
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", counted)
+    wrong_seed = next(bundle.val_source.iter_epoch(epoch=0, seed=2718))
+    with pytest.raises(PeriodicTrainingCacheError, match="differs from the plan"):
+        plan.open_validation_batch(wrong_seed)
+    canonical = next(bundle.val_source.iter_epoch(epoch=0, seed=1729))
+    assert canonical.descriptor_contexts is not None
+    assert all(context.epoch == 0 for context in canonical.descriptor_contexts)
+    assert all(
+        context.augmentation_yaw_be_hex == struct.pack(">d", 0.0).hex()
+        for context in canonical.descriptor_contexts
+    )
+    assert plan.open_validation_batch(canonical).stream_kind == "FULL_RELATION"
+    assert opened == 1
+
+
+def test_plan_has_no_training_checkpoint_or_capture_validation_authority(
+    tmp_path: Path,
+) -> None:
+    bundle = _bundle(tmp_path)
+    plan = _admit(bundle)
+    payload = plan.canonical_bytes()
+    assert b'"authority":0' in payload
+    assert b'"production":false' in payload
+    assert b'"result_claimed":false' in payload
+    assert b"checkpoint" not in payload
+    assert b"capture" in payload
+    assert b"holistic-capture-validation" in payload
+    assert not hasattr(plan, "fit")
+    assert not hasattr(plan, "resume")
+    validation = next(bundle.val_source.iter_epoch(epoch=0, seed=1729))
+    assert validation.descriptor_contexts is not None
+    assert struct.pack(">d", 0.0).hex() in {
+        context.augmentation_yaw_be_hex for context in validation.descriptor_contexts
+    }
diff --git a/tests/test_phaseset_base_cohort_latency_workspace.py b/tests/test_phaseset_base_cohort_latency_workspace.py
new file mode 100644
index 0000000..c97a954
--- /dev/null
+++ b/tests/test_phaseset_base_cohort_latency_workspace.py
@@ -0,0 +1,191 @@
+"""CPU-mock contracts for explicit cuBLAS workspace release."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from phaseset_core import base_cohort_latency as latency
+
+
+_WORKSPACE_BYTES = 32 * 1024 * 1024
+
+
+class _System:
+    def __init__(self, events: list[str]) -> None:
+        self._events = events
+
+    def to(self, device: torch.device) -> _System:
+        self._events.append(f"system.to:{device}")
+        return self
+
+
+def test_release_clears_observed_workspace_then_preserves_zero_guard(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    events: list[str] = []
+    state = {"allocated": _WORKSPACE_BYTES, "reserved": _WORKSPACE_BYTES}
+
+    def synchronize(device: torch.device) -> None:
+        events.append(f"synchronize:{device}")
+
+    def clear_cublas_workspaces() -> None:
+        events.append("clear_cublas_workspaces")
+        assert state["allocated"] == _WORKSPACE_BYTES
+        state["allocated"] = 0
+
+    def empty_cache() -> None:
+        events.append("empty_cache")
+        assert state["allocated"] == 0
+        state["reserved"] = 0
+
+    def memory_allocated(device: torch.device) -> int:
+        events.append(f"memory_allocated:{device}")
+        return state["allocated"]
+
+    def memory_reserved(device: torch.device) -> int:
+        events.append(f"memory_reserved:{device}")
+        return state["reserved"]
+
+    monkeypatch.setattr(
+        latency.torch._C,
+        "_cuda_clearCublasWorkspaces",
+        clear_cublas_workspaces,
+        raising=False,
+    )
+    monkeypatch.setattr(latency.torch.cuda, "synchronize", synchronize)
+    monkeypatch.setattr(latency.torch.cuda, "empty_cache", empty_cache)
+    monkeypatch.setattr(latency.torch.cuda, "memory_allocated", memory_allocated)
+    monkeypatch.setattr(latency.torch.cuda, "memory_reserved", memory_reserved)
+
+    device = torch.device("cuda:0")
+    latency._NvidiaSmiTorchRuntime("GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee").release(
+        _System(events),
+        device,
+    )
+
+    assert state == {"allocated": 0, "reserved": 0}
+    assert events == [
+        "system.to:cpu",
+        "synchronize:cuda:0",
+        "clear_cublas_workspaces",
+        "synchronize:cuda:0",
+        "empty_cache",
+        "memory_allocated:cuda:0",
+        "memory_reserved:cuda:0",
+    ]
+
+
+@pytest.mark.parametrize(
+    ("allocated", "reserved"),
+    [
+        (1, 0),
+        (0, 1),
+        (_WORKSPACE_BYTES, _WORKSPACE_BYTES),
+    ],
+)
+def test_release_still_rejects_any_real_remaining_cuda_allocation(
+    monkeypatch: pytest.MonkeyPatch,
+    allocated: int,
+    reserved: int,
+) -> None:
+    events: list[str] = []
+    monkeypatch.setattr(
+        latency.torch._C,
+        "_cuda_clearCublasWorkspaces",
+        lambda: events.append("clear"),
+        raising=False,
+    )
+    monkeypatch.setattr(
+        latency.torch.cuda,
+        "synchronize",
+        lambda device: events.append(f"sync:{device}"),
+    )
+    monkeypatch.setattr(latency.torch.cuda, "empty_cache", lambda: events.append("empty"))
+    monkeypatch.setattr(latency.torch.cuda, "memory_allocated", lambda _device: allocated)
+    monkeypatch.setattr(latency.torch.cuda, "memory_reserved", lambda _device: reserved)
+
+    with pytest.raises(latency.BaseCohortLatencyError, match="allocation remained"):
+        latency._NvidiaSmiTorchRuntime("GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee").release(
+            _System(events), torch.device("cuda:0")
+        )
+    assert events[:5] == [
+        "system.to:cpu",
+        "sync:cuda:0",
+        "clear",
+        "sync:cuda:0",
+        "empty",
+    ]
+
+
+def test_release_fails_closed_when_workspace_api_is_absent(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    events: list[str] = []
+    monkeypatch.setattr(latency.torch, "_C", SimpleNamespace())
+    monkeypatch.setattr(
+        latency.torch.cuda,
+        "synchronize",
+        lambda device: events.append(f"sync:{device}"),
+    )
+    monkeypatch.setattr(latency.torch.cuda, "empty_cache", lambda: events.append("empty"))
+
+    with pytest.raises(latency.BaseCohortLatencyError, match="API is unavailable") as captured:
+        latency._NvidiaSmiTorchRuntime("GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee").release(
+            _System(events), torch.device("cuda:0")
+        )
+    assert isinstance(captured.value.__cause__, AttributeError)
+    assert events == ["system.to:cpu", "sync:cuda:0"]
+
+
+def test_release_fails_closed_when_workspace_clear_raises(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    events: list[str] = []
+
+    def fail_clear() -> None:
+        events.append("clear")
+        raise RuntimeError("private fixture failure")
+
+    monkeypatch.setattr(
+        latency.torch._C,
+        "_cuda_clearCublasWorkspaces",
+        fail_clear,
+        raising=False,
+    )
+    monkeypatch.setattr(
+        latency.torch.cuda,
+        "synchronize",
+        lambda device: events.append(f"sync:{device}"),
+    )
+    monkeypatch.setattr(latency.torch.cuda, "empty_cache", lambda: events.append("empty"))
+
+    with pytest.raises(latency.BaseCohortLatencyError, match="workspace clear failed") as captured:
+        latency._NvidiaSmiTorchRuntime("GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee").release(
+            _System(events), torch.device("cuda:0")
+        )
+    assert isinstance(captured.value.__cause__, RuntimeError)
+    assert str(captured.value.__cause__) == "private fixture failure"
+    assert events == ["system.to:cpu", "sync:cuda:0", "clear"]
+
+
+def test_workspace_delta_does_not_change_frozen_resource_workload_or_schedule() -> None:
+    assert latency.CUDA_ALLOCATOR_LIMIT_BYTES == 2 * 1024**3
+    assert latency.MINIMUM_FREE_MIB == 8192
+    assert (
+        latency.FIXTURE_BATCH,
+        latency.FIXTURE_ACTORS,
+        latency.FIXTURE_FRAMES,
+        latency.FIXTURE_JOINTS,
+        latency.FIXTURE_COORDINATES,
+        latency.FIXTURE_EMBEDDING_DIM,
+    ) == (1, 32, 200, 22, 3, 512)
+    assert (
+        latency.ROUND_COUNT,
+        latency.ROWS_PER_ROUND,
+        latency.WARMUPS_PER_VISIT,
+        latency.TIMED_PER_VISIT,
+        latency.SAMPLES_PER_ROW,
+    ) == (9, 9, 5, 11, 99)

From 578f52faac2c0786938b8f16ee983d327f142574 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 16:08:43 -0500
Subject: [PATCH 21/95] Integrate complete capture cache and host qualification
 controller

---
 RELEASE_FILES.sha256                          |   28 +-
 STATUS.md                                     |   64 +-
 docs/BASE_COHORT_QUALIFICATION.md             |   29 +-
 docs/CAPTURE_VALIDATION.md                    |   43 +-
 docs/HOST_BASE_QUALIFICATION.md               |   98 ++
 docs/HOST_RUNTIME_INTEGRATION.md              |   33 +-
 docs/PERIODIC_CAPTURE_TRAINING_CACHE.md       |   76 +
 docs/PERIODIC_DESCRIPTOR_CACHE_V2.md          |   10 +-
 docs/PERIODIC_TRAINING_CACHE_PLAN.md          |   14 +-
 src/phaseset_core/base_qualification_host.py  | 1053 ++++++++++++++
 src/phaseset_core/capture_validation.py       |  100 +-
 src/phaseset_core/frozen_clip_text.py         |    4 +-
 src/phaseset_core/host.py                     |  321 ++++-
 .../periodic_capture_training_cache.py        | 1044 ++++++++++++++
 src/phaseset_core/training.py                 |   47 +-
 tests/test_periodic_capture_training_cache.py |  497 +++++++
 ...test_phaseset_capture_validation_cached.py |  220 +++
 .../test_phaseset_host_base_qualification.py  | 1277 +++++++++++++++++
 tests/test_phaseset_residual_cached_encode.py |  306 ++++
 19 files changed, 5202 insertions(+), 62 deletions(-)
 create mode 100644 docs/HOST_BASE_QUALIFICATION.md
 create mode 100644 docs/PERIODIC_CAPTURE_TRAINING_CACHE.md
 create mode 100644 src/phaseset_core/base_qualification_host.py
 create mode 100644 src/phaseset_core/periodic_capture_training_cache.py
 create mode 100644 tests/test_periodic_capture_training_cache.py
 create mode 100644 tests/test_phaseset_capture_validation_cached.py
 create mode 100644 tests/test_phaseset_host_base_qualification.py
 create mode 100644 tests/test_phaseset_residual_cached_encode.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index dd0505d..67e4dad 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,15 +11,15 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-2c8e488dc4cc70f115641167dd6fc5e55b024a187fbbbb961139a98b57654c23	27678	STATUS.md
+d4dc6505cc0e954e24203a82a174da71e48f463c899e5108bd677b82119207de	30475	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
-a269e19f1b058d560967befa723f34281728f28caa0ddc470c8c1612b48cb006	7991	docs/BASE_COHORT_QUALIFICATION.md
+2970469ad0787e74ab4fabce2275486eb50c30c38f3cb7cf8f2800551d16991f	9540	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
 81ecc1cc742604f47833a0aa5b4c567165aa01e9c500d05157e325bd3b4ccdb3	4281	docs/CAPTION_ROLE_PROVENANCE.md
 62f6077a4d1cf0142e85ac28455a090d732b550874e47ea5644403566500f784	4644	docs/CAPTURE_PREPARED_STORAGE.md
-9906509270b1f307fadf0253f9ece494a9e4bd20b3c50d9769ace3e40771f4c9	7237	docs/CAPTURE_VALIDATION.md
+e66e0530b02a222d0b3c46590290d8ebcdafa21d7cd3acf2d751355cc1f8e558	9492	docs/CAPTURE_VALIDATION.md
 e9331b64297d6c4586ccb22022c972fad657ac2b3f9202203be62b067a38ca82	3686	docs/CUDA_LIFECYCLE_OBSERVATION.md
 e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA_PROTOCOL.md
 2a18716b57d21362672faaaf76944821764302064b7d7d931f9ec6a0d3f4c598	5564	docs/EMBODY_ADAPTER_INTEGRATION.md
@@ -28,12 +28,14 @@ e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
 97b7b7e68b7896220b38661b094505b47c711a431f546e6da9772cfedcf6beaa	14238	docs/FROZEN_CLIP_TEXT_ADAPTER.md
 f2c59540bfc4dcdbb5013b76eb20090a7dd61c805523121995ed9206c843f9c8	3711	docs/FROZEN_MHA_RUNTIME.md
-2643d17f18c64105ea4c4ef7873167fc3abf2169efdf4b2e946f20b9081da855	17150	docs/HOST_RUNTIME_INTEGRATION.md
+d179a05895c57e8c01c0b3115170f23127171e273571d9d239add4be8e219958	5453	docs/HOST_BASE_QUALIFICATION.md
+97bae9d94ed80f0871d39437f6a2e416bca912fad80a658964f754edded17cbf	18360	docs/HOST_RUNTIME_INTEGRATION.md
 5dd898347d3646cde4a74e10afa8ba1d12703e3d0c6df51e5113659a31808086	3606	docs/LATENCY_PROGRESS_JOURNAL.md
 f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
-aaed799e60152b2ee9e30d76f8eaa62587c97b133ba57a0a5ea36117029e35cb	7570	docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
-75d5f35864a5884f3dd548652fcde47f48b6da11c6d8430049f142954ea71756	3042	docs/PERIODIC_TRAINING_CACHE_PLAN.md
+a9e215279d354999c1f949f776a131b8cf4cec850abd81459c5c0d8cee0c8b03	4356	docs/PERIODIC_CAPTURE_TRAINING_CACHE.md
+6e9b9513bcfdbe76fbd28472225cb1fa185029a2b272e0b3f7d45e4a581f57c6	7886	docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
+00794af0ae3a3cf00c8d276183a34c3f0bca08e00f749d1f71e6444248a7ed0d	3353	docs/PERIODIC_TRAINING_CACHE_PLAN.md
 e19e4a6f20b5263faede0001581210885ffbb72c684fdf30482d3058d7937bbb	4448	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_PUBLIC_20260825_144456.json
 f94d7d7a270ad9c4055ee024693d8a20a9e916c9624771754db580ac963e6d53	1305	docs/PHASEPAIR_ANNOTATION_CAPTION_LINEAGE_V2_REQUIRED_CODE_SUCCESSOR_PUBLIC_20260825_144222.json
 69b5e67cbb59c9c658e5feb11d3f6c01afbc5e2d26a1acd2d6dba8f81f9e67ff	4296	docs/PHASEPAIR_B0_ANNOTATION_CAPTION_CLOSURE_SUCCESSOR_PUBLIC_V4_20260825_145013.json
@@ -133,11 +135,12 @@ d1aa8d6837a40ed062dc941a5052c25c6304b2f60e97782af306a83ef4078842	72640	src/phase
 ecda879d3f531ebb747b561612fa34df37f1ecbd8e1977d5089d38b01ec63131	29076	src/phaseset_core/base_cohort_qualification.py
 d2d71766c1c6af95480d507960d7186bbec9ff8a424f6e79ce6611fd0aa2e611	38885	src/phaseset_core/base_cohort_resolver.py
 3d4f31b6a773081af41f1d9d45ee98986ea33ef6f954070564373f995af1f2e9	40593	src/phaseset_core/base_cohort_validation.py
+eada42c2c19b9da0ba2e07213823b50855cee66ebdf8ccbf49005a992e9cbd18	43333	src/phaseset_core/base_qualification_host.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
 121dcb6b948b9a3a4878ed376b3692dcbbd4b19f7ea147a7d28341c64b77d296	36926	src/phaseset_core/caption_role_provenance.py
 d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
 0eaf0057ccf6ab6a4b83c1fa6e649399c4f783a79fab301e0778bafb5699daa9	47086	src/phaseset_core/capture_prepared_storage.py
-3ef5fb44f1af4c9f1599178af3e5c3363091f4ec7838f03b189acf08dac00bce	38524	src/phaseset_core/capture_validation.py
+7a146f3c81122ed6492fb6dc30194f68e6e3b86e2a25d29a5313607eb18470b6	42026	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
@@ -146,13 +149,14 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
-566a4e1c0c8ec4ab26dc1a26950134762f653006e39a4ca8e5d17813ed6dca14	68520	src/phaseset_core/frozen_clip_text.py
-e76f255c27c310682ac363e3d0f4fa223b07b85522e607ab3a4ef111cd246332	92373	src/phaseset_core/host.py
+38f8e5a85e02f22c7600c66b88a48d9f1b84ef8ed7dfd7ff3e01fd2960761faf	68520	src/phaseset_core/frozen_clip_text.py
+9ae29038b3a32ea0fdaada8e5bcbfb0c65b8a047be9c78069f159b1f6960352e	106049	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
 9e7fa84554d5676f8ef75049b17b53580395c3c60be0f75fcb55996ccc8ef36d	37720	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
+21ab159e434cc504867d22a22894a8e6a82afa77dde88a466773b5549a004850	42190	src/phaseset_core/periodic_capture_training_cache.py
 10a3dcde2653e4ffa3eeaba06426736d47c9ebb2caa565cc0698df744658d249	55700	src/phaseset_core/periodic_descriptor_cache_v2.py
 8e36e1a62d55962e255d1d08d3608de0fcc31dbacc879051b8e1d88d8d3988c8	35602	src/phaseset_core/periodic_training_cache.py
 a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phaseset_core/pipeline.py
@@ -164,7 +168,7 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
-0b91f3a5f1f4eea3d95c98bf899e12fb7de28394046468a05a24eaf2b6c55491	159407	src/phaseset_core/training.py
+c84486cc35709f4ac9fd17119deffeeea6569499800e59a143e53c6667ecd386	161053	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
 d5d69092cc271059400773ef68447e06068aa802d5e9c82193704c6b65c71f59	18827	tests/test_caption_role_provenance.py
 ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test_capture_validation_source_lineage_v3.py
@@ -173,6 +177,7 @@ af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
 9a05b2a025a694f94e158d2de74242d2a820efbc8973920001895114e7a894fb	14880	tests/test_periodic_cache_source_lineage.py
+8dccac5551ec4d519f98b4b81d8f10ba47bd1f19ece6168ce137c51dc2ca2f0e	18170	tests/test_periodic_capture_training_cache.py
 9c8ff961404bb8d5da86a30c3f533fe8d1909cdfbd76ebb496262ff222121d51	24769	tests/test_periodic_descriptor_cache_v2.py
 0397dbfdfc5f3cd356d437dfec762bcc498e9847eb1c30ae8f71f4139b4e9dd7	22302	tests/test_periodic_training_cache.py
 af9b5adc42d82b9a0aa4ed744bb069e36b7dd041c1957fdc97d17eb814a0fc0c	17284	tests/test_phasepair_core_base_qualification.py
@@ -215,6 +220,7 @@ c88e5b91823b22ebb7a4019acb328040c6b42be7acc03738949439e4efbe443f	10126	tests/tes
 ec0609bf54766ffec53a1d7e4af7e778e2c2efbd9f4d2c4c06984814a066ca7f	21782	tests/test_phaseset_capture_prepared_storage.py
 a02d460a28bd6aeb372fa66e779cb4a65c86536ab951ccd3b6c30ed8091cbd15	8371	tests/test_phaseset_capture_training.py
 beacbd78c68771001b60ff7c6271e6d99c92d24d83f251e1074fc4ce50d1216c	15511	tests/test_phaseset_capture_validation.py
+f936263319934da3fac8ad2dead96cadb6218950809ae0fe300f25d058743612	8105	tests/test_phaseset_capture_validation_cached.py
 5dbbb81d6725f1185f4c12dc85653b6eac1e47ae2ffb7fb36f5b951eb8dea632	8848	tests/test_phaseset_cli.py
 c3060c5567214d195e024152f2d427ff3a63f3cc5fef1ded01e73d6887a3431e	13653	tests/test_phaseset_cli_production_bridge.py
 a53fe5c6b53adb5c80421842f806fc45e8305360348d7065aa0ddfb0b9316dcb	6546	tests/test_phaseset_contracts.py
@@ -228,6 +234,7 @@ dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/tes
 04df5b4a8d75bed006f16b56910e4082d4104a45cc8aeb11d6932ede645777cb	14111	tests/test_phaseset_frozen_clip_cache_rehydrate.py
 937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
 4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
+2f4dae243d0886a5e50d203fd8d2af9075012506f5dea6984730fb437b6325fe	46809	tests/test_phaseset_host_base_qualification.py
 14f6d4b54d78876822e6f96440a32360ea4d4654c1d18d8d8662fe0f4dc42bf8	23309	tests/test_phaseset_host_capture_storage.py
 6eb892149a9b3dee02f48e816acfe0026a0efaed8985a32eedf412bad1edd141	11008	tests/test_phaseset_host_intent_binding.py
 d133ca3a4df71baaea1cc40f68d049b56a7fcfc0d0861a87e5916c9637bdadb2	16345	tests/test_phaseset_latency_progress_journal.py
@@ -241,6 +248,7 @@ aae917cd95bde322f4ec7cea693fd5d0c926d51f0a366e7be160426472ad7973	15770	tests/tes
 f6837c3ac23f4209cb3ec3eadb899d0bdcd4b4259c901b021254ffe7b3688492	8482	tests/test_phaseset_preprocessing.py
 64016f9369540dae8b87654f43d09df9b9c07a3c4af06fa45567008b57140193	45251	tests/test_phaseset_production.py
 a1fbdfe55e50f67bb6568d5eba276e24beeb5a44373c516f0bd7758bd5742127	12486	tests/test_phaseset_publication.py
+44552bf3de846bc0ad98e4e4185cc9a19ae2f3172ac8047f4875644afb1010d5	10587	tests/test_phaseset_residual_cached_encode.py
 005a91d472a715e98b3a733cde102ecffc70222e1572d6100bb18f87ce9e3361	33889	tests/test_phaseset_residual_resume.py
 3f7e4d75e8bd29e6b90c8742d8b5aeccdbe08f454257c43a05c14165ae01d327	16439	tests/test_phaseset_scaling.py
 9147a58a884062ca0b549e33aa7141c9e3d8729db1132f328673d59e93b13177	6762	tests/test_phaseset_split_audit.py
diff --git a/STATUS.md b/STATUS.md
index 1523ab4..7351f46 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -13,6 +13,16 @@ evidence, not training results or scientific claims.
 
 ## Server continuation
 
+- The cached complete-capture evaluator and private host qualification
+  controller now pass one combined Linux server regression: 1334 tests,
+  2 existing skips and 39 subtests in 643.90 seconds, with source unchanged.
+  This closes software integration only. Native data access, all formal
+  training, actual cohort qualification and sealed scores remain absent.
+  Fresh static review identified pending early timer-capability admission,
+  exception-safe timer restoration and post-admission source/output/lease
+  failure classification corrections in the qualification controller. See
+  [known host runtime boundaries](docs/HOST_BASE_QUALIFICATION.md).
+
 - The combined capture-lineage, prepared-window cache-plan and CUDA-workspace
   correction passed 157 focused and 1285 full Linux server tests, two existing
   skips and 39 subtests, with source unchanged. A separate actual K=32 GPU
@@ -31,11 +41,33 @@ evidence, not training results or scientific claims.
   server tests, two existing skips and 39 subtests, source unchanged.
   A separate prepared-window periodic plan passed 76 combined tests, admitting
   the exact twenty-epoch source/cache census for each registered seed and
-  reopening each selected shard without fallback. It is not the main-task
-  capture cache or an integrated training/checkpoint/resume implementation.
+  reopening each selected shard without fallback. A separate complete-capture
+  plan now binds the holistic capture source and its cache without relabeling
+  prepared-window auxiliary validation. Neither plan is yet an integrated
+  training/checkpoint/resume implementation.
   See [capture storage](docs/CAPTURE_PREPARED_STORAGE.md) and
   [window cache plan](docs/PERIODIC_TRAINING_CACHE_PLAN.md).
 
+- The complete-capture plan enumerates the actual twenty-epoch prepared source
+  and every holistic validation window for one registered seed, requiring its
+  closed cache census before returning explicit sealed streams. The residual
+  runtime now has a separate cached encoding entry, and a cached holistic
+  evaluator shares the original complete pooling/gallery oracle while changing
+  only periodic descriptor acquisition. The original uncached APIs remain.
+  A focused server suite covering the cached residual model, frozen-text source
+  binding and complete-capture plan passed 121 tests in 271.25 seconds with
+  source unchanged. The complete combined regression then passed 1305 tests,
+  2 existing skips and 39 subtests in 613.86 seconds, again with source
+  unchanged. Three actual official CLIP forwards retained the established
+  outputs and caller RNG. The later cached holistic evaluator passed 108
+  focused server tests in 151.03 seconds with source unchanged; its subsequent
+  combined 1334-test regression is reported above. These were
+  software-source enumeration and text observations, not twenty epochs of
+  training. Training-loop, checkpoint/resume and host cache-plan consumption
+  remain unfinished. See
+  [complete-capture cache plan](docs/PERIODIC_CAPTURE_TRAINING_CACHE.md) and
+  [capture validation](docs/CAPTURE_VALIDATION.md).
+
 - Prepared-v2 batches now retain their exact descriptor source/window/epoch/yaw
   contexts. Repeated rotation of an already contextualized batch is rejected.
   The live CLIP training-source pin was updated and actual official text
@@ -56,8 +88,9 @@ evidence, not training results or scientific claims.
   without an uncached fallback. The model/cache/control suite passed 98 Linux
   server tests; full regression passed 1228 tests, 2 existing skips and
   39 subtests in 401.07 s, source unchanged. These are software fixtures,
-  not real data or measured acceleration. Training-row selection, complete
-  epoch census and checkpoint/resume cache consumption remain unfinished.
+  not real data or measured acceleration. Plan admission now covers training
+  rows and the complete epoch census, but training-loop and checkpoint/resume
+  cache consumption remain unfinished.
   See [descriptor model integration](docs/PERIODIC_DESCRIPTOR_CACHE_V2.md).
   The host now retains the canonical admitted plan/matrix/config instead of
   reading fields absent from the actual command intent. Commands and handler
@@ -79,7 +112,7 @@ evidence, not training results or scientific claims.
   software fixtures, not a real nine-run cohort, formal CUDA latency session,
   winner or licensed-data cache. The later explicit cache-model API is reported
   above; training/resume cache consumption and host qualification command
-  assembly remain unfinished. See
+  execution with real cohort inputs remain unfinished. See
   [base qualification](docs/BASE_COHORT_QUALIFICATION.md),
   [progress journal](docs/LATENCY_PROGRESS_JOURNAL.md) and
   [descriptor cache](docs/PERIODIC_DESCRIPTOR_CACHE_V2.md).
@@ -109,11 +142,16 @@ evidence, not training results or scientific claims.
   A separate unmocked full-width B0/B1/B2 selected-state-to-gallery witness
   passed all three lanes in 19.36 s on analytic K2/K3, T200 captures. It uses
   explicit checkpoint-cursor and text fixtures, not completed formal epochs,
-  real captions or an actual nine-run cohort. At that earlier snapshot, latency and qualification assembly were
-  unfinished; their newer software verification is reported above. Actual
-  qualification execution and host command assembly remain unfinished. The merged source
-  then passed 1124 complete Linux server tests, 2 existing skips and 39 subtests
-  in 315.67 s with the source census unchanged. See
+  real captions or an actual nine-run cohort. The merged source at that stage
+  passed 1124 complete Linux server tests, 2 existing skips and 39 subtests in
+  315.67 s with the source census unchanged. Latency and qualification assembly
+  were unfinished at that snapshot; their newer software verification is
+  reported above. A private host qualification controller is now implemented,
+  and its corrected focused server suite passed 131 tests in 37.07 seconds
+  with source unchanged. The first attempt's seven fixture API failures and
+  123 passes remain retained; only the test file changed for the successful
+  retry. These software fixtures do not constitute actual qualification of a
+  trained cohort. See
   [completed-base capture scoring](docs/BASE_COHORT_VALIDATION.md).
 
 - Strict known-host SSH login to the registered host now succeeds. The initial
@@ -274,7 +312,11 @@ evidence, not training results or scientific claims.
   Fifty-one focused server tests passed, including actual tiny residual
   optimization/checkpoint/resume equality and zero execution on invalid prior
   checkpoint bytes. See [resume details](docs/RESIDUAL_RESUME.md).
-  The remaining preparation/qualification/evaluation host commands are not yet wired.
+  Preparation and sealed evaluation host commands remain unwired. The separate
+  base-qualification controller passed 131 focused server tests after a
+  test-only correction; the first attempt's seven fixture API failures and
+  123 passes remain retained. No actual nine-run completion is claimed; see
+  [host base qualification](docs/HOST_BASE_QUALIFICATION.md).
   Four actual tiny server fits verified observer behavior without changing
   checkpoint/model/loss/validation outputs. These are engineering fixtures,
   not formal training attempts. GitHub CI passed both Windows runtimes and the
diff --git a/docs/BASE_COHORT_QUALIFICATION.md b/docs/BASE_COHORT_QUALIFICATION.md
index 2ab0d8d..6707f41 100644
--- a/docs/BASE_COHORT_QUALIFICATION.md
+++ b/docs/BASE_COHORT_QUALIFICATION.md
@@ -118,6 +118,27 @@ independently retained execution evidence through the existing backend flow.
 This does not introduce an extra user-signature requirement, turn hashes into
 data rights, or authorize access to sealed test data.
 
+## Private host composition
+
+The [host qualification controller](HOST_BASE_QUALIFICATION.md) implements the
+private orchestration seam from the existing `qualify-base` CLI request to this
+resolver, scorer, latency runner, journal and assembler. The CLI still supplies
+only the attempt root; it cannot submit a winner, score, timing, parameter
+count, checkpoint choice or authorization object.
+
+Before the scorer places a model on CUDA, the controller applies the same
+registered-device identity, minimum free-memory and allocator-cap policy used
+by latency. After scoring it releases scorer references, synchronizes, clears
+this process's cuBLAS workspaces, empties the allocator cache and retains the
+exact-zero guard before starting the independent latency session. A scoring
+failure remains primary even if cleanup also fails, and resource causes remain
+HOLD outcomes rather than being converted into model-quality failures.
+
+After a complete journal-agreeing latency result, the controller calls the
+existing assembler, retains its exact bytes and lets the existing production
+adapter recompute them before the backend callback. The controller creates no
+new scientific selector or human-authorization mechanism.
+
 ## Progress, verification and remaining work
 
 The optional typed observer connects to the
@@ -133,5 +154,9 @@ overlap and must not be added as independent experiments.
 
 Tests use explicit software fixtures and injected observations. No actual
 81-visit CUDA timing session, real completed training cohort, qualified winner
-or host `qualify-base` command completion is claimed. Private host command
-composition and all data-dependent execution remain unfinished.
+or data-dependent host `qualify-base` completion is claimed. The host controller
+passed 131 focused server tests in 37.07 seconds with source unchanged,
+including the real CLI-to-backend path and a structurally supplied qualification
+callback. The first attempt's 123 passes and seven fixture API failures remain
+retained. Its correction changed only the test file, not production guards.
+These software tests did not produce real nine-run inputs or a qualified winner.
diff --git a/docs/CAPTURE_VALIDATION.md b/docs/CAPTURE_VALIDATION.md
index 2da6cc8..29e1099 100644
--- a/docs/CAPTURE_VALIDATION.md
+++ b/docs/CAPTURE_VALIDATION.md
@@ -3,8 +3,9 @@
 `capture_pooling.py` and `capture_validation.py` implement the main task's
 numeric model-to-gallery path. `PhaseSetTrainingRuntime.fit(train_source,
 capture_validation_source)` uses it for validation checkpoint selection. The
-private-host on-disk capture source and the upstream licensed-data preparation
-remain separate integration work; this is not a declaration of data readiness.
+private host can load the on-disk capture source, but upstream licensed-data
+preparation and provenance remain separate work; this is not a declaration of
+data readiness.
 
 ## Complete capture inputs
 
@@ -37,7 +38,9 @@ to its actual consumed manifest/NPZ and canonical global ordinal. A source
 must contain either no such lineage or a complete, single-manifest sequence.
 It does not change capture pooling, text order or gallery semantics, and it
 does not by itself connect descriptor caching to this evaluator. The separate
-prepared-window cache plan cannot replace holistic capture validation.
+prepared-window cache plan cannot replace holistic capture validation. The
+[complete-capture descriptor plan](PERIODIC_CAPTURE_TRAINING_CACHE.md) provides
+that distinct typed connection.
 
 ## Encoding and pooling
 
@@ -62,6 +65,28 @@ model/gallery tensor estimate; this is not a total allocator/RSS guarantee.
 Resource failures reject the complete operation, never sample windows,
 captures, people, or edges. CUDA OOM remains an explicit resource failure.
 
+## Explicit cached descriptor path
+
+`run_capture_validation_cached` uses the same complete source, fixed-tree
+pooling, full gallery, objective and capture-macro metric implementation as the
+uncached runner. It accepts an admitted
+`PeriodicDescriptorCaptureTrainingPlan`, binds it to the residual system and
+complete training configuration, and asks the plan to rebuild and validate the
+whole capture source before any model forward or score.
+
+For each actual window in that rebuilt source, the plan reopens one verified
+`CachedPairChunkStream`; `ResidualRetrievalSystem.encode_trainable_cached`
+passes it explicitly to the registered periodic encoder. There is no silent
+cache-miss fallback and no cache of frozen-base or learned residual outputs.
+The attached frozen CLIP feature batch is validated and consumed exactly as in
+the uncached path; cached validation does not re-encode text. Prepared-window
+auxiliary validation is not accepted as a substitute for the holistic source.
+
+The original `run_capture_validation` API remains unchanged. Both entry points
+share the same core, and the cached wrapper/core are included in the scoped
+live-function identity checks. These checks detect in-process drift within the
+declared runtime; they are not arbitrary-Python isolation.
+
 ## Metrics and checkpoints
 
 The gallery contains one motion row per capture and every admitted holistic
@@ -117,3 +142,15 @@ Embody captures, official holistic descriptions, a rights grant, complete host
 command composition, the nine-run base qualification, or sealed evaluation.
 See [host integration](HOST_RUNTIME_INTEGRATION.md) and
 [prepared window storage](PREPARED_DATA_V2.md).
+
+The complete capture descriptor plan and cached residual model entry later
+passed a 121-test focused server suite in 271.25 seconds with source unchanged;
+the complete combined regression passed 1305 tests, 2 existing skips and 39
+subtests in 613.86 seconds, also with source unchanged. Three official CLIP
+forwards retained the established outputs and caller RNG. Those suites
+enumerated the twenty-epoch software source census but did not train for twenty
+epochs or produce a checkpoint. The cached capture runner then passed 108
+focused server tests in 151.03 seconds with source unchanged. Its complete
+combined regression, including host qualification integration, passed 1334
+tests, 2 existing skips and 39 subtests in 643.90 seconds, source unchanged. Training,
+checkpoint/resume and host lifecycle integration for the plan remain pending.
diff --git a/docs/HOST_BASE_QUALIFICATION.md b/docs/HOST_BASE_QUALIFICATION.md
new file mode 100644
index 0000000..fff74d8
--- /dev/null
+++ b/docs/HOST_BASE_QUALIFICATION.md
@@ -0,0 +1,98 @@
+# Private host base qualification
+
+The private host controller composes the already defined base-cohort resolver,
+holistic capture scorer, CUDA latency session, durable progress journal,
+qualification assembler and production adapter. It does not change the public
+selection rule and does not let a CLI caller submit scientific results.
+
+## Request and input boundary
+
+The public command remains `qualify-base --terminal-root`. The authenticated
+host configuration supplies the closed attempt registry, output and journal
+locations, registered device identity, frozen protocol/launcher identities and
+latency in-process wall-time bound. Concrete host names, ports, device identifiers, private paths,
+receipts and data stay outside the public tree.
+
+The controller requires the exact registered nine-run history: each run keeps
+failed predecessors and ends in its only successful terminal. The resolver
+then verifies terminal-bound latest checkpoint bytes and the distinct selected
+validation-best checkpoint where applicable. Missing or inconsistent rows stop
+before scoring. A registry digest records consumed bytes; it is not proof of
+permission or data provenance.
+
+## Closed execution order
+
+For a formal call, the controller performs this sequence:
+
+1. Load the canonical registry and resolve all nine completed chains and their
+   selected checkpoint payloads.
+2. Before capture scoring, observe the registered CUDA device, require the
+   registered minimum free memory, establish the process allocator cap and
+   require an initially clean allocator.
+3. Recompute the complete holistic validation gallery for every selected base
+   checkpoint. Submitted scores and parameter counts are not accepted.
+4. Release scorer objects, clear this process's cuBLAS workspaces, empty the
+   allocator cache and require zero allocated and reserved bytes. The device
+   identity and free-memory observation are checked again.
+5. Run the complete cyclic latency session with its typed write-once observer.
+   The returned session and durable journal terminal must agree.
+6. Assemble the qualification with the existing selector, retain the exact
+   qualification and dependency bytes, and pass them to the existing
+   production adapter for independent recomputation and backend callback.
+
+Scoring and latency share the same physical-device and precision binding, but
+latency still performs its own admission. The scoring preflight does not weaken
+the latency gate. Cleanup affects only allocations and workspaces owned by the
+current process and does not stop other jobs.
+
+## Failure and timeout behavior
+
+CUDA identity, free-memory, allocator, workspace and OOM failures are retained
+as HOLD outcomes. If scoring and cleanup both fail, the original scoring error
+and its resource cause remain primary while the cleanup type is recorded
+separately. A successful score cannot enter latency unless cleanup reaches the
+exact-zero boundary.
+
+The in-process interval timer is supported only where the required POSIX timer
+API exists. It refuses an inherited deadline. Normal exit restores the prior
+handler; exception-safe restoration is pending as detailed below.
+Deployment must also provide its separately frozen hard timeout because a
+process signal cannot guarantee that a blocked native call returns. A hard kill
+may leave only a valid prefix of the progress journal and the start record;
+that prefix is not a qualification.
+
+An injected runtime exists only for software contract tests. Even a complete
+synthetic journal from that seam returns a nonformal HOLD and cannot reach
+qualification assembly or authentication.
+
+## Verification status
+
+The controller passed 131 focused server tests in 37.07 seconds with source
+unchanged. Tests cover resource admission/release, failure preservation, timer
+portability, the actual CLI-to-backend path and a structurally supplied
+host-to-production-adapter callback. The first attempt's 123 passes and seven
+fixture API failures in 35.21 seconds remain retained. A test-only successor
+fixed journal bindings, CLI stream arguments, fake-CUDA test isolation and a
+class-name assertion; production controller and host bytes were unchanged.
+These software fixtures do not establish a real nine-run cohort, formal
+81-visit CUDA latency session, qualified winner or data-dependent completion.
+
+The combined source, including cached complete-capture validation, subsequently
+passed 1334 tests, 2 existing skips and 39 subtests in 643.90 seconds on the
+Linux server, with source unchanged. No formal cohort was produced by this
+regression.
+
+Static review of this engineering milestone identified two remaining runtime
+boundary corrections: check POSIX/main-thread timer capability before scoring,
+and preserve timer-handler restoration if timer setup or cleanup fails;
+classify post-admission source/output/lease failures at the qualification host
+boundary as runtime outcomes rather than CLI input errors. These corrections
+and their fault-injection tests are not included in the observed 1334-test
+source. Formal qualification remains unexecuted; this feature-branch milestone
+is not an empirical release.
+
+The earlier K=32 B0/B1/B2 diagnostic verified the corrected per-process CUDA
+release mechanics at registered width, but used untrained models, one warmup
+and one observed forward per model. It is not the formal latency session or a
+host qualification result. Earlier failed observations remain part of the
+history.
diff --git a/docs/HOST_RUNTIME_INTEGRATION.md b/docs/HOST_RUNTIME_INTEGRATION.md
index 47d13c4..5013771 100644
--- a/docs/HOST_RUNTIME_INTEGRATION.md
+++ b/docs/HOST_RUNTIME_INTEGRATION.md
@@ -178,13 +178,22 @@ from phaseset_core.host import main
 raise SystemExit(main(PUBLIC_CLI_ARGUMENTS, config_path=PRIVATE_HOST_CONFIG))
 ```
 
-The private host config schema is `phaseset-private-host-v1`.  It points to a
+The original private host config schema is `phaseset-private-host-v1`. It points to a
 prepared index, an existing receipt record plus the exact artifact file for
 each receipt digest, the installed source-tree digest, and runtime facts
 (device, BF16 qualification decision, edge budget, checkpoint cadence, and
 resume metadata).  Paths may be absolute or relative to the private config.
 They are never serialized by the public CLI.
 
+The backward-compatible `phaseset-private-host-v2` requires the
+closed `base_qualification` configuration. Its `qualify-base` implementation
+resolves the actual nine terminal histories, rescoring their selected
+checkpoints on complete validation captures before an independent CUDA
+latency observation and qualification assembly. See
+[host base qualification](HOST_BASE_QUALIFICATION.md) for the exact command,
+latency in-process wall-time bound, runtime admission, evidence and failure semantics. Existing
+v1 configurations are not silently upgraded or assigned a qualification.
+
 For residual runs, the optional `residual` object points to the one canonical
 base-qualification artifact and one canonical capacity-audit artifact per
 registered seed. The host process derives and verifies their SHA-256 values;
@@ -219,17 +228,20 @@ rights assertion when Embody approval is unknown.
   reconstructs those same dependencies and passes the verified predecessor
   record to the unchanged runtime. Historical cache-file SHA provenance is not
   retroactively claimed when only energy floors were consumed.
-- `prepare-data`, `audit-split`, `qualify-base`, `build-periodic-cache`,
+- `prepare-data`, `audit-split`, `build-periodic-cache`,
   `evaluate`, `bootstrap`, and `render-paper` are not
   implemented here.  Calling one raises before a `BackendExecution` exists.
 - The public `resume` parser has no stop-after-step option. It accepts B0/B1/B2
   and registered final system IDs; the host implements base and residual resume
   and recovers identity from the verified attempt ledger.
-- The current public training runtime does not accept a precomputed periodic
-  descriptor cache. The host authenticates the complete cache payload and
-  consumes its energy floors, but periodic descriptors are recomputed by the
-  model path. Wiring cached descriptors into training is a lower-level public
-  runtime gap and must not be represented as cache consumption in reports.
+- The residual encoder and complete-capture evaluator now expose explicit
+  reader-backed descriptor-cache APIs. The training loop and this host's
+  residual construction/checkpoint/resume path do not yet consume those APIs.
+  The existing host cache argument authenticates the complete cache payload
+  and consumes its energy floors, while its model path still recomputes
+  descriptors. That historical behavior must not be represented as descriptor
+  cache consumption in reports. See the
+  [complete-capture cache plan](PERIODIC_CAPTURE_TRAINING_CACHE.md).
 - A hard power loss before the first atomic runtime checkpoint has no resumable
   payload. The host refuses such a resume instead of manufacturing a checkpoint
   receipt. SIGKILL/power loss cannot write a terminal synchronously; terminal
@@ -294,3 +306,10 @@ Negative admissions with changed matrix, command census or handler digest
 leave the snapshot unbound and still allow the subsequent canonical request.
 These tests close the concrete missing-field/poisoning defects, not a complete
 licensed-data CLI training run or the unimplemented commands listed above.
+
+The host qualification integration passed 131 focused Linux server tests in
+37.07 seconds with source unchanged. Tests enter the real CLI/factory/adapter/
+backend path, validate typed holds and independently recheck completion
+artifacts. A preceding run with seven fixture/API failures is retained; only
+the tests changed for this rerun, not production code. These are software
+fixtures, not nine trained bases, formal CUDA latency rows or a selected base.
diff --git a/docs/PERIODIC_CAPTURE_TRAINING_CACHE.md b/docs/PERIODIC_CAPTURE_TRAINING_CACHE.md
new file mode 100644
index 0000000..475196c
--- /dev/null
+++ b/docs/PERIODIC_CAPTURE_TRAINING_CACHE.md
@@ -0,0 +1,76 @@
+# Complete-capture periodic descriptor plan
+
+`PeriodicDescriptorCaptureTrainingPlan` is the authority-zero planning seam
+for one residual system and one registered seed. It combines the existing
+twenty-epoch prepared-training request census with the main task's complete
+holistic capture-validation source. It does not run training, select a
+checkpoint, grant data access or claim a result.
+
+## Complete admission
+
+`admit_periodic_descriptor_capture_training_plan` accepts an exact residual
+`TrainingConfig`, a prepared-v2 training source, a holistic
+`CaptureValidationSource`, and separate authenticated descriptor-cache readers
+for training and capture validation. It accepts no caller-authored plan digest,
+partial key list or cache-miss fallback.
+
+Admission enumerates every actual training row for epochs 0 through 19 using
+the selected seed. It separately enumerates every window in every holistic
+validation capture at validation epoch zero and positive-zero yaw. Capture
+requests derive their source NPZ, storage manifest and canonical global window
+ordinal from the loaded capture source; a loop index or source start frame is
+not substituted for lineage.
+
+The required training and capture key sets must each equal the complete census
+of their corresponding cache reader. Source manifests, full source censuses,
+capture plans, window and caption counts, numeric batches including padding,
+energy floors, edge limits and descriptor families remain bound. Admission
+preserves Python, NumPy and CPU Torch RNG, and preserves CUDA RNG when CUDA was
+already initialized. These checks establish consistency of supplied artifacts,
+not data rights or protection from arbitrary code in the process.
+
+## Explicit selection
+
+`plan.open_training_batch(batch)` revalidates one exact contextualized training
+row and returns its sealed descriptor stream. Before cached holistic validation,
+`plan.validate_capture_validation_source(source)` rebuilds and verifies the
+entire capture source and cache census. The evaluator then calls
+`plan.open_capture_validation_window(capture, window_position)` only for windows
+from that rebuilt source.
+
+Systems 02 and 03 select marginal-power and mean/difference-DCT descriptors;
+systems 04, 06, 07 and 08 select full-relation descriptors. Systems 01 and 05,
+base models, missing shards, wrong batches, foreign families and changed reader
+censuses are rejected rather than routed to an uncached fallback.
+
+`ResidualRetrievalSystem.encode_trainable_cached` is the corresponding model
+entry. It supplies the verified stream to the registered PhaseSet encoder while
+leaving the frozen base, residual head and learned outputs uncached. The
+uncached model method remains available for existing callers.
+
+`run_capture_validation_cached` shares the original holistic evaluator's
+complete pooling, gallery, objective and capture-macro metric core. Its only
+alternate branch is periodic descriptor acquisition. It validates the whole
+capture source before any model forward or gallery score and consumes the
+already attached frozen text features without invoking CLIP again. The original
+`run_capture_validation` signature and uncached behavior remain available.
+
+## Verification and remaining integration
+
+A registered-server focused software suite covering the cached residual model,
+frozen-text source binding and complete capture plan passed 121 tests in
+271.25 seconds with source unchanged. The complete combined regression then
+passed 1305 tests, 2 existing skips and 39 subtests in 613.86 seconds, again
+with source unchanged. Three actual official CLIP forwards retained the
+established output values and caller RNG. Those observations used software
+fixtures and did not perform optimization, finish an epoch, load licensed data
+or produce a checkpoint.
+
+The later cached holistic runner passed 108 focused server tests in 151.03
+seconds with source unchanged. Its complete combined regression with host
+qualification integration passed 1334 tests, 2 existing skips and 39 subtests
+in 643.90 seconds, source unchanged. The complete training lifecycle still needs a typed
+plan handoff for initialization, all epochs, checkpoint/report binding and
+resume. Host storage loading and standalone plan admission are not that lifecycle
+integration. A real-data cache, acceleration claim, formal score and
+qualification remain absent.
diff --git a/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md b/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
index cde5493..53d9e81 100644
--- a/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
+++ b/docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
@@ -125,9 +125,13 @@ The [prepared-window cache plan](PERIODIC_TRAINING_CACHE_PLAN.md) now admits
 an exact complete 20-epoch training census and one val/epoch-zero census for
 each official seed. Its selector reopens the authenticated reader on every
 request; the reader exposes only a read-only complete key-census tuple.
-This is an engineering window-validation seam, not holistic capture
-validation. Training initialization, complete-capture cache composition and
-checkpoint/resume consumption still require explicit integration.
+This remains an engineering window-validation seam, not holistic capture
+validation. A separate [complete-capture plan](PERIODIC_CAPTURE_TRAINING_CACHE.md)
+now binds the same complete training census to every actual holistic
+validation window. The residual system has an explicit reader-backed cached
+encoding method, and [capture validation](CAPTURE_VALIDATION.md) can consume
+that plan through its shared complete-gallery evaluator. Training-loop,
+initialization and checkpoint/resume consumption still require integration.
 The model checks actual batch bytes but does not invent source window ordinals
 or silently select a different epoch/yaw context. Host preparation must supply
 the correct already verified reader handle. No real-data cache, acceleration,
diff --git a/docs/PERIODIC_TRAINING_CACHE_PLAN.md b/docs/PERIODIC_TRAINING_CACHE_PLAN.md
index 9d6532e..478b7b1 100644
--- a/docs/PERIODIC_TRAINING_CACHE_PLAN.md
+++ b/docs/PERIODIC_TRAINING_CACHE_PLAN.md
@@ -5,9 +5,11 @@ formal-residual configuration, prepared-v2 train/val sources and authenticated
 `PeriodicDescriptorCacheV2` readers. It accepts no caller-authored plan digest,
 key subset or cache-miss fallback.
 
-This API is an engineering prepared-window seam. It does not implement the
+This API remains an engineering prepared-window seam. It does not implement the
 main task's holistic capture validation, start training, restore a checkpoint
-or establish data rights.
+or establish data rights. A separate
+[complete-capture plan](PERIODIC_CAPTURE_TRAINING_CACHE.md) now covers the
+holistic validation source without relabeling this auxiliary validation input.
 
 ## Complete requests, one seed at a time
 
@@ -53,6 +55,8 @@ exercise all three seeds, twenty epochs, family mapping, closed census,
 resource order, request mutants and repeated reader opens.
 
 No optimization, trained checkpoint, real-data cache, speedup, formal result or
-holistic validation is claimed by that observation. Complete-capture plans,
-the retrieval runtime's cached entry, initialization/report/checkpoint binding
-and resume remain distinct integration work.
+holistic validation is claimed by that observation. The complete-capture plan
+and explicit cached model entry are implemented as separate typed APIs. Their
+training-loop, initialization/report, checkpoint and resume integration remains
+unfinished; this prepared-window plan is not upgraded into main-task evidence
+by those later components.
diff --git a/src/phaseset_core/base_qualification_host.py b/src/phaseset_core/base_qualification_host.py
new file mode 100644
index 0000000..e56bf22
--- /dev/null
+++ b/src/phaseset_core/base_qualification_host.py
@@ -0,0 +1,1053 @@
+"""Private host controller for one actual nine-row base qualification.
+
+The public CLI supplies only an attempt root.  This module consumes a frozen
+private registry, resolves and scores the actual selected checkpoints, runs
+one complete latency session with a durable observer, and invokes the existing
+qualification assembler.  It never accepts submitted scores, latencies,
+parameter counts, or a winner.
+"""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from dataclasses import asdict, dataclass, field
+from datetime import datetime, timezone
+import gc
+import hashlib
+import json
+import os
+from pathlib import Path
+import re
+import signal
+import stat
+from typing import Final, Iterator
+
+from . import base_cohort_latency as latency_module
+from . import base_cohort_qualification as assembly_module
+from . import base_cohort_resolver as resolver_module
+from . import base_cohort_validation as scoring_module
+from . import latency_progress_journal as journal_module
+from . import production as production_module
+from . import training as training_module
+from .capture_validation import CaptureValidationResourceLimit, CaptureValidationSource
+from .experiments import base_run_ids
+
+
+CONFIG_SCHEMA: Final = "phaseset-private-base-qualification-controller-v2"
+REGISTRY_SCHEMA: Final = "phaseset-private-base-attempt-registry-v1"
+START_SCHEMA: Final = "phaseset-private-base-qualification-start-v1"
+FAILURE_SCHEMA: Final = "phaseset-private-base-qualification-failure-v2"
+GRANT_BASIS_SCHEMA: Final = "phaseset-private-base-qualification-grant-basis-v2"
+AUTHORIZATION_SCHEMA: Final = "phaseset-private-base-qualification-authorization-v1"
+COMPLETION_SCHEMA: Final = "phaseset-private-base-qualification-completion-v2"
+SCORING_CUDA_SCHEMA: Final = "phaseset-private-base-scoring-cuda-v1"
+MAX_REGISTRY_BYTES: Final = 1024 * 1024
+MAX_OUTPUT_BYTES: Final = 8 * 1024 * 1024
+MAX_WALL_TIMEOUT_SECONDS: Final = 7 * 24 * 60 * 60
+_SESSION_ID: Final = re.compile(r"[A-Za-z0-9][A-Za-z0-9._-]{0,95}")
+_CUDA_UUID: Final = re.compile(
+    r"GPU-[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}"
+)
+
+
+class HostBaseQualificationError(ValueError):
+    """The private controller configuration or retained evidence is invalid."""
+
+
+class _NonformalRuntimeHold(RuntimeError):
+    """An explicitly injected engineering runtime reached its closed boundary."""
+
+
+class _ScoringCudaHold(RuntimeError):
+    """The actual scorer CUDA admission or zero-allocation boundary held."""
+
+    def __init__(self, failure_code: str, message: str) -> None:
+        super().__init__(message)
+        self.failure_code = failure_code
+
+
+@dataclass(frozen=True, slots=True)
+class _ScoringCudaState:
+    runtime: object
+    admission_observation: latency_module.SharedCudaObservation
+    runtime_identity: latency_module.BaseLatencyRuntimeIdentity
+    post_context_observation: latency_module.SharedCudaObservation
+
+
+@dataclass(frozen=True, slots=True)
+class HostBaseQualificationConfig:
+    registry_path: Path
+    registry_sha256: str
+    output_root: Path
+    journal_parent: Path
+    session_id: str
+    registered_cuda_uuid: str
+    latency_protocol_sha256: str
+    launcher_sha256: str
+    wall_timeout_seconds: int
+    schema: str = CONFIG_SCHEMA
+
+    def __post_init__(self) -> None:
+        for name in ("registry_path", "output_root", "journal_parent"):
+            if not isinstance(getattr(self, name), Path):
+                raise TypeError(f"{name} must be pathlib.Path")
+        for name in (
+            "registry_sha256",
+            "latency_protocol_sha256",
+            "launcher_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        if type(self.session_id) is not str or _SESSION_ID.fullmatch(self.session_id) is None:
+            raise HostBaseQualificationError("qualification session_id is not one safe component")
+        if (
+            type(self.registered_cuda_uuid) is not str
+            or _CUDA_UUID.fullmatch(self.registered_cuda_uuid) is None
+        ):
+            raise HostBaseQualificationError("registered CUDA UUID is invalid")
+        if (
+            type(self.wall_timeout_seconds) is not int
+            or not 1 <= self.wall_timeout_seconds <= MAX_WALL_TIMEOUT_SECONDS
+        ):
+            raise HostBaseQualificationError("formal latency wall timeout is outside its bound")
+        if self.schema != CONFIG_SCHEMA:
+            raise HostBaseQualificationError("qualification controller schema changed")
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class HostBaseQualificationAuthentication:
+    authorization: production_module.PrivateBaseQualificationAuthorization
+    qualification_sha256: str
+    adapter_sha256: str
+    grant_path: Path
+    qualification_path: Path
+    grant_raw: bytes = field(repr=False)
+    qualification_raw: bytes = field(repr=False)
+
+    def __post_init__(self) -> None:
+        if type(self.authorization) is not production_module.PrivateBaseQualificationAuthorization:
+            raise HostBaseQualificationError("qualification authorization type is invalid")
+        for name in ("qualification_sha256", "adapter_sha256"):
+            _lower_sha256(getattr(self, name), name)
+        if not isinstance(self.grant_path, Path) or not isinstance(
+            self.qualification_path, Path
+        ):
+            raise TypeError("qualification authentication paths must be pathlib.Path")
+        if type(self.grant_raw) is not bytes or type(self.qualification_raw) is not bytes:
+            raise TypeError("qualification authentication bytes must be exact bytes")
+        if _sha256(self.grant_raw) != self.authorization.external_grant_sha256:
+            raise HostBaseQualificationError("authorization does not bind the grant bytes")
+        if _sha256(self.qualification_raw) != self.qualification_sha256:
+            raise HostBaseQualificationError("authentication does not bind qualification bytes")
+
+    def verifies(
+        self,
+        authorization: object,
+        *,
+        qualification_sha256: object,
+        adapter_sha256: object,
+    ) -> bool:
+        if (
+            authorization != self.authorization
+            or qualification_sha256 != self.qualification_sha256
+            or adapter_sha256 != self.adapter_sha256
+        ):
+            return False
+        try:
+            grant, grant_sha = resolver_module._read_owned_file(
+                self.grant_path,
+                label="qualification grant",
+                maximum_bytes=MAX_OUTPUT_BYTES,
+                expected_sha256=self.authorization.external_grant_sha256,
+            )
+            qualification, qualification_sha = resolver_module._read_owned_file(
+                self.qualification_path,
+                label="qualification artifact",
+                maximum_bytes=MAX_OUTPUT_BYTES,
+                expected_sha256=self.qualification_sha256,
+            )
+        except (OSError, TypeError, ValueError, RuntimeError):
+            return False
+        return (
+            grant == self.grant_raw
+            and grant_sha == self.authorization.external_grant_sha256
+            and qualification == self.qualification_raw
+            and qualification_sha == self.qualification_sha256
+        )
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class HostBaseQualificationRun:
+    execution: production_module.BackendExecution
+    authentication: HostBaseQualificationAuthentication | None
+    journal_path: Path | None
+    registry_sha256: str
+    formal: bool
+
+    def __post_init__(self) -> None:
+        if type(self.execution) is not production_module.BackendExecution:
+            raise HostBaseQualificationError("controller execution type is invalid")
+        _lower_sha256(self.registry_sha256, "registry_sha256")
+        if type(self.formal) is not bool:
+            raise TypeError("formal must be an exact bool")
+        if self.execution.outcome == "COMPLETED":
+            if not self.formal or type(self.authentication) is not HostBaseQualificationAuthentication:
+                raise HostBaseQualificationError("only a formal run may return completion")
+        elif self.authentication is not None or self.execution.semantic_evidence is not None:
+            raise HostBaseQualificationError("non-completion cannot expose qualification evidence")
+
+
+def _sha256(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _utc_now() -> str:
+    return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise HostBaseQualificationError(f"{label} must be lowercase SHA-256 hex")
+    return value
+
+
+def _canonical_json_bytes(value: object) -> bytes:
+    try:
+        return (
+            json.dumps(
+                value,
+                sort_keys=True,
+                separators=(",", ":"),
+                ensure_ascii=True,
+                allow_nan=False,
+            ).encode("ascii")
+            + b"\n"
+        )
+    except (TypeError, ValueError, OverflowError, RecursionError) as error:
+        raise HostBaseQualificationError("controller value is not canonical JSON") from error
+
+
+def _without_duplicates(pairs: list[tuple[str, object]]) -> dict[str, object]:
+    result: dict[str, object] = {}
+    for key, value in pairs:
+        if key in result:
+            raise HostBaseQualificationError("registry JSON repeats a key")
+        result[key] = value
+    return result
+
+
+def _parse_canonical_object(raw: bytes, label: str) -> dict[str, object]:
+    try:
+        value = json.loads(
+            raw,
+            object_pairs_hook=_without_duplicates,
+            parse_float=lambda _value: (_ for _ in ()).throw(
+                HostBaseQualificationError(f"{label} cannot contain floats")
+            ),
+            parse_constant=lambda _value: (_ for _ in ()).throw(
+                HostBaseQualificationError(f"{label} cannot contain constants")
+            ),
+        )
+    except HostBaseQualificationError:
+        raise
+    except (UnicodeDecodeError, json.JSONDecodeError, RecursionError) as error:
+        raise HostBaseQualificationError(f"{label} is not valid bounded JSON") from error
+    if type(value) is not dict or raw != _canonical_json_bytes(value):
+        raise HostBaseQualificationError(f"{label} is not one canonical object")
+    return value
+
+
+def _write_once(path: Path, raw: bytes) -> Path:
+    if type(raw) is not bytes or not raw or len(raw) > MAX_OUTPUT_BYTES:
+        raise HostBaseQualificationError("controller output bytes exceed their bound")
+    flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL
+    for name in ("O_BINARY", "O_CLOEXEC", "O_NOFOLLOW"):
+        flags |= int(getattr(os, name, 0))
+    try:
+        fd = os.open(path, flags, 0o600)
+    except OSError as error:
+        raise HostBaseQualificationError("controller output already exists or cannot be created") from error
+    try:
+        offset = 0
+        while offset < len(raw):
+            written = os.write(fd, raw[offset:])
+            if written <= 0:
+                raise OSError("short controller artifact write")
+            offset += written
+        os.fsync(fd)
+    finally:
+        os.close(fd)
+    if os.name == "posix":
+        directory_fd = os.open(path.parent, os.O_RDONLY | os.O_DIRECTORY | os.O_CLOEXEC)
+        try:
+            os.fsync(directory_fd)
+        finally:
+            os.close(directory_fd)
+    return path
+
+
+def _write_json_once(path: Path, value: object) -> tuple[Path, bytes]:
+    raw = _canonical_json_bytes(value)
+    return _write_once(path, raw), raw
+
+
+def _checked_directory(path: Path, label: str) -> Path:
+    if not isinstance(path, Path):
+        raise TypeError(f"{label} must be pathlib.Path")
+    try:
+        metadata = path.lstat()
+    except OSError as error:
+        raise HostBaseQualificationError(f"{label} is absent") from error
+    if not stat.S_ISDIR(metadata.st_mode) or path.is_symlink():
+        raise HostBaseQualificationError(f"{label} must be a non-symlink directory")
+    return path
+
+
+def load_base_attempt_registry(
+    path: Path,
+    *,
+    expected_sha256: str,
+) -> tuple[tuple[resolver_module.BaseAttemptRegistryRow, ...], bytes, str]:
+    """Read one canonical private registry into the resolver's exact rows."""
+
+    expected = _lower_sha256(expected_sha256, "registry expected SHA-256")
+    try:
+        raw, digest = resolver_module._read_owned_file(
+            path,
+            label="base attempt registry",
+            maximum_bytes=MAX_REGISTRY_BYTES,
+            expected_sha256=expected,
+        )
+    except (OSError, TypeError, ValueError, RuntimeError) as error:
+        raise HostBaseQualificationError("base attempt registry bytes are invalid") from error
+    value = _parse_canonical_object(raw, "base attempt registry")
+    if set(value) != {
+        "authority",
+        "production",
+        "result_claimed",
+        "rows",
+        "schema",
+    }:
+        raise HostBaseQualificationError("base attempt registry keys are not closed")
+    if (
+        type(value["authority"]) is not int
+        or value["authority"] != 0
+        or type(value["production"]) is not bool
+        or value["production"] is not False
+        or type(value["result_claimed"]) is not bool
+        or value["result_claimed"] is not False
+        or value["schema"] != REGISTRY_SCHEMA
+        or type(value["rows"]) is not list
+    ):
+        raise HostBaseQualificationError("base attempt registry header is invalid")
+    rows: list[resolver_module.BaseAttemptRegistryRow] = []
+    for index, item in enumerate(value["rows"]):
+        if type(item) is not dict or set(item) != {
+            "attempt_id",
+            "run_id",
+            "terminal_outcome",
+            "terminal_sha256",
+        }:
+            raise HostBaseQualificationError(f"base attempt registry row {index} is invalid")
+        try:
+            rows.append(resolver_module.BaseAttemptRegistryRow(**item))
+        except (TypeError, ValueError, RuntimeError) as error:
+            raise HostBaseQualificationError(
+                f"base attempt registry row {index} is invalid"
+            ) from error
+    try:
+        checked = resolver_module._checked_registry_rows(tuple(rows))
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise HostBaseQualificationError("base attempt registry census is invalid") from error
+    run_order = {run_id: index for index, run_id in enumerate(base_run_ids())}
+    if tuple(run_order[row.run_id] for row in checked) != tuple(
+        sorted(run_order[row.run_id] for row in checked)
+    ):
+        raise HostBaseQualificationError("base attempt registry rows changed registered run order")
+    for run_id in base_run_ids():
+        history = tuple(row for row in checked if row.run_id == run_id)
+        if history[-1].terminal_outcome != "SUCCEEDED" or any(
+            row.terminal_outcome == "SUCCEEDED" for row in history[:-1]
+        ):
+            raise HostBaseQualificationError(
+                "each base registry history must end in its only successful attempt"
+            )
+    return checked, raw, digest
+
+
+@contextmanager
+def _formal_latency_timeout(
+    seconds: int,
+    *,
+    _os_name: str | None = None,
+    _signal_api: object | None = None,
+) -> Iterator[None]:
+    """Install the process-local formal timeout; the outer hard-kill is separate."""
+
+    os_name = os.name if _os_name is None else _os_name
+    signal_api = signal if _signal_api is None else _signal_api
+    if os_name != "posix" or not all(
+        hasattr(signal_api, name)
+        for name in ("SIGALRM", "ITIMER_REAL", "setitimer", "getitimer")
+    ):
+        raise HostBaseQualificationError("formal latency timeout requires POSIX interval timers")
+    if type(seconds) is not int or not 1 <= seconds <= MAX_WALL_TIMEOUT_SECONDS:
+        raise HostBaseQualificationError("formal latency timeout is outside its bound")
+    prior_timer = signal_api.getitimer(signal_api.ITIMER_REAL)
+    if prior_timer != (0.0, 0.0):
+        raise HostBaseQualificationError("formal latency refuses an inherited process timer")
+    prior_handler = signal_api.getsignal(signal_api.SIGALRM)
+
+    def timeout_handler(_signum: int, _frame: object) -> None:
+        raise TimeoutError("formal base latency wall timeout expired")
+
+    signal_api.signal(signal_api.SIGALRM, timeout_handler)
+    signal_api.setitimer(signal_api.ITIMER_REAL, float(seconds), 0.0)
+    try:
+        yield
+    finally:
+        signal_api.setitimer(signal_api.ITIMER_REAL, 0.0, 0.0)
+        signal_api.signal(signal_api.SIGALRM, prior_handler)
+
+
+def _journal_terminal_payload(
+    read: journal_module.LatencyJournalReadResult,
+) -> dict[str, object]:
+    if not read.records:
+        raise HostBaseQualificationError("latency journal has no committed records")
+    value = _parse_canonical_object(read.records[-1].raw, "latency terminal event")
+    if value.get("event_type") != "TERMINAL" or type(value.get("payload")) is not dict:
+        raise HostBaseQualificationError("latency journal did not end in a terminal event")
+    return value["payload"]
+
+
+def _scoring_precision(admission: resolver_module.BaseCohortAdmission) -> str:
+    try:
+        decision = training_module.resolve_precision(
+            training_module.TrainingConfig(
+                stage="base",
+                seed=1729,
+                device=admission.device,
+                request_bf16=admission.request_bf16,
+                bf16_runtime_qualified=admission.bf16_runtime_qualified,
+                edge_budget=admission.edge_budget,
+                checkpoint_every_updates=admission.checkpoint_every_updates,
+                synthetic_contract=False,
+            )
+        )
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise HostBaseQualificationError(
+            "base scoring CUDA precision could not be resolved"
+        ) from error
+    return decision.mode
+
+
+def _admit_scoring_cuda(
+    config: HostBaseQualificationConfig,
+    admission: resolver_module.BaseCohortAdmission,
+    *,
+    _runtime: object | None = None,
+) -> _ScoringCudaState:
+    """Apply the latency UUID/free/cap admission before any capture scoring."""
+
+    runtime = (
+        latency_module._NvidiaSmiTorchRuntime(config.registered_cuda_uuid)
+        if _runtime is None
+        else _runtime
+    )
+    try:
+        device = latency_module.torch.device(admission.device)
+        if device.type != "cuda":
+            raise HostBaseQualificationError("base capture scoring requires CUDA")
+        observed = runtime.snapshot()
+        if type(observed) is not latency_module.SharedCudaObservation:
+            raise _ScoringCudaHold(
+                "RUNTIME_DRIFT",
+                "base scoring admission snapshot is malformed",
+            )
+        latency_module._validate_snapshot(
+            observed,
+            registered_uuid=config.registered_cuda_uuid,
+            runtime=None,
+        )
+        if observed.memory_free_mib < latency_module.MINIMUM_FREE_MIB:
+            raise _ScoringCudaHold(
+                "RESOURCE_LIMIT",
+                "base scoring admission has less than 8 GiB free"
+            )
+        identity = runtime.initialize(
+            device=device,
+            precision_mode=_scoring_precision(admission),
+            admission_observation=observed,
+        )
+        if type(identity) is not latency_module.BaseLatencyRuntimeIdentity:
+            raise _ScoringCudaHold(
+                "RUNTIME_DRIFT",
+                "base scoring CUDA identity is malformed",
+            )
+        post_context = runtime.snapshot()
+        if type(post_context) is not latency_module.SharedCudaObservation:
+            raise _ScoringCudaHold(
+                "RUNTIME_DRIFT",
+                "base scoring post-context snapshot is malformed",
+            )
+        latency_module._validate_snapshot(
+            post_context,
+            registered_uuid=config.registered_cuda_uuid,
+            runtime=identity,
+        )
+        if post_context.memory_free_mib < latency_module.MINIMUM_FREE_MIB:
+            raise _ScoringCudaHold(
+                "RESOURCE_LIMIT",
+                "base scoring CUDA context has less than 8 GiB free"
+            )
+    except (HostBaseQualificationError, _ScoringCudaHold):
+        raise
+    except (TypeError, ValueError, RuntimeError) as error:
+        raise _ScoringCudaHold(
+            latency_module._failure_code(error),
+            "base scoring CUDA admission failed",
+        ) from error
+    return _ScoringCudaState(
+        runtime=runtime,
+        admission_observation=observed,
+        runtime_identity=identity,
+        post_context_observation=post_context,
+    )
+
+
+def _strip_exception_tracebacks(error: BaseException) -> None:
+    """Release scorer frames while retaining exact exception objects and chaining."""
+
+    pending: list[BaseException] = [error]
+    seen: set[int] = set()
+    while pending:
+        current = pending.pop()
+        if id(current) in seen:
+            continue
+        seen.add(id(current))
+        current.__traceback__ = None
+        for linked in (current.__cause__, current.__context__):
+            if linked is not None:
+                pending.append(linked)
+
+
+def _release_scoring_cuda(
+    state: _ScoringCudaState,
+    *,
+    _torch_api: object | None = None,
+) -> latency_module.SharedCudaObservation:
+    """Clear scorer objects/workspaces, then retain the exact-zero latency gate."""
+
+    torch_api = latency_module.torch if _torch_api is None else _torch_api
+    gc.collect()
+    try:
+        try:
+            device = torch_api.device(state.runtime_identity.device)
+        except (AttributeError, TypeError, ValueError, RuntimeError) as error:
+            raise _ScoringCudaHold(
+                "RUNTIME_DRIFT",
+                "base scoring CUDA release device is malformed",
+            ) from error
+        state.runtime.synchronize(device)
+        clear_workspaces = getattr(
+            getattr(torch_api, "_C", None),
+            "_cuda_clearCublasWorkspaces",
+            None,
+        )
+        if not callable(clear_workspaces):
+            raise _ScoringCudaHold(
+                "RUNTIME_DRIFT",
+                "Torch CUDA cuBLAS workspace clear API is unavailable after scoring"
+            )
+        clear_workspaces()
+        state.runtime.synchronize(device)
+        torch_api.cuda.empty_cache()
+        allocated = int(torch_api.cuda.memory_allocated(device))
+        reserved = int(torch_api.cuda.memory_reserved(device))
+        if allocated != 0 or reserved != 0:
+            raise _ScoringCudaHold(
+                "RESOURCE_LIMIT",
+                "base scoring CUDA allocation remained before latency"
+            )
+        after = state.runtime.snapshot()
+        if type(after) is not latency_module.SharedCudaObservation:
+            raise _ScoringCudaHold(
+                "RUNTIME_DRIFT",
+                "base scoring release snapshot is malformed",
+            )
+        latency_module._validate_snapshot(
+            after,
+            registered_uuid=state.runtime_identity.device_uuid,
+            runtime=state.runtime_identity,
+        )
+        if after.memory_free_mib < latency_module.MINIMUM_FREE_MIB:
+            raise _ScoringCudaHold(
+                "RESOURCE_LIMIT",
+                "base scoring release has less than 8 GiB free"
+            )
+    except (HostBaseQualificationError, _ScoringCudaHold):
+        raise
+    except (AttributeError, TypeError, ValueError, RuntimeError) as error:
+        raise _ScoringCudaHold(
+            latency_module._failure_code(error),
+            "base scoring CUDA release failed",
+        ) from error
+    return after
+
+
+def _failure_code(error: BaseException, stage: str) -> tuple[str, str]:
+    if isinstance(error, _NonformalRuntimeHold):
+        return "HELD", "NONFORMAL_TEST_RUNTIME"
+    if isinstance(error, _ScoringCudaHold):
+        return "HELD", f"BASE_SCORING_CUDA_{error.failure_code}"
+    if isinstance(error, latency_module.BaseCohortLatencyHold):
+        return "HELD", f"LATENCY_{error.failure_code}"
+    if isinstance(error, (TimeoutError, journal_module.LatencyJournalWriteError)):
+        return "HELD", "LATENCY_OBSERVATION_INCOMPLETE"
+    if isinstance(error, resolver_module.BaseCohortResolutionError):
+        return "HELD", "BASE_COHORT_INPUT_INVALID"
+    if isinstance(error, HostBaseQualificationError) and stage in {
+        "REGISTRY",
+        "RESOLVE",
+    }:
+        return "HELD", "BASE_COHORT_INPUT_INVALID"
+    chain: list[BaseException] = []
+    current: BaseException | None = error
+    seen: set[int] = set()
+    while current is not None and id(current) not in seen:
+        seen.add(id(current))
+        chain.append(current)
+        current = current.__cause__
+    if any(
+        isinstance(item, (CaptureValidationResourceLimit, latency_module.torch.cuda.OutOfMemoryError))
+        for item in chain
+    ):
+        return "HELD", "BASE_CAPTURE_SCORE_RESOURCE_LIMIT"
+    if isinstance(error, scoring_module.BaseCohortValidationError):
+        return "FAILED", "BASE_CAPTURE_SCORE_FAILED"
+    if isinstance(error, assembly_module.BaseCohortQualificationError):
+        return "FAILED", "BASE_QUALIFICATION_ASSEMBLY_FAILED"
+    return "FAILED", "BASE_QUALIFICATION_CONTROLLER_FAILED"
+
+
+def _persist_failure(
+    output_directory: Path,
+    *,
+    error: BaseException,
+    stage: str,
+    registry_sha256: str,
+    journal_path: Path | None,
+    journal_sha256: str | None,
+    secondary_cleanup_error_type: str | None,
+    completed_at_utc: str,
+) -> tuple[production_module.BackendExecution, Path]:
+    outcome, failure_code = _failure_code(error, stage)
+    path, _raw = _write_json_once(
+        output_directory / "failure.json",
+        {
+            "authority": 0,
+            "completed_at_utc": completed_at_utc,
+            "error_type": type(error).__qualname__,
+            "failure_code": failure_code,
+            "formal_qualification_completed": False,
+            "journal_present": journal_path is not None,
+            "journal_read_sha256": journal_sha256,
+            "production": False,
+            "registry_sha256": registry_sha256,
+            "result_claimed": False,
+            "schema": FAILURE_SCHEMA,
+            "secondary_cleanup_error_type": secondary_cleanup_error_type,
+            "stage": stage,
+            "status": outcome,
+        },
+    )
+    return (
+        production_module.BackendExecution(
+            outcome=outcome,
+            completed_at_utc=completed_at_utc,
+            artifacts=(path,),
+        ),
+        path,
+    )
+
+
+def run_host_base_qualification(
+    attempt_root: Path,
+    *,
+    config: HostBaseQualificationConfig,
+    admission: resolver_module.BaseCohortAdmission,
+    validation_source: CaptureValidationSource,
+    output_directory: Path,
+    adapter_sha256: str,
+    _test_runtime: object | None = None,
+) -> HostBaseQualificationRun:
+    """Execute the actual chain; an injected runtime can only return nonformal HOLD."""
+
+    if type(config) is not HostBaseQualificationConfig:
+        raise TypeError("config must be exact HostBaseQualificationConfig")
+    if type(admission) is not resolver_module.BaseCohortAdmission:
+        raise TypeError("admission must be exact BaseCohortAdmission")
+    if type(validation_source) is not CaptureValidationSource:
+        raise TypeError("validation_source must be exact CaptureValidationSource")
+    _lower_sha256(adapter_sha256, "adapter_sha256")
+    root = _checked_directory(attempt_root, "base attempt root")
+    output = _checked_directory(output_directory, "qualification output directory")
+    output_root = _checked_directory(
+        config.output_root,
+        "base qualification output root",
+    )
+    journal_parent = _checked_directory(
+        config.journal_parent,
+        "latency journal parent",
+    )
+    if len({root.resolve(), output_root.resolve(), journal_parent.resolve()}) != 3:
+        raise HostBaseQualificationError(
+            "attempt, qualification-output, and journal roots must be distinct"
+        )
+    if output.resolve() != (output_root / config.session_id).resolve():
+        raise HostBaseQualificationError(
+            "qualification output directory differs from its frozen session path"
+        )
+    formal = _test_runtime is None
+    stage = "REGISTRY"
+    registry_digest = config.registry_sha256
+    journal_path: Path | None = None
+    journal_sha256: str | None = None
+    scoring_cuda_path: Path | None = None
+    scoring_cuda_raw: bytes | None = None
+    secondary_cleanup_error_type: str | None = None
+    start_path: Path | None = None
+    try:
+        registry_rows, registry_raw, registry_digest = load_base_attempt_registry(
+            config.registry_path,
+            expected_sha256=config.registry_sha256,
+        )
+        start_path, _ = _write_json_once(
+            output / "controller-start.json",
+            {
+                "adapter_sha256": adapter_sha256,
+                "authority": 0,
+                "formal_execution_requested": formal,
+                "journal_binding": {
+                    "latency_protocol_sha256": config.latency_protocol_sha256,
+                    "launcher_sha256": config.launcher_sha256,
+                    "registered_cuda_uuid": config.registered_cuda_uuid,
+                },
+                "production": False,
+                "registry_sha256": registry_digest,
+                "result_claimed": False,
+                "schema": START_SCHEMA,
+                "session_id": config.session_id,
+                "started_at_utc": _utc_now(),
+                "source_tree_sha256": admission.source_tree_sha256,
+                "train_manifest_sha256": admission.train_manifest_sha256,
+                "val_census_sha256": admission.val_manifest_sha256,
+                "wall_timeout_seconds": config.wall_timeout_seconds,
+            },
+        )
+        stage = "RESOLVE"
+        cohort = resolver_module.resolve_completed_base_cohort(
+            root,
+            registry_rows=registry_rows,
+            admission=admission,
+        )
+        stage = "SCORE"
+        if formal:
+            scoring_state = _admit_scoring_cuda(config, admission)
+            score_error: Exception | None = None
+            scored = None
+            try:
+                scored = scoring_module.score_resolved_base_cohort(
+                    cohort,
+                    admission=admission,
+                    validation_source=validation_source,
+                )
+            except Exception as error:
+                score_error = error
+                _strip_exception_tracebacks(error)
+            release_error: Exception | None = None
+            try:
+                post_score_observation = _release_scoring_cuda(scoring_state)
+            except Exception as error:
+                release_error = error
+                _strip_exception_tracebacks(error)
+            if score_error is not None:
+                if release_error is not None:
+                    secondary_cleanup_error_type = type(release_error).__qualname__
+                raise score_error
+            if release_error is not None:
+                raise release_error
+            if type(scored) is not scoring_module.BaseCohortValidationResult:
+                raise HostBaseQualificationError(
+                    "base scorer returned an invalid cohort result"
+                )
+            if any(
+                row.device != scoring_state.runtime_identity.device
+                or row.precision_mode != scoring_state.runtime_identity.precision_mode
+                for row in scored.observations
+            ):
+                raise HostBaseQualificationError(
+                    "base scorer device or precision differs from CUDA admission"
+                )
+            scoring_cuda_path, scoring_cuda_raw = _write_json_once(
+                output / "scoring-cuda.json",
+                {
+                    "admission_observation": scoring_state.admission_observation.payload(),
+                    "allocator_limit_bytes": latency_module.CUDA_ALLOCATOR_LIMIT_BYTES,
+                    "authority": 0,
+                    "post_context_observation": (
+                        scoring_state.post_context_observation.payload()
+                    ),
+                    "post_score_observation": post_score_observation.payload(),
+                    "production": False,
+                    "registered_cuda_uuid": config.registered_cuda_uuid,
+                    "result_claimed": False,
+                    "runtime_identity": asdict(scoring_state.runtime_identity),
+                    "schema": SCORING_CUDA_SCHEMA,
+                    "scored_cohort_sha256": scored.sha256,
+                    "status": "RESOURCE_BOUND_SCORING_COMPLETED",
+                },
+            )
+        else:
+            scored = scoring_module.score_resolved_base_cohort(
+                cohort,
+                admission=admission,
+                validation_source=validation_source,
+            )
+        bindings = journal_module.LatencyJournalBindings(
+            resolved_cohort_sha256=latency_module._resolved_cohort_sha256(cohort),
+            scored_cohort_sha256=scored.sha256,
+            source_tree_sha256=cohort.source_tree_sha256,
+            latency_protocol_sha256=config.latency_protocol_sha256,
+            wrapper_sha256=config.launcher_sha256,
+            registered_cuda_uuid=config.registered_cuda_uuid,
+        )
+        stage = "LATENCY"
+        with journal_module.create_latency_progress_journal(
+            config.journal_parent,
+            session_id=config.session_id,
+            bindings=bindings,
+        ) as writer:
+            journal_path = writer.path
+            with _formal_latency_timeout(config.wall_timeout_seconds):
+                if _test_runtime is None:
+                    latency = latency_module.run_base_cohort_latency_session(
+                        cohort,
+                        admission=admission,
+                        scored_cohort=scored,
+                        registered_cuda_uuid=config.registered_cuda_uuid,
+                        observer=writer,
+                    )
+                else:
+                    latency = latency_module.run_base_cohort_latency_session(
+                        cohort,
+                        admission=admission,
+                        scored_cohort=scored,
+                        registered_cuda_uuid=config.registered_cuda_uuid,
+                        observer=writer,
+                        _runtime=_test_runtime,
+                    )
+        read = journal_module.read_latency_progress_journal(
+            journal_path,
+            expected_bindings=bindings,
+        )
+        journal_sha256 = read.sha256
+        terminal_payload = _journal_terminal_payload(read)
+        if (
+            read.status != journal_module.COMPLETED_STATUS
+            or read.terminal_outcome != "COMPLETED"
+            or terminal_payload.get("outcome") != "COMPLETED"
+            or terminal_payload.get("failure_code") is not None
+            or terminal_payload.get("latency_session_sha256") != latency.sha256
+        ):
+            raise HostBaseQualificationError(
+                "latency call result and durable terminal do not form one completion"
+            )
+        if not formal:
+            raise _NonformalRuntimeHold(
+                "injected latency runtime is an engineering observation, not qualification"
+            )
+        if (
+            type(latency) is not latency_module.BaseCohortLatencySession
+            or latency.actual_runtime is not True
+            or latency.complete is not True
+            or latency.formal_samples_complete is not True
+            or latency.failure_code is not None
+            or latency.status != latency_module.ACTUAL_COMPLETE_STATUS
+        ):
+            raise HostBaseQualificationError("formal latency session is incomplete")
+        stage = "ASSEMBLY"
+        assembled = assembly_module.assemble_base_cohort_qualification(
+            cohort,
+            admission=admission,
+            scored_cohort=scored,
+            latency_session=latency,
+        )
+        if _sha256(registry_raw) != registry_digest:
+            raise HostBaseQualificationError("owned registry bytes changed in memory")
+        registry_after, registry_after_digest = resolver_module._read_owned_file(
+            config.registry_path,
+            label="base attempt registry",
+            maximum_bytes=MAX_REGISTRY_BYTES,
+            expected_sha256=registry_digest,
+        )
+        if registry_after != registry_raw or registry_after_digest != registry_digest:
+            raise HostBaseQualificationError("base attempt registry changed during qualification")
+        if (
+            validation_source.census_sha256 != admission.val_manifest_sha256
+            or validation_source.manifest_sha256 != scored.source_manifest_sha256
+            or latency.sha256 != assembled.dependency_binding.latency_session_sha256
+        ):
+            raise HostBaseQualificationError("qualification source or latency changed after assembly")
+        stage = "PERSIST"
+        if scoring_cuda_path is None or scoring_cuda_raw is None:
+            raise AssertionError("formal qualification lost scoring CUDA evidence")
+        qualification_path = _write_once(output / "base-qualification.json", assembled.artifact)
+        dependency_path, dependency_raw = _write_json_once(
+            output / "dependency-binding.json",
+            asdict(assembled.dependency_binding),
+        )
+        grant_path, grant_raw = _write_json_once(
+            output / "grant-basis.json",
+            {
+                "adapter_sha256": adapter_sha256,
+                "assembly_sha256": assembled.sha256,
+                "authority": 0,
+                "dependency_binding_sha256": assembled.dependency_binding.sha256,
+                "journal_read_sha256": journal_sha256,
+                "latency_session_sha256": latency.sha256,
+                "production": False,
+                "qualification_sha256": _sha256(assembled.artifact),
+                "registry_sha256": registry_digest,
+                "result_claimed": False,
+                "row_binding_sha256s": [row.sha256 for row in assembled.row_bindings],
+                "schema": GRANT_BASIS_SCHEMA,
+                "scoring_cuda_sha256": _sha256(scoring_cuda_raw),
+            },
+        )
+        authorization = production_module.PrivateBaseQualificationAuthorization(
+            authorized_at_utc=_utc_now(),
+            external_grant_sha256=_sha256(grant_raw),
+            validation_manifest_sha256=assembled.qualification.validation_manifest_sha256,
+            query_census_sha256=assembled.qualification.query_census_sha256,
+            evaluator_sha256=assembled.qualification.evaluator_sha256,
+            score_rows_sha256=assembled.qualification.score_rows_sha256,
+        )
+        authorization_path, authorization_raw = _write_json_once(
+            output / "authorization.json",
+            {
+                **asdict(authorization),
+                "authority": 0,
+                "production": False,
+                "result_claimed": False,
+                "schema": AUTHORIZATION_SCHEMA,
+            },
+        )
+        qualification_sha256 = _sha256(assembled.artifact)
+        completed_at_utc = _utc_now()
+        completion_path, _completion_raw = _write_json_once(
+            output / "completion.json",
+            {
+                "artifact_sha256s": {
+                    "authorization": _sha256(authorization_raw),
+                    "dependency_binding": _sha256(dependency_raw),
+                    "grant_basis": _sha256(grant_raw),
+                    "qualification": qualification_sha256,
+                    "scoring_cuda": _sha256(scoring_cuda_raw),
+                },
+                "assembly_sha256": assembled.sha256,
+                "authority": 0,
+                "completed_at_utc": completed_at_utc,
+                "journal_read_sha256": journal_sha256,
+                "production": False,
+                "registry_sha256": registry_digest,
+                "result_claimed": False,
+                "schema": COMPLETION_SCHEMA,
+                "status": "COMPLETED_PENDING_EXISTING_ADAPTER_RECOMPUTATION",
+            },
+        )
+        authentication = HostBaseQualificationAuthentication(
+            authorization=authorization,
+            qualification_sha256=qualification_sha256,
+            adapter_sha256=adapter_sha256,
+            grant_path=grant_path,
+            qualification_path=qualification_path,
+            grant_raw=grant_raw,
+            qualification_raw=assembled.artifact,
+        )
+        execution = production_module.BackendExecution(
+            outcome="COMPLETED",
+            completed_at_utc=completed_at_utc,
+            artifacts=(
+                qualification_path,
+                scoring_cuda_path,
+                dependency_path,
+                grant_path,
+                authorization_path,
+                completion_path,
+            ),
+            semantic_evidence=assembled.execution_evidence,
+        )
+        return HostBaseQualificationRun(
+            execution=execution,
+            authentication=authentication,
+            journal_path=journal_path,
+            registry_sha256=registry_digest,
+            formal=True,
+        )
+    except Exception as error:
+        if journal_path is not None and journal_path.exists():
+            try:
+                # A structurally complete terminal remains nonformal unless it
+                # agrees with the actual call outcome consumed above.
+                if "bindings" in locals():
+                    observed = journal_module.read_latency_progress_journal(
+                        journal_path,
+                        expected_bindings=bindings,
+                    )
+                    journal_sha256 = observed.sha256
+            except (OSError, TypeError, ValueError, RuntimeError):
+                journal_sha256 = None
+        execution, _failure_path = _persist_failure(
+            output,
+            error=error,
+            stage=stage,
+            registry_sha256=registry_digest,
+            journal_path=journal_path,
+            journal_sha256=journal_sha256,
+            secondary_cleanup_error_type=secondary_cleanup_error_type,
+            completed_at_utc=_utc_now(),
+        )
+        prefix = tuple(
+            path for path in (start_path, scoring_cuda_path) if path is not None
+        )
+        artifacts = (*prefix, *execution.artifacts)
+        return HostBaseQualificationRun(
+            execution=production_module.BackendExecution(
+                outcome=execution.outcome,
+                completed_at_utc=execution.completed_at_utc,
+                artifacts=artifacts,
+            ),
+            authentication=None,
+            journal_path=journal_path,
+            registry_sha256=registry_digest,
+            formal=formal,
+        )
+
+
+__all__ = [
+    "CONFIG_SCHEMA",
+    "HostBaseQualificationAuthentication",
+    "HostBaseQualificationConfig",
+    "HostBaseQualificationError",
+    "HostBaseQualificationRun",
+    "MAX_WALL_TIMEOUT_SECONDS",
+    "REGISTRY_SCHEMA",
+    "load_base_attempt_registry",
+    "run_host_base_qualification",
+]
diff --git a/src/phaseset_core/capture_validation.py b/src/phaseset_core/capture_validation.py
index 7b49aa0..e44eca5 100644
--- a/src/phaseset_core/capture_validation.py
+++ b/src/phaseset_core/capture_validation.py
@@ -10,11 +10,11 @@
 
 from __future__ import annotations
 
-from dataclasses import dataclass, field
-from fractions import Fraction
 import hashlib
 import json
-from typing import Final, Literal
+from dataclasses import dataclass, field
+from fractions import Fraction
+from typing import TYPE_CHECKING, Final, Literal
 
 import numpy as np
 import torch
@@ -48,6 +48,11 @@
     resolve_precision,
 )
 
+if TYPE_CHECKING:
+    from .periodic_capture_training_cache import (
+        PeriodicDescriptorCaptureTrainingPlan,
+    )
+
 
 STATUS: Final = "CAPTURE_VALIDATION_SEAM_AUTHORITY0"
 SCHEMA: Final = "phaseset-capture-validation-source-v1"
@@ -660,6 +665,75 @@ def run_capture_validation(
     translated to ``RESOURCE_LIMIT``.
     """
 
+    return _run_capture_validation_core(
+        system,
+        config,
+        source,
+        descriptor_plan=None,
+        max_encoded_bytes=max_encoded_bytes,
+        max_cuda_static_tensor_bytes=max_cuda_static_tensor_bytes,
+    )
+
+
+def run_capture_validation_cached(
+    system: ResidualRetrievalSystem,
+    config: TrainingConfig,
+    source: CaptureValidationSource,
+    descriptor_plan: PeriodicDescriptorCaptureTrainingPlan,
+    *,
+    max_encoded_bytes: int = DEFAULT_MAX_ENCODED_BYTES,
+    max_cuda_static_tensor_bytes: int = DEFAULT_MAX_CUDA_STATIC_TENSOR_BYTES,
+) -> CaptureValidationResult:
+    """Run the same full-capture oracle using explicit verified descriptor streams."""
+
+    # Local import avoids a module cycle: the plan owns capture-source checks.
+    from .periodic_capture_training_cache import (
+        PeriodicDescriptorCaptureTrainingPlan,
+    )
+
+    if type(system) is not ResidualRetrievalSystem:
+        raise CaptureValidationError(
+            "cached capture validation requires exact ResidualRetrievalSystem"
+        )
+    if type(config) is not TrainingConfig:
+        raise CaptureValidationError("config must be exact TrainingConfig")
+    if type(descriptor_plan) is not PeriodicDescriptorCaptureTrainingPlan:
+        raise CaptureValidationError(
+            "descriptor_plan must be an admitted exact capture training plan"
+        )
+    if (
+        config.stage != "residual"
+        or descriptor_plan.system_id != system.system_id
+        or descriptor_plan.config_sha256 != config.sha256
+        or descriptor_plan.seed != config.seed
+        or descriptor_plan.epochs != config.epochs
+        or descriptor_plan.edge_budget != config.edge_budget
+    ):
+        raise CaptureValidationError(
+            "descriptor plan differs from the residual system or training config"
+        )
+    checked_source = descriptor_plan.validate_capture_validation_source(source)
+    return _run_capture_validation_core(
+        system,
+        config,
+        checked_source,
+        descriptor_plan=descriptor_plan,
+        max_encoded_bytes=max_encoded_bytes,
+        max_cuda_static_tensor_bytes=max_cuda_static_tensor_bytes,
+    )
+
+
+def _run_capture_validation_core(
+    system: BaseRetrievalSystem | ResidualRetrievalSystem,
+    config: TrainingConfig,
+    source: CaptureValidationSource,
+    *,
+    descriptor_plan: PeriodicDescriptorCaptureTrainingPlan | None,
+    max_encoded_bytes: int,
+    max_cuda_static_tensor_bytes: int,
+) -> CaptureValidationResult:
+    """Shared full-capture implementation; only periodic descriptor acquisition varies."""
+
     if not isinstance(system, (BaseRetrievalSystem, ResidualRetrievalSystem)):
         raise CaptureValidationError("system must use a registered retrieval interface")
     if type(config) is not TrainingConfig:
@@ -722,11 +796,23 @@ def run_capture_validation(
                 base_rows: list[np.ndarray] = []
                 token_rows: list[np.ndarray] = []
                 mask_rows: list[np.ndarray] = []
-                for window in capture.windows:
+                for window_position, window in enumerate(capture.windows):
                     with _autocast_context(precision, device):
                         if residual:
                             assert isinstance(system, ResidualRetrievalSystem)
-                            tokens, band_mask = system.encode_trainable(window.groups)
+                            if descriptor_plan is None:
+                                tokens, band_mask = system.encode_trainable(window.groups)
+                            else:
+                                descriptor_stream = (
+                                    descriptor_plan.open_capture_validation_window(
+                                        capture,
+                                        window_position,
+                                    )
+                                )
+                                tokens, band_mask = system.encode_trainable_cached(
+                                    window.groups,
+                                    descriptor_stream,
+                                )
                             base = system.encode_frozen_base(window.groups)
                         else:
                             assert isinstance(system, BaseRetrievalSystem)
@@ -898,7 +984,8 @@ def run_capture_validation(
 _CAPTURE_RUNTIME_BINDINGS = tuple(
     (name, globals()[name])
     for name in (
-        "CaptureValidationSource", "run_capture_validation", "_snapshot_source",
+        "CaptureValidationSource", "run_capture_validation",
+        "run_capture_validation_cached", "_run_capture_validation_core", "_snapshot_source",
         "_capture_fractions", "pool_capture_windows", "capture_r1_contributions",
         "validate_retrieval_dataset", "variable_positive_symmetric_infonce",
     )
@@ -921,4 +1008,5 @@ def _verified_capture_validation_bindings():
     "CaptureValidationSource",
     "CaptureValidationWindow",
     "run_capture_validation",
+    "run_capture_validation_cached",
 ]
diff --git a/src/phaseset_core/frozen_clip_text.py b/src/phaseset_core/frozen_clip_text.py
index f1d41ba..c17378d 100644
--- a/src/phaseset_core/frozen_clip_text.py
+++ b/src/phaseset_core/frozen_clip_text.py
@@ -157,8 +157,8 @@
     ),
     (
         "phaseset_core.training",
-        159_407,
-        "0b91f3a5f1f4eea3d95c98bf899e12fb7de28394046468a05a24eaf2b6c55491",
+        161_053,
+        "c84486cc35709f4ac9fd17119deffeeea6569499800e59a143e53c6667ecd386",
     ),
 )
 
diff --git a/src/phaseset_core/host.py b/src/phaseset_core/host.py
index 3c89b52..e71ff32 100644
--- a/src/phaseset_core/host.py
+++ b/src/phaseset_core/host.py
@@ -7,8 +7,8 @@
 existing ``ExecutionHold`` boundary.
 
 The implemented command surface is intentionally small: ``preflight``,
-``run-base``, ``run-residual``, and base/residual ``resume``. Other commands raise
-before a ``BackendExecution`` can be manufactured.
+``run-base``, ``qualify-base``, ``run-residual``, and base/residual ``resume``.
+Other commands raise before a ``BackendExecution`` can be manufactured.
 """
 
 from __future__ import annotations
@@ -32,6 +32,15 @@
 import numpy as np
 import torch
 from phaseset_core import cli
+from phaseset_core.base_qualification_host import (
+    CONFIG_SCHEMA as BASE_QUALIFICATION_CONTROLLER_SCHEMA,
+    HostBaseQualificationAuthentication,
+    HostBaseQualificationConfig,
+    HostBaseQualificationError,
+    load_base_attempt_registry,
+    run_host_base_qualification,
+)
+from phaseset_core.base_cohort_resolver import BaseCohortAdmission
 from phaseset_core.capture_prepared_storage import (
     CapturePreparedStorageError,
     CaptureStorageLimits,
@@ -73,6 +82,7 @@
 )
 from phaseset_core.production import (
     BackendExecution,
+    PrivateBaseQualificationAuthorization,
     PrivateReceiptAssertions,
     ProductionRuntimeAdapter,
 )
@@ -91,6 +101,7 @@
 )
 
 HOST_CONFIG_SCHEMA = "phaseset-private-host-v1"
+HOST_CONFIG_QUALIFICATION_SCHEMA = "phaseset-private-host-v2"
 PREPARED_INDEX_SCHEMA = "phaseset-prepared-index-v1"
 PREPARED_SPLIT_SCHEMA = "phaseset-prepared-training-split-v1"
 CAPTURE_VALIDATION_INDEX_SCHEMA = "phaseset-host-capture-validation-index-v1"
@@ -131,6 +142,32 @@ class HostConfigurationError(ValueError):
     """The private host configuration or prepared seam is not closed."""
 
 
+def _qualify_base_request_is_closed(request: cli.CLICommandRequest) -> bool:
+    """Whether qualify-base carries exactly its one factory-only path."""
+
+    return request.terminal_root is not None and all(
+        value is None
+        for value in (
+            request.run_id,
+            request.seed,
+            request.split,
+            request.system_id,
+            request.source_manifest,
+            request.prepared_root,
+            request.split_manifest,
+            request.base_checkpoint,
+            request.periodic_cache,
+            request.cache_root,
+            request.checkpoint_directory,
+            request.resume_checkpoint,
+            request.stop_after_global_step,
+            request.evaluation_manifest,
+            request.statistical_report,
+            request.attempt_directory,
+        )
+    )
+
+
 class _AttemptLease:
     """Process-released exclusive lease held for one attempt's full lifetime."""
 
@@ -719,6 +756,7 @@ class HostConfig:
     resume_retry_class: str
     corrective_change_sha256: str
     residual_artifacts: ResidualArtifacts | None
+    base_qualification: HostBaseQualificationConfig | None = None
 
     @classmethod
     def load(cls, path: str | Path) -> HostConfig:
@@ -731,13 +769,22 @@ def load(cls, path: str | Path) -> HostConfig:
             "schema",
             "source_tree_sha256",
         }
-        if frozenset(value) not in {
-            frozenset(required_keys),
-            frozenset(required_keys | {"residual"}),
-        }:
+        optional_keys = frozenset({"residual", "base_qualification"})
+        if not required_keys.issubset(value) or not set(value).issubset(
+            required_keys | optional_keys
+        ):
             raise HostConfigurationError("host configuration keys are not closed")
-        if value["schema"] != HOST_CONFIG_SCHEMA:
+        if value["schema"] not in {
+            HOST_CONFIG_SCHEMA,
+            HOST_CONFIG_QUALIFICATION_SCHEMA,
+        }:
             raise HostConfigurationError("host configuration schema is not registered")
+        if (value["schema"] == HOST_CONFIG_SCHEMA) != (
+            "base_qualification" not in value
+        ):
+            raise HostConfigurationError(
+                "base qualification configuration requires private host schema v2"
+            )
         receipts = value["receipts"]
         runtime = value["runtime"]
         if type(receipts) is not dict or type(runtime) is not dict:
@@ -800,6 +847,65 @@ def load(cls, path: str | Path) -> HostConfig:
                     for seed in SEEDS
                 },
             )
+        qualification_value = value.get("base_qualification")
+        base_qualification: HostBaseQualificationConfig | None = None
+        if qualification_value is not None:
+            if type(qualification_value) is not dict:
+                raise HostConfigurationError(
+                    "base qualification configuration must be an object"
+                )
+            _closed_keys(
+                qualification_value,
+                {
+                    "journal_parent",
+                    "latency_protocol_sha256",
+                    "launcher_sha256",
+                    "output_root",
+                    "registered_cuda_uuid",
+                    "registry_path",
+                    "registry_sha256",
+                    "schema",
+                    "session_id",
+                    "wall_timeout_seconds",
+                },
+                "base qualification configuration",
+            )
+            if qualification_value["schema"] != BASE_QUALIFICATION_CONTROLLER_SCHEMA:
+                raise HostConfigurationError(
+                    "base qualification controller schema is not registered"
+                )
+            try:
+                base_qualification = HostBaseQualificationConfig(
+                    registry_path=_resolve_config_path(
+                        config_path.parent,
+                        qualification_value["registry_path"],
+                        "base attempt registry",
+                    ),
+                    registry_sha256=qualification_value["registry_sha256"],
+                    output_root=_resolve_config_path(
+                        config_path.parent,
+                        qualification_value["output_root"],
+                        "base qualification output root",
+                    ),
+                    journal_parent=_resolve_config_path(
+                        config_path.parent,
+                        qualification_value["journal_parent"],
+                        "base latency journal parent",
+                    ),
+                    session_id=qualification_value["session_id"],
+                    registered_cuda_uuid=qualification_value["registered_cuda_uuid"],
+                    latency_protocol_sha256=qualification_value[
+                        "latency_protocol_sha256"
+                    ],
+                    launcher_sha256=qualification_value["launcher_sha256"],
+                    wall_timeout_seconds=qualification_value[
+                        "wall_timeout_seconds"
+                    ],
+                )
+            except (HostBaseQualificationError, TypeError, ValueError) as error:
+                raise HostConfigurationError(
+                    "base qualification configuration is invalid"
+                ) from error
         return cls(
             path=config_path,
             prepared_index=_resolve_config_path(
@@ -824,6 +930,7 @@ def load(cls, path: str | Path) -> HostConfig:
             resume_retry_class=runtime["resume_retry_class"],
             corrective_change_sha256=corrective,
             residual_artifacts=residual_artifacts,
+            base_qualification=base_qualification,
         )
 
     def load_receipts(self) -> PrivateReceiptAssertions:
@@ -860,6 +967,60 @@ def validate_runtime(self) -> None:
         }:
             raise HostConfigurationError("runtime resume_retry_class is not registered")
 
+    def validate_base_qualification_request(
+        self,
+        request: cli.CLICommandRequest,
+    ) -> None:
+        """Close the qualify-base private inputs without running a model."""
+
+        if request.command != "qualify-base":
+            return
+        qualification = self.base_qualification
+        if qualification is None:
+            raise HostConfigurationError(
+                "qualify-base requires a base qualification controller configuration"
+            )
+        if not _qualify_base_request_is_closed(request):
+            raise HostConfigurationError(
+                "qualify-base accepts only one --terminal-root input"
+            )
+        if request.terminal_root is None:  # narrowed by the closed-input predicate
+            raise AssertionError("closed qualify-base request lost terminal_root")
+        for value, label in (
+            (request.terminal_root, "base terminal root"),
+            (qualification.output_root, "base qualification output root"),
+            (qualification.journal_parent, "base latency journal parent"),
+        ):
+            _reject_symlink_components(value, label)
+            if not value.is_dir():
+                raise HostConfigurationError(f"{label} must be an existing directory")
+        if request.terminal_root.resolve() in {
+            qualification.output_root.resolve(),
+            qualification.journal_parent.resolve(),
+        }:
+            raise HostConfigurationError(
+                "attempt, qualification-output, and journal roots must be distinct"
+            )
+        if qualification.output_root.resolve() == qualification.journal_parent.resolve():
+            raise HostConfigurationError(
+                "qualification output and journal roots must be distinct"
+            )
+        try:
+            load_base_attempt_registry(
+                qualification.registry_path,
+                expected_sha256=qualification.registry_sha256,
+            )
+        except (HostBaseQualificationError, OSError, TypeError, ValueError) as error:
+            raise HostConfigurationError(
+                "base attempt registry manifest is invalid"
+            ) from error
+        output = qualification.output_root / qualification.session_id
+        journal = qualification.journal_parent / qualification.session_id
+        if output.exists() or output.is_symlink() or journal.exists() or journal.is_symlink():
+            raise HostConfigurationError(
+                "base qualification session output or journal already exists"
+            )
+
     def load_sources(
         self,
     ) -> tuple[TrainingDataSource, TrainingDataSource | CaptureValidationSource]:
@@ -1601,6 +1762,12 @@ def __init__(self, config: HostConfig, request: cli.CLICommandRequest) -> None:
         self._config = config
         self._request = request
         self._admission_request: RuntimeAdmissionRequest | None = None
+        self._authenticated_adapter_sha256: str | None = None
+        self._qualification_adapter: ProductionRuntimeAdapter | None = None
+        self._qualification_authentication: (
+            HostBaseQualificationAuthentication | None
+        ) = None
+        self._qualification_intent: CommandIntent | None = None
         self.backend_sha256 = _sha256_bytes(Path(__file__).read_bytes())
 
     def authenticate(
@@ -1609,7 +1776,6 @@ def authenticate(
         receipts: PrivateReceiptAssertions,
         adapter_sha256: str,
     ) -> bool:
-        del adapter_sha256
         if receipts != self._config.load_receipts():
             return False
         plan = build_experiment_plan()
@@ -1628,10 +1794,53 @@ def authenticate(
         # parsed CLI command.  Retain the exact runner-created admission request so
         # attempt ledgers never read fields that CommandIntent does not define.
         if self._admission_request is not None:
-            return request == self._admission_request
+            return (
+                request == self._admission_request
+                and adapter_sha256 == self._authenticated_adapter_sha256
+            )
         self._admission_request = request
+        self._authenticated_adapter_sha256 = _lower_sha256(
+            adapter_sha256,
+            "production adapter SHA-256",
+        )
         return True
 
+    def bind_qualification_adapter(self, adapter: ProductionRuntimeAdapter) -> None:
+        """Bind the one adapter whose private slot receives actual derived evidence."""
+
+        if self._request.command != "qualify-base":
+            raise HostConfigurationError(
+                "base qualification adapter binding is command-specific"
+            )
+        if type(adapter) is not ProductionRuntimeAdapter:
+            raise TypeError("qualification adapter must be exact ProductionRuntimeAdapter")
+        if getattr(adapter, "_backend", None) is not self:
+            raise HostConfigurationError("qualification adapter belongs to another backend")
+        if self._qualification_adapter is not None:
+            raise HostConfigurationError("qualification adapter was already bound")
+        self._qualification_adapter = adapter
+
+    def authenticate_base_qualification(
+        self,
+        intent: CommandIntent,
+        authorization: PrivateBaseQualificationAuthorization,
+        qualification_sha256: str,
+        adapter_sha256: str,
+    ) -> bool:
+        """Match production recomputation to the controller's retained real bytes."""
+
+        expected = self._qualification_authentication
+        return (
+            intent.command == "qualify-base"
+            and intent == self._qualification_intent
+            and type(expected) is HostBaseQualificationAuthentication
+            and expected.verifies(
+                authorization,
+                qualification_sha256=qualification_sha256,
+                adapter_sha256=adapter_sha256,
+            )
+        )
+
     def _attempt_plan_bindings(self) -> tuple[str, str, str]:
         request = self._admission_request
         if request is None:
@@ -1655,6 +1864,8 @@ def execute(self, intent: CommandIntent) -> BackendExecution:
             return self._run_residual(intent)
         if intent.command == "resume":
             return self._resume_base(intent)
+        if intent.command == "qualify-base":
+            return self._qualify_base(intent)
         raise HostConfigurationError(
             f"host harness does not implement command {intent.command}; no result was produced"
         )
@@ -1671,6 +1882,91 @@ def _training_config(self, *, stage: str, seed: int) -> TrainingConfig:
             synthetic_contract=False,
         )
 
+    def _qualify_base(self, intent: CommandIntent) -> BackendExecution:
+        request = self._request
+        qualification = self._config.base_qualification
+        adapter = self._qualification_adapter
+        admission_request = self._admission_request
+        if (
+            qualification is None
+            or adapter is None
+            or admission_request is None
+            or self._authenticated_adapter_sha256 is None
+        ):
+            raise HostConfigurationError(
+                "qualify-base controller is not fully admitted and bound"
+            )
+        if (
+            not _qualify_base_request_is_closed(request)
+            or intent.run_id is not None
+            or intent.seed is not None
+            or intent.split is not None
+        ):
+            raise HostConfigurationError(
+                "qualify-base accepts only the registered terminal root"
+            )
+        if request.terminal_root is None:  # narrowed by the closed-input predicate
+            raise AssertionError("closed qualify-base request lost terminal_root")
+        train, val = self._config.load_sources()
+        if type(val) is not CaptureValidationSource or val.split != "val":
+            raise HostConfigurationError(
+                "qualify-base requires the complete holistic capture val source"
+            )
+        cohort_admission = BaseCohortAdmission(
+            plan_sha256=admission_request.plan_sha256,
+            matrix_sha256=admission_request.matrix_sha256,
+            training_config_sha256=admission_request.training_config_sha256,
+            source_tree_sha256=self._config.source_tree_sha256,
+            train_manifest_sha256=train.manifest_sha256,
+            val_manifest_sha256=val.census_sha256,
+            device=self._config.device,
+            request_bf16=self._config.request_bf16,
+            bf16_runtime_qualified=self._config.bf16_runtime_qualified,
+            edge_budget=self._config.edge_budget,
+            checkpoint_every_updates=self._config.checkpoint_every_updates,
+        )
+        output_directory = qualification.output_root / qualification.session_id
+        try:
+            output_directory.mkdir(mode=0o700, parents=False, exist_ok=False)
+        except OSError as error:
+            raise HostConfigurationError(
+                "base qualification output directory cannot be created once"
+            ) from error
+        lease = _AttemptLease.acquire(output_directory)
+        try:
+            result = run_host_base_qualification(
+                request.terminal_root.resolve(),
+                config=qualification,
+                admission=cohort_admission,
+                validation_source=val,
+                output_directory=output_directory,
+                adapter_sha256=self._authenticated_adapter_sha256,
+            )
+            if result.execution.outcome == "COMPLETED":
+                authentication = result.authentication
+                if type(authentication) is not HostBaseQualificationAuthentication:
+                    raise HostConfigurationError(
+                        "completed qualification lost controller authentication"
+                    )
+                if getattr(adapter, "_base_qualification_authorization", None) is not None:
+                    raise HostConfigurationError(
+                        "production adapter already contains qualification authorization"
+                    )
+                self._qualification_authentication = authentication
+                self._qualification_intent = intent
+                # ProductionRuntimeAdapter intentionally validates this exact
+                # object after independently rebuilding the semantic artifact.
+                # The private host derives it here from actual controller output;
+                # no CLI/user digest is accepted as authorization.
+                object.__setattr__(
+                    adapter,
+                    "_base_qualification_authorization",
+                    authentication.authorization,
+                )
+            return result.execution
+        finally:
+            lease.release()
+
     def _execute_base_runtime(
         self,
         *,
@@ -2235,6 +2531,7 @@ def __call__(self, request: cli.CLICommandRequest) -> ProductionRuntimeAdapter:
             config = HostConfig.load(self._config_path)
             receipts = config.load_receipts()
             config.validate_runtime()
+            config.validate_base_qualification_request(request)
             # Preflight verifies the authenticated prepared tree, materializes the
             # bounded capture source, and streams every concrete train batch once.
             config.load_sources()
@@ -2264,7 +2561,10 @@ def __call__(self, request: cli.CLICommandRequest) -> ProductionRuntimeAdapter:
                 holds = ("HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",)
             raise ExecutionHold(request.command, holds) from error
         backend = PhaseSetHostBackend(config, request)
-        return ProductionRuntimeAdapter(backend, receipts)
+        adapter = ProductionRuntimeAdapter(backend, receipts)
+        if request.command == "qualify-base":
+            backend.bind_qualification_adapter(adapter)
+        return adapter
 
 
 def main(argv: Sequence[str] | None = None, *, config_path: str | Path) -> int:
@@ -2295,6 +2595,7 @@ def entrypoint(argv: Sequence[str] | None = None) -> int:
 __all__ = [
     "HostConfig",
     "HostConfigurationError",
+    "HOST_CONFIG_QUALIFICATION_SCHEMA",
     "PrivatePreparedDataSource",
     "RuntimeAdapterFactory",
     "entrypoint",
diff --git a/src/phaseset_core/periodic_capture_training_cache.py b/src/phaseset_core/periodic_capture_training_cache.py
new file mode 100644
index 0000000..5289553
--- /dev/null
+++ b/src/phaseset_core/periodic_capture_training_cache.py
@@ -0,0 +1,1044 @@
+"""Sealed descriptor plans for prepared training plus holistic capture validation.
+
+This authority-zero, data-free planning layer authenticates no path and runs no
+model.  It derives one complete seed-specific descriptor plan from exact
+already-loaded sources and readers.  The existing prepared-window plan keeps
+its original engineering-only meaning.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+import hashlib
+from types import MappingProxyType
+from typing import Final
+
+import numpy as np
+
+from .capture_validation import (
+    CaptureDescriptorWindowSource,
+    CaptureValidationCapture,
+    CaptureValidationSource,
+)
+from .controls import system_spec
+from .periodic_descriptor_cache_v2 import (
+    CachedDescriptorBatch,
+    CachedPairChunkStream,
+    DescriptorCacheSourceBinding,
+    DescriptorStreamKind,
+    DescriptorWindowContext,
+    PeriodicDescriptorCacheV2,
+    prepared_group_batch_sha256,
+)
+from .periodic_training_cache import (
+    _CACHEABLE_SYSTEMS,
+    _EXPECTED_POLICIES,
+    _batch_identity,
+    _canonical_json,
+    _capture_rng,
+    _contexts_sha256,
+    _enforce_edge_budget,
+    _enumerate_rows,
+    _lower_sha256,
+    _restore_rng,
+    _rng_matches,
+    _source_binding_value,
+    _validate_opened,
+    _validate_reader_snapshot,
+    PeriodicDescriptorPlanRow,
+    PeriodicTrainingCacheError,
+    RequestIdentity,
+)
+from .prepared_data_v2 import PreparedTrainingDataSourceV2
+from .training import OFFICIAL_SEEDS, RESIDUAL_EPOCHS, RetrievalTrainingBatch, TrainingConfig
+
+
+AUTHORITY: Final = 0
+PRODUCTION: Final = False
+RESULT_CLAIMED: Final = False
+PLAN_SCHEMA: Final = "phaseset-periodic-descriptor-capture-training-plan-v1"
+CAPTURE_VALIDATION_SEMANTICS: Final = "complete-holistic-capture-validation"
+
+_PLAN_TOKEN: Final = object()
+
+CaptureRequestIdentity = tuple[
+    str,
+    str,
+    int,
+    int,
+    str,
+    str,
+    int,
+    str,
+    str,
+    int,
+    int,
+]
+
+
+class PeriodicCaptureTrainingCacheError(PeriodicTrainingCacheError):
+    """An exact train-plus-capture descriptor plan could not be used."""
+
+
+def _sha256(value: bytes) -> str:
+    return hashlib.sha256(value).hexdigest()
+
+
+def _capture_request_sha256(
+    *,
+    capture_commitment: str,
+    capture_plan_sha256: str,
+    window_position: int,
+    source_start_frame: int,
+    window_commitment: str,
+    source_window_npz_sha256: str,
+    window_ordinal: int,
+    contexts_sha256: str,
+    batch_input_sha256: str,
+    edge_count: int,
+) -> str:
+    return _sha256(
+        _canonical_json(
+            {
+                "batch_input_sha256": batch_input_sha256,
+                "capture_commitment": capture_commitment,
+                "capture_plan_sha256": capture_plan_sha256,
+                "contexts_sha256": contexts_sha256,
+                "edge_count": edge_count,
+                "source_start_frame": source_start_frame,
+                "source_window_npz_sha256": source_window_npz_sha256,
+                "window_commitment": window_commitment,
+                "window_ordinal": window_ordinal,
+                "window_position": window_position,
+            }
+        )
+    )
+
+
+def _capture_request_identity(
+    *,
+    capture_commitment: str,
+    capture_plan_sha256: str,
+    window_position: int,
+    source_start_frame: int,
+    window_commitment: str,
+    source_window_npz_sha256: str,
+    window_ordinal: int,
+    contexts_sha256: str,
+    batch_input_sha256: str,
+    edge_count: int,
+) -> CaptureRequestIdentity:
+    return (
+        capture_commitment,
+        capture_plan_sha256,
+        window_position,
+        source_start_frame,
+        window_commitment,
+        source_window_npz_sha256,
+        window_ordinal,
+        contexts_sha256,
+        batch_input_sha256,
+        edge_count,
+        1,
+    )
+
+
+@dataclass(frozen=True, slots=True)
+class PeriodicCaptureDescriptorPlanRow:
+    """One exact B=1 capture window and its admitted descriptor shard."""
+
+    execution_position: int
+    capture_position: int
+    capture_commitment: str
+    capture_plan_sha256: str
+    window_position: int
+    source_start_frame: int
+    window_commitment: str
+    storage_manifest_sha256: str
+    source_window_npz_sha256: str
+    window_ordinal: int
+    contexts_sha256: str
+    batch_input_sha256: str
+    cache_key_sha256: str
+    request_sha256: str
+    edge_count: int
+    window_count: int = 1
+
+    def __post_init__(self) -> None:
+        for name in (
+            "execution_position",
+            "capture_position",
+            "window_position",
+            "source_start_frame",
+            "window_ordinal",
+            "edge_count",
+            "window_count",
+        ):
+            value = getattr(self, name)
+            if type(value) is not int or value < 0:
+                raise TypeError(f"{name} must be an exact nonnegative int")
+        if self.edge_count < 1 or self.window_count != 1:
+            raise PeriodicCaptureTrainingCacheError(
+                "capture descriptor rows require one window and at least one edge"
+            )
+        for name in (
+            "capture_commitment",
+            "capture_plan_sha256",
+            "window_commitment",
+            "storage_manifest_sha256",
+            "source_window_npz_sha256",
+            "contexts_sha256",
+            "batch_input_sha256",
+            "cache_key_sha256",
+            "request_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        expected = _capture_request_sha256(
+            capture_commitment=self.capture_commitment,
+            capture_plan_sha256=self.capture_plan_sha256,
+            window_position=self.window_position,
+            source_start_frame=self.source_start_frame,
+            window_commitment=self.window_commitment,
+            source_window_npz_sha256=self.source_window_npz_sha256,
+            window_ordinal=self.window_ordinal,
+            contexts_sha256=self.contexts_sha256,
+            batch_input_sha256=self.batch_input_sha256,
+            edge_count=self.edge_count,
+        )
+        if self.request_sha256 != expected:
+            raise PeriodicCaptureTrainingCacheError("capture row request digest is inconsistent")
+
+    def _identity(self) -> CaptureRequestIdentity:
+        return _capture_request_identity(
+            capture_commitment=self.capture_commitment,
+            capture_plan_sha256=self.capture_plan_sha256,
+            window_position=self.window_position,
+            source_start_frame=self.source_start_frame,
+            window_commitment=self.window_commitment,
+            source_window_npz_sha256=self.source_window_npz_sha256,
+            window_ordinal=self.window_ordinal,
+            contexts_sha256=self.contexts_sha256,
+            batch_input_sha256=self.batch_input_sha256,
+            edge_count=self.edge_count,
+        )
+
+    def _value(self) -> dict[str, object]:
+        return {
+            "batch_input_sha256": self.batch_input_sha256,
+            "cache_key_sha256": self.cache_key_sha256,
+            "capture_commitment": self.capture_commitment,
+            "capture_plan_sha256": self.capture_plan_sha256,
+            "capture_position": self.capture_position,
+            "contexts_sha256": self.contexts_sha256,
+            "edge_count": self.edge_count,
+            "execution_position": self.execution_position,
+            "request_sha256": self.request_sha256,
+            "source_start_frame": self.source_start_frame,
+            "source_window_npz_sha256": self.source_window_npz_sha256,
+            "storage_manifest_sha256": self.storage_manifest_sha256,
+            "window_commitment": self.window_commitment,
+            "window_count": self.window_count,
+            "window_ordinal": self.window_ordinal,
+            "window_position": self.window_position,
+        }
+
+
+def _checked_cache_census(
+    cache: PeriodicDescriptorCacheV2,
+    label: str,
+) -> tuple[str, ...]:
+    census = cache.cache_key_census
+    if (
+        type(census) is not tuple
+        or not census
+        or census != tuple(sorted(census))
+        or len(set(census)) != len(census)
+    ):
+        raise PeriodicCaptureTrainingCacheError(f"{label} cache key census is invalid")
+    for index, value in enumerate(census):
+        _lower_sha256(value, f"{label} cache key {index}")
+    return census
+
+
+def _snapshot_capture_source(value: object) -> CaptureValidationSource:
+    if type(value) is not CaptureValidationSource:
+        raise TypeError("capture source must be exact CaptureValidationSource")
+    return CaptureValidationSource(value.split, value.manifest_sha256, value.captures)
+
+
+def _capture_source_counts(
+    source: CaptureValidationSource,
+) -> tuple[int, int, int, str]:
+    capture_count = len(source.captures)
+    window_count = sum(len(capture.windows) for capture in source.captures)
+    caption_count = sum(
+        len(capture.holistic_text.caption_commitments) for capture in source.captures
+    )
+    descriptor_sources = tuple(
+        window.descriptor_source for capture in source.captures for window in capture.windows
+    )
+    if not descriptor_sources or any(
+        type(value) is not CaptureDescriptorWindowSource for value in descriptor_sources
+    ):
+        raise PeriodicCaptureTrainingCacheError(
+            "capture plan requires v2 descriptor lineage on every window"
+        )
+    typed_sources = tuple(value for value in descriptor_sources if value is not None)
+    storage_manifest = typed_sources[0].storage_manifest_sha256
+    if any(value.storage_manifest_sha256 != storage_manifest for value in typed_sources):
+        raise PeriodicCaptureTrainingCacheError(
+            "capture descriptor rows do not share one storage manifest"
+        )
+    if tuple(value.window_ordinal for value in typed_sources) != tuple(range(window_count)):
+        raise PeriodicCaptureTrainingCacheError(
+            "capture descriptor ordinals differ from canonical global order"
+        )
+    return capture_count, window_count, caption_count, storage_manifest
+
+
+def _checked_capture_values(
+    capture: CaptureValidationCapture,
+    window_position: int,
+    *,
+    seed: int,
+    edge_budget: int,
+    expected_storage_manifest_sha256: str,
+) -> tuple[
+    CaptureRequestIdentity,
+    tuple[DescriptorWindowContext, ...],
+    str,
+    int,
+]:
+    assert type(capture) is CaptureValidationCapture
+    if type(window_position) is not int:
+        raise TypeError("window_position must be an exact int")
+    if not 0 <= window_position < len(capture.windows):
+        raise PeriodicCaptureTrainingCacheError("capture window position is out of range")
+    window = capture.windows[window_position]
+    descriptor_source = window.descriptor_source
+    if type(descriptor_source) is not CaptureDescriptorWindowSource:
+        raise PeriodicCaptureTrainingCacheError("capture window lacks exact v2 descriptor lineage")
+    if descriptor_source.storage_manifest_sha256 != expected_storage_manifest_sha256:
+        raise PeriodicCaptureTrainingCacheError(
+            "capture window storage manifest differs from the plan"
+        )
+    context = window.descriptor_context(seed=seed)
+    contexts = (context,)
+    edge_count = sum(count * (count - 1) // 2 for count in window.groups.actor_counts)
+    if edge_count < 1:
+        raise PeriodicCaptureTrainingCacheError("capture descriptor rows require at least one edge")
+    _enforce_edge_budget(edge_count, edge_budget)
+    batch_digest = prepared_group_batch_sha256(window.groups)
+    context_digest = _contexts_sha256(contexts)
+    identity = _capture_request_identity(
+        capture_commitment=capture.capture_commitment.hex(),
+        capture_plan_sha256=capture.plan.sha256,
+        window_position=window_position,
+        source_start_frame=capture.plan.source_start_frames[window_position],
+        window_commitment=window.window_commitment.hex(),
+        source_window_npz_sha256=descriptor_source.source_window_npz_sha256,
+        window_ordinal=descriptor_source.window_ordinal,
+        contexts_sha256=context_digest,
+        batch_input_sha256=batch_digest,
+        edge_count=edge_count,
+    )
+    return identity, contexts, batch_digest, edge_count
+
+
+def _capture_row(
+    *,
+    capture: CaptureValidationCapture,
+    capture_position: int,
+    window_position: int,
+    execution_position: int,
+    contexts: tuple[DescriptorWindowContext, ...],
+    batch_input_sha256: str,
+    edge_count: int,
+    opened: CachedDescriptorBatch,
+) -> PeriodicCaptureDescriptorPlanRow:
+    window = capture.windows[window_position]
+    descriptor_source = window.descriptor_source
+    assert type(descriptor_source) is CaptureDescriptorWindowSource
+    context_digest = _contexts_sha256(contexts)
+    request_digest = _capture_request_sha256(
+        capture_commitment=capture.capture_commitment.hex(),
+        capture_plan_sha256=capture.plan.sha256,
+        window_position=window_position,
+        source_start_frame=capture.plan.source_start_frames[window_position],
+        window_commitment=window.window_commitment.hex(),
+        source_window_npz_sha256=descriptor_source.source_window_npz_sha256,
+        window_ordinal=descriptor_source.window_ordinal,
+        contexts_sha256=context_digest,
+        batch_input_sha256=batch_input_sha256,
+        edge_count=edge_count,
+    )
+    return PeriodicCaptureDescriptorPlanRow(
+        execution_position=execution_position,
+        capture_position=capture_position,
+        capture_commitment=capture.capture_commitment.hex(),
+        capture_plan_sha256=capture.plan.sha256,
+        window_position=window_position,
+        source_start_frame=capture.plan.source_start_frames[window_position],
+        window_commitment=window.window_commitment.hex(),
+        storage_manifest_sha256=descriptor_source.storage_manifest_sha256,
+        source_window_npz_sha256=descriptor_source.source_window_npz_sha256,
+        window_ordinal=descriptor_source.window_ordinal,
+        contexts_sha256=context_digest,
+        batch_input_sha256=batch_input_sha256,
+        cache_key_sha256=opened.cache_key_sha256,
+        request_sha256=request_digest,
+        edge_count=edge_count,
+    )
+
+
+def _enumerate_capture_rows(
+    source: CaptureValidationSource,
+    cache: PeriodicDescriptorCacheV2,
+    *,
+    seed: int,
+    stream_kind: DescriptorStreamKind,
+    edge_budget: int,
+    start_position: int,
+    storage_manifest_sha256: str,
+    source_binding: DescriptorCacheSourceBinding,
+    floors_sha256: str,
+    index_sha256: str,
+    cache_key_census: tuple[str, ...],
+) -> tuple[
+    tuple[PeriodicCaptureDescriptorPlanRow, ...],
+    Mapping[CaptureRequestIdentity, PeriodicCaptureDescriptorPlanRow],
+]:
+    rows: list[PeriodicCaptureDescriptorPlanRow] = []
+    lookup: dict[CaptureRequestIdentity, PeriodicCaptureDescriptorPlanRow] = {}
+    keys: set[str] = set()
+    for capture_position, capture in enumerate(source.captures):
+        for window_position, window in enumerate(capture.windows):
+            if len(rows) >= len(cache_key_census):
+                raise PeriodicCaptureTrainingCacheError(
+                    "capture source row census exceeds its closed cache census"
+                )
+            identity, contexts, batch_digest, edge_count = _checked_capture_values(
+                capture,
+                window_position,
+                seed=seed,
+                edge_budget=edge_budget,
+                expected_storage_manifest_sha256=storage_manifest_sha256,
+            )
+            _validate_reader_snapshot(
+                cache,
+                index_sha256=index_sha256,
+                source_binding=source_binding,
+                floors_sha256=floors_sha256,
+                cache_key_census=cache_key_census,
+            )
+            opened = cache.open_batch(window.groups, contexts)
+            stream = opened.stream(stream_kind)
+            if type(stream) is not CachedPairChunkStream:
+                raise PeriodicCaptureTrainingCacheError(
+                    "capture cache returned an unsealed descriptor stream"
+                )
+            row = _capture_row(
+                capture=capture,
+                capture_position=capture_position,
+                window_position=window_position,
+                execution_position=start_position + len(rows),
+                contexts=contexts,
+                batch_input_sha256=batch_digest,
+                edge_count=edge_count,
+                opened=opened,
+            )
+            _validate_opened(
+                opened,
+                row=row,
+                contexts=contexts,
+                floors_sha256=floors_sha256,
+            )
+            if row._identity() != identity:
+                raise PeriodicCaptureTrainingCacheError(
+                    "capture shard request differs from the exact source window"
+                )
+            if identity in lookup or row.cache_key_sha256 in keys:
+                raise PeriodicCaptureTrainingCacheError(
+                    "capture plan repeats an exact descriptor request"
+                )
+            rows.append(row)
+            lookup[identity] = row
+            keys.add(row.cache_key_sha256)
+    if not rows:
+        raise PeriodicCaptureTrainingCacheError("capture source produced no plan rows")
+    if tuple(sorted(keys)) != cache_key_census:
+        raise PeriodicCaptureTrainingCacheError(
+            "capture required key set differs from the closed reader census"
+        )
+    return tuple(rows), MappingProxyType(lookup)
+
+
+@dataclass(frozen=True, slots=True)
+class PeriodicDescriptorCaptureTrainingPlan:
+    """Sealed complete train plus holistic capture descriptor plan."""
+
+    system_id: str
+    seed: int
+    epochs: int
+    stream_kind: DescriptorStreamKind
+    config_sha256: str
+    edge_budget: int
+    energy_floors_sha256: str
+    train_source_manifest_sha256: str
+    capture_source_manifest_sha256: str
+    capture_source_census_sha256: str
+    capture_storage_manifest_sha256: str
+    train_cache_index_sha256: str
+    capture_cache_index_sha256: str
+    train_cache_source_binding: DescriptorCacheSourceBinding
+    capture_cache_source_binding: DescriptorCacheSourceBinding
+    train_cache_key_census: tuple[str, ...]
+    capture_cache_key_census: tuple[str, ...]
+    capture_count: int
+    window_count: int
+    caption_count: int
+    train_rows: tuple[PeriodicDescriptorPlanRow, ...]
+    capture_rows: tuple[PeriodicCaptureDescriptorPlanRow, ...]
+    _train_cache: PeriodicDescriptorCacheV2 = field(repr=False, compare=False)
+    _capture_cache: PeriodicDescriptorCacheV2 = field(repr=False, compare=False)
+    _train_lookup: Mapping[RequestIdentity, PeriodicDescriptorPlanRow] = field(
+        repr=False,
+        compare=False,
+    )
+    _capture_lookup: Mapping[
+        CaptureRequestIdentity,
+        PeriodicCaptureDescriptorPlanRow,
+    ] = field(repr=False, compare=False)
+    _seal: object = field(repr=False, compare=False)
+
+    def __post_init__(self) -> None:
+        if self._seal is not _PLAN_TOKEN:
+            raise PeriodicCaptureTrainingCacheError(
+                "capture training plans must come from complete admission"
+            )
+        if (
+            self.system_id not in _CACHEABLE_SYSTEMS
+            or self.stream_kind != _CACHEABLE_SYSTEMS[self.system_id]
+        ):
+            raise PeriodicCaptureTrainingCacheError("plan system or stream is not cacheable")
+        if self.seed not in OFFICIAL_SEEDS or self.epochs != RESIDUAL_EPOCHS:
+            raise PeriodicCaptureTrainingCacheError("plan seed or epoch census changed")
+        if type(self.edge_budget) is not int or self.edge_budget < 1:
+            raise TypeError("edge_budget must be an exact positive int")
+        for name in (
+            "config_sha256",
+            "energy_floors_sha256",
+            "train_source_manifest_sha256",
+            "capture_source_manifest_sha256",
+            "capture_source_census_sha256",
+            "capture_storage_manifest_sha256",
+            "train_cache_index_sha256",
+            "capture_cache_index_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        for name in ("capture_count", "window_count", "caption_count"):
+            value = getattr(self, name)
+            if type(value) is not int or value < 1:
+                raise TypeError(f"{name} must be an exact positive int")
+        if (
+            type(self.train_cache_source_binding) is not DescriptorCacheSourceBinding
+            or type(self.capture_cache_source_binding) is not DescriptorCacheSourceBinding
+            or self.train_cache_source_binding.prepared_manifest_sha256
+            != self.train_source_manifest_sha256
+            or self.capture_cache_source_binding.prepared_manifest_sha256
+            != self.capture_storage_manifest_sha256
+        ):
+            raise PeriodicCaptureTrainingCacheError(
+                "cache source bindings differ from their consumed source manifests"
+            )
+        if (
+            type(self.train_rows) is not tuple
+            or not self.train_rows
+            or any(type(row) is not PeriodicDescriptorPlanRow for row in self.train_rows)
+            or any(row.split != "train" for row in self.train_rows)
+        ):
+            raise PeriodicCaptureTrainingCacheError("training row census is invalid")
+        if (
+            type(self.capture_rows) is not tuple
+            or len(self.capture_rows) != self.window_count
+            or any(type(row) is not PeriodicCaptureDescriptorPlanRow for row in self.capture_rows)
+        ):
+            raise PeriodicCaptureTrainingCacheError("capture row census is invalid")
+        all_positions = tuple(row.execution_position for row in self.train_rows + self.capture_rows)
+        if all_positions != tuple(range(len(all_positions))):
+            raise PeriodicCaptureTrainingCacheError("plan execution positions are not canonical")
+        if tuple(row.window_ordinal for row in self.capture_rows) != tuple(
+            range(self.window_count)
+        ):
+            raise PeriodicCaptureTrainingCacheError("capture row ordinals are not canonical")
+        capture_positions = tuple(sorted({row.capture_position for row in self.capture_rows}))
+        if capture_positions != tuple(range(self.capture_count)):
+            raise PeriodicCaptureTrainingCacheError(
+                "capture positions are not complete and canonical"
+            )
+        if tuple(row.capture_position for row in self.capture_rows) != tuple(
+            sorted(row.capture_position for row in self.capture_rows)
+        ):
+            raise PeriodicCaptureTrainingCacheError("capture rows are not grouped canonically")
+        if len({row.capture_commitment for row in self.capture_rows}) != self.capture_count:
+            raise PeriodicCaptureTrainingCacheError(
+                "capture commitments are not complete and unique"
+            )
+        if any(
+            row.storage_manifest_sha256 != self.capture_storage_manifest_sha256
+            for row in self.capture_rows
+        ):
+            raise PeriodicCaptureTrainingCacheError(
+                "capture rows differ from the consumed storage manifest"
+            )
+        for position in capture_positions:
+            rows = tuple(row for row in self.capture_rows if row.capture_position == position)
+            if (
+                tuple(row.window_position for row in rows) != tuple(range(len(rows)))
+                or len({row.capture_commitment for row in rows}) != 1
+                or len({row.capture_plan_sha256 for row in rows}) != 1
+            ):
+                raise PeriodicCaptureTrainingCacheError(
+                    "capture-local row order or ownership is invalid"
+                )
+        if tuple(sorted(row.cache_key_sha256 for row in self.train_rows)) != (
+            self.train_cache_key_census
+        ):
+            raise PeriodicCaptureTrainingCacheError("training rows differ from cache key census")
+        if tuple(sorted(row.cache_key_sha256 for row in self.capture_rows)) != (
+            self.capture_cache_key_census
+        ):
+            raise PeriodicCaptureTrainingCacheError("capture rows differ from cache key census")
+        for census, label in (
+            (self.train_cache_key_census, "training"),
+            (self.capture_cache_key_census, "capture"),
+        ):
+            if (
+                type(census) is not tuple
+                or not census
+                or census != tuple(sorted(census))
+                or len(set(census)) != len(census)
+            ):
+                raise PeriodicCaptureTrainingCacheError(f"{label} cache key census is invalid")
+            for index, value in enumerate(census):
+                _lower_sha256(value, f"{label} cache key {index}")
+        if set(self._train_lookup) != {row._identity() for row in self.train_rows}:
+            raise PeriodicCaptureTrainingCacheError("training lookup differs from rows")
+        if set(self._capture_lookup) != {row._identity() for row in self.capture_rows}:
+            raise PeriodicCaptureTrainingCacheError("capture lookup differs from rows")
+        if any(self._train_lookup[row._identity()] != row for row in self.train_rows):
+            raise PeriodicCaptureTrainingCacheError("training lookup values differ from rows")
+        if any(self._capture_lookup[row._identity()] != row for row in self.capture_rows):
+            raise PeriodicCaptureTrainingCacheError("capture lookup values differ from rows")
+
+    @property
+    def authority(self) -> int:
+        return AUTHORITY
+
+    @property
+    def production(self) -> bool:
+        return PRODUCTION
+
+    @property
+    def result_claimed(self) -> bool:
+        return RESULT_CLAIMED
+
+    def canonical_bytes(self) -> bytes:
+        value = {
+            "authority": AUTHORITY,
+            "caption_count": self.caption_count,
+            "capture_cache_index_sha256": self.capture_cache_index_sha256,
+            "capture_cache_key_census": list(self.capture_cache_key_census),
+            "capture_cache_source_binding": _source_binding_value(
+                self.capture_cache_source_binding
+            ),
+            "capture_count": self.capture_count,
+            "capture_rows": [row._value() for row in self.capture_rows],
+            "capture_source_census_sha256": self.capture_source_census_sha256,
+            "capture_source_manifest_sha256": self.capture_source_manifest_sha256,
+            "capture_storage_manifest_sha256": self.capture_storage_manifest_sha256,
+            "capture_validation_semantics": CAPTURE_VALIDATION_SEMANTICS,
+            "config_sha256": self.config_sha256,
+            "edge_budget": self.edge_budget,
+            "energy_floors_sha256": self.energy_floors_sha256,
+            "epochs": self.epochs,
+            "external_authentication_asserted": False,
+            "production": PRODUCTION,
+            "result_claimed": RESULT_CLAIMED,
+            "schema": PLAN_SCHEMA,
+            "seed": self.seed,
+            "stream_kind": self.stream_kind,
+            "system_id": self.system_id,
+            "train_cache_index_sha256": self.train_cache_index_sha256,
+            "train_cache_key_census": list(self.train_cache_key_census),
+            "train_cache_source_binding": _source_binding_value(self.train_cache_source_binding),
+            "train_rows": [row._value() for row in self.train_rows],
+            "train_source_manifest_sha256": self.train_source_manifest_sha256,
+            "window_count": self.window_count,
+        }
+        return _canonical_json(value)
+
+    @property
+    def sha256(self) -> str:
+        return _sha256(self.canonical_bytes())
+
+    def open_training_batch(
+        self,
+        batch: RetrievalTrainingBatch,
+    ) -> CachedPairChunkStream:
+        """Reopen one exact admitted training shard without fallback."""
+
+        checked, identity = _batch_identity(
+            batch,
+            split="train",
+            seed=self.seed,
+            expected_manifest_sha256=self.train_source_manifest_sha256,
+            epochs=self.epochs,
+        )
+        row = self._train_lookup.get(identity)
+        if row is None:
+            raise PeriodicCaptureTrainingCacheError(
+                "training batch is not an exact admitted plan row"
+            )
+        _validate_reader_snapshot(
+            self._train_cache,
+            index_sha256=self.train_cache_index_sha256,
+            source_binding=self.train_cache_source_binding,
+            floors_sha256=self.energy_floors_sha256,
+            cache_key_census=self.train_cache_key_census,
+        )
+        _enforce_edge_budget(row.edge_count, self.edge_budget)
+        contexts = checked.descriptor_contexts
+        assert contexts is not None
+        opened = self._train_cache.open_batch(checked.groups, contexts)
+        _validate_opened(
+            opened,
+            row=row,
+            contexts=contexts,
+            floors_sha256=self.energy_floors_sha256,
+        )
+        stream = opened.stream(self.stream_kind)
+        if type(stream) is not CachedPairChunkStream:
+            raise PeriodicCaptureTrainingCacheError(
+                "training cache returned an unsealed descriptor stream"
+            )
+        return stream
+
+    def validate_capture_validation_source(
+        self,
+        source: CaptureValidationSource,
+    ) -> CaptureValidationSource:
+        """Return a rebuilt exact source after checking full gallery and motion identity."""
+
+        checked = _snapshot_capture_source(source)
+        capture_count, window_count, caption_count, storage_manifest = _capture_source_counts(
+            checked
+        )
+        if (
+            checked.manifest_sha256 != self.capture_source_manifest_sha256
+            or checked.census_sha256 != self.capture_source_census_sha256
+            or storage_manifest != self.capture_storage_manifest_sha256
+            or capture_count != self.capture_count
+            or window_count != self.window_count
+            or caption_count != self.caption_count
+        ):
+            raise PeriodicCaptureTrainingCacheError(
+                "capture source differs from the complete admitted census"
+            )
+        actual_rows: list[CaptureRequestIdentity] = []
+        for capture in checked.captures:
+            for window_position in range(len(capture.windows)):
+                identity, _contexts, _batch_digest, _edge_count = _checked_capture_values(
+                    capture,
+                    window_position,
+                    seed=self.seed,
+                    edge_budget=self.edge_budget,
+                    expected_storage_manifest_sha256=(self.capture_storage_manifest_sha256),
+                )
+                actual_rows.append(identity)
+        if tuple(actual_rows) != tuple(row._identity() for row in self.capture_rows):
+            raise PeriodicCaptureTrainingCacheError(
+                "capture source window rows differ from the admitted plan"
+            )
+        _validate_reader_snapshot(
+            self._capture_cache,
+            index_sha256=self.capture_cache_index_sha256,
+            source_binding=self.capture_cache_source_binding,
+            floors_sha256=self.energy_floors_sha256,
+            cache_key_census=self.capture_cache_key_census,
+        )
+        return checked
+
+    def open_capture_validation_window(
+        self,
+        capture: CaptureValidationCapture,
+        window_position: int,
+    ) -> CachedPairChunkStream:
+        """Reopen one exact admitted capture-window shard without fallback."""
+
+        if type(capture) is not CaptureValidationCapture:
+            raise TypeError("capture must be exact CaptureValidationCapture")
+        checked_capture = CaptureValidationCapture(
+            capture.plan,
+            capture.windows,
+            capture.holistic_text,
+            capture.component_label,
+        )
+        identity, contexts, _batch_digest, _edge_count = _checked_capture_values(
+            checked_capture,
+            window_position,
+            seed=self.seed,
+            edge_budget=self.edge_budget,
+            expected_storage_manifest_sha256=self.capture_storage_manifest_sha256,
+        )
+        row = self._capture_lookup.get(identity)
+        if row is None:
+            raise PeriodicCaptureTrainingCacheError(
+                "capture window is not an exact admitted plan row"
+            )
+        _validate_reader_snapshot(
+            self._capture_cache,
+            index_sha256=self.capture_cache_index_sha256,
+            source_binding=self.capture_cache_source_binding,
+            floors_sha256=self.energy_floors_sha256,
+            cache_key_census=self.capture_cache_key_census,
+        )
+        _enforce_edge_budget(row.edge_count, self.edge_budget)
+        opened = self._capture_cache.open_batch(
+            checked_capture.windows[window_position].groups,
+            contexts,
+        )
+        _validate_opened(
+            opened,
+            row=row,
+            contexts=contexts,
+            floors_sha256=self.energy_floors_sha256,
+        )
+        stream = opened.stream(self.stream_kind)
+        if type(stream) is not CachedPairChunkStream:
+            raise PeriodicCaptureTrainingCacheError(
+                "capture cache returned an unsealed descriptor stream"
+            )
+        return stream
+
+
+def _admit_periodic_descriptor_capture_training_plan(
+    *,
+    system_id: str,
+    config: TrainingConfig,
+    train_source: PreparedTrainingDataSourceV2,
+    capture_validation_source: CaptureValidationSource,
+    train_cache: PeriodicDescriptorCacheV2,
+    capture_validation_cache: PeriodicDescriptorCacheV2,
+) -> PeriodicDescriptorCaptureTrainingPlan:
+    if type(config) is not TrainingConfig:
+        raise TypeError("config must be an exact TrainingConfig")
+    if config.stage != "residual" or config.synthetic_contract:
+        raise PeriodicCaptureTrainingCacheError(
+            "capture descriptor plans require an exact formal residual configuration"
+        )
+    if config.seed not in OFFICIAL_SEEDS or config.epochs != RESIDUAL_EPOCHS:
+        raise PeriodicCaptureTrainingCacheError("plan seed or epoch census changed")
+    if type(system_id) is not str or system_id not in _CACHEABLE_SYSTEMS:
+        raise PeriodicCaptureTrainingCacheError(
+            "capture plans support residual systems 02, 03, 04, 06, 07, and 08 only"
+        )
+    stream_kind = _CACHEABLE_SYSTEMS[system_id]
+    specification = system_spec(system_id)
+    if (
+        specification.feature_policy != _EXPECTED_POLICIES[system_id]
+        or specification.residual_enabled is not True
+    ):
+        raise PeriodicCaptureTrainingCacheError("registered system feature policy changed")
+    if type(train_source) is not PreparedTrainingDataSourceV2:
+        raise TypeError("train_source must be exact PreparedTrainingDataSourceV2")
+    if train_source.split != "train":
+        raise PeriodicCaptureTrainingCacheError("prepared source must be train-only")
+    checked_capture_source = _snapshot_capture_source(capture_validation_source)
+    capture_count, window_count, caption_count, storage_manifest = _capture_source_counts(
+        checked_capture_source
+    )
+    if (
+        type(train_cache) is not PeriodicDescriptorCacheV2
+        or type(capture_validation_cache) is not PeriodicDescriptorCacheV2
+    ):
+        raise TypeError("caches must be exact PeriodicDescriptorCacheV2 readers")
+    if train_cache is capture_validation_cache:
+        raise PeriodicCaptureTrainingCacheError(
+            "training and capture validation require distinct readers"
+        )
+    train_binding = train_cache.source_binding
+    capture_binding = capture_validation_cache.source_binding
+    if (
+        type(train_binding) is not DescriptorCacheSourceBinding
+        or type(capture_binding) is not DescriptorCacheSourceBinding
+    ):
+        raise PeriodicCaptureTrainingCacheError("cache source binding type changed")
+    if train_binding.prepared_manifest_sha256 != train_source.manifest_sha256:
+        raise PeriodicCaptureTrainingCacheError(
+            "training cache binding differs from the prepared train source"
+        )
+    if capture_binding.prepared_manifest_sha256 != storage_manifest:
+        raise PeriodicCaptureTrainingCacheError(
+            "capture cache binding differs from the consumed storage manifest"
+        )
+    if train_cache.energy_floors_sha256 != capture_validation_cache.energy_floors_sha256:
+        raise PeriodicCaptureTrainingCacheError(
+            "training and capture cache energy-floor digests differ"
+        )
+    if not np.array_equal(train_cache.energy_floors, capture_validation_cache.energy_floors):
+        raise PeriodicCaptureTrainingCacheError(
+            "training and capture cache energy-floor bytes differ"
+        )
+    train_census = _checked_cache_census(train_cache, "training")
+    capture_census = _checked_cache_census(capture_validation_cache, "capture")
+    config_sha256 = config.sha256
+    train_manifest = train_source.manifest_sha256
+    capture_manifest = checked_capture_source.manifest_sha256
+    capture_source_census = checked_capture_source.census_sha256
+    floors_sha256 = train_cache.energy_floors_sha256
+    train_index = train_cache.index_sha256
+    capture_index = capture_validation_cache.index_sha256
+    _validate_reader_snapshot(
+        train_cache,
+        index_sha256=train_index,
+        source_binding=train_binding,
+        floors_sha256=floors_sha256,
+        cache_key_census=train_census,
+    )
+    _validate_reader_snapshot(
+        capture_validation_cache,
+        index_sha256=capture_index,
+        source_binding=capture_binding,
+        floors_sha256=floors_sha256,
+        cache_key_census=capture_census,
+    )
+    train_rows, train_lookup = _enumerate_rows(
+        train_source,
+        train_cache,
+        split="train",
+        seed=config.seed,
+        epochs=tuple(range(RESIDUAL_EPOCHS)),
+        stream_kind=stream_kind,
+        edge_budget=config.edge_budget,
+        start_position=0,
+        source_binding=train_binding,
+        floors_sha256=floors_sha256,
+        index_sha256=train_index,
+        cache_key_census=train_census,
+    )
+    capture_rows, capture_lookup = _enumerate_capture_rows(
+        checked_capture_source,
+        capture_validation_cache,
+        seed=config.seed,
+        stream_kind=stream_kind,
+        edge_budget=config.edge_budget,
+        start_position=len(train_rows),
+        storage_manifest_sha256=storage_manifest,
+        source_binding=capture_binding,
+        floors_sha256=floors_sha256,
+        index_sha256=capture_index,
+        cache_key_census=capture_census,
+    )
+    if (
+        config.sha256 != config_sha256
+        or train_source.manifest_sha256 != train_manifest
+        or checked_capture_source.manifest_sha256 != capture_manifest
+        or checked_capture_source.census_sha256 != capture_source_census
+        or train_source.split != "train"
+        or checked_capture_source.split != "val"
+    ):
+        raise PeriodicCaptureTrainingCacheError(
+            "configuration or source identity changed during admission"
+        )
+    _validate_reader_snapshot(
+        train_cache,
+        index_sha256=train_index,
+        source_binding=train_binding,
+        floors_sha256=floors_sha256,
+        cache_key_census=train_census,
+    )
+    _validate_reader_snapshot(
+        capture_validation_cache,
+        index_sha256=capture_index,
+        source_binding=capture_binding,
+        floors_sha256=floors_sha256,
+        cache_key_census=capture_census,
+    )
+    return PeriodicDescriptorCaptureTrainingPlan(
+        system_id=system_id,
+        seed=config.seed,
+        epochs=RESIDUAL_EPOCHS,
+        stream_kind=stream_kind,
+        config_sha256=config_sha256,
+        edge_budget=config.edge_budget,
+        energy_floors_sha256=floors_sha256,
+        train_source_manifest_sha256=train_manifest,
+        capture_source_manifest_sha256=capture_manifest,
+        capture_source_census_sha256=capture_source_census,
+        capture_storage_manifest_sha256=storage_manifest,
+        train_cache_index_sha256=train_index,
+        capture_cache_index_sha256=capture_index,
+        train_cache_source_binding=train_binding,
+        capture_cache_source_binding=capture_binding,
+        train_cache_key_census=train_census,
+        capture_cache_key_census=capture_census,
+        capture_count=capture_count,
+        window_count=window_count,
+        caption_count=caption_count,
+        train_rows=train_rows,
+        capture_rows=capture_rows,
+        _train_cache=train_cache,
+        _capture_cache=capture_validation_cache,
+        _train_lookup=train_lookup,
+        _capture_lookup=capture_lookup,
+        _seal=_PLAN_TOKEN,
+    )
+
+
+def admit_periodic_descriptor_capture_training_plan(
+    *,
+    system_id: str,
+    config: TrainingConfig,
+    train_source: PreparedTrainingDataSourceV2,
+    capture_validation_source: CaptureValidationSource,
+    train_cache: PeriodicDescriptorCacheV2,
+    capture_validation_cache: PeriodicDescriptorCacheV2,
+) -> PeriodicDescriptorCaptureTrainingPlan:
+    """Derive one exact authority-zero train-plus-capture cache plan."""
+
+    snapshot = _capture_rng()
+    try:
+        plan = _admit_periodic_descriptor_capture_training_plan(
+            system_id=system_id,
+            config=config,
+            train_source=train_source,
+            capture_validation_source=capture_validation_source,
+            train_cache=train_cache,
+            capture_validation_cache=capture_validation_cache,
+        )
+    except Exception as error:
+        if not _rng_matches(snapshot):
+            _restore_rng(snapshot)
+            raise PeriodicCaptureTrainingCacheError(
+                "capture plan admission changed caller RNG state"
+            ) from error
+        raise
+    if not _rng_matches(snapshot):
+        _restore_rng(snapshot)
+        raise PeriodicCaptureTrainingCacheError("capture plan admission changed caller RNG state")
+    return plan
+
+
+__all__ = [
+    "AUTHORITY",
+    "CAPTURE_VALIDATION_SEMANTICS",
+    "PLAN_SCHEMA",
+    "PRODUCTION",
+    "PeriodicCaptureDescriptorPlanRow",
+    "PeriodicCaptureTrainingCacheError",
+    "PeriodicDescriptorCaptureTrainingPlan",
+    "RESULT_CLAIMED",
+    "admit_periodic_descriptor_capture_training_plan",
+]
diff --git a/src/phaseset_core/training.py b/src/phaseset_core/training.py
index b09813b..8ae07e3 100644
--- a/src/phaseset_core/training.py
+++ b/src/phaseset_core/training.py
@@ -59,7 +59,7 @@
     build_group_base,
 )
 from .objectives import PhaseSetRetrievalHead, variable_positive_symmetric_infonce
-from .periodic_descriptor_cache_v2 import DescriptorWindowContext
+from .periodic_descriptor_cache_v2 import CachedPairChunkStream, DescriptorWindowContext
 
 
 STATUS: Final = "DATA_FREE_EXECUTABLE_TRAINING_RUNTIME_NONPRODUCTION_AUTHORITY0"
@@ -613,14 +613,55 @@ def train(self, mode: bool = True) -> ResidualRetrievalSystem:
         self.frozen_base.eval()
         return self
 
-    def encode_trainable(self, groups: PreparedGroupBatch) -> tuple[Tensor, Tensor]:
-        output = self.periodic_encoder(groups)
+    def _validated_trainable_output(
+        self,
+        output: object,
+    ) -> tuple[Tensor, Tensor]:
+        """Apply the unchanged residual-token checks to either explicit path."""
+
         if type(output) is not GroupTokenOutput:
             raise TrainingRuntimeError("residual encoder must return exact GroupTokenOutput")
         if output.tokens.ndim != 3 or output.tokens.shape[-1] != self.embedding_dim:
             raise TrainingRuntimeError("residual token width differs from training system")
         return output.tokens.float().contiguous(), output.band_mask.contiguous()
 
+    def encode_trainable(self, groups: PreparedGroupBatch) -> tuple[Tensor, Tensor]:
+        return self._validated_trainable_output(self.periodic_encoder(groups))
+
+    def encode_trainable_cached(
+        self,
+        groups: PreparedGroupBatch,
+        descriptor_stream: CachedPairChunkStream,
+    ) -> tuple[Tensor, Tensor]:
+        """Encode only from one explicit verified descriptor stream.
+
+        The cache remains a substitute for the fixed periodic descriptors only.
+        Frozen-base outputs and learned residual outputs are never cached here.
+        """
+
+        from .controls import PhaseSetSystem, _ControlledPhaseSetEncoder
+
+        if self.system_id not in ("02", "03", "04", "06", "07", "08"):
+            raise TrainingRuntimeError(
+                "this registered residual system does not admit cached descriptors"
+            )
+        periodic = self.periodic_encoder
+        if (
+            type(periodic) is not PhaseSetSystem
+            or periodic.system_id != self.system_id
+            or type(periodic.encoder) is not _ControlledPhaseSetEncoder
+        ):
+            raise TrainingRuntimeError(
+                "cached residual encoding requires the exact registered PhaseSet encoder"
+            )
+        if type(descriptor_stream) is not CachedPairChunkStream:
+            raise TypeError("descriptor_stream must be exact CachedPairChunkStream")
+        output = periodic.forward_cached(
+            groups,
+            descriptor_stream=descriptor_stream,
+        )
+        return self._validated_trainable_output(output)
+
     def encode_frozen_base(self, groups: PreparedGroupBatch) -> Tensor:
         with torch.no_grad():
             output = self.frozen_base(groups)
diff --git a/tests/test_periodic_capture_training_cache.py b/tests/test_periodic_capture_training_cache.py
new file mode 100644
index 0000000..59d116e
--- /dev/null
+++ b/tests/test_periodic_capture_training_cache.py
@@ -0,0 +1,497 @@
+"""Focused analytic contracts for the train-plus-capture descriptor plan."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, replace
+import hashlib
+from pathlib import Path
+import random
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import capture_prepared_storage as capture_storage
+from phaseset_core import capture_validation, prepared_data_v2, training
+from phaseset_core.periodic import ResourceLimitError
+from phaseset_core.periodic_capture_training_cache import (
+    CAPTURE_VALIDATION_SEMANTICS,
+    PLAN_SCHEMA,
+    PeriodicCaptureTrainingCacheError,
+    admit_periodic_descriptor_capture_training_plan,
+)
+from phaseset_core.periodic_descriptor_cache_v2 import (
+    CachedPairChunkStream,
+    DescriptorCacheSourceBinding,
+    PeriodicDescriptorCacheV2,
+    PeriodicDescriptorCacheWriterV2,
+)
+from test_periodic_training_cache import _block, _write_cache, _write_sources
+from test_phaseset_capture_prepared_storage import _frozen_text, _source
+
+
+_MAX_BYTES = 32 * 1024 * 1024
+_FLOORS = np.ascontiguousarray(
+    np.asarray([(index + 1) * 1.0e-12 for index in range(6)], dtype=np.float64)
+)
+
+
+def _digest(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+def _capture_binding(storage_manifest_sha256: str) -> DescriptorCacheSourceBinding:
+    return DescriptorCacheSourceBinding(
+        prepared_manifest_sha256=storage_manifest_sha256,
+        source_tree_sha256=_digest("capture/source-tree"),
+        environment_sha256=_digest("capture/environment"),
+    )
+
+
+def _write_capture_source(root: Path) -> capture_validation.CaptureValidationSource:
+    result = capture_storage.write_capture_validation_source_v2(root, _source())
+    return capture_storage.load_capture_validation_source(
+        result.manifest_path,
+        expected_manifest_sha256=result.manifest_sha256,
+    )
+
+
+def _write_capture_cache(
+    root: Path,
+    source: capture_validation.CaptureValidationSource,
+    *,
+    seeds: tuple[int, ...] = (1729,),
+    window_limit: int | None = None,
+) -> PeriodicDescriptorCacheV2:
+    descriptors = [
+        window.descriptor_source for capture in source.captures for window in capture.windows
+    ]
+    assert descriptors and all(
+        type(value) is capture_validation.CaptureDescriptorWindowSource for value in descriptors
+    )
+    storage_manifest = descriptors[0].storage_manifest_sha256
+    binding = _capture_binding(storage_manifest)
+    writer = PeriodicDescriptorCacheWriterV2(
+        root,
+        source_binding=binding,
+        energy_floors=_FLOORS,
+    )
+    written = 0
+    for seed in seeds:
+        for capture in source.captures:
+            for window in capture.windows:
+                if window_limit is not None and written >= window_limit:
+                    break
+                writer.add_batch(window.groups, (window.descriptor_context(seed=seed),))
+                written += 1
+            if window_limit is not None and written >= window_limit:
+                break
+        if window_limit is not None and written >= window_limit:
+            break
+    artifact = writer.finalize()
+    return PeriodicDescriptorCacheV2(
+        root,
+        expected_index_sha256=artifact.index_sha256,
+        expected_source_binding=binding,
+        energy_floors=_FLOORS,
+    )
+
+
+@dataclass(frozen=True)
+class _Bundle:
+    train_source: prepared_data_v2.PreparedTrainingDataSourceV2
+    capture_source: capture_validation.CaptureValidationSource
+    train_cache: PeriodicDescriptorCacheV2
+    capture_cache: PeriodicDescriptorCacheV2
+    config: training.TrainingConfig
+
+
+def _bundle(tmp_path: Path, *, seed: int = 1729) -> _Bundle:
+    train_source, _unused_window_val = _write_sources(tmp_path / "prepared")
+    capture_source = _write_capture_source(tmp_path / "capture-source")
+    train_cache = _write_cache(
+        tmp_path / "train-cache",
+        train_source,
+        seed=seed,
+        epochs=tuple(range(training.RESIDUAL_EPOCHS)),
+        floors=_FLOORS,
+    )
+    capture_cache = _write_capture_cache(
+        tmp_path / "capture-cache",
+        capture_source,
+        seeds=(seed,),
+    )
+    return _Bundle(
+        train_source,
+        capture_source,
+        train_cache,
+        capture_cache,
+        training.TrainingConfig(stage="residual", seed=seed),
+    )
+
+
+def _admit(bundle: _Bundle, *, system_id: str = "08"):
+    return admit_periodic_descriptor_capture_training_plan(
+        system_id=system_id,
+        config=bundle.config,
+        train_source=bundle.train_source,
+        capture_validation_source=bundle.capture_source,
+        train_cache=bundle.train_cache,
+        capture_validation_cache=bundle.capture_cache,
+    )
+
+
+def _rng_snapshot():
+    state = np.random.get_state()
+    return (
+        random.getstate(),
+        (state[0], np.array(state[1], copy=True), *state[2:]),
+        torch.random.get_rng_state().clone(),
+    )
+
+
+def _assert_rng_equal(before) -> None:
+    python_before, numpy_before, torch_before = before
+    current = np.random.get_state()
+    assert random.getstate() == python_before
+    assert current[0] == numpy_before[0]
+    assert np.array_equal(current[1], numpy_before[1])
+    assert current[2:] == numpy_before[2:]
+    assert torch.equal(torch.random.get_rng_state(), torch_before)
+
+
+def test_complete_plan_binds_twenty_epoch_train_and_every_capture_window(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path)
+    original = PeriodicDescriptorCacheV2.open_batch
+    calls = 0
+
+    def counted(self, batch, contexts):
+        nonlocal calls
+        calls += 1
+        return original(self, batch, contexts)
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", counted)
+    before = _rng_snapshot()
+    cuda_initialized_before = torch.cuda.is_initialized()
+    plan = _admit(bundle, system_id="06")
+    _assert_rng_equal(before)
+    assert torch.cuda.is_initialized() is cuda_initialized_before
+    window_count = sum(len(capture.windows) for capture in bundle.capture_source.captures)
+    caption_count = sum(
+        len(capture.holistic_text.caption_commitments) for capture in bundle.capture_source.captures
+    )
+    assert calls == training.RESIDUAL_EPOCHS + window_count
+    assert plan.authority == 0
+    assert plan.production is False
+    assert plan.result_claimed is False
+    assert plan.stream_kind == "FULL_RELATION"
+    assert plan.capture_count == len(bundle.capture_source.captures)
+    assert plan.window_count == window_count
+    assert plan.caption_count == caption_count
+    assert plan.capture_source_manifest_sha256 == bundle.capture_source.manifest_sha256
+    assert plan.capture_source_census_sha256 == bundle.capture_source.census_sha256
+    assert (
+        plan.capture_storage_manifest_sha256
+        == bundle.capture_source.captures[0].windows[0].descriptor_source.storage_manifest_sha256
+    )
+    assert plan.capture_storage_manifest_sha256 != plan.capture_source_manifest_sha256
+    assert len(plan.train_rows) == training.RESIDUAL_EPOCHS
+    assert {row.epoch for row in plan.train_rows} == set(range(training.RESIDUAL_EPOCHS))
+    assert len(plan.capture_rows) == window_count
+    assert tuple(row.execution_position for row in plan.capture_rows) == tuple(
+        range(training.RESIDUAL_EPOCHS, training.RESIDUAL_EPOCHS + window_count)
+    )
+    assert tuple(row.window_ordinal for row in plan.capture_rows) == tuple(range(window_count))
+    assert tuple(sorted(row.cache_key_sha256 for row in plan.capture_rows)) == (
+        bundle.capture_cache.cache_key_census
+    )
+    expected = tuple(
+        (
+            capture.capture_commitment.hex(),
+            capture.plan.sha256,
+            window_position,
+            capture.plan.source_start_frames[window_position],
+            window.window_commitment.hex(),
+            window.descriptor_source.source_window_npz_sha256,
+            window.descriptor_source.window_ordinal,
+        )
+        for capture in bundle.capture_source.captures
+        for window_position, window in enumerate(capture.windows)
+    )
+    observed = tuple(
+        (
+            row.capture_commitment,
+            row.capture_plan_sha256,
+            row.window_position,
+            row.source_start_frame,
+            row.window_commitment,
+            row.source_window_npz_sha256,
+            row.window_ordinal,
+        )
+        for row in plan.capture_rows
+    )
+    assert observed == expected
+    raw = plan.canonical_bytes()
+    assert raw.endswith(b"\n")
+    assert PLAN_SCHEMA.encode("ascii") in raw
+    assert CAPTURE_VALIDATION_SEMANTICS.encode("ascii") in raw
+    assert len(plan.sha256) == 64
+
+    checked = plan.validate_capture_validation_source(bundle.capture_source)
+    assert checked.census_sha256 == bundle.capture_source.census_sha256
+    assert calls == training.RESIDUAL_EPOCHS + window_count
+    train_batch = next(bundle.train_source.iter_epoch(epoch=7, seed=1729))
+    assert type(plan.open_training_batch(train_batch)) is CachedPairChunkStream
+    for capture in checked.captures:
+        for window_position in range(len(capture.windows)):
+            stream = plan.open_capture_validation_window(capture, window_position)
+            assert type(stream) is CachedPairChunkStream
+            assert stream.stream_kind == "FULL_RELATION"
+    assert calls == training.RESIDUAL_EPOCHS + 2 * window_count + 1
+    _assert_rng_equal(before)
+    assert torch.cuda.is_initialized() is cuda_initialized_before
+
+
+def test_registered_capture_plan_stream_mapping(tmp_path: Path) -> None:
+    bundle = _bundle(tmp_path)
+    expected = {
+        "02": "MARGINAL_POWER",
+        "03": "MEAN_DIFFERENCE_DCT",
+        "04": "FULL_RELATION",
+        "06": "FULL_RELATION",
+        "07": "FULL_RELATION",
+        "08": "FULL_RELATION",
+    }
+    for system_id, stream_kind in expected.items():
+        assert _admit(bundle, system_id=system_id).stream_kind == stream_kind
+
+
+def test_full_scientific_source_census_including_text_is_rechecked(
+    tmp_path: Path,
+) -> None:
+    bundle = _bundle(tmp_path)
+    plan = _admit(bundle)
+    first = bundle.capture_source.captures[0]
+    replacement_axes = tuple(
+        17 + index for index in range(len(first.holistic_text.caption_commitments))
+    )
+    changed_capture = replace(
+        first,
+        holistic_text=_frozen_text("changed-holistic-gallery", replacement_axes),
+    )
+    changed = capture_validation.CaptureValidationSource(
+        "val",
+        bundle.capture_source.manifest_sha256,
+        (changed_capture, *bundle.capture_source.captures[1:]),
+    )
+    assert changed.captures[0].plan == bundle.capture_source.captures[0].plan
+    assert tuple(window.window_commitment for window in changed.captures[0].windows) == tuple(
+        window.window_commitment for window in bundle.capture_source.captures[0].windows
+    )
+    assert (
+        sum(len(capture.holistic_text.caption_commitments) for capture in changed.captures)
+        == plan.caption_count
+    )
+    assert changed.census_sha256 != bundle.capture_source.census_sha256
+    with pytest.raises(
+        PeriodicCaptureTrainingCacheError,
+        match="complete admitted census",
+    ):
+        plan.validate_capture_validation_source(changed)
+
+    changed_manifest = capture_validation.CaptureValidationSource(
+        "val",
+        _digest("changed-upstream-manifest"),
+        bundle.capture_source.captures,
+    )
+    with pytest.raises(
+        PeriodicCaptureTrainingCacheError,
+        match="complete admitted census",
+    ):
+        plan.validate_capture_validation_source(changed_manifest)
+
+
+def test_legacy_capture_source_and_prepared_window_val_never_enter_plan(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path)
+    calls = 0
+    original = PeriodicDescriptorCacheV2.open_batch
+
+    def counted(self, batch, contexts):
+        nonlocal calls
+        calls += 1
+        return original(self, batch, contexts)
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", counted)
+    with pytest.raises(
+        PeriodicCaptureTrainingCacheError,
+        match="v2 descriptor lineage",
+    ):
+        admit_periodic_descriptor_capture_training_plan(
+            system_id="08",
+            config=bundle.config,
+            train_source=bundle.train_source,
+            capture_validation_source=_source(),
+            train_cache=bundle.train_cache,
+            capture_validation_cache=bundle.capture_cache,
+        )
+    assert calls == 0
+
+    _train, prepared_window_val = _write_sources(tmp_path / "other-prepared")
+    with pytest.raises(TypeError, match="exact CaptureValidationSource"):
+        admit_periodic_descriptor_capture_training_plan(
+            system_id="08",
+            config=bundle.config,
+            train_source=bundle.train_source,
+            capture_validation_source=prepared_window_val,
+            train_cache=bundle.train_cache,
+            capture_validation_cache=bundle.capture_cache,
+        )
+    assert calls == 0
+
+
+def test_capture_selector_rejects_stale_npz_lineage_before_reader_open(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path)
+    plan = _admit(bundle)
+    first = bundle.capture_source.captures[0]
+    window = first.windows[0]
+    descriptor = window.descriptor_source
+    assert type(descriptor) is capture_validation.CaptureDescriptorWindowSource
+    changed_descriptor = replace(descriptor, source_window_npz_sha256=_digest("stale-npz"))
+    changed_window = replace(window, descriptor_source=changed_descriptor)
+    changed_capture = replace(first, windows=(changed_window, *first.windows[1:]))
+    calls = 0
+
+    def forbidden(*_args, **_kwargs):
+        nonlocal calls
+        calls += 1
+        raise AssertionError("stale selector must not open a shard")
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", forbidden)
+    with pytest.raises(PeriodicCaptureTrainingCacheError, match="not an exact admitted"):
+        plan.open_capture_validation_window(changed_capture, 0)
+    assert calls == 0
+
+
+def test_uncached_and_base_systems_reject_before_any_shard_open(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path)
+    calls = 0
+
+    def forbidden(*_args, **_kwargs):
+        nonlocal calls
+        calls += 1
+        raise AssertionError("cache must not open")
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", forbidden)
+    for system_id in ("01", "05", "B0", "00"):
+        with pytest.raises(PeriodicCaptureTrainingCacheError):
+            _admit(bundle, system_id=system_id)
+    assert calls == 0
+
+
+@pytest.mark.parametrize("mode", ["missing", "extra"])
+def test_capture_reader_census_must_equal_all_capture_windows(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    mode: str,
+) -> None:
+    bundle = _bundle(tmp_path)
+    cache = _write_capture_cache(
+        tmp_path / f"capture-cache-{mode}",
+        bundle.capture_source,
+        seeds=(1729,) if mode == "missing" else (1729, 2718),
+        window_limit=2 if mode == "missing" else None,
+    )
+    original = PeriodicDescriptorCacheV2.open_batch
+    capture_opens = 0
+
+    def counted(self, batch, contexts):
+        nonlocal capture_opens
+        if self is cache:
+            capture_opens += 1
+        return original(self, batch, contexts)
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", counted)
+    expected = "exceeds its closed cache census" if mode == "missing" else "required key set"
+    with pytest.raises(PeriodicCaptureTrainingCacheError, match=expected):
+        admit_periodic_descriptor_capture_training_plan(
+            system_id="08",
+            config=bundle.config,
+            train_source=bundle.train_source,
+            capture_validation_source=bundle.capture_source,
+            train_cache=bundle.train_cache,
+            capture_validation_cache=cache,
+        )
+    all_windows = sum(len(capture.windows) for capture in bundle.capture_source.captures)
+    assert capture_opens == (2 if mode == "missing" else all_windows)
+
+
+def _two_actor_train_source(
+    root: Path,
+) -> prepared_data_v2.PreparedTrainingDataSourceV2:
+    result = prepared_data_v2.write_prepared_training_tree_v2(
+        root,
+        train_blocks=(_block("budget/train", actors=2, ordinal=10),),
+        val_blocks=(_block("budget/val", actors=2, ordinal=20),),
+        max_total_edges=1,
+        max_batch_size=1,
+        max_batch_bytes=_MAX_BYTES,
+        max_decoded_batch_bytes=_MAX_BYTES,
+    )
+    train_source, _val_source = prepared_data_v2.load_prepared_training_sources_v2(
+        result.index_path,
+        expected_index_sha256=result.index_sha256,
+        max_batch_bytes=_MAX_BYTES,
+        max_decoded_batch_bytes=_MAX_BYTES,
+    )
+    return train_source
+
+
+def test_capture_edge_budget_fails_before_capture_reader_open(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    train_source = _two_actor_train_source(tmp_path / "budget-prepared")
+    train_cache = _write_cache(
+        tmp_path / "budget-train-cache",
+        train_source,
+        seed=1729,
+        epochs=tuple(range(training.RESIDUAL_EPOCHS)),
+        floors=_FLOORS,
+    )
+    capture_source = _write_capture_source(tmp_path / "budget-capture-source")
+    capture_cache = _write_capture_cache(
+        tmp_path / "budget-capture-cache",
+        capture_source,
+    )
+    capture_opens = 0
+    original = PeriodicDescriptorCacheV2.open_batch
+
+    def counted(self, batch, contexts):
+        nonlocal capture_opens
+        if self is capture_cache:
+            capture_opens += 1
+        return original(self, batch, contexts)
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", counted)
+    with pytest.raises(ResourceLimitError):
+        admit_periodic_descriptor_capture_training_plan(
+            system_id="08",
+            config=training.TrainingConfig(stage="residual", seed=1729, edge_budget=1),
+            train_source=train_source,
+            capture_validation_source=capture_source,
+            train_cache=train_cache,
+            capture_validation_cache=capture_cache,
+        )
+    assert capture_opens == 0
diff --git a/tests/test_phaseset_capture_validation_cached.py b/tests/test_phaseset_capture_validation_cached.py
new file mode 100644
index 0000000..88b79fb
--- /dev/null
+++ b/tests/test_phaseset_capture_validation_cached.py
@@ -0,0 +1,220 @@
+"""Server-only contracts for cached holistic capture validation."""
+
+from __future__ import annotations
+
+import copy
+from dataclasses import replace
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+from phaseset_core import capture_validation, frozen_clip_text, training
+from phaseset_core.periodic_capture_training_cache import (
+    PeriodicCaptureTrainingCacheError,
+    PeriodicDescriptorCaptureTrainingPlan,
+)
+from phaseset_core.periodic_descriptor_cache_v2 import (
+    DescriptorCacheV2Error,
+    PeriodicDescriptorCacheV2,
+)
+from test_periodic_capture_training_cache import (
+    _FLOORS,
+    _admit,
+    _assert_rng_equal,
+    _bundle,
+    _rng_snapshot,
+)
+
+
+def _system(system_id: str = "08") -> training.ResidualRetrievalSystem:
+    with torch.random.fork_rng():
+        torch.manual_seed(1729)
+        frozen_base = training.build_registered_base_training_system("B0").group_base
+        return training.build_registered_residual_training_system(
+            system_id,
+            frozen_base,
+            seed=1729,
+            energy_floors=_FLOORS,
+            edge_budget=32_768,
+        )
+
+
+def _assert_same_result(left, right) -> None:
+    assert np.array_equal(left.dataset.scores, right.dataset.scores)
+    assert left.dataset.motion_commitments == right.dataset.motion_commitments
+    assert left.dataset.caption_commitments == right.dataset.caption_commitments
+    assert left.dataset.positive_motion_indices == right.dataset.positive_motion_indices
+    assert np.array_equal(left.dataset.group_sizes, right.dataset.group_sizes)
+    assert left.dataset.component_labels == right.dataset.component_labels
+    assert left.text_to_motion_capture_r1 == right.text_to_motion_capture_r1
+    assert left.motion_to_text_capture_r1 == right.motion_to_text_capture_r1
+    assert left.primary_capture_r1 == right.primary_capture_r1
+    assert left.loss == right.loss
+    assert left.source_census_sha256 == right.source_census_sha256
+    assert left.scores_float64_sha256 == right.scores_float64_sha256
+    assert left.encoded_bytes == right.encoded_bytes
+
+
+def test_reader_backed_cached_runner_matches_uncached_full_capture_gallery(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path)
+    plan = _admit(bundle, system_id="08")
+    uncached_system = _system().train()
+    cached_system = copy.deepcopy(uncached_system)
+    uncached_modes = tuple(module.training for module in uncached_system.modules())
+    cached_modes = tuple(module.training for module in cached_system.modules())
+    states = {key: value.detach().clone() for key, value in cached_system.state_dict().items()}
+    original_open = PeriodicDescriptorCacheV2.open_batch
+    capture_opens = 0
+
+    def counted_open(self, groups, contexts):
+        nonlocal capture_opens
+        if self is bundle.capture_cache:
+            capture_opens += 1
+        return original_open(self, groups, contexts)
+
+    def forbidden_text_encode(*_args, **_kwargs):
+        raise AssertionError("persisted holistic text must not be re-encoded")
+
+    monkeypatch.setattr(PeriodicDescriptorCacheV2, "open_batch", counted_open)
+    monkeypatch.setattr(
+        frozen_clip_text.FrozenClipTextAdapter,
+        "encode",
+        forbidden_text_encode,
+    )
+    before = _rng_snapshot()
+    uncached = capture_validation.run_capture_validation(
+        uncached_system,
+        bundle.config,
+        bundle.capture_source,
+    )
+    cached = capture_validation.run_capture_validation_cached(
+        cached_system,
+        bundle.config,
+        bundle.capture_source,
+        plan,
+    )
+    _assert_rng_equal(before)
+    _assert_same_result(uncached, cached)
+    assert capture_opens == sum(len(capture.windows) for capture in bundle.capture_source.captures)
+    assert uncached_modes == tuple(module.training for module in uncached_system.modules())
+    assert cached_modes == tuple(module.training for module in cached_system.modules())
+    assert all(torch.equal(value, cached_system.state_dict()[key]) for key, value in states.items())
+    assert all(parameter.grad is None for parameter in cached_system.parameters())
+
+
+def test_complete_source_drift_and_missing_shard_fail_without_scores_or_fallback(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path)
+    plan = _admit(bundle)
+    system = _system()
+    calls = {"cached": 0, "uncached": 0, "scores": 0}
+    original_cached = training.ResidualRetrievalSystem.encode_trainable_cached
+    original_scores = training.ResidualRetrievalSystem.scores
+
+    def counted_cached(self, groups, stream):
+        calls["cached"] += 1
+        return original_cached(self, groups, stream)
+
+    def forbidden_uncached(*_args, **_kwargs):
+        calls["uncached"] += 1
+        raise AssertionError("cached runner must not fall back")
+
+    def counted_scores(self, *args, **kwargs):
+        calls["scores"] += 1
+        return original_scores(self, *args, **kwargs)
+
+    monkeypatch.setattr(
+        training.ResidualRetrievalSystem,
+        "encode_trainable_cached",
+        counted_cached,
+    )
+    monkeypatch.setattr(
+        training.ResidualRetrievalSystem,
+        "encode_trainable",
+        forbidden_uncached,
+    )
+    monkeypatch.setattr(training.ResidualRetrievalSystem, "scores", counted_scores)
+    changed = replace(bundle.capture_source, manifest_sha256="f" * 64)
+    with pytest.raises(PeriodicCaptureTrainingCacheError, match="complete admitted census"):
+        capture_validation.run_capture_validation_cached(
+            system,
+            bundle.config,
+            changed,
+            plan,
+        )
+    assert calls == {"cached": 0, "uncached": 0, "scores": 0}
+
+    first_key = plan.capture_rows[0].cache_key_sha256
+    (bundle.capture_cache.root / "shards" / f"{first_key}.pdc2").unlink()
+    with pytest.raises(DescriptorCacheV2Error):
+        capture_validation.run_capture_validation_cached(
+            system,
+            bundle.config,
+            bundle.capture_source,
+            plan,
+        )
+    assert calls == {"cached": 0, "uncached": 0, "scores": 0}
+
+
+def test_wrong_system_or_descriptor_family_is_rejected_without_fallback(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path)
+    plan = _admit(bundle, system_id="08")
+    system = _system("08")
+    other = _system("06")
+    with pytest.raises(capture_validation.CaptureValidationError, match="differs"):
+        capture_validation.run_capture_validation_cached(
+            other,
+            bundle.config,
+            bundle.capture_source,
+            plan,
+        )
+
+    original_open = PeriodicDescriptorCaptureTrainingPlan.open_capture_validation_window
+    uncached_calls = 0
+
+    def wrong_family(self, capture, window_position):
+        stream = original_open(self, capture, window_position)
+        return stream.batch.stream("MARGINAL_POWER")
+
+    def forbidden_uncached(*_args, **_kwargs):
+        nonlocal uncached_calls
+        uncached_calls += 1
+        raise AssertionError("wrong cached family must not trigger uncached fallback")
+
+    monkeypatch.setattr(
+        PeriodicDescriptorCaptureTrainingPlan,
+        "open_capture_validation_window",
+        wrong_family,
+    )
+    monkeypatch.setattr(
+        training.ResidualRetrievalSystem,
+        "encode_trainable",
+        forbidden_uncached,
+    )
+    with pytest.raises((DescriptorCacheV2Error, RuntimeError, ValueError)):
+        capture_validation.run_capture_validation_cached(
+            system,
+            bundle.config,
+            bundle.capture_source,
+            plan,
+        )
+    assert uncached_calls == 0
+
+
+def test_public_uncached_api_and_runtime_binding_order_remain_compatible() -> None:
+    assert capture_validation.__all__[-2:] == [
+        "run_capture_validation",
+        "run_capture_validation_cached",
+    ]
+    source_type, public_runner = capture_validation._verified_capture_validation_bindings()
+    assert source_type is capture_validation.CaptureValidationSource
+    assert public_runner is capture_validation.run_capture_validation
diff --git a/tests/test_phaseset_host_base_qualification.py b/tests/test_phaseset_host_base_qualification.py
new file mode 100644
index 0000000..2f94955
--- /dev/null
+++ b/tests/test_phaseset_host_base_qualification.py
@@ -0,0 +1,1277 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+from dataclasses import fields, replace
+from fractions import Fraction
+import hashlib
+import inspect
+import io
+import json
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+from phaseset_core import base_qualification_host as controller
+from phaseset_core import base_cohort_qualification as assembly
+from phaseset_core import cli
+from phaseset_core import experiments
+from phaseset_core import host
+from phaseset_core import latency_progress_journal as journal
+from phaseset_core.experiments import base_run_ids
+from phaseset_core.production import BackendExecution, PrivateReceiptAssertions
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+PUBLIC_CONFIG = PROJECT_ROOT / "configs" / "phaseset" / "training.json"
+SHA_A = "a" * 64
+SHA_B = "b" * 64
+SHA_C = "c" * 64
+CUDA_UUID = "GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee"
+
+
+def _canonical(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _digest(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _registry_raw(*, reverse: bool = False) -> bytes:
+    run_ids = list(base_run_ids())
+    if reverse:
+        run_ids.reverse()
+    return _canonical(
+        {
+            "authority": 0,
+            "production": False,
+            "result_claimed": False,
+            "rows": [
+                {
+                    "attempt_id": f"actual-attempt-{index:02d}",
+                    "run_id": run_id,
+                    "terminal_outcome": "SUCCEEDED",
+                    "terminal_sha256": hashlib.sha256(run_id.encode("ascii")).hexdigest(),
+                }
+                for index, run_id in enumerate(run_ids)
+            ],
+            "schema": controller.REGISTRY_SCHEMA,
+        }
+    )
+
+
+def _controller_config(tmp_path: Path, registry: Path) -> controller.HostBaseQualificationConfig:
+    output_root = tmp_path / "output"
+    journal_parent = tmp_path / "journal"
+    output_root.mkdir()
+    journal_parent.mkdir()
+    return controller.HostBaseQualificationConfig(
+        registry_path=registry,
+        registry_sha256=_digest(registry.read_bytes()),
+        output_root=output_root,
+        journal_parent=journal_parent,
+        session_id="session-001",
+        registered_cuda_uuid=CUDA_UUID,
+        latency_protocol_sha256=SHA_A,
+        launcher_sha256=SHA_B,
+        wall_timeout_seconds=3600,
+    )
+
+
+def _admission():
+    return controller.resolver_module.BaseCohortAdmission(
+        plan_sha256=SHA_A,
+        matrix_sha256=SHA_B,
+        training_config_sha256=SHA_C,
+        source_tree_sha256=_digest(b"source"),
+        train_manifest_sha256=_digest(b"train"),
+        val_manifest_sha256=_digest(b"val"),
+        device="cuda",
+        request_bf16=False,
+        bf16_runtime_qualified=False,
+        edge_budget=32,
+        checkpoint_every_updates=1,
+    )
+
+
+def _cuda_observation(captured_unix_ns: int = 1):
+    return controller.latency_module.SharedCudaObservation(
+        captured_unix_ns=captured_unix_ns,
+        device_uuid=CUDA_UUID,
+        memory_total_mib=49_000,
+        memory_free_mib=40_000,
+        memory_used_mib=100,
+        gpu_utilization_percent=0,
+        memory_utilization_percent=0,
+        compute_mode="Default",
+        driver_version="test-driver",
+    )
+
+
+def _cuda_identity():
+    return controller.latency_module.BaseLatencyRuntimeIdentity(
+        device="cuda:0",
+        device_uuid=CUDA_UUID,
+        device_name="fictional test GPU",
+        torch_version="test-torch",
+        cuda_runtime_version="test-cuda",
+        cudnn_version=1,
+        driver_version="test-driver",
+        nvidia_total_memory_mib=49_000,
+        compute_mode="Default",
+        torch_total_memory_bytes=49_000 * 1024 * 1024,
+        initial_allocated_bytes=0,
+        initial_reserved_bytes=0,
+        environment_sha256=_digest(b"environment"),
+        precision_mode="FP32",
+    )
+
+
+class _FakeCaptureValidationSource:
+    split = "val"
+    census_sha256 = _digest(b"val")
+    manifest_sha256 = _digest(b"capture-source")
+
+
+@contextmanager
+def _no_timeout(_seconds: int):
+    yield
+
+
+def _emit_completed_journal(observer, session_sha256: str) -> None:
+    observer.session_start(
+        admission_observation_sha256=_digest(b"admission observation"),
+        started_unix_ns=1,
+    )
+    observer.context_ready(
+        runtime_sha256=_digest(b"runtime"),
+        post_context_observation_sha256=_digest(b"post context"),
+    )
+    for round_index, round_rows in enumerate(
+        controller.latency_module.cyclic_visit_schedule()
+    ):
+        for ordinal, run_id in enumerate(round_rows):
+            event = {
+                "round_index": round_index,
+                "ordinal_in_round": ordinal,
+                "run_id": run_id,
+            }
+            observer.visit_started(
+                **event,
+                before_observation_sha256=_digest(
+                    f"before/{round_index}/{ordinal}".encode("ascii")
+                ),
+            )
+            for warmup_index in range(5):
+                observer.warmup_completed(**event, warmup_index=warmup_index)
+            for sample_index in range(11):
+                observer.timed_sample_completed(
+                    **event,
+                    sample_index=sample_index,
+                    sample_ns=sample_index + 1,
+                )
+            observer.visit_ended(
+                **event,
+                outcome="COMPLETED",
+                after_observation_sha256=_digest(
+                    f"after/{round_index}/{ordinal}".encode("ascii")
+                ),
+                peak_allocated_bytes=1,
+                peak_reserved_bytes=2,
+                failure_code=None,
+            )
+    observer.terminal(
+        outcome="COMPLETED",
+        latency_session_sha256=session_sha256,
+        failure_code=None,
+    )
+
+
+def _patch_pre_latency(monkeypatch: pytest.MonkeyPatch) -> tuple[object, object]:
+    cohort = SimpleNamespace(source_tree_sha256=_digest(b"source"))
+    scored = SimpleNamespace(sha256=_digest(b"scored"))
+    monkeypatch.setattr(controller, "CaptureValidationSource", _FakeCaptureValidationSource)
+    monkeypatch.setattr(
+        controller.resolver_module,
+        "resolve_completed_base_cohort",
+        lambda *_args, **_kwargs: cohort,
+    )
+    monkeypatch.setattr(
+        controller.scoring_module,
+        "score_resolved_base_cohort",
+        lambda *_args, **_kwargs: scored,
+    )
+    monkeypatch.setattr(
+        controller.latency_module,
+        "_resolved_cohort_sha256",
+        lambda value: _digest(b"resolved") if value is cohort else pytest.fail("cohort drift"),
+    )
+    monkeypatch.setattr(controller, "_formal_latency_timeout", _no_timeout)
+    return cohort, scored
+
+
+def test_public_cli_qualify_base_carries_only_terminal_root(tmp_path: Path) -> None:
+    namespace = cli.build_parser().parse_args(
+        [
+            "qualify-base",
+            "--config",
+            str(PUBLIC_CONFIG),
+            "--terminal-root",
+            str(tmp_path / "terminals"),
+        ]
+    )
+    request = cli._build_command_request(namespace)
+    assert request.command == "qualify-base"
+    assert request.terminal_root == tmp_path / "terminals"
+    assert {
+        field.name
+        for field in fields(request)
+        if field.name not in {"command", "terminal_root"}
+        and getattr(request, field.name) is not None
+    } == set()
+    signature = inspect.signature(controller.run_host_base_qualification)
+    assert not {
+        "winner",
+        "scores",
+        "latencies",
+        "parameter_count",
+        "qualification",
+        "authorization",
+    } & set(signature.parameters)
+
+
+@pytest.mark.parametrize(
+    "changed",
+    [
+        {"registered_cuda_uuid": "aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee"},
+        {"registered_cuda_uuid": "GPU-AAAAAAAA-BBBB-CCCC-DDDD-EEEEEEEEEEEE"},
+        {"registered_cuda_uuid": "GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeef" + " "},
+        {"wall_timeout_seconds": True},
+        {"wall_timeout_seconds": controller.MAX_WALL_TIMEOUT_SECONDS + 1},
+        {"session_id": "../escape"},
+    ],
+)
+def test_controller_configuration_rejects_noncanonical_values(
+    tmp_path: Path,
+    changed: dict[str, object],
+) -> None:
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    values = {
+        "registry_path": registry,
+        "registry_sha256": _digest(registry.read_bytes()),
+        "output_root": tmp_path,
+        "journal_parent": tmp_path,
+        "session_id": "session-001",
+        "registered_cuda_uuid": CUDA_UUID,
+        "latency_protocol_sha256": SHA_A,
+        "launcher_sha256": SHA_B,
+        "wall_timeout_seconds": 3600,
+    }
+    values.update(changed)
+    with pytest.raises((controller.HostBaseQualificationError, TypeError)):
+        controller.HostBaseQualificationConfig(**values)
+
+
+def test_attempt_registry_is_exact_canonical_nine_run_snapshot(tmp_path: Path) -> None:
+    path = tmp_path / "registry.json"
+    raw = _registry_raw()
+    path.write_bytes(raw)
+    rows, consumed, digest = controller.load_base_attempt_registry(
+        path,
+        expected_sha256=_digest(raw),
+    )
+    assert consumed == raw
+    assert digest == _digest(raw)
+    assert tuple(row.run_id for row in rows) == base_run_ids()
+
+    path.write_bytes(_registry_raw(reverse=True))
+    with pytest.raises(controller.HostBaseQualificationError, match="run order"):
+        controller.load_base_attempt_registry(
+            path,
+            expected_sha256=_digest(path.read_bytes()),
+        )
+
+    noncanonical = raw[:-1] + b" \n"
+    path.write_bytes(noncanonical)
+    with pytest.raises(controller.HostBaseQualificationError, match="canonical"):
+        controller.load_base_attempt_registry(
+            path,
+            expected_sha256=_digest(noncanonical),
+        )
+
+
+def test_private_host_v2_loads_closed_qualification_configuration(
+    tmp_path: Path,
+) -> None:
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    value = {
+        "base_qualification": {
+            "journal_parent": "journals",
+            "latency_protocol_sha256": SHA_A,
+            "launcher_sha256": SHA_B,
+            "output_root": "outputs",
+            "registered_cuda_uuid": CUDA_UUID,
+            "registry_path": "registry.json",
+            "registry_sha256": _digest(registry.read_bytes()),
+            "schema": controller.CONFIG_SCHEMA,
+            "session_id": "qualification-001",
+            "wall_timeout_seconds": 3600,
+        },
+        "prepared_index": "prepared-index.json",
+        "receipts": {
+            "artifacts": {
+                name: f"receipts/{name}.json" for name in host.RECEIPT_DIGEST_FIELDS
+            },
+            "record": "receipts/record.json",
+        },
+        "runtime": {
+            "bf16_runtime_qualified": False,
+            "checkpoint_every_updates": 1,
+            "corrective_change_sha256": SHA_C,
+            "device": "cuda",
+            "edge_budget": 32,
+            "max_batch_bytes": 1024,
+            "request_bf16": False,
+            "resume_retry_class": "RESOURCE",
+        },
+        "schema": host.HOST_CONFIG_QUALIFICATION_SCHEMA,
+        "source_tree_sha256": _digest(b"source tree"),
+    }
+    config_path = tmp_path / "host.json"
+    config_path.write_bytes(_canonical(value))
+    loaded = host.HostConfig.load(config_path)
+    assert loaded.base_qualification is not None
+    assert loaded.base_qualification.registry_path == registry.resolve()
+    assert loaded.base_qualification.wall_timeout_seconds == 3600
+    with pytest.raises(host.HostConfigurationError, match="only one"):
+        loaded.validate_base_qualification_request(
+            cli.CLICommandRequest(
+                command="qualify-base",
+                terminal_root=tmp_path / "terminals",
+                base_checkpoint=tmp_path / "incompatible-checkpoint.pt",
+            )
+        )
+
+    value["schema"] = host.HOST_CONFIG_SCHEMA
+    config_path.write_bytes(_canonical(value))
+    with pytest.raises(host.HostConfigurationError, match="schema v2"):
+        host.HostConfig.load(config_path)
+
+
+def test_injected_latency_runtime_retains_complete_journal_but_holds(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    terminal_root = tmp_path / "terminals"
+    terminal_root.mkdir()
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    config = _controller_config(tmp_path, registry)
+    output_directory = config.output_root / config.session_id
+    output_directory.mkdir()
+    cohort, scored = _patch_pre_latency(monkeypatch)
+    session_sha = _digest(b"engineering latency session")
+
+    def fake_latency(*_args, **kwargs):
+        assert kwargs["_runtime"] is fake_runtime
+        _emit_completed_journal(kwargs["observer"], session_sha)
+        return SimpleNamespace(sha256=session_sha)
+
+    def forbidden_assembly(*_args, **_kwargs):
+        pytest.fail("nonformal latency reached qualification assembly")
+
+    fake_runtime = object()
+    monkeypatch.setattr(
+        controller.latency_module,
+        "run_base_cohort_latency_session",
+        fake_latency,
+    )
+    monkeypatch.setattr(
+        controller.assembly_module,
+        "assemble_base_cohort_qualification",
+        forbidden_assembly,
+    )
+    result = controller.run_host_base_qualification(
+        terminal_root,
+        config=config,
+        admission=_admission(),
+        validation_source=_FakeCaptureValidationSource(),
+        output_directory=output_directory,
+        adapter_sha256=SHA_C,
+        _test_runtime=fake_runtime,
+    )
+    assert result.execution.outcome == "HELD"
+    assert result.formal is False
+    assert result.authentication is None
+    assert result.execution.semantic_evidence is None
+    failure = json.loads((output_directory / "failure.json").read_bytes())
+    assert failure["failure_code"] == "NONFORMAL_TEST_RUNTIME"
+    recovered = journal.read_latency_progress_journal(
+        result.journal_path,
+        expected_bindings=journal.LatencyJournalBindings(
+            resolved_cohort_sha256=(
+                controller.latency_module._resolved_cohort_sha256(cohort)
+            ),
+            scored_cohort_sha256=scored.sha256,
+            source_tree_sha256=cohort.source_tree_sha256,
+            latency_protocol_sha256=config.latency_protocol_sha256,
+            wrapper_sha256=config.launcher_sha256,
+            registered_cuda_uuid=config.registered_cuda_uuid,
+        ),
+    )
+    assert recovered.status == journal.COMPLETED_STATUS
+    assert recovered.formal_latency is False
+
+
+def test_formal_call_never_passes_runtime_injection(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    terminal_root = tmp_path / "terminals"
+    terminal_root.mkdir()
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    config = _controller_config(tmp_path, registry)
+    output_directory = config.output_root / config.session_id
+    output_directory.mkdir()
+    cohort, _old_scored = _patch_pre_latency(monkeypatch)
+    session_sha = _digest(b"substituted formal latency session")
+    observed_kwargs: dict[str, object] = {}
+    ordered_events: list[str] = []
+    identity = _cuda_identity()
+    state = controller._ScoringCudaState(
+        runtime=SimpleNamespace(),
+        admission_observation=_cuda_observation(1),
+        runtime_identity=identity,
+        post_context_observation=_cuda_observation(2),
+    )
+    scored = SimpleNamespace(
+        sha256=_digest(b"scored"),
+        observations=(
+            SimpleNamespace(device="cuda:0", precision_mode="FP32"),
+        ),
+    )
+    monkeypatch.setattr(
+        controller.resolver_module,
+        "resolve_completed_base_cohort",
+        lambda *_args, **_kwargs: cohort,
+    )
+    monkeypatch.setattr(
+        controller.scoring_module,
+        "score_resolved_base_cohort",
+        lambda *_args, **_kwargs: (ordered_events.append("score"), scored)[1],
+    )
+    monkeypatch.setattr(
+        controller.scoring_module,
+        "BaseCohortValidationResult",
+        SimpleNamespace,
+    )
+    monkeypatch.setattr(
+        controller,
+        "_admit_scoring_cuda",
+        lambda *_args: (ordered_events.append("admit"), state)[1],
+    )
+    monkeypatch.setattr(
+        controller,
+        "_release_scoring_cuda",
+        lambda _state: (ordered_events.append("release"), _cuda_observation(3))[1],
+    )
+
+    def fake_latency(*_args, **kwargs):
+        ordered_events.append("latency")
+        observed_kwargs.update(kwargs)
+        _emit_completed_journal(kwargs["observer"], session_sha)
+        return SimpleNamespace(sha256=session_sha)
+
+    monkeypatch.setattr(
+        controller.latency_module,
+        "run_base_cohort_latency_session",
+        fake_latency,
+    )
+    result = controller.run_host_base_qualification(
+        terminal_root,
+        config=config,
+        admission=_admission(),
+        validation_source=_FakeCaptureValidationSource(),
+        output_directory=output_directory,
+        adapter_sha256=SHA_C,
+    )
+    assert "_runtime" not in observed_kwargs
+    assert ordered_events == ["admit", "score", "release", "latency"]
+    assert result.execution.outcome != "COMPLETED"
+    assert result.authentication is None
+
+
+def test_scoring_cuda_admission_and_release_keep_original_zero_gates(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    config = _controller_config(tmp_path, registry)
+    observations = iter((_cuda_observation(1), _cuda_observation(2)))
+    identity = _cuda_identity()
+    runtime_events: list[str] = []
+    cuda_initialized_before = controller.latency_module.torch.cuda.is_initialized()
+    monkeypatch.setattr(controller, "_scoring_precision", lambda _admission: "FP32")
+
+    class FakeRuntime:
+        def snapshot(self):
+            runtime_events.append("snapshot")
+            return next(observations)
+
+        def initialize(self, *, device, precision_mode, admission_observation):
+            runtime_events.append("initialize")
+            assert str(device) == "cuda"
+            assert precision_mode == "FP32"
+            assert admission_observation.captured_unix_ns == 1
+            return identity
+
+    admitted = controller._admit_scoring_cuda(
+        config,
+        _admission(),
+        _runtime=FakeRuntime(),
+    )
+    assert admitted.runtime_identity is identity
+    assert runtime_events == ["snapshot", "initialize", "snapshot"]
+    assert controller.latency_module.torch.cuda.is_initialized() is cuda_initialized_before
+
+    release_events: list[str] = []
+
+    class ReleaseRuntime:
+        def synchronize(self, device):
+            assert device == "cuda:0"
+            release_events.append("synchronize")
+
+        def snapshot(self):
+            release_events.append("snapshot")
+            return _cuda_observation(3)
+
+    fake_torch = SimpleNamespace(
+        device=lambda value: value,
+        _C=SimpleNamespace(
+            _cuda_clearCublasWorkspaces=lambda: release_events.append(
+                "clear_cublas_workspaces"
+            )
+        ),
+        cuda=SimpleNamespace(
+            empty_cache=lambda: release_events.append("empty_cache"),
+            memory_allocated=lambda _device: 0,
+            memory_reserved=lambda _device: 0,
+        ),
+    )
+    released = controller._release_scoring_cuda(
+        controller._ScoringCudaState(
+            runtime=ReleaseRuntime(),
+            admission_observation=_cuda_observation(1),
+            runtime_identity=identity,
+            post_context_observation=_cuda_observation(2),
+        ),
+        _torch_api=fake_torch,
+    )
+    assert released.captured_unix_ns == 3
+    assert release_events == [
+        "synchronize",
+        "clear_cublas_workspaces",
+        "synchronize",
+        "empty_cache",
+        "snapshot",
+    ]
+
+    fake_torch.cuda.memory_reserved = lambda _device: 1
+    with pytest.raises(controller._ScoringCudaHold, match="remained"):
+        controller._release_scoring_cuda(
+            controller._ScoringCudaState(
+                runtime=ReleaseRuntime(),
+                admission_observation=_cuda_observation(1),
+                runtime_identity=identity,
+                post_context_observation=_cuda_observation(2),
+            ),
+            _torch_api=fake_torch,
+        )
+
+    fake_torch._C = SimpleNamespace()
+    with pytest.raises(controller._ScoringCudaHold) as missing:
+        controller._release_scoring_cuda(
+            controller._ScoringCudaState(
+                runtime=ReleaseRuntime(),
+                admission_observation=_cuda_observation(1),
+                runtime_identity=identity,
+                post_context_observation=_cuda_observation(2),
+            ),
+            _torch_api=fake_torch,
+        )
+    assert missing.value.failure_code == "RUNTIME_DRIFT"
+
+
+@pytest.mark.parametrize("mode", ["admission-observation", "identity", "post-context"])
+def test_scoring_cuda_malformed_runtime_values_are_runtime_drift_holds(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    mode: str,
+) -> None:
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    config = _controller_config(tmp_path, registry)
+    cuda_initialized_before = controller.latency_module.torch.cuda.is_initialized()
+    monkeypatch.setattr(controller, "_scoring_precision", lambda _admission: "FP32")
+    observations = iter(
+        (
+            object() if mode == "admission-observation" else _cuda_observation(1),
+            object() if mode == "post-context" else _cuda_observation(2),
+        )
+    )
+
+    class MalformedRuntime:
+        def snapshot(self):
+            return next(observations)
+
+        def initialize(self, **_kwargs):
+            return object() if mode == "identity" else _cuda_identity()
+
+    with pytest.raises(controller._ScoringCudaHold) as held:
+        controller._admit_scoring_cuda(
+            config,
+            _admission(),
+            _runtime=MalformedRuntime(),
+        )
+    assert held.value.failure_code == "RUNTIME_DRIFT"
+    assert controller.latency_module.torch.cuda.is_initialized() is cuda_initialized_before
+
+
+def test_scoring_precision_forwards_actual_admission_to_real_cpu_config(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    admission = replace(_admission(), device="cpu")
+    original = controller.training_module.resolve_precision
+    observed: list[controller.training_module.TrainingConfig] = []
+    cuda_initialized_before = controller.latency_module.torch.cuda.is_initialized()
+
+    def observed_resolution(config):
+        observed.append(config)
+        return original(config)
+
+    monkeypatch.setattr(
+        controller.training_module,
+        "resolve_precision",
+        observed_resolution,
+    )
+    assert controller._scoring_precision(admission) == "FP32"
+    assert len(observed) == 1
+    config = observed[0]
+    assert type(config) is controller.training_module.TrainingConfig
+    assert config.stage == "base"
+    assert config.seed == 1729
+    assert config.device == admission.device
+    assert config.request_bf16 is admission.request_bf16
+    assert config.bf16_runtime_qualified is admission.bf16_runtime_qualified
+    assert config.edge_budget == admission.edge_budget
+    assert config.checkpoint_every_updates == admission.checkpoint_every_updates
+    assert config.synthetic_contract is False
+    assert controller.latency_module.torch.cuda.is_initialized() is cuda_initialized_before
+
+
+def test_scoring_cuda_malformed_release_snapshot_is_runtime_drift_hold() -> None:
+    class MalformedReleaseRuntime:
+        def synchronize(self, _device):
+            return None
+
+        def snapshot(self):
+            return object()
+
+    fake_torch = SimpleNamespace(
+        device=lambda value: value,
+        _C=SimpleNamespace(_cuda_clearCublasWorkspaces=lambda: None),
+        cuda=SimpleNamespace(
+            empty_cache=lambda: None,
+            memory_allocated=lambda _device: 0,
+            memory_reserved=lambda _device: 0,
+        ),
+    )
+    state = controller._ScoringCudaState(
+        runtime=MalformedReleaseRuntime(),
+        admission_observation=_cuda_observation(1),
+        runtime_identity=_cuda_identity(),
+        post_context_observation=_cuda_observation(2),
+    )
+    with pytest.raises(controller._ScoringCudaHold) as held:
+        controller._release_scoring_cuda(state, _torch_api=fake_torch)
+    assert held.value.failure_code == "RUNTIME_DRIFT"
+
+
+def test_scoring_cuda_malformed_release_device_is_runtime_drift_hold() -> None:
+    fake_torch = SimpleNamespace(
+        device=lambda _value: (_ for _ in ()).throw(ValueError("bad device")),
+    )
+    state = controller._ScoringCudaState(
+        runtime=SimpleNamespace(),
+        admission_observation=_cuda_observation(1),
+        runtime_identity=_cuda_identity(),
+        post_context_observation=_cuda_observation(2),
+    )
+    with pytest.raises(controller._ScoringCudaHold) as held:
+        controller._release_scoring_cuda(state, _torch_api=fake_torch)
+    assert held.value.failure_code == "RUNTIME_DRIFT"
+
+
+def test_scoring_resource_failure_retains_original_cause_and_secondary_cleanup(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    terminal_root = tmp_path / "terminals"
+    terminal_root.mkdir()
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    config = _controller_config(tmp_path, registry)
+    output_directory = config.output_root / config.session_id
+    output_directory.mkdir()
+    _patch_pre_latency(monkeypatch)
+    state = controller._ScoringCudaState(
+        runtime=SimpleNamespace(),
+        admission_observation=_cuda_observation(1),
+        runtime_identity=_cuda_identity(),
+        post_context_observation=_cuda_observation(2),
+    )
+
+    class CleanupBoom(RuntimeError):
+        pass
+
+    resource_error = controller.CaptureValidationResourceLimit("capture budget")
+    score_error = controller.scoring_module.BaseCohortValidationError("score failed")
+    score_error.__cause__ = resource_error
+
+    monkeypatch.setattr(controller, "_admit_scoring_cuda", lambda *_args: state)
+    monkeypatch.setattr(
+        controller.scoring_module,
+        "score_resolved_base_cohort",
+        lambda *_args, **_kwargs: (_ for _ in ()).throw(score_error),
+    )
+    monkeypatch.setattr(
+        controller,
+        "_release_scoring_cuda",
+        lambda _state: (_ for _ in ()).throw(CleanupBoom("cleanup")),
+    )
+    result = controller.run_host_base_qualification(
+        terminal_root,
+        config=config,
+        admission=_admission(),
+        validation_source=_FakeCaptureValidationSource(),
+        output_directory=output_directory,
+        adapter_sha256=SHA_C,
+    )
+    assert result.execution.outcome == "HELD"
+    failure = json.loads((output_directory / "failure.json").read_bytes())
+    assert failure["stage"] == "SCORE"
+    assert failure["error_type"] == "BaseCohortValidationError"
+    assert failure["failure_code"] == "BASE_CAPTURE_SCORE_RESOURCE_LIMIT"
+    assert failure["secondary_cleanup_error_type"] == CleanupBoom.__qualname__
+    assert score_error.__cause__ is resource_error
+    assert result.journal_path is None
+
+
+def test_structural_controller_completion_persists_typed_evidence(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Software branch coverage only; every result-bearing dependency is patched."""
+
+    terminal_root = tmp_path / "terminals"
+    terminal_root.mkdir()
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    config = _controller_config(tmp_path, registry)
+    output_directory = config.output_root / config.session_id
+    output_directory.mkdir()
+    cohort, _old_scored = _patch_pre_latency(monkeypatch)
+    structural = _typed_structural_assembly()
+    identity = _cuda_identity()
+    state = controller._ScoringCudaState(
+        runtime=SimpleNamespace(),
+        admission_observation=_cuda_observation(1),
+        runtime_identity=identity,
+        post_context_observation=_cuda_observation(2),
+    )
+    scored = SimpleNamespace(
+        sha256=_digest(b"scored"),
+        source_manifest_sha256=_FakeCaptureValidationSource.manifest_sha256,
+        observations=(
+            SimpleNamespace(device="cuda:0", precision_mode="FP32"),
+        ),
+    )
+    session_sha = structural.dependency_binding.latency_session_sha256
+    latency = SimpleNamespace(
+        sha256=session_sha,
+        actual_runtime=True,
+        complete=True,
+        formal_samples_complete=True,
+        failure_code=None,
+        status=controller.latency_module.ACTUAL_COMPLETE_STATUS,
+    )
+    monkeypatch.setattr(
+        controller.resolver_module,
+        "resolve_completed_base_cohort",
+        lambda *_args, **_kwargs: cohort,
+    )
+    monkeypatch.setattr(
+        controller.scoring_module,
+        "score_resolved_base_cohort",
+        lambda *_args, **_kwargs: scored,
+    )
+    monkeypatch.setattr(
+        controller.scoring_module,
+        "BaseCohortValidationResult",
+        SimpleNamespace,
+    )
+    monkeypatch.setattr(controller, "_admit_scoring_cuda", lambda *_args: state)
+    monkeypatch.setattr(
+        controller,
+        "_release_scoring_cuda",
+        lambda _state: _cuda_observation(3),
+    )
+
+    def fake_latency(*_args, **kwargs):
+        assert "_runtime" not in kwargs
+        _emit_completed_journal(kwargs["observer"], session_sha)
+        return latency
+
+    monkeypatch.setattr(
+        controller.latency_module,
+        "run_base_cohort_latency_session",
+        fake_latency,
+    )
+    monkeypatch.setattr(
+        controller.latency_module,
+        "BaseCohortLatencySession",
+        SimpleNamespace,
+    )
+    monkeypatch.setattr(
+        controller.assembly_module,
+        "assemble_base_cohort_qualification",
+        lambda *_args, **_kwargs: structural,
+    )
+    result = controller.run_host_base_qualification(
+        terminal_root,
+        config=config,
+        admission=_admission(),
+        validation_source=_FakeCaptureValidationSource(),
+        output_directory=output_directory,
+        adapter_sha256=SHA_C,
+    )
+    assert result.execution.outcome == "COMPLETED"
+    assert result.formal is True
+    assert result.authentication is not None
+    assert result.authentication.verifies(
+        result.authentication.authorization,
+        qualification_sha256=_digest(structural.artifact),
+        adapter_sha256=SHA_C,
+    )
+    assert result.execution.semantic_evidence is structural.execution_evidence
+    assert (output_directory / "scoring-cuda.json") in result.execution.artifacts
+    assert (output_directory / "base-qualification.json").read_bytes() == structural.artifact
+
+
+def test_authentication_reopens_exact_controller_bytes(tmp_path: Path) -> None:
+    grant = b"grant bytes\n"
+    qualification = b"qualification bytes\n"
+    grant_path = tmp_path / "grant.json"
+    qualification_path = tmp_path / "qualification.json"
+    grant_path.write_bytes(grant)
+    qualification_path.write_bytes(qualification)
+    authorization = controller.production_module.PrivateBaseQualificationAuthorization(
+        authorized_at_utc="2026-09-08T00:00:00Z",
+        external_grant_sha256=_digest(grant),
+        validation_manifest_sha256=SHA_A,
+        query_census_sha256=SHA_B,
+        evaluator_sha256=SHA_C,
+        score_rows_sha256=_digest(b"rows"),
+    )
+    value = controller.HostBaseQualificationAuthentication(
+        authorization=authorization,
+        qualification_sha256=_digest(qualification),
+        adapter_sha256=_digest(b"adapter"),
+        grant_path=grant_path,
+        qualification_path=qualification_path,
+        grant_raw=grant,
+        qualification_raw=qualification,
+    )
+    assert value.verifies(
+        authorization,
+        qualification_sha256=_digest(qualification),
+        adapter_sha256=_digest(b"adapter"),
+    )
+    qualification_path.write_bytes(qualification + b"tamper")
+    assert not value.verifies(
+        authorization,
+        qualification_sha256=_digest(qualification),
+        adapter_sha256=_digest(b"adapter"),
+    )
+
+
+def _receipts() -> PrivateReceiptAssertions:
+    return PrivateReceiptAssertions(
+        admitted_at_utc="2026-09-08T00:00:00Z",
+        data_manifest_sha256=_digest(b"data"),
+        prepared_data_manifest_sha256=_digest(b"prepared"),
+        split_audit_sha256=_digest(b"split"),
+        rights_assertion_sha256=_digest(b"rights"),
+        caption_manifest_sha256=_digest(b"captions"),
+        score_execution_census_sha256=_digest(b"score census"),
+        validation_evaluation_census_sha256=_digest(b"val census"),
+        hard_gallery_freeze_binding_sha256=_digest(b"freeze"),
+        validation_hard_gallery_collection_sha256=_digest(b"gallery"),
+        runtime_assertion_sha256=_digest(b"runtime assertion"),
+        execution_assertion_sha256=_digest(b"execution assertion"),
+        authority=1,
+    )
+
+
+def _typed_structural_assembly() -> assembly.BaseCohortQualificationAssembly:
+    counts = {"B0": 100, "B1": 200, "B2": 300}
+    fractions = {"B0": Fraction(1, 2), "B1": Fraction(3, 4), "B2": Fraction(3, 4)}
+    rows = []
+    for ordinal, run_id in enumerate(base_run_ids()):
+        _role, seed, system_id = experiments.parse_run_id(run_id)
+        rows.append(
+            assembly.CombinedBaseScoreBinding(
+                run_id=run_id,
+                attempt_id=f"structural-attempt-{ordinal}",
+                system_id=system_id,
+                seed=seed,
+                terminal_sha256=_digest(f"terminal-{ordinal}".encode("ascii")),
+                selected_checkpoint_sha256=_digest(
+                    f"checkpoint-{ordinal}".encode("ascii")
+                ),
+                selected_checkpoint_state_digest=_digest(
+                    f"state-{ordinal}".encode("ascii")
+                ),
+                validation_score_artifact_sha256=_digest(
+                    f"score-{ordinal}".encode("ascii")
+                ),
+                latency_row_sha256=_digest(f"latency-{ordinal}".encode("ascii")),
+                latency_samples_sha256=_digest(
+                    f"samples-{ordinal}".encode("ascii")
+                ),
+                parameter_census_sha256=_digest(
+                    f"parameters-{system_id}".encode("ascii")
+                ),
+                parameter_count=counts[system_id],
+                frozen_runtime_latency_ns=100 + ordinal,
+                bidirectional_capture_r1=fractions[system_id],
+                validation_manifest_sha256=_digest(b"val-census"),
+                validation_source_manifest_sha256=_digest(b"val-source"),
+                query_census_sha256=_digest(b"val-census"),
+                evaluator_sha256=_digest(b"placeholder-evaluator"),
+                runtime_sha256=_digest(b"runtime"),
+                environment_sha256=_digest(b"environment"),
+                precision_mode="FP32",
+            )
+        )
+    selector = controller.production_module.current_base_qualification_artifact_bytes()
+    evaluator = _digest(selector)
+    rebound = tuple(replace(row, evaluator_sha256=evaluator) for row in rows)
+    score_rows = assembly._base_scores(rebound)
+    qualification = experiments.qualify_base(score_rows)
+    dependency = assembly.BaseQualificationDependencyBinding(
+        admission_sha256=_digest(b"admission"),
+        resolved_cohort_sha256=_digest(b"resolved"),
+        scored_cohort_sha256=_digest(b"scored"),
+        latency_session_sha256=_digest(b"latency-session"),
+        plan_sha256=_digest(b"plan"),
+        matrix_sha256=_digest(b"matrix"),
+        training_config_sha256=_digest(b"config"),
+        source_tree_sha256=_digest(b"source-tree"),
+        train_manifest_sha256=_digest(b"train"),
+        admitted_val_census_sha256=_digest(b"val-census"),
+        validation_source_manifest_sha256=_digest(b"val-source"),
+        query_census_sha256=_digest(b"val-census"),
+        resolver_source_sha256=_digest(b"resolver-source"),
+        scorer_source_sha256=_digest(b"scorer-source"),
+        latency_source_sha256=_digest(b"latency-source"),
+        assembler_source_sha256=_digest(b"assembler-source"),
+        production_source_sha256=_digest(b"production-source"),
+        selector_artifact_sha256=evaluator,
+        runtime_sha256=_digest(b"runtime"),
+        environment_sha256=_digest(b"environment"),
+        precision_mode="FP32",
+    )
+    evidence = controller.production_module.BaseQualificationExecutionEvidence(
+        score_rows=score_rows,
+        selector_artifact=selector,
+    )
+    return assembly.BaseCohortQualificationAssembly(
+        qualification=qualification,
+        execution_evidence=evidence,
+        artifact=experiments.canonical_base_qualification_bytes(qualification),
+        row_bindings=rebound,
+        dependency_binding=dependency,
+    )
+
+
+def test_cli_factory_adapter_backend_reaches_typed_controller_before_model(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    terminal_root = tmp_path / "terminals"
+    output_root = tmp_path / "outputs"
+    journal_parent = tmp_path / "journals"
+    terminal_root.mkdir()
+    output_root.mkdir()
+    journal_parent.mkdir()
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    qualification = controller.HostBaseQualificationConfig(
+        registry_path=registry,
+        registry_sha256=_digest(registry.read_bytes()),
+        output_root=output_root,
+        journal_parent=journal_parent,
+        session_id="cli-session",
+        registered_cuda_uuid=CUDA_UUID,
+        latency_protocol_sha256=SHA_A,
+        launcher_sha256=SHA_B,
+        wall_timeout_seconds=7200,
+    )
+    receipts = _receipts()
+    fake_val = _FakeCaptureValidationSource()
+    fake_train = SimpleNamespace(manifest_sha256=_digest(b"train"))
+    calls: list[dict[str, object]] = []
+
+    class FakeConfig:
+        source_tree_sha256 = _digest(b"source")
+        device = "cuda"
+        request_bf16 = False
+        bf16_runtime_qualified = False
+        edge_budget = 32
+        checkpoint_every_updates = 1
+        base_qualification = qualification
+
+        def load_receipts(self):
+            return receipts
+
+        def validate_runtime(self):
+            return None
+
+        def validate_base_qualification_request(self, request):
+            assert request.command == "qualify-base"
+            assert request.terminal_root == terminal_root
+
+        def load_sources(self):
+            return fake_train, fake_val
+
+    def fake_controller(attempt_root: Path, **kwargs):
+        calls.append({"attempt_root": attempt_root, **kwargs})
+        artifact = kwargs["output_directory"] / "nonformal-hold.json"
+        artifact.write_bytes(b"nonformal host test hold\n")
+        return controller.HostBaseQualificationRun(
+            execution=BackendExecution(
+                outcome="HELD",
+                completed_at_utc="2026-09-08T00:00:01Z",
+                artifacts=(artifact,),
+            ),
+            authentication=None,
+            journal_path=None,
+            registry_sha256=qualification.registry_sha256,
+            formal=True,
+        )
+
+    monkeypatch.setattr(host.HostConfig, "load", lambda _path: FakeConfig())
+    monkeypatch.setattr(host, "CaptureValidationSource", _FakeCaptureValidationSource)
+    monkeypatch.setattr(host, "run_host_base_qualification", fake_controller)
+    output = io.StringIO()
+    errors = io.StringIO()
+    exit_code = cli.main(
+        [
+            "qualify-base",
+            "--config",
+            str(PUBLIC_CONFIG),
+            "--terminal-root",
+            str(terminal_root),
+        ],
+        runtime_adapter_factory=host.RuntimeAdapterFactory(tmp_path / "host.json"),
+        stdout=output,
+        stderr=errors,
+    )
+    assert exit_code == cli.EXIT_HOLD
+    assert errors.getvalue() == ""
+    emitted = json.loads(output.getvalue())
+    assert emitted["command"] == "qualify-base"
+    assert emitted["outcome"] == "HELD"
+    assert len(calls) == 1
+    call = calls[0]
+    assert call["attempt_root"] == terminal_root.resolve()
+    assert type(call["admission"]) is controller.resolver_module.BaseCohortAdmission
+    assert call["validation_source"] is fake_val
+    assert call["config"] is qualification
+    assert call["adapter_sha256"] == call["adapter_sha256"].lower()
+
+
+def test_structural_completion_reaches_production_recompute_and_callback(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Contract fixture only: no model, cohort, score, or latency was executed."""
+
+    terminal_root = tmp_path / "terminals"
+    output_root = tmp_path / "outputs"
+    journal_parent = tmp_path / "journals"
+    terminal_root.mkdir()
+    output_root.mkdir()
+    journal_parent.mkdir()
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    qualification_config = controller.HostBaseQualificationConfig(
+        registry_path=registry,
+        registry_sha256=_digest(registry.read_bytes()),
+        output_root=output_root,
+        journal_parent=journal_parent,
+        session_id="structural-session",
+        registered_cuda_uuid=CUDA_UUID,
+        latency_protocol_sha256=SHA_A,
+        launcher_sha256=SHA_B,
+        wall_timeout_seconds=7200,
+    )
+    receipts = _receipts()
+    fake_val = _FakeCaptureValidationSource()
+    fake_train = SimpleNamespace(manifest_sha256=_digest(b"train"))
+    structural = _typed_structural_assembly()
+    callback_observed: list[str] = []
+
+    class FakeConfig:
+        source_tree_sha256 = _digest(b"source")
+        device = "cuda"
+        request_bf16 = False
+        bf16_runtime_qualified = False
+        edge_budget = 32
+        checkpoint_every_updates = 1
+        base_qualification = qualification_config
+
+        def load_receipts(self):
+            return receipts
+
+        def validate_runtime(self):
+            return None
+
+        def validate_base_qualification_request(self, request):
+            assert request.command == "qualify-base"
+            assert request.terminal_root == terminal_root
+
+        def load_sources(self):
+            return fake_train, fake_val
+
+    original_authenticate = host.PhaseSetHostBackend.authenticate_base_qualification
+
+    def observed_authenticate(self, *args, **kwargs):
+        result = original_authenticate(self, *args, **kwargs)
+        callback_observed.append("accepted" if result else "rejected")
+        return result
+
+    def structural_controller(attempt_root: Path, **kwargs):
+        assert attempt_root == terminal_root.resolve()
+        output_directory = kwargs["output_directory"]
+        qualification_path = output_directory / "base-qualification.json"
+        grant_path = output_directory / "structural-grant.json"
+        qualification_path.write_bytes(structural.artifact)
+        grant_raw = b"structural software fixture; not a formal grant\n"
+        grant_path.write_bytes(grant_raw)
+        authorization = (
+            controller.production_module.PrivateBaseQualificationAuthorization(
+                authorized_at_utc="2026-09-08T00:00:01Z",
+                external_grant_sha256=_digest(grant_raw),
+                validation_manifest_sha256=(
+                    structural.qualification.validation_manifest_sha256
+                ),
+                query_census_sha256=structural.qualification.query_census_sha256,
+                evaluator_sha256=structural.qualification.evaluator_sha256,
+                score_rows_sha256=structural.qualification.score_rows_sha256,
+            )
+        )
+        authentication = controller.HostBaseQualificationAuthentication(
+            authorization=authorization,
+            qualification_sha256=_digest(structural.artifact),
+            adapter_sha256=kwargs["adapter_sha256"],
+            grant_path=grant_path,
+            qualification_path=qualification_path,
+            grant_raw=grant_raw,
+            qualification_raw=structural.artifact,
+        )
+        return controller.HostBaseQualificationRun(
+            execution=BackendExecution(
+                outcome="COMPLETED",
+                completed_at_utc="2026-09-08T00:00:02Z",
+                artifacts=(qualification_path, grant_path),
+                semantic_evidence=structural.execution_evidence,
+            ),
+            authentication=authentication,
+            journal_path=None,
+            registry_sha256=qualification_config.registry_sha256,
+            formal=True,
+        )
+
+    monkeypatch.setattr(host.HostConfig, "load", lambda _path: FakeConfig())
+    monkeypatch.setattr(host, "CaptureValidationSource", _FakeCaptureValidationSource)
+    monkeypatch.setattr(host, "run_host_base_qualification", structural_controller)
+    monkeypatch.setattr(
+        host.PhaseSetHostBackend,
+        "authenticate_base_qualification",
+        observed_authenticate,
+    )
+    output = io.StringIO()
+    errors = io.StringIO()
+    exit_code = cli.main(
+        [
+            "qualify-base",
+            "--config",
+            str(PUBLIC_CONFIG),
+            "--terminal-root",
+            str(terminal_root),
+        ],
+        runtime_adapter_factory=host.RuntimeAdapterFactory(tmp_path / "host.json"),
+        stdout=output,
+        stderr=errors,
+    )
+    assert exit_code == cli.EXIT_OK
+    assert errors.getvalue() == ""
+    emitted = json.loads(output.getvalue())
+    assert emitted["outcome"] == "COMPLETED"
+    assert emitted["production"] is True
+    assert emitted["result_claimed"] is False
+    assert callback_observed == ["accepted"]
+
+
+def test_process_timer_refuses_inherited_deadline_without_mutating_os() -> None:
+    signal_api = SimpleNamespace(
+        SIGALRM=14,
+        ITIMER_REAL=0,
+        getitimer=lambda _timer: (1.0, 0.0),
+        getsignal=lambda _signal: None,
+        setitimer=lambda *_args: pytest.fail("inherited timer was replaced"),
+        signal=lambda *_args: pytest.fail("inherited handler was replaced"),
+    )
+    with pytest.raises(controller.HostBaseQualificationError, match="inherited"):
+        with controller._formal_latency_timeout(
+            3600,
+            _os_name="posix",
+            _signal_api=signal_api,
+        ):
+            pytest.fail("inherited timer was replaced")
+
+
+def test_process_timer_fails_closed_when_platform_has_no_interval_timer() -> None:
+    with pytest.raises(controller.HostBaseQualificationError, match="requires POSIX"):
+        with controller._formal_latency_timeout(
+            3600,
+            _os_name="nt",
+            _signal_api=SimpleNamespace(),
+        ):
+            pytest.fail("unsupported timer platform was admitted")
diff --git a/tests/test_phaseset_residual_cached_encode.py b/tests/test_phaseset_residual_cached_encode.py
new file mode 100644
index 0000000..99c9999
--- /dev/null
+++ b/tests/test_phaseset_residual_cached_encode.py
@@ -0,0 +1,306 @@
+from __future__ import annotations
+
+import copy
+import hashlib
+from pathlib import Path
+import random
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core import training
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+from phaseset_core.models import PhaseSetModelError
+from phaseset_core.objectives import PhaseSetRetrievalHead
+from phaseset_core.periodic import ResourceLimitError
+from phaseset_core.periodic_descriptor_cache_v2 import (
+    CachedPairChunkStream,
+    DescriptorCacheSourceBinding,
+    DescriptorWindowContext,
+    PeriodicDescriptorCacheV2,
+    PeriodicDescriptorCacheWriterV2,
+)
+
+
+def _digest(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+def _floors(offset: float = 0.0) -> np.ndarray:
+    return np.ascontiguousarray(
+        np.asarray(
+            [offset + (index + 1) * 1.0e-12 for index in range(6)],
+            dtype=np.float64,
+        )
+    )
+
+
+def _batch(*, numeric_offset: float = 0.0) -> PreparedGroupBatch:
+    time_steps = 200
+    actor_count = 3
+    time = np.arange(time_steps, dtype=np.float64)[:, None]
+    joint = np.arange(22, dtype=np.float64)[None, :]
+    skeletons = np.zeros((1, actor_count, time_steps, 22, 3), dtype=np.float32)
+    for actor in range(actor_count):
+        skeletons[0, actor, :, :, 0] = 0.013 * (actor + 1) + 0.0007 * time * (joint + 1.0)
+        skeletons[0, actor, :, :, 1] = 0.009 * (actor + 1) + 0.0003 * time * (23.0 - joint)
+        skeletons[0, actor, :, :, 2] = np.sin((time + joint + 5.0 * actor) / 29.0)
+    skeletons[0, 0, 17, 3, 0] += np.float32(numeric_offset)
+    actors = tuple(bytes([index + 1]) * 32 for index in range(actor_count))
+    return PreparedGroupBatch(
+        skeletons=np.ascontiguousarray(skeletons),
+        actor_mask=np.ones((1, actor_count), dtype=np.bool_),
+        frame_mask=np.ones((1, time_steps), dtype=np.bool_),
+        track_mask=np.ones((1, actor_count, time_steps, 22), dtype=np.bool_),
+        actor_commitments=(actors,),
+        group_commitments=(group_commitment(actors),),
+    )
+
+
+def _context() -> tuple[DescriptorWindowContext, ...]:
+    return (
+        DescriptorWindowContext.from_yaw(
+            prepared_manifest_sha256=_digest("prepared-manifest"),
+            source_batch_sha256=_digest("source-npz"),
+            window_sha256=_digest("window"),
+            window_ordinal=7,
+            split="val",
+            seed=1729,
+            epoch=0,
+            augmentation_yaw=0.0,
+        ),
+    )
+
+
+def _binding() -> DescriptorCacheSourceBinding:
+    return DescriptorCacheSourceBinding(
+        prepared_manifest_sha256=_digest("prepared-manifest"),
+        source_tree_sha256=_digest("source-tree"),
+        environment_sha256=_digest("environment"),
+    )
+
+
+def _cached_streams(
+    root: Path,
+    batch: PreparedGroupBatch,
+    floors: np.ndarray,
+) -> dict[str, CachedPairChunkStream]:
+    writer = PeriodicDescriptorCacheWriterV2(
+        root,
+        source_binding=_binding(),
+        energy_floors=floors,
+    )
+    writer.add_batch(batch, _context())
+    artifact = writer.finalize()
+    reader = PeriodicDescriptorCacheV2(
+        root,
+        expected_index_sha256=artifact.index_sha256,
+        expected_source_binding=_binding(),
+        energy_floors=floors,
+    )
+    opened = reader.open_batch(batch, _context())
+    return {
+        kind: opened.stream(kind)
+        for kind in ("FULL_RELATION", "MARGINAL_POWER", "MEAN_DIFFERENCE_DCT")
+    }
+
+
+def _system(
+    system_id: str,
+    floors: np.ndarray,
+    *,
+    edge_budget: int = 32,
+) -> training.ResidualRetrievalSystem:
+    frozen_base = training.build_registered_base_training_system("B0").group_base
+    return training.build_registered_residual_training_system(
+        system_id,
+        frozen_base,
+        seed=1729,
+        energy_floors=floors,
+        edge_budget=edge_budget,
+    )
+
+
+def _gradients(module: torch.nn.Module) -> dict[str, torch.Tensor | None]:
+    return {
+        name: None if parameter.grad is None else parameter.grad.detach().clone()
+        for name, parameter in module.named_parameters()
+    }
+
+
+def _assert_gradients_equal(
+    left: dict[str, torch.Tensor | None],
+    right: dict[str, torch.Tensor | None],
+) -> None:
+    assert left.keys() == right.keys()
+    for name in left:
+        if left[name] is None:
+            assert right[name] is None, name
+        else:
+            assert right[name] is not None, name
+            assert torch.equal(left[name], right[name]), name
+
+
+@pytest.mark.parametrize(
+    ("system_id", "stream_kind"),
+    (
+        ("02", "MARGINAL_POWER"),
+        ("03", "MEAN_DIFFERENCE_DCT"),
+        ("04", "FULL_RELATION"),
+        ("06", "FULL_RELATION"),
+        ("07", "FULL_RELATION"),
+        ("08", "FULL_RELATION"),
+    ),
+)
+def test_registered_residual_cached_method_matches_uncached_tokens_and_mask(
+    tmp_path: Path,
+    system_id: str,
+    stream_kind: str,
+) -> None:
+    batch = _batch()
+    floors = _floors()
+    streams = _cached_streams(tmp_path / "cache", batch, floors)
+    reference = _system(system_id, floors).eval()
+    candidate = copy.deepcopy(reference).eval()
+
+    expected = reference.encode_trainable(batch)
+    actual = candidate.encode_trainable_cached(batch, streams[stream_kind])
+
+    assert torch.equal(actual[0], expected[0])
+    assert torch.equal(actual[1], expected[1])
+    assert actual[0].dtype == torch.float32
+    assert actual[0].is_contiguous()
+    assert actual[1].is_contiguous()
+
+
+def test_cached_stream_is_reiterable_and_preserves_rng_and_vjp(tmp_path: Path) -> None:
+    batch = _batch()
+    floors = _floors()
+    stream = _cached_streams(tmp_path / "cache", batch, floors)["FULL_RELATION"]
+    reference = _system("06", floors).eval()
+    first = copy.deepcopy(reference).eval()
+    second = copy.deepcopy(reference).eval()
+
+    expected_tokens, expected_mask = reference.encode_trainable(batch)
+    expected_tokens.square().sum().backward()
+    expected_gradients = _gradients(reference)
+
+    random.seed(19)
+    np.random.seed(23)
+    torch.manual_seed(29)
+    python_before = random.getstate()
+    numpy_before = np.random.get_state()
+    torch_before = torch.get_rng_state().clone()
+
+    first_tokens, first_mask = first.encode_trainable_cached(batch, stream)
+    first_tokens.square().sum().backward()
+    first_gradients = _gradients(first)
+    second_tokens, second_mask = second.encode_trainable_cached(batch, stream)
+    second_tokens.square().sum().backward()
+    second_gradients = _gradients(second)
+
+    assert random.getstate() == python_before
+    numpy_after = np.random.get_state()
+    assert numpy_after[0] == numpy_before[0]
+    assert np.array_equal(numpy_after[1], numpy_before[1])
+    assert numpy_after[2:] == numpy_before[2:]
+    assert torch.equal(torch.get_rng_state(), torch_before)
+    assert torch.equal(first_tokens, expected_tokens)
+    assert torch.equal(second_tokens, expected_tokens)
+    assert torch.equal(first_mask, expected_mask)
+    assert torch.equal(second_mask, expected_mask)
+    _assert_gradients_equal(first_gradients, expected_gradients)
+    _assert_gradients_equal(second_gradients, expected_gradients)
+
+
+def test_cached_method_rejects_wrong_batch_stream_floors_and_resource_limit(
+    tmp_path: Path,
+) -> None:
+    batch = _batch()
+    floors = _floors()
+    streams = _cached_streams(tmp_path / "cache", batch, floors)
+    system = _system("08", floors).eval()
+
+    with pytest.raises(PhaseSetModelError, match="actual model batch"):
+        system.encode_trainable_cached(_batch(numeric_offset=0.125), streams["FULL_RELATION"])
+    with pytest.raises(PhaseSetModelError, match="stream kind"):
+        system.encode_trainable_cached(batch, streams["MARGINAL_POWER"])
+    with pytest.raises(PhaseSetModelError, match="energy floors"):
+        _system("08", _floors(1.0e-8)).eval().encode_trainable_cached(
+            batch,
+            streams["FULL_RELATION"],
+        )
+    with pytest.raises(ResourceLimitError):
+        _system("08", floors, edge_budget=2).eval().encode_trainable_cached(
+            batch,
+            streams["FULL_RELATION"],
+        )
+    with pytest.raises(TypeError, match="exact CachedPairChunkStream"):
+        system.encode_trainable_cached(batch, None)  # type: ignore[arg-type]
+
+
+@pytest.mark.parametrize("system_id", ("01", "05"))
+def test_noncacheable_registered_residuals_are_rejected(
+    tmp_path: Path,
+    system_id: str,
+) -> None:
+    batch = _batch()
+    floors = _floors()
+    stream = _cached_streams(tmp_path / "cache", batch, floors)["FULL_RELATION"]
+    with pytest.raises(training.TrainingRuntimeError, match="does not admit"):
+        _system(system_id, floors).encode_trainable_cached(batch, stream)
+
+
+def test_nonregistered_encoder_and_method_drift_are_rejected(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    batch = _batch()
+    floors = _floors()
+    stream = _cached_streams(tmp_path / "cache", batch, floors)["FULL_RELATION"]
+
+    class UnregisteredPeriodic(torch.nn.Module):
+        system_id = "08"
+        embedding_dim = 512
+
+        def forward(self, _groups: PreparedGroupBatch) -> object:
+            pytest.fail("unregistered periodic encoder was executed")
+
+        def forward_cached(
+            self,
+            _groups: PreparedGroupBatch,
+            *,
+            descriptor_stream: CachedPairChunkStream,
+        ) -> object:
+            del descriptor_stream
+            pytest.fail("unregistered cached encoder was executed")
+
+    frozen_base = training.build_registered_base_training_system("B0").group_base
+    unregistered = training.ResidualRetrievalSystem(
+        frozen_base,
+        UnregisteredPeriodic(),
+        PhaseSetRetrievalHead(512, text_hidden_dim=512, residual_lambda_init=0.0),
+        embedding_dim=512,
+    )
+    with pytest.raises(training.TrainingRuntimeError, match="exact registered"):
+        unregistered.encode_trainable_cached(batch, stream)
+
+    registered = _system("08", floors)
+    config = training.TrainingConfig(
+        stage="residual",
+        seed=1729,
+        device="cpu",
+        request_bf16=False,
+        bf16_runtime_qualified=False,
+        edge_budget=32,
+        checkpoint_every_updates=1,
+        synthetic_contract=False,
+    )
+    monkeypatch.setattr(
+        training.ResidualRetrievalSystem,
+        "encode_trainable_cached",
+        lambda *_args, **_kwargs: pytest.fail("drifted cached method executed"),
+    )
+    with pytest.raises(training.TrainingRuntimeError, match="method identity changed"):
+        training._validate_registered_formal_system(registered, config)

From d6d8089275c80ad65ace2af71904ef1960da5487 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Tue, 8 Sep 2026 17:34:46 -0500
Subject: [PATCH 22/95] Integrate complete descriptor cache construction and
 cached residual lifecycle

---
 RELEASE_FILES.sha256                          |   23 +-
 STATUS.md                                     |   47 +-
 docs/CACHED_RESIDUAL_TRAINING.md              |   55 +
 docs/HOST_BASE_QUALIFICATION.md               |   30 +-
 docs/HOST_RUNTIME_INTEGRATION.md              |   18 +-
 docs/PERIODIC_CACHE_EXECUTION.md              |   61 +
 src/phaseset_core/base_qualification_host.py  |  114 +-
 src/phaseset_core/frozen_clip_text.py         |    4 +-
 src/phaseset_core/host.py                     |   81 +-
 src/phaseset_core/periodic_cache_execution.py | 1474 +++++++++++++++++
 src/phaseset_core/training.py                 |  668 ++++++--
 tests/test_cached_residual_lifecycle.py       |  534 ++++++
 ...test_phaseset_capture_validation_cached.py |    6 +-
 .../test_phaseset_host_base_qualification.py  |  349 +++-
 .../test_phaseset_periodic_cache_execution.py |  419 +++++
 15 files changed, 3625 insertions(+), 258 deletions(-)
 create mode 100644 docs/CACHED_RESIDUAL_TRAINING.md
 create mode 100644 docs/PERIODIC_CACHE_EXECUTION.md
 create mode 100644 src/phaseset_core/periodic_cache_execution.py
 create mode 100644 tests/test_cached_residual_lifecycle.py
 create mode 100644 tests/test_phaseset_periodic_cache_execution.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 67e4dad..2be6fa2 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -11,12 +11,13 @@ b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.
 fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
-d4dc6505cc0e954e24203a82a174da71e48f463c899e5108bd677b82119207de	30475	STATUS.md
+e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
 2970469ad0787e74ab4fabce2275486eb50c30c38f3cb7cf8f2800551d16991f	9540	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
+e32c97a223c2b04278b9ce3c5d3b25590b390bc82dbf29e99b06d08d6c96b268	3054	docs/CACHED_RESIDUAL_TRAINING.md
 81ecc1cc742604f47833a0aa5b4c567165aa01e9c500d05157e325bd3b4ccdb3	4281	docs/CAPTION_ROLE_PROVENANCE.md
 62f6077a4d1cf0142e85ac28455a090d732b550874e47ea5644403566500f784	4644	docs/CAPTURE_PREPARED_STORAGE.md
 e66e0530b02a222d0b3c46590290d8ebcdafa21d7cd3acf2d751355cc1f8e558	9492	docs/CAPTURE_VALIDATION.md
@@ -28,11 +29,12 @@ e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
 97b7b7e68b7896220b38661b094505b47c711a431f546e6da9772cfedcf6beaa	14238	docs/FROZEN_CLIP_TEXT_ADAPTER.md
 f2c59540bfc4dcdbb5013b76eb20090a7dd61c805523121995ed9206c843f9c8	3711	docs/FROZEN_MHA_RUNTIME.md
-d179a05895c57e8c01c0b3115170f23127171e273571d9d239add4be8e219958	5453	docs/HOST_BASE_QUALIFICATION.md
-97bae9d94ed80f0871d39437f6a2e416bca912fad80a658964f754edded17cbf	18360	docs/HOST_RUNTIME_INTEGRATION.md
+d03a1569556beb1b3ad7f7f501d7bdc40e271f6912c173e403f75458fd6ed238	6158	docs/HOST_BASE_QUALIFICATION.md
+e38d6f254f71d0027a0b82e1dc3ec95d23a9041831d21d41e96acf10b81e155f	19223	docs/HOST_RUNTIME_INTEGRATION.md
 5dd898347d3646cde4a74e10afa8ba1d12703e3d0c6df51e5113659a31808086	3606	docs/LATENCY_PROGRESS_JOURNAL.md
 f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
+1cbb857ba962b29eae75d206aa49657f481e559085e062a2ef51ab4a6d87a220	3440	docs/PERIODIC_CACHE_EXECUTION.md
 a9e215279d354999c1f949f776a131b8cf4cec850abd81459c5c0d8cee0c8b03	4356	docs/PERIODIC_CAPTURE_TRAINING_CACHE.md
 6e9b9513bcfdbe76fbd28472225cb1fa185029a2b272e0b3f7d45e4a581f57c6	7886	docs/PERIODIC_DESCRIPTOR_CACHE_V2.md
 00794af0ae3a3cf00c8d276183a34c3f0bca08e00f749d1f71e6444248a7ed0d	3353	docs/PERIODIC_TRAINING_CACHE_PLAN.md
@@ -135,7 +137,7 @@ d1aa8d6837a40ed062dc941a5052c25c6304b2f60e97782af306a83ef4078842	72640	src/phase
 ecda879d3f531ebb747b561612fa34df37f1ecbd8e1977d5089d38b01ec63131	29076	src/phaseset_core/base_cohort_qualification.py
 d2d71766c1c6af95480d507960d7186bbec9ff8a424f6e79ce6611fd0aa2e611	38885	src/phaseset_core/base_cohort_resolver.py
 3d4f31b6a773081af41f1d9d45ee98986ea33ef6f954070564373f995af1f2e9	40593	src/phaseset_core/base_cohort_validation.py
-eada42c2c19b9da0ba2e07213823b50855cee66ebdf8ccbf49005a992e9cbd18	43333	src/phaseset_core/base_qualification_host.py
+ba59fcfa317aae5d0bf8e5330ede0eea2f399c5a4ebe7700b2f49029bdb53ff9	46345	src/phaseset_core/base_qualification_host.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
 121dcb6b948b9a3a4878ed376b3692dcbbd4b19f7ea147a7d28341c64b77d296	36926	src/phaseset_core/caption_role_provenance.py
 d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
@@ -149,13 +151,14 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
-38f8e5a85e02f22c7600c66b88a48d9f1b84ef8ed7dfd7ff3e01fd2960761faf	68520	src/phaseset_core/frozen_clip_text.py
-9ae29038b3a32ea0fdaada8e5bcbfb0c65b8a047be9c78069f159b1f6960352e	106049	src/phaseset_core/host.py
+877302cdb2ad891eb064d5bb228d124b66042865b78fa3826f66cf7bacf01166	68520	src/phaseset_core/frozen_clip_text.py
+6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
 9e7fa84554d5676f8ef75049b17b53580395c3c60be0f75fcb55996ccc8ef36d	37720	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
+e69b0d27d479b4499bae73c96f7854f9d07539043a0b66f0f75f6bc3e23c1f51	55415	src/phaseset_core/periodic_cache_execution.py
 21ab159e434cc504867d22a22894a8e6a82afa77dde88a466773b5549a004850	42190	src/phaseset_core/periodic_capture_training_cache.py
 10a3dcde2653e4ffa3eeaba06426736d47c9ebb2caa565cc0698df744658d249	55700	src/phaseset_core/periodic_descriptor_cache_v2.py
 8e36e1a62d55962e255d1d08d3608de0fcc31dbacc879051b8e1d88d8d3988c8	35602	src/phaseset_core/periodic_training_cache.py
@@ -168,8 +171,9 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
-c84486cc35709f4ac9fd17119deffeeea6569499800e59a143e53c6667ecd386	161053	src/phaseset_core/training.py
+168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
+ae334bee8e2762f022020942141cb52f3285d3d9cb07d53574b6c37f57b2a556	20274	tests/test_cached_residual_lifecycle.py
 d5d69092cc271059400773ef68447e06068aa802d5e9c82193704c6b65c71f59	18827	tests/test_caption_role_provenance.py
 ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test_capture_validation_source_lineage_v3.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
@@ -220,7 +224,7 @@ c88e5b91823b22ebb7a4019acb328040c6b42be7acc03738949439e4efbe443f	10126	tests/tes
 ec0609bf54766ffec53a1d7e4af7e778e2c2efbd9f4d2c4c06984814a066ca7f	21782	tests/test_phaseset_capture_prepared_storage.py
 a02d460a28bd6aeb372fa66e779cb4a65c86536ab951ccd3b6c30ed8091cbd15	8371	tests/test_phaseset_capture_training.py
 beacbd78c68771001b60ff7c6271e6d99c92d24d83f251e1074fc4ce50d1216c	15511	tests/test_phaseset_capture_validation.py
-f936263319934da3fac8ad2dead96cadb6218950809ae0fe300f25d058743612	8105	tests/test_phaseset_capture_validation_cached.py
+0281be4e47421dc8c52839ea99a45d32531f6ce21a6f726a6ec32ea5ca6a3d4b	8300	tests/test_phaseset_capture_validation_cached.py
 5dbbb81d6725f1185f4c12dc85653b6eac1e47ae2ffb7fb36f5b951eb8dea632	8848	tests/test_phaseset_cli.py
 c3060c5567214d195e024152f2d427ff3a63f3cc5fef1ded01e73d6887a3431e	13653	tests/test_phaseset_cli_production_bridge.py
 a53fe5c6b53adb5c80421842f806fc45e8305360348d7065aa0ddfb0b9316dcb	6546	tests/test_phaseset_contracts.py
@@ -234,7 +238,7 @@ dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/tes
 04df5b4a8d75bed006f16b56910e4082d4104a45cc8aeb11d6932ede645777cb	14111	tests/test_phaseset_frozen_clip_cache_rehydrate.py
 937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
 4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
-2f4dae243d0886a5e50d203fd8d2af9075012506f5dea6984730fb437b6325fe	46809	tests/test_phaseset_host_base_qualification.py
+357695439cffc89282c6d51573817964b603b3d3a76325e641ed89136d8650e2	58152	tests/test_phaseset_host_base_qualification.py
 14f6d4b54d78876822e6f96440a32360ea4d4654c1d18d8d8662fe0f4dc42bf8	23309	tests/test_phaseset_host_capture_storage.py
 6eb892149a9b3dee02f48e816acfe0026a0efaed8985a32eedf412bad1edd141	11008	tests/test_phaseset_host_intent_binding.py
 d133ca3a4df71baaea1cc40f68d049b56a7fcfc0d0861a87e5916c9637bdadb2	16345	tests/test_phaseset_latency_progress_journal.py
@@ -242,6 +246,7 @@ a75f583fd0a233760b1a421ca51680afe4195c33f9ef4bce6e9a1737828f207e	14907	tests/tes
 e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test_phaseset_morlet.py
 6d7005585882e25be9479542aac96a5b2aeb1bddc5619a0f37c57f61f2cbe6c3	6348	tests/test_phaseset_objectives.py
 52ccd9d4ea8351d5aaaa82eb99d84c6bd240c8b6147a506fd65dbca3d1394331	11547	tests/test_phaseset_periodic.py
+709e51151dc6e5a6e05de49987e303f9118c30f159f19513245a6d4f1793977b	14857	tests/test_phaseset_periodic_cache_execution.py
 6ebbf2ece33ff2390036e5ce55dcd34a0459211b79922219971c27bee37844a0	10278	tests/test_phaseset_pipeline.py
 82c15cd86cd1ac918d4cec678814ad61509ae7405173a73871c2f2172e82faeb	17328	tests/test_phaseset_prepared_data_v2.py
 aae917cd95bde322f4ec7cea693fd5d0c926d51f0a366e7be160426472ad7973	15770	tests/test_phaseset_prepared_host_integration.py
diff --git a/STATUS.md b/STATUS.md
index 7351f46..1a1da76 100644
--- a/STATUS.md
+++ b/STATUS.md
@@ -13,15 +13,31 @@ evidence, not training results or scientific claims.
 
 ## Server continuation
 
+- Complete two-store descriptor-cache construction and cached residual
+  checkpoint/resume now pass one combined 270-test Linux server cohort in
+  429.82 seconds, with source unchanged. It also includes the qualification
+  timer/runtime corrections and Windows readonly-shard fault-fixture fix.
+  The actual official CLIP outputs retain their two exact embedding goldens
+  and unchanged RNG under the new training-source binding. The same source
+  subsequently passed 1360 full Linux tests, 2 existing skips and 39 subtests
+  in 743.26 seconds, with source unchanged. Cross-platform CI acceptance is
+  tracked separately; no formal run is
+  implied. See [cache construction](docs/PERIODIC_CACHE_EXECUTION.md) and
+  [cached training/resume](docs/CACHED_RESIDUAL_TRAINING.md).
+
 - The cached complete-capture evaluator and private host qualification
   controller now pass one combined Linux server regression: 1334 tests,
   2 existing skips and 39 subtests in 643.90 seconds, with source unchanged.
   This closes software integration only. Native data access, all formal
   training, actual cohort qualification and sealed scores remain absent.
-  Fresh static review identified pending early timer-capability admission,
-  exception-safe timer restoration and post-admission source/output/lease
-  failure classification corrections in the qualification controller. See
-  [known host runtime boundaries](docs/HOST_BASE_QUALIFICATION.md).
+  The current successor now implements early POSIX/main-thread timer
+  admission, exception-safe timer restoration, and qualify-base-only runtime
+  classification for post-admission source/output/lease failures. Static review
+  passed, and its focused Linux server run passed 145 tests in 64.24 seconds
+  with source unchanged. That successor is not part of the observed 1334-test
+  source; it is included in the newer 1360-test cohort above. Windows CI
+  acceptance remains separate. See
+  [host runtime boundaries](docs/HOST_BASE_QUALIFICATION.md).
 
 - The combined capture-lineage, prepared-window cache-plan and CUDA-workspace
   correction passed 157 focused and 1285 full Linux server tests, two existing
@@ -43,8 +59,9 @@ evidence, not training results or scientific claims.
   the exact twenty-epoch source/cache census for each registered seed and
   reopening each selected shard without fallback. A separate complete-capture
   plan now binds the holistic capture source and its cache without relabeling
-  prepared-window auxiliary validation. Neither plan is yet an integrated
-  training/checkpoint/resume implementation.
+  prepared-window auxiliary validation. At that predecessor milestone the plans
+  were not yet integrated into training/checkpoint/resume; the current explicit
+  cached lifecycle is described above.
   See [capture storage](docs/CAPTURE_PREPARED_STORAGE.md) and
   [window cache plan](docs/PERIODIC_TRAINING_CACHE_PLAN.md).
 
@@ -63,8 +80,9 @@ evidence, not training results or scientific claims.
   focused server tests in 151.03 seconds with source unchanged; its subsequent
   combined 1334-test regression is reported above. These were
   software-source enumeration and text observations, not twenty epochs of
-  training. Training-loop, checkpoint/resume and host cache-plan consumption
-  remain unfinished. See
+  training. At that predecessor milestone, training-loop, checkpoint/resume
+  and host cache-plan consumption were unfinished. The newer core cached
+  lifecycle is reported above; host end-to-end injection remains separate. See
   [complete-capture cache plan](docs/PERIODIC_CAPTURE_TRAINING_CACHE.md) and
   [capture validation](docs/CAPTURE_VALIDATION.md).
 
@@ -76,7 +94,8 @@ evidence, not training results or scientific claims.
   the observed representation without bypassing device or resource checks.
   The combined Linux server suite passed 136 focused tests and 1257 full
   tests, 2 existing skips and 39 subtests in 413.42 s, source unchanged.
-  Complete training cache-plan/checkpoint/resume integration is still pending;
+  Complete training cache-plan/checkpoint/resume integration was still pending
+  at that predecessor milestone;
   these observations are not real-data training or a K32 performance result.
   See [source lineage](docs/PREPARED_DATA_V2.md) and
   [CUDA identity](docs/BASE_COHORT_QUALIFICATION.md).
@@ -88,9 +107,9 @@ evidence, not training results or scientific claims.
   without an uncached fallback. The model/cache/control suite passed 98 Linux
   server tests; full regression passed 1228 tests, 2 existing skips and
   39 subtests in 401.07 s, source unchanged. These are software fixtures,
-  not real data or measured acceleration. Plan admission now covers training
-  rows and the complete epoch census, but training-loop and checkpoint/resume
-  cache consumption remain unfinished.
+  not real data or measured acceleration. At that predecessor milestone plan
+  admission covered training rows and the complete epoch census, while the
+  training-loop/checkpoint/resume consumption reported above was unfinished.
   See [descriptor model integration](docs/PERIODIC_DESCRIPTOR_CACHE_V2.md).
   The host now retains the canonical admitted plan/matrix/config instead of
   reading fields absent from the actual command intent. Commands and handler
@@ -111,8 +130,8 @@ evidence, not training results or scientific claims.
   hardlink/oversize case split. These are explicit
   software fixtures, not a real nine-run cohort, formal CUDA latency session,
   winner or licensed-data cache. The later explicit cache-model API is reported
-  above; training/resume cache consumption and host qualification command
-  execution with real cohort inputs remain unfinished. See
+  above. Current host end-to-end cached training/resume and host qualification
+  command execution with real cohort inputs remain unfinished. See
   [base qualification](docs/BASE_COHORT_QUALIFICATION.md),
   [progress journal](docs/LATENCY_PROGRESS_JOURNAL.md) and
   [descriptor cache](docs/PERIODIC_DESCRIPTOR_CACHE_V2.md).
diff --git a/docs/CACHED_RESIDUAL_TRAINING.md b/docs/CACHED_RESIDUAL_TRAINING.md
new file mode 100644
index 0000000..59872d6
--- /dev/null
+++ b/docs/CACHED_RESIDUAL_TRAINING.md
@@ -0,0 +1,55 @@
+# Cached residual training and resume
+
+The residual lifecycle accepts an explicitly admitted
+`PeriodicDescriptorCaptureTrainingPlan` for systems 02/03/04/06/07/08. It does
+not autodetect caches, substitute a window-level validation task for the full
+capture gallery, or fall back to uncached descriptors on a miss.
+
+Pass the same exact plan to both
+`construct_registered_residual_seed_bound_system(..., descriptor_plan=plan)`
+and `PhaseSetTrainingRuntime(..., descriptor_plan=plan)`. Admission checks its
+system, official seed, formal twenty-epoch configuration, edge budget, energy
+floors, train manifest and complete holistic capture census. Systems 01/05 and
+base systems do not accept descriptor plans.
+
+Every gradient-cache first pass and differentiable replay reopens the current
+batch through `plan.open_training_batch(batch)` and calls
+`encode_trainable_cached`. Readers, streams and learned activations are not
+stored in checkpoints. A replay error restores the post-objective RNG state
+and propagates before an optimizer or scheduler step; no failed cache read is
+replaced by recomputation. Holistic validation calls the shared
+`run_capture_validation_cached` implementation for every accepted window.
+
+## Serialized compatibility
+
+Uncached initialization, checkpoint and report schemas and bytes remain v1;
+they do not gain nullable cache fields. Cached execution uses additive schemas:
+
+- `phaseset-training-initialization-binding-v2-cached-descriptor-plan`
+- `phaseset-training-checkpoint-v2-cached-descriptor-plan`
+- `phaseset-training-report-v2-cached-descriptor-plan`
+
+The cached checkpoint binds the complete plan digest and a dataloader digest
+over the train manifest, holistic capture census and that plan. Resume must
+reconstruct the same live plan and revalidate those identities before restoring
+state. Cached and uncached checkpoints are not implicitly migrated into one
+another. Paths, descriptor payloads, reader objects and stream cursors are
+excluded from serialization.
+
+## Evidence and remaining integration
+
+The combined registered-server software cohort passed 270 focused tests in
+429.82 seconds with source unchanged. It includes real analytic cache writers,
+readers and plan admission, small-model lifecycle tests, failure injection and
+the platform corrections. The official frozen CLIP adapter also retained its
+two exact embedding goldens and unchanged RNG after the training-source pin
+was updated. The same source then passed 1360 full Linux tests, 2 existing
+skips and 39 subtests in 743.26 seconds, with source unchanged. These
+observations are not formal model-quality results.
+
+Full-width cached numerical equivalence and host-level plan/configuration and
+checkpoint-ledger injection are separate work. The current CLI host does not
+yet dispatch this cached lifecycle end to end. Native data, fitted scientific
+floors, the nine qualified bases and the formal residual runs remain required.
+See [cache construction](PERIODIC_CACHE_EXECUTION.md) and
+[complete plan admission](PERIODIC_CAPTURE_TRAINING_CACHE.md).
diff --git a/docs/HOST_BASE_QUALIFICATION.md b/docs/HOST_BASE_QUALIFICATION.md
index fff74d8..489c74a 100644
--- a/docs/HOST_BASE_QUALIFICATION.md
+++ b/docs/HOST_BASE_QUALIFICATION.md
@@ -54,8 +54,12 @@ separately. A successful score cannot enter latency unless cleanup reaches the
 exact-zero boundary.
 
 The in-process interval timer is supported only where the required POSIX timer
-API exists. It refuses an inherited deadline. Normal exit restores the prior
-handler; exception-safe restoration is pending as detailed below.
+API exists. The current controller checks that API, the Python main thread
+and the absence of an inherited deadline before registry resolution or scoring,
+then rechecks immediately before latency timer installation. Its cleanup always
+attempts both disarm and prior-handler restoration, and a cleanup error cannot
+replace the primary scoring or latency error. Timer capability or restoration
+drift is a runtime HOLD, not a scientific failure.
 Deployment must also provide its separately frozen hard timeout because a
 process signal cannot guarantee that a blocked native call returns. A hard kill
 may leave only a valid prefix of the progress journal and the start record;
@@ -82,14 +86,20 @@ passed 1334 tests, 2 existing skips and 39 subtests in 643.90 seconds on the
 Linux server, with source unchanged. No formal cohort was produced by this
 regression.
 
-Static review of this engineering milestone identified two remaining runtime
-boundary corrections: check POSIX/main-thread timer capability before scoring,
-and preserve timer-handler restoration if timer setup or cleanup fails;
-classify post-admission source/output/lease failures at the qualification host
-boundary as runtime outcomes rather than CLI input errors. These corrections
-and their fault-injection tests are not included in the observed 1334-test
-source. Formal qualification remains unexecuted; this feature-branch milestone
-is not an empirical release.
+Follow-up code now implements the two reviewed runtime-boundary
+corrections: the early and exception-safe timer behavior described above, and
+qualify-base-only classification of post-admission source/output/lease runtime
+failures as HOLD outcomes rather than CLI input errors. Other commands and the
+v1 private-host schema retain their prior behavior. Fault-injection tests use a
+typed portable timer facade so Windows does not need to emulate POSIX signals;
+a separate test exercises the real POSIX handler and timer restoration.
+
+The successor passed static checks, fresh static review, and 145 focused Linux
+server tests in 64.24 seconds with source unchanged. Those code and test changes
+are not included in the observed 1334-test source, and cross-platform CI
+acceptance remains pending, so this paragraph does not claim the portability
+defect is empirically closed. Formal qualification remains unexecuted; this
+feature-branch milestone is not an empirical release.
 
 The earlier K=32 B0/B1/B2 diagnostic verified the corrected per-process CUDA
 release mechanics at registered width, but used untrained models, one warmup
diff --git a/docs/HOST_RUNTIME_INTEGRATION.md b/docs/HOST_RUNTIME_INTEGRATION.md
index 5013771..853ba0e 100644
--- a/docs/HOST_RUNTIME_INTEGRATION.md
+++ b/docs/HOST_RUNTIME_INTEGRATION.md
@@ -235,8 +235,9 @@ rights assertion when Embody approval is unknown.
   and registered final system IDs; the host implements base and residual resume
   and recovers identity from the verified attempt ledger.
 - The residual encoder and complete-capture evaluator now expose explicit
-  reader-backed descriptor-cache APIs. The training loop and this host's
-  residual construction/checkpoint/resume path do not yet consume those APIs.
+  reader-backed descriptor-cache APIs. The training loop now consumes explicit
+  admitted plans, including cached checkpoint/resume. This host's residual
+  construction/checkpoint/resume path does not yet inject those plans.
   The existing host cache argument authenticates the complete cache payload
   and consumes its energy floors, while its model path still recomputes
   descriptors. That historical behavior must not be represented as descriptor
@@ -313,3 +314,16 @@ backend path, validate typed holds and independently recheck completion
 artifacts. A preceding run with seven fixture/API failures is retained; only
 the tests changed for this rerun, not production code. These are software
 fixtures, not nine trained bases, formal CUDA latency rows or a selected base.
+
+The subsequent controller boundary correction passed 145 focused tests and was
+included in a 270-test combined cached-training/cache-construction cohort.
+Both observed runs kept the source unchanged. Early main-thread/POSIX timer
+checks, cleanup preservation and qualify-base-only runtime HOLD classification
+are implemented; Windows CI acceptance is a separate observation.
+
+The [descriptor cache builder](PERIODIC_CACHE_EXECUTION.md) and
+[cached residual lifecycle](CACHED_RESIDUAL_TRAINING.md) are callable core
+interfaces, not yet an end-to-end cached CLI host. Typed build evidence,
+private configuration, direct plan injection and ledger/resume linkage remain
+explicit follow-up integration. The existing uncached host does not silently
+select a cache based on files found on disk.
diff --git a/docs/PERIODIC_CACHE_EXECUTION.md b/docs/PERIODIC_CACHE_EXECUTION.md
new file mode 100644
index 0000000..2702ed5
--- /dev/null
+++ b/docs/PERIODIC_CACHE_EXECUTION.md
@@ -0,0 +1,61 @@
+# Complete periodic descriptor cache construction
+
+`periodic_cache_execution` builds and consumes one seed-specific private cache
+tree. It composes the actual v2 descriptor writers/readers and complete plan
+admission; it does not estimate energy floors, choose a base, run a model or
+authorize data.
+
+The entry points are `load_qualified_energy_floor_input`,
+`build_periodic_descriptor_cache_for_seed` and
+`consume_periodic_descriptor_cache_build`. They require one official seed, its
+formal twenty-epoch residual configuration, canonical selected-base
+qualification, the exact winning checkpoint, supplied qualified floor bytes,
+an admitted prepared-v2 train source, a complete holistic capture-val source,
+source/environment identities and explicit resource bounds.
+
+The writer enumerates every train batch in epochs 0..19 and every accepted
+capture-validation window once, then admits the complete plans for
+02/03/04/06/07/08. Systems 01/05 do not receive fabricated descriptor plans.
+No person, edge or window is silently dropped to fit a resource limit.
+
+The train writer is constrained by both its per-cache and combined limits;
+the capture writer receives only the positive remaining shard/byte allowance.
+An exceeding shard is rejected before it is written. File reads compare
+portable path/descriptor identity and independently verify full before/after
+metadata on each interface, including the Windows ctime distinction.
+
+## Immutable output and consumption
+
+The output root must be new. It contains `train/`, `capture-validation/`, six
+`plans/<system>.json` files, and a final `build.json`. The last file binds the
+seed/configuration, selected base/terminal/checkpoint, consumed floor
+record/payload/vector, both source and cache identities/censuses, six admitted
+plans, code/environment and resource bounds. It is published only after the
+complete build and admission succeed. Partial failed roots are preserved and
+are not resumed or overwritten by this version.
+
+Consumption requires a separately retained expected build-manifest digest.
+It reopens both readers, repeats all six complete admissions, compares stored
+plan bytes, checks the live checkpoint and training-code identities, and
+reconstructs the exact canonical manifest. A self-consistent replacement JSON
+file or an asserted success boolean is insufficient.
+
+Cache contents remain private: commitments can still be linkable, and
+descriptor shards must not be treated as automatically publishable data.
+
+## Verification boundary
+
+The combined 270-test registered-server cohort includes actual complete
+twenty-epoch and full-capture analytic construction/consumption, no-overwrite,
+wrong split/checkpoint, mutated plan, extra member, strict floor payload,
+aggregate disk limits and canonical type-drift tests. It passed with unchanged
+source. Full regression on the same source passed 1360 tests, 2 existing skips
+and 39 subtests in 743.26 seconds. These are software tests, not licensed-data
+construction or training.
+
+The existing floor-only record proves linkage to supplied seed/base bytes;
+it does not prove how the six values were scientifically fitted. A separately
+specified actor-once training-only floor collector and provenance are still
+required for formal use. Diagnostic constants are not a substitute. Host CLI
+dispatch and typed production evidence also remain separate integration work.
+See [cached residual lifecycle](CACHED_RESIDUAL_TRAINING.md).
diff --git a/src/phaseset_core/base_qualification_host.py b/src/phaseset_core/base_qualification_host.py
index e56bf22..e80838c 100644
--- a/src/phaseset_core/base_qualification_host.py
+++ b/src/phaseset_core/base_qualification_host.py
@@ -20,6 +20,7 @@
 import re
 import signal
 import stat
+import threading
 from typing import Final, Iterator
 
 from . import base_cohort_latency as latency_module
@@ -66,6 +67,10 @@ def __init__(self, failure_code: str, message: str) -> None:
         self.failure_code = failure_code
 
 
+class _FormalLatencyTimerHold(RuntimeError):
+    """The required process-local latency timer is unavailable or drifted."""
+
+
 @dataclass(frozen=True, slots=True)
 class _ScoringCudaState:
     runtime: object
@@ -379,39 +384,113 @@ def load_base_attempt_registry(
     return checked, raw, digest
 
 
+def _validate_formal_latency_timer_support(
+    *,
+    _os_name: str | None = None,
+    _signal_api: object | None = None,
+    _threading_api: object | None = None,
+) -> None:
+    """Check the real process timer boundary without mutating timer state."""
+
+    os_name = os.name if _os_name is None else _os_name
+    signal_api = signal if _signal_api is None else _signal_api
+    threading_api = threading if _threading_api is None else _threading_api
+    if os_name != "posix" or not all(
+        hasattr(signal_api, name)
+        for name in (
+            "SIGALRM",
+            "ITIMER_REAL",
+            "getitimer",
+            "getsignal",
+            "setitimer",
+            "signal",
+        )
+    ):
+        raise _FormalLatencyTimerHold(
+            "formal latency timeout requires POSIX interval timers"
+        )
+    if not all(
+        callable(getattr(threading_api, name, None))
+        for name in ("current_thread", "main_thread")
+    ):
+        raise _FormalLatencyTimerHold("formal latency timer thread API is unavailable")
+    try:
+        if threading_api.current_thread() is not threading_api.main_thread():
+            raise _FormalLatencyTimerHold(
+                "formal latency timeout requires the Python main thread"
+            )
+        prior_timer = signal_api.getitimer(signal_api.ITIMER_REAL)
+        signal_api.getsignal(signal_api.SIGALRM)
+    except _FormalLatencyTimerHold:
+        raise
+    except (AttributeError, OSError, RuntimeError, TypeError, ValueError) as error:
+        raise _FormalLatencyTimerHold(
+            "formal latency timer capability check failed"
+        ) from error
+    if prior_timer != (0.0, 0.0):
+        raise _FormalLatencyTimerHold(
+            "formal latency refuses an inherited process timer"
+        )
+
+
 @contextmanager
 def _formal_latency_timeout(
     seconds: int,
     *,
     _os_name: str | None = None,
     _signal_api: object | None = None,
+    _threading_api: object | None = None,
 ) -> Iterator[None]:
     """Install the process-local formal timeout; the outer hard-kill is separate."""
 
-    os_name = os.name if _os_name is None else _os_name
     signal_api = signal if _signal_api is None else _signal_api
-    if os_name != "posix" or not all(
-        hasattr(signal_api, name)
-        for name in ("SIGALRM", "ITIMER_REAL", "setitimer", "getitimer")
-    ):
-        raise HostBaseQualificationError("formal latency timeout requires POSIX interval timers")
     if type(seconds) is not int or not 1 <= seconds <= MAX_WALL_TIMEOUT_SECONDS:
         raise HostBaseQualificationError("formal latency timeout is outside its bound")
-    prior_timer = signal_api.getitimer(signal_api.ITIMER_REAL)
-    if prior_timer != (0.0, 0.0):
-        raise HostBaseQualificationError("formal latency refuses an inherited process timer")
-    prior_handler = signal_api.getsignal(signal_api.SIGALRM)
+    _validate_formal_latency_timer_support(
+        _os_name=_os_name,
+        _signal_api=signal_api,
+        _threading_api=_threading_api,
+    )
+    try:
+        prior_handler = signal_api.getsignal(signal_api.SIGALRM)
+    except (AttributeError, OSError, RuntimeError, TypeError, ValueError) as error:
+        raise _FormalLatencyTimerHold(
+            "formal latency timer handler could not be read"
+        ) from error
 
     def timeout_handler(_signum: int, _frame: object) -> None:
         raise TimeoutError("formal base latency wall timeout expired")
 
-    signal_api.signal(signal_api.SIGALRM, timeout_handler)
-    signal_api.setitimer(signal_api.ITIMER_REAL, float(seconds), 0.0)
+    primary_error: BaseException | None = None
     try:
+        try:
+            signal_api.signal(signal_api.SIGALRM, timeout_handler)
+            signal_api.setitimer(signal_api.ITIMER_REAL, float(seconds), 0.0)
+        except (AttributeError, OSError, RuntimeError, TypeError, ValueError) as error:
+            raise _FormalLatencyTimerHold(
+                "formal latency timer could not be installed"
+            ) from error
         yield
+    except BaseException as error:
+        primary_error = error
+        raise
     finally:
-        signal_api.setitimer(signal_api.ITIMER_REAL, 0.0, 0.0)
-        signal_api.signal(signal_api.SIGALRM, prior_handler)
+        cleanup_error: BaseException | None = None
+        try:
+            signal_api.setitimer(signal_api.ITIMER_REAL, 0.0, 0.0)
+        except BaseException as error:
+            cleanup_error = error
+        try:
+            signal_api.signal(signal_api.SIGALRM, prior_handler)
+        except BaseException as error:
+            if cleanup_error is None:
+                cleanup_error = error
+        if primary_error is None and cleanup_error is not None:
+            if isinstance(cleanup_error, Exception):
+                raise _FormalLatencyTimerHold(
+                    "formal latency timer could not be restored"
+                ) from cleanup_error
+            raise cleanup_error
 
 
 def _journal_terminal_payload(
@@ -607,6 +686,8 @@ def _failure_code(error: BaseException, stage: str) -> tuple[str, str]:
         return "HELD", f"BASE_SCORING_CUDA_{error.failure_code}"
     if isinstance(error, latency_module.BaseCohortLatencyHold):
         return "HELD", f"LATENCY_{error.failure_code}"
+    if isinstance(error, _FormalLatencyTimerHold):
+        return "HELD", "LATENCY_RUNTIME_DRIFT"
     if isinstance(error, (TimeoutError, journal_module.LatencyJournalWriteError)):
         return "HELD", "LATENCY_OBSERVATION_INCOMPLETE"
     if isinstance(error, resolver_module.BaseCohortResolutionError):
@@ -714,7 +795,7 @@ def run_host_base_qualification(
             "qualification output directory differs from its frozen session path"
         )
     formal = _test_runtime is None
-    stage = "REGISTRY"
+    stage = "LATENCY_PREFLIGHT" if formal else "REGISTRY"
     registry_digest = config.registry_sha256
     journal_path: Path | None = None
     journal_sha256: str | None = None
@@ -723,6 +804,9 @@ def run_host_base_qualification(
     secondary_cleanup_error_type: str | None = None
     start_path: Path | None = None
     try:
+        if formal:
+            _validate_formal_latency_timer_support()
+            stage = "REGISTRY"
         registry_rows, registry_raw, registry_digest = load_base_attempt_registry(
             config.registry_path,
             expected_sha256=config.registry_sha256,
diff --git a/src/phaseset_core/frozen_clip_text.py b/src/phaseset_core/frozen_clip_text.py
index c17378d..ae2fb10 100644
--- a/src/phaseset_core/frozen_clip_text.py
+++ b/src/phaseset_core/frozen_clip_text.py
@@ -157,8 +157,8 @@
     ),
     (
         "phaseset_core.training",
-        161_053,
-        "c84486cc35709f4ac9fd17119deffeeea6569499800e59a143e53c6667ecd386",
+        175_962,
+        "168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c",
     ),
 )
 
diff --git a/src/phaseset_core/host.py b/src/phaseset_core/host.py
index e71ff32..c6abc62 100644
--- a/src/phaseset_core/host.py
+++ b/src/phaseset_core/host.py
@@ -168,6 +168,22 @@ def _qualify_base_request_is_closed(request: cli.CLICommandRequest) -> bool:
     )
 
 
+def _qualify_base_runtime_hold(*, prepared_data: bool) -> ExecutionHold:
+    """Map only post-admission qualify-base runtime drift to the public HOLD seam."""
+
+    if type(prepared_data) is not bool:
+        raise TypeError("prepared_data must be exact bool")
+    holds = (
+        (
+            "HOLD_PREPARED_DATA_MANIFEST_ABSENT",
+            "HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",
+        )
+        if prepared_data
+        else ("HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",)
+    )
+    return ExecutionHold("qualify-base", holds)
+
+
 class _AttemptLease:
     """Process-released exclusive lease held for one attempt's full lifetime."""
 
@@ -1907,32 +1923,44 @@ def _qualify_base(self, intent: CommandIntent) -> BackendExecution:
             )
         if request.terminal_root is None:  # narrowed by the closed-input predicate
             raise AssertionError("closed qualify-base request lost terminal_root")
-        train, val = self._config.load_sources()
-        if type(val) is not CaptureValidationSource or val.split != "val":
-            raise HostConfigurationError(
-                "qualify-base requires the complete holistic capture val source"
-            )
-        cohort_admission = BaseCohortAdmission(
-            plan_sha256=admission_request.plan_sha256,
-            matrix_sha256=admission_request.matrix_sha256,
-            training_config_sha256=admission_request.training_config_sha256,
-            source_tree_sha256=self._config.source_tree_sha256,
-            train_manifest_sha256=train.manifest_sha256,
-            val_manifest_sha256=val.census_sha256,
-            device=self._config.device,
-            request_bf16=self._config.request_bf16,
-            bf16_runtime_qualified=self._config.bf16_runtime_qualified,
-            edge_budget=self._config.edge_budget,
-            checkpoint_every_updates=self._config.checkpoint_every_updates,
-        )
+        try:
+            train, val = self._config.load_sources()
+            if type(val) is not CaptureValidationSource or val.split != "val":
+                raise HostConfigurationError(
+                    "qualify-base requires the complete holistic capture val source"
+                )
+            cohort_admission = BaseCohortAdmission(
+                plan_sha256=admission_request.plan_sha256,
+                matrix_sha256=admission_request.matrix_sha256,
+                training_config_sha256=admission_request.training_config_sha256,
+                source_tree_sha256=self._config.source_tree_sha256,
+                train_manifest_sha256=train.manifest_sha256,
+                val_manifest_sha256=val.census_sha256,
+                device=self._config.device,
+                request_bf16=self._config.request_bf16,
+                bf16_runtime_qualified=self._config.bf16_runtime_qualified,
+                edge_budget=self._config.edge_budget,
+                checkpoint_every_updates=self._config.checkpoint_every_updates,
+            )
+        except (
+            AttributeError,
+            HostConfigurationError,
+            OSError,
+            RuntimeError,
+            TypeError,
+            ValueError,
+        ) as error:
+            raise _qualify_base_runtime_hold(prepared_data=True) from error
         output_directory = qualification.output_root / qualification.session_id
         try:
             output_directory.mkdir(mode=0o700, parents=False, exist_ok=False)
         except OSError as error:
-            raise HostConfigurationError(
-                "base qualification output directory cannot be created once"
-            ) from error
-        lease = _AttemptLease.acquire(output_directory)
+            raise _qualify_base_runtime_hold(prepared_data=False) from error
+        try:
+            lease = _AttemptLease.acquire(output_directory)
+        except (HostConfigurationError, OSError, RuntimeError) as error:
+            raise _qualify_base_runtime_hold(prepared_data=False) from error
+        primary_error: BaseException | None = None
         try:
             result = run_host_base_qualification(
                 request.terminal_root.resolve(),
@@ -1964,8 +1992,15 @@ def _qualify_base(self, intent: CommandIntent) -> BackendExecution:
                     authentication.authorization,
                 )
             return result.execution
+        except BaseException as error:
+            primary_error = error
+            raise
         finally:
-            lease.release()
+            try:
+                lease.release()
+            except Exception as error:
+                if primary_error is None:
+                    raise _qualify_base_runtime_hold(prepared_data=False) from error
 
     def _execute_base_runtime(
         self,
diff --git a/src/phaseset_core/periodic_cache_execution.py b/src/phaseset_core/periodic_cache_execution.py
new file mode 100644
index 0000000..e15594e
--- /dev/null
+++ b/src/phaseset_core/periodic_cache_execution.py
@@ -0,0 +1,1474 @@
+"""Build and consume one immutable seed-specific periodic descriptor cache.
+
+This private module composes the existing prepared-data source, capture source,
+descriptor writer/reader, and complete six-system plan admission.  It does not
+estimate energy floors, execute a model, authorize data, or claim a result.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+import hashlib
+import io
+import json
+import os
+from pathlib import Path
+import stat
+from typing import Final
+import zipfile
+
+import numpy as np
+
+from .capture_validation import (
+    CaptureDescriptorWindowSource,
+    CaptureValidationSource,
+)
+from .execution import (
+    PeriodicCacheRecord,
+    parse_periodic_cache_bytes,
+)
+from .experiments import (
+    SEEDS,
+    BaseQualification,
+    canonical_base_qualification_bytes,
+)
+from .periodic import validate_energy_floors
+from .periodic_capture_training_cache import (
+    PeriodicDescriptorCaptureTrainingPlan,
+    admit_periodic_descriptor_capture_training_plan,
+)
+from .periodic_descriptor_cache_v2 import (
+    DescriptorCacheArtifact,
+    DescriptorCacheBounds,
+    DescriptorCacheSourceBinding,
+    PeriodicDescriptorCacheV2,
+    PeriodicDescriptorCacheWriterV2,
+    energy_floors_sha256,
+)
+from .prepared_data_v2 import PreparedTrainingDataSourceV2
+from .training import (
+    OFFICIAL_SEEDS,
+    RESIDUAL_EPOCHS,
+    RetrievalTrainingBatch,
+    TrainingConfig,
+    training_code_artifact_sha256,
+)
+
+
+AUTHORITY: Final = 0
+PRODUCTION: Final = False
+RESULT_CLAIMED: Final = False
+EXTERNAL_AUTHENTICATION_ASSERTED: Final = False
+BUILD_SCHEMA: Final = "phaseset-periodic-descriptor-cache-build-v1"
+BUILD_STATUS: Final = "COMPLETE_PRIVATE_CACHE_BUILD_AUTHORITY0"
+FLOOR_INPUT_SCHEMA: Final = "phaseset-qualified-energy-floor-input-v1"
+FLOOR_RECORD_NAME: Final = "record.json"
+FLOOR_PAYLOAD_NAME: Final = "cache-content.npz"
+BUILD_MANIFEST_NAME: Final = "build.json"
+TRAIN_CACHE_NAME: Final = "train"
+CAPTURE_CACHE_NAME: Final = "capture-validation"
+PLAN_DIRECTORY_NAME: Final = "plans"
+MAX_FLOOR_RECORD_BYTES: Final = 64 * 1024
+MAX_FLOOR_PAYLOAD_BYTES: Final = 1024 * 1024
+MAX_FLOOR_NPY_BYTES: Final = 4096
+
+CACHEABLE_SYSTEM_STREAMS: Final[tuple[tuple[str, str], ...]] = (
+    ("02", "MARGINAL_POWER"),
+    ("03", "MEAN_DIFFERENCE_DCT"),
+    ("04", "FULL_RELATION"),
+    ("06", "FULL_RELATION"),
+    ("07", "FULL_RELATION"),
+    ("08", "FULL_RELATION"),
+)
+
+_FLOOR_TOKEN: Final = object()
+_RESULT_TOKEN: Final = object()
+
+
+class PeriodicCacheExecutionError(ValueError):
+    """The seed-specific cache build or its consumed bytes are invalid."""
+
+
+@dataclass(frozen=True, slots=True)
+class PeriodicCacheExecutionBounds:
+    """Explicit bounds above the two existing descriptor-cache bounds."""
+
+    descriptor: DescriptorCacheBounds = DescriptorCacheBounds()
+    max_checkpoint_bytes: int = 8 * 1024 * 1024 * 1024
+    max_combined_shards: int = 524_288
+    max_combined_shard_bytes: int = 2 * (1 << 40)
+    max_build_manifest_bytes: int = 4 * 1024 * 1024
+    max_plan_bytes: int = 256 * 1024 * 1024
+
+    def __post_init__(self) -> None:
+        if type(self.descriptor) is not DescriptorCacheBounds:
+            raise TypeError("descriptor must be exact DescriptorCacheBounds")
+        for name in (
+            "max_checkpoint_bytes",
+            "max_combined_shards",
+            "max_combined_shard_bytes",
+            "max_build_manifest_bytes",
+            "max_plan_bytes",
+        ):
+            value = getattr(self, name)
+            if type(value) is not int or value < 1:
+                raise TypeError(f"{name} must be an exact positive int")
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class QualifiedEnergyFloorInput:
+    """Exact consumed v1 floor bytes bound to a canonical base selection.
+
+    The type proves byte and selection linkage only.  It does not invent the
+    still-missing scientific estimator that creates training-final floors.
+    """
+
+    record: PeriodicCacheRecord
+    energy_floors: np.ndarray = field(repr=False)
+    record_json_bytes: bytes = field(repr=False)
+    payload_npz_bytes: bytes = field(repr=False)
+    record_sha256: str
+    payload_sha256: str
+    qualification_sha256: str
+    energy_floors_sha256: str
+    _seal: object = field(repr=False, compare=False)
+
+    def __post_init__(self) -> None:
+        if self._seal is not _FLOOR_TOKEN:
+            raise PeriodicCacheExecutionError(
+                "qualified floor input must come from the same-byte v1 loader"
+            )
+        if type(self.record) is not PeriodicCacheRecord:
+            raise TypeError("record must be exact PeriodicCacheRecord")
+        if type(self.record_json_bytes) is not bytes or type(self.payload_npz_bytes) is not bytes:
+            raise TypeError("floor artifacts must be exact built-in bytes")
+        for name in (
+            "record_sha256",
+            "payload_sha256",
+            "qualification_sha256",
+            "energy_floors_sha256",
+        ):
+            _lower_sha256(getattr(self, name), name)
+        if _sha256(self.record_json_bytes) != self.record_sha256:
+            raise PeriodicCacheExecutionError("floor record bytes changed")
+        if _sha256(self.payload_npz_bytes) != self.payload_sha256:
+            raise PeriodicCacheExecutionError("floor payload bytes changed")
+        floors = _frozen_floors(self.energy_floors)
+        if energy_floors_sha256(floors) != self.energy_floors_sha256:
+            raise PeriodicCacheExecutionError("floor vector bytes changed")
+        object.__setattr__(self, "energy_floors", floors)
+
+    def __repr__(self) -> str:
+        return (
+            "QualifiedEnergyFloorInput(seed="
+            f"{self.record.seed}, record_sha256={self.record_sha256!r}, "
+            "<floor bytes hidden>)"
+        )
+
+
+@dataclass(frozen=True, slots=True, repr=False)
+class PeriodicDescriptorCacheBuildResult:
+    """Canonical manifest plus live readers and six sealed admitted plans."""
+
+    root: Path = field(repr=False)
+    seed: int
+    config_sha256: str
+    qualification_sha256: str
+    manifest_json_bytes: bytes = field(repr=False)
+    manifest_sha256: str
+    train_cache: PeriodicDescriptorCacheV2 = field(repr=False, compare=False)
+    capture_validation_cache: PeriodicDescriptorCacheV2 = field(
+        repr=False,
+        compare=False,
+    )
+    plans: tuple[PeriodicDescriptorCaptureTrainingPlan, ...] = field(
+        repr=False,
+        compare=False,
+    )
+    _seal: object = field(repr=False, compare=False)
+
+    def __post_init__(self) -> None:
+        if self._seal is not _RESULT_TOKEN:
+            raise PeriodicCacheExecutionError(
+                "cache result must come from build or complete consumption verification"
+            )
+        if not isinstance(self.root, Path):
+            raise TypeError("result root must be a pathlib.Path")
+        if type(self.seed) is not int or self.seed not in OFFICIAL_SEEDS:
+            raise PeriodicCacheExecutionError("result seed is outside the census")
+        for name in ("config_sha256", "qualification_sha256", "manifest_sha256"):
+            _lower_sha256(getattr(self, name), name)
+        if type(self.manifest_json_bytes) is not bytes:
+            raise TypeError("manifest_json_bytes must be exact built-in bytes")
+        if _sha256(self.manifest_json_bytes) != self.manifest_sha256:
+            raise PeriodicCacheExecutionError("result manifest bytes changed")
+        if (
+            type(self.train_cache) is not PeriodicDescriptorCacheV2
+            or type(self.capture_validation_cache) is not PeriodicDescriptorCacheV2
+            or type(self.plans) is not tuple
+            or tuple(plan.system_id for plan in self.plans)
+            != tuple(system_id for system_id, _ in CACHEABLE_SYSTEM_STREAMS)
+            or any(type(plan) is not PeriodicDescriptorCaptureTrainingPlan for plan in self.plans)
+        ):
+            raise PeriodicCacheExecutionError("result readers or plan census changed")
+
+    def __repr__(self) -> str:
+        return (
+            f"PeriodicDescriptorCacheBuildResult(seed={self.seed}, "
+            f"manifest_sha256={self.manifest_sha256!r}, <private tree hidden>)"
+        )
+
+
+def _sha256(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _lower_sha256(value: object, label: str) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or value.lower() != value
+        or any(character not in "0123456789abcdef" for character in value)
+    ):
+        raise TypeError(f"{label} must be one lowercase SHA-256 hex digest")
+    return value
+
+
+def _canonical_json(value: object) -> bytes:
+    return (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+
+
+def _pairs_no_duplicates(pairs: list[tuple[str, object]]) -> dict[str, object]:
+    value: dict[str, object] = {}
+    for key, item in pairs:
+        if key in value:
+            raise PeriodicCacheExecutionError(f"duplicate JSON key: {key}")
+        value[key] = item
+    return value
+
+
+def _reject_float(value: str) -> object:
+    raise PeriodicCacheExecutionError(f"JSON floating point is forbidden: {value}")
+
+
+def _parse_canonical_json(raw: bytes, label: str) -> dict[str, object]:
+    if type(raw) is not bytes:
+        raise TypeError(f"{label} must be exact built-in bytes")
+    try:
+        value = json.loads(
+            raw.decode("ascii"),
+            object_pairs_hook=_pairs_no_duplicates,
+            parse_float=_reject_float,
+            parse_constant=_reject_float,
+        )
+    except (UnicodeDecodeError, json.JSONDecodeError, RecursionError) as error:
+        raise PeriodicCacheExecutionError(f"{label} is not bounded canonical JSON") from error
+    if type(value) is not dict or _canonical_json(value) != raw:
+        raise PeriodicCacheExecutionError(f"{label} is not exact canonical JSON")
+    return value
+
+
+def _closed(value: object, keys: set[str], label: str) -> dict[str, object]:
+    if type(value) is not dict or set(value) != keys:
+        raise PeriodicCacheExecutionError(f"{label} keys are not closed")
+    return value
+
+
+def _reject_symlink_components(path: Path, label: str) -> None:
+    for component in (path, *path.parents):
+        if component.is_symlink():
+            raise PeriodicCacheExecutionError(f"{label} cannot traverse a symlink")
+
+
+def _stable_stat_identity(
+    value: os.stat_result,
+) -> tuple[int, int, int, int, int, int, int]:
+    return (
+        value.st_mode,
+        value.st_dev,
+        value.st_ino,
+        value.st_size,
+        value.st_mtime_ns,
+        value.st_ctime_ns,
+        value.st_nlink,
+    )
+
+
+def _cross_interface_identity(
+    value: os.stat_result,
+) -> tuple[int, int, int, int, int]:
+    # Windows path and descriptor APIs can expose different ctime semantics.
+    # Compare ctime and nlink only before/after within the same API.
+    return (
+        value.st_mode,
+        value.st_dev,
+        value.st_ino,
+        value.st_size,
+        value.st_mtime_ns,
+    )
+
+
+def _open_owned(
+    path: Path,
+    label: str,
+) -> tuple[int, os.stat_result, os.stat_result]:
+    _reject_symlink_components(path, label)
+    try:
+        before = path.lstat()
+    except OSError as error:
+        raise PeriodicCacheExecutionError(f"{label} is absent") from error
+    if not stat.S_ISREG(before.st_mode):
+        raise PeriodicCacheExecutionError(f"{label} must be a regular non-symlink file")
+    flags = os.O_RDONLY | getattr(os, "O_BINARY", 0) | getattr(os, "O_NOFOLLOW", 0)
+    try:
+        descriptor = os.open(path, flags)
+    except OSError as error:
+        raise PeriodicCacheExecutionError(f"{label} cannot be opened safely") from error
+    opened = os.fstat(descriptor)
+    if not stat.S_ISREG(opened.st_mode) or _cross_interface_identity(
+        opened
+    ) != _cross_interface_identity(before):
+        os.close(descriptor)
+        raise PeriodicCacheExecutionError(f"{label} identity changed before consumption")
+    return descriptor, before, opened
+
+
+def _verify_after_read(
+    descriptor: int,
+    path: Path,
+    path_before: os.stat_result,
+    opened_before: os.stat_result,
+    label: str,
+) -> None:
+    try:
+        opened_after = os.fstat(descriptor)
+        path_after = path.lstat()
+    except OSError as error:
+        raise PeriodicCacheExecutionError(f"{label} changed during consumption") from error
+    if (
+        not stat.S_ISREG(opened_after.st_mode)
+        or not stat.S_ISREG(path_after.st_mode)
+        or _stable_stat_identity(opened_before) != _stable_stat_identity(opened_after)
+        or _stable_stat_identity(path_before) != _stable_stat_identity(path_after)
+        or _cross_interface_identity(opened_after) != _cross_interface_identity(path_after)
+    ):
+        raise PeriodicCacheExecutionError(f"{label} changed during consumption")
+
+
+def _read_owned(path: Path, *, maximum: int, label: str) -> bytes:
+    if type(maximum) is not int or maximum < 1:
+        raise TypeError("maximum must be an exact positive int")
+    descriptor, path_before, opened = _open_owned(path, label)
+    try:
+        if opened.st_size > maximum:
+            raise PeriodicCacheExecutionError(f"{label} exceeds its byte bound")
+        chunks: list[bytes] = []
+        total = 0
+        while True:
+            chunk = os.read(descriptor, min(1024 * 1024, maximum + 1 - total))
+            if not chunk:
+                break
+            chunks.append(chunk)
+            total += len(chunk)
+            if total > maximum:
+                raise PeriodicCacheExecutionError(f"{label} exceeds its byte bound")
+        raw = b"".join(chunks)
+        if len(raw) != opened.st_size:
+            raise PeriodicCacheExecutionError(f"{label} size changed during consumption")
+        _verify_after_read(descriptor, path, path_before, opened, label)
+    finally:
+        os.close(descriptor)
+    return raw
+
+
+def _hash_owned(path: Path, *, maximum: int, label: str) -> tuple[str, int]:
+    if type(maximum) is not int or maximum < 1:
+        raise TypeError("maximum must be an exact positive int")
+    descriptor, path_before, opened = _open_owned(path, label)
+    digest = hashlib.sha256()
+    total = 0
+    try:
+        if opened.st_size > maximum:
+            raise PeriodicCacheExecutionError(f"{label} exceeds its byte bound")
+        while True:
+            chunk = os.read(descriptor, 1024 * 1024)
+            if not chunk:
+                break
+            digest.update(chunk)
+            total += len(chunk)
+            if total > maximum:
+                raise PeriodicCacheExecutionError(f"{label} exceeds its byte bound")
+        if total != opened.st_size:
+            raise PeriodicCacheExecutionError(f"{label} size changed during consumption")
+        _verify_after_read(descriptor, path, path_before, opened, label)
+    finally:
+        os.close(descriptor)
+    return digest.hexdigest(), total
+
+
+def _existing_directory(path: Path, label: str) -> Path:
+    _reject_symlink_components(path, label)
+    try:
+        value = path.resolve(strict=True)
+        metadata = value.lstat()
+    except OSError as error:
+        raise PeriodicCacheExecutionError(f"{label} is absent") from error
+    if not stat.S_ISDIR(metadata.st_mode) or value.is_symlink():
+        raise PeriodicCacheExecutionError(f"{label} must be a non-symlink directory")
+    return value
+
+
+def _exact_names(root: Path, expected: set[str], label: str) -> None:
+    try:
+        names = {entry.name for entry in os.scandir(root)}
+    except OSError as error:
+        raise PeriodicCacheExecutionError(f"{label} cannot be enumerated") from error
+    if names != expected:
+        raise PeriodicCacheExecutionError(f"{label} member census changed")
+
+
+def _write_exclusive(path: Path, raw: bytes, label: str) -> None:
+    if type(raw) is not bytes:
+        raise TypeError("published artifacts must be exact built-in bytes")
+    flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL | getattr(os, "O_BINARY", 0)
+    try:
+        descriptor = os.open(path, flags, 0o600)
+        try:
+            view = memoryview(raw)
+            while view:
+                written = os.write(descriptor, view)
+                if written < 1:
+                    raise OSError("short write")
+                view = view[written:]
+            os.fsync(descriptor)
+        finally:
+            os.close(descriptor)
+    except OSError as error:
+        raise PeriodicCacheExecutionError(f"{label} exclusive publish failed") from error
+    if _read_owned(path, maximum=max(1, len(raw)), label=label) != raw:
+        raise PeriodicCacheExecutionError(f"{label} post-publish bytes changed")
+
+
+def _frozen_floors(value: object) -> np.ndarray:
+    try:
+        checked = validate_energy_floors(value)
+    except (TypeError, ValueError) as error:
+        raise PeriodicCacheExecutionError("energy floors are invalid") from error
+    frozen = np.array(checked, dtype=np.float64, copy=True, order="C")
+    frozen.setflags(write=False)
+    return frozen
+
+
+def _qualification_facts(
+    qualification: object,
+    expected_sha256: object,
+    seed: object,
+) -> tuple[BaseQualification, str, str, str, str]:
+    if type(seed) is not int or seed not in OFFICIAL_SEEDS or tuple(SEEDS) != OFFICIAL_SEEDS:
+        raise PeriodicCacheExecutionError("cache seed is outside the frozen census")
+    try:
+        expected = _lower_sha256(expected_sha256, "qualification_sha256")
+        raw = canonical_base_qualification_bytes(
+            qualification,
+            expected_sha256=expected,
+        )
+    except (TypeError, ValueError) as error:
+        raise PeriodicCacheExecutionError("base qualification is not canonical") from error
+    if type(qualification) is not BaseQualification or _sha256(raw) != expected:
+        raise PeriodicCacheExecutionError("base qualification digest mismatch")
+    position = OFFICIAL_SEEDS.index(seed)
+    system_id = qualification.winner_system_id
+    run_id = f"phaseset-run-v1/BASE_QUALIFICATION/{seed}/{system_id}"
+    return (
+        qualification,
+        expected,
+        run_id,
+        qualification.winner_terminal_sha256s[position],
+        qualification.winner_checkpoint_sha256s[position],
+    )
+
+
+def _decode_floor_payload(raw: bytes) -> np.ndarray:
+    try:
+        with zipfile.ZipFile(io.BytesIO(raw), mode="r") as archive:
+            rows = archive.infolist()
+            if (
+                len(rows) != 1
+                or rows[0].filename != "energy_floors.npy"
+                or rows[0].is_dir()
+                or rows[0].flag_bits & 0x1
+                or rows[0].file_size > MAX_FLOOR_NPY_BYTES
+            ):
+                raise PeriodicCacheExecutionError(
+                    "floor payload member census or decoded bound changed"
+                )
+        with np.load(io.BytesIO(raw), allow_pickle=False) as values:
+            if values.files != ["energy_floors"]:
+                raise PeriodicCacheExecutionError("floor payload keys are not exact")
+            floors = np.array(values["energy_floors"], copy=True, order="C")
+    except PeriodicCacheExecutionError:
+        raise
+    except (OSError, ValueError, zipfile.BadZipFile, RuntimeError) as error:
+        raise PeriodicCacheExecutionError("floor payload is not safe numeric NPZ") from error
+    return _frozen_floors(floors)
+
+
+def load_qualified_energy_floor_input(
+    root: str | Path,
+    *,
+    seed: int,
+    qualification: BaseQualification,
+    qualification_sha256: str,
+) -> QualifiedEnergyFloorInput:
+    """Load the two exact legacy v1 files and retain their consumed bytes."""
+
+    checked_qualification, qualification_digest, run_id, terminal, _checkpoint = (
+        _qualification_facts(qualification, qualification_sha256, seed)
+    )
+    directory = _existing_directory(Path(root), "qualified energy-floor root")
+    _exact_names(
+        directory,
+        {FLOOR_RECORD_NAME, FLOOR_PAYLOAD_NAME},
+        "qualified energy-floor root",
+    )
+    record_raw = _read_owned(
+        directory / FLOOR_RECORD_NAME,
+        maximum=MAX_FLOOR_RECORD_BYTES,
+        label="qualified energy-floor record",
+    )
+    payload_raw = _read_owned(
+        directory / FLOOR_PAYLOAD_NAME,
+        maximum=MAX_FLOOR_PAYLOAD_BYTES,
+        label="qualified energy-floor payload",
+    )
+    try:
+        record = parse_periodic_cache_bytes(record_raw)
+    except (TypeError, ValueError) as error:
+        raise PeriodicCacheExecutionError("qualified energy-floor record is invalid") from error
+    if (
+        record.seed != seed
+        or record.qualified_base_system_id != checked_qualification.winner_system_id
+        or record.source_run_id != run_id
+        or record.base_terminal_sha256 != terminal
+        or record.qualification_sha256 != qualification_digest
+        or record.cache_content_sha256 != _sha256(payload_raw)
+    ):
+        raise PeriodicCacheExecutionError("qualified energy-floor bytes differ from base selection")
+    floors = _decode_floor_payload(payload_raw)
+    return QualifiedEnergyFloorInput(
+        record=record,
+        energy_floors=floors,
+        record_json_bytes=record_raw,
+        payload_npz_bytes=payload_raw,
+        record_sha256=_sha256(record_raw),
+        payload_sha256=_sha256(payload_raw),
+        qualification_sha256=qualification_digest,
+        energy_floors_sha256=energy_floors_sha256(floors),
+        _seal=_FLOOR_TOKEN,
+    )
+
+
+def _validate_floor_input(
+    value: object,
+    *,
+    seed: int,
+    qualification: BaseQualification,
+    qualification_sha256: str,
+) -> QualifiedEnergyFloorInput:
+    if type(value) is not QualifiedEnergyFloorInput or value._seal is not _FLOOR_TOKEN:
+        raise TypeError("floors must be exact QualifiedEnergyFloorInput")
+    checked = QualifiedEnergyFloorInput(
+        record=value.record,
+        energy_floors=value.energy_floors,
+        record_json_bytes=value.record_json_bytes,
+        payload_npz_bytes=value.payload_npz_bytes,
+        record_sha256=value.record_sha256,
+        payload_sha256=value.payload_sha256,
+        qualification_sha256=value.qualification_sha256,
+        energy_floors_sha256=value.energy_floors_sha256,
+        _seal=_FLOOR_TOKEN,
+    )
+    _qualification, expected, run_id, terminal, _checkpoint = _qualification_facts(
+        qualification,
+        qualification_sha256,
+        seed,
+    )
+    record = checked.record
+    if (
+        checked.qualification_sha256 != expected
+        or record.seed != seed
+        or record.qualified_base_system_id != qualification.winner_system_id
+        or record.source_run_id != run_id
+        or record.base_terminal_sha256 != terminal
+        or record.qualification_sha256 != expected
+        or record.cache_content_sha256 != checked.payload_sha256
+        or parse_periodic_cache_bytes(checked.record_json_bytes) != record
+        or not np.array_equal(
+            _decode_floor_payload(checked.payload_npz_bytes),
+            checked.energy_floors,
+        )
+    ):
+        raise PeriodicCacheExecutionError("qualified energy-floor input binding changed")
+    return checked
+
+
+def _capture_snapshot_and_facts(
+    source: object,
+) -> tuple[CaptureValidationSource, str, int, int, int]:
+    if type(source) is not CaptureValidationSource:
+        raise TypeError("capture_source must be exact CaptureValidationSource")
+    checked = CaptureValidationSource(source.split, source.manifest_sha256, source.captures)
+    descriptors = tuple(
+        window.descriptor_source for capture in checked.captures for window in capture.windows
+    )
+    if not descriptors or any(
+        type(row) is not CaptureDescriptorWindowSource for row in descriptors
+    ):
+        raise PeriodicCacheExecutionError(
+            "capture source requires complete authenticated v2 window lineage"
+        )
+    storage_manifest = descriptors[0].storage_manifest_sha256
+    if any(row.storage_manifest_sha256 != storage_manifest for row in descriptors):
+        raise PeriodicCacheExecutionError("capture storage manifest changed across windows")
+    window_count = len(descriptors)
+    caption_count = sum(
+        len(capture.holistic_text.caption_commitments) for capture in checked.captures
+    )
+    return checked, storage_manifest, len(checked.captures), window_count, caption_count
+
+
+def _validate_inputs(
+    *,
+    seed: object,
+    config: object,
+    qualification: object,
+    qualification_sha256: object,
+    winner_checkpoint: object,
+    floors: object,
+    train_source: object,
+    capture_source: object,
+    source_tree_sha256: object,
+    environment_sha256: object,
+    bounds: object,
+) -> tuple[
+    TrainingConfig,
+    BaseQualification,
+    str,
+    str,
+    str,
+    QualifiedEnergyFloorInput,
+    PreparedTrainingDataSourceV2,
+    CaptureValidationSource,
+    str,
+    int,
+    int,
+    int,
+    str,
+    str,
+    PeriodicCacheExecutionBounds,
+    Path,
+]:
+    if type(config) is not TrainingConfig:
+        raise TypeError("config must be exact TrainingConfig")
+    if (
+        type(seed) is not int
+        or config.stage != "residual"
+        or config.seed != seed
+        or config.synthetic_contract
+        or config.epochs != RESIDUAL_EPOCHS
+    ):
+        raise PeriodicCacheExecutionError(
+            "cache build requires the exact formal 20-epoch residual config for its seed"
+        )
+    if type(bounds) is not PeriodicCacheExecutionBounds:
+        raise TypeError("bounds must be exact PeriodicCacheExecutionBounds")
+    if bounds.descriptor.max_edges_per_shard > config.edge_budget:
+        raise PeriodicCacheExecutionError(
+            "descriptor shard edge bound exceeds the formal runtime edge budget"
+        )
+    checked_qualification, qualification_digest, run_id, terminal, checkpoint = (
+        _qualification_facts(qualification, qualification_sha256, seed)
+    )
+    checked_floors = _validate_floor_input(
+        floors,
+        seed=seed,
+        qualification=checked_qualification,
+        qualification_sha256=qualification_digest,
+    )
+    if type(train_source) is not PreparedTrainingDataSourceV2:
+        raise TypeError("train_source must be exact PreparedTrainingDataSourceV2")
+    if train_source.split != "train":
+        raise PeriodicCacheExecutionError("descriptor cache source must be train-only")
+    checked_capture, storage_manifest, capture_count, window_count, caption_count = (
+        _capture_snapshot_and_facts(capture_source)
+    )
+    source_digest = _lower_sha256(source_tree_sha256, "source_tree_sha256")
+    environment_digest = _lower_sha256(environment_sha256, "environment_sha256")
+    if not isinstance(winner_checkpoint, (str, Path)):
+        raise TypeError("winner_checkpoint must be a path")
+    checkpoint_path = Path(winner_checkpoint)
+    actual_checkpoint, _size = _hash_owned(
+        checkpoint_path,
+        maximum=bounds.max_checkpoint_bytes,
+        label="selected winner checkpoint",
+    )
+    if actual_checkpoint != checkpoint:
+        raise PeriodicCacheExecutionError(
+            "selected winner checkpoint bytes differ from base qualification"
+        )
+    return (
+        config,
+        checked_qualification,
+        qualification_digest,
+        run_id,
+        terminal,
+        checked_floors,
+        train_source,
+        checked_capture,
+        storage_manifest,
+        capture_count,
+        window_count,
+        caption_count,
+        source_digest,
+        environment_digest,
+        bounds,
+        checkpoint_path,
+    )
+
+
+def _binding_value(binding: DescriptorCacheSourceBinding) -> dict[str, str]:
+    return {
+        "environment_sha256": binding.environment_sha256,
+        "prepared_manifest_sha256": binding.prepared_manifest_sha256,
+        "source_tree_sha256": binding.source_tree_sha256,
+    }
+
+
+def _bounds_value(bounds: PeriodicCacheExecutionBounds) -> dict[str, object]:
+    descriptor = bounds.descriptor
+    return {
+        "descriptor": {
+            "max_decoded_shard_bytes": descriptor.max_decoded_shard_bytes,
+            "max_edges_per_shard": descriptor.max_edges_per_shard,
+            "max_index_bytes": descriptor.max_index_bytes,
+            "max_shard_bytes": descriptor.max_shard_bytes,
+            "max_shards": descriptor.max_shards,
+            "max_total_shard_bytes": descriptor.max_total_shard_bytes,
+        },
+        "max_build_manifest_bytes": bounds.max_build_manifest_bytes,
+        "max_checkpoint_bytes": bounds.max_checkpoint_bytes,
+        "max_combined_shard_bytes": bounds.max_combined_shard_bytes,
+        "max_combined_shards": bounds.max_combined_shards,
+        "max_plan_bytes": bounds.max_plan_bytes,
+    }
+
+
+def _plan_value(plan: PeriodicDescriptorCaptureTrainingPlan) -> dict[str, object]:
+    return {
+        "path": f"{PLAN_DIRECTORY_NAME}/{plan.system_id}.json",
+        "sha256": plan.sha256,
+        "stream_kind": plan.stream_kind,
+        "system_id": plan.system_id,
+    }
+
+
+def _cache_value(
+    artifact: DescriptorCacheArtifact,
+    binding: DescriptorCacheSourceBinding,
+    relative_index: str,
+) -> dict[str, object]:
+    return {
+        "index_path": relative_index,
+        "index_sha256": artifact.index_sha256,
+        "shard_count": artifact.shard_count,
+        "source_binding": _binding_value(binding),
+        "total_shard_bytes": artifact.total_shard_bytes,
+    }
+
+
+def _manifest_value(
+    *,
+    seed: int,
+    config: TrainingConfig,
+    qualification: BaseQualification,
+    qualification_sha256: str,
+    run_id: str,
+    terminal_sha256: str,
+    checkpoint_sha256: str,
+    floors: QualifiedEnergyFloorInput,
+    source_tree_sha256: str,
+    environment_sha256: str,
+    training_code_sha256: str,
+    train_source: PreparedTrainingDataSourceV2,
+    capture_source: CaptureValidationSource,
+    storage_manifest_sha256: str,
+    capture_count: int,
+    window_count: int,
+    caption_count: int,
+    train_artifact: DescriptorCacheArtifact,
+    capture_artifact: DescriptorCacheArtifact,
+    train_binding: DescriptorCacheSourceBinding,
+    capture_binding: DescriptorCacheSourceBinding,
+    plans: tuple[PeriodicDescriptorCaptureTrainingPlan, ...],
+    bounds: PeriodicCacheExecutionBounds,
+) -> dict[str, object]:
+    return {
+        "authority": AUTHORITY,
+        "bounds": _bounds_value(bounds),
+        "capture_source": {
+            "caption_count": caption_count,
+            "capture_count": capture_count,
+            "census_sha256": capture_source.census_sha256,
+            "manifest_sha256": capture_source.manifest_sha256,
+            "storage_manifest_sha256": storage_manifest_sha256,
+            "window_count": window_count,
+        },
+        "capture_validation_cache": _cache_value(
+            capture_artifact,
+            capture_binding,
+            f"{CAPTURE_CACHE_NAME}/index.json",
+        ),
+        "config_sha256": config.sha256,
+        "energy_floor_input": {
+            "energy_floors_sha256": floors.energy_floors_sha256,
+            "payload_sha256": floors.payload_sha256,
+            "record_sha256": floors.record_sha256,
+            "schema": FLOOR_INPUT_SCHEMA,
+        },
+        "environment_sha256": environment_sha256,
+        "external_authentication_asserted": EXTERNAL_AUTHENTICATION_ASSERTED,
+        "plans": [_plan_value(plan) for plan in plans],
+        "production": PRODUCTION,
+        "qualification": {
+            "sha256": qualification_sha256,
+            "winner_checkpoint_sha256": checkpoint_sha256,
+            "winner_system_id": qualification.winner_system_id,
+            "winner_terminal_sha256": terminal_sha256,
+            "winner_seed_run_id": run_id,
+        },
+        "result_claimed": RESULT_CLAIMED,
+        "schema": BUILD_SCHEMA,
+        "seed": seed,
+        "source_tree_sha256": source_tree_sha256,
+        "status": BUILD_STATUS,
+        "train_cache": _cache_value(
+            train_artifact,
+            train_binding,
+            f"{TRAIN_CACHE_NAME}/index.json",
+        ),
+        "train_source_manifest_sha256": train_source.manifest_sha256,
+        "training_code_artifact_sha256": training_code_sha256,
+    }
+
+
+def _check_combined_bounds(
+    train: DescriptorCacheArtifact,
+    capture: DescriptorCacheArtifact,
+    bounds: PeriodicCacheExecutionBounds,
+) -> None:
+    if train.shard_count + capture.shard_count > bounds.max_combined_shards:
+        raise PeriodicCacheExecutionError("combined cache exceeds its shard-count bound")
+    if train.total_shard_bytes + capture.total_shard_bytes > bounds.max_combined_shard_bytes:
+        raise PeriodicCacheExecutionError("combined cache exceeds its shard-byte bound")
+
+
+def _bounded_writer_limits(
+    descriptor: DescriptorCacheBounds,
+    *,
+    remaining_shards: int,
+    remaining_bytes: int,
+    label: str,
+) -> DescriptorCacheBounds:
+    if type(remaining_shards) is not int or type(remaining_bytes) is not int:
+        raise TypeError("remaining aggregate cache bounds must be exact ints")
+    if remaining_shards < 1 or remaining_bytes < 1:
+        raise PeriodicCacheExecutionError(f"combined cache has no remaining capacity for {label}")
+    return DescriptorCacheBounds(
+        max_index_bytes=descriptor.max_index_bytes,
+        max_shard_bytes=descriptor.max_shard_bytes,
+        max_decoded_shard_bytes=descriptor.max_decoded_shard_bytes,
+        max_shards=min(descriptor.max_shards, remaining_shards),
+        max_total_shard_bytes=min(
+            descriptor.max_total_shard_bytes,
+            remaining_bytes,
+        ),
+        max_edges_per_shard=descriptor.max_edges_per_shard,
+    )
+
+
+def _admit_all_plans(
+    *,
+    config: TrainingConfig,
+    train_source: PreparedTrainingDataSourceV2,
+    capture_source: CaptureValidationSource,
+    train_cache: PeriodicDescriptorCacheV2,
+    capture_cache: PeriodicDescriptorCacheV2,
+) -> tuple[PeriodicDescriptorCaptureTrainingPlan, ...]:
+    plans = tuple(
+        admit_periodic_descriptor_capture_training_plan(
+            system_id=system_id,
+            config=config,
+            train_source=train_source,
+            capture_validation_source=capture_source,
+            train_cache=train_cache,
+            capture_validation_cache=capture_cache,
+        )
+        for system_id, _stream in CACHEABLE_SYSTEM_STREAMS
+    )
+    if tuple((plan.system_id, plan.stream_kind) for plan in plans) != CACHEABLE_SYSTEM_STREAMS:
+        raise PeriodicCacheExecutionError("six-system descriptor plan mapping changed")
+    if len({plan.sha256 for plan in plans}) != len(plans):
+        raise PeriodicCacheExecutionError("six-system descriptor plans are not distinct")
+    return plans
+
+
+def _claim_output_root(path: Path) -> Path:
+    _reject_symlink_components(path, "periodic descriptor output root")
+    try:
+        path.mkdir(parents=False, exist_ok=False)
+    except OSError as error:
+        raise PeriodicCacheExecutionError("periodic descriptor output root must be new") from error
+    return path.resolve()
+
+
+def _result(
+    *,
+    root: Path,
+    seed: int,
+    config_sha256: str,
+    qualification_sha256: str,
+    manifest: bytes,
+    train_cache: PeriodicDescriptorCacheV2,
+    capture_cache: PeriodicDescriptorCacheV2,
+    plans: tuple[PeriodicDescriptorCaptureTrainingPlan, ...],
+) -> PeriodicDescriptorCacheBuildResult:
+    return PeriodicDescriptorCacheBuildResult(
+        root=root,
+        seed=seed,
+        config_sha256=config_sha256,
+        qualification_sha256=qualification_sha256,
+        manifest_json_bytes=manifest,
+        manifest_sha256=_sha256(manifest),
+        train_cache=train_cache,
+        capture_validation_cache=capture_cache,
+        plans=plans,
+        _seal=_RESULT_TOKEN,
+    )
+
+
+def build_periodic_descriptor_cache_for_seed(
+    *,
+    seed: int,
+    config: TrainingConfig,
+    qualification: BaseQualification,
+    qualification_sha256: str,
+    winner_checkpoint: str | Path,
+    floors: QualifiedEnergyFloorInput,
+    train_source: PreparedTrainingDataSourceV2,
+    capture_source: CaptureValidationSource,
+    output_root: str | Path,
+    source_tree_sha256: str,
+    environment_sha256: str,
+    bounds: PeriodicCacheExecutionBounds = PeriodicCacheExecutionBounds(),
+) -> PeriodicDescriptorCacheBuildResult:
+    """Build two complete descriptor stores and admit all six cached systems."""
+
+    try:
+        (
+            checked_config,
+            checked_qualification,
+            qualification_digest,
+            run_id,
+            terminal,
+            checked_floors,
+            checked_train,
+            checked_capture,
+            storage_manifest,
+            capture_count,
+            window_count,
+            caption_count,
+            source_digest,
+            environment_digest,
+            checked_bounds,
+            checkpoint_path,
+        ) = _validate_inputs(
+            seed=seed,
+            config=config,
+            qualification=qualification,
+            qualification_sha256=qualification_sha256,
+            winner_checkpoint=winner_checkpoint,
+            floors=floors,
+            train_source=train_source,
+            capture_source=capture_source,
+            source_tree_sha256=source_tree_sha256,
+            environment_sha256=environment_sha256,
+            bounds=bounds,
+        )
+        checkpoint_sha256 = checked_qualification.winner_checkpoint_sha256s[
+            OFFICIAL_SEEDS.index(seed)
+        ]
+        training_code_sha256 = training_code_artifact_sha256()
+        train_binding = DescriptorCacheSourceBinding(
+            prepared_manifest_sha256=checked_train.manifest_sha256,
+            source_tree_sha256=source_digest,
+            environment_sha256=environment_digest,
+        )
+        capture_binding = DescriptorCacheSourceBinding(
+            prepared_manifest_sha256=storage_manifest,
+            source_tree_sha256=source_digest,
+            environment_sha256=environment_digest,
+        )
+        root = _claim_output_root(Path(output_root))
+        train_writer_bounds = _bounded_writer_limits(
+            checked_bounds.descriptor,
+            remaining_shards=checked_bounds.max_combined_shards,
+            remaining_bytes=checked_bounds.max_combined_shard_bytes,
+            label="training cache",
+        )
+        train_writer = PeriodicDescriptorCacheWriterV2(
+            root / TRAIN_CACHE_NAME,
+            source_binding=train_binding,
+            energy_floors=checked_floors.energy_floors,
+            bounds=train_writer_bounds,
+        )
+        for epoch in range(RESIDUAL_EPOCHS):
+            for batch in checked_train.iter_epoch(epoch=epoch, seed=seed):
+                if type(batch) is not RetrievalTrainingBatch or batch.split != "train":
+                    raise PeriodicCacheExecutionError(
+                        "prepared train iterator emitted a non-training batch"
+                    )
+                contexts = batch.descriptor_contexts
+                if type(contexts) is not tuple or len(contexts) != batch.motion_count:
+                    raise PeriodicCacheExecutionError(
+                        "prepared train batch lacks complete descriptor contexts"
+                    )
+                train_writer.add_batch(batch.groups, contexts)
+        train_artifact = train_writer.finalize()
+
+        capture_writer_bounds = _bounded_writer_limits(
+            checked_bounds.descriptor,
+            remaining_shards=(checked_bounds.max_combined_shards - train_artifact.shard_count),
+            remaining_bytes=(
+                checked_bounds.max_combined_shard_bytes - train_artifact.total_shard_bytes
+            ),
+            label="capture validation cache",
+        )
+        capture_writer = PeriodicDescriptorCacheWriterV2(
+            root / CAPTURE_CACHE_NAME,
+            source_binding=capture_binding,
+            energy_floors=checked_floors.energy_floors,
+            bounds=capture_writer_bounds,
+        )
+        for capture in checked_capture.captures:
+            for window in capture.windows:
+                capture_writer.add_batch(
+                    window.groups,
+                    (window.descriptor_context(seed=seed),),
+                )
+        capture_artifact = capture_writer.finalize()
+        _check_combined_bounds(train_artifact, capture_artifact, checked_bounds)
+
+        train_cache = PeriodicDescriptorCacheV2(
+            train_artifact.root,
+            expected_index_sha256=train_artifact.index_sha256,
+            expected_source_binding=train_binding,
+            energy_floors=checked_floors.energy_floors,
+            bounds=checked_bounds.descriptor,
+        )
+        capture_cache = PeriodicDescriptorCacheV2(
+            capture_artifact.root,
+            expected_index_sha256=capture_artifact.index_sha256,
+            expected_source_binding=capture_binding,
+            energy_floors=checked_floors.energy_floors,
+            bounds=checked_bounds.descriptor,
+        )
+        plans = _admit_all_plans(
+            config=checked_config,
+            train_source=checked_train,
+            capture_source=checked_capture,
+            train_cache=train_cache,
+            capture_cache=capture_cache,
+        )
+        plan_root = root / PLAN_DIRECTORY_NAME
+        plan_root.mkdir(parents=False, exist_ok=False)
+        for plan in plans:
+            raw = plan.canonical_bytes()
+            if len(raw) > checked_bounds.max_plan_bytes:
+                raise PeriodicCacheExecutionError("descriptor plan exceeds its byte bound")
+            _write_exclusive(
+                plan_root / f"{plan.system_id}.json",
+                raw,
+                f"descriptor plan {plan.system_id}",
+            )
+
+        actual_checkpoint, _size = _hash_owned(
+            checkpoint_path,
+            maximum=checked_bounds.max_checkpoint_bytes,
+            label="selected winner checkpoint",
+        )
+        if (
+            actual_checkpoint != checkpoint_sha256
+            or checked_train.manifest_sha256 != train_source.manifest_sha256
+            or checked_capture.census_sha256 != capture_source.census_sha256
+            or checked_capture.manifest_sha256 != capture_source.manifest_sha256
+            or checked_config.sha256 != config.sha256
+            or training_code_artifact_sha256() != training_code_sha256
+        ):
+            raise PeriodicCacheExecutionError("cache inputs changed during construction")
+        checked_floors = _validate_floor_input(
+            checked_floors,
+            seed=seed,
+            qualification=checked_qualification,
+            qualification_sha256=qualification_digest,
+        )
+        manifest_value = _manifest_value(
+            seed=seed,
+            config=checked_config,
+            qualification=checked_qualification,
+            qualification_sha256=qualification_digest,
+            run_id=run_id,
+            terminal_sha256=terminal,
+            checkpoint_sha256=checkpoint_sha256,
+            floors=checked_floors,
+            source_tree_sha256=source_digest,
+            environment_sha256=environment_digest,
+            training_code_sha256=training_code_sha256,
+            train_source=checked_train,
+            capture_source=checked_capture,
+            storage_manifest_sha256=storage_manifest,
+            capture_count=capture_count,
+            window_count=window_count,
+            caption_count=caption_count,
+            train_artifact=train_artifact,
+            capture_artifact=capture_artifact,
+            train_binding=train_binding,
+            capture_binding=capture_binding,
+            plans=plans,
+            bounds=checked_bounds,
+        )
+        manifest = _canonical_json(manifest_value)
+        if len(manifest) > checked_bounds.max_build_manifest_bytes:
+            raise PeriodicCacheExecutionError("build manifest exceeds its byte bound")
+        _write_exclusive(root / BUILD_MANIFEST_NAME, manifest, "descriptor build manifest")
+        _exact_names(
+            root,
+            {
+                BUILD_MANIFEST_NAME,
+                TRAIN_CACHE_NAME,
+                CAPTURE_CACHE_NAME,
+                PLAN_DIRECTORY_NAME,
+            },
+            "periodic descriptor output root",
+        )
+        return _result(
+            root=root,
+            seed=seed,
+            config_sha256=checked_config.sha256,
+            qualification_sha256=qualification_digest,
+            manifest=manifest,
+            train_cache=train_cache,
+            capture_cache=capture_cache,
+            plans=plans,
+        )
+    except PeriodicCacheExecutionError:
+        raise
+    except (OSError, TypeError, ValueError, RuntimeError) as error:
+        raise PeriodicCacheExecutionError(
+            "periodic descriptor cache construction failed"
+        ) from error
+
+
+def _manifest_cache_row(value: object, label: str) -> dict[str, object]:
+    row = _closed(
+        value,
+        {
+            "index_path",
+            "index_sha256",
+            "shard_count",
+            "source_binding",
+            "total_shard_bytes",
+        },
+        label,
+    )
+    _lower_sha256(row["index_sha256"], f"{label} index_sha256")
+    if type(row["shard_count"]) is not int or row["shard_count"] < 1:
+        raise PeriodicCacheExecutionError(f"{label} shard_count is invalid")
+    if type(row["total_shard_bytes"]) is not int or row["total_shard_bytes"] < 1:
+        raise PeriodicCacheExecutionError(f"{label} total_shard_bytes is invalid")
+    return row
+
+
+def _index_counts(
+    path: Path,
+    *,
+    expected_sha256: str,
+    bounds: DescriptorCacheBounds,
+) -> tuple[int, int]:
+    raw = _read_owned(path, maximum=bounds.max_index_bytes, label="descriptor cache index")
+    if _sha256(raw) != expected_sha256:
+        raise PeriodicCacheExecutionError("descriptor cache index digest changed")
+    value = _parse_canonical_json(raw, "descriptor cache index")
+    rows = value.get("shards")
+    total = value.get("total_shard_bytes")
+    if type(rows) is not list or type(total) is not int or total < 1:
+        raise PeriodicCacheExecutionError("descriptor cache index census is invalid")
+    return len(rows), total
+
+
+def consume_periodic_descriptor_cache_build(
+    root: str | Path,
+    *,
+    expected_manifest_sha256: str,
+    seed: int,
+    config: TrainingConfig,
+    qualification: BaseQualification,
+    qualification_sha256: str,
+    winner_checkpoint: str | Path,
+    floors: QualifiedEnergyFloorInput,
+    train_source: PreparedTrainingDataSourceV2,
+    capture_source: CaptureValidationSource,
+    source_tree_sha256: str,
+    environment_sha256: str,
+    bounds: PeriodicCacheExecutionBounds = PeriodicCacheExecutionBounds(),
+) -> PeriodicDescriptorCacheBuildResult:
+    """Reopen exact build bytes and repeat reader plus six-plan admission."""
+
+    try:
+        (
+            checked_config,
+            checked_qualification,
+            qualification_digest,
+            run_id,
+            terminal,
+            checked_floors,
+            checked_train,
+            checked_capture,
+            storage_manifest,
+            capture_count,
+            window_count,
+            caption_count,
+            source_digest,
+            environment_digest,
+            checked_bounds,
+            checkpoint_path,
+        ) = _validate_inputs(
+            seed=seed,
+            config=config,
+            qualification=qualification,
+            qualification_sha256=qualification_sha256,
+            winner_checkpoint=winner_checkpoint,
+            floors=floors,
+            train_source=train_source,
+            capture_source=capture_source,
+            source_tree_sha256=source_tree_sha256,
+            environment_sha256=environment_sha256,
+            bounds=bounds,
+        )
+        expected_manifest = _lower_sha256(
+            expected_manifest_sha256,
+            "expected_manifest_sha256",
+        )
+        directory = _existing_directory(Path(root), "periodic descriptor build root")
+        _exact_names(
+            directory,
+            {
+                BUILD_MANIFEST_NAME,
+                TRAIN_CACHE_NAME,
+                CAPTURE_CACHE_NAME,
+                PLAN_DIRECTORY_NAME,
+            },
+            "periodic descriptor build root",
+        )
+        manifest = _read_owned(
+            directory / BUILD_MANIFEST_NAME,
+            maximum=checked_bounds.max_build_manifest_bytes,
+            label="descriptor build manifest",
+        )
+        if _sha256(manifest) != expected_manifest:
+            raise PeriodicCacheExecutionError("descriptor build manifest digest mismatch")
+        value = _closed(
+            _parse_canonical_json(manifest, "descriptor build manifest"),
+            {
+                "authority",
+                "bounds",
+                "capture_source",
+                "capture_validation_cache",
+                "config_sha256",
+                "energy_floor_input",
+                "environment_sha256",
+                "external_authentication_asserted",
+                "plans",
+                "production",
+                "qualification",
+                "result_claimed",
+                "schema",
+                "seed",
+                "source_tree_sha256",
+                "status",
+                "train_cache",
+                "train_source_manifest_sha256",
+                "training_code_artifact_sha256",
+            },
+            "descriptor build manifest",
+        )
+        train_row = _manifest_cache_row(value["train_cache"], "train cache")
+        capture_row = _manifest_cache_row(
+            value["capture_validation_cache"],
+            "capture validation cache",
+        )
+        if (
+            train_row["index_path"] != f"{TRAIN_CACHE_NAME}/index.json"
+            or capture_row["index_path"] != f"{CAPTURE_CACHE_NAME}/index.json"
+        ):
+            raise PeriodicCacheExecutionError("descriptor index relative paths changed")
+        train_binding = DescriptorCacheSourceBinding(
+            prepared_manifest_sha256=checked_train.manifest_sha256,
+            source_tree_sha256=source_digest,
+            environment_sha256=environment_digest,
+        )
+        capture_binding = DescriptorCacheSourceBinding(
+            prepared_manifest_sha256=storage_manifest,
+            source_tree_sha256=source_digest,
+            environment_sha256=environment_digest,
+        )
+        train_cache = PeriodicDescriptorCacheV2(
+            directory / TRAIN_CACHE_NAME,
+            expected_index_sha256=train_row["index_sha256"],  # type: ignore[arg-type]
+            expected_source_binding=train_binding,
+            energy_floors=checked_floors.energy_floors,
+            bounds=checked_bounds.descriptor,
+        )
+        capture_cache = PeriodicDescriptorCacheV2(
+            directory / CAPTURE_CACHE_NAME,
+            expected_index_sha256=capture_row["index_sha256"],  # type: ignore[arg-type]
+            expected_source_binding=capture_binding,
+            energy_floors=checked_floors.energy_floors,
+            bounds=checked_bounds.descriptor,
+        )
+        train_count, train_bytes = _index_counts(
+            directory / TRAIN_CACHE_NAME / "index.json",
+            expected_sha256=train_cache.index_sha256,
+            bounds=checked_bounds.descriptor,
+        )
+        capture_shards, capture_bytes = _index_counts(
+            directory / CAPTURE_CACHE_NAME / "index.json",
+            expected_sha256=capture_cache.index_sha256,
+            bounds=checked_bounds.descriptor,
+        )
+        train_artifact = DescriptorCacheArtifact(
+            root=directory / TRAIN_CACHE_NAME,
+            index_path=directory / TRAIN_CACHE_NAME / "index.json",
+            index_sha256=train_cache.index_sha256,
+            shard_count=train_count,
+            total_shard_bytes=train_bytes,
+        )
+        capture_artifact = DescriptorCacheArtifact(
+            root=directory / CAPTURE_CACHE_NAME,
+            index_path=directory / CAPTURE_CACHE_NAME / "index.json",
+            index_sha256=capture_cache.index_sha256,
+            shard_count=capture_shards,
+            total_shard_bytes=capture_bytes,
+        )
+        _check_combined_bounds(train_artifact, capture_artifact, checked_bounds)
+        plans = _admit_all_plans(
+            config=checked_config,
+            train_source=checked_train,
+            capture_source=checked_capture,
+            train_cache=train_cache,
+            capture_cache=capture_cache,
+        )
+        plan_root = _existing_directory(directory / PLAN_DIRECTORY_NAME, "descriptor plan root")
+        _exact_names(
+            plan_root,
+            {f"{system_id}.json" for system_id, _stream in CACHEABLE_SYSTEM_STREAMS},
+            "descriptor plan root",
+        )
+        for plan in plans:
+            raw = _read_owned(
+                plan_root / f"{plan.system_id}.json",
+                maximum=checked_bounds.max_plan_bytes,
+                label=f"descriptor plan {plan.system_id}",
+            )
+            if raw != plan.canonical_bytes():
+                raise PeriodicCacheExecutionError(
+                    f"descriptor plan {plan.system_id} differs from complete admission"
+                )
+        training_code_sha256 = training_code_artifact_sha256()
+        checkpoint_sha256, _size = _hash_owned(
+            checkpoint_path,
+            maximum=checked_bounds.max_checkpoint_bytes,
+            label="selected winner checkpoint",
+        )
+        expected_value = _manifest_value(
+            seed=seed,
+            config=checked_config,
+            qualification=checked_qualification,
+            qualification_sha256=qualification_digest,
+            run_id=run_id,
+            terminal_sha256=terminal,
+            checkpoint_sha256=checkpoint_sha256,
+            floors=checked_floors,
+            source_tree_sha256=source_digest,
+            environment_sha256=environment_digest,
+            training_code_sha256=training_code_sha256,
+            train_source=checked_train,
+            capture_source=checked_capture,
+            storage_manifest_sha256=storage_manifest,
+            capture_count=capture_count,
+            window_count=window_count,
+            caption_count=caption_count,
+            train_artifact=train_artifact,
+            capture_artifact=capture_artifact,
+            train_binding=train_binding,
+            capture_binding=capture_binding,
+            plans=plans,
+            bounds=checked_bounds,
+        )
+        if value != expected_value or manifest != _canonical_json(expected_value):
+            raise PeriodicCacheExecutionError(
+                "descriptor build manifest differs from live consumed evidence"
+            )
+        return _result(
+            root=directory,
+            seed=seed,
+            config_sha256=checked_config.sha256,
+            qualification_sha256=qualification_digest,
+            manifest=manifest,
+            train_cache=train_cache,
+            capture_cache=capture_cache,
+            plans=plans,
+        )
+    except PeriodicCacheExecutionError:
+        raise
+    except (OSError, TypeError, ValueError, RuntimeError) as error:
+        raise PeriodicCacheExecutionError(
+            "periodic descriptor cache consumption verification failed"
+        ) from error
+
+
+__all__ = [
+    "AUTHORITY",
+    "BUILD_SCHEMA",
+    "BUILD_STATUS",
+    "CACHEABLE_SYSTEM_STREAMS",
+    "EXTERNAL_AUTHENTICATION_ASSERTED",
+    "FLOOR_INPUT_SCHEMA",
+    "PRODUCTION",
+    "PeriodicCacheExecutionBounds",
+    "PeriodicCacheExecutionError",
+    "PeriodicDescriptorCacheBuildResult",
+    "QualifiedEnergyFloorInput",
+    "RESULT_CLAIMED",
+    "build_periodic_descriptor_cache_for_seed",
+    "consume_periodic_descriptor_cache_build",
+    "load_qualified_energy_floor_input",
+]
diff --git a/src/phaseset_core/training.py b/src/phaseset_core/training.py
index 8ae07e3..32d9a7b 100644
--- a/src/phaseset_core/training.py
+++ b/src/phaseset_core/training.py
@@ -38,6 +38,7 @@
 
 if TYPE_CHECKING:
     from .capture_validation import CaptureValidationSource
+    from .periodic_capture_training_cache import PeriodicDescriptorCaptureTrainingPlan
 
 from .contracts import PreparedGroupBatch, validate_prepared_group_batch
 from .execution import (
@@ -65,6 +66,11 @@
 STATUS: Final = "DATA_FREE_EXECUTABLE_TRAINING_RUNTIME_NONPRODUCTION_AUTHORITY0"
 CHECKPOINT_SCHEMA: Final = "phaseset-training-checkpoint-v1"
 REPORT_SCHEMA: Final = "phaseset-training-report-v1"
+CACHED_CHECKPOINT_SCHEMA: Final = "phaseset-training-checkpoint-v2-cached-descriptor-plan"
+CACHED_INITIALIZATION_SCHEMA: Final = (
+    "phaseset-training-initialization-binding-v2-cached-descriptor-plan"
+)
+CACHED_REPORT_SCHEMA: Final = "phaseset-training-report-v2-cached-descriptor-plan"
 OFFICIAL_SEEDS: Final = (1729, 2718, 31415)
 RESIDUAL_SYSTEM_IDS: Final = tuple(f"{index:02d}" for index in range(1, 9))
 REGISTERED_BASE_SYSTEM_IDS: Final = ("B0", "B1", "B2")
@@ -175,14 +181,12 @@ def __post_init__(self) -> None:
             or len(self.motion_positive_ids) != checked.batch_size
         ):
             raise TypeError("motion_positive_ids must contain one bytes[32] per motion")
-        if (
-            type(self.text_positive_ids) is not tuple
-            or len(self.text_positive_ids) != int(self.text_embeddings.shape[0])
+        if type(self.text_positive_ids) is not tuple or len(self.text_positive_ids) != int(
+            self.text_embeddings.shape[0]
         ):
             raise TypeError("text_positive_ids must contain one bytes[32] per text")
-        if (
-            type(self.text_commitments) is not tuple
-            or len(self.text_commitments) != int(self.text_embeddings.shape[0])
+        if type(self.text_commitments) is not tuple or len(self.text_commitments) != int(
+            self.text_embeddings.shape[0]
         ):
             raise TypeError("text_commitments must contain one bytes[32] per text")
         motions = tuple(
@@ -368,6 +372,7 @@ class TrainingInitializationBinding:
     qualified_base_selection_sha256: str | None = None
     residual_capacity_audit_sha256: str | None = None
     schema: str = "phaseset-training-initialization-binding-v1"
+    periodic_descriptor_capture_plan_sha256: str | None = None
 
     def __post_init__(self) -> None:
         if self.stage not in ("base", "residual"):
@@ -416,7 +421,22 @@ def __post_init__(self) -> None:
                 self.qualified_base_selection_sha256,
                 "qualified_base_selection_sha256",
             )
-        if self.schema != "phaseset-training-initialization-binding-v1":
+        plan_sha256 = self.periodic_descriptor_capture_plan_sha256
+        if plan_sha256 is not None:
+            if self.stage != "residual":
+                raise TrainingRuntimeError(
+                    "base initialization cannot bind a periodic descriptor capture plan"
+                )
+            _lower_sha256(
+                plan_sha256,
+                "periodic_descriptor_capture_plan_sha256",
+            )
+        expected_schema = (
+            CACHED_INITIALIZATION_SCHEMA
+            if plan_sha256 is not None
+            else "phaseset-training-initialization-binding-v1"
+        )
+        if self.schema != expected_schema:
             raise TrainingRuntimeError("initialization binding schema changed")
 
     def canonical_bytes(self) -> bytes:
@@ -435,6 +455,10 @@ def canonical_bytes(self) -> bytes:
             "stage": self.stage,
             "system_id": self.system_id,
         }
+        if self.periodic_descriptor_capture_plan_sha256 is not None:
+            value["periodic_descriptor_capture_plan_sha256"] = (
+                self.periodic_descriptor_capture_plan_sha256
+            )
         return json.dumps(value, sort_keys=True, separators=(",", ":")).encode("ascii") + b"\n"
 
     @property
@@ -482,9 +506,7 @@ def __post_init__(self) -> None:
             _lower_sha256(getattr(self, name), name)
         if any(parameter.requires_grad for parameter in self.group_base.parameters()):
             raise TrainingRuntimeError("qualified frozen base parameters must be frozen")
-        if type(self.frozen_logit_scale) is not float or not math.isfinite(
-            self.frozen_logit_scale
-        ):
+        if type(self.frozen_logit_scale) is not float or not math.isfinite(self.frozen_logit_scale):
             raise TrainingRuntimeError("qualified frozen base logit scale must be finite float")
         if (
             _frozen_base_score_state_sha256(
@@ -576,9 +598,7 @@ def __init__(
             raise TypeError("retrieval_head must be PhaseSetRetrievalHead")
         if type(embedding_dim) is not int or embedding_dim < 2:
             raise TypeError("embedding_dim must be an exact int >=2")
-        if type(frozen_base_logit_scale) is not float or not math.isfinite(
-            frozen_base_logit_scale
-        ):
+        if type(frozen_base_logit_scale) is not float or not math.isfinite(frozen_base_logit_scale):
             raise TypeError("frozen_base_logit_scale must be a finite exact float")
         self.frozen_base = frozen_base
         self.periodic_encoder = periodic_encoder
@@ -681,16 +701,21 @@ def scores(
     ) -> Tensor:
         _validate_embedding_pair(base_embeddings, text_embeddings, self.embedding_dim)
         base_scores = (
-            torch.exp(
-                torch.clamp(
-                    self._frozen_base_logit_scale[0],
-                    min=0.0,
-                    max=math.log(100.0),
+            (
+                torch.exp(
+                    torch.clamp(
+                        self._frozen_base_logit_scale[0],
+                        min=0.0,
+                        max=math.log(100.0),
+                    )
                 )
+                * F.normalize(base_embeddings, dim=-1, eps=1e-12)
+                @ F.normalize(text_embeddings, dim=-1, eps=1e-12).T
             )
-            * F.normalize(base_embeddings, dim=-1, eps=1e-12)
-            @ F.normalize(text_embeddings, dim=-1, eps=1e-12).T
-        ).detach().float().contiguous()
+            .detach()
+            .float()
+            .contiguous()
+        )
         retrieval = self.retrieval_head(
             group_tokens.float().contiguous(),
             band_mask.contiguous(),
@@ -700,6 +725,71 @@ def scores(
         return retrieval.scores.contiguous()
 
 
+def _periodic_descriptor_capture_plan_identity(
+    descriptor_plan: object,
+    *,
+    system_id: str,
+    config: TrainingConfig,
+    energy_floors_sha256: str,
+) -> tuple[PeriodicDescriptorCaptureTrainingPlan, str]:
+    """Validate one sealed cache plan without opening a descriptor shard."""
+
+    from .periodic_capture_training_cache import (
+        PeriodicDescriptorCaptureTrainingPlan,
+    )
+
+    if type(descriptor_plan) is not PeriodicDescriptorCaptureTrainingPlan:
+        raise TypeError("descriptor_plan must be exact PeriodicDescriptorCaptureTrainingPlan")
+    if (
+        config.stage != "residual"
+        or config.synthetic_contract
+        or descriptor_plan.system_id != system_id
+        or descriptor_plan.seed != config.seed
+        or descriptor_plan.epochs != config.epochs
+        or descriptor_plan.config_sha256 != config.sha256
+        or descriptor_plan.edge_budget != config.edge_budget
+        or descriptor_plan.energy_floors_sha256 != energy_floors_sha256
+        or descriptor_plan.authority != 0
+        or descriptor_plan.production is not False
+        or descriptor_plan.result_claimed is not False
+    ):
+        raise TrainingRuntimeError(
+            "periodic descriptor capture plan differs from the residual runtime"
+        )
+    try:
+        digest = _lower_sha256(
+            descriptor_plan.sha256,
+            "periodic_descriptor_capture_plan_sha256",
+        )
+    except (TypeError, ValueError) as error:
+        raise TrainingRuntimeError(
+            "periodic descriptor capture plan digest is malformed"
+        ) from error
+    return descriptor_plan, digest
+
+
+def _residual_energy_floors_sha256(
+    system: ResidualRetrievalSystem,
+    config: TrainingConfig,
+) -> str:
+    """Read the exact registered residual floor identity without a forward."""
+
+    from .controls import PhaseSetSystem, _ControlledPhaseSetEncoder
+    from .periodic_descriptor_cache_v2 import energy_floors_sha256
+
+    periodic = system.periodic_encoder
+    if (
+        type(periodic) is not PhaseSetSystem
+        or periodic.system_id != system.system_id
+        or type(periodic.encoder) is not _ControlledPhaseSetEncoder
+        or periodic.encoder.edge_budget != config.edge_budget
+    ):
+        raise TrainingRuntimeError(
+            "cached residual runtime requires the exact registered PhaseSet encoder"
+        )
+    return energy_floors_sha256(periodic.encoder._energy_floors)
+
+
 def _capture_project_method_identities() -> tuple[tuple[type[object], str, object], ...]:
     """Freeze Python method descriptors before a formal system can be constructed."""
 
@@ -755,9 +845,7 @@ def _assert_registered_method_integrity(
 
     _verified_capture_validation_bindings()
     for name, expected in _FORMAL_RUNTIME_CONSTANTS.items():
-        if globals().get(name) != expected or type(globals().get(name)) is not type(
-            expected
-        ):
+        if globals().get(name) != expected or type(globals().get(name)) is not type(expected):
             raise TrainingRuntimeError(f"formal runtime constant changed: {name}")
     for name, expected in _FORMAL_TRAINING_FUNCTION_IDENTITIES:
         if globals().get(name) is not expected:
@@ -829,13 +917,16 @@ def _assert_registered_method_integrity(
 
 
 def _registered_spec_sha256(value: dict[str, object]) -> str:
-    raw = json.dumps(
-        value,
-        sort_keys=True,
-        separators=(",", ":"),
-        ensure_ascii=True,
-        allow_nan=False,
-    ).encode("ascii") + b"\n"
+    raw = (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
     return _sha256(raw)
 
 
@@ -1040,11 +1131,9 @@ def _validate_registered_formal_system(
         system.system_id not in RESIDUAL_SYSTEM_IDS
         or system.embedding_dim != REGISTERED_EMBEDDING_DIM
         or periodic.embedding_dim != REGISTERED_EMBEDDING_DIM
-        or periodic.encoder.half_edge_encoder[0].out_features
-        != REGISTERED_PERIODIC_HIDDEN_DIM
+        or periodic.encoder.half_edge_encoder[0].out_features != REGISTERED_PERIODIC_HIDDEN_DIM
         or periodic.encoder.edge_budget != config.edge_budget
-        or system.retrieval_head.text_band_mlp.embedding_dim
-        != REGISTERED_EMBEDDING_DIM
+        or system.retrieval_head.text_band_mlp.embedding_dim != REGISTERED_EMBEDDING_DIM
         or text_projection[0].out_features != REGISTERED_TEXT_HIDDEN_DIM
         or tuple(frozen_logit.shape) != (1,)
         or frozen_logit.dtype != torch.float32
@@ -1163,9 +1252,7 @@ def audit_residual_training_parameter_counts(
         if not isinstance(system, ResidualRetrievalSystem) or system.system_id != system_id:
             raise TrainingRuntimeError("residual capacity mapping identity mismatch")
         counts[system_id] = sum(
-            parameter.numel()
-            for parameter in system.parameters()
-            if parameter.requires_grad
+            parameter.numel() for parameter in system.parameters() if parameter.requires_grad
         )
     full = counts["08"]
     if full <= 0:
@@ -1244,13 +1331,9 @@ def _validate_registered_residual_capacity_audit(
             "expected capacity audit sha256",
         )
     except TypeError as error:
-        raise TrainingRuntimeError(
-            "expected capacity audit digest is malformed"
-        ) from error
+        raise TrainingRuntimeError("expected capacity audit digest is malformed") from error
     if value.sha256 != expected_capacity:
-        raise TrainingRuntimeError(
-            "capacity audit differs from the trusted expected digest"
-        )
+        raise TrainingRuntimeError("capacity audit differs from the trusted expected digest")
     _validate_qualified_frozen_base(
         qualified_base,
         expected_selection_sha256=expected_qualification_sha256,
@@ -1358,6 +1441,26 @@ class TrainingReport:
     schema: str = REPORT_SCHEMA
 
 
+@dataclass(frozen=True, slots=True)
+class CachedResidualTrainingReport(TrainingReport):
+    """A residual report that binds one complete descriptor-cache plan."""
+
+    periodic_descriptor_capture_plan_sha256: str = ""
+    dataloader_state_sha256: str = ""
+    schema: str = CACHED_REPORT_SCHEMA
+
+    def __post_init__(self) -> None:
+        if self.stage != "residual":
+            raise TrainingRuntimeError("cached descriptor reports must be residual")
+        _lower_sha256(
+            self.periodic_descriptor_capture_plan_sha256,
+            "periodic_descriptor_capture_plan_sha256",
+        )
+        _lower_sha256(self.dataloader_state_sha256, "dataloader_state_sha256")
+        if self.schema != CACHED_REPORT_SCHEMA:
+            raise TrainingRuntimeError("cached descriptor report schema changed")
+
+
 @dataclass(slots=True)
 class _CursorState:
     epoch: int = 0
@@ -1382,14 +1485,32 @@ class _CacheRow:
     rng_before: dict[str, object]
 
 
+def _scoped_capture_validation_functions() -> tuple[object, object, object]:
+    """Return the exact capture callables after their module closes live drift."""
+
+    from .capture_validation import (
+        CaptureValidationSource,
+        _verified_capture_validation_bindings,
+        run_capture_validation,
+        run_capture_validation_cached,
+    )
+
+    checked_source, checked_uncached = _verified_capture_validation_bindings()
+    if (
+        checked_source is not CaptureValidationSource
+        or checked_uncached is not run_capture_validation
+    ):
+        raise TrainingRuntimeError("capture runtime binding verifier changed its ABI")
+    return CaptureValidationSource, run_capture_validation, run_capture_validation_cached
+
+
 def _source_identity(
-    source: object, split: Split,
+    source: object,
+    split: Split,
 ) -> tuple[TrainingDataSource | CaptureValidationSource, str]:
     # Lazy import avoids the capture evaluator's intentional system-interface
     # dependency on this module. Only the concrete val-only source is admitted.
-    from .capture_validation import _verified_capture_validation_bindings
-
-    CaptureValidationSource, _ = _verified_capture_validation_bindings()
+    CaptureValidationSource, _, _ = _scoped_capture_validation_functions()
 
     if type(source) is CaptureValidationSource:
         if split != "val":
@@ -1408,6 +1529,81 @@ def _source_identity(
     return source, digest
 
 
+def _descriptor_plan_sources(
+    plan: PeriodicDescriptorCaptureTrainingPlan,
+    train_source: object,
+    val_source: object,
+) -> tuple[TrainingDataSource, CaptureValidationSource]:
+    """Bind the complete prepared-train and holistic-capture inputs pre-forward."""
+
+    from .prepared_data_v2 import PreparedTrainingDataSourceV2
+
+    CaptureValidationSource, _, _ = _scoped_capture_validation_functions()
+
+    if type(train_source) is not PreparedTrainingDataSourceV2:
+        raise TypeError("cached descriptor training requires exact PreparedTrainingDataSourceV2")
+    if (
+        train_source.split != "train"
+        or train_source.manifest_sha256 != plan.train_source_manifest_sha256
+    ):
+        raise TrainingRuntimeError(
+            "cached descriptor training source differs from the admitted plan"
+        )
+    if type(val_source) is not CaptureValidationSource:
+        raise TypeError("cached descriptor training requires exact CaptureValidationSource")
+    checked = plan.validate_capture_validation_source(val_source)
+    if (
+        checked.split != "val"
+        or checked.manifest_sha256 != plan.capture_source_manifest_sha256
+        or checked.census_sha256 != plan.capture_source_census_sha256
+    ):
+        raise TrainingRuntimeError(
+            "cached capture validation source differs from the admitted plan"
+        )
+    return train_source, checked
+
+
+def _cached_dataloader_state_sha256(
+    train_manifest_sha256: str,
+    val_manifest_sha256: str,
+    descriptor_plan_sha256: str,
+) -> str:
+    value = {
+        "periodic_descriptor_capture_plan_sha256": _lower_sha256(
+            descriptor_plan_sha256,
+            "periodic_descriptor_capture_plan_sha256",
+        ),
+        "schema": "phaseset-training-dataloader-state-v2-cached-descriptor-plan",
+        "train_manifest_sha256": _lower_sha256(
+            train_manifest_sha256,
+            "train_manifest_sha256",
+        ),
+        "val_manifest_sha256": _lower_sha256(
+            val_manifest_sha256,
+            "val_manifest_sha256",
+        ),
+    }
+    raw = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("ascii") + b"\n"
+    return _sha256(raw)
+
+
+def _runtime_descriptor_plan_identity(
+    descriptor_plan: object | None,
+    system: BaseRetrievalSystem | ResidualRetrievalSystem,
+    config: TrainingConfig,
+) -> tuple[PeriodicDescriptorCaptureTrainingPlan | None, str | None]:
+    if descriptor_plan is None:
+        return None, None
+    if not isinstance(system, ResidualRetrievalSystem):
+        raise TrainingRuntimeError("base runtime cannot use a periodic descriptor capture plan")
+    return _periodic_descriptor_capture_plan_identity(
+        descriptor_plan,
+        system_id=system.system_id,
+        config=config,
+        energy_floors_sha256=_residual_energy_floors_sha256(system, config),
+    )
+
+
 def _materialize_epoch(
     source: TrainingDataSource,
     *,
@@ -1505,8 +1701,7 @@ def _capture_macro_bidirectional_r1(
         row = logits[motion_index]
         maximum = torch.max(row)
         tied = tuple(
-            int(index)
-            for index in torch.nonzero(row == maximum, as_tuple=False).flatten().tolist()
+            int(index) for index in torch.nonzero(row == maximum, as_tuple=False).flatten().tolist()
         )
         chosen = min(tied, key=lambda index: text_commitments[index])
         motion_hits.append(int(bool(positive[motion_index, chosen].item())))
@@ -1537,9 +1732,7 @@ def _capture_macro_bidirectional_r1(
             len(text_indices),
         )
         capture_values.append((motion_value + text_value) / 2)
-    return float(
-        sum(capture_values, start=Fraction(0, 1)) / len(capture_values)
-    )
+    return float(sum(capture_values, start=Fraction(0, 1)) / len(capture_values))
 
 
 def _capture_rng() -> dict[str, object]:
@@ -1579,7 +1772,9 @@ def _restore_rng(value: Mapping[str, object]) -> None:
         torch.set_rng_state(cpu_state.cpu())
         cuda_states = value["torch_cuda"]
         if torch.cuda.is_available():
-            if type(cuda_states) is not list or any(type(item) is not Tensor for item in cuda_states):
+            if type(cuda_states) is not list or any(
+                type(item) is not Tensor for item in cuda_states
+            ):
                 raise TypeError
             torch.cuda.set_rng_state_all([item.cpu() for item in cuda_states])
     except (KeyError, TypeError, ValueError, RuntimeError) as error:
@@ -1650,10 +1845,7 @@ def training_system_state_sha256(
         if not (
             optimizable_only
             and isinstance(system, ResidualRetrievalSystem)
-            and (
-                name.startswith("frozen_base.")
-                or name == "_frozen_base_logit_scale"
-            )
+            and (name.startswith("frozen_base.") or name == "_frozen_base_logit_scale")
         )
     }
     if not selected:
@@ -1784,9 +1976,10 @@ class tree. Opaque attributes are excluded rather than represented by an
         "stage": "residual" if isinstance(system, ResidualRetrievalSystem) else "base",
         "system_id": system.system_id,
     }
-    raw = json.dumps(payload, sort_keys=True, separators=(",", ":"), allow_nan=False).encode(
-        "ascii"
-    ) + b"\n"
+    raw = (
+        json.dumps(payload, sort_keys=True, separators=(",", ":"), allow_nan=False).encode("ascii")
+        + b"\n"
+    )
     return _sha256(raw)
 
 
@@ -1826,6 +2019,7 @@ def _construct_seed_bound_system(
     frozen_base_checkpoint_sha256: str | None = None,
     qualified_base_selection_sha256: str | None = None,
     residual_capacity_audit: ResidualCapacityAudit | None = None,
+    periodic_descriptor_capture_plan_sha256: str | None = None,
 ) -> tuple[
     BaseRetrievalSystem | ResidualRetrievalSystem,
     TrainingInitializationBinding,
@@ -1855,6 +2049,14 @@ def _construct_seed_bound_system(
             raise TrainingRuntimeError(
                 "formal residual construction requires an all-system capacity audit"
             )
+        checked_plan_sha256 = (
+            _lower_sha256(
+                periodic_descriptor_capture_plan_sha256,
+                "periodic_descriptor_capture_plan_sha256",
+            )
+            if periodic_descriptor_capture_plan_sha256 is not None
+            else None
+        )
     else:
         if frozen_base_checkpoint_sha256 is not None:
             raise TrainingRuntimeError("base construction cannot bind a frozen checkpoint")
@@ -1864,6 +2066,11 @@ def _construct_seed_bound_system(
             raise TrainingRuntimeError("base construction cannot bind a base selection receipt")
         if residual_capacity_audit is not None:
             raise TrainingRuntimeError("base construction cannot bind a residual capacity audit")
+        if periodic_descriptor_capture_plan_sha256 is not None:
+            raise TrainingRuntimeError(
+                "base construction cannot bind a periodic descriptor capture plan"
+            )
+        checked_plan_sha256 = None
 
     ambient_rng = _capture_rng()
     try:
@@ -1915,6 +2122,12 @@ def _construct_seed_bound_system(
         residual_capacity_audit_sha256=(
             residual_capacity_audit.sha256 if residual_capacity_audit is not None else None
         ),
+        periodic_descriptor_capture_plan_sha256=checked_plan_sha256,
+        schema=(
+            CACHED_INITIALIZATION_SCHEMA
+            if checked_plan_sha256 is not None
+            else "phaseset-training-initialization-binding-v1"
+        ),
     )
     return system, binding
 
@@ -1995,25 +2208,19 @@ def _backend_flag_targets() -> tuple[tuple[object, str, bool], ...]:
         (torch.backends.cudnn, "deterministic", True),
     )
     return tuple(
-        (owner, name, expected)
-        for owner, name, expected in candidates
-        if hasattr(owner, name)
+        (owner, name, expected) for owner, name, expected in candidates if hasattr(owner, name)
     )
 
 
 def _capture_numerical_runtime_flags() -> dict[str, object]:
     flags: dict[str, object] = {
         "deterministic_algorithms": torch.are_deterministic_algorithms_enabled(),
-        "deterministic_warn_only": (
-            torch.is_deterministic_algorithms_warn_only_enabled()
-        ),
+        "deterministic_warn_only": (torch.is_deterministic_algorithms_warn_only_enabled()),
         "float32_matmul_precision": torch.get_float32_matmul_precision(),
         "mha_fastpath_enabled": bool(torch.backends.mha.get_fastpath_enabled()),
     }
     for owner, name, _expected in _backend_flag_targets():
-        owner_name = (
-            "cuda.matmul" if owner is torch.backends.cuda.matmul else "cudnn"
-        )
+        owner_name = "cuda.matmul" if owner is torch.backends.cuda.matmul else "cudnn"
         flags[f"{owner_name}.{name}"] = bool(getattr(owner, name))
     return flags
 
@@ -2028,9 +2235,7 @@ def _install_frozen_numerical_runtime() -> None:
 
 def _restore_numerical_runtime_flags(flags: Mapping[str, object]) -> None:
     for owner, name, _expected in _backend_flag_targets():
-        owner_name = (
-            "cuda.matmul" if owner is torch.backends.cuda.matmul else "cudnn"
-        )
+        owner_name = "cuda.matmul" if owner is torch.backends.cuda.matmul else "cudnn"
         value = flags.get(f"{owner_name}.{name}")
         if type(value) is bool:
             setattr(owner, name, value)
@@ -2055,9 +2260,7 @@ def _assert_frozen_numerical_runtime(device: torch.device) -> None:
         "mha_fastpath_enabled": False,
     }
     for owner, name, value in _backend_flag_targets():
-        owner_name = (
-            "cuda.matmul" if owner is torch.backends.cuda.matmul else "cudnn"
-        )
+        owner_name = "cuda.matmul" if owner is torch.backends.cuda.matmul else "cudnn"
         expected[f"{owner_name}.{name}"] = value
     if observed != expected:
         raise TrainingRuntimeError("frozen numerical runtime flags changed")
@@ -2164,6 +2367,7 @@ def construct_registered_residual_seed_bound_system(
     residual_capacity_audit: ResidualCapacityAudit,
     expected_qualification_sha256: str,
     expected_capacity_audit_sha256: str,
+    descriptor_plan: PeriodicDescriptorCaptureTrainingPlan | None = None,
 ) -> tuple[ResidualRetrievalSystem, TrainingInitializationBinding]:
     """Construct one formal 01--08 residual exclusively from the closed registry."""
 
@@ -2175,6 +2379,18 @@ def construct_registered_residual_seed_bound_system(
         raise TypeError("qualified_base must be exact QualifiedFrozenBase")
     if qualified_base.seed != config.seed:
         raise TrainingRuntimeError("qualified base seed differs from residual seed")
+    checked_plan_sha256: str | None = None
+    if descriptor_plan is not None:
+        from .periodic import validate_energy_floors
+        from .periodic_descriptor_cache_v2 import energy_floors_sha256
+
+        checked_floors = validate_energy_floors(energy_floors)
+        _, checked_plan_sha256 = _periodic_descriptor_capture_plan_identity(
+            descriptor_plan,
+            system_id=system_id,
+            config=config,
+            energy_floors_sha256=energy_floors_sha256(checked_floors),
+        )
     _validate_registered_residual_capacity_audit(
         residual_capacity_audit,
         qualified_base,
@@ -2201,14 +2417,11 @@ def construct_registered_residual_seed_bound_system(
         and residual_capacity_audit.energy_floors_sha256 == floor_sha
         and residual_capacity_audit.frozen_base_checkpoint_sha256
         == qualified_base.checkpoint_sha256
-        and residual_capacity_audit.frozen_base_state_sha256
-        == qualified_base.frozen_state_sha256
+        and residual_capacity_audit.frozen_base_state_sha256 == qualified_base.frozen_state_sha256
         and residual_capacity_audit.qualified_base_selection_sha256
         == qualified_base.selection_receipt_sha256
-        and residual_capacity_audit.code_artifact_sha256
-        == training_code_artifact_sha256()
-        and residual_capacity_audit.environment_sha256
-        == training_environment_sha256()
+        and residual_capacity_audit.code_artifact_sha256 == training_code_artifact_sha256()
+        and residual_capacity_audit.environment_sha256 == training_environment_sha256()
     )
     if not expected_context:
         raise TrainingRuntimeError("residual capacity audit context mismatch")
@@ -2226,12 +2439,11 @@ def construct_registered_residual_seed_bound_system(
         frozen_base_checkpoint_sha256=qualified_base.checkpoint_sha256,
         qualified_base_selection_sha256=qualified_base.selection_receipt_sha256,
         residual_capacity_audit=residual_capacity_audit,
+        periodic_descriptor_capture_plan_sha256=checked_plan_sha256,
     )
     if not isinstance(system, ResidualRetrievalSystem):
         raise AssertionError("closed residual registry returned the wrong type")
-    if residual_capacity_audit.behavior_for(system_id) != training_system_behavior_sha256(
-        system
-    ):
+    if residual_capacity_audit.behavior_for(system_id) != training_system_behavior_sha256(system):
         raise TrainingRuntimeError("residual behavior differs from the capacity cohort")
     return system, binding
 
@@ -2255,9 +2467,7 @@ def _atomic_torch_save(path: Path, payload: dict[str, object]) -> CheckpointArti
             # cannot overwrite a checkpoint won by a concurrent writer.
             os.link(temporary, path, follow_symlinks=False)
         except FileExistsError as error:
-            raise TrainingCheckpointError(
-                "immutable checkpoint target already exists"
-            ) from error
+            raise TrainingCheckpointError("immutable checkpoint target already exists") from error
         except OSError as error:
             if path.exists() or path.is_symlink():
                 raise TrainingCheckpointError(
@@ -2338,9 +2548,7 @@ def _validate_qualified_frozen_base(
                 "expected base selection sha256",
             )
         except TypeError as error:
-            raise TrainingCheckpointError(
-                "expected base selection digest is malformed"
-            ) from error
+            raise TrainingCheckpointError("expected base selection digest is malformed") from error
         if selection != trusted_selection:
             raise TrainingCheckpointError(
                 "qualified base differs from the trusted selection digest"
@@ -2370,9 +2578,8 @@ def _validate_qualified_frozen_base(
     payload_state_digest = payload.get("state_digest")
     payload_without_digest = dict(payload)
     payload_without_digest.pop("state_digest", None)
-    if (
-        type(payload_state_digest) is not str
-        or payload_state_digest != _stable_hash(payload_without_digest)
+    if type(payload_state_digest) is not str or payload_state_digest != _stable_hash(
+        payload_without_digest
     ):
         raise TrainingCheckpointError("qualified base checkpoint state digest mismatch")
     updates_per_epoch = _checkpoint_exact_int(
@@ -2421,9 +2628,7 @@ def _validate_qualified_frozen_base(
     }
     for name, expected in expected_payload.items():
         if payload.get(name) != expected:
-            raise TrainingCheckpointError(
-                f"qualified base checkpoint {name} evidence mismatch"
-            )
+            raise TrainingCheckpointError(f"qualified base checkpoint {name} evidence mismatch")
     if value.code_artifact_sha256 != current_code:
         raise TrainingCheckpointError("qualified base code artifact changed")
     if value.environment_sha256 != current_environment:
@@ -2460,12 +2665,8 @@ def _validate_qualified_frozen_base(
             "system_id": expected_system_id,
         }
     )
-    if training_system_behavior_sha256(behavior_system) != payload.get(
-        "behavior_sha256"
-    ):
-        raise TrainingCheckpointError(
-            "qualified base behavior differs from checkpoint evidence"
-        )
+    if training_system_behavior_sha256(behavior_system) != payload.get("behavior_sha256"):
+        raise TrainingCheckpointError("qualified base behavior differs from checkpoint evidence")
     expected_frozen_state = _frozen_base_score_state_sha256(
         value.group_base,
         value.frozen_logit_scale,
@@ -2503,9 +2704,7 @@ def load_qualified_frozen_base(
     system_id = qualification.winner_system_id
     if system_id not in REGISTERED_BASE_SYSTEM_IDS:
         raise TrainingCheckpointError("qualified base winner is outside the census")
-    expected_checkpoint = qualification.winner_checkpoint_sha256s[
-        OFFICIAL_SEEDS.index(seed)
-    ]
+    expected_checkpoint = qualification.winner_checkpoint_sha256s[OFFICIAL_SEEDS.index(seed)]
     payload, artifact = _load_torch_checkpoint(
         Path(checkpoint_path),
         expected_sha256=expected_checkpoint,
@@ -2826,9 +3025,7 @@ def _validate_restored_optimizer_scheduler(
                 "checkpoint AdamW parameter state is incomplete or malformed"
             )
         if _optimizer_step_value(record["step"]) != global_step:
-            raise TrainingCheckpointError(
-                "checkpoint optimizer step disagrees with global_step"
-            )
+            raise TrainingCheckpointError("checkpoint optimizer step disagrees with global_step")
         for name in ("exp_avg", "exp_avg_sq"):
             moment = record[name]
             if (
@@ -2838,9 +3035,7 @@ def _validate_restored_optimizer_scheduler(
                 or moment.dtype != parameter.dtype
                 or not bool(torch.isfinite(moment).all().item())
             ):
-                raise TrainingCheckpointError(
-                    f"checkpoint AdamW {name} tensor is malformed"
-                )
+                raise TrainingCheckpointError(f"checkpoint AdamW {name} tensor is malformed")
         if bool((record["exp_avg_sq"] < 0).any().item()):  # type: ignore[operator]
             raise TrainingCheckpointError("checkpoint AdamW exp_avg_sq is negative")
 
@@ -2870,7 +3065,9 @@ def _validate_restored_optimizer_scheduler(
     ("_capture_numerical_runtime_flags", _capture_numerical_runtime_flags),
     ("_capture_rng", _capture_rng),
     ("_capture_macro_bidirectional_r1", _capture_macro_bidirectional_r1),
+    ("_cached_dataloader_state_sha256", _cached_dataloader_state_sha256),
     ("_cuda_environment_inventory", _cuda_environment_inventory),
+    ("_descriptor_plan_sources", _descriptor_plan_sources),
     ("_frozen_numerical_runtime", _frozen_numerical_runtime),
     ("_group_effective_batches", _group_effective_batches),
     ("_install_frozen_numerical_runtime", _install_frozen_numerical_runtime),
@@ -2878,8 +3075,15 @@ def _validate_restored_optimizer_scheduler(
     ("_load_torch_checkpoint", _load_torch_checkpoint),
     ("_materialize_epoch", _materialize_epoch),
     ("_positive_mask", _positive_mask),
+    (
+        "_periodic_descriptor_capture_plan_identity",
+        _periodic_descriptor_capture_plan_identity,
+    ),
+    ("_residual_energy_floors_sha256", _residual_energy_floors_sha256),
     ("_restore_numerical_runtime_flags", _restore_numerical_runtime_flags),
     ("_restore_rng", _restore_rng),
+    ("_runtime_descriptor_plan_identity", _runtime_descriptor_plan_identity),
+    ("_scoped_capture_validation_functions", _scoped_capture_validation_functions),
     (
         "_rebuild_registered_residual_capacity_audit",
         _rebuild_registered_residual_capacity_audit,
@@ -2927,6 +3131,7 @@ def __init__(
         *,
         initialization_binding: TrainingInitializationBinding | None = None,
         checkpoint_observer: CheckpointObserver | None = None,
+        descriptor_plan: PeriodicDescriptorCaptureTrainingPlan | None = None,
     ) -> None:
         if type(config) is not TrainingConfig:
             raise TypeError("config must be exact TrainingConfig")
@@ -2944,6 +3149,18 @@ def __init__(
             raise TrainingRuntimeError(
                 "formal training requires a seed-bound initialization binding"
             )
+        checked_plan, checked_plan_sha256 = _runtime_descriptor_plan_identity(
+            descriptor_plan,
+            system,
+            config,
+        )
+        binding_plan_sha256 = (
+            initialization_binding.periodic_descriptor_capture_plan_sha256
+            if initialization_binding is not None
+            else None
+        )
+        if binding_plan_sha256 != checked_plan_sha256:
+            raise TrainingRuntimeError("initialization binding and descriptor plan identity differ")
         if not config.synthetic_contract:
             _validate_registered_formal_system(system, config)
         actual_initial_state = training_system_state_sha256(
@@ -2956,13 +3173,10 @@ def __init__(
                 initialization_binding.stage != config.stage
                 or initialization_binding.seed != config.seed
                 or initialization_binding.system_id != system.system_id
-                or initialization_binding.initial_optimizable_state_sha256
-                != actual_initial_state
+                or initialization_binding.initial_optimizable_state_sha256 != actual_initial_state
                 or initialization_binding.behavior_sha256 != actual_behavior
-                or initialization_binding.code_artifact_sha256
-                != training_code_artifact_sha256()
-                or initialization_binding.environment_sha256
-                != training_environment_sha256()
+                or initialization_binding.code_artifact_sha256 != training_code_artifact_sha256()
+                or initialization_binding.environment_sha256 != training_environment_sha256()
             ):
                 raise TrainingRuntimeError(
                     "initialization binding does not match the live training system"
@@ -2989,6 +3203,8 @@ def __init__(
         self.system = system
         self.config = config
         self.initialization_binding = initialization_binding
+        self.descriptor_plan = checked_plan
+        self.periodic_descriptor_capture_plan_sha256 = checked_plan_sha256
         self.initial_optimizable_state_sha256 = actual_initial_state
         self.behavior_sha256 = actual_behavior
         self.device = torch.device(config.device)
@@ -3006,7 +3222,26 @@ def __init__(
         self._total_steps = 0
         self._numeric_runtime_active = False
 
+    def _assert_live_descriptor_plan(self) -> None:
+        plan, digest = _runtime_descriptor_plan_identity(
+            self.descriptor_plan,
+            self.system,
+            self.config,
+        )
+        binding_digest = (
+            self.initialization_binding.periodic_descriptor_capture_plan_sha256
+            if self.initialization_binding is not None
+            else None
+        )
+        if (
+            plan is not self.descriptor_plan
+            or digest != self.periodic_descriptor_capture_plan_sha256
+            or binding_digest != digest
+        ):
+            raise TrainingRuntimeError("live periodic descriptor capture plan changed")
+
     def _assert_live_formal_binding(self, *, require_initial_state: bool) -> None:
+        self._assert_live_descriptor_plan()
         if self.config.synthetic_contract:
             return
         if self._numeric_runtime_active:
@@ -3021,10 +3256,14 @@ def _assert_live_formal_binding(self, *, require_initial_state: bool) -> None:
             raise TrainingRuntimeError("training environment changed")
         if training_system_behavior_sha256(self.system) != binding.behavior_sha256:
             raise TrainingRuntimeError("live formal behavior diverged from initialization binding")
-        if require_initial_state and training_system_state_sha256(
-            self.system,
-            optimizable_only=True,
-        ) != binding.initial_optimizable_state_sha256:
+        if (
+            require_initial_state
+            and training_system_state_sha256(
+                self.system,
+                optimizable_only=True,
+            )
+            != binding.initial_optimizable_state_sha256
+        ):
             raise TrainingRuntimeError("live formal state diverged before optimizer creation")
         if isinstance(self.system, ResidualRetrievalSystem):
             frozen_state = _frozen_base_score_state_sha256(
@@ -3032,12 +3271,12 @@ def _assert_live_formal_binding(self, *, require_initial_state: bool) -> None:
                 float(self.system._frozen_base_logit_scale.detach().cpu().item()),
             )
             if frozen_state != binding.frozen_base_state_sha256:
-                raise TrainingRuntimeError(
-                    "live frozen base diverged from initialization binding"
-                )
+                raise TrainingRuntimeError("live frozen base diverged from initialization binding")
 
     def _trainable_parameters(self) -> list[nn.Parameter]:
-        parameters = [parameter for parameter in self.system.parameters() if parameter.requires_grad]
+        parameters = [
+            parameter for parameter in self.system.parameters() if parameter.requires_grad
+        ]
         if not parameters:
             raise TrainingRuntimeError("training system has no trainable parameters")
         return parameters
@@ -3081,7 +3320,9 @@ def _seed_new_run(self) -> None:
     def _checkpoint_payload(self) -> dict[str, object]:
         state = self._state
         payload: dict[str, object] = {
-            "schema": CHECKPOINT_SCHEMA,
+            "schema": (
+                CACHED_CHECKPOINT_SCHEMA if self.descriptor_plan is not None else CHECKPOINT_SCHEMA
+            ),
             "authority": 0,
             "production": False,
             "result_claimed": False,
@@ -3153,6 +3394,15 @@ def _checkpoint_payload(self) -> dict[str, object]:
             "scheduler": self._checked_scheduler.state_dict(),
             "rng": _capture_rng(),
         }
+        if self.periodic_descriptor_capture_plan_sha256 is not None:
+            payload["periodic_descriptor_capture_plan_sha256"] = (
+                self.periodic_descriptor_capture_plan_sha256
+            )
+            payload["dataloader_state_sha256"] = _cached_dataloader_state_sha256(
+                self._train_manifest,
+                self._val_manifest,
+                self.periodic_descriptor_capture_plan_sha256,
+            )
         payload["state_digest"] = _stable_hash(payload)
         return payload
 
@@ -3188,8 +3438,14 @@ def _restore_checkpoint(
         if type(digest) is not str or digest != _stable_hash(payload):
             raise TrainingCheckpointError("checkpoint state digest mismatch")
         payload["state_digest"] = digest
+        if self.descriptor_plan is None and "periodic_descriptor_capture_plan_sha256" in payload:
+            raise TrainingCheckpointError(
+                "uncached runtime cannot resume a cached descriptor checkpoint"
+            )
         bindings = {
-            "schema": CHECKPOINT_SCHEMA,
+            "schema": (
+                CACHED_CHECKPOINT_SCHEMA if self.descriptor_plan is not None else CHECKPOINT_SCHEMA
+            ),
             "stage": self.config.stage,
             "seed": self.config.seed,
             "system_id": self.system.system_id,
@@ -3203,6 +3459,15 @@ def _restore_checkpoint(
             "code_artifact_sha256": training_code_artifact_sha256(),
             "environment_sha256": training_environment_sha256(),
         }
+        if self.periodic_descriptor_capture_plan_sha256 is not None:
+            bindings["periodic_descriptor_capture_plan_sha256"] = (
+                self.periodic_descriptor_capture_plan_sha256
+            )
+            bindings["dataloader_state_sha256"] = _cached_dataloader_state_sha256(
+                self._train_manifest,
+                self._val_manifest,
+                self.periodic_descriptor_capture_plan_sha256,
+            )
         if self.initialization_binding is None:
             if any(
                 payload.get(key) is not None
@@ -3231,9 +3496,7 @@ def _restore_checkpoint(
             bindings.update(
                 {
                     "initialization_binding_sha256": self.initialization_binding.sha256,
-                    "initial_optimizable_state_sha256": (
-                        self.initial_optimizable_state_sha256
-                    ),
+                    "initial_optimizable_state_sha256": (self.initial_optimizable_state_sha256),
                     "factory_sha256": self.initialization_binding.factory_sha256,
                     "code_artifact_sha256": self.initialization_binding.code_artifact_sha256,
                     "frozen_base_checkpoint_sha256": (
@@ -3290,6 +3553,19 @@ def _restore_checkpoint(
         self._latest = artifact
         self._assert_live_formal_binding(require_initial_state=False)
 
+    def _encode_cached_training_batch(
+        self,
+        batch: RetrievalTrainingBatch,
+    ) -> tuple[Tensor, Tensor]:
+        if not isinstance(self.system, ResidualRetrievalSystem):
+            raise TrainingRuntimeError("cached descriptor plan requires a residual system")
+        self._assert_live_descriptor_plan()
+        plan = self.descriptor_plan
+        if plan is None:
+            raise TrainingRuntimeError("cached descriptor plan was lost")
+        stream = plan.open_training_batch(batch)
+        return self.system.encode_trainable_cached(batch.groups, stream)
+
     def _prepare_cache(
         self, microbatches: tuple[RetrievalTrainingBatch, ...]
     ) -> tuple[list[_CacheRow], Tensor, tuple[bytes, ...], tuple[bytes, ...]]:
@@ -3306,7 +3582,10 @@ def _prepare_cache(
                     band_mask = None
                     base_embedding = None
                 else:
-                    primary, band_mask = self.system.encode_trainable(batch.groups)
+                    if self.descriptor_plan is None:
+                        primary, band_mask = self.system.encode_trainable(batch.groups)
+                    else:
+                        primary, band_mask = self._encode_cached_training_batch(batch)
                     base_embedding = self.system.encode_frozen_base(batch.groups)
             primary_leaf = primary.detach().float().contiguous().requires_grad_(True)
             rows.append(
@@ -3327,9 +3606,7 @@ def _scores_from_cache(self, rows: list[_CacheRow], text: Tensor) -> Tensor:
         primary = torch.cat([row.primary_leaf for row in rows]).contiguous()
         if isinstance(self.system, BaseRetrievalSystem):
             return self.system.scores(primary, text)
-        masks = torch.cat(
-            [row.band_mask for row in rows if row.band_mask is not None]
-        ).contiguous()
+        masks = torch.cat([row.band_mask for row in rows if row.band_mask is not None]).contiguous()
         bases = torch.cat(
             [row.base_embedding for row in rows if row.base_embedding is not None]
         ).contiguous()
@@ -3349,20 +3626,29 @@ def _train_update(self, microbatches: tuple[RetrievalTrainingBatch, ...]) -> flo
         # dropout-bearing head cannot repeat the same mask on the next update.
         rng_after = _capture_rng()
         loss.backward()
-        for row in rows:
-            gradient = row.primary_leaf.grad
-            if gradient is None:
-                raise TrainingRuntimeError("gradient cache did not receive representation gradients")
-            _restore_rng(row.rng_before)
-            with _autocast_context(self.precision, self.device):
-                if isinstance(self.system, BaseRetrievalSystem):
-                    replay = self.system.encode_trainable(row.batch.groups)
-                else:
-                    replay, replay_mask = self.system.encode_trainable(row.batch.groups)
-                    if row.band_mask is None or not torch.equal(replay_mask, row.band_mask):
-                        raise TrainingRuntimeError("residual replay changed its deterministic band mask")
-            torch.autograd.backward(replay, gradient)
-        _restore_rng(rng_after)
+        try:
+            for row in rows:
+                gradient = row.primary_leaf.grad
+                if gradient is None:
+                    raise TrainingRuntimeError(
+                        "gradient cache did not receive representation gradients"
+                    )
+                _restore_rng(row.rng_before)
+                with _autocast_context(self.precision, self.device):
+                    if isinstance(self.system, BaseRetrievalSystem):
+                        replay = self.system.encode_trainable(row.batch.groups)
+                    else:
+                        if self.descriptor_plan is None:
+                            replay, replay_mask = self.system.encode_trainable(row.batch.groups)
+                        else:
+                            replay, replay_mask = self._encode_cached_training_batch(row.batch)
+                        if row.band_mask is None or not torch.equal(replay_mask, row.band_mask):
+                            raise TrainingRuntimeError(
+                                "residual replay changed its deterministic band mask"
+                            )
+                torch.autograd.backward(replay, gradient)
+        finally:
+            _restore_rng(rng_after)
         gradient_norm = torch.nn.utils.clip_grad_norm_(
             self._trainable_parameters(),
             GRADIENT_CLIP_NORM,
@@ -3379,19 +3665,35 @@ def _validation(
     ) -> tuple[float, float, int, int]:
         from .capture_validation import CaptureValidationSource, run_capture_validation
 
-        if not self.config.synthetic_contract:
-            from .capture_validation import _verified_capture_validation_bindings
+        run_capture_validation_cached = None
 
-            CaptureValidationSource, run_capture_validation = _verified_capture_validation_bindings()
+        if not self.config.synthetic_contract:
+            (
+                CaptureValidationSource,
+                run_capture_validation,
+                run_capture_validation_cached,
+            ) = _scoped_capture_validation_functions()
         if type(source) is CaptureValidationSource:
             checked = CaptureValidationSource(source.split, source.manifest_sha256, source.captures)
             if checked.census_sha256 != self._val_manifest:
                 raise TrainingRuntimeError("capture validation census changed after fit admission")
             edges = tuple(
                 window.groups.actor_counts[0] * (window.groups.actor_counts[0] - 1) // 2
-                for capture in checked.captures for window in capture.windows
+                for capture in checked.captures
+                for window in capture.windows
             )
-            result = run_capture_validation(self.system, self.config, checked)
+            if self.descriptor_plan is None:
+                result = run_capture_validation(self.system, self.config, checked)
+            else:
+                self._assert_live_descriptor_plan()
+                if run_capture_validation_cached is None:
+                    raise TrainingRuntimeError("cached capture validation binding is unavailable")
+                result = run_capture_validation_cached(
+                    self.system,
+                    self.config,
+                    checked,
+                    self.descriptor_plan,
+                )
             if result.source_census_sha256 != self._val_manifest:
                 raise TrainingRuntimeError("scored capture census differs from admitted validation")
             return float(result.primary_capture_r1), result.loss, sum(edges), max(edges)
@@ -3473,7 +3775,7 @@ def _report(self, status: Literal["COMPLETED", "INTERRUPTED"]) -> TrainingReport
             self._state.best_checkpoint_name,
             self._state.best_checkpoint_sha256,
         )
-        return TrainingReport(
+        report = TrainingReport(
             status=status,
             stage=self.config.stage,
             system_id=self.system.system_id,
@@ -3520,6 +3822,22 @@ def _report(self, status: Literal["COMPLETED", "INTERRUPTED"]) -> TrainingReport
             val_manifest_sha256=self._val_manifest,
             last_train_loss=self._state.last_train_loss,
         )
+        if self.periodic_descriptor_capture_plan_sha256 is None:
+            return report
+        values = {
+            field.name: getattr(report, field.name)
+            for field in dataclass_fields(TrainingReport)
+            if field.name != "schema"
+        }
+        return CachedResidualTrainingReport(
+            **values,
+            periodic_descriptor_capture_plan_sha256=(self.periodic_descriptor_capture_plan_sha256),
+            dataloader_state_sha256=_cached_dataloader_state_sha256(
+                self._train_manifest,
+                self._val_manifest,
+                self.periodic_descriptor_capture_plan_sha256,
+            ),
+        )
 
     def fit(
         self,
@@ -3542,9 +3860,23 @@ def fit(
         checkpoints bind the complete source census rather than actor-set IDs.
         """
 
+        if self.descriptor_plan is not None:
+            self._assert_live_descriptor_plan()
+            train_source, val_source = _descriptor_plan_sources(
+                self.descriptor_plan,
+                train_source,
+                val_source,
+            )
         self._assert_live_formal_binding(require_initial_state=True)
         train, self._train_manifest = _source_identity(train_source, "train")
         val, self._val_manifest = _source_identity(val_source, "val")
+        if self.descriptor_plan is not None and (
+            self._train_manifest != self.descriptor_plan.train_source_manifest_sha256
+            or self._val_manifest != self.descriptor_plan.capture_source_census_sha256
+        ):
+            raise TrainingRuntimeError(
+                "runtime data identities differ from the cached descriptor plan"
+            )
         first_epoch = _materialize_epoch(
             train,
             split="train",
@@ -3557,8 +3889,7 @@ def fit(
             self.config.effective_global_batch,
         )
         if not self.config.synthetic_contract and any(
-            sum(batch.motion_count for batch in group)
-            != self.config.effective_global_batch
+            sum(batch.motion_count for batch in group) != self.config.effective_global_batch
             for group in first_groups
         ):
             raise TrainingRuntimeError(
@@ -3574,9 +3905,7 @@ def fit(
                     resume_record,
                 )
             ):
-                raise TrainingCheckpointError(
-                    "resume binding was provided without a checkpoint"
-                )
+                raise TrainingCheckpointError("resume binding was provided without a checkpoint")
             if any(self.checkpoint_directory.glob("checkpoint-*.pt")):
                 raise TrainingCheckpointError("new run requires an empty checkpoint directory")
             self._seed_new_run()
@@ -3588,11 +3917,9 @@ def fit(
                         "formal resume requires a verified attempt-ledger binding"
                     )
                 try:
-                    ledger_sha256 = (
-                        execution_module.verified_resume_checkpoint_payload_sha256(
+                    ledger_sha256 = execution_module.verified_resume_checkpoint_payload_sha256(
                         resume_attempt_root,
                         resume_record,
-                        )
                     )
                 except (ExecutionContractError, TypeError) as error:
                     raise TrainingCheckpointError(
@@ -3612,11 +3939,9 @@ def fit(
                 )
             elif resume_attempt_root is not None:
                 try:
-                    ledger_sha256 = (
-                        execution_module.verified_resume_checkpoint_payload_sha256(
+                    ledger_sha256 = execution_module.verified_resume_checkpoint_payload_sha256(
                         resume_attempt_root,
                         resume_record,
-                        )
                     )
                 except (ExecutionContractError, TypeError) as error:
                     raise TrainingCheckpointError(
@@ -3675,9 +4000,7 @@ def fit(
                             "formal sampler emitted a partial 128-window effective batch"
                         )
                     if self._state.update_index > len(groups):
-                        raise TrainingCheckpointError(
-                            "resumed update cursor exceeds epoch plan"
-                        )
+                        raise TrainingCheckpointError("resumed update cursor exceeds epoch plan")
                     for update_index in range(self._state.update_index, len(groups)):
                         microbatches = groups[update_index]
                         loss = self._train_update(microbatches)
@@ -3691,11 +4014,7 @@ def fit(
                             maximum,
                         )
                         self._state.last_train_loss = loss
-                        if (
-                            self._state.global_step
-                            % self.config.checkpoint_every_updates
-                            == 0
-                        ):
+                        if self._state.global_step % self.config.checkpoint_every_updates == 0:
                             self._write_checkpoint("update")
                         if (
                             stop_after_global_step is not None
@@ -3735,6 +4054,7 @@ def fit(
     "BASE_EPOCHS",
     "BASE_LEARNING_RATE",
     "BaseRetrievalSystem",
+    "CachedResidualTrainingReport",
     "CHECKPOINT_SCHEMA",
     "CheckpointArtifact",
     "CheckpointObserver",
diff --git a/tests/test_cached_residual_lifecycle.py b/tests/test_cached_residual_lifecycle.py
new file mode 100644
index 0000000..2a7588a
--- /dev/null
+++ b/tests/test_cached_residual_lifecycle.py
@@ -0,0 +1,534 @@
+"""Focused engineering checks for the cached residual lifecycle seam.
+
+The fixtures are analytic and grant no data, training, or result authority.
+The complete descriptor plan is the real sealed 20-epoch plan; test-only
+monkeypatches keep checkpoint lifecycle work small without weakening its
+production admission rules.
+"""
+
+from __future__ import annotations
+
+from fractions import Fraction
+import hashlib
+import json
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+from torch import nn
+
+from phaseset_core import capture_validation, execution, prepared_data_v2, training
+from phaseset_core.contracts import PreparedGroupBatch, validate_prepared_group_batch
+from phaseset_core.controls import PhaseSetSystem
+from phaseset_core.models import GroupBaseOutput
+from phaseset_core.objectives import PhaseSetRetrievalHead
+from phaseset_core.periodic_capture_training_cache import (
+    PeriodicCaptureTrainingCacheError,
+)
+from test_periodic_capture_training_cache import _FLOORS, _admit, _bundle
+
+
+def _digest(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+class _TinyFrozenBase(nn.Module):
+    """Small deterministic base used only by the engineering lifecycle tests."""
+
+    def __init__(self, width: int) -> None:
+        super().__init__()
+        self.register_buffer(
+            "template",
+            torch.linspace(0.125, 0.5, width, dtype=torch.float32),
+        )
+
+    def forward(self, groups: PreparedGroupBatch) -> GroupBaseOutput:
+        checked = validate_prepared_group_batch(groups)
+        device = self.template.device
+        actor_mask = torch.tensor(checked.actor_mask, dtype=torch.bool, device=device)
+        group = self.template[None, :].expand(checked.batch_size, -1).contiguous()
+        actors = group[:, None, :].expand(-1, checked.skeletons.shape[1], -1).contiguous()
+        actors = torch.where(actor_mask[..., None], actors, torch.zeros_like(actors))
+        return GroupBaseOutput(group, actors, actor_mask.contiguous())
+
+
+def _tiny_system(system_id: str = "08") -> training.ResidualRetrievalSystem:
+    torch.manual_seed(7301)
+    periodic = PhaseSetSystem(
+        system_id,
+        embedding_dim=4,
+        hidden_dim=4,
+        energy_floors=_FLOORS,
+        edge_budget=32_768,
+    )
+    return training.ResidualRetrievalSystem(
+        _TinyFrozenBase(4),
+        periodic,
+        PhaseSetRetrievalHead(4, text_hidden_dim=4, residual_lambda_init=0.0),
+        embedding_dim=4,
+        frozen_base_logit_scale=1.0,
+    )
+
+
+def _binding(
+    system: training.ResidualRetrievalSystem,
+    config: training.TrainingConfig,
+    plan_sha256: str | None,
+) -> training.TrainingInitializationBinding:
+    return training.TrainingInitializationBinding(
+        stage="residual",
+        seed=config.seed,
+        system_id=system.system_id,
+        factory_sha256=_digest("fixture/factory"),
+        code_artifact_sha256=training.training_code_artifact_sha256(),
+        environment_sha256=training.training_environment_sha256(),
+        behavior_sha256=training.training_system_behavior_sha256(system),
+        initial_optimizable_state_sha256=training.training_system_state_sha256(
+            system,
+            optimizable_only=True,
+        ),
+        frozen_base_checkpoint_sha256=_digest("fixture/base-checkpoint"),
+        frozen_base_state_sha256=training._frozen_base_score_state_sha256(
+            system.frozen_base,
+            float(system._frozen_base_logit_scale.item()),
+        ),
+        qualified_base_selection_sha256=_digest("fixture/base-selection"),
+        residual_capacity_audit_sha256=_digest("fixture/capacity"),
+        periodic_descriptor_capture_plan_sha256=plan_sha256,
+        schema=(
+            training.CACHED_INITIALIZATION_SCHEMA
+            if plan_sha256 is not None
+            else "phaseset-training-initialization-binding-v1"
+        ),
+    )
+
+
+def _runtime(
+    root: Path,
+    plan,
+    monkeypatch: pytest.MonkeyPatch,
+    *,
+    system_id: str = "08",
+) -> training.PhaseSetTrainingRuntime:
+    monkeypatch.setattr(training, "_validate_registered_formal_system", lambda *_: None)
+    system = _tiny_system(system_id)
+    config = plan_config(plan)
+    binding = _binding(system, config, plan.sha256)
+    return training.PhaseSetTrainingRuntime(
+        system,
+        config,
+        root,
+        initialization_binding=binding,
+        descriptor_plan=plan,
+    )
+
+
+def plan_config(plan) -> training.TrainingConfig:
+    return training.TrainingConfig(
+        stage="residual",
+        seed=plan.seed,
+        edge_budget=plan.edge_budget,
+    )
+
+
+def _narrow_text(
+    batch: training.RetrievalTrainingBatch,
+) -> training.RetrievalTrainingBatch:
+    return training.RetrievalTrainingBatch(
+        groups=batch.groups,
+        text_embeddings=batch.text_embeddings[:, :4].clone().contiguous(),
+        motion_positive_ids=batch.motion_positive_ids,
+        text_positive_ids=batch.text_positive_ids,
+        text_commitments=batch.text_commitments,
+        split=batch.split,
+        descriptor_contexts=batch.descriptor_contexts,
+    )
+
+
+def _state(module: nn.Module) -> dict[str, torch.Tensor]:
+    return {name: value.detach().cpu().clone() for name, value in module.state_dict().items()}
+
+
+def _assert_state_equal(
+    left: dict[str, torch.Tensor],
+    right: dict[str, torch.Tensor],
+) -> None:
+    assert left.keys() == right.keys()
+    for name in left:
+        assert torch.equal(left[name], right[name]), name
+
+
+def _tiny_optimizer_update(
+    self: training.PhaseSetTrainingRuntime,
+    _microbatches: tuple[training.RetrievalTrainingBatch, ...],
+) -> float:
+    optimizer = self._checked_optimizer
+    optimizer.zero_grad(set_to_none=True)
+    terms = [parameter.reshape(-1)[0] for parameter in self._trainable_parameters()]
+    loss = torch.stack(terms).sum()
+    loss.backward()
+    optimizer.step()
+    self._checked_scheduler.step()
+    return float(loss.detach().cpu().item())
+
+
+def _fixed_validation(
+    _self: training.PhaseSetTrainingRuntime,
+    _source: object,
+) -> tuple[float, float, int, int]:
+    return 0.5, 0.25, 3, 3
+
+
+def _resume_record() -> execution.ResumeRecord:
+    return execution.ResumeRecord(
+        new_attempt_id="a" * 32,
+        predecessor_attempt_id="b" * 32,
+        run_id="08-seed1729-20990101T000000Z",
+        created_at_utc="2099-01-01T00:00:00Z",
+        predecessor_terminal_sha256=_digest("fixture/terminal"),
+        checkpoint_receipt_sha256=_digest("fixture/checkpoint-receipt"),
+        corrective_change_sha256=_digest("fixture/corrective-change"),
+        retry_class="IMPLEMENTATION",
+    )
+
+
+def test_uncached_initialization_and_checkpoint_keep_v1_shape(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    system = _tiny_system()
+    config = training.TrainingConfig(stage="residual", seed=1729)
+    binding = _binding(system, config, None)
+    value = json.loads(binding.canonical_bytes())
+
+    assert value["schema"] == "phaseset-training-initialization-binding-v1"
+    assert "periodic_descriptor_capture_plan_sha256" not in value
+    assert binding.periodic_descriptor_capture_plan_sha256 is None
+    positional = training.TrainingInitializationBinding(
+        binding.stage,
+        binding.seed,
+        binding.system_id,
+        binding.factory_sha256,
+        binding.code_artifact_sha256,
+        binding.environment_sha256,
+        binding.behavior_sha256,
+        binding.initial_optimizable_state_sha256,
+        binding.frozen_base_checkpoint_sha256,
+        binding.frozen_base_state_sha256,
+        binding.qualified_base_selection_sha256,
+        binding.residual_capacity_audit_sha256,
+        binding.schema,
+    )
+    assert positional == binding
+    monkeypatch.setattr(training, "_validate_registered_formal_system", lambda *_: None)
+    runtime = training.PhaseSetTrainingRuntime(
+        system,
+        config,
+        tmp_path / "uncached",
+        initialization_binding=binding,
+    )
+    runtime._train_manifest = _digest("fixture/train")
+    runtime._val_manifest = _digest("fixture/val")
+    runtime._initialize_optimizer(1)
+    runtime._seed_new_run()
+    artifact = runtime._write_checkpoint("update")
+    payload, _ = training._load_torch_checkpoint(
+        artifact.path,
+        expected_sha256=artifact.sha256,
+    )
+    assert payload["schema"] == training.CHECKPOINT_SCHEMA
+    assert "periodic_descriptor_capture_plan_sha256" not in payload
+
+
+def test_cached_gradient_cache_reopens_stream_for_first_pass_and_recompute(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path / "bundle")
+    plan = _admit(bundle, system_id="08")
+    runtime = _runtime(tmp_path / "runtime", plan, monkeypatch)
+    runtime._initialize_optimizer(1)
+    batch = _narrow_text(next(bundle.train_source.iter_epoch(epoch=0, seed=1729)))
+    calls: list[training.RetrievalTrainingBatch] = []
+    plan_type = type(plan)
+    original = plan_type.open_training_batch
+
+    def counted(self, value):
+        calls.append(value)
+        return original(self, value)
+
+    def forbidden(*_args, **_kwargs):
+        raise AssertionError("cached lifecycle must not call the uncached encoder")
+
+    monkeypatch.setattr(plan_type, "open_training_batch", counted)
+    monkeypatch.setattr(training.ResidualRetrievalSystem, "encode_trainable", forbidden)
+    loss = runtime._train_update((batch,))
+
+    assert np.isfinite(loss)
+    assert calls == [batch, batch]
+
+
+def test_cached_replay_selector_failure_restores_rng_and_never_steps(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path / "bundle")
+    plan = _admit(bundle, system_id="08")
+    runtime = _runtime(tmp_path / "runtime", plan, monkeypatch)
+    runtime._initialize_optimizer(1)
+    batch = _narrow_text(next(bundle.train_source.iter_epoch(epoch=0, seed=1729)))
+    before_parameters = _state(runtime.system)
+    before_scheduler = runtime._checked_scheduler.state_dict()
+    plan_type = type(plan)
+    original_open = plan_type.open_training_batch
+    original_restore = training._restore_rng
+    open_calls = 0
+    restored_rng_sha256: list[str] = []
+
+    def fail_replay(self, value):
+        nonlocal open_calls
+        open_calls += 1
+        if open_calls == 2:
+            raise PeriodicCaptureTrainingCacheError("fixture replay selector failure")
+        return original_open(self, value)
+
+    def record_restore(value):
+        restored_rng_sha256.append(training._stable_hash(value))
+        original_restore(value)
+
+    def forbidden_step(*_args, **_kwargs):
+        raise AssertionError("optimizer step must not run after replay failure")
+
+    monkeypatch.setattr(plan_type, "open_training_batch", fail_replay)
+    monkeypatch.setattr(training, "_restore_rng", record_restore)
+    monkeypatch.setattr(runtime._checked_optimizer, "step", forbidden_step)
+
+    with pytest.raises(
+        PeriodicCaptureTrainingCacheError,
+        match="fixture replay selector failure",
+    ):
+        runtime._train_update((batch,))
+
+    assert open_calls == 2
+    assert len(restored_rng_sha256) == 2
+    assert training._stable_hash(training._capture_rng()) == restored_rng_sha256[-1]
+    _assert_state_equal(before_parameters, _state(runtime.system))
+    assert runtime._checked_scheduler.state_dict() == before_scheduler
+
+
+def test_capture_validation_dispatches_exact_cached_runner(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path / "bundle")
+    plan = _admit(bundle, system_id="08")
+    runtime = _runtime(tmp_path / "runtime", plan, monkeypatch)
+    runtime._val_manifest = bundle.capture_source.census_sha256
+    observed: list[tuple[object, ...]] = []
+
+    def cached(system, config, source, descriptor_plan):
+        observed.append((system, config, source, descriptor_plan))
+        return SimpleNamespace(
+            primary_capture_r1=Fraction(1, 2),
+            loss=0.75,
+            source_census_sha256=source.census_sha256,
+        )
+
+    monkeypatch.setattr(
+        capture_validation,
+        "_verified_capture_validation_bindings",
+        lambda: (
+            capture_validation.CaptureValidationSource,
+            capture_validation.run_capture_validation,
+        ),
+    )
+    monkeypatch.setattr(capture_validation, "run_capture_validation_cached", cached)
+    metric, loss, edges, maximum = runtime._validation(bundle.capture_source)
+
+    expected_edges = tuple(
+        window.groups.actor_counts[0] * (window.groups.actor_counts[0] - 1) // 2
+        for capture in bundle.capture_source.captures
+        for window in capture.windows
+    )
+    assert len(observed) == 1
+    actual_system, actual_config, actual_source, actual_plan = observed[0]
+    assert actual_system is runtime.system
+    assert actual_config is runtime.config
+    assert actual_plan is plan
+    assert type(actual_source) is capture_validation.CaptureValidationSource
+    assert actual_source.split == bundle.capture_source.split
+    assert actual_source.manifest_sha256 == bundle.capture_source.manifest_sha256
+    assert actual_source.census_sha256 == bundle.capture_source.census_sha256
+    assert (metric, loss, edges, maximum) == (
+        0.5,
+        0.75,
+        sum(expected_edges),
+        max(expected_edges),
+    )
+
+
+def test_cached_checkpoint_resume_matches_uninterrupted_and_binds_plan(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path / "bundle")
+    plan = _admit(bundle, system_id="08")
+    source_type = prepared_data_v2.PreparedTrainingDataSourceV2
+    original_iter = source_type.iter_epoch
+
+    def full_effective_batch(self, *, epoch: int, seed: int):
+        values = tuple(original_iter(self, epoch=epoch, seed=seed))
+        assert len(values) == 1 and values[0].motion_count == 1
+        return values * training.EFFECTIVE_GLOBAL_BATCH
+
+    monkeypatch.setattr(source_type, "iter_epoch", full_effective_batch)
+    monkeypatch.setattr(training.PhaseSetTrainingRuntime, "_train_update", _tiny_optimizer_update)
+    monkeypatch.setattr(training.PhaseSetTrainingRuntime, "_validation", _fixed_validation)
+
+    uninterrupted = _runtime(tmp_path / "uninterrupted", plan, monkeypatch)
+    uninterrupted_report = uninterrupted.fit(
+        bundle.train_source,
+        bundle.capture_source,
+        stop_after_global_step=2,
+    )
+
+    partial = _runtime(tmp_path / "resumed", plan, monkeypatch)
+    partial_report = partial.fit(
+        bundle.train_source,
+        bundle.capture_source,
+        stop_after_global_step=1,
+    )
+    monkeypatch.setattr(
+        training.execution_module,
+        "verified_resume_checkpoint_payload_sha256",
+        lambda *_args: partial_report.latest_checkpoint.sha256,
+    )
+    resumed = _runtime(tmp_path / "resumed", plan, monkeypatch)
+    resumed_report = resumed.fit(
+        bundle.train_source,
+        bundle.capture_source,
+        resume_checkpoint=partial_report.latest_checkpoint.path,
+        resume_checkpoint_sha256=partial_report.latest_checkpoint.sha256,
+        resume_attempt_root=tmp_path / "attempt",
+        resume_record=_resume_record(),
+        stop_after_global_step=2,
+    )
+
+    _assert_state_equal(_state(uninterrupted.system), _state(resumed.system))
+    assert training._stable_hash(uninterrupted._checked_optimizer.state_dict()) == (
+        training._stable_hash(resumed._checked_optimizer.state_dict())
+    )
+    assert uninterrupted._checked_scheduler.state_dict() == (
+        resumed._checked_scheduler.state_dict()
+    )
+    assert uninterrupted_report.global_step == resumed_report.global_step == 2
+    assert uninterrupted_report.periodic_descriptor_capture_plan_sha256 == plan.sha256
+    assert resumed_report.periodic_descriptor_capture_plan_sha256 == plan.sha256
+    assert uninterrupted_report.dataloader_state_sha256 == (resumed_report.dataloader_state_sha256)
+    assert uninterrupted_report.schema == training.CACHED_REPORT_SCHEMA
+    payload, _artifact = training._load_torch_checkpoint(
+        resumed_report.latest_checkpoint.path,
+        expected_sha256=resumed_report.latest_checkpoint.sha256,
+    )
+    assert payload["schema"] == training.CACHED_CHECKPOINT_SCHEMA
+    assert payload["periodic_descriptor_capture_plan_sha256"] == plan.sha256
+    assert payload["dataloader_state_sha256"] == resumed_report.dataloader_state_sha256
+
+
+def test_cached_checkpoint_digest_drift_and_uncached_resume_are_rejected(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path / "bundle")
+    plan = _admit(bundle, system_id="08")
+    runtime = _runtime(tmp_path / "runtime", plan, monkeypatch)
+    runtime._train_manifest = plan.train_source_manifest_sha256
+    runtime._val_manifest = plan.capture_source_census_sha256
+    runtime._initialize_optimizer(1)
+    runtime._seed_new_run()
+    artifact = runtime._write_checkpoint("update")
+    payload, _ = training._load_torch_checkpoint(
+        artifact.path,
+        expected_sha256=artifact.sha256,
+    )
+    payload.pop("state_digest")
+    payload["periodic_descriptor_capture_plan_sha256"] = _digest("wrong-plan")
+    payload["state_digest"] = training._stable_hash(payload)
+    changed = training._atomic_torch_save(tmp_path / "changed.pt", payload)
+
+    replacement = _runtime(tmp_path / "replacement", plan, monkeypatch)
+    replacement._train_manifest = plan.train_source_manifest_sha256
+    replacement._val_manifest = plan.capture_source_census_sha256
+    replacement._initialize_optimizer(1)
+    with pytest.raises(training.TrainingCheckpointError, match="plan_sha256 binding"):
+        replacement._restore_checkpoint(changed.path, expected_sha256=changed.sha256)
+
+    uncached_system = _tiny_system()
+    uncached = training.PhaseSetTrainingRuntime(
+        uncached_system,
+        bundle.config,
+        tmp_path / "uncached",
+        initialization_binding=_binding(uncached_system, bundle.config, None),
+    )
+    uncached._train_manifest = plan.train_source_manifest_sha256
+    uncached._val_manifest = plan.capture_source_census_sha256
+    uncached._initialize_optimizer(1)
+    with pytest.raises(training.TrainingCheckpointError, match="uncached runtime"):
+        uncached._restore_checkpoint(artifact.path, expected_sha256=artifact.sha256)
+
+
+def test_plan_source_and_system_mismatch_fail_before_fit_forward(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path / "bundle")
+    plan = _admit(bundle, system_id="08")
+    runtime = _runtime(tmp_path / "runtime", plan, monkeypatch)
+    changed_capture = capture_validation.CaptureValidationSource(
+        "val",
+        _digest("changed-capture-manifest"),
+        bundle.capture_source.captures,
+    )
+
+    def forbidden(*_args, **_kwargs):
+        raise AssertionError("invalid sources must fail before formal model checks")
+
+    monkeypatch.setattr(runtime, "_assert_live_formal_binding", forbidden)
+    with pytest.raises(PeriodicCaptureTrainingCacheError):
+        runtime.fit(bundle.train_source, changed_capture)
+
+    system01 = _tiny_system("01")
+    binding01 = _binding(system01, bundle.config, plan.sha256)
+    with pytest.raises(training.TrainingRuntimeError, match="differs from the residual runtime"):
+        training.PhaseSetTrainingRuntime(
+            system01,
+            bundle.config,
+            tmp_path / "system01",
+            initialization_binding=binding01,
+            descriptor_plan=plan,
+        )
+
+
+def test_cached_binding_digest_is_required_before_formal_validation(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    bundle = _bundle(tmp_path / "bundle")
+    plan = _admit(bundle, system_id="08")
+    system = _tiny_system()
+    binding = _binding(system, bundle.config, None)
+
+    def forbidden(*_args, **_kwargs):
+        raise AssertionError("binding mismatch must precede formal model validation")
+
+    monkeypatch.setattr(training, "_validate_registered_formal_system", forbidden)
+    with pytest.raises(training.TrainingRuntimeError, match="binding and descriptor plan"):
+        training.PhaseSetTrainingRuntime(
+            system,
+            bundle.config,
+            tmp_path / "runtime",
+            initialization_binding=binding,
+            descriptor_plan=plan,
+        )
diff --git a/tests/test_phaseset_capture_validation_cached.py b/tests/test_phaseset_capture_validation_cached.py
index 88b79fb..8ba1dc9 100644
--- a/tests/test_phaseset_capture_validation_cached.py
+++ b/tests/test_phaseset_capture_validation_cached.py
@@ -151,7 +151,11 @@ def counted_scores(self, *args, **kwargs):
     assert calls == {"cached": 0, "uncached": 0, "scores": 0}
 
     first_key = plan.capture_rows[0].cache_key_sha256
-    (bundle.capture_cache.root / "shards" / f"{first_key}.pdc2").unlink()
+    shard = bundle.capture_cache.root / "shards" / f"{first_key}.pdc2"
+    # The fixture deliberately removes an immutable shard; Windows requires
+    # clearing its read-only attribute before the missing-file fault injection.
+    shard.chmod(0o600)
+    shard.unlink()
     with pytest.raises(DescriptorCacheV2Error):
         capture_validation.run_capture_validation_cached(
             system,
diff --git a/tests/test_phaseset_host_base_qualification.py b/tests/test_phaseset_host_base_qualification.py
index 2f94955..d07624b 100644
--- a/tests/test_phaseset_host_base_qualification.py
+++ b/tests/test_phaseset_host_base_qualification.py
@@ -143,11 +143,6 @@ class _FakeCaptureValidationSource:
     manifest_sha256 = _digest(b"capture-source")
 
 
-@contextmanager
-def _no_timeout(_seconds: int):
-    yield
-
-
 def _emit_completed_journal(observer, session_sha256: str) -> None:
     observer.session_start(
         admission_observation_sha256=_digest(b"admission observation"),
@@ -200,6 +195,39 @@ def _emit_completed_journal(observer, session_sha256: str) -> None:
 def _patch_pre_latency(monkeypatch: pytest.MonkeyPatch) -> tuple[object, object]:
     cohort = SimpleNamespace(source_tree_sha256=_digest(b"source"))
     scored = SimpleNamespace(sha256=_digest(b"scored"))
+    process_thread = object()
+    signal_facade = SimpleNamespace(
+        SIGALRM=14,
+        ITIMER_REAL=0,
+        getitimer=lambda _which: (0.0, 0.0),
+        getsignal=lambda _signal_number: object(),
+        setitimer=lambda _which, _seconds, _interval: None,
+        signal=lambda _signal_number, _handler: None,
+    )
+    threading_facade = SimpleNamespace(
+        current_thread=lambda: process_thread,
+        main_thread=lambda: process_thread,
+    )
+    real_timer_preflight = controller._validate_formal_latency_timer_support
+    real_timer_timeout = controller._formal_latency_timeout
+
+    def portable_timer_preflight(**_ignored: object) -> None:
+        real_timer_preflight(
+            _os_name="posix",
+            _signal_api=signal_facade,
+            _threading_api=threading_facade,
+        )
+
+    @contextmanager
+    def portable_timer_timeout(seconds: int):
+        with real_timer_timeout(
+            seconds,
+            _os_name="posix",
+            _signal_api=signal_facade,
+            _threading_api=threading_facade,
+        ):
+            yield
+
     monkeypatch.setattr(controller, "CaptureValidationSource", _FakeCaptureValidationSource)
     monkeypatch.setattr(
         controller.resolver_module,
@@ -216,7 +244,12 @@ def _patch_pre_latency(monkeypatch: pytest.MonkeyPatch) -> tuple[object, object]
         "_resolved_cohort_sha256",
         lambda value: _digest(b"resolved") if value is cohort else pytest.fail("cohort drift"),
     )
-    monkeypatch.setattr(controller, "_formal_latency_timeout", _no_timeout)
+    monkeypatch.setattr(
+        controller,
+        "_validate_formal_latency_timer_support",
+        portable_timer_preflight,
+    )
+    monkeypatch.setattr(controller, "_formal_latency_timeout", portable_timer_timeout)
     return cohort, scored
 
 
@@ -1116,6 +1149,142 @@ def fake_controller(attempt_root: Path, **kwargs):
     assert call["adapter_sha256"] == call["adapter_sha256"].lower()
 
 
+@pytest.mark.parametrize(
+    ("failure_site", "expected_holds"),
+    [
+        (
+            "source",
+            [
+                "HOLD_PREPARED_DATA_MANIFEST_ABSENT",
+                "HOLD_EXTERNAL_RECEIPTS_UNVERIFIED",
+            ],
+        ),
+        ("output", ["HOLD_EXTERNAL_RECEIPTS_UNVERIFIED"]),
+        ("lease-acquire", ["HOLD_EXTERNAL_RECEIPTS_UNVERIFIED"]),
+        ("lease-release", ["HOLD_EXTERNAL_RECEIPTS_UNVERIFIED"]),
+    ],
+)
+def test_cli_qualify_base_post_admission_runtime_drift_is_hold_not_contract_error(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    failure_site: str,
+    expected_holds: list[str],
+) -> None:
+    terminal_root = tmp_path / "terminals"
+    output_root = tmp_path / "outputs"
+    journal_parent = tmp_path / "journals"
+    terminal_root.mkdir()
+    output_root.mkdir()
+    journal_parent.mkdir()
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    qualification = controller.HostBaseQualificationConfig(
+        registry_path=registry,
+        registry_sha256=_digest(registry.read_bytes()),
+        output_root=output_root,
+        journal_parent=journal_parent,
+        session_id=f"runtime-drift-{failure_site}",
+        registered_cuda_uuid=CUDA_UUID,
+        latency_protocol_sha256=SHA_A,
+        launcher_sha256=SHA_B,
+        wall_timeout_seconds=7200,
+    )
+    receipts = _receipts()
+    fake_val = _FakeCaptureValidationSource()
+    fake_train = SimpleNamespace(manifest_sha256=_digest(b"train"))
+    source_calls = 0
+
+    class FakeConfig:
+        source_tree_sha256 = _digest(b"source")
+        device = "cuda"
+        request_bf16 = False
+        bf16_runtime_qualified = False
+        edge_budget = 32
+        checkpoint_every_updates = 1
+        base_qualification = qualification
+
+        def load_receipts(self):
+            return receipts
+
+        def validate_runtime(self):
+            return None
+
+        def validate_base_qualification_request(self, request):
+            assert request.command == "qualify-base"
+            assert request.terminal_root == terminal_root
+
+        def load_sources(self):
+            nonlocal source_calls
+            source_calls += 1
+            if source_calls == 1 and failure_site == "output":
+                (output_root / qualification.session_id).mkdir()
+            if source_calls == 2 and failure_site == "source":
+                raise host.HostConfigurationError(
+                    "prepared manifest changed after admission"
+                )
+            return fake_train, fake_val
+
+    class FailingReleaseLease:
+        def release(self):
+            raise OSError("lease release failed")
+
+    if failure_site == "lease-acquire":
+        monkeypatch.setattr(
+            host._AttemptLease,
+            "acquire",
+            lambda _directory: (_ for _ in ()).throw(
+                host.HostConfigurationError("lease acquisition failed")
+            ),
+        )
+    elif failure_site == "lease-release":
+        monkeypatch.setattr(
+            host._AttemptLease,
+            "acquire",
+            lambda _directory: FailingReleaseLease(),
+        )
+
+    def controller_call(*_args, **kwargs):
+        if failure_site != "lease-release":
+            pytest.fail("controller ran after pre-controller runtime drift")
+        artifact = kwargs["output_directory"] / "retained-controller-hold.json"
+        artifact.write_bytes(b"retained controller hold\n")
+        return controller.HostBaseQualificationRun(
+            execution=BackendExecution(
+                outcome="HELD",
+                completed_at_utc="2026-09-08T00:00:01Z",
+                artifacts=(artifact,),
+            ),
+            authentication=None,
+            journal_path=None,
+            registry_sha256=qualification.registry_sha256,
+            formal=True,
+        )
+
+    monkeypatch.setattr(host.HostConfig, "load", lambda _path: FakeConfig())
+    monkeypatch.setattr(host, "CaptureValidationSource", _FakeCaptureValidationSource)
+    monkeypatch.setattr(host, "run_host_base_qualification", controller_call)
+    output = io.StringIO()
+    errors = io.StringIO()
+    exit_code = cli.main(
+        [
+            "qualify-base",
+            "--config",
+            str(PUBLIC_CONFIG),
+            "--terminal-root",
+            str(terminal_root),
+        ],
+        runtime_adapter_factory=host.RuntimeAdapterFactory(tmp_path / "host.json"),
+        stdout=output,
+        stderr=errors,
+    )
+    assert exit_code == cli.EXIT_HOLD
+    assert errors.getvalue() == ""
+    emitted = json.loads(output.getvalue())
+    assert emitted["status"] == "HELD"
+    assert emitted["hold_codes"] == expected_holds
+    assert source_calls == 2
+
+
 def test_structural_completion_reaches_production_recompute_and_callback(
     tmp_path: Path,
     monkeypatch: pytest.MonkeyPatch,
@@ -1249,6 +1418,50 @@ def structural_controller(attempt_root: Path, **kwargs):
     assert callback_observed == ["accepted"]
 
 
+def test_formal_timer_runtime_hold_precedes_registry_and_scoring(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    terminal_root = tmp_path / "terminals"
+    terminal_root.mkdir()
+    registry = tmp_path / "registry.json"
+    registry.write_bytes(_registry_raw())
+    config = _controller_config(tmp_path, registry)
+    output_directory = config.output_root / config.session_id
+    output_directory.mkdir()
+    monkeypatch.setattr(controller, "CaptureValidationSource", _FakeCaptureValidationSource)
+    monkeypatch.setattr(
+        controller,
+        "_validate_formal_latency_timer_support",
+        lambda: (_ for _ in ()).throw(
+            controller._FormalLatencyTimerHold("timer unavailable")
+        ),
+    )
+    monkeypatch.setattr(
+        controller,
+        "load_base_attempt_registry",
+        lambda *_args, **_kwargs: pytest.fail("registry opened before timer preflight"),
+    )
+    monkeypatch.setattr(
+        controller.scoring_module,
+        "score_resolved_base_cohort",
+        lambda *_args, **_kwargs: pytest.fail("scorer ran before timer preflight"),
+    )
+    result = controller.run_host_base_qualification(
+        terminal_root,
+        config=config,
+        admission=_admission(),
+        validation_source=_FakeCaptureValidationSource(),
+        output_directory=output_directory,
+        adapter_sha256=SHA_C,
+    )
+    assert result.execution.outcome == "HELD"
+    failure = json.loads((output_directory / "failure.json").read_bytes())
+    assert failure["stage"] == "LATENCY_PREFLIGHT"
+    assert failure["failure_code"] == "LATENCY_RUNTIME_DRIFT"
+    assert not (output_directory / "controller-start.json").exists()
+
+
 def test_process_timer_refuses_inherited_deadline_without_mutating_os() -> None:
     signal_api = SimpleNamespace(
         SIGALRM=14,
@@ -1258,7 +1471,7 @@ def test_process_timer_refuses_inherited_deadline_without_mutating_os() -> None:
         setitimer=lambda *_args: pytest.fail("inherited timer was replaced"),
         signal=lambda *_args: pytest.fail("inherited handler was replaced"),
     )
-    with pytest.raises(controller.HostBaseQualificationError, match="inherited"):
+    with pytest.raises(controller._FormalLatencyTimerHold, match="inherited"):
         with controller._formal_latency_timeout(
             3600,
             _os_name="posix",
@@ -1268,10 +1481,130 @@ def test_process_timer_refuses_inherited_deadline_without_mutating_os() -> None:
 
 
 def test_process_timer_fails_closed_when_platform_has_no_interval_timer() -> None:
-    with pytest.raises(controller.HostBaseQualificationError, match="requires POSIX"):
+    with pytest.raises(controller._FormalLatencyTimerHold, match="requires POSIX"):
         with controller._formal_latency_timeout(
             3600,
             _os_name="nt",
             _signal_api=SimpleNamespace(),
         ):
             pytest.fail("unsupported timer platform was admitted")
+
+
+def test_process_timer_preflight_requires_the_python_main_thread() -> None:
+    signal_api = SimpleNamespace(
+        SIGALRM=14,
+        ITIMER_REAL=0,
+        getitimer=lambda _timer: (0.0, 0.0),
+        getsignal=lambda _signal: None,
+        setitimer=lambda *_args: None,
+        signal=lambda *_args: None,
+    )
+    current = object()
+    main = object()
+    with pytest.raises(controller._FormalLatencyTimerHold, match="main thread"):
+        controller._validate_formal_latency_timer_support(
+            _os_name="posix",
+            _signal_api=signal_api,
+            _threading_api=SimpleNamespace(
+                current_thread=lambda: current,
+                main_thread=lambda: main,
+            ),
+        )
+
+
+@pytest.mark.parametrize("failure_site", ["install", "cleanup"])
+def test_process_timer_restores_handler_after_timer_api_failure(
+    failure_site: str,
+) -> None:
+    original_handler = object()
+
+    class SignalFacade:
+        SIGALRM = 14
+        ITIMER_REAL = 0
+
+        def __init__(self) -> None:
+            self.handler = original_handler
+            self.armed = False
+
+        def getitimer(self, _timer):
+            return (0.0, 0.0)
+
+        def getsignal(self, _signal):
+            return self.handler
+
+        def signal(self, _signal, handler):
+            self.handler = handler
+
+        def setitimer(self, _timer, seconds, _interval):
+            if seconds > 0.0:
+                if failure_site == "install":
+                    raise OSError("arm failed")
+                self.armed = True
+                return None
+            self.armed = False
+            if failure_site == "cleanup":
+                raise OSError("disarm failed")
+            return None
+
+    facade = SignalFacade()
+    with pytest.raises(controller._FormalLatencyTimerHold):
+        with controller._formal_latency_timeout(
+            3600,
+            _os_name="posix",
+            _signal_api=facade,
+        ):
+            assert failure_site == "cleanup"
+    assert facade.handler is original_handler
+    assert facade.armed is False
+
+
+def test_process_timer_cleanup_does_not_replace_primary_error() -> None:
+    original_handler = object()
+
+    class PrimaryBoom(RuntimeError):
+        pass
+
+    class CleanupFailingSignalFacade:
+        SIGALRM = 14
+        ITIMER_REAL = 0
+
+        def __init__(self) -> None:
+            self.handler = original_handler
+
+        def getitimer(self, _timer):
+            return (0.0, 0.0)
+
+        def getsignal(self, _signal):
+            return self.handler
+
+        def signal(self, _signal, handler):
+            self.handler = handler
+
+        def setitimer(self, _timer, seconds, _interval):
+            if seconds == 0.0:
+                raise OSError("disarm failed")
+            return None
+
+    facade = CleanupFailingSignalFacade()
+    with pytest.raises(PrimaryBoom, match="primary"):
+        with controller._formal_latency_timeout(
+            3600,
+            _os_name="posix",
+            _signal_api=facade,
+        ):
+            raise PrimaryBoom("primary")
+    assert facade.handler is original_handler
+
+
+@pytest.mark.skipif(
+    controller.os.name != "posix",
+    reason="the real interval-timer API is POSIX-only",
+)
+def test_real_posix_interval_timer_restores_process_state() -> None:
+    if controller.signal.getitimer(controller.signal.ITIMER_REAL) != (0.0, 0.0):
+        pytest.skip("test process already has an interval timer")
+    original_handler = controller.signal.getsignal(controller.signal.SIGALRM)
+    with controller._formal_latency_timeout(60):
+        assert controller.signal.getitimer(controller.signal.ITIMER_REAL)[0] > 0.0
+    assert controller.signal.getitimer(controller.signal.ITIMER_REAL) == (0.0, 0.0)
+    assert controller.signal.getsignal(controller.signal.SIGALRM) == original_handler
diff --git a/tests/test_phaseset_periodic_cache_execution.py b/tests/test_phaseset_periodic_cache_execution.py
new file mode 100644
index 0000000..3484011
--- /dev/null
+++ b/tests/test_phaseset_periodic_cache_execution.py
@@ -0,0 +1,419 @@
+"""Focused software contracts for the private periodic-cache builder.
+
+These fixtures are analytic and carry no empirical result.  Numerical execution
+is reserved for the registered server test lane.
+"""
+
+from __future__ import annotations
+
+from dataclasses import replace
+import hashlib
+import io
+import json
+from pathlib import Path
+import shutil
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from phaseset_core import (
+    capture_validation,
+    execution,
+    experiments,
+    periodic_cache_execution as cache_execution,
+    training,
+)
+from phaseset_core.periodic_cache_execution import (
+    CACHEABLE_SYSTEM_STREAMS,
+    PeriodicCacheExecutionBounds,
+    PeriodicCacheExecutionError,
+    build_periodic_descriptor_cache_for_seed,
+    consume_periodic_descriptor_cache_build,
+    load_qualified_energy_floor_input,
+)
+from test_periodic_capture_training_cache import _write_capture_source
+from test_periodic_training_cache import _write_sources
+
+
+_SEED = 1729
+_FLOORS = np.ascontiguousarray(
+    np.asarray([(index + 1) * 1.0e-12 for index in range(6)], dtype=np.float64)
+)
+
+
+def _sha(raw: bytes) -> str:
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _digest(label: str) -> str:
+    return _sha(label.encode("ascii"))
+
+
+def _qualification(
+    checkpoint_sha256s: dict[int, str],
+) -> tuple[experiments.BaseQualification, str]:
+    scores = {"B0": (60, 62, 58), "B1": (70, 71, 69), "B2": (65, 66, 64)}
+    rows: list[experiments.BaseScore] = []
+    for index, run_id in enumerate(experiments.base_run_ids(), start=1):
+        _, seed, system_id = experiments.parse_run_id(run_id)
+        checkpoint = (
+            checkpoint_sha256s[seed] if system_id == "B1" else _digest(f"checkpoint/{run_id}")
+        )
+        rows.append(
+            experiments.BaseScore(
+                run_id=run_id,
+                bidirectional_r1_numerator=scores[system_id][experiments.SEEDS.index(seed)],
+                bidirectional_r1_denominator=100,
+                parameter_count={"B0": 10_000, "B1": 11_000, "B2": 12_000}[system_id],
+                frozen_runtime_latency_ns={
+                    "B0": 1_000,
+                    "B1": 1_100,
+                    "B2": 1_200,
+                }[system_id],
+                terminal_sha256=_digest(f"terminal/{index}"),
+                selected_checkpoint_sha256=checkpoint,
+                split="validation",
+                validation_manifest_sha256=_digest("capture-validation-manifest"),
+                query_census_sha256=_digest("capture-validation-census"),
+                evaluator_sha256=_digest("capture-evaluator"),
+                score_artifact_sha256=_digest(f"score/{index}"),
+            )
+        )
+    qualification = experiments.qualify_base(tuple(rows))
+    raw = experiments.canonical_base_qualification_bytes(qualification)
+    return qualification, _sha(raw)
+
+
+def _floor_payload(*, extra_key: bool = False) -> bytes:
+    stream = io.BytesIO()
+    if extra_key:
+        np.savez(stream, energy_floors=_FLOORS, ignored=np.zeros(1, dtype=np.float64))
+    else:
+        np.savez(stream, energy_floors=_FLOORS)
+    return stream.getvalue()
+
+
+def _write_floor_root(
+    root: Path,
+    *,
+    qualification: experiments.BaseQualification,
+    qualification_sha256: str,
+    payload: bytes | None = None,
+) -> Path:
+    root.mkdir()
+    raw_payload = _floor_payload() if payload is None else payload
+    record = execution.PeriodicCacheRecord(
+        seed=_SEED,
+        qualified_base_system_id=qualification.winner_system_id,
+        source_run_id=(
+            f"phaseset-run-v1/BASE_QUALIFICATION/{_SEED}/{qualification.winner_system_id}"
+        ),
+        created_at_utc="2026-09-08T00:00:00Z",
+        base_terminal_sha256=qualification.winner_terminal_sha256s[experiments.SEEDS.index(_SEED)],
+        qualification_sha256=qualification_sha256,
+        cache_content_sha256=_sha(raw_payload),
+    )
+    (root / "record.json").write_bytes(execution.canonical_periodic_cache_bytes(record))
+    (root / "cache-content.npz").write_bytes(raw_payload)
+    return root
+
+
+def _bundle(tmp_path: Path):
+    tmp_path.mkdir(parents=True, exist_ok=True)
+    checkpoint = tmp_path / "winner-checkpoint.pt"
+    checkpoint.write_bytes(b"analytic selected checkpoint fixture\n")
+    qualification, qualification_sha256 = _qualification(
+        {
+            1729: _sha(checkpoint.read_bytes()),
+            2718: _digest("checkpoint/winner/2718"),
+            31415: _digest("checkpoint/winner/31415"),
+        }
+    )
+    floor_root = _write_floor_root(
+        tmp_path / "qualified-floors",
+        qualification=qualification,
+        qualification_sha256=qualification_sha256,
+    )
+    floors = load_qualified_energy_floor_input(
+        floor_root,
+        seed=_SEED,
+        qualification=qualification,
+        qualification_sha256=qualification_sha256,
+    )
+    train_source, auxiliary_val = _write_sources(tmp_path / "prepared")
+    capture_source = _write_capture_source(tmp_path / "capture")
+    return (
+        checkpoint,
+        qualification,
+        qualification_sha256,
+        floors,
+        train_source,
+        auxiliary_val,
+        capture_source,
+        training.TrainingConfig(stage="residual", seed=_SEED),
+    )
+
+
+def _arguments(tmp_path: Path) -> dict[str, object]:
+    (
+        checkpoint,
+        qualification,
+        qualification_sha256,
+        floors,
+        train_source,
+        _auxiliary_val,
+        capture_source,
+        config,
+    ) = _bundle(tmp_path)
+    return {
+        "seed": _SEED,
+        "config": config,
+        "qualification": qualification,
+        "qualification_sha256": qualification_sha256,
+        "winner_checkpoint": checkpoint,
+        "floors": floors,
+        "train_source": train_source,
+        "capture_source": capture_source,
+        "source_tree_sha256": _digest("registered-source-tree"),
+        "environment_sha256": _digest("registered-environment"),
+    }
+
+
+def test_build_and_consume_complete_twenty_epoch_and_capture_plan(tmp_path: Path) -> None:
+    arguments = _arguments(tmp_path)
+    root = tmp_path / "periodic-descriptor-build"
+    built = build_periodic_descriptor_cache_for_seed(
+        output_root=root,
+        **arguments,
+    )
+
+    assert built.root == root.resolve()
+    assert built.manifest_sha256 == _sha(built.manifest_json_bytes)
+    assert [plan.system_id for plan in built.plans] == [
+        system_id for system_id, _stream in CACHEABLE_SYSTEM_STREAMS
+    ]
+    assert [plan.stream_kind for plan in built.plans] == [
+        stream for _system_id, stream in CACHEABLE_SYSTEM_STREAMS
+    ]
+    assert {entry.name for entry in root.iterdir()} == {
+        "build.json",
+        "train",
+        "capture-validation",
+        "plans",
+    }
+    value = json.loads(built.manifest_json_bytes)
+    assert value["authority"] == 0
+    assert value["production"] is False
+    assert value["result_claimed"] is False
+    assert value["external_authentication_asserted"] is False
+    assert value["train_cache"]["shard_count"] == training.RESIDUAL_EPOCHS
+    capture_source = arguments["capture_source"]
+    assert type(capture_source) is capture_validation.CaptureValidationSource
+    expected_windows = sum(len(capture.windows) for capture in capture_source.captures)
+    assert value["capture_validation_cache"]["shard_count"] == expected_windows
+    assert value["capture_source"]["window_count"] == expected_windows
+    assert str(tmp_path).encode() not in built.manifest_json_bytes
+
+    consumed = consume_periodic_descriptor_cache_build(
+        root,
+        expected_manifest_sha256=built.manifest_sha256,
+        **arguments,
+    )
+    assert consumed.manifest_json_bytes == built.manifest_json_bytes
+    assert tuple(plan.sha256 for plan in consumed.plans) == tuple(
+        plan.sha256 for plan in built.plans
+    )
+    assert consumed.train_cache.cache_key_census == built.train_cache.cache_key_census
+    assert (
+        consumed.capture_validation_cache.cache_key_census
+        == built.capture_validation_cache.cache_key_census
+    )
+
+
+def test_floor_loader_requires_exact_two_file_tree_and_exact_npz_key(
+    tmp_path: Path,
+) -> None:
+    checkpoint = b"floor loader checkpoint"
+    qualification, qualification_sha256 = _qualification(
+        {
+            1729: _sha(checkpoint),
+            2718: _digest("floor/2718"),
+            31415: _digest("floor/31415"),
+        }
+    )
+    extra_payload = _floor_payload(extra_key=True)
+    root = _write_floor_root(
+        tmp_path / "floors-extra-key",
+        qualification=qualification,
+        qualification_sha256=qualification_sha256,
+        payload=extra_payload,
+    )
+    with pytest.raises(PeriodicCacheExecutionError, match="member census"):
+        load_qualified_energy_floor_input(
+            root,
+            seed=_SEED,
+            qualification=qualification,
+            qualification_sha256=qualification_sha256,
+        )
+
+    root = _write_floor_root(
+        tmp_path / "floors-extra-file",
+        qualification=qualification,
+        qualification_sha256=qualification_sha256,
+    )
+    (root / "unregistered.txt").write_text("unregistered", encoding="ascii")
+    with pytest.raises(PeriodicCacheExecutionError, match="member census"):
+        load_qualified_energy_floor_input(
+            root,
+            seed=_SEED,
+            qualification=qualification,
+            qualification_sha256=qualification_sha256,
+        )
+
+
+def test_preflight_rejects_auxiliary_val_checkpoint_drift_and_reused_root(
+    tmp_path: Path,
+) -> None:
+    arguments = _arguments(tmp_path)
+    auxiliary_val = _bundle(tmp_path / "other")[5]
+    invalid = dict(arguments)
+    invalid["train_source"] = auxiliary_val
+    with pytest.raises(PeriodicCacheExecutionError, match="train-only"):
+        build_periodic_descriptor_cache_for_seed(
+            output_root=tmp_path / "must-not-exist",
+            **invalid,
+        )
+    assert not (tmp_path / "must-not-exist").exists()
+
+    checkpoint = arguments["winner_checkpoint"]
+    assert isinstance(checkpoint, Path)
+    checkpoint.write_bytes(b"changed after qualification\n")
+    with pytest.raises(PeriodicCacheExecutionError, match="checkpoint bytes differ"):
+        build_periodic_descriptor_cache_for_seed(
+            output_root=tmp_path / "must-not-exist-either",
+            **arguments,
+        )
+    assert not (tmp_path / "must-not-exist-either").exists()
+
+    checkpoint.write_bytes(b"analytic selected checkpoint fixture\n")
+    reused = tmp_path / "already-present"
+    reused.mkdir()
+    with pytest.raises(PeriodicCacheExecutionError, match="output root must be new"):
+        build_periodic_descriptor_cache_for_seed(output_root=reused, **arguments)
+    assert list(reused.iterdir()) == []
+
+
+def test_consumption_rejects_plan_mutation_and_extra_tree_member(tmp_path: Path) -> None:
+    arguments = _arguments(tmp_path)
+    built = build_periodic_descriptor_cache_for_seed(
+        output_root=tmp_path / "built",
+        **arguments,
+    )
+    plan_mutant = tmp_path / "plan-mutant"
+    shutil.copytree(built.root, plan_mutant)
+    plan_path = plan_mutant / "plans" / "02.json"
+    plan_path.write_bytes(plan_path.read_bytes() + b" ")
+    with pytest.raises(PeriodicCacheExecutionError, match="differs from complete admission"):
+        consume_periodic_descriptor_cache_build(
+            plan_mutant,
+            expected_manifest_sha256=built.manifest_sha256,
+            **arguments,
+        )
+
+    extra_member = tmp_path / "extra-member"
+    shutil.copytree(built.root, extra_member)
+    (extra_member / "unexpected").write_bytes(b"unexpected")
+    with pytest.raises(PeriodicCacheExecutionError, match="member census"):
+        consume_periodic_descriptor_cache_build(
+            extra_member,
+            expected_manifest_sha256=built.manifest_sha256,
+            **arguments,
+        )
+
+    bool_mutant = tmp_path / "bool-mutant"
+    shutil.copytree(built.root, bool_mutant)
+    manifest_path = bool_mutant / "build.json"
+    value = json.loads(manifest_path.read_bytes())
+    value["authority"] = False
+    raw = (
+        json.dumps(
+            value,
+            sort_keys=True,
+            separators=(",", ":"),
+            ensure_ascii=True,
+            allow_nan=False,
+        ).encode("ascii")
+        + b"\n"
+    )
+    manifest_path.write_bytes(raw)
+    with pytest.raises(PeriodicCacheExecutionError, match="live consumed evidence"):
+        consume_periodic_descriptor_cache_build(
+            bool_mutant,
+            expected_manifest_sha256=_sha(raw),
+            **arguments,
+        )
+
+
+@pytest.mark.parametrize(
+    ("max_shards", "max_bytes"),
+    [(5, 1 << 40), (524_288, 1)],
+)
+def test_combined_bounds_stop_writer_before_exceeding_disk_census(
+    tmp_path: Path,
+    max_shards: int,
+    max_bytes: int,
+) -> None:
+    arguments = _arguments(tmp_path / "inputs")
+    root = tmp_path / "bounded-build"
+    bounds = PeriodicCacheExecutionBounds(
+        max_combined_shards=max_shards,
+        max_combined_shard_bytes=max_bytes,
+    )
+    with pytest.raises(PeriodicCacheExecutionError, match="construction failed"):
+        build_periodic_descriptor_cache_for_seed(
+            output_root=root,
+            bounds=bounds,
+            **arguments,
+        )
+    shards = tuple(root.rglob("*.pdc2"))
+    assert len(shards) <= max_shards
+    assert sum(path.stat().st_size for path in shards) <= max_bytes
+    assert not (root / "build.json").exists()
+
+
+def test_cross_interface_identity_excludes_only_nonportable_metadata() -> None:
+    common = {
+        "st_mode": 0o100600,
+        "st_dev": 7,
+        "st_ino": 11,
+        "st_size": 13,
+        "st_mtime_ns": 17,
+        "st_nlink": 1,
+    }
+    path_stat = SimpleNamespace(**common, st_ctime_ns=19)
+    descriptor_stat = SimpleNamespace(**common, st_ctime_ns=23)
+    assert cache_execution._cross_interface_identity(  # noqa: SLF001
+        path_stat
+    ) == cache_execution._cross_interface_identity(descriptor_stat)  # noqa: SLF001
+    assert cache_execution._stable_stat_identity(  # noqa: SLF001
+        path_stat
+    ) != cache_execution._stable_stat_identity(descriptor_stat)  # noqa: SLF001
+
+
+def test_floor_bytes_and_result_objects_are_defensive_snapshots(tmp_path: Path) -> None:
+    arguments = _arguments(tmp_path)
+    floors = arguments["floors"]
+    original = floors.energy_floors.copy()
+    with pytest.raises(ValueError):
+        floors.energy_floors[0] = 1.0
+    assert np.array_equal(floors.energy_floors, original)
+
+    built = build_periodic_descriptor_cache_for_seed(
+        output_root=tmp_path / "built",
+        **arguments,
+    )
+    with pytest.raises(PeriodicCacheExecutionError, match="record bytes changed"):
+        replace(floors, record_sha256="f" * 64)
+    with pytest.raises(PeriodicCacheExecutionError, match="manifest bytes changed"):
+        replace(built, manifest_sha256="f" * 64)

From 093cfc7ac30d1c786b874533e1aba304afa0840a Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 15:27:14 -0500
Subject: [PATCH 23/95] Implement PhaseSet V2 directional temporal coordination
 research APIs

---
 README.md                                     |   19 +-
 RELEASE_FILES.sha256                          |   14 +-
 configs/phaseset_v2_experiment_matrix.json    | 1114 +++++++++++++++++
 docs/PHASESET_EXPERIMENT_PLAN.md              |    4 +
 docs/PHASESET_RESEARCH_CONTRACT.md            |    5 +
 docs/PHASESET_V2_AMENDMENT.md                 |   86 ++
 docs/PHASESET_V2_MECHANISM_VERIFICATION.md    |   79 ++
 src/phaseset_core/directional_phase.py        |  300 +++++
 src/phaseset_core/temporal_coordination.py    |  362 ++++++
 .../test_phaseset_host_base_qualification.py  |  119 ++
 tests/test_phaseset_v2_coordination.py        |  308 +++++
 11 files changed, 2405 insertions(+), 5 deletions(-)
 create mode 100644 configs/phaseset_v2_experiment_matrix.json
 create mode 100644 docs/PHASESET_V2_AMENDMENT.md
 create mode 100644 docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 create mode 100644 src/phaseset_core/directional_phase.py
 create mode 100644 src/phaseset_core/temporal_coordination.py
 create mode 100644 tests/test_phaseset_v2_coordination.py

diff --git a/README.md b/README.md
index 1b85f00..f435124 100644
--- a/README.md
+++ b/README.md
@@ -40,7 +40,24 @@ multi-person skeleton sequence
   [20-Hz rate contract](docs/PHASESET_20HZ_MORLET_CONTRACT.md) and
   [canonical-byte portability policy](docs/MORLET_PORTABILITY.md).
 
-## Current milestone
+## PhaseSet-V2 research upgrade — 2026-09-26
+
+The new [V2 amendment](docs/PHASESET_V2_AMENDMENT.md) introduces signed local
+phase, time-varying actor–edge incidence, chronological capture readout, and
+verified counterfactual coordination supervision. The
+[87-stage matrix](configs/phaseset_v2_experiment_matrix.json) supersedes the
+affected legacy 33-run plan. New modules are independent research APIs; the
+formal V2 host runner and literature reproductions are not yet implemented.
+
+[Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
+records 117 focused CPU tests and one full-width CUDA forward/backward witness.
+There are still **0/87 formal V2 stages** and no native-data retrieval result.
+Missing approved main data, licensed body-model assets, exact handoff fold
+assignments, and human relational verification are explicit dependencies.
+The descriptions and score equation below document the preserved V1 core,
+not completed V2 experiments or a submission-ready paper.
+
+## Preserved V1 milestone
 
 This source snapshot declares `v0.2.1`, the portable-verification patch for the
 stable data-free multi-person core and execution-contract milestone. It is a
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 2be6fa2..f6289a3 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -8,13 +8,14 @@ e9938bfe4bbd1ee2357396d1a6a8788e511389e297c28d7247d9e16a9a8137fc	392	.gitignore
 a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTING.md
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-fc86a807b30083c26785c2c06fa7dfd59bf83d89e6076da2a4b23841c47215cc	9209	README.md
+1cb23b08139df6d86e581e23468210a6edbc42da72d2498fd1448a48b7773619	10220	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
+77e1f34b68a74c455a391916dcdd4b508d24307be5cd29b2e3484eb9838cc80f	24054	configs/phaseset_v2_experiment_matrix.json
 2970469ad0787e74ab4fabce2275486eb50c30c38f3cb7cf8f2800551d16991f	9540	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
 e32c97a223c2b04278b9ce3c5d3b25590b390bc82dbf29e99b06d08d6c96b268	3054	docs/CACHED_RESIDUAL_TRAINING.md
@@ -61,10 +62,12 @@ d588d52a1d2674ba5dde7543915dc77299457a840af35aad83f96191f9913e24	37391	docs/PHAS
 1da1df6293c4f77f42ce0b91e62b8884b0c0055b199e99e7ebbac46760db1c27	9930	docs/PHASEPAIR_SIGNAL_SPEC_20260824_165840.md
 cc7106ec61da31a6ccbea1ac5b151b6275a8d8ccf8dc6cce9377680ce30e6b55	21155	docs/PHASEPAIR_TRAINING_EXECUTION_SCHEMA_AMENDMENT_20260824_234612.md
 c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASESET_20HZ_MORLET_CONTRACT.md
-846ffa812d14c5e7652a503d19230e7954bdedbc630af33560cd99380124d86b	11188	docs/PHASESET_EXPERIMENT_PLAN.md
+0ae55c2d5142c294e0915dc8c64a9919a11d6218b437f4f90b4ecc903599a4d6	11413	docs/PHASESET_EXPERIMENT_PLAN.md
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
-ecb85faa3b442450f6ecc8d6f2fff77baf19e833254fa8a715127d9127742f63	13825	docs/PHASESET_RESEARCH_CONTRACT.md
+cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
+53149d3cbfa8997ce46bf397320783af6b96a3b13f6532baccf4680cc7500273	5185	docs/PHASESET_V2_AMENDMENT.md
+4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
@@ -147,6 +150,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
+82c8c4a10263d12ccc94518ecbb08ed8902d7352104bd5d50e6dd1ae9e749efb	13149	src/phaseset_core/directional_phase.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
@@ -170,6 +174,7 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
+d29db7a6cec9a7bf02b16425961b2075a24d25a4774171d19157327d0b18cd11	15922	src/phaseset_core/temporal_coordination.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
@@ -238,7 +243,7 @@ dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/tes
 04df5b4a8d75bed006f16b56910e4082d4104a45cc8aeb11d6932ede645777cb	14111	tests/test_phaseset_frozen_clip_cache_rehydrate.py
 937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
 4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
-357695439cffc89282c6d51573817964b603b3d3a76325e641ed89136d8650e2	58152	tests/test_phaseset_host_base_qualification.py
+841bb5bd72b98885cdc50c95a92022bacd5d9ba63f9f3776ccaaf9df06ac6de6	62702	tests/test_phaseset_host_base_qualification.py
 14f6d4b54d78876822e6f96440a32360ea4d4654c1d18d8d8662fe0f4dc42bf8	23309	tests/test_phaseset_host_capture_storage.py
 6eb892149a9b3dee02f48e816acfe0026a0efaed8985a32eedf412bad1edd141	11008	tests/test_phaseset_host_intent_binding.py
 d133ca3a4df71baaea1cc40f68d049b56a7fcfc0d0861a87e5916c9637bdadb2	16345	tests/test_phaseset_latency_progress_journal.py
@@ -261,6 +266,7 @@ d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/tes
 e6e9600a244af88f731e9cc981f19478b5760db8c254dfd8455960ffe789ef1a	12321	tests/test_phaseset_streaming_autograd.py
 ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test_phaseset_torch_periodic.py
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
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 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
 45b42266bc1c42c36af7eb7d07be2fc85d8b1d0a539ffb3674f98b47e1327254	4973	tests/test_release_artifact_audit.py
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
diff --git a/configs/phaseset_v2_experiment_matrix.json b/configs/phaseset_v2_experiment_matrix.json
new file mode 100644
index 0000000..9725ee6
--- /dev/null
+++ b/configs/phaseset_v2_experiment_matrix.json
@@ -0,0 +1,1114 @@
+{
+  "schema": "phaseset-v2-training-matrix-v1",
+  "amendment_date": "2026-09-26",
+  "status": "PLANNED_NOT_LAUNCHABLE",
+  "supersedes": "legacy_33_run_matrix",
+  "seeds": [
+    1729,
+    2718,
+    31415
+  ],
+  "budget": {
+    "gpu_hours": 300,
+    "formal_stages": 87,
+    "max_formal_starts": 90,
+    "max_pilot_stages": 12,
+    "max_architecture_revisions": 2,
+    "max_root_cause_restarts": 3,
+    "base_max_epochs": 30,
+    "residual_max_epochs": 20,
+    "pilot_hyperparameter_adjustments": 1
+  },
+  "primary_split": {
+    "train_captures": 400,
+    "validation_captures": 96,
+    "test_captures": 76,
+    "final_test_components": 3
+  },
+  "pilot_and_group_fold_components": null,
+  "group_fold_components_source": "Referenced ChatGPT handoff archive; attachment not yet available",
+  "frozen_strong_baseline": null,
+  "success_thresholds": {
+    "retrieval_mean_r1_gain_pp": 2,
+    "relation_challenge_gain_pp": 5,
+    "positive_group_folds": 3,
+    "nonperiodic_max_regression_pp": 1,
+    "inference_cost_ratio": 2
+  },
+  "relation_challenge": {
+    "minimum_verified_text_pairs": 200,
+    "minimum_real_captures": 100,
+    "minimum_participant_components": 8,
+    "blind_human_annotators": 2
+  },
+  "ablations": {
+    "A1": "speed_only_frontend",
+    "A2": "remove_local_and_capture_order",
+    "A3": "pair_bag",
+    "A4": "shuffle_explicit_incidence",
+    "A5": "remove_explicit_phase_fields",
+    "A6": "mean_difference_signal_DCT",
+    "A7": "generic_local_relation_matching_same_counterfactual_data",
+    "A8": "no_counterfactual_objective",
+    "A9": "legacy_representation_calibrated_score_only"
+  },
+  "terminal_states": [
+    "READY_FOR_SUBMISSION",
+    "COMPLETE_NOT_SUPPORTED",
+    "BLOCKED_DATA",
+    "BLOCKED_RIGHTS",
+    "BLOCKED_RUNTIME",
+    "BLOCKED_RELATIONAL_EVIDENCE",
+    "BLOCKED_BUDGET",
+    "FAILED_IMPLEMENTATION"
+  ],
+  "runs": [
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+    {
+      "id": "V2-031",
+      "group": "mechanism_ablation",
+      "split": "main",
+      "system": "A3",
+      "seed": 1729,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-007"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-032",
+      "group": "mechanism_ablation",
+      "split": "main",
+      "system": "A3",
+      "seed": 2718,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-008"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-033",
+      "group": "mechanism_ablation",
+      "split": "main",
+      "system": "A3",
+      "seed": 31415,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-009"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-034",
+      "group": "mechanism_ablation",
+      "split": "main",
+      "system": "A4",
+      "seed": 1729,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-007"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-035",
+      "group": "mechanism_ablation",
+      "split": "main",
+      "system": "A4",
+      "seed": 2718,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-008"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-036",
+      "group": "mechanism_ablation",
+      "split": "main",
+      "system": "A4",
+      "seed": 31415,
+      "max_epochs": 20,
+      "max_steps": null,
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+        "V2-009"
+      ],
+      "status": "PLANNED"
+    },
+    {
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+      "group": "mechanism_ablation",
+      "split": "main",
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+      "seed": 1729,
+      "max_epochs": 20,
+      "max_steps": null,
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+        "V2-007"
+      ],
+      "status": "PLANNED"
+    },
+    {
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+      ],
+      "status": "PLANNED"
+    },
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+      "group": "mechanism_ablation",
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+      "status": "PLANNED"
+    },
+    {
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+      "split": "main",
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+      ],
+      "status": "PLANNED"
+    },
+    {
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+      ],
+      "status": "PLANNED"
+    },
+    {
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+      "group": "mechanism_ablation",
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+      "status": "PLANNED"
+    },
+    {
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+      "group": "mechanism_ablation",
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+        "V2-009"
+      ],
+      "status": "PLANNED"
+    },
+    {
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+        "V2-007"
+      ],
+      "status": "PLANNED"
+    },
+    {
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+      "group": "mechanism_ablation",
+      "split": "main",
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+      "seed": 2718,
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+        "V2-008"
+      ],
+      "status": "PLANNED"
+    },
+    {
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+      "group": "mechanism_ablation",
+      "split": "main",
+      "system": "A8",
+      "seed": 31415,
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+        "V2-009"
+      ],
+      "status": "PLANNED"
+    },
+    {
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+      "group": "mechanism_ablation",
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+      "depends_on": [
+        "V2-007"
+      ],
+      "status": "PLANNED"
+    },
+    {
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+      "group": "mechanism_ablation",
+      "split": "main",
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+        "V2-008"
+      ],
+      "status": "PLANNED"
+    },
+    {
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+      "group": "mechanism_ablation",
+      "split": "main",
+      "system": "A9",
+      "seed": 31415,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-009"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-052",
+      "group": "group_held_out",
+      "split": "group_fold_1",
+      "system": "B2",
+      "seed": 1729,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-053",
+      "group": "group_held_out",
+      "split": "group_fold_1",
+      "system": "PhaseSet-V2",
+      "seed": 1729,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-052"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-054",
+      "group": "group_held_out",
+      "split": "group_fold_1",
+      "system": "pilot_frozen_strong_baseline",
+      "seed": 1729,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
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+      "group": "group_held_out",
+      "split": "group_fold_1",
+      "system": "B2",
+      "seed": 2718,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-056",
+      "group": "group_held_out",
+      "split": "group_fold_1",
+      "system": "PhaseSet-V2",
+      "seed": 2718,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-055"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-057",
+      "group": "group_held_out",
+      "split": "group_fold_1",
+      "system": "pilot_frozen_strong_baseline",
+      "seed": 2718,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
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+      "group": "group_held_out",
+      "split": "group_fold_1",
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+      "seed": 31415,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-059",
+      "group": "group_held_out",
+      "split": "group_fold_1",
+      "system": "PhaseSet-V2",
+      "seed": 31415,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-058"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-060",
+      "group": "group_held_out",
+      "split": "group_fold_1",
+      "system": "pilot_frozen_strong_baseline",
+      "seed": 31415,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-061",
+      "group": "group_held_out",
+      "split": "group_fold_2",
+      "system": "B2",
+      "seed": 1729,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-062",
+      "group": "group_held_out",
+      "split": "group_fold_2",
+      "system": "PhaseSet-V2",
+      "seed": 1729,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-061"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-063",
+      "group": "group_held_out",
+      "split": "group_fold_2",
+      "system": "pilot_frozen_strong_baseline",
+      "seed": 1729,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
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+      "group": "group_held_out",
+      "split": "group_fold_2",
+      "system": "B2",
+      "seed": 2718,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-065",
+      "group": "group_held_out",
+      "split": "group_fold_2",
+      "system": "PhaseSet-V2",
+      "seed": 2718,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-064"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-066",
+      "group": "group_held_out",
+      "split": "group_fold_2",
+      "system": "pilot_frozen_strong_baseline",
+      "seed": 2718,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-067",
+      "group": "group_held_out",
+      "split": "group_fold_2",
+      "system": "B2",
+      "seed": 31415,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-068",
+      "group": "group_held_out",
+      "split": "group_fold_2",
+      "system": "PhaseSet-V2",
+      "seed": 31415,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-067"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-069",
+      "group": "group_held_out",
+      "split": "group_fold_2",
+      "system": "pilot_frozen_strong_baseline",
+      "seed": 31415,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-070",
+      "group": "group_held_out",
+      "split": "group_fold_3",
+      "system": "B2",
+      "seed": 1729,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-071",
+      "group": "group_held_out",
+      "split": "group_fold_3",
+      "system": "PhaseSet-V2",
+      "seed": 1729,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-070"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-072",
+      "group": "group_held_out",
+      "split": "group_fold_3",
+      "system": "pilot_frozen_strong_baseline",
+      "seed": 1729,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-073",
+      "group": "group_held_out",
+      "split": "group_fold_3",
+      "system": "B2",
+      "seed": 2718,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-074",
+      "group": "group_held_out",
+      "split": "group_fold_3",
+      "system": "PhaseSet-V2",
+      "seed": 2718,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-073"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-075",
+      "group": "group_held_out",
+      "split": "group_fold_3",
+      "system": "pilot_frozen_strong_baseline",
+      "seed": 2718,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-076",
+      "group": "group_held_out",
+      "split": "group_fold_3",
+      "system": "B2",
+      "seed": 31415,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-077",
+      "group": "group_held_out",
+      "split": "group_fold_3",
+      "system": "PhaseSet-V2",
+      "seed": 31415,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-076"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-078",
+      "group": "group_held_out",
+      "split": "group_fold_3",
+      "system": "pilot_frozen_strong_baseline",
+      "seed": 31415,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-079",
+      "group": "external_dyadic",
+      "split": "Inter-X",
+      "system": "B2",
+      "seed": 1729,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-080",
+      "group": "external_dyadic",
+      "split": "Inter-X",
+      "system": "PhaseSet-V2",
+      "seed": 1729,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-079"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-081",
+      "group": "external_dyadic",
+      "split": "Inter-X",
+      "system": "MIME-original",
+      "seed": 1729,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-082",
+      "group": "external_dyadic",
+      "split": "Inter-X",
+      "system": "B2",
+      "seed": 2718,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-083",
+      "group": "external_dyadic",
+      "split": "Inter-X",
+      "system": "PhaseSet-V2",
+      "seed": 2718,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-082"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-084",
+      "group": "external_dyadic",
+      "split": "Inter-X",
+      "system": "MIME-original",
+      "seed": 2718,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-085",
+      "group": "external_dyadic",
+      "split": "Inter-X",
+      "system": "B2",
+      "seed": 31415,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-086",
+      "group": "external_dyadic",
+      "split": "Inter-X",
+      "system": "PhaseSet-V2",
+      "seed": 31415,
+      "max_epochs": 20,
+      "max_steps": null,
+      "depends_on": [
+        "V2-085"
+      ],
+      "status": "PLANNED"
+    },
+    {
+      "id": "V2-087",
+      "group": "external_dyadic",
+      "split": "Inter-X",
+      "system": "MIME-original",
+      "seed": 31415,
+      "max_epochs": 30,
+      "max_steps": null,
+      "depends_on": [],
+      "status": "PLANNED"
+    }
+  ]
+}
diff --git a/docs/PHASESET_EXPERIMENT_PLAN.md b/docs/PHASESET_EXPERIMENT_PLAN.md
index f20e3a3..3fc0e0e 100644
--- a/docs/PHASESET_EXPERIMENT_PLAN.md
+++ b/docs/PHASESET_EXPERIMENT_PLAN.md
@@ -1,5 +1,9 @@
 # PhaseSet frozen experiment and statistics plan
 
+> Historical V1 plan. The affected 33-run census is superseded by the
+> [2026-09-26 V2 amendment](PHASESET_V2_AMENDMENT.md) and its 87-stage matrix.
+> Preserve this file for legacy reproducibility; do not run both matrices.
+
 **Plan ID:** `phaseset-multiperson-20260825`
 **Frozen at:** 2026-08-25 20:43:38 +08:00
 **State:** `FROZEN / NOT_EXECUTED / SEALED_TEST_CLOSED`
diff --git a/docs/PHASESET_RESEARCH_CONTRACT.md b/docs/PHASESET_RESEARCH_CONTRACT.md
index 083fe2f..171ef0a 100644
--- a/docs/PHASESET_RESEARCH_CONTRACT.md
+++ b/docs/PHASESET_RESEARCH_CONTRACT.md
@@ -1,5 +1,10 @@
 # PhaseSet frozen research contract
 
+> Historical V1 contract. Signed local phase, temporal incidence, calibrated
+> scoring, counterfactual supervision, and the formal census are amended by
+> [PhaseSet-V2](PHASESET_V2_AMENDMENT.md). The original contract and code remain
+> preserved; V2 has no real-data result or scientific acceptance yet.
+
 **Contract ID:** `phaseset-multiperson-20260825`
 **Frozen at:** 2026-08-25 20:43:38 +08:00
 **Target:** ICASSP 2027, Multimedia Signal Processing primary topic
diff --git a/docs/PHASESET_V2_AMENDMENT.md b/docs/PHASESET_V2_AMENDMENT.md
new file mode 100644
index 0000000..b04d6ea
--- /dev/null
+++ b/docs/PHASESET_V2_AMENDMENT.md
@@ -0,0 +1,86 @@
+# PhaseSet-V2 研究修订 — 2026-09-26
+
+此修订执行用户指定的“升级至顶会程度”聊天。研究目标是区分
+“个体动作相近、协同关系不同”的原生多人场景。旧协议和历史证据保留；
+受影响的正式训练使用本修订的87阶段矩阵，不再叠加旧33-run矩阵。
+
+## 固定的三个升级
+
+1. 方向保持的局部相位：保留带符号的XYZ关节速度，区分pelvis运动与
+   身体相对pelvis运动。沿用既有20Hz六频带核，提取局部复交叉响应、
+   coherence、端点能量和支持。低能量相位标记为不可观测。
+2. 时间关系：保留actor–edge incidence，沿同一个人物及同一条边的时间轨迹
+   编码，再做集合读出；完整capture按实际窗口起始时间做有序读出。
+3. 协同语言监督：匹配完整、端点绑定的关系表示，使用variable-positive
+   InfoNCE及有核实假标签的反事实softplus损失。人物重编号不是负例；
+   泛泛动作描述在相位扰动后仍可能为真，不能自动标假。
+
+评分单独校准：两分支先变为cosine，在共同学习尺度上以sigmoid权重混合。
+A9使用旧表示、只改变校准，不能把尺度修复归为新机制贡献。
+A6对平均与差分信号先做DCT，保留交叉项；不以端点自功率平均冒充它。
+
+坐标约定为共享世界坐标。世界方向反相不自动等于面对面镜像手势的
+语义反相。所有相位操作仍由motion产生，文本不能更改物理字段。
+全人对时间复杂度为O(K²)；流式显存不代表线性时间。
+
+## 实验与停止边界
+
+| 阶段 | 正式训练阶段 |
+|---|---:|
+| B0/B1/B2，三seed | 9 |
+| TMR-Set/WaMo-Set/MIME-Set，三seed | 9 |
+| 旧PhaseSet与V2 residual，三seed | 6 |
+| A1–A9，各三seed | 27 |
+| 三group-held-out folds，各三seed的B2、V2、冻结强对手 | 27 |
+| Inter-X：三seed的B2、V2、原MIME | 9 |
+| 合计 | 87 |
+
+机器矩阵见 [配置](../configs/phaseset_v2_experiment_matrix.json)。
+seeds为1729/2718/31415。base最多30epoch、residual最多20epoch；
+最多12短pilot、两轮结构返工、全局三次有根因的故障重启，
+正式启动最多90，总保护预算300 GPU-hours。先profile代表性任务，再冻结
+max_steps；pilot只允许一次有依据的统一超参调整。达到限制后交付真实终态。
+
+性能比较保留文献原架构及辅助损失的合理多人适配；机制比较固定B2、
+文本来源及相近有效容量。TMR普通双塔不能替代完整TMR，WaMo功率头不能
+替代原方法，MIME多人适配及收敛情况须明确。外折独立训练，权重不跨折复用。
+Inter-X如在其训练集训练，只能称外部双人适用性验证。
+
+A1速度输入、A2无时间顺序、A3pair-bag、A4错误incidence、A5无显式phase、
+A6真实mean/difference DCT、A7相同反事实数据的普通局部关系匹配、
+A8无反事实目标、A9只校准旧表示。每一贡献必须有对应可推翻它的对照。
+
+## 数据和科学验收
+
+保留400/96/76原split及三个最终test组件。具体pilot/三外折组件来自
+聊天交付包；目前该附件未传入，配置保留null且不得启动正式矩阵。
+没有观察到真实数据时，不执行正式训练或生成主表数字。
+
+真实关系挑战至少200核实最小文本对、100capture、8参与者组件，
+由两名不知道模型分数的人工标注者审核。不能以机器改写凑数。
+先确认原生数据中的可观察关系是否足够；不自动追加Multi-TPC。
+
+内部科学目标：主双向R@1三seed均值比最强合格文献baseline至少+2pp；
+关系挑战至少+5pp；三个外折平均效应为正；方向/结构/反事实对应消融
+支持并进行预登记多重比较；超过A9；非周期相对B2退化不超过1pp；
+相同硬件和缓存规则下端到端推理默认不超过强对手2倍。报告不确定性、
+组件级结果及效率trade-off；这些目标不保证会议录用。
+
+终态采用READY_FOR_SUBMISSION、COMPLETE_NOT_SUPPORTED、
+BLOCKED_DATA/RIGHTS/RUNTIME/RELATIONAL_EVIDENCE/BUDGET或FAILED_IMPLEMENTATION。
+软件测试、PDF编译和同模型家族审查不替代真实科学验收。
+结论与正负结果一致；不追加seed、epoch、数据集或更换test追逐结果。
+
+## 本次实现的边界
+
+两个新模块实现固定物理前端、checkpointed时间incidence、
+有序capture读出、共同尺度评分和核实负例损失。它们作为独立研究API，
+尚未接入现有host正式训练、文献适配或数据标注。
+默认40帧patch/20帧hop及关系GRU是待交付包核对的实现起点，
+不作为已冻结的最终超参。新前端的训练能量阈值需重新拟合；
+旧五速度通道阈值不能直接套用。机制检查使用分析运动，不产生检索成绩。
+
+正文与图表最终来自同一冻结aggregate，提供可编辑源、真实限制和失败案例。
+本日查证 [ICASSP2027官方征稿](https://2027.ieeeicassp.org/call-for-papers/)
+显示full-paper截止2026-09-23，已过。研究实现继续，投稿目标需按真实开放的
+会议及当届模板/页数/披露规则确认。
diff --git a/docs/PHASESET_V2_MECHANISM_VERIFICATION.md b/docs/PHASESET_V2_MECHANISM_VERIFICATION.md
new file mode 100644
index 0000000..6136d4f
--- /dev/null
+++ b/docs/PHASESET_V2_MECHANISM_VERIFICATION.md
@@ -0,0 +1,79 @@
+# PhaseSet-V2 mechanism verification — 2026-09-26
+
+This is implementation evidence, not a retrieval benchmark, convergence claim,
+or submission-ready paper. The scientific upgrade is described in
+[the V2 amendment](PHASESET_V2_AMENDMENT.md); its
+[87-stage matrix](../configs/phaseset_v2_experiment_matrix.json) supersedes the
+affected legacy 33-run plan.
+
+## What was implemented
+
+- Signed world-coordinate pelvis velocity and pelvis-relative joint velocity;
+  six unchanged 20-Hz Morlet kernels; local complex cross-response, endpoint
+  energies, coherence, support, and explicit phase observability.
+- Shared actor and edge temporal encoders with time-varying incidence moments,
+  fixed 64-edge checkpoint/recompute blocks, and an ordered capture readout.
+- A common-scale cosine mixture, complete endpoint-bound relation matching,
+  variable-positive symmetric InfoNCE, and verified-negative-only softplus loss.
+- A true mean/difference signal DCT control, preserving its cross terms.
+- A portable structural test journal using the real state machine. Production
+  POSIX journal durability and runtime admission were not relaxed.
+
+These are independent research APIs, not yet a formal V2 host execution path.
+No TMR/WaMo/MIME reproduction, V2 text annotation, training-only energy-floor
+fit, or native-data training is claimed here.
+
+## Observed server checks
+
+The existing isolated environment was reused: Python 3.12.12, NumPy 2.4.6,
+Torch 2.12.0+cu126, CUDA 12.6. Numerical checks ran on the registered experiment
+server, not on the local workstation.
+
+| Check | Observed result |
+|---|---|
+| Focused V2, Morlet, legacy signal, host controller, and journal regressions | 117 passed, 1 CUDA-only test skipped; 32.47 s |
+| Width-512 CUDA forward/backward, K=3 | 1 passed; 7.43 s |
+| CUDA device | NVIDIA RTX A6000 |
+| Peak allocated / reserved memory in that witness | 143,108,608 / 165,675,008 bytes |
+| Source changes during either attempt | none; exact digest comparison passed |
+
+Mechanism tests include speed-only in-phase/antiphase collision versus signed
+phase separation; endpoint conjugation; observed stationary support with
+unobservable phase; no fabricated missing-joint velocity; non-incident edge
+stability; actor permutations and padding; exact K=2 positive-zero topology
+outputs and gradients; pair-only equivalence; checkpoint/eager forward and
+parameter gradients at K=3 and K=13 (78 edges, crossing the 64-edge boundary);
+chronological capture order; calibration; and verified-counterfactual exclusion.
+
+The width-512 GPU witness is **not** an arbitrary-K stress study, a complete
+end-to-end training/resume run, or a resource comparison with literature methods.
+The same-family GPT-5.6-Sol code review remains provisional scientific evidence.
+Both execution receipts and raw logs stay private; their closed receipt digests
+are:
+
+- CPU: `e1a9a5efa7089c0a5ae8263a712492ba57032dc6b2d440794ee25d73dbefc42f`
+- CUDA: `2952fdbe06d81afb23ae89389a52bbe747c1cb37ca58d11cb2580fd7dc895e1c`
+
+Windows CI is required for the portable structural journal fix. A Linux server
+check alone is not evidence of Windows execution; use the commit-bound CI result.
+
+## Scientific execution state
+
+Formal V2 stages completed: **0 / 87**. Sealed test remains closed.
+
+The project-owned data directory was inspected read-only on this date: only
+the supplemental Multi-TPC data was observed. Licensed Embody main motion data,
+neutral SMPL-X model assets, and a corresponding access approval were not found
+in the checked project locations. This is not a claim that approval could not
+exist elsewhere.
+
+The referenced chat's ZIP/Markdown handoff was not attached to the accessible
+conversation. Exact pilot and three-fold component assignments remain null;
+they must be recovered, not invented. Two blinded human reviewers and the real
+relational challenge are still outstanding. No synthetic result is promoted
+to main-data evidence, and no auxiliary dataset silently replaces Embody.
+
+Current execution state is **BLOCKED_DATA**, with rights, handoff, and human
+relational-evidence dependencies recorded separately. Once those inputs exist,
+the next step is the bounded native-data audit/pilot, then the frozen formal
+matrix—not additional data-free audit scaffolding.
diff --git a/src/phaseset_core/directional_phase.py b/src/phaseset_core/directional_phase.py
new file mode 100644
index 0000000..a1f27d4
--- /dev/null
+++ b/src/phaseset_core/directional_phase.py
@@ -0,0 +1,300 @@
+"""Direction-preserving, local six-band relations for PhaseSet-V2.
+
+Coordinates are shared world coordinates. Slot zero is pelvis velocity; the
+remaining 21 slots are joint velocities relative to that actor's pelvis.
+This convention does not identify anatomical mirror gestures as antiphase.
+Physical features are fixed preprocessing, independent of text or actor IDs.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import math
+from typing import Iterator
+
+import numpy as np
+
+from .contracts import PreparedGroupBatch, validate_prepared_group_batch
+from .morlet import SAMPLE_RATE_HZ, morlet_kernel_bank
+from .periodic import ResourceLimitError, validate_energy_floors
+
+
+PHASE_FEATURE_DIM = 7
+ACTOR_FEATURE_DIM = 132
+
+
+@dataclass(frozen=True)
+class LocalPhaseConfig:
+    patch_frames: int = 40
+    hop_frames: int = 20
+    epsilon: float = 1e-12
+    edge_budget: int = 32_768
+    max_response_bytes: int = 1_073_741_824
+
+    def __post_init__(self) -> None:
+        for name in ("patch_frames", "hop_frames", "edge_budget", "max_response_bytes"):
+            if type(getattr(self, name)) is not int or getattr(self, name) < 1:
+                raise ValueError(f"{name} must be a positive integer")
+        if not math.isfinite(self.epsilon) or self.epsilon <= 0:
+            raise ValueError("epsilon must be positive and finite")
+
+
+@dataclass(frozen=True)
+class DirectionalPhaseField:
+    """One canonical group; response memory grows with actors, not pairs."""
+
+    responses: tuple[np.ndarray, ...]  # six [K,N_b,22,3] complex128 arrays
+    response_masks: tuple[np.ndarray, ...]  # six [K,N_b,22,3] masks
+    response_centers: tuple[np.ndarray, ...]
+    intervals: tuple[tuple[int, int], ...]
+    actor_features: np.ndarray  # [K,P,132], signed mean and per-channel RMS
+    actor_patch_mask: np.ndarray  # [K,P]
+    root_positions: np.ndarray  # [K,P,3]
+    root_patch_mask: np.ndarray  # [K,P]
+    energy_floors: np.ndarray
+    config: LocalPhaseConfig
+
+    @property
+    def actor_count(self) -> int:
+        return int(self.actor_features.shape[0])
+
+    @property
+    def patch_count(self) -> int:
+        return len(self.intervals)
+
+    @property
+    def pair_count(self) -> int:
+        return self.actor_count * (self.actor_count - 1) // 2
+
+
+@dataclass(frozen=True)
+class LocalPairChunk:
+    endpoints: np.ndarray  # [E,2], unordered canonical pairs
+    features: np.ndarray  # [E,P,6,7]
+    phase_mask: np.ndarray  # [E,P,6]
+    support_mask: np.ndarray  # [E,P,6]; low coherence does not remove edges
+
+    def reverse_features(self) -> np.ndarray:
+        """Reverse both endpoints: conjugate C and exchange endpoint energies."""
+        reversed_values = self.features.copy()
+        reversed_values[..., 1] *= -1.0
+        reversed_values[..., 3] = self.features[..., 4]
+        reversed_values[..., 4] = self.features[..., 3]
+        reversed_values[reversed_values == 0.0] = 0.0
+        return reversed_values
+
+
+def _readonly(value: np.ndarray) -> np.ndarray:
+    value.setflags(write=False)
+    return value
+
+
+def signed_joint_velocity(batch: PreparedGroupBatch) -> tuple[np.ndarray, np.ndarray]:
+    """Preserve signed XYZ; a velocity needs both frames and the pelvis tracked."""
+    checked = validate_prepared_group_batch(batch)
+    coordinates = checked.skeletons.astype(np.float64)
+    values = np.zeros_like(coordinates)
+    mask = np.zeros_like(coordinates, dtype=np.bool_)
+    displacement = (coordinates[:, :, 1:] - coordinates[:, :, :-1]) * SAMPLE_RATE_HZ
+    joint_valid = checked.track_mask[:, :, 1:] & checked.track_mask[:, :, :-1]
+    frame_valid = checked.frame_mask[:, 1:] & checked.frame_mask[:, :-1]
+    joint_valid &= frame_valid[:, None, :, None] & checked.actor_mask[:, :, None, None]
+    pelvis_valid = joint_valid[..., :1]
+    relative = displacement - displacement[..., :1, :]
+    relative[..., 0, :] = displacement[..., 0, :]
+    observed = joint_valid & pelvis_valid
+    values[:, :, 1:] = np.where(observed[..., None], relative, 0.0)
+    mask[:, :, 1:] = np.broadcast_to(observed[..., None], relative.shape)
+    values[values == 0.0] = 0.0
+    return _readonly(values), _readonly(mask)
+
+
+def _patch_intervals(length: int, config: LocalPhaseConfig) -> tuple[tuple[int, int], ...]:
+    width = min(length, config.patch_frames)
+    starts = list(range(0, length - width + 1, config.hop_frames))
+    if starts[-1] != length - width:
+        starts.append(length - width)
+    return tuple((start, start + width) for start in starts)
+
+
+def directional_phase_fields(
+    batch: PreparedGroupBatch,
+    *,
+    energy_floors: np.ndarray,
+    config: LocalPhaseConfig = LocalPhaseConfig(),
+) -> tuple[DirectionalPhaseField, ...]:
+    """Compute each actor/band once, preserving the unchanged physical kernels.
+
+    Floors must be fitted for this signed-vector frontend using training only.
+    Legacy five-speed-channel fitted floors are not interchangeable with these.
+    """
+    checked = validate_prepared_group_batch(batch)
+    floors = validate_energy_floors(energy_floors)
+    required_edges = sum(count * (count - 1) // 2 for count in checked.actor_counts)
+    if required_edges > config.edge_budget:
+        raise ResourceLimitError(required_edges=required_edges, edge_budget=config.edge_budget)
+    bands = morlet_kernel_bank()
+    projected = sum(
+        count * sum(max(0, length - band.length + 1) for band in bands) * 66 * 17
+        for count, length in zip(checked.actor_counts, checked.valid_lengths, strict=True)
+    )
+    if projected > config.max_response_bytes:
+        raise MemoryError("RESOURCE_LIMIT: actor Morlet responses exceed the byte budget")
+    signed, signed_mask = signed_joint_velocity(checked)
+    results = []
+    for row, (count, length) in enumerate(
+        zip(checked.actor_counts, checked.valid_lengths, strict=True)
+    ):
+        values = signed[row, :count, :length].reshape(count, length, 66)
+        masks = signed_mask[row, :count, :length].reshape(count, length, 66)
+        responses, response_masks, centers = [], [], []
+        for band in bands:
+            if length < band.length:
+                transformed = np.zeros((count, 0, 22, 3), dtype=np.complex128)
+                valid = np.zeros(transformed.shape, dtype=np.bool_)
+                positions = np.zeros(0, dtype=np.float64)
+            else:
+                windows = np.lib.stride_tricks.sliding_window_view(values, band.length, axis=1)
+                mask_windows = np.lib.stride_tricks.sliding_window_view(masks, band.length, axis=1)
+                valid = mask_windows.all(axis=-1).reshape(count, -1, 22, 3)
+                transformed = np.einsum("ktcl,l->ktc", windows, band.kernel).reshape(
+                    count, -1, 22, 3
+                )
+                transformed = np.where(valid, transformed, 0.0 + 0.0j)
+                positions = np.arange(length - band.length + 1) + (band.length - 1) / 2
+            responses.append(_readonly(np.ascontiguousarray(transformed)))
+            response_masks.append(_readonly(np.ascontiguousarray(valid)))
+            centers.append(_readonly(positions))
+        intervals = _patch_intervals(length, config)
+        actor_features = np.zeros((count, len(intervals), ACTOR_FEATURE_DIM), dtype=np.float32)
+        actor_mask = np.zeros((count, len(intervals)), dtype=np.bool_)
+        roots = np.zeros((count, len(intervals), 3), dtype=np.float32)
+        root_mask = np.zeros_like(actor_mask)
+        for patch, (start, stop) in enumerate(intervals):
+            local_values, local_mask = values[:, start:stop], masks[:, start:stop]
+            denominator = local_mask.sum(axis=1).clip(min=1)
+            mean = local_values.sum(axis=1) / denominator
+            rms = np.sqrt((local_values**2).sum(axis=1) / denominator)
+            actor_features[:, patch] = np.concatenate((mean, rms), axis=-1)
+            actor_mask[:, patch] = local_mask.any(axis=(1, 2))
+            pelvis_mask = checked.track_mask[row, :count, start:stop, 0]
+            pelvis_values = checked.skeletons[row, :count, start:stop, 0].astype(np.float64)
+            roots[:, patch] = (
+                np.where(pelvis_mask[..., None], pelvis_values, 0.0).sum(axis=1)
+                / pelvis_mask.sum(axis=1).clip(min=1)[:, None]
+            )
+            root_mask[:, patch] = pelvis_mask.any(axis=1)
+        results.append(
+            DirectionalPhaseField(
+                tuple(responses),
+                tuple(response_masks),
+                tuple(centers),
+                intervals,
+                _readonly(actor_features),
+                _readonly(actor_mask),
+                _readonly(roots),
+                _readonly(root_mask),
+                floors,
+                config,
+            )
+        )
+    return tuple(results)
+
+
+def canonical_edge_pairs(actor_count: int, start: int, stop: int) -> np.ndarray:
+    """Address a contiguous lexicographic edge block without storing all edges."""
+    total = actor_count * (actor_count - 1) // 2
+    if not 0 <= start <= stop <= total:
+        raise ValueError("edge interval is outside the complete unordered graph")
+    result = np.empty((stop - start, 2), dtype=np.int64)
+    if stop == start:
+        return result
+    low, high = 0, actor_count - 1
+    while low + 1 < high:
+        middle = (low + high) // 2
+        if middle * (2 * actor_count - middle - 1) // 2 <= start:
+            low = middle
+        else:
+            high = middle
+    left = low
+    right = left + 1 + start - left * (2 * actor_count - left - 1) // 2
+    for offset in range(stop - start):
+        result[offset] = left, right
+        right += 1
+        if right == actor_count:
+            left += 1
+            right = left + 1
+    return result
+
+
+def local_pair_chunk(field: DirectionalPhaseField, start: int, stop: int) -> LocalPairChunk:
+    pairs = canonical_edge_pairs(field.actor_count, start, stop)
+    features = np.zeros((len(pairs), field.patch_count, 6, PHASE_FEATURE_DIM), dtype=np.float64)
+    phase_mask = np.zeros(features.shape[:-1], dtype=np.bool_)
+    support_mask = np.zeros_like(phase_mask)
+    for band, (response, mask, centers) in enumerate(
+        zip(field.responses, field.response_masks, field.response_centers, strict=True)
+    ):
+        for patch, (window_start, window_stop) in enumerate(field.intervals):
+            selected = (centers >= window_start) & (centers < window_stop)
+            for edge, (left, right) in enumerate(pairs):
+                common = mask[left, selected] & mask[right, selected]
+                count = int(common.sum())
+                if count == 0:
+                    continue
+                a, b = (
+                    response[left, selected][common],
+                    response[right, selected][common],
+                )
+                aa = float(np.vdot(a, a).real)
+                bb = float(np.vdot(b, b).real)
+                cross = complex(np.sum(a * b.conj()))
+                left_power, right_power = aa / count, bb / count
+                observed = (
+                    left_power > field.energy_floors[band]
+                    and right_power > field.energy_floors[band]
+                )
+                correlation = cross / math.sqrt(aa * bb + field.config.epsilon) if observed else 0j
+                features[edge, patch, band] = (
+                    correlation.real,
+                    correlation.imag,
+                    abs(correlation),
+                    math.log1p(left_power),
+                    math.log1p(right_power),
+                    count / max(1, int(selected.sum()) * 66),
+                    float(observed),
+                )
+                phase_mask[edge, patch, band] = observed
+                support_mask[edge, patch, band] = True
+    if not np.isfinite(features).all():
+        raise ValueError("local phase features became nonfinite")
+    features[features == 0.0] = 0.0
+    return LocalPairChunk(
+        _readonly(pairs),
+        _readonly(features),
+        _readonly(phase_mask),
+        _readonly(support_mask),
+    )
+
+
+def iter_local_pair_chunks(
+    field: DirectionalPhaseField, *, edge_chunk_size: int = 256
+) -> Iterator[LocalPairChunk]:
+    if type(edge_chunk_size) is not int or edge_chunk_size < 1:
+        raise ValueError("edge_chunk_size must be a positive integer")
+    for start in range(0, field.pair_count, edge_chunk_size):
+        yield local_pair_chunk(field, start, min(field.pair_count, start + edge_chunk_size))
+
+
+def mean_difference_signal_dct(left: np.ndarray, right: np.ndarray) -> tuple[np.ndarray, ...]:
+    """Orthonormal DCT-II of mean/difference signals, including cross terms."""
+    if left.shape != right.shape or left.ndim != 2 or left.shape[0] < 1:
+        raise ValueError("DCT inputs must have matching nonempty [T,C] shapes")
+    if not np.isfinite(left).all() or not np.isfinite(right).all():
+        raise ValueError("DCT inputs must be finite")
+    length = left.shape[0]
+    basis = np.cos(
+        np.pi / length * (np.arange(length) + 0.5)[None, :] * np.arange(length)[:, None]
+    ) * math.sqrt(2.0 / length)
+    basis[0] /= math.sqrt(2.0)
+    return basis @ ((left + right) / 2), basis @ ((left - right) / 2)
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
new file mode 100644
index 0000000..b599e7f
--- /dev/null
+++ b/src/phaseset_core/temporal_coordination.py
@@ -0,0 +1,362 @@
+"""Temporal actor-edge incidence, ordered capture readout and language loss.
+
+This is the V2 research module, not a licensed-data runner or a trained model.
+Edges are processed in fixed 64-edge blocks. Checkpointed blocks recompute
+physical relations and neural activations in backward rather than retaining
+all edge-token activations. All-pair time remains quadratic in actor count.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import math
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+from torch.utils.checkpoint import checkpoint
+
+from .directional_phase import (
+    ACTOR_FEATURE_DIM,
+    PHASE_FEATURE_DIM,
+    DirectionalPhaseField,
+    local_pair_chunk,
+)
+from .objectives import variable_positive_symmetric_infonce
+
+
+class MaskedTemporalEncoder(nn.Module):
+    """Missing positions neither update state nor create a biased output."""
+
+    def __init__(self, width: int) -> None:
+        super().__init__()
+        self.cell = nn.GRUCell(width, width)
+
+    def forward(self, sequence: Tensor, mask: Tensor) -> tuple[Tensor, Tensor]:
+        if sequence.ndim != 3 or mask.shape != sequence.shape[:2] or mask.dtype != torch.bool:
+            raise ValueError("temporal inputs require [N,P,D] and bool[N,P]")
+        state = sequence.new_zeros((sequence.shape[0], sequence.shape[2]))
+        outputs = []
+        for position in range(sequence.shape[1]):
+            valid = mask[:, position, None]
+            proposal = self.cell(sequence[:, position], state)
+            state = torch.where(valid, proposal, state)
+            outputs.append(torch.where(valid, state, torch.zeros_like(state)))
+        if not outputs:
+            raise ValueError("temporal sequence is empty")
+        return torch.stack(outputs, dim=1), state
+
+
+@dataclass(frozen=True)
+class TemporalCoordinationOutput:
+    patch_tokens: Tensor
+    patch_mask: Tensor
+    embedding: Tensor
+    topology_nodes: Tensor
+    topology_mask: Tensor
+    valid_pair_count: Tensor
+
+
+class TemporalIncidenceEncoder(nn.Module):
+    """Shared endpoint-bound relations followed along their time trajectories."""
+
+    def __init__(
+        self,
+        width: int = 512,
+        *,
+        use_topology: bool = True,
+        strip_phase: bool = False,
+        checkpoint_blocks: bool = True,
+    ) -> None:
+        super().__init__()
+        if type(width) is not int or width < 2:
+            raise ValueError("width must be an integer >=2")
+        self.width = width
+        self.use_topology = use_topology
+        self.strip_phase = strip_phase
+        self.checkpoint_blocks = checkpoint_blocks
+        self.actor_projection = nn.Sequential(nn.Linear(ACTOR_FEATURE_DIM, width), nn.GELU())
+        self.half_edge = nn.Sequential(
+            nn.Linear(2 * width + 6 * PHASE_FEATURE_DIM + 3, width),
+            nn.GELU(),
+            nn.Linear(width, width),
+        )
+        self.symmetric_pair = nn.Sequential(nn.Linear(3 * width, width), nn.GELU())
+        self.node_mlp = nn.Sequential(
+            nn.Linear(3 * width + 2, width), nn.GELU(), nn.Linear(width, width)
+        )
+        self.node_temporal = MaskedTemporalEncoder(width)
+        self.edge_delta = nn.Sequential(
+            nn.Linear(3 * width, width), nn.GELU(), nn.Linear(width, width)
+        )
+        self.edge_temporal = MaskedTemporalEncoder(width)
+        self.group_temporal = MaskedTemporalEncoder(width)
+        self.topology_scale = nn.Parameter(torch.tensor(0.1))
+
+    def _half_edges(
+        self, field: DirectionalPhaseField, hidden: Tensor, start: int, stop: int
+    ) -> tuple[Tensor, Tensor, Tensor, Tensor]:
+        chunk = local_pair_chunk(field, start, stop)
+        device = hidden.device
+        endpoints = torch.tensor(chunk.endpoints.copy(), dtype=torch.int64, device=device)
+        forward = torch.tensor(chunk.features.copy(), dtype=hidden.dtype, device=device)
+        reverse = torch.tensor(chunk.reverse_features(), dtype=hidden.dtype, device=device)
+        if self.strip_phase:
+            forward[..., :2] = 0.0
+            reverse[..., :2] = 0.0
+        left, right = endpoints[:, 0], endpoints[:, 1]
+        roots = torch.tensor(field.root_positions.copy(), dtype=hidden.dtype, device=device)
+        root_mask = torch.tensor(field.root_patch_mask.copy(), device=device)
+        relative_root = roots[right] - roots[left]
+        relative_root = torch.where(
+            (root_mask[left] & root_mask[right])[..., None],
+            relative_root,
+            torch.zeros_like(relative_root),
+        )
+        left_hidden, right_hidden = hidden[left], hidden[right]
+        a = self.half_edge(
+            torch.cat(
+                (
+                    left_hidden,
+                    right_hidden,
+                    forward.flatten(start_dim=2),
+                    relative_root,
+                ),
+                dim=-1,
+            )
+        )
+        b = self.half_edge(
+            torch.cat(
+                (
+                    right_hidden,
+                    left_hidden,
+                    reverse.flatten(start_dim=2),
+                    -relative_root,
+                ),
+                dim=-1,
+            )
+        )
+        mask = torch.tensor(chunk.support_mask.any(axis=-1).copy(), device=device)
+        actor_mask = torch.tensor(field.actor_patch_mask.copy(), device=device)
+        mask = mask & actor_mask[left] & actor_mask[right]
+        return (
+            torch.where(mask[..., None], a, torch.zeros_like(a)),
+            torch.where(mask[..., None], b, torch.zeros_like(b)),
+            endpoints,
+            mask,
+        )
+
+    def _node_block(
+        self, field: DirectionalPhaseField, hidden: Tensor, start: int, stop: int
+    ) -> tuple[Tensor, Tensor, Tensor]:
+        a, b, endpoints, mask = self._half_edges(field, hidden, start, stop)
+        # K x 64 incidence block, not an all-pair K x K adjacency or score map.
+        left = F.one_hot(endpoints[:, 0], field.actor_count).T.to(torch.float64)
+        right = F.one_hot(endpoints[:, 1], field.actor_count).T.to(torch.float64)
+        shape = (field.actor_count, field.patch_count, self.width)
+        a64, b64 = a.to(torch.float64), b.to(torch.float64)
+        total = (left @ a64.flatten(1) + right @ b64.flatten(1)).reshape(shape)
+        second = (left @ a64.square().flatten(1) + right @ b64.square().flatten(1)).reshape(shape)
+        degree = (left + right) @ mask.to(torch.float64)
+        return total, second, degree
+
+    def _edge_block(
+        self,
+        field: DirectionalPhaseField,
+        hidden: Tensor,
+        nodes: Tensor,
+        topology_mask: Tensor,
+        start: int,
+        stop: int,
+    ) -> tuple[Tensor, Tensor]:
+        a, b, endpoints, mask = self._half_edges(field, hidden, start, stop)
+        pair = self.symmetric_pair(torch.cat((a + b, (a - b).abs(), a * b), dim=-1))
+        left_nodes, right_nodes = nodes[endpoints[:, 0]], nodes[endpoints[:, 1]]
+        delta = self.edge_delta(
+            torch.cat(
+                (pair, left_nodes + right_nodes, (left_nodes - right_nodes).abs()),
+                dim=-1,
+            )
+        )
+        eligible = topology_mask[endpoints[:, 0]] | topology_mask[endpoints[:, 1]]
+        delta = torch.where(eligible[..., None], delta, torch.zeros_like(delta))
+        encoded, _ = self.edge_temporal(pair + self.topology_scale.tanh() * delta, mask)
+        return encoded.to(torch.float64).sum(dim=0), mask.to(torch.int64).sum(dim=0)
+
+    def _run_block(self, function, *inputs):
+        if self.checkpoint_blocks and torch.is_grad_enabled():
+            return checkpoint(function, *inputs, use_reentrant=False)
+        return function(*inputs)
+
+    def forward(self, field: DirectionalPhaseField) -> TemporalCoordinationOutput:
+        device = next(self.parameters()).device
+        actor_values = torch.tensor(field.actor_features.copy(), dtype=torch.float32, device=device)
+        actor_mask = torch.tensor(field.actor_patch_mask.copy(), device=device)
+        hidden = self.actor_projection(actor_values)
+        hidden = torch.where(actor_mask[..., None], hidden, torch.zeros_like(hidden))
+        shape = (field.actor_count, field.patch_count, self.width)
+        total = torch.zeros(shape, dtype=torch.float64, device=device)
+        second = torch.zeros_like(total)
+        degree = torch.zeros(shape[:2], dtype=torch.float64, device=device)
+        for start in range(0, field.pair_count, 64):
+            stop = min(field.pair_count, start + 64)
+
+            def node_block(value, block_start=start, block_stop=stop):
+                return self._node_block(field, value, block_start, block_stop)
+
+            local_total, local_second, local_degree = self._run_block(node_block, hidden)
+            total, second, degree = (
+                total + local_total,
+                second + local_second,
+                degree + local_degree,
+            )
+        denominator = degree.clamp_min(1)[..., None]
+        mean = total / denominator
+        m2 = (second / denominator - mean.square()).clamp_min(0)
+        global_mean = total.sum(dim=0) / degree.sum(dim=0).clamp_min(1)[:, None]
+        coverage = degree / (field.actor_count - 1)
+        stats = torch.cat(
+            (
+                hidden,
+                (mean - global_mean).to(hidden.dtype),
+                m2.to(hidden.dtype),
+                coverage[..., None].to(hidden.dtype),
+                (1 - coverage)[..., None].to(hidden.dtype),
+            ),
+            dim=-1,
+        )
+        topology_mask = actor_mask & (degree >= 2) & self.use_topology
+        nodes = self.node_mlp(stats)
+        nodes = torch.where(topology_mask[..., None], nodes, torch.zeros_like(nodes))
+        nodes, _ = self.node_temporal(nodes, topology_mask)
+        nodes = torch.where(topology_mask[..., None], nodes, torch.zeros_like(nodes))
+        patch_sum = torch.zeros((field.patch_count, self.width), dtype=torch.float64, device=device)
+        pair_count = torch.zeros(field.patch_count, dtype=torch.int64, device=device)
+        for start in range(0, field.pair_count, 64):
+            stop = min(field.pair_count, start + 64)
+
+            def edge_block(value, node_value, block_start=start, block_stop=stop):
+                return self._edge_block(
+                    field, value, node_value, topology_mask, block_start, block_stop
+                )
+
+            local_sum, local_count = self._run_block(edge_block, hidden, nodes)
+            patch_sum, pair_count = patch_sum + local_sum, pair_count + local_count
+        patch_mask = pair_count > 0
+        patches = (patch_sum / pair_count.clamp_min(1)[:, None]).to(hidden.dtype)
+        patches, embedding = self.group_temporal(patches[None], patch_mask[None])
+        return TemporalCoordinationOutput(
+            F.normalize(patches[0], dim=-1),
+            patch_mask,
+            F.normalize(embedding[0], dim=-1),
+            nodes,
+            topology_mask,
+            pair_count,
+        )
+
+
+class OrderedCaptureReadout(nn.Module):
+    """Sort by physical window start, then encode the complete ordered capture."""
+
+    def __init__(self, width: int = 512) -> None:
+        super().__init__()
+        self.temporal = MaskedTemporalEncoder(width)
+
+    def forward(self, windows: Tensor, start_seconds: Tensor, mask: Tensor) -> Tensor:
+        if (
+            windows.ndim != 2
+            or start_seconds.shape != windows.shape[:1]
+            or mask.shape != windows.shape[:1]
+        ):
+            raise ValueError("capture needs [W,D] windows and [W] starts/mask")
+        if mask.dtype != torch.bool or not bool(torch.isfinite(start_seconds).all()):
+            raise ValueError("capture starts must be finite and mask bool")
+        ordered = torch.argsort(start_seconds, stable=True)
+        observed_starts = start_seconds[ordered][mask[ordered]]
+        if observed_starts.numel() > 1 and bool(
+            (observed_starts[1:] <= observed_starts[:-1]).any()
+        ):
+            raise ValueError("valid capture windows must have distinct start times")
+        _, embedding = self.temporal(windows[ordered][None], mask[ordered][None])
+        return F.normalize(embedding[0], dim=-1)
+
+
+class CalibratedCoordinationScore(nn.Module):
+    """Blend both branches as cosines on one learned similarity scale."""
+
+    def __init__(self, initial_temperature: float = 0.07) -> None:
+        super().__init__()
+        if not math.isfinite(initial_temperature) or initial_temperature <= 0:
+            raise ValueError("initial_temperature must be positive")
+        self.mixture_logit = nn.Parameter(torch.tensor(0.0))
+        self.log_scale = nn.Parameter(torch.tensor(math.log(1 / initial_temperature)))
+
+    def forward(self, global_cosine: Tensor, coordination_cosine: Tensor) -> Tensor:
+        if global_cosine.shape != coordination_cosine.shape:
+            raise ValueError("both cosine score matrices must have the same shape")
+        for value in (global_cosine, coordination_cosine):
+            if not bool(torch.isfinite(value).all()) or bool((value.abs() > 1.00001).any()):
+                raise ValueError("calibration consumes cosines, not temperature-scaled logits")
+        alpha = self.mixture_logit.sigmoid()
+        scale = self.log_scale.clamp(math.log(1e-3), math.log(100)).exp()
+        return scale * ((1 - alpha) * global_cosine + alpha * coordination_cosine)
+
+
+def complete_relation_text_scores(
+    relation_tokens: Tensor, relation_mask: Tensor, text_embeddings: Tensor
+) -> Tensor:
+    """Match whole endpoint-bound tokens to text; never recombine separate maxima."""
+    if relation_tokens.ndim != 3 or relation_mask.shape != relation_tokens.shape[:2]:
+        raise ValueError("relations need [B,R,D] tokens and [B,R] mask")
+    if relation_mask.dtype != torch.bool or text_embeddings.ndim != 2:
+        raise ValueError("relation mask must be bool and text embeddings [M,D]")
+    if relation_tokens.shape[-1] != text_embeddings.shape[-1]:
+        raise ValueError("relation and text projections must share a width")
+    similarities = torch.einsum(
+        "brd,md->brm",
+        F.normalize(relation_tokens, dim=-1),
+        F.normalize(text_embeddings, dim=-1),
+    )
+    selected = similarities.masked_fill(~relation_mask[..., None], -torch.inf).amax(dim=1)
+    return torch.where(relation_mask.any(dim=1)[:, None], selected, torch.zeros_like(selected))
+
+
+def verified_counterfactual_loss(
+    positive_scores: Tensor,
+    negative_scores: Tensor,
+    verified_negative_mask: Tensor,
+    *,
+    margin: float = 0.2,
+) -> Tensor:
+    """A caller must supply a verified-false mask; generic descriptions get no loss."""
+    if (
+        positive_scores.shape != negative_scores.shape
+        or verified_negative_mask.shape != positive_scores.shape
+        or verified_negative_mask.dtype != torch.bool
+    ):
+        raise ValueError("counterfactual scores and bool verified mask must have matching shapes")
+    if not math.isfinite(margin) or margin < 0:
+        raise ValueError("margin must be finite and nonnegative")
+    penalties = F.softplus(margin + negative_scores - positive_scores)
+    if not bool(verified_negative_mask.any()):
+        return (positive_scores.sum() + negative_scores.sum()) * 0.0
+    return penalties[verified_negative_mask].mean()
+
+
+def coordination_objective(
+    scores: Tensor,
+    positive_mask: Tensor,
+    *,
+    cf_positive_scores: Tensor,
+    cf_negative_scores: Tensor,
+    verified_negative_mask: Tensor,
+    cf_weight: float = 0.2,
+    margin: float = 0.2,
+) -> Tensor:
+    if not math.isfinite(cf_weight) or cf_weight < 0:
+        raise ValueError("cf_weight must be finite and nonnegative")
+    _, _, contrastive = variable_positive_symmetric_infonce(scores, positive_mask)
+    counterfactual = verified_counterfactual_loss(
+        cf_positive_scores, cf_negative_scores, verified_negative_mask, margin=margin
+    )
+    return contrastive + cf_weight * counterfactual
diff --git a/tests/test_phaseset_host_base_qualification.py b/tests/test_phaseset_host_base_qualification.py
index d07624b..1966bc6 100644
--- a/tests/test_phaseset_host_base_qualification.py
+++ b/tests/test_phaseset_host_base_qualification.py
@@ -210,6 +210,115 @@ def _patch_pre_latency(monkeypatch: pytest.MonkeyPatch) -> tuple[object, object]
     )
     real_timer_preflight = controller._validate_formal_latency_timer_support
     real_timer_timeout = controller._formal_latency_timeout
+    journal_writers: dict[Path, object] = {}
+
+    class PortableJournalWriter:
+        """Exercise the real journal schema/state machine without POSIX dir-fd I/O."""
+
+        _EVENT_METHODS = {
+            "session_start": "SESSION_START",
+            "context_ready": "CONTEXT_READY",
+            "visit_started": "VISIT_START",
+            "warmup_completed": "WARMUP_COMPLETED",
+            "timed_sample_completed": "TIMED_SAMPLE_COMPLETED",
+            "visit_ended": "VISIT_END",
+            "terminal": "TERMINAL",
+        }
+
+        def __init__(
+            self,
+            path: Path,
+            session_id: str,
+            bindings: journal.LatencyJournalBindings,
+        ) -> None:
+            self.path = path
+            self.session_id = session_id
+            self.bindings = bindings
+            self.records: list[journal.LatencyJournalRecord] = []
+            self.state = journal._JournalState()
+            self.previous_sha256 = journal.ZERO_SHA256
+            self.closed = False
+
+        def __enter__(self):
+            return self
+
+        def __exit__(self, _type, _value, _traceback) -> None:
+            self.closed = True
+
+        def __getattr__(self, name: str):
+            try:
+                event_type = self._EVENT_METHODS[name]
+            except KeyError as error:
+                raise AttributeError(name) from error
+
+            def append(**payload: object) -> journal.LatencyJournalRecord:
+                if self.closed or len(self.records) >= journal.MAX_EVENTS:
+                    raise journal.LatencyJournalWriteError(
+                        "portable structural journal is closed or exhausted"
+                    )
+                if event_type == "SESSION_START":
+                    # The real writer injects its own admitted bindings here;
+                    # session_start callers do not provide this field.
+                    payload["bindings"] = self.bindings.payload()
+                sequence = len(self.records)
+                next_state = self.state.transition(event_type, payload)
+                raw = journal._canonical_json_bytes(
+                    journal._event_payload(
+                        sequence=sequence,
+                        session_id=self.session_id,
+                        event_type=event_type,
+                        previous_event_sha256=self.previous_sha256,
+                        binding_sha256=self.bindings.sha256,
+                        recorded_unix_ns=sequence + 1,
+                        payload=payload,
+                    )
+                )
+                event_sha256 = _digest(raw)
+                record = journal.LatencyJournalRecord(
+                    sequence=sequence,
+                    event_type=event_type,
+                    previous_event_sha256=self.previous_sha256,
+                    binding_sha256=self.bindings.sha256,
+                    raw=raw,
+                    marker_raw=journal._completion_marker_raw(sequence, event_sha256),
+                )
+                self.records.append(record)
+                self.state = next_state
+                self.previous_sha256 = event_sha256
+                return record
+
+            return append
+
+    def portable_create_journal(
+        parent: Path,
+        *,
+        session_id: str,
+        bindings: journal.LatencyJournalBindings,
+        **_ignored: object,
+    ) -> PortableJournalWriter:
+        path = parent / session_id
+        path.mkdir(mode=0o700, exist_ok=False)
+        writer = PortableJournalWriter(path, session_id, bindings)
+        journal_writers[path.resolve()] = writer
+        return writer
+
+    def portable_read_journal(
+        path: Path,
+        *,
+        expected_bindings: journal.LatencyJournalBindings,
+    ) -> journal.LatencyJournalReadResult:
+        writer = journal_writers.get(path.resolve())
+        if writer is None or writer.bindings != expected_bindings:
+            raise journal.LatencyJournalError(
+                "portable structural journal binding is unavailable"
+            )
+        return journal._read_result(
+            session_id=writer.session_id,
+            bindings=writer.bindings,
+            records=writer.records,
+            tail=(),
+            terminal_outcome=writer.state.terminal_outcome,
+        )
 
     def portable_timer_preflight(**_ignored: object) -> None:
         real_timer_preflight(
@@ -250,6 +359,16 @@ def portable_timer_timeout(seconds: int):
         portable_timer_preflight,
     )
     monkeypatch.setattr(controller, "_formal_latency_timeout", portable_timer_timeout)
+    monkeypatch.setattr(
+        controller.journal_module,
+        "create_latency_progress_journal",
+        portable_create_journal,
+    )
+    monkeypatch.setattr(
+        controller.journal_module,
+        "read_latency_progress_journal",
+        portable_read_journal,
+    )
     return cohort, scored
 
 
diff --git a/tests/test_phaseset_v2_coordination.py b/tests/test_phaseset_v2_coordination.py
new file mode 100644
index 0000000..c72fb79
--- /dev/null
+++ b/tests/test_phaseset_v2_coordination.py
@@ -0,0 +1,308 @@
+"""Mechanism checks, not retrieval results or native-data training."""
+
+from __future__ import annotations
+
+import copy
+import itertools
+import json
+import os
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.contracts import (
+    PreparedGroupBatch,
+    group_commitment,
+    skeleton_to_activity,
+)
+from phaseset_core.directional_phase import (
+    LocalPhaseConfig,
+    canonical_edge_pairs,
+    directional_phase_fields,
+    iter_local_pair_chunks,
+    local_pair_chunk,
+    mean_difference_signal_dct,
+    signed_joint_velocity,
+)
+from phaseset_core.periodic import ResourceLimitError
+from phaseset_core.temporal_coordination import (
+    CalibratedCoordinationScore,
+    OrderedCaptureReadout,
+    TemporalIncidenceEncoder,
+    complete_relation_text_scores,
+    coordination_objective,
+    verified_counterfactual_loss,
+)
+
+
+def motion(phases=(0.0, 0.0, np.pi / 2), *, order=None, padding=0, missing=False):
+    count, length = len(phases), 200
+    skeletons = np.zeros((1, count + padding, length + 1, 22, 3), dtype=np.float32)
+    actor_mask = np.zeros((1, count + padding), dtype=np.bool_)
+    track = np.zeros(skeletons.shape[:-1], dtype=np.bool_)
+    keys = tuple(bytes([index + 1]) * 32 for index in range(count))
+    rows = tuple(range(count)) if order is None else tuple(order)
+    identifiers = [None] * (count + padding)
+    time = np.arange(length) / 20
+    for destination, actor in enumerate(rows):
+        actor_mask[0, destination] = True
+        identifiers[destination] = keys[actor]
+        skeletons[0, destination, :length, :, 1] = np.float32(actor * 2)
+        displacement = np.sin(2 * np.pi * 1.125 * time + phases[actor]).astype(np.float32)
+        skeletons[0, destination, :length, 1:, 0] = displacement[:, None]
+        track[0, destination, :length] = not missing
+    if missing:
+        skeletons[:] = 0.0
+        # A prepared actor must have at least one tracked joint. Isolated
+        # samples provide no observed two-frame velocity or Morlet support.
+        track[0, :count, 0, 0] = True
+    frame = np.zeros((1, length + 1), dtype=np.bool_)
+    frame[:, :length] = True
+    return PreparedGroupBatch(
+        skeletons,
+        actor_mask,
+        frame,
+        track,
+        (tuple(identifiers),),
+        (group_commitment(keys),),
+    )
+
+
+def field(batch=None, **kwargs):
+    return directional_phase_fields(
+        motion() if batch is None else batch,
+        energy_floors=np.zeros(6, dtype=np.float64),
+        **kwargs,
+    )[0]
+
+
+def test_speed_frontend_collision_but_signed_phase_separates_antiphase():
+    inphase = motion((0.0, 0.0))
+    antiphase = motion((0.0, np.pi))
+    old_a, old_b = skeleton_to_activity(inphase), skeleton_to_activity(antiphase)
+    np.testing.assert_allclose(old_a.activities, old_b.activities, atol=2e-6, rtol=1e-6)
+    a, b = (
+        local_pair_chunk(field(inphase), 0, 1),
+        local_pair_chunk(field(antiphase), 0, 1),
+    )
+    assert a.phase_mask[0, :, 1].all() and b.phase_mask[0, :, 1].all()
+    assert float(a.features[0, :, 1, 0].mean()) > 0.999
+    assert float(b.features[0, :, 1, 0].mean()) < -0.999
+
+
+def test_endpoint_exchange_is_complex_conjugation_with_energy_swap():
+    values = local_pair_chunk(field(), 0, 3)
+    reversed_values = values.reverse_features()
+    np.testing.assert_array_equal(reversed_values[..., 0], values.features[..., 0])
+    np.testing.assert_allclose(reversed_values[..., 1], -values.features[..., 1], atol=0)
+    np.testing.assert_array_equal(reversed_values[..., 3], values.features[..., 4])
+    np.testing.assert_array_equal(reversed_values[..., 4], values.features[..., 3])
+
+
+def test_stationary_motion_has_unobservable_phase_and_observed_support():
+    batch = motion((0.0, 0.0))
+    skeletons = np.array(batch.skeletons)
+    skeletons[:, :, :, :, 0] = 0.0
+    stationary = PreparedGroupBatch(
+        skeletons,
+        batch.actor_mask.copy(),
+        batch.frame_mask.copy(),
+        batch.track_mask.copy(),
+        batch.actor_commitments,
+        batch.group_commitments,
+    )
+    values = local_pair_chunk(field(stationary), 0, 1)
+    assert values.support_mask.any()
+    assert not values.phase_mask.any()
+    assert (values.features[..., :5] == 0).all()
+    assert not np.signbit(values.features[..., :5]).any()
+
+
+def test_missing_pelvis_invalidates_relative_velocity_without_fabrication():
+    batch = motion((0.0, 0.0))
+    track = np.array(batch.track_mask)
+    track[0, 0, 20:25, 0] = False
+    skeletons = np.array(batch.skeletons)
+    skeletons[0, 0, 20:25, 0] = 0.0
+    batch = PreparedGroupBatch(
+        skeletons,
+        batch.actor_mask.copy(),
+        batch.frame_mask.copy(),
+        track,
+        batch.actor_commitments,
+        batch.group_commitments,
+    )
+    values, mask = signed_joint_velocity(batch)
+    assert not mask[0, 0, 20:26].any()
+    assert np.all(values[0, 0, 20:26] == 0)
+    assert mask[0, 1, 20:26].all()
+
+
+def test_delay_changes_only_incident_relations():
+    unchanged = local_pair_chunk(field(motion((0.0, 0.0, 0.0))), 0, 3)
+    delayed = local_pair_chunk(field(motion((0.0, 0.0, np.pi / 2))), 0, 3)
+    np.testing.assert_array_equal(unchanged.features[0], delayed.features[0])
+    assert not np.array_equal(unchanged.features[1:], delayed.features[1:])
+
+
+def test_all_actor_permutations_and_padding_leave_physical_relations_exact():
+    reference = local_pair_chunk(field(), 0, 3)
+    for order in itertools.permutations(range(3)):
+        candidate = local_pair_chunk(field(motion(order=order, padding=4)), 0, 3)
+        np.testing.assert_array_equal(reference.features, candidate.features)
+        np.testing.assert_array_equal(reference.support_mask, candidate.support_mask)
+
+
+def test_edge_addressing_and_chunks_cover_every_pair_without_dense_graph():
+    expected = np.array(list(itertools.combinations(range(8), 2)))
+    for start in range(len(expected)):
+        np.testing.assert_array_equal(
+            canonical_edge_pairs(8, start, len(expected)), expected[start:]
+        )
+    source = field(motion(tuple(index / 7 for index in range(8))))
+    chunks = list(iter_local_pair_chunks(source, edge_chunk_size=5))
+    np.testing.assert_array_equal(np.concatenate([value.endpoints for value in chunks]), expected)
+    np.testing.assert_array_equal(
+        np.concatenate([value.features for value in chunks]),
+        local_pair_chunk(source, 0, len(expected)).features,
+    )
+    with pytest.raises(ResourceLimitError):
+        field(motion(), config=LocalPhaseConfig(edge_budget=2))
+
+
+def test_true_mean_difference_dct_retains_cross_terms():
+    signal = np.arange(12, dtype=np.float64).reshape(6, 2) - 4
+    same_mean, same_difference = mean_difference_signal_dct(signal, signal)
+    opposite_mean, opposite_difference = mean_difference_signal_dct(signal, -signal)
+    np.testing.assert_allclose(same_difference, 0, atol=0)
+    np.testing.assert_allclose(opposite_mean, 0, atol=0)
+    np.testing.assert_allclose(same_mean, opposite_difference, atol=0)
+    np.testing.assert_allclose(np.square(same_mean).sum(), np.square(signal).sum(), rtol=1e-14)
+
+
+def test_k2_topology_and_parameter_gradients_are_exact_zero_and_pair_only_equal():
+    torch.manual_seed(1729)
+    full = TemporalIncidenceEncoder(32)
+    pair = copy.deepcopy(full)
+    pair.use_topology = False
+    source = field(motion((0.0, np.pi / 2)))
+    result, pair_result = full(source), pair(source)
+    assert torch.equal(result.embedding, pair_result.embedding)
+    assert torch.count_nonzero(result.topology_nodes) == 0
+    assert not bool(torch.signbit(result.topology_nodes).any())
+    result.embedding.sum().backward()
+    topology = list(full.node_mlp.parameters()) + list(full.node_temporal.parameters())
+    topology += list(full.edge_delta.parameters()) + [full.topology_scale]
+    for parameter in topology:
+        assert parameter.grad is not None and torch.count_nonzero(parameter.grad) == 0
+        assert not bool(torch.signbit(parameter.grad).any())
+
+
+@pytest.mark.parametrize("actor_count", [3, 13])
+def test_temporal_encoder_is_actor_invariant_and_checkpoint_gradients_match(actor_count):
+    torch.manual_seed(2718)
+    model = TemporalIncidenceEncoder(32)
+    eager = copy.deepcopy(model)
+    eager.checkpoint_blocks = False
+    phases = tuple(index / 7 for index in range(actor_count))
+    source = field(motion(phases))
+    first, second = model(source), eager(source)
+    assert torch.equal(first.embedding, second.embedding)
+    permuted = model(field(motion(phases, order=tuple(reversed(range(actor_count))), padding=3)))
+    assert torch.equal(first.embedding, permuted.embedding)
+    first.embedding.sum().backward()
+    second.embedding.sum().backward()
+    for left, right in zip(model.parameters(), eager.parameters(), strict=True):
+        assert left.grad is not None and right.grad is not None
+        torch.testing.assert_close(left.grad, right.grad, rtol=1e-6, atol=1e-7)
+
+
+def test_group_with_no_derived_support_has_exact_zero_neural_output():
+    torch.manual_seed(31415)
+    result = TemporalIncidenceEncoder(32)(field(motion(missing=True)))
+    assert not result.patch_mask.any()
+    assert torch.count_nonzero(result.embedding) == 0
+    assert not torch.signbit(result.embedding).any()
+
+
+def test_capture_readout_distinguishes_AB_from_BA_and_sorts_storage_order():
+    torch.manual_seed(1729)
+    reader = OrderedCaptureReadout(8)
+    windows = torch.randn(2, 8)
+    starts, mask = torch.tensor([0.0, 10.0]), torch.ones(2, dtype=torch.bool)
+    ab, ba = reader(windows, starts, mask), reader(windows.flip(0), starts, mask)
+    assert not torch.allclose(ab, ba)
+    assert torch.equal(ab, reader(windows.flip(0), starts.flip(0), mask))
+    with pytest.raises(ValueError, match="distinct"):
+        reader(windows, torch.tensor([0.0, 0.0]), mask)
+
+
+def test_calibration_can_correct_a_three_logit_error_and_rejects_scaled_inputs():
+    score = CalibratedCoordinationScore(initial_temperature=0.1)
+    base = torch.tensor([[0.5, 0.8]])  # wrong candidate leads by 3 base logits
+    coordination = torch.tensor([[1.0, -1.0]])
+    result = score(base, coordination)
+    assert result[0, 0] > result[0, 1]
+    with pytest.raises(ValueError, match="cosines"):
+        score(base * 10, coordination)
+
+
+def test_complete_relation_matching_never_recombines_components_of_two_relations():
+    tokens = torch.tensor([[[1.0, 0.0], [0.0, 1.0]]])
+    text = torch.tensor([[1.0, 1.0]])
+    result = complete_relation_text_scores(tokens, torch.ones(1, 2, dtype=torch.bool), text)
+    torch.testing.assert_close(result, torch.tensor([[2**-0.5]]))
+
+
+def test_counterfactual_loss_excludes_unverified_examples_and_has_finite_gradients():
+    positive = torch.tensor([0.2, -100.0], requires_grad=True)
+    negative = torch.tensor([0.1, 100.0], requires_grad=True)
+    valid = torch.tensor([True, False])
+    loss = verified_counterfactual_loss(positive, negative, valid)
+    torch.testing.assert_close(loss, torch.nn.functional.softplus(torch.tensor(0.1)))
+    loss.backward()
+    assert positive.grad[1] == negative.grad[1] == 0
+    empty = verified_counterfactual_loss(positive, negative, torch.zeros(2, dtype=torch.bool))
+    assert empty == 0
+    logits = torch.tensor([[2.0, 0.0, 1.0], [0.0, 2.0, 0.0]], requires_grad=True)
+    positives = torch.tensor([[True, False, True], [False, True, False]])
+    objective = coordination_objective(
+        logits,
+        positives,
+        cf_positive_scores=positive,
+        cf_negative_scores=negative,
+        verified_negative_mask=valid,
+    )
+    objective.backward()
+    assert torch.isfinite(logits.grad).all()
+
+
+@pytest.mark.skipif(
+    os.environ.get("PHASESET_V2_CUDA_WITNESS") != "1", reason="server CUDA witness only"
+)
+def test_full_width_cuda_forward_backward_under_two_gib():
+    torch.manual_seed(1729)
+    total = torch.cuda.get_device_properties(0).total_memory
+    torch.cuda.set_per_process_memory_fraction(2 * 1024**3 / total, 0)
+    model = TemporalIncidenceEncoder(512).cuda()
+    output = model(field())
+    assert output.embedding.shape == (512,)
+    assert torch.isfinite(output.embedding).all()
+    output.embedding.sum().backward()
+    assert all(p.grad is not None and torch.isfinite(p.grad).all() for p in model.parameters())
+    assert any(torch.count_nonzero(p.grad) for p in model.node_mlp.parameters())
+    assert torch.cuda.max_memory_allocated() < 2 * 1024**3
+    print(
+        json.dumps(
+            {
+                "witness": "mechanism_only_not_retrieval_result",
+                "width": 512,
+                "actors": 3,
+                "peak_allocated_bytes": torch.cuda.max_memory_allocated(),
+                "peak_reserved_bytes": torch.cuda.max_memory_reserved(),
+                "device": torch.cuda.get_device_name(0),
+            },
+            sort_keys=True,
+        )
+    )

From d984c726bd9fea07b72658b432c98f4b5fd910c2 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 16:22:06 -0500
Subject: [PATCH 24/95] Align PhaseSet V2 with supplied handoff splits and
 temporal contracts

---
 configs/phaseset_v2_experiment_matrix.json | 39 ++++++++++--
 docs/PHASESET_V2_AMENDMENT.md              | 24 ++++++--
 docs/PHASESET_V2_HANDOFF_INTEGRATION.md    | 51 ++++++++++++++++
 src/phaseset_core/directional_phase.py     |  2 +-
 src/phaseset_core/temporal_coordination.py | 54 +++++++++++++---
 tests/test_phaseset_v2_coordination.py     | 71 +++++++++++++++++++++-
 6 files changed, 218 insertions(+), 23 deletions(-)
 create mode 100644 docs/PHASESET_V2_HANDOFF_INTEGRATION.md

diff --git a/configs/phaseset_v2_experiment_matrix.json b/configs/phaseset_v2_experiment_matrix.json
index 9725ee6..b1ec476 100644
--- a/configs/phaseset_v2_experiment_matrix.json
+++ b/configs/phaseset_v2_experiment_matrix.json
@@ -17,16 +17,47 @@
     "max_root_cause_restarts": 3,
     "base_max_epochs": 30,
     "residual_max_epochs": 20,
-    "pilot_hyperparameter_adjustments": 1
+    "pilot_hyperparameter_adjustments": 1,
+    "pilot_max_formal_step_fraction": 0.2,
+    "allowed_uniform_epoch_expansion": {"base": 60, "residual": 40},
+    "epoch_expansion_must_precede_formal_freeze": true
   },
   "primary_split": {
     "train_captures": 400,
     "validation_captures": 96,
     "test_captures": 76,
-    "final_test_components": 3
+    "final_test_components": 3,
+    "validation_component_labels": ["C00"],
+    "sealed_test_component_labels": ["C09", "C11", "C15"]
+  },
+  "pilot_and_group_fold_components": {
+    "pilot_train": ["C01", "C02"],
+    "pilot_validation": ["C03"],
+    "development": ["C00", "C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08", "C10", "C12", "C13", "C14"],
+    "fold_validation": ["C03"],
+    "folds": [
+      {"id": "fold_0", "run_split": "group_fold_1", "held_out": ["C00", "C06", "C10", "C14"], "train": ["C01", "C02", "C04", "C05", "C07", "C08", "C12", "C13"]},
+      {"id": "fold_1", "run_split": "group_fold_2", "held_out": ["C04", "C07", "C12"], "train": ["C00", "C01", "C02", "C05", "C06", "C08", "C10", "C13", "C14"]},
+      {"id": "fold_2", "run_split": "group_fold_3", "held_out": ["C05", "C08", "C13"], "train": ["C00", "C01", "C02", "C04", "C06", "C07", "C10", "C12", "C14"]}
+    ],
+    "reuse_main_checkpoints": false
+  },
+  "group_fold_components_source": "User-supplied PhaseSet_Codex_Handoff_2026-09-26.md, section 5.2",
+  "pilot_allocation": {"TMR-Set": 3, "WaMo-Set": 3, "MIME-Set": 3, "B2": 1, "PhaseSet-V2-head": 2},
+  "mechanism_contract": {
+    "residual_base": "B2",
+    "temporal_layers": 2,
+    "width": 512,
+    "local_statistics_frames": 40,
+    "hop_frames": 20,
+    "initial_relation_mixture": 0.1,
+    "no_periodic_support": "global_branch_fallback",
+    "head_parameter_relative_tolerance": 0.05,
+    "dummy_capacity_parameters": false,
+    "dct_mean_signal": "(a+b)/2",
+    "dct_difference_signal": "a-b",
+    "capture_temporal_state": "consistent_actor_and_edge_tracks_before_set_readout"
   },
-  "pilot_and_group_fold_components": null,
-  "group_fold_components_source": "Referenced ChatGPT handoff archive; attachment not yet available",
   "frozen_strong_baseline": null,
   "success_thresholds": {
     "retrieval_mean_r1_gain_pp": 2,
diff --git a/docs/PHASESET_V2_AMENDMENT.md b/docs/PHASESET_V2_AMENDMENT.md
index b04d6ea..64f936b 100644
--- a/docs/PHASESET_V2_AMENDMENT.md
+++ b/docs/PHASESET_V2_AMENDMENT.md
@@ -39,10 +39,14 @@ A6对平均与差分信号先做DCT，保留交叉项；不以端点自功率平
 seeds为1729/2718/31415。base最多30epoch、residual最多20epoch；
 最多12短pilot、两轮结构返工、全局三次有根因的故障重启，
 正式启动最多90，总保护预算300 GPU-hours。先profile代表性任务，再冻结
-max_steps；pilot只允许一次有依据的统一超参调整。达到限制后交付真实终态。
+max_steps；每个pilot不超过对应正式阶段20%的optimizer steps。
+默认pilot为三个文献家族各3次、B2一次、V2 head两次，总计12次。
+只允许冻结前一次有依据的统一调整；epoch可统一扩大至base60/residual40，
+但仍受300 GPU-hours和已冻结max_steps约束。达到限制后交付真实终态。
 
 性能比较保留文献原架构及辅助损失的合理多人适配；机制比较固定B2、
-文本来源及相近有效容量。TMR普通双塔不能替代完整TMR，WaMo功率头不能
+文本来源及相近有效容量（head参数默认差异不超过5%，不计无用dummy参数）。
+TMR普通双塔不能替代完整TMR，WaMo功率头不能
 替代原方法，MIME多人适配及收敛情况须明确。外折独立训练，权重不跨折复用。
 Inter-X如在其训练集训练，只能称外部双人适用性验证。
 
@@ -52,8 +56,12 @@ A8无反事实目标、A9只校准旧表示。每一贡献必须有对应可推
 
 ## 数据和科学验收
 
-保留400/96/76原split及三个最终test组件。具体pilot/三外折组件来自
-聊天交付包；目前该附件未传入，配置保留null且不得启动正式矩阵。
+保留400/96/76原split，主validation为C00，最终test封存C09/C11/C15。
+用户提供的`PhaseSet_Codex_Handoff_2026-09-26.md`已接入：pilot训练C01/C02、
+验证C03；fold0留出C00/C06/C10/C14，fold1留出C04/C07/C12，
+fold2留出C05/C08/C13。各折训练开发组件中除C03及本折held-out的所有组件，
+checkpoint selection统一C03，不复用主实验checkpoint。
+冻结强对手B*只能按pilot验证从TMR-Set/WaMo-Set/MIME-Set选择。
 没有观察到真实数据时，不执行正式训练或生成主表数字。
 
 真实关系挑战至少200核实最小文本对、100capture、8参与者组件，
@@ -76,8 +84,12 @@ BLOCKED_DATA/RIGHTS/RUNTIME/RELATIONAL_EVIDENCE/BUDGET或FAILED_IMPLEMENTATION
 两个新模块实现固定物理前端、checkpointed时间incidence、
 有序capture读出、共同尺度评分和核实负例损失。它们作为独立研究API，
 尚未接入现有host正式训练、文献适配或数据标注。
-默认40帧patch/20帧hop及关系GRU是待交付包核对的实现起点，
-不作为已冻结的最终超参。新前端的训练能量阈值需重新拟合；
+handoff确认40帧统计/20帧hop，低频核支持约4秒，不能声称1秒精确定位。
+起始时间编码为两层512D；两层状态可跨chunk连续，但host层的整capture
+actor/edge轨迹、halo卷积和关系packet读出尚待接入，不能以窗口embedding
+的有序读出冒充它们。评分起始alpha=0.1，无周期支持时显式回退global。
+本次精确组件配置不等于pilot超参已冻结或正式矩阵可启动。
+新前端的训练能量阈值需重新拟合；
 旧五速度通道阈值不能直接套用。机制检查使用分析运动，不产生检索成绩。
 
 正文与图表最终来自同一冻结aggregate，提供可编辑源、真实限制和失败案例。
diff --git a/docs/PHASESET_V2_HANDOFF_INTEGRATION.md b/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
new file mode 100644
index 0000000..28af36f
--- /dev/null
+++ b/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
@@ -0,0 +1,51 @@
+# 2026-09-26 handoff integration
+
+The user-supplied Markdown handoff resolves the missing component definitions.
+It is preserved privately; no ZIP, licensed data, body model or download grant
+is asserted to have been supplied with it.
+
+The 87-stage JSON now encodes the exact C01/C02 pilot learning, C03 selection,
+ten out-of-fold components in three folds, and sealed C09/C11/C15 exclusion.
+Handoff fold indices 0/1/2 map explicitly to existing run split labels 1/2/3.
+The total remains 87 planned stages, not 87 completed experiments. Strong
+comparator selection and exact step limits still require the real pilot.
+
+The bounded implementation changes are:
+
+- Mean/difference DCT uses `(a+b)/2` and `a-b`, not half the difference.
+- Two masked recurrent layers carry both track states across temporal chunks.
+- Shared-cosine fusion starts at alpha 0.1 and accepts an explicit support mask
+  to fall back to the global branch for unsupported motion rows.
+- Pilot allocation includes B2, each pilot is capped at 20% of its formal
+  optimizer steps, and mechanism head capacity tolerance is 5% without dummy
+  parameters. Any permitted 60/40 epoch expansion precedes formal freeze and
+  remains subject to the 300 GPU-hour cap.
+
+## Observed software verification
+
+Fresh GPT-5.6-Sol review found no blocking issue; semantic review remains
+provisional. Server-only execution of the V2 coordination, Morlet and legacy
+PhasePair signal suites: **65 passed, 1 CUDA-only skip in 9.89 s**. The separate
+512D, three-actor CUDA forward/backward witness: **1 passed in 5.17 s**, RTX
+A6000, peak allocated **180,931,072 bytes**, reserved **205,520,896 bytes**.
+These are analytic mechanism checks, not motion-language retrieval results.
+
+No real native-data training, human relational challenge, sealed-test scores,
+paper aggregate or scientific acceptance is implied. Whole-capture actor/edge
+state orchestration, long-capture halo convolution, relation-packet integration
+with the host runner, and legitimate literature adaptations remain to finish.
+The prior exact-commit CI and anonymous-codeload receipts retain their original
+scope and are not relabeled as verification of this new revision.
+
+## Asset discovery correction
+
+The current [official Embody page](https://www.meta.com/emerging-tech/codec-avatars/embody-3d/)
+exposes an organization-email field and XRCIA acceptance/download form.
+Do not insist on an approval email as the only possible access evidence.
+The [official repository](https://github.com/facebookresearch/embody-3d#download-data)
+documents the resulting download links. Neither this discovery nor the
+repository's code license establishes that restricted captures may be used or
+deployed anywhere; the actual data license and grant must be respected.
+The [SMPL-X model instructions](https://github.com/vchoutas/smplx#downloading-the-model)
+require registration and the model license. A Python package or pytest fixture
+with a model-like filename is not the licensed body-model asset.
diff --git a/src/phaseset_core/directional_phase.py b/src/phaseset_core/directional_phase.py
index a1f27d4..e564405 100644
--- a/src/phaseset_core/directional_phase.py
+++ b/src/phaseset_core/directional_phase.py
@@ -297,4 +297,4 @@ def mean_difference_signal_dct(left: np.ndarray, right: np.ndarray) -> tuple[np.
         np.pi / length * (np.arange(length) + 0.5)[None, :] * np.arange(length)[:, None]
     ) * math.sqrt(2.0 / length)
     basis[0] /= math.sqrt(2.0)
-    return basis @ ((left + right) / 2), basis @ ((left - right) / 2)
+    return basis @ ((left + right) / 2), basis @ (left - right)
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
index b599e7f..51e51d4 100644
--- a/src/phaseset_core/temporal_coordination.py
+++ b/src/phaseset_core/temporal_coordination.py
@@ -26,25 +26,43 @@
 
 
 class MaskedTemporalEncoder(nn.Module):
-    """Missing positions neither update state nor create a biased output."""
+    """Two shared temporal layers; missing positions do not update either state.
+
+    ``encode_chunk`` carries both layers across ordered chunks of one track.
+    Reset its state for a different capture or a declared track discontinuity.
+    """
 
     def __init__(self, width: int) -> None:
         super().__init__()
-        self.cell = nn.GRUCell(width, width)
+        self.cells = nn.ModuleList([nn.GRUCell(width, width), nn.GRUCell(width, width)])
 
     def forward(self, sequence: Tensor, mask: Tensor) -> tuple[Tensor, Tensor]:
+        outputs, states = self.encode_chunk(sequence, mask)
+        return outputs, states[-1]
+
+    def encode_chunk(
+        self, sequence: Tensor, mask: Tensor, states: Tensor | None = None
+    ) -> tuple[Tensor, Tensor]:
         if sequence.ndim != 3 or mask.shape != sequence.shape[:2] or mask.dtype != torch.bool:
             raise ValueError("temporal inputs require [N,P,D] and bool[N,P]")
-        state = sequence.new_zeros((sequence.shape[0], sequence.shape[2]))
+        expected = (len(self.cells), sequence.shape[0], sequence.shape[2])
+        if states is None:
+            states = sequence.new_zeros(expected)
+        elif states.shape != expected or states.dtype != sequence.dtype or states.device != sequence.device:
+            raise ValueError("chunk states must match [2,N,D], dtype and device")
+        layer_states = list(states.unbind(0))
         outputs = []
         for position in range(sequence.shape[1]):
             valid = mask[:, position, None]
-            proposal = self.cell(sequence[:, position], state)
-            state = torch.where(valid, proposal, state)
-            outputs.append(torch.where(valid, state, torch.zeros_like(state)))
+            value = sequence[:, position]
+            for layer, cell in enumerate(self.cells):
+                proposal = cell(value, layer_states[layer])
+                layer_states[layer] = torch.where(valid, proposal, layer_states[layer])
+                value = layer_states[layer]
+            outputs.append(torch.where(valid, value, torch.zeros_like(value)))
         if not outputs:
             raise ValueError("temporal sequence is empty")
-        return torch.stack(outputs, dim=1), state
+        return torch.stack(outputs, dim=1), torch.stack(layer_states)
 
 
 @dataclass(frozen=True)
@@ -284,20 +302,36 @@ def forward(self, windows: Tensor, start_seconds: Tensor, mask: Tensor) -> Tenso
 class CalibratedCoordinationScore(nn.Module):
     """Blend both branches as cosines on one learned similarity scale."""
 
-    def __init__(self, initial_temperature: float = 0.07) -> None:
+    def __init__(self, initial_temperature: float = 0.07, initial_mixture: float = 0.1) -> None:
         super().__init__()
         if not math.isfinite(initial_temperature) or initial_temperature <= 0:
             raise ValueError("initial_temperature must be positive")
-        self.mixture_logit = nn.Parameter(torch.tensor(0.0))
+        if not math.isfinite(initial_mixture) or not 0 < initial_mixture < 1:
+            raise ValueError("initial_mixture must be strictly between zero and one")
+        self.mixture_logit = nn.Parameter(torch.tensor(math.log(initial_mixture / (1 - initial_mixture))))
         self.log_scale = nn.Parameter(torch.tensor(math.log(1 / initial_temperature)))
 
-    def forward(self, global_cosine: Tensor, coordination_cosine: Tensor) -> Tensor:
+    def forward(
+        self,
+        global_cosine: Tensor,
+        coordination_cosine: Tensor,
+        *,
+        coordination_support: Tensor | None = None,
+    ) -> Tensor:
         if global_cosine.shape != coordination_cosine.shape:
             raise ValueError("both cosine score matrices must have the same shape")
         for value in (global_cosine, coordination_cosine):
             if not bool(torch.isfinite(value).all()) or bool((value.abs() > 1.00001).any()):
                 raise ValueError("calibration consumes cosines, not temperature-scaled logits")
         alpha = self.mixture_logit.sigmoid()
+        if coordination_support is not None:
+            if coordination_support.dtype != torch.bool:
+                raise ValueError("coordination_support must be boolean")
+            if coordination_support.shape == global_cosine.shape[:1] and global_cosine.ndim == 2:
+                coordination_support = coordination_support[:, None]
+            elif coordination_support.shape != global_cosine.shape:
+                raise ValueError("coordination_support must match scores or the motion row axis")
+            alpha = torch.where(coordination_support, alpha, torch.zeros_like(alpha))
         scale = self.log_scale.clamp(math.log(1e-3), math.log(100)).exp()
         return scale * ((1 - alpha) * global_cosine + alpha * coordination_cosine)
 
diff --git a/tests/test_phaseset_v2_coordination.py b/tests/test_phaseset_v2_coordination.py
index c72fb79..3758fba 100644
--- a/tests/test_phaseset_v2_coordination.py
+++ b/tests/test_phaseset_v2_coordination.py
@@ -6,6 +6,7 @@
 import itertools
 import json
 import os
+from pathlib import Path
 
 import numpy as np
 import pytest
@@ -28,6 +29,7 @@
 from phaseset_core.periodic import ResourceLimitError
 from phaseset_core.temporal_coordination import (
     CalibratedCoordinationScore,
+    MaskedTemporalEncoder,
     OrderedCaptureReadout,
     TemporalIncidenceEncoder,
     complete_relation_text_scores,
@@ -77,6 +79,39 @@ def field(batch=None, **kwargs):
     )[0]
 
 
+def test_handoff_fold_components_are_exact_disjoint_and_bound_to_87_stage_matrix():
+    matrix = json.loads(
+        (Path(__file__).parents[1] / "configs/phaseset_v2_experiment_matrix.json").read_text()
+    )
+    components = matrix["pilot_and_group_fold_components"]
+    assert components["pilot_train"] == ["C01", "C02"]
+    assert components["pilot_validation"] == components["fold_validation"] == ["C03"]
+    assert [row["held_out"] for row in components["folds"]] == [
+        ["C00", "C06", "C10", "C14"], ["C04", "C07", "C12"], ["C05", "C08", "C13"]
+    ]
+    sealed = set(matrix["primary_split"]["sealed_test_component_labels"])
+    assert sealed == {"C09", "C11", "C15"}
+    development = set(components["development"])
+    assert not development & sealed
+    assert len(development) == 13
+    held_out = []
+    for fold in components["folds"]:
+        train, test = set(fold["train"]), set(fold["held_out"])
+        assert train == development - test - {"C03"}
+        assert not (train | test) & sealed
+        held_out.extend(fold["held_out"])
+        rows = [row for row in matrix["runs"] if row["split"] == fold["run_split"]]
+        assert len(rows) == 9
+        assert {row["seed"] for row in rows} == {1729, 2718, 31415}
+    assert len(held_out) == len(set(held_out)) == 10
+    assert len(matrix["runs"]) == 87
+    assert sum(matrix["pilot_allocation"].values()) == 12
+    assert matrix["pilot_allocation"]["B2"] == 1
+    assert matrix["budget"]["pilot_max_formal_step_fraction"] == 0.2
+    assert matrix["frozen_strong_baseline"] is None  # selection requires real pilot
+    assert all(row["status"] == "PLANNED" for row in matrix["runs"])
+
+
 def test_speed_frontend_collision_but_signed_phase_separates_antiphase():
     inphase = motion((0.0, 0.0))
     antiphase = motion((0.0, np.pi))
@@ -177,7 +212,7 @@ def test_true_mean_difference_dct_retains_cross_terms():
     opposite_mean, opposite_difference = mean_difference_signal_dct(signal, -signal)
     np.testing.assert_allclose(same_difference, 0, atol=0)
     np.testing.assert_allclose(opposite_mean, 0, atol=0)
-    np.testing.assert_allclose(same_mean, opposite_difference, atol=0)
+    np.testing.assert_allclose(2 * same_mean, opposite_difference, atol=0)
     np.testing.assert_allclose(np.square(same_mean).sum(), np.square(signal).sum(), rtol=1e-14)
 
 
@@ -239,7 +274,7 @@ def test_capture_readout_distinguishes_AB_from_BA_and_sorts_storage_order():
 
 
 def test_calibration_can_correct_a_three_logit_error_and_rejects_scaled_inputs():
-    score = CalibratedCoordinationScore(initial_temperature=0.1)
+    score = CalibratedCoordinationScore(initial_temperature=0.1, initial_mixture=0.2)
     base = torch.tensor([[0.5, 0.8]])  # wrong candidate leads by 3 base logits
     coordination = torch.tensor([[1.0, -1.0]])
     result = score(base, coordination)
@@ -248,6 +283,38 @@ def test_calibration_can_correct_a_three_logit_error_and_rejects_scaled_inputs()
         score(base * 10, coordination)
 
 
+def test_two_layer_temporal_state_continues_across_chunks_without_order_loss():
+    torch.manual_seed(1729)
+    encoder = MaskedTemporalEncoder(8)
+    sequence = torch.randn(2, 9, 8)
+    mask = torch.ones(2, 9, dtype=torch.bool)
+    mask[0, 3:5] = False
+    whole, final_states = encoder.encode_chunk(sequence, mask)
+    first, states = encoder.encode_chunk(sequence[:, :4], mask[:, :4])
+    second, continued_states = encoder.encode_chunk(sequence[:, 4:], mask[:, 4:], states)
+    assert states.shape == (2, 2, 8)
+    assert torch.equal(whole, torch.cat((first, second), dim=1))
+    assert torch.equal(final_states, continued_states)
+    assert torch.count_nonzero(whole[0, 3:5]) == 0
+    whole.sum().backward()
+    assert all(p.grad is not None and torch.isfinite(p.grad).all() for p in encoder.parameters())
+
+
+def test_calibration_default_and_unsupported_motion_falls_back_without_relation_gradient():
+    score = CalibratedCoordinationScore(initial_temperature=0.1)
+    torch.testing.assert_close(score.mixture_logit.sigmoid(), torch.tensor(0.1))
+    base = torch.tensor([[0.5, -0.8], [0.2, -0.1]])
+    relation = torch.tensor([[-1.0, 1.0], [0.9, 0.8]], requires_grad=True)
+    result = score(base, relation, coordination_support=torch.tensor([False, True]))
+    torch.testing.assert_close(result[0], 10 * base[0])
+    result.sum().backward()
+    assert torch.count_nonzero(relation.grad[0]) == 0
+    assert torch.count_nonzero(relation.grad[1]) == 2
+    assert score.mixture_logit.grad is not None and score.mixture_logit.grad != 0
+    with pytest.raises(ValueError, match="boolean"):
+        score(base, relation, coordination_support=torch.ones(2))
+
+
 def test_complete_relation_matching_never_recombines_components_of_two_relations():
     tokens = torch.tensor([[[1.0, 0.0], [0.0, 1.0]]])
     text = torch.tensor([[1.0, 1.0]])

From 3a51ccfee31a29009fa8d616cce543dc26022bb4 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 16:28:10 -0500
Subject: [PATCH 25/95] Refresh tracked release manifest after handoff
 integration

---
 RELEASE_FILES.sha256 | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index f6289a3..891ff3d 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -15,7 +15,7 @@ e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
-77e1f34b68a74c455a391916dcdd4b508d24307be5cd29b2e3484eb9838cc80f	24054	configs/phaseset_v2_experiment_matrix.json
+238dd64decf965481abf5d16d6226fc7c6beca0ac79a71c01ed777b4b3878b3a	25670	configs/phaseset_v2_experiment_matrix.json
 2970469ad0787e74ab4fabce2275486eb50c30c38f3cb7cf8f2800551d16991f	9540	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
 e32c97a223c2b04278b9ce3c5d3b25590b390bc82dbf29e99b06d08d6c96b268	3054	docs/CACHED_RESIDUAL_TRAINING.md
@@ -66,7 +66,8 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
-53149d3cbfa8997ce46bf397320783af6b96a3b13f6532baccf4680cc7500273	5185	docs/PHASESET_V2_AMENDMENT.md
+55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
+acb763608bcf9f7ba0b25b7492a30af06def41e7471cc89a9c06af7a6c8ef46f	2999	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
@@ -150,7 +151,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
-82c8c4a10263d12ccc94518ecbb08ed8902d7352104bd5d50e6dd1ae9e749efb	13149	src/phaseset_core/directional_phase.py
+ae58fa5a0b26e80a2be244900273d6f67b8ef1a194e4461b0dd897303f4f05a9	13143	src/phaseset_core/directional_phase.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
@@ -174,7 +175,7 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
-d29db7a6cec9a7bf02b16425961b2075a24d25a4774171d19157327d0b18cd11	15922	src/phaseset_core/temporal_coordination.py
+1676dd74a2a8b7034ac0e9f5a688ea99eae8559d8c33bd161beb27693e4bb472	17791	src/phaseset_core/temporal_coordination.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
@@ -266,7 +267,7 @@ d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/tes
 e6e9600a244af88f731e9cc981f19478b5760db8c254dfd8455960ffe789ef1a	12321	tests/test_phaseset_streaming_autograd.py
 ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test_phaseset_torch_periodic.py
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
-1eb98f77a10ee360364fde1df59bbab8574e16a0ff3cb5a508521024719ef61f	12772	tests/test_phaseset_v2_coordination.py
+444e99989264742972aa93e8b5667c5948fc0efb7c6d91d9eb6f7fb80b300af8	16168	tests/test_phaseset_v2_coordination.py
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
 45b42266bc1c42c36af7eb7d07be2fc85d8b1d0a539ffb3674f98b47e1327254	4973	tests/test_release_artifact_audit.py
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py

From 7d893151baece2575094e13b9b9c74bb440c4b1a Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 17:13:08 -0500
Subject: [PATCH 26/95] Add halo convolution and actor history with native
 asset qualification

---
 RELEASE_FILES.sha256                       | 10 ++--
 docs/EMBODY_ADAPTER_INTEGRATION.md         | 39 ++++++++++++++--
 docs/PHASESET_V2_HANDOFF_INTEGRATION.md    | 17 ++++++-
 src/phaseset_core/directional_phase.py     | 37 +++++++++++----
 src/phaseset_core/temporal_coordination.py | 15 +++++-
 tests/test_phaseset_v2_coordination.py     | 53 +++++++++++++++++++++-
 6 files changed, 150 insertions(+), 21 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 891ff3d..e0958e1 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -24,7 +24,7 @@ e32c97a223c2b04278b9ce3c5d3b25590b390bc82dbf29e99b06d08d6c96b268	3054	docs/CACHE
 e66e0530b02a222d0b3c46590290d8ebcdafa21d7cd3acf2d751355cc1f8e558	9492	docs/CAPTURE_VALIDATION.md
 e9331b64297d6c4586ccb22022c972fad657ac2b3f9202203be62b067a38ca82	3686	docs/CUDA_LIFECYCLE_OBSERVATION.md
 e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA_PROTOCOL.md
-2a18716b57d21362672faaaf76944821764302064b7d7d931f9ec6a0d3f4c598	5564	docs/EMBODY_ADAPTER_INTEGRATION.md
+887fcea5997d1ace10037dcc80139221de95e4d32f01f633c1bf44731a19f53b	7646	docs/EMBODY_ADAPTER_INTEGRATION.md
 3633e37c3eae3289d552a363e565bee027ebd83661f1c44ea136f9498de9e5af	19059	docs/EXECUTION_PROTOCOL.md
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
@@ -67,7 +67,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
-acb763608bcf9f7ba0b25b7492a30af06def41e7471cc89a9c06af7a6c8ef46f	2999	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
+282226f8a73af4c9dd0b61528acd1299ec5bd06685e0622c45562fa05c7d02d5	3858	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
@@ -151,7 +151,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
-ae58fa5a0b26e80a2be244900273d6f67b8ef1a194e4461b0dd897303f4f05a9	13143	src/phaseset_core/directional_phase.py
+53f0800ba8403a7c38abb27923cc62a7ef6560ba856a469e538fdea74eb55a12	14230	src/phaseset_core/directional_phase.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
@@ -175,7 +175,7 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
-1676dd74a2a8b7034ac0e9f5a688ea99eae8559d8c33bd161beb27693e4bb472	17791	src/phaseset_core/temporal_coordination.py
+62a95b37532c00bad794af4127c6bd4b0d7b61662d834b7f6e0a90aed6d943f9	18209	src/phaseset_core/temporal_coordination.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
@@ -267,7 +267,7 @@ d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/tes
 e6e9600a244af88f731e9cc981f19478b5760db8c254dfd8455960ffe789ef1a	12321	tests/test_phaseset_streaming_autograd.py
 ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test_phaseset_torch_periodic.py
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
-444e99989264742972aa93e8b5667c5948fc0efb7c6d91d9eb6f7fb80b300af8	16168	tests/test_phaseset_v2_coordination.py
+a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
 45b42266bc1c42c36af7eb7d07be2fc85d8b1d0a539ffb3674f98b47e1327254	4973	tests/test_release_artifact_audit.py
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
diff --git a/docs/EMBODY_ADAPTER_INTEGRATION.md b/docs/EMBODY_ADAPTER_INTEGRATION.md
index dc05ded..f6e953b 100644
--- a/docs/EMBODY_ADAPTER_INTEGRATION.md
+++ b/docs/EMBODY_ADAPTER_INTEGRATION.md
@@ -1,8 +1,39 @@
 # Embody SMPL-X adapter integration
 
-Status: implemented integration seam. No Embody asset was available or evaluated, and
-the contract fixtures in the unit tests are not empirical data or experimental
-results.
+Status: implemented integration seam. A caller-licensed official locked-head
+neutral model passed a server loader/batch qualification on 2026-09-26.
+This is not native-capture training or retrieval evidence. Contract fixtures
+remain nonempirical and are not experimental results.
+
+## Actual asset qualification, 2026-09-26
+
+The official Embody example uses locked-head SMPL-X. The SMPL-X download page
+warns that its shape space differs from v1.1; do not silently substitute that
+model. The observed `lockedhead_20230207` neutral NPZ matched its official
+archive's checksum. With the existing evaluator and isolated server runtime,
+the `3 → 1 → 3` frame-batch loader check passed in 4.865 seconds, returning
+finite float32 `[3,22,3]` joints and repeatable outputs. This qualifies loading
+and short-tail compatibility, not a learned model or a dataset benchmark.
+
+The downloaded human-text archives were independently matched to the existing
+participant-disjoint component manifest: 572 eligible captures, 16 components,
+and 400/96/76 captures with no participant/capture overlap. Native holistic
+file presence is **398/96/76**: two training captures lack holistic text.
+As preregistered, those are excluded from the holistic task before any model
+scores, with no split reassignment. The 400-capture training universe remains
+unchanged for tasks whose required annotations are present. Test caption files
+were sealed by digest without decoding their semantic contents.
+
+Dataset archives, body models, identifiers, signed URLs, private receipts and
+paths remain private. These observations do not grant access to another user;
+each reproducer must obtain their own official dataset/model authorization.
+The next server check decoded one actual four-actor pilot-training window:
+native tracks had 3600 frames per actor, the first accepted 300-frame source
+window converted to finite float32 `[4,200,22,3]` in 11.048 seconds with zero
+interpolated actor frames. This uses real Embody parameters and the official
+body model, not fixture arrays. It does not read caption semantics, create
+retrieval scores, persist a prepared training cache, or train a model.
+Full-corpus motion auditing and the formal training matrix remain separate gates.
 
 ## Native boundary
 
@@ -70,7 +101,7 @@ dynamic collation.
 
 ## Access and license gates not implemented in code
 
-- Embody data access is not established. The official repository says a user
+- Embody data access is not established by this adapter. The official repository says a user
   must complete the release form before downloads work, that approved access
   supplies 21 links, and that the dataset is governed by the XRCIA license.
   This adapter neither requests nor proves that approval.
diff --git a/docs/PHASESET_V2_HANDOFF_INTEGRATION.md b/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
index 28af36f..49d64c6 100644
--- a/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
+++ b/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
@@ -32,7 +32,7 @@ These are analytic mechanism checks, not motion-language retrieval results.
 
 No real native-data training, human relational challenge, sealed-test scores,
 paper aggregate or scientific acceptance is implied. Whole-capture actor/edge
-state orchestration, long-capture halo convolution, relation-packet integration
+state host orchestration, disk-backed response streaming, relation-packet integration
 with the host runner, and legitimate literature adaptations remain to finish.
 The prior exact-commit CI and anonymous-codeload receipts retain their original
 scope and are not relabeled as verification of this new revision.
@@ -49,3 +49,18 @@ deployed anywhere; the actual data license and grant must be respected.
 The [SMPL-X model instructions](https://github.com/vchoutas/smplx#downloading-the-model)
 require registration and the model license. A Python package or pytest fixture
 with a model-like filename is not the licensed body-model asset.
+
+## Capture-core follow-up, 2026-09-26
+
+Shared two-layer actor temporal states now precede half-edge construction;
+actor histories are not pooled or reset between local patches in one field.
+Morlet convolution uses complete kernel halos around each central response
+block (default 1024 frames), computing each response once. The resulting
+complete response cache still has an explicit 1 GiB limit: this is bounded
+temporary convolution, **not** a completed disk-streaming host.
+
+The updated modules passed server CPU checks: **70 passed, 1 CUDA-only skip**
+in 13.64 seconds, including chunk lengths 1/7/31/64 versus unchunked responses,
+gap-mask equality, actor-state continuity, K=2 exact-zero regression, and the
+legacy signal/Morlet tests. Before/after source snapshots are identical.
+This is software qualification; formal V2 training remains 0/87.
diff --git a/src/phaseset_core/directional_phase.py b/src/phaseset_core/directional_phase.py
index e564405..1800564 100644
--- a/src/phaseset_core/directional_phase.py
+++ b/src/phaseset_core/directional_phase.py
@@ -30,9 +30,16 @@ class LocalPhaseConfig:
     epsilon: float = 1e-12
     edge_budget: int = 32_768
     max_response_bytes: int = 1_073_741_824
+    convolution_chunk_frames: int = 1024
 
     def __post_init__(self) -> None:
-        for name in ("patch_frames", "hop_frames", "edge_budget", "max_response_bytes"):
+        for name in (
+            "patch_frames",
+            "hop_frames",
+            "edge_budget",
+            "max_response_bytes",
+            "convolution_chunk_frames",
+        ):
             if type(getattr(self, name)) is not int or getattr(self, name) < 1:
                 raise ValueError(f"{name} must be a positive integer")
         if not math.isfinite(self.epsilon) or self.epsilon <= 0:
@@ -154,13 +161,27 @@ def directional_phase_fields(
                 valid = np.zeros(transformed.shape, dtype=np.bool_)
                 positions = np.zeros(0, dtype=np.float64)
             else:
-                windows = np.lib.stride_tricks.sliding_window_view(values, band.length, axis=1)
-                mask_windows = np.lib.stride_tricks.sliding_window_view(masks, band.length, axis=1)
-                valid = mask_windows.all(axis=-1).reshape(count, -1, 22, 3)
-                transformed = np.einsum("ktcl,l->ktc", windows, band.kernel).reshape(
-                    count, -1, 22, 3
-                )
-                transformed = np.where(valid, transformed, 0.0 + 0.0j)
+                response_count = length - band.length + 1
+                transformed = np.zeros((count, response_count, 22, 3), dtype=np.complex128)
+                valid = np.zeros(transformed.shape, dtype=np.bool_)
+                # Each central response is written once. The input extends by
+                # kernel_length-1 past the response block: full physical halo,
+                # never independent chunk convolution or a shortened kernel.
+                for start in range(0, response_count, config.convolution_chunk_frames):
+                    stop = min(response_count, start + config.convolution_chunk_frames)
+                    halo_stop = stop + band.length - 1
+                    windows = np.lib.stride_tricks.sliding_window_view(
+                        values[:, start:halo_stop], band.length, axis=1
+                    )
+                    mask_windows = np.lib.stride_tricks.sliding_window_view(
+                        masks[:, start:halo_stop], band.length, axis=1
+                    )
+                    local_valid = mask_windows.all(axis=-1).reshape(count, stop - start, 22, 3)
+                    local_values = np.einsum("ktcl,l->ktc", windows, band.kernel).reshape(
+                        count, stop - start, 22, 3
+                    )
+                    valid[:, start:stop] = local_valid
+                    transformed[:, start:stop] = np.where(local_valid, local_values, 0.0 + 0.0j)
                 positions = np.arange(length - band.length + 1) + (band.length - 1) / 2
             responses.append(_readonly(np.ascontiguousarray(transformed)))
             response_masks.append(_readonly(np.ascontiguousarray(valid)))
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
index 51e51d4..1422a6f 100644
--- a/src/phaseset_core/temporal_coordination.py
+++ b/src/phaseset_core/temporal_coordination.py
@@ -48,7 +48,11 @@ def encode_chunk(
         expected = (len(self.cells), sequence.shape[0], sequence.shape[2])
         if states is None:
             states = sequence.new_zeros(expected)
-        elif states.shape != expected or states.dtype != sequence.dtype or states.device != sequence.device:
+        elif (
+            states.shape != expected
+            or states.dtype != sequence.dtype
+            or states.device != sequence.device
+        ):
             raise ValueError("chunk states must match [2,N,D], dtype and device")
         layer_states = list(states.unbind(0))
         outputs = []
@@ -94,6 +98,7 @@ def __init__(
         self.strip_phase = strip_phase
         self.checkpoint_blocks = checkpoint_blocks
         self.actor_projection = nn.Sequential(nn.Linear(ACTOR_FEATURE_DIM, width), nn.GELU())
+        self.actor_temporal = MaskedTemporalEncoder(width)
         self.half_edge = nn.Sequential(
             nn.Linear(2 * width + 6 * PHASE_FEATURE_DIM + 3, width),
             nn.GELU(),
@@ -212,6 +217,10 @@ def forward(self, field: DirectionalPhaseField) -> TemporalCoordinationOutput:
         actor_mask = torch.tensor(field.actor_patch_mask.copy(), device=device)
         hidden = self.actor_projection(actor_values)
         hidden = torch.where(actor_mask[..., None], hidden, torch.zeros_like(hidden))
+        # Preserve each actor's own history before forming endpoint-bound
+        # relations. A field spans one capture; no set pooling or window reset
+        # is inserted between local patches along a consistent actor track.
+        hidden, _ = self.actor_temporal(hidden, actor_mask)
         shape = (field.actor_count, field.patch_count, self.width)
         total = torch.zeros(shape, dtype=torch.float64, device=device)
         second = torch.zeros_like(total)
@@ -308,7 +317,9 @@ def __init__(self, initial_temperature: float = 0.07, initial_mixture: float = 0
             raise ValueError("initial_temperature must be positive")
         if not math.isfinite(initial_mixture) or not 0 < initial_mixture < 1:
             raise ValueError("initial_mixture must be strictly between zero and one")
-        self.mixture_logit = nn.Parameter(torch.tensor(math.log(initial_mixture / (1 - initial_mixture))))
+        self.mixture_logit = nn.Parameter(
+            torch.tensor(math.log(initial_mixture / (1 - initial_mixture)))
+        )
         self.log_scale = nn.Parameter(torch.tensor(math.log(1 / initial_temperature)))
 
     def forward(
diff --git a/tests/test_phaseset_v2_coordination.py b/tests/test_phaseset_v2_coordination.py
index 3758fba..da46574 100644
--- a/tests/test_phaseset_v2_coordination.py
+++ b/tests/test_phaseset_v2_coordination.py
@@ -87,7 +87,9 @@ def test_handoff_fold_components_are_exact_disjoint_and_bound_to_87_stage_matrix
     assert components["pilot_train"] == ["C01", "C02"]
     assert components["pilot_validation"] == components["fold_validation"] == ["C03"]
     assert [row["held_out"] for row in components["folds"]] == [
-        ["C00", "C06", "C10", "C14"], ["C04", "C07", "C12"], ["C05", "C08", "C13"]
+        ["C00", "C06", "C10", "C14"],
+        ["C04", "C07", "C12"],
+        ["C05", "C08", "C13"],
     ]
     sealed = set(matrix["primary_split"]["sealed_test_component_labels"])
     assert sealed == {"C09", "C11", "C15"}
@@ -135,6 +137,55 @@ def test_endpoint_exchange_is_complex_conjugation_with_energy_swap():
     np.testing.assert_array_equal(reversed_values[..., 4], values.features[..., 3])
 
 
+@pytest.mark.parametrize("chunk_frames", [1, 7, 31, 64])
+def test_halo_convolution_matches_unchunked_responses_and_gap_masks(chunk_frames):
+    batch = motion()
+    coordinates, tracked = np.array(batch.skeletons), np.array(batch.track_mask)
+    tracked[0, 1, 63:67] = False
+    coordinates[0, 1, 63:67] = 0.0
+    batch = PreparedGroupBatch(
+        coordinates,
+        batch.actor_mask.copy(),
+        batch.frame_mask.copy(),
+        tracked,
+        batch.actor_commitments,
+        batch.group_commitments,
+    )
+    reference = field(batch, config=LocalPhaseConfig(convolution_chunk_frames=1000))
+    candidate = field(batch, config=LocalPhaseConfig(convolution_chunk_frames=chunk_frames))
+    for left, right in zip(reference.responses, candidate.responses, strict=True):
+        np.testing.assert_array_equal(left, right)
+    for left, right in zip(reference.response_masks, candidate.response_masks, strict=True):
+        np.testing.assert_array_equal(left, right)
+    assert not candidate.response_masks[0][1].all()
+    np.testing.assert_array_equal(
+        local_pair_chunk(reference, 0, 3).features,
+        local_pair_chunk(candidate, 0, 3).features,
+    )
+
+
+def test_actor_temporal_history_is_encoded_before_incident_messages():
+    torch.manual_seed(31415)
+    model = TemporalIncidenceEncoder(8)
+    source = field()
+    captured = []
+    handle = model.actor_temporal.register_forward_hook(
+        lambda module, inputs, output: captured.append((inputs, output))
+    )
+    try:
+        model(source)
+    finally:
+        handle.remove()
+    assert len(captured) == 1
+    (values, mask), (outputs, final) = captured[0]
+    whole, whole_states = model.actor_temporal.encode_chunk(values, mask)
+    first, carried = model.actor_temporal.encode_chunk(values[:, :3], mask[:, :3])
+    tail, carried = model.actor_temporal.encode_chunk(values[:, 3:], mask[:, 3:], carried)
+    assert torch.equal(outputs, torch.cat((first, tail), dim=1))
+    assert torch.equal(outputs, whole) and torch.equal(final, whole_states[-1])
+    assert torch.equal(carried, whole_states)
+
+
 def test_stationary_motion_has_unobservable_phase_and_observed_support():
     batch = motion((0.0, 0.0))
     skeletons = np.array(batch.skeletons)

From d9e5866886a85e3da4ef993fcdf340985fe8c1b7 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 17:42:50 -0500
Subject: [PATCH 27/95] Preserve continuous capture coordinates and actor-edge
 phase trajectories

---
 RELEASE_FILES.sha256                      |   8 +-
 docs/EMBODY_ADAPTER_INTEGRATION.md        |  12 ++
 docs/PHASESET_V2_HANDOFF_INTEGRATION.md   |  36 ++++
 src/phaseset_core/continuous_capture.py   | 250 ++++++++++++++++++++++
 src/phaseset_core/directional_phase.py    |  81 ++++++-
 tests/test_phaseset_continuous_capture.py | 152 +++++++++++++
 6 files changed, 526 insertions(+), 13 deletions(-)
 create mode 100644 src/phaseset_core/continuous_capture.py
 create mode 100644 tests/test_phaseset_continuous_capture.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index e0958e1..3b251d0 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -24,7 +24,7 @@ e32c97a223c2b04278b9ce3c5d3b25590b390bc82dbf29e99b06d08d6c96b268	3054	docs/CACHE
 e66e0530b02a222d0b3c46590290d8ebcdafa21d7cd3acf2d751355cc1f8e558	9492	docs/CAPTURE_VALIDATION.md
 e9331b64297d6c4586ccb22022c972fad657ac2b3f9202203be62b067a38ca82	3686	docs/CUDA_LIFECYCLE_OBSERVATION.md
 e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA_PROTOCOL.md
-887fcea5997d1ace10037dcc80139221de95e4d32f01f633c1bf44731a19f53b	7646	docs/EMBODY_ADAPTER_INTEGRATION.md
+579b0a0baefe6cb92cdf25beee878ec0cd3c4ac2f5b9d8d4cfe31dd9e909c41d	8390	docs/EMBODY_ADAPTER_INTEGRATION.md
 3633e37c3eae3289d552a363e565bee027ebd83661f1c44ea136f9498de9e5af	19059	docs/EXECUTION_PROTOCOL.md
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
@@ -67,7 +67,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
-282226f8a73af4c9dd0b61528acd1299ec5bd06685e0622c45562fa05c7d02d5	3858	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
+960ed92900b4c17c81ed7e84af8bc2a33d89ac739106b559a363d86d3350ddae	6049	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
@@ -148,10 +148,11 @@ d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phases
 0eaf0057ccf6ab6a4b83c1fa6e649399c4f783a79fab301e0778bafb5699daa9	47086	src/phaseset_core/capture_prepared_storage.py
 7a146f3c81122ed6492fb6dc30194f68e6e3b86e2a25d29a5313607eb18470b6	42026	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
+8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
-53f0800ba8403a7c38abb27923cc62a7ef6560ba856a469e538fdea74eb55a12	14230	src/phaseset_core/directional_phase.py
+409f4d0a2b765ec7498d8307fdf7be2daf82b131d06b6a3a3ddb70d4eb8bd151	16128	src/phaseset_core/directional_phase.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
@@ -233,6 +234,7 @@ beacbd78c68771001b60ff7c6271e6d99c92d24d83f251e1074fc4ce50d1216c	15511	tests/tes
 0281be4e47421dc8c52839ea99a45d32531f6ce21a6f726a6ec32ea5ca6a3d4b	8300	tests/test_phaseset_capture_validation_cached.py
 5dbbb81d6725f1185f4c12dc85653b6eac1e47ae2ffb7fb36f5b951eb8dea632	8848	tests/test_phaseset_cli.py
 c3060c5567214d195e024152f2d427ff3a63f3cc5fef1ded01e73d6887a3431e	13653	tests/test_phaseset_cli_production_bridge.py
+02b7700b556d31b9d49ebcaaa3500278ea7e46f47314f9414896abf73ba5b74c	7046	tests/test_phaseset_continuous_capture.py
 a53fe5c6b53adb5c80421842f806fc45e8305360348d7065aa0ddfb0b9316dcb	6546	tests/test_phaseset_contracts.py
 a74143036a41dd9d8779dc4a9b70fe20e5b38d013ef23778d13e73c11273dbd8	26066	tests/test_phaseset_controls.py
 a17a50e801c389b4e24b2840f2aafcb7b476cbef86e2e142ad0af6245a54718f	4515	tests/test_phaseset_data.py
diff --git a/docs/EMBODY_ADAPTER_INTEGRATION.md b/docs/EMBODY_ADAPTER_INTEGRATION.md
index f6e953b..e7dc5a2 100644
--- a/docs/EMBODY_ADAPTER_INTEGRATION.md
+++ b/docs/EMBODY_ADAPTER_INTEGRATION.md
@@ -99,6 +99,18 @@ body-22 extraction defense in depth, 300-frame windows, 30-to-20 Hz resampling,
 missing-run rejection/interpolation, group canonicalization, augmentation, and
 dynamic collation.
 
+For the V2 continuous path, pass the same complete numeric capture to
+`prepare_continuous_capture`, then `continuous_directional_phase_field`.
+This preserves one coordinate frame, absolute window positions, anti-alias
+and Morlet halos, and actor/edge trajectories across ten-second boundaries.
+It does not replace the original window API or authorize data access.
+
+The licensed native qualification now also covers one complete four-person
+120-second training capture: official SMPL-X conversion to `[4,2400,22,3]`,
+12 accepted windows, 119 patches and an untrained 512D temporal forward, in
+63.55 seconds on server CPU. No trained scores or captions were consumed.
+The entire main-corpus conversion, calibration and training are not implied.
+
 ## Access and license gates not implemented in code
 
 - Embody data access is not established by this adapter. The official repository says a user
diff --git a/docs/PHASESET_V2_HANDOFF_INTEGRATION.md b/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
index 49d64c6..952dde4 100644
--- a/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
+++ b/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
@@ -64,3 +64,39 @@ in 13.64 seconds, including chunk lengths 1/7/31/64 versus unchunked responses,
 gap-mask equality, actor-state continuity, K=2 exact-zero regression, and the
 legacy signal/Morlet tests. Before/after source snapshots are identical.
 This is software qualification; formal V2 training remains 0/87.
+
+## Continuous native capture follow-up, 2026-09-26
+
+`prepare_continuous_capture` now preserves one group origin/yaw and the complete
+absolute timeline across accepted ten-second intervals. The original 300-frame
+missing-data decisions are unchanged; rejected windows remain zero/masked on
+the timeline, rather than concatenating their neighbors. Anti-alias FIR chunks
+include the full halo, and targets touching rejected FIR support are masked.
+Original short-gap observation masks remain false. Complete windows are used;
+any trailing source frames are explicitly recorded.
+
+`continuous_directional_phase_field` consumes the unbatched capture seam,
+preserving signed velocity and six-band support across window boundaries. The
+legacy short-window `PreparedGroupBatch` contract has not been relaxed.
+Actor, node, edge and group temporal encoders therefore see one whole field,
+not separately initialized ten-second fields. Base-window views share the
+same capture-wide coordinates and retain their absolute source starts.
+
+Server-only checks passed **106 tests, 1 CUDA-only skip in 18.24 s**, including
+FIR chunk lengths 1/7/31/300/1024, actor permutation bit equality, rejected
+intervals, retained short gaps, shared yaw, absolute window offsets, cross-seam
+Morlet support, the original preprocessing/pipeline and legacy signal suites.
+Source snapshots before and after execution are identical.
+
+A real native four-actor, 120-second training capture passed the complete
+official-model conversion and **untrained** 512D temporal forward in **63.55 s**:
+3600 source frames, `[4,2400,22,3]` at 20 Hz, 12 accepted windows, 119 local
+patches and six complete-graph edges. There was no caption semantic reading,
+trained retrieval score, optimization or test evaluation. Zero energy floors
+were solely a numerical fixture, not production calibration.
+
+This closes the continuous numeric seam and a native full-capture forward,
+not the full-corpus training host, disk-backed cache/backward scheduling,
+training-only floor fit, literature baselines or scientific experiments.
+The response cache still enforces its 1 GiB RAM limit without sampling actors
+or edges. Formal training remains **0/87**.
diff --git a/src/phaseset_core/continuous_capture.py b/src/phaseset_core/continuous_capture.py
new file mode 100644
index 0000000..973f666
--- /dev/null
+++ b/src/phaseset_core/continuous_capture.py
@@ -0,0 +1,250 @@
+"""Capture-wide coordinates and resampling, without ten-second state resets.
+
+The original window API remains unchanged. This V2 seam preserves absolute
+time, applies the same source-window missing-data decisions, and carries one
+group origin/yaw across every accepted window. Rejected intervals stay on the
+timeline as exact +0 with false masks; they are never concatenated away.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+import math
+
+import numpy as np
+
+from .contracts import group_commitment
+from .pipeline import (
+    PrivateCaptureArrays,
+    PreparationResourceLimit,
+    PreparedGroupSample,
+    _capture_lineage_sha256,
+    _window_lineage_sha256,
+)
+from .preprocessing import (
+    SOURCE_WINDOW_FRAMES,
+    TARGET_WINDOW_FRAMES,
+    PreprocessingError,
+    WindowDecision,
+    assess_missing_window,
+    canonicalize_group,
+    design_antialias_fir,
+    interpolate_accepted_gaps,
+)
+
+
+@dataclass(frozen=True)
+class PreparedContinuousCapture:
+    skeletons: np.ndarray = field(repr=False)  # float32 [K,T,22,3], absolute 20 Hz
+    track_mask: np.ndarray = field(repr=False)  # bool [K,T,22]
+    actor_commitments: tuple[bytes, ...]
+    source_sha256: str
+    group_commitment: bytes
+    decisions: tuple[WindowDecision, ...]
+    trailing_source_frames: int
+    reference_frame: int
+    group_center: np.ndarray
+    augmentation_yaw: float
+
+    def __post_init__(self) -> None:
+        if (
+            type(self.skeletons) is not np.ndarray
+            or self.skeletons.dtype != np.dtype(np.float32)
+            or self.skeletons.ndim != 4
+            or self.skeletons.shape[0] < 2
+            or self.skeletons.shape[2:] != (22, 3)
+            or not self.skeletons.flags.c_contiguous
+        ):
+            raise PreprocessingError("continuous skeletons must be float32 [K>=2,T,22,3]")
+        if (
+            type(self.track_mask) is not np.ndarray
+            or self.track_mask.dtype != np.dtype(np.bool_)
+            or self.track_mask.shape != self.skeletons.shape[:-1]
+            or not self.track_mask.flags.c_contiguous
+        ):
+            raise PreprocessingError("continuous tracking must be bool [K,T,22]")
+        if not bool(np.isfinite(self.skeletons).all()):
+            raise PreprocessingError("continuous skeletons must be finite")
+        if bool((self.skeletons[~self.track_mask].view(np.uint32) != 0).any()):
+            raise PreprocessingError("untracked continuous skeletons must be exact +0")
+        if (
+            type(self.decisions) is not tuple
+            or not self.decisions
+            or any(type(decision) is not WindowDecision for decision in self.decisions)
+            or self.frame_count != len(self.decisions) * TARGET_WINDOW_FRAMES
+            or not any(decision.accepted for decision in self.decisions)
+        ):
+            raise PreprocessingError("window decisions must cover the complete timeline")
+        if (
+            type(self.actor_commitments) is not tuple
+            or len(self.actor_commitments) != self.actor_count
+            or tuple(sorted(self.actor_commitments)) != self.actor_commitments
+            or group_commitment(self.actor_commitments) != self.group_commitment
+        ):
+            raise PreprocessingError("continuous actor lineage must be canonical")
+        if (
+            type(self.source_sha256) is not str
+            or len(self.source_sha256) != 64
+            or any(c not in "0123456789abcdef" for c in self.source_sha256)
+        ):
+            raise PreprocessingError("source_sha256 must be lowercase SHA-256 hex")
+        if type(self.trailing_source_frames) is not int or not 0 <= self.trailing_source_frames < 300:
+            raise PreprocessingError("trailing source frames must be in [0,300)")
+        if (
+            type(self.reference_frame) is not int
+            or not 0 <= self.reference_frame < self.frame_count
+            or not bool(self.track_mask[:, self.reference_frame].all())
+        ):
+            raise PreprocessingError("capture origin must use one fully observed target frame")
+        if (
+            type(self.group_center) is not np.ndarray
+            or self.group_center.shape != (3,)
+            or not bool(np.isfinite(self.group_center).all())
+            or isinstance(self.augmentation_yaw, bool)
+            or not isinstance(self.augmentation_yaw, (int, float))
+            or not math.isfinite(self.augmentation_yaw)
+        ):
+            raise PreprocessingError("capture transform must be finite")
+        self.skeletons.setflags(write=False)
+        self.track_mask.setflags(write=False)
+        self.group_center.setflags(write=False)
+
+    @property
+    def actor_count(self) -> int:
+        return self.skeletons.shape[0]
+
+    @property
+    def frame_count(self) -> int:
+        return self.skeletons.shape[1]
+
+    def windows(self) -> tuple[PreparedGroupSample, ...]:
+        """Base-model views of accepted windows, all in this capture's frame."""
+        return tuple(
+            PreparedGroupSample(
+                skeletons=self.skeletons[:, index * 200 : (index + 1) * 200],
+                track_mask=self.track_mask[:, index * 200 : (index + 1) * 200],
+                actor_commitments=self.actor_commitments,
+                group_commitment=self.group_commitment,
+                source_sha256=self.source_sha256,
+                window_sha256=_window_lineage_sha256(self.source_sha256, index * 300),
+                source_start_frame=index * 300,
+                augmentation_yaw=self.augmentation_yaw,
+                decision=decision,
+            )
+            for index, decision in enumerate(self.decisions)
+            if decision.accepted
+        )
+
+
+def _capture_filter(values: np.ndarray, kernel: np.ndarray, chunk_frames: int) -> np.ndarray:
+    """Use the full FIR halo at chunk seams; pad only true capture endpoints."""
+    half = kernel.size // 2
+    flat = values.reshape(len(values), -1)
+    padded = np.pad(flat, ((half, half), (0, 0)), mode="edge")
+    filtered = np.empty_like(flat)
+    for start in range(0, len(values), chunk_frames):
+        stop = min(start + chunk_frames, len(values))
+        windows = np.lib.stride_tricks.sliding_window_view(
+            padded[start : stop + kernel.size - 1], kernel.size, axis=0
+        )
+        filtered[start:stop] = np.einsum("tcf,f->tc", windows, kernel, optimize=False)
+    return filtered.reshape(values.shape)
+
+
+def prepare_continuous_capture(
+    capture: PrivateCaptureArrays,
+    *,
+    augmentation_yaw: float = 0.0,
+    resample_chunk_frames: int = 1024,
+    max_preparation_bytes: int = 1_073_741_824,
+) -> PreparedContinuousCapture:
+    """Prepare all complete ten-second intervals, retaining one shared frame.
+
+    Interpolation eligibility is still assessed per original 300-frame window,
+    including the 95%, seven-frame and bounded-gap rules. Anti-alias filtering
+    spans adjacent accepted windows. A target touching a rejected interval's
+    FIR footprint is conservatively masked; no long gap is imputed. Original
+    short-gap observation masks are retained. No participant is removed.
+    """
+    if type(capture) is not PrivateCaptureArrays:
+        raise TypeError("capture must be exactly PrivateCaptureArrays")
+    for name, value in (
+        ("resample_chunk_frames", resample_chunk_frames),
+        ("max_preparation_bytes", max_preparation_bytes),
+    ):
+        if type(value) is not int or value < 1:
+            raise ValueError(f"{name} must be a positive exact int")
+    full_windows = capture.frame_count // SOURCE_WINDOW_FRAMES
+    if full_windows == 0:
+        raise PreprocessingError("capture has no complete source window")
+    source_length = full_windows * SOURCE_WINDOW_FRAMES
+    # Conservative bound for the linear-sized preparation buffers, independent
+    # of the all-edge budget checked by the physical relation frontend.
+    if source_length * capture.actor_count * 66 * 64 > max_preparation_bytes:
+        raise PreparationResourceLimit("RESOURCE_LIMIT: continuous preparation exceeds budget")
+    order = sorted(range(capture.actor_count), key=lambda i: capture.actor_commitments[i])
+    motion = capture.joints[:source_length, order, :22]
+    joint_mask = capture.track_mask[:source_length, order, :22]
+    observed = joint_mask[..., 0]
+    if not np.array_equal(joint_mask, np.broadcast_to(observed[..., None], joint_mask.shape)):
+        raise PreprocessingError("body-22 tracking must be uniform within each actor-frame")
+    decisions = tuple(
+        assess_missing_window(observed[start : start + SOURCE_WINDOW_FRAMES])
+        for start in range(0, source_length, SOURCE_WINDOW_FRAMES)
+    )
+    if not any(decision.accepted for decision in decisions):
+        raise PreprocessingError("capture has no accepted source window")
+    work = np.zeros(motion.shape, dtype=np.float64)
+    eligible = np.zeros(source_length, dtype=np.bool_)
+    for index, decision in enumerate(decisions):
+        if decision.accepted:
+            start, stop = index * SOURCE_WINDOW_FRAMES, (index + 1) * SOURCE_WINDOW_FRAMES
+            work[start:stop] = interpolate_accepted_gaps(
+                motion[start:stop], observed_mask=observed[start:stop]
+            ).values
+            eligible[start:stop] = True
+    kernel = design_antialias_fir()
+    filtered = _capture_filter(work, kernel, resample_chunk_frames)
+    output_length = full_windows * TARGET_WINDOW_FRAMES
+    # Exact rational source locations: even target frames are source integers,
+    # odd frames are half-integers. Avoid floating-point near-integer mask drift.
+    twice_positions = np.arange(output_length, dtype=np.int64) * 3
+    lower = twice_positions // 2
+    fractional = twice_positions % 2 != 0
+    upper = np.minimum(lower + 1, source_length - 1)
+    fraction = (fractional.astype(np.float64) * 0.5).reshape(-1, 1, 1, 1)
+    values = filtered[lower] * (1.0 - fraction) + filtered[upper] * fraction
+    mask = observed[lower].copy()
+    mask[fractional] &= observed[upper[fractional]]
+    half = kernel.size // 2
+    footprint_ok = np.lib.stride_tricks.sliding_window_view(
+        np.pad(eligible, (half, half), mode="edge"), kernel.size
+    ).all(axis=-1)
+    target_ok = footprint_ok[lower].copy()
+    target_ok[fractional] &= footprint_ok[upper[fractional]]
+    mask &= target_ok[:, None]
+    canonical = canonicalize_group(
+        values,
+        np.zeros((output_length, capture.actor_count), dtype=np.float64),
+        observed_mask=mask,
+        augmentation_yaw=augmentation_yaw,
+    )
+    skeletons = np.ascontiguousarray(
+        np.where(mask[..., None, None], canonical.positions, 0.0).transpose(1, 0, 2, 3),
+        dtype=np.float32,
+    )
+    track_mask = np.ascontiguousarray(np.broadcast_to(mask.T[..., None], skeletons.shape[:-1]))
+    skeletons.setflags(write=False)
+    track_mask.setflags(write=False)
+    return PreparedContinuousCapture(
+        skeletons=skeletons,
+        track_mask=track_mask,
+        actor_commitments=tuple(capture.actor_commitments[index] for index in order),
+        source_sha256=_capture_lineage_sha256(capture),
+        group_commitment=capture.group_commitment,
+        decisions=decisions,
+        trailing_source_frames=capture.frame_count - source_length,
+        reference_frame=canonical.reference_frame,
+        group_center=canonical.group_center,
+        augmentation_yaw=canonical.augmentation_yaw,
+    )
diff --git a/src/phaseset_core/directional_phase.py b/src/phaseset_core/directional_phase.py
index 1800564..2691459 100644
--- a/src/phaseset_core/directional_phase.py
+++ b/src/phaseset_core/directional_phase.py
@@ -15,6 +15,7 @@
 import numpy as np
 
 from .contracts import PreparedGroupBatch, validate_prepared_group_batch
+from .continuous_capture import PreparedContinuousCapture
 from .morlet import SAMPLE_RATE_HZ, morlet_kernel_bank
 from .periodic import ResourceLimitError, validate_energy_floors
 
@@ -99,13 +100,24 @@ def _readonly(value: np.ndarray) -> np.ndarray:
 def signed_joint_velocity(batch: PreparedGroupBatch) -> tuple[np.ndarray, np.ndarray]:
     """Preserve signed XYZ; a velocity needs both frames and the pelvis tracked."""
     checked = validate_prepared_group_batch(batch)
-    coordinates = checked.skeletons.astype(np.float64)
+    return _signed_velocity_arrays(
+        checked.skeletons, checked.track_mask, checked.frame_mask, checked.actor_mask
+    )
+
+
+def _signed_velocity_arrays(
+    skeletons: np.ndarray,
+    track_mask: np.ndarray,
+    frame_mask: np.ndarray,
+    actor_mask: np.ndarray,
+) -> tuple[np.ndarray, np.ndarray]:
+    coordinates = skeletons.astype(np.float64)
     values = np.zeros_like(coordinates)
     mask = np.zeros_like(coordinates, dtype=np.bool_)
     displacement = (coordinates[:, :, 1:] - coordinates[:, :, :-1]) * SAMPLE_RATE_HZ
-    joint_valid = checked.track_mask[:, :, 1:] & checked.track_mask[:, :, :-1]
-    frame_valid = checked.frame_mask[:, 1:] & checked.frame_mask[:, :-1]
-    joint_valid &= frame_valid[:, None, :, None] & checked.actor_mask[:, :, None, None]
+    joint_valid = track_mask[:, :, 1:] & track_mask[:, :, :-1]
+    frame_valid = frame_mask[:, 1:] & frame_mask[:, :-1]
+    joint_valid &= frame_valid[:, None, :, None] & actor_mask[:, :, None, None]
     pelvis_valid = joint_valid[..., :1]
     relative = displacement - displacement[..., :1, :]
     relative[..., 0, :] = displacement[..., 0, :]
@@ -137,20 +149,69 @@ def directional_phase_fields(
     """
     checked = validate_prepared_group_batch(batch)
     floors = validate_energy_floors(energy_floors)
-    required_edges = sum(count * (count - 1) // 2 for count in checked.actor_counts)
+    return _directional_fields_from_arrays(
+        checked.skeletons,
+        checked.track_mask,
+        checked.frame_mask,
+        checked.actor_mask,
+        checked.actor_counts,
+        checked.valid_lengths,
+        floors,
+        config,
+    )
+
+
+def continuous_directional_phase_field(
+    capture: PreparedContinuousCapture,
+    *,
+    energy_floors: np.ndarray,
+    config: LocalPhaseConfig = LocalPhaseConfig(),
+) -> DirectionalPhaseField:
+    """Analyze a whole capture without the legacy short-window time limit.
+
+    The timeline, signed derivatives, Morlet support and patch locations span
+    ten-second boundaries. This remains an explicitly bounded RAM response
+    cache, not a disk-streaming training host. Floors remain training-only.
+    """
+    if type(capture) is not PreparedContinuousCapture:
+        raise TypeError("capture must be exactly PreparedContinuousCapture")
+    floors = validate_energy_floors(energy_floors)
+    return _directional_fields_from_arrays(
+        capture.skeletons[None],
+        capture.track_mask[None],
+        np.ones((1, capture.frame_count), dtype=np.bool_),
+        np.ones((1, capture.actor_count), dtype=np.bool_),
+        (capture.actor_count,),
+        (capture.frame_count,),
+        floors,
+        config,
+    )[0]
+
+
+def _directional_fields_from_arrays(
+    skeletons: np.ndarray,
+    track_mask: np.ndarray,
+    frame_mask: np.ndarray,
+    actor_mask: np.ndarray,
+    actor_counts: tuple[int, ...],
+    valid_lengths: tuple[int, ...],
+    floors: np.ndarray,
+    config: LocalPhaseConfig,
+) -> tuple[DirectionalPhaseField, ...]:
+    required_edges = sum(count * (count - 1) // 2 for count in actor_counts)
     if required_edges > config.edge_budget:
         raise ResourceLimitError(required_edges=required_edges, edge_budget=config.edge_budget)
     bands = morlet_kernel_bank()
     projected = sum(
         count * sum(max(0, length - band.length + 1) for band in bands) * 66 * 17
-        for count, length in zip(checked.actor_counts, checked.valid_lengths, strict=True)
+        for count, length in zip(actor_counts, valid_lengths, strict=True)
     )
     if projected > config.max_response_bytes:
         raise MemoryError("RESOURCE_LIMIT: actor Morlet responses exceed the byte budget")
-    signed, signed_mask = signed_joint_velocity(checked)
+    signed, signed_mask = _signed_velocity_arrays(skeletons, track_mask, frame_mask, actor_mask)
     results = []
     for row, (count, length) in enumerate(
-        zip(checked.actor_counts, checked.valid_lengths, strict=True)
+        zip(actor_counts, valid_lengths, strict=True)
     ):
         values = signed[row, :count, :length].reshape(count, length, 66)
         masks = signed_mask[row, :count, :length].reshape(count, length, 66)
@@ -198,8 +259,8 @@ def directional_phase_fields(
             rms = np.sqrt((local_values**2).sum(axis=1) / denominator)
             actor_features[:, patch] = np.concatenate((mean, rms), axis=-1)
             actor_mask[:, patch] = local_mask.any(axis=(1, 2))
-            pelvis_mask = checked.track_mask[row, :count, start:stop, 0]
-            pelvis_values = checked.skeletons[row, :count, start:stop, 0].astype(np.float64)
+            pelvis_mask = track_mask[row, :count, start:stop, 0]
+            pelvis_values = skeletons[row, :count, start:stop, 0].astype(np.float64)
             roots[:, patch] = (
                 np.where(pelvis_mask[..., None], pelvis_values, 0.0).sum(axis=1)
                 / pelvis_mask.sum(axis=1).clip(min=1)[:, None]
diff --git a/tests/test_phaseset_continuous_capture.py b/tests/test_phaseset_continuous_capture.py
new file mode 100644
index 0000000..276227c
--- /dev/null
+++ b/tests/test_phaseset_continuous_capture.py
@@ -0,0 +1,152 @@
+from __future__ import annotations
+
+from dataclasses import replace
+import math
+
+import numpy as np
+import pytest
+
+from phaseset_core.continuous_capture import prepare_continuous_capture
+from phaseset_core.directional_phase import LocalPhaseConfig, continuous_directional_phase_field
+from phaseset_core.pipeline import (
+    PrivateCaptureArrays,
+    PreparationResourceLimit,
+    prepare_window,
+)
+from phaseset_core.preprocessing import PreprocessingError
+
+
+def _capture(frames: int = 600, actors: int = 3) -> PrivateCaptureArrays:
+    joints = np.zeros((frames, actors, 22, 3), dtype=np.float32)
+    t = np.arange(frames, dtype=np.float64) / 30
+    for actor in range(actors):
+        joints[:, actor, :, 0] = (t + actor * 2)[:, None]
+        joints[:, actor, :, 1] = np.arange(22) * 0.01
+        joints[:, actor, :, 2] = np.sin(2 * np.pi * 1.6875 * t + actor * 0.5)[:, None]
+    return PrivateCaptureArrays(
+        joints,
+        np.ones(joints.shape[:-1], dtype=np.bool_),
+        tuple(bytes([actor + 1]) * 32 for actor in range(actors)),
+        "a" * 64,
+    )
+
+
+def test_origin_and_base_window_views_do_not_reset_at_ten_seconds() -> None:
+    prepared = prepare_continuous_capture(_capture())
+    assert prepared.skeletons.shape == (3, 400, 22, 3)
+    assert prepared.reference_frame == 0
+    assert np.allclose(prepared.skeletons[:, 0, 0].mean(axis=0), 0, atol=2e-7)
+    # A translating group retains ten seconds of travel instead of resetting
+    # its centroid to zero at the second window's first frame.
+    assert prepared.skeletons[:, 200, 0, 0].mean() > 9.9
+    views = prepared.windows()
+    assert [sample.source_start_frame for sample in views] == [0, 300]
+    np.testing.assert_array_equal(views[1].skeletons, prepared.skeletons[:, 200:])
+    assert views[1].skeletons[:, 0, 0, 0].mean() > 9.9
+
+
+@pytest.mark.parametrize("chunk", [1, 7, 31, 300, 1024])
+def test_fir_chunk_halos_preserve_exact_full_capture_outputs(chunk: int) -> None:
+    capture = _capture()
+    reference = prepare_continuous_capture(capture, resample_chunk_frames=5000)
+    actual = prepare_continuous_capture(capture, resample_chunk_frames=chunk)
+    np.testing.assert_array_equal(actual.skeletons.view(np.uint32), reference.skeletons.view(np.uint32))
+    np.testing.assert_array_equal(actual.track_mask, reference.track_mask)
+
+
+def test_single_complete_window_retains_legacy_numerical_convention() -> None:
+    capture = _capture(300)
+    legacy = prepare_window(capture, source_start_frame=0)
+    actual = prepare_continuous_capture(capture).windows()[0]
+    np.testing.assert_allclose(actual.skeletons, legacy.skeletons, atol=2e-6, rtol=2e-6)
+    np.testing.assert_array_equal(actual.track_mask, legacy.track_mask)
+    assert actual.window_sha256 == legacy.window_sha256
+
+
+def test_actor_permutations_are_bitwise_identical_after_canonical_preparation() -> None:
+    capture = _capture()
+    permutation = [2, 0, 1]
+    changed = PrivateCaptureArrays(
+        capture.joints[:, permutation],
+        capture.track_mask[:, permutation],
+        tuple(capture.actor_commitments[index] for index in permutation),
+        capture.source_sha256,
+    )
+    original, permuted = prepare_continuous_capture(capture), prepare_continuous_capture(changed)
+    np.testing.assert_array_equal(original.skeletons.view(np.uint32), permuted.skeletons.view(np.uint32))
+    assert original.actor_commitments == permuted.actor_commitments
+
+
+def test_short_gaps_are_interpolated_but_original_observation_mask_is_retained() -> None:
+    capture = _capture()
+    mask, values = capture.track_mask.copy(), capture.joints.copy()
+    mask[100:107, 1] = False
+    values[100:107, 1] = np.nan
+    prepared = prepare_continuous_capture(replace(capture, joints=values, track_mask=mask))
+    assert all(decision.accepted for decision in prepared.decisions)
+    assert prepared.decisions[0].missing_actor_frames == 7
+    assert not prepared.track_mask[1, 67:72].all()
+    assert not np.any(prepared.skeletons[~prepared.track_mask].view(np.uint32))
+
+
+def test_rejected_intervals_keep_absolute_time_and_do_not_pollute_fir_neighbors() -> None:
+    capture = _capture(900)
+    mask, values = capture.track_mask.copy(), capture.joints.copy()
+    mask[400:408, 1] = False
+    values[400:408, 1] = np.nan
+    prepared = prepare_continuous_capture(replace(capture, joints=values, track_mask=mask))
+    assert [decision.accepted for decision in prepared.decisions] == [True, False, True]
+    assert prepared.frame_count == 600
+    assert not prepared.track_mask[:, 200:400].any()
+    assert not prepared.track_mask[:, 199].any()
+    assert not prepared.track_mask[:, 400].any()
+    assert prepared.track_mask[:, 410].all()
+    assert [window.source_start_frame for window in prepared.windows()] == [0, 600]
+    assert not prepared.skeletons[:, 200:400].view(np.uint32).any()
+
+
+def test_rejected_leading_window_does_not_become_an_artificial_origin() -> None:
+    capture = _capture()
+    mask = capture.track_mask.copy()
+    mask[:8, 0] = False
+    prepared = prepare_continuous_capture(replace(capture, track_mask=mask))
+    assert prepared.reference_frame >= 210
+    assert [sample.source_start_frame for sample in prepared.windows()] == [300]
+
+
+def test_yaw_is_shared_for_the_whole_capture_and_never_actor_specific() -> None:
+    capture = _capture()
+    original = prepare_continuous_capture(capture)
+    rotated = prepare_continuous_capture(capture, augmentation_yaw=math.pi / 2)
+    np.testing.assert_allclose(rotated.skeletons[..., 0], original.skeletons[..., 2], atol=1e-6)
+    np.testing.assert_allclose(rotated.skeletons[..., 2], -original.skeletons[..., 0], atol=1e-6)
+
+
+def test_short_tail_is_declared_not_mixed_into_complete_windows() -> None:
+    prepared = prepare_continuous_capture(_capture(637))
+    assert prepared.frame_count == 400
+    assert prepared.trailing_source_frames == 37
+
+
+def test_resource_limits_and_no_eligible_windows_are_explicit() -> None:
+    with pytest.raises(PreparationResourceLimit, match="RESOURCE_LIMIT"):
+        prepare_continuous_capture(_capture(), max_preparation_bytes=1)
+    capture = _capture()
+    mask = capture.track_mask.copy()
+    mask[::2, 0] = False
+    with pytest.raises(PreprocessingError, match="no accepted"):
+        prepare_continuous_capture(replace(capture, track_mask=mask))
+
+
+def test_continuous_phase_has_real_support_across_ten_second_seams() -> None:
+    prepared = prepare_continuous_capture(_capture())
+    field = continuous_directional_phase_field(prepared, energy_floors=np.zeros(6))
+    assert field.actor_count == 3 and field.intervals[-1][1] == 400
+    assert (180, 220) in field.intervals
+    for centers, mask in zip(field.response_centers, field.response_masks, strict=True):
+        at_boundary = (centers >= 195) & (centers <= 205)
+        assert at_boundary.any() and mask[:, at_boundary].all()
+    with pytest.raises(MemoryError, match="RESOURCE_LIMIT"):
+        continuous_directional_phase_field(
+            prepared, energy_floors=np.zeros(6), config=LocalPhaseConfig(max_response_bytes=1)
+        )

From 97ba196b822bcb1116f1bfae5444f281762b5d96 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 17:57:22 -0500
Subject: [PATCH 28/95] Record complete native input audit and prescore task
 eligibility

---
 RELEASE_FILES.sha256               |  2 +-
 docs/EMBODY_ADAPTER_INTEGRATION.md | 23 +++++++++++++++++++++++
 2 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 3b251d0..075466c 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -24,7 +24,7 @@ e32c97a223c2b04278b9ce3c5d3b25590b390bc82dbf29e99b06d08d6c96b268	3054	docs/CACHE
 e66e0530b02a222d0b3c46590290d8ebcdafa21d7cd3acf2d751355cc1f8e558	9492	docs/CAPTURE_VALIDATION.md
 e9331b64297d6c4586ccb22022c972fad657ac2b3f9202203be62b067a38ca82	3686	docs/CUDA_LIFECYCLE_OBSERVATION.md
 e91eb48eea7b2b4db91184825f012c62301fc649cfd656ab1723ddfddf634da7	14306	docs/DATA_PROTOCOL.md
-579b0a0baefe6cb92cdf25beee878ec0cd3c4ac2f5b9d8d4cfe31dd9e909c41d	8390	docs/EMBODY_ADAPTER_INTEGRATION.md
+7246495a01908041d7a3d56268f92a5cfadc44655292a2d93f50a94748338f33	9755	docs/EMBODY_ADAPTER_INTEGRATION.md
 3633e37c3eae3289d552a363e565bee027ebd83661f1c44ea136f9498de9e5af	19059	docs/EXECUTION_PROTOCOL.md
 75c1180867d7f9e527212dcbb543e6b5d91a687fdbba70a7e3c75d97a5aa7104	8069	docs/EXPERIMENT_CODE_REVIEW_20260824_220209.md
 dc015b9fb4bba90e3f69759d77f3940ee2a364391ec1a3f429a0cbb881b77990	6989	docs/EXPERIMENT_IMPLEMENTATION_REPORT_20260824_220209.md
diff --git a/docs/EMBODY_ADAPTER_INTEGRATION.md b/docs/EMBODY_ADAPTER_INTEGRATION.md
index e7dc5a2..5f5ed6f 100644
--- a/docs/EMBODY_ADAPTER_INTEGRATION.md
+++ b/docs/EMBODY_ADAPTER_INTEGRATION.md
@@ -111,6 +111,29 @@ The licensed native qualification now also covers one complete four-person
 63.55 seconds on server CPU. No trained scores or captions were consumed.
 The entire main-corpus conversion, calibration and training are not implied.
 
+## Full native main-corpus input audit, 2026-09-26
+
+Server-only inspection of both complete licensed motion archives passed in
+339.93 seconds: **572 captures, 2,261 native actor records and 15,827 selected
+NPY members**. Every selected member was CRC-checked, and all seven native
+tracks were validated for exact dimensions/frame counts, binary tracking masks
+and observed finite/float32-representable values. This is an input audit, not
+SMPL-X conversion or model evaluation of the full corpus.
+
+The original participant-disjoint universe remains **400/96/76**. The frozen
+nonoverlapping ten-second rule accepts **4,894/1,276/1,055** windows and rejects
+**18/11/2** in train/validation/test. One test capture has no accepted complete
+window; no motion is padded, repeated or borrowed to keep it. After additionally
+requiring the existing human holistic description, the confirmatory task has
+**398/96/75** eligible captures. Exclusions precede all model scores; there is
+no split reassignment. Original K counts, 545 four-person and 27 three-person,
+describe the universe, not an assertion of task-eligible K subgroup counts.
+
+Caption semantics and retrieval scores were not inspected. Full development
+body-model conversion, training-only calibration, baselines and scientific
+evaluation remain separate stages; input-audit success is not scientific
+acceptance.
+
 ## Access and license gates not implemented in code
 
 - Embody data access is not established by this adapter. The official repository says a user

From 2b2a4baea220bbbc6b4b5ea4e134ba1f8e57de4a Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 18:17:28 -0500
Subject: [PATCH 29/95] Record source-bound continuous CUDA mechanism
 qualification

---
 RELEASE_FILES.sha256                    |  2 +-
 docs/PHASESET_V2_HANDOFF_INTEGRATION.md | 14 ++++++++++++++
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 075466c..b8cff7b 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -67,7 +67,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
-960ed92900b4c17c81ed7e84af8bc2a33d89ac739106b559a363d86d3350ddae	6049	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
+0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
diff --git a/docs/PHASESET_V2_HANDOFF_INTEGRATION.md b/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
index 952dde4..abdd2be 100644
--- a/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
+++ b/docs/PHASESET_V2_HANDOFF_INTEGRATION.md
@@ -100,3 +100,17 @@ not the full-corpus training host, disk-backed cache/backward scheduling,
 training-only floor fit, literature baselines or scientific experiments.
 The response cache still enforces its 1 GiB RAM limit without sampling actors
 or edges. Formal training remains **0/87**.
+
+## Updated CUDA mechanism witness, 2026-09-26
+
+The same continuous-capture source passed a fresh server-only 512D, three-actor
+CUDA forward/backward check: **1 passed in 11.30 s**, NVIDIA RTX A6000, peak
+allocated **206,455,808 bytes**, reserved **249,561,088 bytes**. Admission
+required observed device use below 500 MiB; the allocator was bounded to 2 GiB.
+Source snapshots match and the final attempt exit is zero. This supersedes no
+earlier receipt: the earlier peaks above belong to their earlier source.
+
+The witness uses an analytic input and no optimizer or retrieval scores. It
+does not qualify full-corpus training, establish accuracy, or count as one of
+the 87 formal stages. Actual development body-model conversion is a separate
+private server job; final-test captures are excluded from that conversion.

From db0c088197fa4be60405675a7ba849123d9d1a87 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 19:37:46 -0500
Subject: [PATCH 30/95] Add signed-vector training calibration and disk-backed
 capture responses

---
 RELEASE_FILES.sha256                         |   7 +-
 docs/PHASESET_V2_CALIBRATION_AND_CACHE.md    |  79 ++++++++
 src/phaseset_core/continuous_phase_cache.py  | 162 +++++++++++++++++
 src/phaseset_core/directional_calibration.py | 154 ++++++++++++++++
 src/phaseset_core/directional_phase.py       |  31 +++-
 tests/test_continuous_phase_cache.py         | 153 ++++++++++++++++
 tests/test_directional_calibration.py        | 178 +++++++++++++++++++
 7 files changed, 757 insertions(+), 7 deletions(-)
 create mode 100644 docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 create mode 100644 src/phaseset_core/continuous_phase_cache.py
 create mode 100644 src/phaseset_core/directional_calibration.py
 create mode 100644 tests/test_continuous_phase_cache.py
 create mode 100644 tests/test_directional_calibration.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index b8cff7b..32be0ba 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -67,6 +67,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
+22cfa400a4c4c282c3ebea0f3baa13d27a12cc9b24bc1747cc08be80dccea3b1	4740	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
@@ -149,10 +150,12 @@ d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phases
 7a146f3c81122ed6492fb6dc30194f68e6e3b86e2a25d29a5313607eb18470b6	42026	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
+1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
-409f4d0a2b765ec7498d8307fdf7be2daf82b131d06b6a3a3ddb70d4eb8bd151	16128	src/phaseset_core/directional_phase.py
+9c9f86e262a7cf00809b16447dc5f5015a193e3af8eef703046c5e9468ababef	6825	src/phaseset_core/directional_calibration.py
+5fde45b3a8e25b095fa30bd8729ab7d2732a9909453462b87b04244aee5ef228	17265	src/phaseset_core/directional_phase.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
@@ -184,6 +187,8 @@ ae334bee8e2762f022020942141cb52f3285d3d9cb07d53574b6c37f57b2a556	20274	tests/tes
 d5d69092cc271059400773ef68447e06068aa802d5e9c82193704c6b65c71f59	18827	tests/test_caption_role_provenance.py
 ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test_capture_validation_source_lineage_v3.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
+5ca49ce282fc8cff3654af8af56ade66cfa6b732afd7dcca3e92ffe06d4ce64a	6030	tests/test_continuous_phase_cache.py
+5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
diff --git a/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md b/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
new file mode 100644
index 0000000..8ea6374
--- /dev/null
+++ b/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
@@ -0,0 +1,79 @@
+# Signed-vector calibration and continuous physical cache
+
+This extends the V2 continuous numeric seam. It is not the completed training
+runner, literature-baseline reproduction, or a retrieval-results release.
+
+## Calibration population and arithmetic
+
+`actor_patch_power_observations` collects each actor/local-patch/band once,
+without enumerating edges, reading captions, or consulting energy thresholds.
+It uses the same response centers, scalar masks and complex squared-energy
+units as `local_pair_chunk`. Marginal actor support is not replaced by the
+smaller intersection with any particular partner.
+
+The estimator remains the previously specified linear fifth percentile:
+`h=(n-1)/20`, with exact integer rank and fractional interpolation. Observed
+exact-zero power is included; missing kernel support is excluded and counted.
+This policy is not tuned on scores. The V2 population is **signed-vector
+actor/local-patch power**, not the legacy five-speed-channel window population;
+legacy fitted values are not reusable here.
+
+`fit_directional_energy_floors` requires the full expected capture census and
+that invocation's training components. Repeated, unexpected or incomplete
+captures and non-training components fail without a returned fit. Runtime
+limits fail explicitly rather than sample observations. Final C09/C11/C15
+are never calibration components. C00 can be training in appropriate external
+folds; it is excluded from main calibration. C03 is excluded from pilot/fold
+calibration but is part of the main training split. Every fit must use its own
+declared training census, never reuse another fold's thresholds.
+
+## Response storage and reuse
+
+`write_continuous_phase_cache` writes a fresh **private** directory containing
+complete complex128 actor/band responses and bool masks as NPY memory maps.
+Convolution keeps the original full kernel halo and response arithmetic.
+Centers, signed actor features, root features, masks and absolute intervals
+are preserved. There is no actor, edge, time or missing-interval sampling.
+Disk bytes/free space are admitted before the directory is created, existing
+attempts are not overwritten, and `metadata.json` is written last.
+
+The physical response cache does not fit or bake in thresholds. Its zero-floor
+field is collection-only. `load_continuous_phase_cache` requires explicit
+fitted floors and expected capture source lineage; thresholds are applied when
+constructing local relations, without reconvolving motion. Main/pilot/fold
+fits may reuse fixed physical responses but cannot share learned checkpoints
+or calibration populations. The existing execution manifest remains
+responsible for preprocessing/source admission and file-integrity checks.
+
+The original RAM API and its 1 GiB response gate remain unchanged. The disk
+backend avoids retaining the complete response arrays as heap allocations;
+linear velocity/features, OS page cache and neural activations still consume
+memory. It does **not** establish constant resident memory or finish long-
+capture neural backward scheduling, augmentation, production CLI integration,
+or full-corpus training. All real-data artifacts remain private.
+
+Software and native qualification results are recorded only after server
+execution. Synthetic correctness and a one-capture native fit do not amount
+to full-training calibration, formal optimization or scientific acceptance.
+
+## Observed server qualification, 2026-09-27
+
+The calibration/cache tests together with continuous preprocessing, V2
+coordination, pipeline and legacy signal/Morlet regression passed **133 tests,
+1 CUDA-only skip in 22.17 seconds** on server CPU. Source before/after matched.
+Checks include exact RAM/mapped-response parity, pair features, immutable
+thresholds, unequal actor masks, calibration isolation and cached gradients.
+
+The same previously qualified **real four-actor, 120-second training capture**
+then passed disk-cache creation, one-capture calibration, read-only reload and
+**untrained 512D forward/backward** in **12.42 seconds**, using one CPU thread.
+The cache contains 64,038,532 bytes; measured cache write was 1.51 seconds,
+calibration 0.15 seconds, forward 2.82 seconds and backward 5.67 seconds.
+There are 2,400 frames, 119 patches, six edges and 49 finite gradient tensors,
+with nonzero half-edge/topology gradients. These are single qualification
+measurements, not p50/p95 benchmarks or evidence of retrieval improvement.
+
+The one-capture thresholds are **not** the complete training/fold fit. No
+captions, final-test input, optimizer steps or retrieval scores were used.
+Formal training remains **0/87**; production host integration, complete fits,
+faithful literature baselines, pilot and scientific evaluation are unfinished.
diff --git a/src/phaseset_core/continuous_phase_cache.py b/src/phaseset_core/continuous_phase_cache.py
new file mode 100644
index 0000000..1b9b39c
--- /dev/null
+++ b/src/phaseset_core/continuous_phase_cache.py
@@ -0,0 +1,162 @@
+"""Disk-backed, text-independent signed-vector response cache for one capture.
+
+Complete Morlet responses use NPY memory maps and the unchanged full-halo
+arithmetic. No actor, edge, time position or rejected interval is sampled away.
+Linear velocity/features and neural trajectories still use RAM; this is not
+a completed training host or a constant-resident-memory guarantee.
+"""
+
+from __future__ import annotations
+
+from dataclasses import asdict
+import json
+from pathlib import Path
+import shutil
+
+import numpy as np
+
+from .continuous_capture import PreparedContinuousCapture
+from .directional_phase import (
+    ACTOR_FEATURE_DIM,
+    DirectionalPhaseField,
+    LocalPhaseConfig,
+    _directional_fields_from_arrays,
+)
+from .morlet import morlet_kernel_bank
+from .periodic import validate_energy_floors
+
+
+CACHE_SCHEMA = "phaseset-continuous-signed-response-cache-v1"
+
+
+def write_continuous_phase_cache(
+    capture: PreparedContinuousCapture,
+    directory: str | Path,
+    *,
+    config: LocalPhaseConfig = LocalPhaseConfig(),
+    max_disk_bytes: int = 16 * 2**30,
+) -> DirectionalPhaseField:
+    """Write a fresh private physical cache; zero floors are collection-only.
+
+    Calibration reads marginal powers independently of these placeholder
+    floors. Reload with that split's fitted floors before computing relations.
+    metadata.json is written last; an interrupted directory is not complete.
+    """
+    if type(capture) is not PreparedContinuousCapture:
+        raise TypeError("capture must be exactly PreparedContinuousCapture")
+    if type(max_disk_bytes) is not int or max_disk_bytes < 1:
+        raise ValueError("disk byte budget must be a positive integer")
+    destination = Path(directory)
+    if destination.exists():
+        raise FileExistsError("a physical cache must not overwrite an existing attempt")
+    response_counts = tuple(
+        max(0, capture.frame_count - band.length + 1) for band in morlet_kernel_bank()
+    )
+    patches = (
+        capture.frame_count - min(capture.frame_count, config.patch_frames)
+    ) // config.hop_frames + 2
+    # Six complex128+bool responses, centers, actor/root features and masks.
+    projected = (
+        sum(response_counts) * (capture.actor_count * 66 * 17 + 8)
+        + capture.actor_count * patches * ((ACTOR_FEATURE_DIM + 3) * 4 + 2)
+        + 65536
+    )
+    if projected > max_disk_bytes or shutil.disk_usage(destination.parent).free < projected + 2**27:
+        raise MemoryError("RESOURCE_LIMIT: complete physical cache exceeds disk budget/free space")
+    destination.mkdir(mode=0o700)
+    field = _directional_fields_from_arrays(
+        capture.skeletons[None],
+        capture.track_mask[None],
+        np.ones((1, capture.frame_count), dtype=np.bool_),
+        np.ones((1, capture.actor_count), dtype=np.bool_),
+        (capture.actor_count,),
+        (capture.frame_count,),
+        validate_energy_floors(np.zeros(6, dtype=np.float64)),
+        config,
+        response_directory=destination,
+    )[0]
+    small_arrays = {
+        "actor-features": field.actor_features,
+        "actor-patch-mask": field.actor_patch_mask,
+        "root-positions": field.root_positions,
+        "root-patch-mask": field.root_patch_mask,
+    }
+    for index, (response, mask, centers) in enumerate(
+        zip(field.responses, field.response_masks, field.response_centers, strict=True), start=1
+    ):
+        # Empty bands have no mapping and are saved as ordinary empty NPYs.
+        if not (destination / f"response-{index}.npy").exists():
+            small_arrays[f"response-{index}"] = response
+            small_arrays[f"response-mask-{index}"] = mask
+        small_arrays[f"centers-{index}"] = centers
+    for name, value in small_arrays.items():
+        with (destination / f"{name}.npy").open("xb") as stream:
+            np.save(stream, value, allow_pickle=False)
+    metadata = {
+        "schema": CACHE_SCHEMA,
+        "source_sha256": capture.source_sha256,
+        "actor_count": capture.actor_count,
+        "frame_count": capture.frame_count,
+        "intervals": field.intervals,
+        "config": asdict(config),
+        "energy_floors": "not_fitted_in_physical_cache",
+    }
+    with (destination / "metadata.json").open("x", encoding="utf-8") as stream:
+        json.dump(metadata, stream, sort_keys=True)
+        stream.write("\n")
+    return field
+
+
+def load_continuous_phase_cache(
+    directory: str | Path,
+    *,
+    expected_source_sha256: str,
+    energy_floors: np.ndarray,
+) -> DirectionalPhaseField:
+    """Read-only maps with declared capture lineage and explicit fitted floors.
+
+    Source admission/file checks belong to the existing execution manifest;
+    this loader is not an independent data-rights or content-integrity gate.
+    It checks cache schema and array headers without loading every response.
+    """
+    root = Path(directory)
+    metadata = json.loads((root / "metadata.json").read_text(encoding="utf-8"))
+    if metadata["schema"] != CACHE_SCHEMA or metadata["source_sha256"] != expected_source_sha256:
+        raise ValueError("physical cache schema or capture source lineage differs")
+    config = LocalPhaseConfig(**metadata["config"])
+    intervals = tuple(tuple(row) for row in metadata["intervals"])
+    actors, patches = metadata["actor_count"], len(intervals)
+
+    def mapped(name, dtype, shape):
+        value = np.load(root / f"{name}.npy", allow_pickle=False, mmap_mode="r")
+        if value.dtype != np.dtype(dtype) or value.shape != shape or not value.flags.c_contiguous:
+            raise ValueError(f"physical cache array header differs: {name}")
+        return value
+
+    counts = tuple(
+        max(0, metadata["frame_count"] - band.length + 1) for band in morlet_kernel_bank()
+    )
+    responses = tuple(
+        mapped(f"response-{index}", np.complex128, (actors, count, 22, 3))
+        for index, count in enumerate(counts, start=1)
+    )
+    masks = tuple(
+        mapped(f"response-mask-{index}", np.bool_, response.shape)
+        for index, response in enumerate(responses, start=1)
+    )
+    centers = tuple(
+        mapped(f"centers-{index}", np.float64, (count,))
+        for index, count in enumerate(counts, start=1)
+    )
+    return DirectionalPhaseField(
+        responses,
+        masks,
+        centers,
+        intervals,
+        mapped("actor-features", np.float32, (actors, patches, ACTOR_FEATURE_DIM)),
+        mapped("actor-patch-mask", np.bool_, (actors, patches)),
+        mapped("root-positions", np.float32, (actors, patches, 3)),
+        mapped("root-patch-mask", np.bool_, (actors, patches)),
+        validate_energy_floors(energy_floors),
+        config,
+    )
diff --git a/src/phaseset_core/directional_calibration.py b/src/phaseset_core/directional_calibration.py
new file mode 100644
index 0000000..c398f14
--- /dev/null
+++ b/src/phaseset_core/directional_calibration.py
@@ -0,0 +1,154 @@
+"""Train-only energy calibration for the signed-vector, capture-wide frontend.
+
+The population is one observation per actor/local patch/band, not per pair.
+Observed zero power is included; absent kernel support is excluded and counted.
+The inherited fifth-percentile estimator is fixed before scores are produced.
+It must be refitted on each pilot/main/fold's complete training population.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+from dataclasses import dataclass
+
+import numpy as np
+
+from .directional_phase import DirectionalPhaseField
+from .periodic import validate_energy_floors
+
+
+SEALED_EMBODY_COMPONENTS = frozenset(("C09", "C11", "C15"))
+CALIBRATION_METHOD = "signed-vector-actor-patch-power/linear-fifth-percentile-v1"
+
+
+@dataclass(frozen=True)
+class DirectionalCalibrationCapture:
+    capture_id: str  # private capture identity, never an actor-set positive ID
+    component: str
+    field: DirectionalPhaseField
+
+
+@dataclass(frozen=True)
+class DirectionalEnergyCalibration:
+    energy_floors: np.ndarray
+    capture_count: int
+    training_components: tuple[str, ...]
+    observed_counts: tuple[int, ...]
+    missing_counts: tuple[int, ...]
+    exact_zero_counts: tuple[int, ...]
+    method: str = CALIBRATION_METHOD
+
+
+def actor_patch_power_observations(
+    field: DirectionalPhaseField,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Return float64[K,P,6] powers and support, with no pair enumeration.
+
+    Units agree with ``local_pair_chunk``: sum of squared complex vector
+    components divided by the number of supported scalar components. Actor
+    support is marginal; a particular pair can have a smaller intersection.
+    No energy threshold or caption is consulted during collection.
+    """
+    powers = np.zeros((field.actor_count, field.patch_count, 6), dtype=np.float64)
+    supported = np.zeros(powers.shape, dtype=np.bool_)
+    for band, (response, mask, centers) in enumerate(
+        zip(field.responses, field.response_masks, field.response_centers, strict=True)
+    ):
+        for patch, (start, stop) in enumerate(field.intervals):
+            selected = (centers >= start) & (centers < stop)
+            for actor in range(field.actor_count):
+                valid = mask[actor, selected]
+                count = int(valid.sum())
+                if count:
+                    values = response[actor, selected][valid]
+                    power = float(np.vdot(values, values).real) / count
+                    if not np.isfinite(power) or power < 0:
+                        raise ValueError("observed signed-vector power must be finite/nonnegative")
+                    powers[actor, patch, band] = power if power else 0.0
+                    supported[actor, patch, band] = True
+    powers.setflags(write=False)
+    supported.setflags(write=False)
+    return powers, supported
+
+
+def _linear_fifth_percentile(values: np.ndarray) -> float:
+    ordered = np.sort(values)
+    if ordered.size == 0:
+        raise ValueError("every band requires at least one training observation")
+    # q=1/20, h=q*(n-1); no rounding-sensitive floating point rank decision.
+    lower, remainder = divmod(ordered.size - 1, 20)
+    first = float(ordered[lower])
+    result = (
+        first if not remainder else (first + (float(ordered[lower + 1]) - first) * (remainder / 20))
+    )
+    return result if result else 0.0
+
+
+def fit_directional_energy_floors(
+    captures: Iterable[DirectionalCalibrationCapture],
+    *,
+    training_components: tuple[str, ...],
+    expected_capture_ids: tuple[str, ...],
+    max_actor_patch_observations: int = 1_000_000,
+) -> DirectionalEnergyCalibration:
+    """Consume exactly the declared training census, or fail without a fit.
+
+    Callers derive component/capture admission from the frozen split manifest.
+    C00 is allowed in the training side of the appropriate external folds;
+    main validation, pilot validation and each held-out fold are excluded by
+    that invocation's admission list. Final sealed components are never fit.
+    Runtime limits cannot sample observations or silently shorten captures.
+    """
+    if (
+        not training_components
+        or any(not isinstance(value, str) or not value for value in training_components)
+        or len(set(training_components)) != len(training_components)
+        or set(training_components) & SEALED_EMBODY_COMPONENTS
+    ):
+        raise ValueError("training components must be nonempty, unique and exclude sealed test")
+    if (
+        not expected_capture_ids
+        or any(not isinstance(value, str) or not value for value in expected_capture_ids)
+        or len(set(expected_capture_ids)) != len(expected_capture_ids)
+    ):
+        raise ValueError("expected training capture census must be nonempty and unique")
+    if type(max_actor_patch_observations) is not int or max_actor_patch_observations < 1:
+        raise ValueError("observation budget must be a positive integer")
+    allowed, expected = set(training_components), set(expected_capture_ids)
+    seen: set[str] = set()
+    samples: list[list[np.ndarray]] = [[] for _ in range(6)]
+    missing = np.zeros(6, dtype=np.int64)
+    total = 0
+    observed_components: set[str] = set()
+    for capture in captures:
+        # Admission precedes physical collection, including for validation rows.
+        if capture.component not in allowed:
+            raise ValueError("calibration capture belongs to a non-training component")
+        if capture.capture_id not in expected or capture.capture_id in seen:
+            raise ValueError("calibration capture is unexpected or repeated")
+        field = capture.field
+        total += field.actor_count * field.patch_count
+        if total > max_actor_patch_observations:
+            raise MemoryError("RESOURCE_LIMIT: complete actor-patch population exceeds budget")
+        powers, support = actor_patch_power_observations(field)
+        for band in range(6):
+            mask = support[..., band]
+            samples[band].append(powers[..., band][mask])
+            missing[band] += int(mask.size - mask.sum())
+        seen.add(capture.capture_id)
+        observed_components.add(capture.component)
+    if seen != expected or observed_components != allowed:
+        raise ValueError("consumed calibration stream differs from the complete training census")
+    values = tuple(np.concatenate(rows) for rows in samples)
+    floors = validate_energy_floors(
+        np.array([_linear_fifth_percentile(row) for row in values], dtype=np.float64)
+    ).copy()
+    floors.setflags(write=False)
+    return DirectionalEnergyCalibration(
+        floors,
+        len(seen),
+        tuple(sorted(allowed)),
+        tuple(int(row.size) for row in values),
+        tuple(int(count) for count in missing),
+        tuple(int(np.count_nonzero(row == 0.0)) for row in values),
+    )
diff --git a/src/phaseset_core/directional_phase.py b/src/phaseset_core/directional_phase.py
index 2691459..fa2e2d9 100644
--- a/src/phaseset_core/directional_phase.py
+++ b/src/phaseset_core/directional_phase.py
@@ -10,6 +10,7 @@
 
 from dataclasses import dataclass
 import math
+from pathlib import Path
 from typing import Iterator
 
 import numpy as np
@@ -197,6 +198,7 @@ def _directional_fields_from_arrays(
     valid_lengths: tuple[int, ...],
     floors: np.ndarray,
     config: LocalPhaseConfig,
+    response_directory: Path | None = None,
 ) -> tuple[DirectionalPhaseField, ...]:
     required_edges = sum(count * (count - 1) // 2 for count in actor_counts)
     if required_edges > config.edge_budget:
@@ -206,13 +208,13 @@ def _directional_fields_from_arrays(
         count * sum(max(0, length - band.length + 1) for band in bands) * 66 * 17
         for count, length in zip(actor_counts, valid_lengths, strict=True)
     )
-    if projected > config.max_response_bytes:
+    if response_directory is not None and len(actor_counts) != 1:
+        raise ValueError("disk response storage is one complete capture per directory")
+    if response_directory is None and projected > config.max_response_bytes:
         raise MemoryError("RESOURCE_LIMIT: actor Morlet responses exceed the byte budget")
     signed, signed_mask = _signed_velocity_arrays(skeletons, track_mask, frame_mask, actor_mask)
     results = []
-    for row, (count, length) in enumerate(
-        zip(actor_counts, valid_lengths, strict=True)
-    ):
+    for row, (count, length) in enumerate(zip(actor_counts, valid_lengths, strict=True)):
         values = signed[row, :count, :length].reshape(count, length, 66)
         masks = signed_mask[row, :count, :length].reshape(count, length, 66)
         responses, response_masks, centers = [], [], []
@@ -223,8 +225,22 @@ def _directional_fields_from_arrays(
                 positions = np.zeros(0, dtype=np.float64)
             else:
                 response_count = length - band.length + 1
-                transformed = np.zeros((count, response_count, 22, 3), dtype=np.complex128)
-                valid = np.zeros(transformed.shape, dtype=np.bool_)
+                shape = (count, response_count, 22, 3)
+                if response_directory is None:
+                    transformed = np.zeros(shape, dtype=np.complex128)
+                    valid = np.zeros(shape, dtype=np.bool_)
+                else:
+                    response_path = response_directory / f"response-{band.index}.npy"
+                    mask_path = response_directory / f"response-mask-{band.index}.npy"
+                    # Claim new files exclusively; never overwrite an earlier cache.
+                    response_path.open("xb").close()
+                    mask_path.open("xb").close()
+                    transformed = np.lib.format.open_memmap(
+                        response_path, mode="w+", dtype=np.complex128, shape=shape
+                    )
+                    valid = np.lib.format.open_memmap(
+                        mask_path, mode="w+", dtype=np.bool_, shape=shape
+                    )
                 # Each central response is written once. The input extends by
                 # kernel_length-1 past the response block: full physical halo,
                 # never independent chunk convolution or a shortened kernel.
@@ -243,6 +259,9 @@ def _directional_fields_from_arrays(
                     )
                     valid[:, start:stop] = local_valid
                     transformed[:, start:stop] = np.where(local_valid, local_values, 0.0 + 0.0j)
+                if response_directory is not None:
+                    transformed.flush()
+                    valid.flush()
                 positions = np.arange(length - band.length + 1) + (band.length - 1) / 2
             responses.append(_readonly(np.ascontiguousarray(transformed)))
             response_masks.append(_readonly(np.ascontiguousarray(valid)))
diff --git a/tests/test_continuous_phase_cache.py b/tests/test_continuous_phase_cache.py
new file mode 100644
index 0000000..02ce980
--- /dev/null
+++ b/tests/test_continuous_phase_cache.py
@@ -0,0 +1,153 @@
+from __future__ import annotations
+
+from dataclasses import replace
+import json
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.continuous_capture import prepare_continuous_capture
+from phaseset_core.continuous_phase_cache import (
+    load_continuous_phase_cache,
+    write_continuous_phase_cache,
+)
+from phaseset_core.directional_calibration import (
+    DirectionalCalibrationCapture,
+    fit_directional_energy_floors,
+)
+from phaseset_core.directional_phase import (
+    LocalPhaseConfig,
+    continuous_directional_phase_field,
+    local_pair_chunk,
+)
+from phaseset_core.pipeline import PrivateCaptureArrays
+from phaseset_core.temporal_coordination import TemporalIncidenceEncoder
+
+
+def _capture():
+    frames, actors = 600, 3
+    joints = np.zeros((frames, actors, 22, 3), dtype=np.float32)
+    for actor in range(actors):
+        joints[:, actor, :, 1] = actor
+        joints[:, actor, 1:, 0] = np.sin(2 * np.pi * 1.125 * np.arange(frames) / 30 + actor * 0.3)[
+            :, None
+        ]
+    return prepare_continuous_capture(
+        PrivateCaptureArrays(
+            joints,
+            np.ones(joints.shape[:-1], dtype=np.bool_),
+            tuple(bytes([actor + 1]) * 32 for actor in range(actors)),
+            "a" * 64,
+        )
+    )
+
+
+@pytest.mark.parametrize("chunk", [1, 31, 1024])
+def test_memory_maps_are_bitwise_equal_to_full_halo_ram_backend(chunk, tmp_path):
+    capture = _capture()
+    floors = np.zeros(6, dtype=np.float64)
+    ram = continuous_directional_phase_field(capture, energy_floors=floors)
+    cache = tmp_path / "physical"
+    write_continuous_phase_cache(
+        capture,
+        cache,
+        config=LocalPhaseConfig(convolution_chunk_frames=chunk, max_response_bytes=1),
+    )
+    disk = load_continuous_phase_cache(
+        cache, expected_source_sha256=capture.source_sha256, energy_floors=floors
+    )
+    for reference, actual in zip(ram.responses, disk.responses, strict=True):
+        assert isinstance(actual, np.memmap) and not actual.flags.writeable
+        np.testing.assert_array_equal(reference.view(np.uint64), actual.view(np.uint64))
+    for name in ("response_masks", "response_centers"):
+        for reference, actual in zip(getattr(ram, name), getattr(disk, name), strict=True):
+            np.testing.assert_array_equal(reference, actual)
+    assert ram.intervals == disk.intervals
+    for name in ("actor_features", "actor_patch_mask", "root_positions", "root_patch_mask"):
+        np.testing.assert_array_equal(getattr(ram, name), getattr(disk, name))
+    np.testing.assert_array_equal(
+        local_pair_chunk(ram, 0, 3).features, local_pair_chunk(disk, 0, 3).features
+    )
+
+
+def test_calibration_reuses_cache_and_applies_floors_without_reconvolution(tmp_path):
+    capture = _capture()
+    raw = write_continuous_phase_cache(capture, tmp_path / "physical")
+    assert not raw.energy_floors.flags.writeable
+    result = fit_directional_energy_floors(
+        (DirectionalCalibrationCapture("capture", "C01", raw),),
+        training_components=("C01",),
+        expected_capture_ids=("capture",),
+    )
+    disk = load_continuous_phase_cache(
+        tmp_path / "physical",
+        expected_source_sha256=capture.source_sha256,
+        energy_floors=result.energy_floors,
+    )
+    np.testing.assert_array_equal(disk.energy_floors, result.energy_floors)
+    assert not disk.energy_floors.flags.writeable
+    expected = replace(raw, energy_floors=result.energy_floors)
+    np.testing.assert_array_equal(
+        local_pair_chunk(expected, 0, 3).features, local_pair_chunk(disk, 0, 3).features
+    )
+
+
+def test_cached_encoder_forward_backward_matches_ram_source(tmp_path):
+    capture = _capture()
+    raw = continuous_directional_phase_field(capture, energy_floors=np.zeros(6, dtype=np.float64))
+    write_continuous_phase_cache(capture, tmp_path / "physical")
+    disk = load_continuous_phase_cache(
+        tmp_path / "physical",
+        expected_source_sha256=capture.source_sha256,
+        energy_floors=np.zeros(6, dtype=np.float64),
+    )
+    torch.manual_seed(1729)
+    model = TemporalIncidenceEncoder(width=8)
+    results, gradients = [], []
+    for source in (raw, disk):
+        model.zero_grad(set_to_none=True)
+        output = model(source)
+        results.append(output.embedding.detach().clone())
+        output.embedding[0].backward()
+        gradients.append(
+            tuple(
+                None if parameter.grad is None else parameter.grad.detach().clone()
+                for parameter in model.parameters()
+            )
+        )
+    assert torch.equal(*results)
+    for first, second in zip(*gradients, strict=True):
+        assert (first is None and second is None) or torch.equal(first, second)
+
+
+def test_limits_and_existing_attempts_do_not_overwrite_data(tmp_path):
+    capture = _capture()
+    with pytest.raises(MemoryError, match="RESOURCE_LIMIT"):
+        write_continuous_phase_cache(capture, tmp_path / "limited", max_disk_bytes=1)
+    assert not (tmp_path / "limited").exists()
+    output = tmp_path / "existing"
+    output.mkdir()
+    (output / "keep.txt").write_text("user")
+    with pytest.raises(FileExistsError):
+        write_continuous_phase_cache(capture, output)
+    assert (output / "keep.txt").read_text() == "user"
+
+
+def test_incomplete_lineage_and_wrong_array_headers_are_rejected(tmp_path):
+    capture = _capture()
+    root = tmp_path / "physical"
+    write_continuous_phase_cache(capture, root)
+    with pytest.raises(ValueError, match="lineage"):
+        load_continuous_phase_cache(
+            root, expected_source_sha256="b" * 64, energy_floors=np.zeros(6, dtype=np.float64)
+        )
+    metadata = json.loads((root / "metadata.json").read_text())
+    metadata["actor_count"] += 1
+    (root / "metadata.json").write_text(json.dumps(metadata))
+    with pytest.raises(ValueError, match="header differs"):
+        load_continuous_phase_cache(
+            root,
+            expected_source_sha256=capture.source_sha256,
+            energy_floors=np.zeros(6, dtype=np.float64),
+        )
diff --git a/tests/test_directional_calibration.py b/tests/test_directional_calibration.py
new file mode 100644
index 0000000..ad2cff7
--- /dev/null
+++ b/tests/test_directional_calibration.py
@@ -0,0 +1,178 @@
+from __future__ import annotations
+
+from dataclasses import replace
+
+import numpy as np
+import pytest
+
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+from phaseset_core.directional_calibration import (
+    DirectionalCalibrationCapture,
+    _linear_fifth_percentile,
+    actor_patch_power_observations,
+    fit_directional_energy_floors,
+)
+from phaseset_core.directional_phase import directional_phase_fields, local_pair_chunk
+
+
+def _field(amplitude=1.0, actors=3):
+    length = 200
+    values = np.zeros((1, actors, length, 22, 3), dtype=np.float32)
+    time = np.arange(length) / 20
+    for actor in range(actors):
+        values[0, actor, :, :, 1] = actor
+        values[0, actor, :, 1:, 0] = (amplitude * np.sin(2 * np.pi * 1.125 * time + actor * 0.7))[
+            :, None
+        ]
+    keys = tuple(bytes([actor + 1]) * 32 for actor in range(actors))
+    batch = PreparedGroupBatch(
+        values,
+        np.ones((1, actors), dtype=np.bool_),
+        np.ones((1, length), dtype=np.bool_),
+        np.ones(values.shape[:-1], dtype=np.bool_),
+        (keys,),
+        (group_commitment(keys),),
+    )
+    return directional_phase_fields(batch, energy_floors=np.zeros(6, dtype=np.float64))[0]
+
+
+def _fit(rows, **kwargs):
+    return fit_directional_energy_floors(
+        rows,
+        training_components=kwargs.pop("training_components", ("C01",)),
+        expected_capture_ids=kwargs.pop("expected_capture_ids", ("first",)),
+        **kwargs,
+    )
+
+
+def test_actor_observations_use_pair_power_units_without_enumerating_edges(monkeypatch):
+    field = _field()
+    pair = local_pair_chunk(field, 0, 1)
+    import phaseset_core.directional_phase as physical
+
+    def no_pairs(*args, **kwargs):
+        raise AssertionError("calibration must not enumerate pairs")
+
+    monkeypatch.setattr(physical, "canonical_edge_pairs", no_pairs)
+    powers, support = actor_patch_power_observations(field)
+    assert powers.shape == support.shape == (3, field.patch_count, 6)
+    assert not powers.flags.writeable and not support.flags.writeable
+    for actor, slot in ((0, 3), (1, 4)):
+        selected = pair.support_mask[0]
+        np.testing.assert_allclose(
+            np.log1p(powers[actor])[selected],
+            pair.features[0, ..., slot][selected],
+            atol=1e-15,
+            rtol=1e-15,
+        )
+
+
+def test_fit_matches_literal_fifth_percentile_and_counts_missing_support():
+    field = _field()
+    powers, support = actor_patch_power_observations(field)
+    result = _fit((DirectionalCalibrationCapture("first", "C01", field),))
+    for band in range(6):
+        observed = powers[..., band][support[..., band]]
+        assert result.energy_floors[band] == pytest.approx(np.quantile(observed, 0.05))
+        assert result.observed_counts[band] == observed.size
+        assert result.missing_counts[band] == 3 * field.patch_count - observed.size
+    assert result.capture_count == 1 and result.training_components == ("C01",)
+    assert not result.energy_floors.flags.writeable
+
+
+def test_actor_marginal_support_is_not_a_particular_pair_intersection():
+    field = _field()
+    responses = tuple(np.ones_like(value) for value in field.responses)
+    masks = tuple(value.copy() for value in field.response_masks)
+    centers = field.response_centers[5]
+    responses[5][0, centers <= 20] = 7.0
+    masks[5][1, centers <= 20] = False
+    changed = replace(field, responses=responses, response_masks=masks)
+    powers, support = actor_patch_power_observations(changed)
+    pair = local_pair_chunk(changed, 0, 1)
+    assert support[0, 0, 5] and support[1, 0, 5]
+    assert powers[0, 0, 5] > 1.0
+    assert powers[1, 0, 5] == 1.0
+    assert pair.features[0, 0, 5, 3] == pytest.approx(np.log1p(1.0))
+
+
+def test_observed_zero_power_is_included_and_has_exact_positive_zero_floor():
+    field = _field(0.0)
+    result = _fit((DirectionalCalibrationCapture("first", "C01", field),))
+    assert not result.energy_floors.view(np.uint64).any()
+    assert result.observed_counts == result.exact_zero_counts
+    assert min(result.observed_counts) > 0
+
+
+def test_no_support_is_not_counted_as_observed_zero():
+    field = _field()
+    absent = replace(
+        field, response_masks=tuple(np.zeros_like(mask) for mask in field.response_masks)
+    )
+    powers, support = actor_patch_power_observations(absent)
+    assert not powers.any() and not support.any()
+    with pytest.raises(ValueError, match="at least one training observation"):
+        _fit((DirectionalCalibrationCapture("first", "C01", absent),))
+
+
+def test_calibration_is_collection_order_invariant_and_ignores_input_thresholds():
+    first, second = _field(), _field(0.25)
+    rows = (
+        DirectionalCalibrationCapture("first", "C01", first),
+        DirectionalCalibrationCapture("second", "C02", second),
+    )
+    options = dict(training_components=("C01", "C02"), expected_capture_ids=("first", "second"))
+    original = _fit(rows, **options)
+    changed = _fit(tuple(reversed(rows)), **options)
+    np.testing.assert_array_equal(
+        original.energy_floors.view(np.uint64), changed.energy_floors.view(np.uint64)
+    )
+    same = _fit((replace(rows[0], field=replace(first, energy_floors=np.full(6, 1e9))),))
+    reference = _fit((rows[0],))
+    np.testing.assert_array_equal(
+        same.energy_floors.view(np.uint64), reference.energy_floors.view(np.uint64)
+    )
+
+
+@pytest.mark.parametrize("component", ["C00", "C03", "C09", "C11", "C15"])
+def test_nontraining_capture_is_rejected_before_observations(component, monkeypatch):
+    import phaseset_core.directional_calibration as calibration
+
+    def never_collect(*args):
+        raise AssertionError("non-training motion must not be observed")
+
+    monkeypatch.setattr(calibration, "actor_patch_power_observations", never_collect)
+    with pytest.raises(ValueError, match="non-training component"):
+        _fit((DirectionalCalibrationCapture("first", component, _field()),))
+
+
+def test_c00_can_be_training_in_an_external_fold_but_sealed_test_never_can():
+    row = DirectionalCalibrationCapture("first", "C00", _field())
+    assert _fit((row,), training_components=("C00",)).capture_count == 1
+    for component in ("C09", "C11", "C15"):
+        with pytest.raises(ValueError, match="exclude sealed test"):
+            _fit((replace(row, component=component),), training_components=(component,))
+
+
+def test_incomplete_duplicate_and_unexpected_capture_censuses_fail():
+    row = DirectionalCalibrationCapture("first", "C01", _field())
+    with pytest.raises(ValueError, match="complete training census"):
+        _fit((row,), expected_capture_ids=("first", "second"))
+    with pytest.raises(ValueError, match="unexpected or repeated"):
+        _fit((row, row))
+    with pytest.raises(ValueError, match="unexpected or repeated"):
+        _fit((replace(row, capture_id="other"),))
+    with pytest.raises(ValueError, match="complete training census"):
+        _fit((row,), training_components=("C01", "C02"))
+
+
+def test_resource_limit_never_samples_the_training_population():
+    row = DirectionalCalibrationCapture("first", "C01", _field())
+    with pytest.raises(MemoryError, match="RESOURCE_LIMIT"):
+        _fit((row,), max_actor_patch_observations=1)
+
+
+@pytest.mark.parametrize("size", [1, 2, 20, 21, 22, 103])
+def test_fixed_quantile_rank_and_interpolation(size):
+    values = np.arange(size, dtype=np.float64)
+    assert _linear_fifth_percentile(values[::-1]) == pytest.approx((size - 1) / 20)

From f28180c03818491b6bcc2bcac3bd979da3f2af09 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 20:17:47 -0500
Subject: [PATCH 31/95] Connect full-capture artifacts to independent
 calibration populations

---
 RELEASE_FILES.sha256                         |  6 +-
 docs/PHASESET_V2_CALIBRATION_AND_CACHE.md    | 50 +++++++++++
 src/phaseset_core/calibration_populations.py | 91 ++++++++++++++++++++
 src/phaseset_core/continuous_capture_io.py   | 73 ++++++++++++++++
 tests/test_calibration_populations.py        | 52 +++++++++++
 tests/test_continuous_capture_io.py          | 86 ++++++++++++++++++
 6 files changed, 357 insertions(+), 1 deletion(-)
 create mode 100644 src/phaseset_core/calibration_populations.py
 create mode 100644 src/phaseset_core/continuous_capture_io.py
 create mode 100644 tests/test_calibration_populations.py
 create mode 100644 tests/test_continuous_capture_io.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 32be0ba..da9d741 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -67,7 +67,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
-22cfa400a4c4c282c3ebea0f3baa13d27a12cc9b24bc1747cc08be80dccea3b1	4740	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
+e924512f74a72a9e9554f7ba2dfd351630e8ae86aa667f707aa7de0a643b19b2	7743	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
@@ -143,6 +143,7 @@ ecda879d3f531ebb747b561612fa34df37f1ecbd8e1977d5089d38b01ec63131	29076	src/phase
 d2d71766c1c6af95480d507960d7186bbec9ff8a424f6e79ce6611fd0aa2e611	38885	src/phaseset_core/base_cohort_resolver.py
 3d4f31b6a773081af41f1d9d45ee98986ea33ef6f954070564373f995af1f2e9	40593	src/phaseset_core/base_cohort_validation.py
 ba59fcfa317aae5d0bf8e5330ede0eea2f399c5a4ebe7700b2f49029bdb53ff9	46345	src/phaseset_core/base_qualification_host.py
+622082f9660a4cc766573edb3bb2ce0548ef5f03bd515aca002f1b190e89c249	3669	src/phaseset_core/calibration_populations.py
 b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phaseset_core/caption_fusion.py
 121dcb6b948b9a3a4878ed376b3692dcbbd4b19f7ea147a7d28341c64b77d296	36926	src/phaseset_core/caption_role_provenance.py
 d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
@@ -150,6 +151,7 @@ d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phases
 7a146f3c81122ed6492fb6dc30194f68e6e3b86e2a25d29a5313607eb18470b6	42026	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
+50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
@@ -184,9 +186,11 @@ a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phase
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
 ae334bee8e2762f022020942141cb52f3285d3d9cb07d53574b6c37f57b2a556	20274	tests/test_cached_residual_lifecycle.py
+358c3ac4470be9c1199a85ab18cadfc977690359875f226fbb4c591b323997bf	2082	tests/test_calibration_populations.py
 d5d69092cc271059400773ef68447e06068aa802d5e9c82193704c6b65c71f59	18827	tests/test_caption_role_provenance.py
 ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test_capture_validation_source_lineage_v3.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
+b0f9a8d920640d3c5455e9eacdeb87feecbaca87a8b1b06affe8ca297c78d075	3593	tests/test_continuous_capture_io.py
 5ca49ce282fc8cff3654af8af56ade66cfa6b732afd7dcca3e92ffe06d4ce64a	6030	tests/test_continuous_phase_cache.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
diff --git a/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md b/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
index 8ea6374..0116851 100644
--- a/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
+++ b/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
@@ -77,3 +77,53 @@ The one-capture thresholds are **not** the complete training/fold fit. No
 captions, final-test input, optimizer steps or retrieval scores were used.
 Formal training remains **0/87**; production host integration, complete fits,
 faithful literature baselines, pilot and scientific evaluation are unfinished.
+
+## Full-development storage and split routing
+
+`continuous_capture_io.load_prepared_continuous_capture` restores a complete
+body22 timeline from the preparation manifest's completed record and its
+three private arrays. The record, array hashes, headers, canonical actor
+lineage and complete window decisions are checked; mmap arrays stay read-only.
+This does not replace dataset rights/split admission by the execution manifest.
+
+`calibration_populations.calibration_populations` reads the V2 matrix's exact
+user-supplied component assignments. It returns five distinct training routes:
+
+- Main: the twelve registered training components; C00 remains validation.
+- Pilot: C01/C02 train, C03 selects.
+- Fold 0: development excluding C03 and held-out C00/C06/C10/C14.
+- Fold 1: development excluding C03 and held-out C04/C07/C12.
+- Fold 2: development excluding C03 and held-out C05/C08/C13.
+
+Thus C00 is training data in folds 1/2, not in main, pilot or fold 0. C03 is
+main training data but never pilot/fold training data. C09/C11/C15 cannot be
+admitted. Distinct folds refit floors and models; no main-checkpoint reuse.
+This helper does not change `PLANNED_NOT_LAUNCHABLE` or launch training.
+
+The private development consumer admits only completed body records, checks
+the frozen eligible census, creates whole-capture physical caches, and waits
+for the producer's successful source-bound receipt before fitting any floors.
+If the producer finishes while a cache snapshot is being processed, it must
+re-read and drain all newly completed records before verifying the terminal
+census. No partial sample can masquerade as complete main/fold calibration.
+The full body/caption/model inputs, physical arrays and fitted population
+files remain private; the public release contains APIs and data-free tests.
+
+The extended server CPU qualification passed **146 tests, one CUDA-only skip
+in 21.06 seconds**, including three private orchestration regressions. The
+producer-completion race is tested with mocks explicitly marked as sequencing
+tests, not as research data or results. The public storage/population tests
+are data-free and included in the package's source release.
+
+Three already prepared **real native development captures** separately passed
+admitted-record restoration, complete physical-cache creation, read-only
+reload and artifact recording in **2.94 seconds** (94,609,294 cache bytes).
+This is a bounded input-preparation qualification, not a 512D model profile,
+training-population fit or retrieval result. No captions, final-test input,
+optimizer steps or fitted floors were used. The complete 494-capture consumer
+has been launched; it is not yet declared complete, and formal training is 0/87.
+
+The disk cache is an input-preparation stage, not a completed V2 training
+host. Shared-yaw actor/root recomputation, long-trajectory backward scheduling,
+CLIP/counterfactual integration, faithful baselines, actual profile/pilot and
+the 87-stage formal matrix remain separate required work.
diff --git a/src/phaseset_core/calibration_populations.py b/src/phaseset_core/calibration_populations.py
new file mode 100644
index 0000000..f887aae
--- /dev/null
+++ b/src/phaseset_core/calibration_populations.py
@@ -0,0 +1,91 @@
+"""Training populations for V2 main, pilot and independent outer folds.
+
+Read the user-supplied matrix rather than inventing component allocations.
+This does not make that planned training matrix launchable or fit any data.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from .directional_calibration import SEALED_EMBODY_COMPONENTS
+
+
+@dataclass(frozen=True)
+class CalibrationPopulation:
+    name: str
+    training_components: tuple[str, ...]
+    validation_components: tuple[str, ...]
+    held_out_components: tuple[str, ...]
+
+
+def calibration_populations(matrix: dict[str, Any]) -> tuple[CalibrationPopulation, ...]:
+    """Derive five exact populations, rejecting inconsistent held-out routes.
+
+    The complete eligible capture census must additionally be established
+    before fitting; this function only translates the registered components.
+    Main validation C00 is training data in folds 1/2, never in main/pilot.
+    """
+    if matrix["schema"] != "phaseset-v2-training-matrix-v1":
+        raise ValueError("V2 matrix schema differs")
+    groups = matrix["pilot_and_group_fold_components"]
+
+    def components(values):
+        if (
+            not values
+            or any(not isinstance(value, str) or not value for value in values)
+            or len(set(values)) != len(values)
+            or set(values) & SEALED_EMBODY_COMPONENTS
+        ):
+            raise ValueError("development components must be unique and exclude sealed test")
+        return set(values)
+
+    development = components(groups["development"])
+    main_validation = components(matrix["primary_split"]["validation_component_labels"])
+    pilot_train = components(groups["pilot_train"])
+    pilot_validation = components(groups["pilot_validation"])
+    fold_validation = components(groups["fold_validation"])
+    if (
+        not (main_validation | pilot_train | pilot_validation | fold_validation) <= development
+        or pilot_train & pilot_validation
+        or fold_validation != pilot_validation
+        or groups["reuse_main_checkpoints"] is not False
+    ):
+        raise ValueError("pilot/main/fold development routes differ")
+    populations = [
+        CalibrationPopulation(
+            "main", tuple(sorted(development - main_validation)), tuple(sorted(main_validation)), ()
+        ),
+        CalibrationPopulation(
+            "pilot", tuple(sorted(pilot_train)), tuple(sorted(pilot_validation)), ()
+        ),
+    ]
+    held_union: set[str] = set()
+    folds = groups["folds"]
+    if len(folds) != 3 or {fold["id"] for fold in folds} != {"fold_0", "fold_1", "fold_2"}:
+        raise ValueError("exactly three distinct registered outer folds are required")
+    for fold in sorted(folds, key=lambda value: value["id"]):
+        held, training = components(fold["held_out"]), components(fold["train"])
+        if (
+            not held <= development
+            or held & (fold_validation | pilot_train | held_union)
+            or training != development - held - fold_validation
+        ):
+            raise ValueError(
+                "outer fold training overlaps or differs from the registered complement"
+            )
+        held_union |= held
+        populations.append(
+            CalibrationPopulation(
+                fold["id"],
+                tuple(sorted(training)),
+                tuple(sorted(fold_validation)),
+                tuple(sorted(held)),
+            )
+        )
+    if held_union != development - pilot_train - fold_validation:
+        raise ValueError(
+            "outer held-out components do not cover the registered evaluation population"
+        )
+    return tuple(populations)
diff --git a/src/phaseset_core/continuous_capture_io.py b/src/phaseset_core/continuous_capture_io.py
new file mode 100644
index 0000000..15cc481
--- /dev/null
+++ b/src/phaseset_core/continuous_capture_io.py
@@ -0,0 +1,73 @@
+"""Read the private body22 artifacts produced by continuous preparation.
+
+This is the V2 full-timeline storage seam, not the legacy window store.
+The frozen execution manifest remains responsible for dataset/split admission.
+No caption, model asset or participant name is needed to load these arrays.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from .continuous_capture import PreparedContinuousCapture
+from .preprocessing import WindowDecision
+
+
+PREPARED_ARRAY_NAMES = ("skeletons.npy", "track_mask.npy", "actor_commitments.npy")
+
+
+def _file_sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(2**20), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def load_prepared_continuous_capture(
+    directory: str | Path,
+    *,
+    expected_metadata: dict[str, Any],
+) -> PreparedContinuousCapture:
+    """Verify the admitted preparation record and load read-only body maps.
+
+    ``expected_metadata`` is the completed record in the producer's private
+    manifest. Only the three fixed array names are read. Full array checks
+    and timeline/canonical lineage validation are intentionally retained.
+    """
+    root = Path(directory)
+    metadata = json.loads((root / "metadata.json").read_text(encoding="utf-8"))
+    if metadata != expected_metadata:
+        raise ValueError("continuous prepared metadata differs from admitted manifest")
+    digests = metadata["artifact_sha256s"]
+    if set(digests) != set(PREPARED_ARRAY_NAMES):
+        raise ValueError("continuous prepared record requires the three body22 arrays")
+    for name in PREPARED_ARRAY_NAMES:
+        if _file_sha256(root / name) != digests[name]:
+            raise ValueError(f"continuous prepared artifact differs: {name}")
+    skeletons = np.asarray(np.load(root / "skeletons.npy", allow_pickle=False, mmap_mode="r"))
+    tracking = np.asarray(np.load(root / "track_mask.npy", allow_pickle=False, mmap_mode="r"))
+    commitments = np.load(root / "actor_commitments.npy", allow_pickle=False)
+    if (
+        commitments.dtype != np.dtype(np.uint8)
+        or commitments.shape != (skeletons.shape[0], 32)
+        or list(skeletons.shape) != metadata["shape"]
+    ):
+        raise ValueError("continuous prepared array headers differ from manifest")
+    return PreparedContinuousCapture(
+        skeletons=skeletons,
+        track_mask=tracking,
+        actor_commitments=tuple(value.tobytes() for value in commitments),
+        source_sha256=metadata["source_sha256"],
+        group_commitment=bytes.fromhex(metadata["group_commitment"]),
+        decisions=tuple(WindowDecision(**value) for value in metadata["window_decisions"]),
+        trailing_source_frames=metadata["trailing_source_frames"],
+        reference_frame=metadata["reference_frame_20fps"],
+        group_center=np.array(metadata["group_center"], dtype=np.float64),
+        augmentation_yaw=metadata["augmentation_yaw"],
+    )
diff --git a/tests/test_calibration_populations.py b/tests/test_calibration_populations.py
new file mode 100644
index 0000000..3d2f3d9
--- /dev/null
+++ b/tests/test_calibration_populations.py
@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+from copy import deepcopy
+import json
+from pathlib import Path
+
+import pytest
+
+from phaseset_core.calibration_populations import calibration_populations
+
+
+def _matrix():
+    return json.loads(
+        (Path(__file__).parents[1] / "configs/phaseset_v2_experiment_matrix.json").read_text()
+    )
+
+
+def test_main_pilot_and_fold_floors_have_distinct_exact_training_routes():
+    matrix = _matrix()
+    routes = {row.name: row for row in calibration_populations(matrix)}
+    assert set(routes) == {"main", "pilot", "fold_0", "fold_1", "fold_2"}
+    assert len(routes["main"].training_components) == 12
+    assert routes["pilot"].training_components == ("C01", "C02")
+    assert routes["pilot"].validation_components == ("C03",)
+    assert "C03" in routes["main"].training_components
+    assert "C00" not in routes["main"].training_components
+    assert "C00" not in routes["fold_0"].training_components
+    assert "C00" in routes["fold_1"].training_components
+    assert "C00" in routes["fold_2"].training_components
+    for route in routes.values():
+        assert not set(route.training_components) & set(route.validation_components)
+        assert not set(route.training_components) & set(route.held_out_components)
+        assert not {"C09", "C11", "C15"} & set(route.training_components)
+    assert matrix["status"] == "PLANNED_NOT_LAUNCHABLE"
+
+
+@pytest.mark.parametrize("mutation", ["sealed", "heldout", "validation", "missing", "reuse"])
+def test_bad_population_routes_cannot_admit_floors(mutation):
+    matrix = deepcopy(_matrix())
+    groups = matrix["pilot_and_group_fold_components"]
+    if mutation == "sealed":
+        groups["development"].append("C15")
+    elif mutation == "heldout":
+        groups["folds"][0]["train"].append("C00")
+    elif mutation == "validation":
+        groups["folds"][0]["train"].append("C03")
+    elif mutation == "missing":
+        groups["folds"][0]["train"].remove("C01")
+    else:
+        groups["reuse_main_checkpoints"] = True
+    with pytest.raises(ValueError):
+        calibration_populations(matrix)
diff --git a/tests/test_continuous_capture_io.py b/tests/test_continuous_capture_io.py
new file mode 100644
index 0000000..6c9cdc5
--- /dev/null
+++ b/tests/test_continuous_capture_io.py
@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+from dataclasses import asdict
+import hashlib
+import json
+
+import numpy as np
+import pytest
+
+from phaseset_core.continuous_capture import prepare_continuous_capture
+from phaseset_core.continuous_capture_io import load_prepared_continuous_capture
+from phaseset_core.pipeline import PrivateCaptureArrays
+
+
+def _artifacts(root):
+    joints = np.zeros((600, 3, 22, 3), dtype=np.float32)
+    joints[..., 0] = np.arange(600, dtype=np.float32)[:, None, None] / 30
+    prepared = prepare_continuous_capture(
+        PrivateCaptureArrays(
+            joints,
+            np.ones(joints.shape[:-1], dtype=np.bool_),
+            tuple(bytes([i + 1]) * 32 for i in range(3)),
+            "a" * 64,
+        )
+    )
+    arrays = {
+        "skeletons.npy": prepared.skeletons,
+        "track_mask.npy": prepared.track_mask,
+        "actor_commitments.npy": np.array(
+            [list(value) for value in prepared.actor_commitments], dtype=np.uint8
+        ),
+    }
+    digests = {}
+    for name, array in arrays.items():
+        np.save(root / name, array, allow_pickle=False)
+        digests[name] = hashlib.sha256((root / name).read_bytes()).hexdigest()
+    metadata = {
+        "shape": list(prepared.skeletons.shape),
+        "source_sha256": prepared.source_sha256,
+        "group_commitment": prepared.group_commitment.hex(),
+        "artifact_sha256s": digests,
+        "window_decisions": [asdict(value) for value in prepared.decisions],
+        "trailing_source_frames": prepared.trailing_source_frames,
+        "reference_frame_20fps": prepared.reference_frame,
+        "group_center": prepared.group_center.tolist(),
+        "augmentation_yaw": prepared.augmentation_yaw,
+    }
+    (root / "metadata.json").write_text(json.dumps(metadata), encoding="utf-8")
+    return prepared, metadata
+
+
+def test_completed_body_artifacts_restore_exact_full_timeline(tmp_path):
+    reference, metadata = _artifacts(tmp_path)
+    restored = load_prepared_continuous_capture(tmp_path, expected_metadata=metadata)
+    np.testing.assert_array_equal(
+        reference.skeletons.view(np.uint32), restored.skeletons.view(np.uint32)
+    )
+    np.testing.assert_array_equal(reference.track_mask, restored.track_mask)
+    assert reference.actor_commitments == restored.actor_commitments
+    assert reference.decisions == restored.decisions
+    assert restored.frame_count == 400 and len(restored.windows()) == 2
+    assert not restored.skeletons.flags.writeable and not restored.track_mask.flags.writeable
+    assert reference.source_sha256 == restored.source_sha256
+
+
+def test_prepared_artifact_drift_is_rejected(tmp_path):
+    _, metadata = _artifacts(tmp_path)
+    with (tmp_path / "track_mask.npy").open("ab") as stream:
+        stream.write(b"drift")
+    with pytest.raises(ValueError, match="artifact differs"):
+        load_prepared_continuous_capture(tmp_path, expected_metadata=metadata)
+
+
+def test_prepared_metadata_must_match_admitted_record(tmp_path):
+    _, metadata = _artifacts(tmp_path)
+    altered = {**metadata, "augmentation_yaw": 1.0}
+    with pytest.raises(ValueError, match="metadata differs"):
+        load_prepared_continuous_capture(tmp_path, expected_metadata=altered)
+
+
+def test_unexpected_array_name_is_not_followed(tmp_path):
+    _, metadata = _artifacts(tmp_path)
+    metadata["artifact_sha256s"]["unexpected.npy"] = "a" * 64
+    (tmp_path / "metadata.json").write_text(json.dumps(metadata), encoding="utf-8")
+    with pytest.raises(ValueError, match="three body22 arrays"):
+        load_prepared_continuous_capture(tmp_path, expected_metadata=metadata)

From 303b59a7b882985b1013ca4bb1407876f9a8ccda Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 20:51:56 -0500
Subject: [PATCH 32/95] Add consistent shared-yaw whole-capture training inputs

---
 RELEASE_FILES.sha256                          |   4 +-
 docs/PHASESET_V2_CALIBRATION_AND_CACHE.md     |  59 +++++-
 .../continuous_training_input.py              | 163 +++++++++++++++
 tests/test_continuous_training_input.py       | 185 ++++++++++++++++++
 4 files changed, 407 insertions(+), 4 deletions(-)
 create mode 100644 src/phaseset_core/continuous_training_input.py
 create mode 100644 tests/test_continuous_training_input.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index da9d741..ebb6916 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -67,7 +67,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
-e924512f74a72a9e9554f7ba2dfd351630e8ae86aa667f707aa7de0a643b19b2	7743	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
+fbaee4e629411c21d5841bd95d4e30957d6b344748d787063d8c705e6a3b9fe4	11091	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
@@ -153,6 +153,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
+a46004b4c5a94dc84029a6cf5a54631e614e67f680c506feb0b92b435702160b	6873	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
@@ -192,6 +193,7 @@ ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 b0f9a8d920640d3c5455e9eacdeb87feecbaca87a8b1b06affe8ca297c78d075	3593	tests/test_continuous_capture_io.py
 5ca49ce282fc8cff3654af8af56ade66cfa6b732afd7dcca3e92ffe06d4ce64a	6030	tests/test_continuous_phase_cache.py
+2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
diff --git a/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md b/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
index 0116851..f10e91f 100644
--- a/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
+++ b/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
@@ -124,6 +124,59 @@ optimizer steps or fitted floors were used. The complete 494-capture consumer
 has been launched; it is not yet declared complete, and formal training is 0/87.
 
 The disk cache is an input-preparation stage, not a completed V2 training
-host. Shared-yaw actor/root recomputation, long-trajectory backward scheduling,
-CLIP/counterfactual integration, faithful baselines, actual profile/pilot and
-the 87-stage formal matrix remain separate required work.
+host. CLIP/counterfactual integration, faithful baselines, actual profile/pilot
+and the 87-stage formal matrix remain separate required work.
+
+## Whole-capture training input and shared yaw
+
+`continuous_training_input.ContinuousTrainingInput.load` binds the body22 and
+physical records through the consumer's `prepared_record`, verifies their
+fixed artifact files, and restores one complete, immutable capture. Load once
+per admitted capture rather than rehashing every response on every step.
+Rights, split admission and the fitted calibration population still belong to
+the frozen execution manifest. A positive key is `source_sha256`, never the
+possibly reused actor-set/group commitment.
+
+`view(yaw_delta=0, allow_shared_yaw=False)` reuses the read-only physical maps.
+A nonzero view rotates **all actors and all frames together** about the same
+existing group origin; it never recenters on a person or resets at a window.
+Original masks, rejected intervals, absolute times and capture identity stay.
+The transform explicitly operates on already prepared float32 body22; it does
+not claim bitwise equivalence to rotating raw parameters before resampling.
+
+Nonzero yaw recomputes complete signed velocity, Morlet responses, actor
+signed-mean/per-channel-RMS and root statistics using the same frontend and
+the unchanged fitted floors. It does **not** rotate the RMS vector, reuse stale
+zero-yaw actor/root values, or assume floating-point phase invariance. The
+RAM/edge resource gates apply to the whole capture; an oversized augmentation
+fails explicitly, with no zero-yaw fallback, dropped actors or shortened time.
+This correctness-first route reconvolves motion and its cost must be measured
+in the actual host profile; augmentation is not advertised as free or mmap-only.
+
+The caller must explicitly decide text yaw eligibility. Captions describing
+world directions cannot silently receive incompatible motion augmentation;
+this data-loading module neither reads nor rewrites any caption. A rejected
+yaw request fails rather than silently proceeding without augmentation.
+The frozen patch/hop/epsilon configuration cannot change between views.
+
+Data-free tests cover exact masks and whole timeline, rotation about the
+shared origin, correct RMS rather than the rotated-RMS shortcut, complete-field
+parity with direct recomputation, full-trajectory gradients, cache drift and
+resource/text-eligibility failures. These are implementation checks, not
+retrieval improvements or formal optimizer runs.
+
+The extended server CPU suite passed **154 tests, one CUDA-only skip in
+26.21 seconds**, with unchanged source and invocation files. The same fixed
+real four-actor, 120-second training capture then passed the admitted
+zero-yaw cache view, full nonzero-yaw recomputation and two untrained **512D
+forward/backward** evaluations in **20.49 seconds** total. Complete fields,
+embeddings and all parameter gradients match direct augmented-field execution
+bitwise in this frozen runtime; 49 gradient tensors are finite and the
+half-edge/topology branches have nonzero gradients. Peak RSS was 1,180,908 KiB.
+
+Measured admission was 0.49 seconds and yaw reconvolution 1.69 seconds. These
+single-capture qualification timings do not establish end-to-end throughput,
+p50/p95 latency or a GPU cost bound. Floors came from the prior fixed
+one-capture QA fit, **not** the full main/pilot/fold population. No captions,
+final-test input, optimizer or retrieval scores were used. The production
+optimizer/CLIP/counterfactual/baseline host and 87 formal stages remain undone.
diff --git a/src/phaseset_core/continuous_training_input.py b/src/phaseset_core/continuous_training_input.py
new file mode 100644
index 0000000..dc962f5
--- /dev/null
+++ b/src/phaseset_core/continuous_training_input.py
@@ -0,0 +1,163 @@
+"""Admitted whole-capture input views, with consistent shared-yaw augmentation.
+
+Zero-yaw views reuse immutable disk responses. Nonzero yaw rotates the entire
+prepared timeline and recomputes *all* physical/actor/root fields, including
+float32 coordinate rounding. Rotating cached per-channel RMS is incorrect.
+This correctness-first RAM route is bounded, not a claim of free augmentation
+or a complete optimizer/CLIP/baseline host. Profile its cost before training.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, replace
+import json
+import math
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from .continuous_capture import PreparedContinuousCapture
+from .continuous_capture_io import _file_sha256, load_prepared_continuous_capture
+from .continuous_phase_cache import load_continuous_phase_cache
+from .directional_phase import (
+    DirectionalPhaseField,
+    LocalPhaseConfig,
+    continuous_directional_phase_field,
+)
+
+
+def shared_yaw_capture(
+    capture: PreparedContinuousCapture, *, yaw_delta: float
+) -> PreparedContinuousCapture:
+    """Rotate every actor/time about the existing common origin; never recenter.
+
+    The input is already antialiased body22. This is an explicit post-prepare
+    float32 training transform, not bitwise equivalence to rotating raw SMPL-X
+    parameters before resampling. Masks, rejected intervals and lineage stay.
+    """
+    if type(capture) is not PreparedContinuousCapture:
+        raise TypeError("capture must be exactly PreparedContinuousCapture")
+    if (
+        isinstance(yaw_delta, bool)
+        or not isinstance(yaw_delta, (int, float))
+        or not math.isfinite(yaw_delta)
+        or not math.isfinite(capture.augmentation_yaw + yaw_delta)
+    ):
+        raise ValueError("yaw_delta and the total yaw must be finite real angles")
+    if yaw_delta == 0.0:
+        return capture
+    values = capture.skeletons.astype(np.float64)
+    cosine, sine = math.cos(yaw_delta), math.sin(yaw_delta)
+    rotated = np.empty_like(values)
+    rotated[..., 0] = cosine * values[..., 0] + sine * values[..., 2]
+    rotated[..., 1] = values[..., 1]
+    rotated[..., 2] = -sine * values[..., 0] + cosine * values[..., 2]
+    skeletons = np.ascontiguousarray(
+        np.where(capture.track_mask[..., None], rotated, 0.0), dtype=np.float32
+    )
+    skeletons[skeletons == 0.0] = 0.0
+    return replace(
+        capture,
+        skeletons=skeletons,
+        augmentation_yaw=capture.augmentation_yaw + yaw_delta,
+    )
+
+
+@dataclass(frozen=True)
+class ContinuousTrainingView:
+    capture: PreparedContinuousCapture
+    phase_field: DirectionalPhaseField
+    reused_physical_cache: bool
+
+    @property
+    def positive_capture_key(self) -> str:
+        """Repeated actor sets are not positives; identity is the source capture."""
+        return self.capture.source_sha256
+
+
+@dataclass(frozen=True)
+class ContinuousTrainingInput:
+    """Load once per admitted capture; request complete views during training.
+
+    Dataset/split/rights/calibration-population admission remains the frozen
+    execution manifest's job. No text is inspected here. The caller must decide
+    whether a caption permits yaw (e.g. world-direction language may not).
+    """
+
+    capture: PreparedContinuousCapture
+    cached_field: DirectionalPhaseField
+
+    @classmethod
+    def load(
+        cls,
+        body_directory: str | Path,
+        cache_directory: str | Path,
+        *,
+        cache_record: dict[str, Any],
+        energy_floors: np.ndarray,
+    ) -> ContinuousTrainingInput:
+        prepared_record = cache_record["prepared_record"]
+        if prepared_record["augmentation_yaw"] != 0.0:
+            raise ValueError("this development cache route requires unaugmented body22")
+        capture = load_prepared_continuous_capture(
+            body_directory, expected_metadata=prepared_record
+        )
+        if cache_record["source_sha256"] != capture.source_sha256:
+            raise ValueError("physical and prepared records refer to different captures")
+        root = Path(cache_directory)
+        names = {
+            "metadata.json",
+            "actor-features.npy",
+            "actor-patch-mask.npy",
+            "root-positions.npy",
+            "root-patch-mask.npy",
+        } | {
+            f"{prefix}-{band}.npy"
+            for prefix in ("response", "response-mask", "centers")
+            for band in range(1, 7)
+        }
+        digests = cache_record["artifact_sha256s"]
+        if set(digests) != names:
+            raise ValueError("physical manifest must contain the complete fixed cache arrays")
+        for name in sorted(names):
+            if _file_sha256(root / name) != digests[name]:
+                raise ValueError(f"physical cache artifact differs: {name}")
+        metadata = json.loads((root / "metadata.json").read_text(encoding="utf-8"))
+        if metadata["frame_count"] != capture.frame_count:
+            raise ValueError("physical cache timeline differs from prepared body22")
+        field = load_continuous_phase_cache(
+            root, expected_source_sha256=capture.source_sha256, energy_floors=energy_floors
+        )
+        if field.actor_count != capture.actor_count or field.patch_count != cache_record["patches"]:
+            raise ValueError("physical cache actor/patch census differs")
+        return cls(capture, field)
+
+    def view(
+        self,
+        *,
+        yaw_delta: float = 0.0,
+        allow_shared_yaw: bool,
+        config: LocalPhaseConfig | None = None,
+    ) -> ContinuousTrainingView:
+        if type(allow_shared_yaw) is not bool:
+            raise ValueError("caption yaw eligibility must be an explicit boolean")
+        rotated = shared_yaw_capture(self.capture, yaw_delta=yaw_delta)
+        if rotated is self.capture:
+            if config is not None and config != self.cached_field.config:
+                raise ValueError("a reused cache cannot change the physical configuration")
+            return ContinuousTrainingView(self.capture, self.cached_field, True)
+        if not allow_shared_yaw:
+            raise ValueError("nonzero yaw is not admitted for this capture's text")
+        selected_config = self.cached_field.config if config is None else config
+        if any(
+            getattr(selected_config, name) != getattr(self.cached_field.config, name)
+            for name in ("patch_frames", "hop_frames", "epsilon")
+        ):
+            raise ValueError("augmentation cannot change the frozen physical configuration")
+        # No zero-yaw features or approximate RMS/complex rotations are reused.
+        # The existing RAM/edge gates explicitly reject oversized full captures.
+        field = continuous_directional_phase_field(
+            rotated, energy_floors=self.cached_field.energy_floors, config=selected_config
+        )
+        return ContinuousTrainingView(rotated, field, False)
diff --git a/tests/test_continuous_training_input.py b/tests/test_continuous_training_input.py
new file mode 100644
index 0000000..dc9ae24
--- /dev/null
+++ b/tests/test_continuous_training_input.py
@@ -0,0 +1,185 @@
+from __future__ import annotations
+
+from dataclasses import asdict
+import hashlib
+import json
+import math
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.continuous_capture import prepare_continuous_capture
+from phaseset_core.continuous_phase_cache import write_continuous_phase_cache
+from phaseset_core.continuous_training_input import ContinuousTrainingInput, shared_yaw_capture
+from phaseset_core.directional_phase import LocalPhaseConfig, continuous_directional_phase_field
+from phaseset_core.pipeline import PrivateCaptureArrays
+from phaseset_core.temporal_coordination import TemporalIncidenceEncoder
+
+
+def _input(tmp_path):
+    frames, actors = 900, 3
+    t = np.arange(frames) / 30
+    joints = np.zeros((frames, actors, 22, 3), dtype=np.float32)
+    for actor in range(actors):
+        joints[:, actor, :, 0] = (actor + np.sin(2 * np.pi * 1.125 * t + actor))[:, None]
+        joints[:, actor, :, 2] = (actor * 0.3 + np.cos(2 * np.pi * 0.75 * t))[:, None]
+        joints[:, actor, 1:, 0] += np.sin(2 * np.pi * 2.53125 * t)[:, None]
+        joints[:, actor, 1:, 2] += 0.2 * np.cos(2 * np.pi * 2.53125 * t + 0.6)[:, None]
+    tracked = np.ones(joints.shape[:-1], dtype=np.bool_)
+    tracked[300:360] = False  # Keep the rejected middle interval on the timeline.
+    capture = prepare_continuous_capture(
+        PrivateCaptureArrays(
+            joints, tracked, tuple(bytes([i + 1]) * 32 for i in range(actors)), "a" * 64
+        )
+    )
+    body, physical = tmp_path / "body", tmp_path / "physical"
+    body.mkdir()
+    arrays = {
+        "skeletons.npy": capture.skeletons,
+        "track_mask.npy": capture.track_mask,
+        "actor_commitments.npy": np.array([list(x) for x in capture.actor_commitments], np.uint8),
+    }
+    for name, value in arrays.items():
+        np.save(body / name, value, allow_pickle=False)
+    prepared = {
+        "shape": list(capture.skeletons.shape),
+        "source_sha256": capture.source_sha256,
+        "group_commitment": capture.group_commitment.hex(),
+        "artifact_sha256s": {
+            name: hashlib.sha256((body / name).read_bytes()).hexdigest() for name in arrays
+        },
+        "window_decisions": [asdict(x) for x in capture.decisions],
+        "trailing_source_frames": capture.trailing_source_frames,
+        "reference_frame_20fps": capture.reference_frame,
+        "group_center": capture.group_center.tolist(),
+        "augmentation_yaw": 0.0,
+    }
+    (body / "metadata.json").write_text(json.dumps(prepared))
+    field = write_continuous_phase_cache(capture, physical)
+    record = {
+        "source_sha256": capture.source_sha256,
+        "patches": field.patch_count,
+        "prepared_record": prepared,
+        "artifact_sha256s": {
+            p.name: hashlib.sha256(p.read_bytes()).hexdigest() for p in physical.iterdir()
+        },
+    }
+    source = ContinuousTrainingInput.load(
+        body, physical, cache_record=record, energy_floors=np.full(6, 1e-10, np.float64)
+    )
+    return source, body, physical, record
+
+
+def test_zero_yaw_reuses_readonly_cache_and_source_capture_positive(tmp_path):
+    source, _, _, _ = _input(tmp_path)
+    view = source.view(allow_shared_yaw=False)
+    assert view.capture is source.capture and view.phase_field is source.cached_field
+    assert view.reused_physical_cache
+    assert view.positive_capture_key == source.capture.source_sha256
+    assert view.positive_capture_key != source.capture.group_commitment.hex()
+    assert not view.phase_field.actor_features.flags.writeable
+
+
+def test_shared_yaw_rotates_whole_capture_without_recenter_or_timeline_reset(tmp_path):
+    source, _, _, _ = _input(tmp_path)
+    view = source.view(yaw_delta=math.pi / 2, allow_shared_yaw=True)
+    assert not view.reused_physical_cache
+    original, rotated = source.capture, view.capture
+    np.testing.assert_allclose(rotated.skeletons[..., 0], original.skeletons[..., 2], atol=1e-7)
+    np.testing.assert_allclose(rotated.skeletons[..., 2], -original.skeletons[..., 0], atol=1e-7)
+    np.testing.assert_array_equal(rotated.track_mask, original.track_mask)
+    np.testing.assert_array_equal(rotated.group_center, original.group_center)
+    assert rotated.decisions == original.decisions and not rotated.decisions[1].accepted
+    assert rotated.reference_frame == original.reference_frame and rotated.frame_count == 600
+    assert rotated.actor_commitments == original.actor_commitments
+    assert rotated.source_sha256 == original.source_sha256
+    assert not rotated.skeletons[~rotated.track_mask].view(np.uint32).any()
+    assert not rotated.skeletons.flags.writeable
+
+
+def test_yaw_view_recomputes_rms_roots_and_physics_bitwise(tmp_path):
+    source, _, _, _ = _input(tmp_path)
+    view = source.view(yaw_delta=0.731, allow_shared_yaw=True)
+    reference = continuous_directional_phase_field(
+        view.capture, energy_floors=source.cached_field.energy_floors
+    )
+    for name in ("actor_features", "actor_patch_mask", "root_positions", "root_patch_mask"):
+        np.testing.assert_array_equal(getattr(view.phase_field, name), getattr(reference, name))
+    for name in ("responses", "response_masks", "response_centers"):
+        for actual, expected in zip(
+            getattr(view.phase_field, name), getattr(reference, name), strict=True
+        ):
+            np.testing.assert_array_equal(actual, expected)
+    assert not np.array_equal(view.phase_field.actor_features, source.cached_field.actor_features)
+    # RMS(x cos(yaw)+z sin(yaw)) cannot be obtained by rotating the RMS vector.
+    old_rms = source.cached_field.actor_features[..., 66:].reshape(3, -1, 22, 3)
+    naive = math.cos(0.731) * old_rms[..., 0] + math.sin(0.731) * old_rms[..., 2]
+    new_rms = view.phase_field.actor_features[..., 66:].reshape(3, -1, 22, 3)[..., 0]
+    assert float(np.abs(new_rms - naive).max()) > 0.01
+    np.testing.assert_array_equal(view.phase_field.energy_floors, source.cached_field.energy_floors)
+
+
+def test_yaw_full_trajectory_checkpointed_backward_equals_direct_field(tmp_path):
+    source, _, _, _ = _input(tmp_path)
+    view = source.view(yaw_delta=-0.4, allow_shared_yaw=True)
+    reference = continuous_directional_phase_field(
+        view.capture, energy_floors=view.phase_field.energy_floors
+    )
+    torch.manual_seed(1729)
+    model = TemporalIncidenceEncoder(width=8)
+    results, gradients = [], []
+    for field in (view.phase_field, reference):
+        model.zero_grad(set_to_none=True)
+        output = model(field)
+        results.append(output.embedding.detach().clone())
+        output.embedding[0].backward()
+        gradients.append(
+            [p.grad.detach().clone() if p.grad is not None else None for p in model.parameters()]
+        )
+    assert torch.equal(*results)
+    for a, b in zip(*gradients, strict=True):
+        assert (a is None and b is None) or torch.equal(a, b)
+
+
+@pytest.mark.parametrize("angle", [True, float("nan"), float("inf"), "1"])
+def test_invalid_yaw_is_rejected(tmp_path, angle):
+    source, _, _, _ = _input(tmp_path)
+    with pytest.raises(ValueError, match="finite"):
+        shared_yaw_capture(source.capture, yaw_delta=angle)
+
+
+def test_unapproved_yaw_or_response_budget_never_falls_back_to_stale_cache(tmp_path):
+    source, _, _, _ = _input(tmp_path)
+    with pytest.raises(ValueError, match="not admitted"):
+        source.view(yaw_delta=0.5, allow_shared_yaw=False)
+    with pytest.raises(MemoryError, match="RESOURCE_LIMIT"):
+        source.view(
+            yaw_delta=0.5, allow_shared_yaw=True, config=LocalPhaseConfig(max_response_bytes=1)
+        )
+    with pytest.raises(ValueError, match="cannot change"):
+        source.view(allow_shared_yaw=True, config=LocalPhaseConfig(patch_frames=20))
+    with pytest.raises(ValueError, match="frozen physical"):
+        source.view(
+            yaw_delta=0.5,
+            allow_shared_yaw=True,
+            config=LocalPhaseConfig(patch_frames=20),
+        )
+
+
+def test_cache_drift_and_wrong_prepared_binding_are_rejected(tmp_path):
+    _, body, physical, record = _input(tmp_path)
+    wrong = {**record, "source_sha256": "b" * 64}
+    with pytest.raises(ValueError, match="different captures"):
+        ContinuousTrainingInput.load(body, physical, cache_record=wrong, energy_floors=np.zeros(6))
+    with (physical / "actor-features.npy").open("ab") as stream:
+        stream.write(b"drift")
+    with pytest.raises(ValueError, match="artifact differs"):
+        ContinuousTrainingInput.load(body, physical, cache_record=record, energy_floors=np.zeros(6))
+
+
+def test_nonzero_cached_preparation_is_not_assumed_unaugmented(tmp_path):
+    _, body, physical, record = _input(tmp_path)
+    wrong = {**record, "prepared_record": {**record["prepared_record"], "augmentation_yaw": 1.0}}
+    with pytest.raises(ValueError, match="unaugmented"):
+        ContinuousTrainingInput.load(body, physical, cache_record=wrong, energy_floors=np.zeros(6))

From 1bd575589b4086ed78b3a8f0dba00bd790dd5c07 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 22:08:59 -0500
Subject: [PATCH 33/95] feat: bind full-capture temporal relations to human
 text and annotation families

---
 RELEASE_FILES.sha256                       |   8 +-
 configs/phaseset_v2_experiment_matrix.json |   6 +
 docs/PHASESET_V2_CALIBRATION_AND_CACHE.md  | 104 +++++++
 src/phaseset_core/continuous_retrieval.py  | 257 ++++++++++++++++++
 src/phaseset_core/temporal_coordination.py | 101 ++++++-
 tests/test_continuous_retrieval.py         | 301 +++++++++++++++++++++
 6 files changed, 773 insertions(+), 4 deletions(-)
 create mode 100644 src/phaseset_core/continuous_retrieval.py
 create mode 100644 tests/test_continuous_retrieval.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index ebb6916..279b244 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -15,7 +15,7 @@ e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
-238dd64decf965481abf5d16d6226fc7c6beca0ac79a71c01ed777b4b3878b3a	25670	configs/phaseset_v2_experiment_matrix.json
+cd6fb3868996ab909d8a382cfc62a35fa5510e465ca697aae0a142afc50b9498	26169	configs/phaseset_v2_experiment_matrix.json
 2970469ad0787e74ab4fabce2275486eb50c30c38f3cb7cf8f2800551d16991f	9540	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
 e32c97a223c2b04278b9ce3c5d3b25590b390bc82dbf29e99b06d08d6c96b268	3054	docs/CACHED_RESIDUAL_TRAINING.md
@@ -67,7 +67,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
-fbaee4e629411c21d5841bd95d4e30957d6b344748d787063d8c705e6a3b9fe4	11091	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
+d7f297d1c028f49e840a0e965c07195ee9a06e67503bac78832c4eaf925c759f	17902	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
@@ -153,6 +153,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
+38f74724372e276257c1014e50e26eb8b2cab482728a6b143799118ad2f47ca9	10545	src/phaseset_core/continuous_retrieval.py
 a46004b4c5a94dc84029a6cf5a54631e614e67f680c506feb0b92b435702160b	6873	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
@@ -182,7 +183,7 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
-62a95b37532c00bad794af4127c6bd4b0d7b61662d834b7f6e0a90aed6d943f9	18209	src/phaseset_core/temporal_coordination.py
+5604397a65a0032d2a14124d34ca3d96a2d6d67e5f4534732bb82f7a0c7b2546	23163	src/phaseset_core/temporal_coordination.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
@@ -193,6 +194,7 @@ ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 b0f9a8d920640d3c5455e9eacdeb87feecbaca87a8b1b06affe8ca297c78d075	3593	tests/test_continuous_capture_io.py
 5ca49ce282fc8cff3654af8af56ade66cfa6b732afd7dcca3e92ffe06d4ce64a	6030	tests/test_continuous_phase_cache.py
+8c11f2e5f37055f9abd3e8c691d81582a79ba702b1e9398da2a29ac688c71f61	12362	tests/test_continuous_retrieval.py
 2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
diff --git a/configs/phaseset_v2_experiment_matrix.json b/configs/phaseset_v2_experiment_matrix.json
index b1ec476..d81f0ea 100644
--- a/configs/phaseset_v2_experiment_matrix.json
+++ b/configs/phaseset_v2_experiment_matrix.json
@@ -51,6 +51,12 @@
     "local_statistics_frames": 40,
     "hop_frames": 20,
     "initial_relation_mixture": 0.1,
+    "relation_language_readout": "0.5 * ordered_capture_cosine + 0.5 * max_whole_directed_packet_cosine",
+    "relation_language_edge_block": 64,
+    "coordination_text_adapter": "shared_512_GELU_512_on_frozen_CLIP",
+    "human_holistic_caption_field": "all_scene_explained_rows_no_questionnaire_fusion",
+    "retrieval_positive_identity": "admitted_parent_annotation_family_not_released_segment_or_actor_set",
+    "parent_capture_task_freeze": "PENDING_lineage_aggregation_gallery_and_statistics",
     "no_periodic_support": "global_branch_fallback",
     "head_parameter_relative_tolerance": 0.05,
     "dummy_capacity_parameters": false,
diff --git a/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md b/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
index f10e91f..6c23367 100644
--- a/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
+++ b/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
@@ -180,3 +180,107 @@ p50/p95 latency or a GPU cost bound. Floors came from the prior fixed
 one-capture QA fit, **not** the full main/pilot/fold population. No captions,
 final-test input, optimizer or retrieval scores were used. The production
 optimizer/CLIP/counterfactual/baseline host and 87 formal stages remain undone.
+
+## Complete-capture sentence score and loss seam
+
+`continuous_retrieval.ContinuousRetrievalSystem` reuses the fixed 512D B2
+anchor and admitted `FrozenClipTextBatch` receipts. B2 stays frozen and in
+evaluation mode even when the residual system trains. Every accepted window
+is encoded in the same capture frame; its fixed mean supplies only the global
+branch. No legacy base temperature is added to the bounded coordination cosine.
+
+The new shared text adapter is 512 -> GELU -> 512. The coordination encoder
+keeps its original complete ordered actor/node/pair/group histories. For
+language, each **directed** packet retains its ordered half-edge and both
+ordered endpoint node contexts; both orientations share update/temporal
+weights. Complete trajectories are streamed in 64-edge blocks and recomputed
+in backward. Tokens are matched to whole sentence vectors, not independently
+maximized subject/action/object fields. No actor ordinal or lineage ID is a
+neural feature.
+
+The pre-pilot rule is fixed in the experiment matrix:
+`r = 0.5 * ordered_capture_cosine + 0.5 * max_directed_packet_cosine`.
+This bounded weighted local cosine is a scoring implementation, not a fourth
+novelty claim. Group temporal parameters are effective, not dummy capacity.
+Language maxima include only energy-observable patches; zero-energy track
+support can retain history but cannot enable the periodic score. Low coherence
+still does not remove an edge. A capture with no observable periodic evidence
+has exact-zero coordination cosine and falls back to the global branch.
+
+No all-pair packet tensor is retained. A block score matrix over text rows has
+an explicit 512 MiB storage gate; callers must batch a larger gallery, never
+drop actors, edges or time to fit it. Activation, physical-cache and complete
+capture host memory are not constant-memory claims. Actual timing and cost
+must be established in the later hardware profile.
+
+The official holistic schema's **all `scene_explained` human sentence rows**
+are used, without concatenating mood/atmosphere questionnaire fields or
+machine-fusing annotations. Row count is variable. Source capture SHA identifies
+the actual motion input, not its positive-target family. Released siblings can
+share the same parent holistic file. `forward` requires separately admitted
+motion/text positive-family keys and labels all siblings as positives; repeated
+actor sets still do not create positives. Schema selection is not proof of motion observability;
+official file provenance and human relation truth remain separate requirements.
+
+`score` accepts arbitrary gallery/CF sentence batches without assigning any
+positive labels; `forward` adds admitted target-family positives only for a valid
+contrastive batch. A gallery chunk need not contain all matching motion rows,
+and false-CF text is never forced to carry a fabricated positive capture key.
+
+The loss interface requires explicit counterfactual scores and verified-false
+masks. It cannot certify those human labels, and no false negative is invented
+from generic captions. Empty explicit tensors cover implementation-only
+contrastive checks or the no-CF control, not a completed full-CF training run.
+The existing low-level atomic checkpoint and RNG helpers can be reused without
+relaxing the old qualified-winner/execution guards. This new seam does not yet
+claim a formal optimizer scheduler, full corpus calibration, faithful TMR/WaMo/
+MIME adaptations, a trained B2 checkpoint, GPU profile/pilot, verified-CF
+dataset, sealed evaluation or any of the 87 formal training stages.
+
+The expanded server CPU regression passed **164 tests, one CUDA-only skip in
+46.50 seconds**, with unchanged source and invocation files. This includes the
+small synthetic optimizer/save/resume sequence and all prior continuous,
+Morlet and legacy pair checks. The first immutable attempt had 163 passes and
+one overly strict stationary-gradient sign-bit assertion: zero derivatives
+included IEEE negative zero. Only that test was corrected to finite numerical
+zero; the exact-positive-zero score and separate K=2 topology value/gradient
+bit-pattern checks remain strict. The failed attempt is retained, not rewritten
+or counted as a formal training restart.
+
+A real development-only audit found **494 released segments but 253 parent
+capture/holistic annotation families**. Exactly 138 families repeat across
+segments; each file family maps to one parent stem, with no cross-component or
+train/validation family duplication. The 494 files contain 2,613 sentence rows
+including these copies (2,144 train, 469 validation); their per-segment row-count
+histogram is 1:2, 4:20, 5:437, 9:4, 10:31. These are not 2,613 independent human
+annotations. Final-test captions were not read.
+
+The first native qualification's two segments shared a holistic file; its
+finite backward was an execution check, not valid evidence of independent
+retrieval targets. This prompted the explicit positive-family interface before
+formal training. Parent-level motion/task aggregation, deduplicated gallery,
+positive mapping, independent sampling/statistics and effective-test sealing
+still require a unified contract freeze. Existing per-segment caches are input
+artifacts, not proof that that task is closed. Do not treat sibling segments as
+false negatives, bootstrap them as independent parent captures, invent a split
+leak, change the participant-disjoint split, or concatenate missing time away.
+
+After separating source and positive-family identities, the fresh server suite
+again passed **164 tests, one CUDA-only skip in 46.98 seconds** with unchanged
+source/invocation. Two **different** real C01 annotation families then passed
+actual frozen-CLIP, complete **120/260-second** motion, all **12/26** accepted
+base windows, **119/259** ordered patches, 10 human sentences and untrained
+512D forward/backward in **151.26 seconds** total. All 55 trainable gradient
+tensors were finite, required branches had nonzero gradients, frozen B2 stayed
+unchanged, and the original group readout remained bitwise identical to the
+previous qualified source in this runtime. Forward/backward were 51.69/71.94
+seconds; peak RSS was 1,856,512 KiB. This is CPU execution qualification, not a
+GPU throughput profile or retrieval result.
+
+The same real-file audit counted **1,334 human answer-row slots when each
+annotation family is counted once**, not 2,613 independent annotations and not
+1,334 independently verified relation examples. No text truncation occurred in
+this two-family check. It used one-capture QA floors and random untrained B2:
+zero optimizer, pilot, formal, verified-CF and final-test runs. Complete parent
+task admission, true human relation evidence and the 87-stage study remain
+unfinished; their pending state must not be replaced by this qualification.
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
new file mode 100644
index 0000000..763c279
--- /dev/null
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -0,0 +1,257 @@
+"""Complete-capture V2 retrieval against admitted, frozen CLIP sentence rows.
+
+This is a trainable score/loss seam, not a formal-attempt scheduler. The caller
+admits split, rights, caption provenance, population floors and the frozen B2
+checkpoint. It must not label machine-fused text or unverified counterfactuals
+as human evidence. No final-test or data discovery happens inside this module.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+from .capture_validation import _validate_text_batch
+from .continuous_training_input import ContinuousTrainingView
+from .frozen_clip_text import FrozenClipTextBatch
+from .pipeline import collate_group_samples, edge_budget_batches
+from .temporal_coordination import (
+    CalibratedCoordinationScore,
+    TemporalIncidenceEncoder,
+    coordination_objective,
+)
+from .training import BaseRetrievalSystem
+
+
+def human_holistic_captions(annotation: dict) -> tuple[str, ...]:
+    """Use every official ``scene_explained`` row, without machine fusion.
+
+    Other parallel questionnaire fields describe mood/atmosphere/relationships;
+    they are not silently concatenated into motion ground truth. This selects
+    a schema field, not proof that an assertion is skeleton-observable. Rights,
+    official-file provenance and the two-human relation audit remain external.
+    """
+    if type(annotation) is not dict:
+        raise ValueError("official holistic annotation must be a JSON object")
+    rows = annotation.get("scene_explained")
+    if (
+        type(rows) is not list
+        or not rows
+        or any(type(row) is not str or not row.strip() for row in rows)
+    ):
+        raise ValueError("scene_explained must contain nonempty human sentence rows")
+    return tuple(rows)
+
+
+def capture_family_positive_mask(
+    motion_keys: tuple[str, ...], text_keys: tuple[str, ...], *, device: torch.device
+) -> Tensor:
+    """Variable positives use admitted target families, never a repeated actor set.
+
+    Released segments can share one parent holistic annotation. These siblings
+    must not become false negatives. Source identity remains separate; the host
+    binds parent/annotation-family membership from real provenance.
+    """
+    for keys in (motion_keys, text_keys):
+        if not keys or any(
+            type(key) is not str or len(key) != 64 or any(c not in "0123456789abcdef" for c in key)
+            for key in keys
+        ):
+            raise ValueError("positive family keys must be nonempty SHA-256 tuples")
+    mask = torch.tensor(
+        [[motion == caption for caption in text_keys] for motion in motion_keys],
+        dtype=torch.bool,
+        device=device,
+    )
+    if not bool(mask.any(dim=0).all() and mask.any(dim=1).all()):
+        raise ValueError("every motion and caption row needs a positive target family")
+    return mask.contiguous()
+
+
+@dataclass(frozen=True)
+class ContinuousScoreOutput:
+    scores: Tensor
+    global_cosine: Tensor
+    coordination_cosine: Tensor
+    periodic_support: Tensor
+    text_receipt_sha256: str
+    capture_keys: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class ContinuousRetrievalOutput:
+    scores: Tensor
+    positive_mask: Tensor
+    global_cosine: Tensor
+    coordination_cosine: Tensor
+    periodic_support: Tensor
+    text_receipt_sha256: str
+    capture_keys: tuple[str, ...]
+
+
+class ContinuousRetrievalSystem(nn.Module):
+    """Fixed B2 global anchor plus full ordered, directed-packet coordination.
+
+    B2 consumes every accepted ten-second view in the capture's common frame.
+    Its fixed window mean is only the legacy global branch; the coordination
+    branch preserves the complete absolute timeline, gaps and two-layer state.
+    The new text adapter is shared, 512->512->512 with GELU. The frozen CLIP
+    tower and B2 remain non-trainable; no base logit temperature is reused.
+    """
+
+    def __init__(
+        self,
+        frozen_b2: BaseRetrievalSystem,
+        *,
+        use_topology: bool = True,
+        strip_phase: bool = False,
+        checkpoint_blocks: bool = True,
+        base_window_batch_size: int = 1,
+        base_edge_budget: int = 32768,
+    ) -> None:
+        super().__init__()
+        if (
+            not isinstance(frozen_b2, BaseRetrievalSystem)
+            or frozen_b2.system_id != "B2"
+            or frozen_b2.embedding_dim != 512
+        ):
+            raise ValueError("V2 requires the fixed 512D B2 anchor, not a selected legacy winner")
+        if type(base_window_batch_size) is not int or base_window_batch_size < 1:
+            raise ValueError("base_window_batch_size must be a positive integer")
+        if type(base_edge_budget) is not int or base_edge_budget < 1:
+            raise ValueError("base_edge_budget must be a positive integer")
+        self.frozen_b2 = frozen_b2.requires_grad_(False).eval()
+        self.coordination = TemporalIncidenceEncoder(
+            width=512,
+            use_topology=use_topology,
+            strip_phase=strip_phase,
+            checkpoint_blocks=checkpoint_blocks,
+        )
+        self.text_adapter = nn.Sequential(nn.Linear(512, 512), nn.GELU(), nn.Linear(512, 512))
+        self.calibration = CalibratedCoordinationScore()
+        self.base_window_batch_size = base_window_batch_size
+        self.base_edge_budget = base_edge_budget
+
+    def train(self, mode: bool = True):
+        super().train(mode)
+        self.frozen_b2.eval()
+        return self
+
+    @torch.no_grad()
+    def encode_global(self, view: ContinuousTrainingView) -> Tensor:
+        """Use all accepted windows, with fixed reduction order across microbatches."""
+        self.frozen_b2.eval()
+        windows = view.capture.windows()
+        batches = edge_budget_batches(
+            windows,
+            max_total_edges=self.base_edge_budget,
+            max_batch_size=self.base_window_batch_size,
+        )
+        embeddings = [
+            self.frozen_b2.encode_trainable(collate_group_samples(batch)) for batch in batches
+        ]
+        # edge_budget_batches sorts this single capture in absolute start order.
+        rows = torch.cat(embeddings)
+        if len(rows) != len(windows):
+            raise RuntimeError("complete global capture encoding omitted a window")
+        return F.normalize(rows.to(torch.float64).mean(dim=0).to(torch.float32), dim=0)
+
+    def score(
+        self,
+        views: tuple[ContinuousTrainingView, ...],
+        text_batch: FrozenClipTextBatch,
+    ) -> ContinuousScoreOutput:
+        """Score arbitrary gallery/CF sentences without assigning positive labels.
+
+        This also supports gallery text batches whose matching motion rows are
+        elsewhere. Counterfactual text must never be given fabricated positive
+        capture keys merely to pass the contrastive training interface.
+        """
+        if (
+            type(views) is not tuple
+            or not views
+            or any(type(view) is not ContinuousTrainingView for view in views)
+        ):
+            raise ValueError("views must be a nonempty tuple of admitted continuous inputs")
+        text, _, receipt_sha = _validate_text_batch(text_batch)
+        device = next(self.coordination.parameters()).device
+        keys = tuple(view.positive_capture_key for view in views)
+        text = text.to(device)
+        adapted_text = self.text_adapter(text)
+        base_rows, relation_rows, supports = [], [], []
+        for view in views:
+            if view.phase_field.actor_count != view.capture.actor_count or (
+                view.phase_field.intervals[-1][1] != view.capture.frame_count
+            ):
+                raise ValueError("the coordination field must span the complete capture")
+            base_rows.append(self.encode_global(view))
+            output = self.coordination.score_text(view.phase_field, adapted_text)
+            relation_rows.append(output.cosine)
+            supports.append(output.periodic_support)
+        global_cosine = F.normalize(torch.stack(base_rows), dim=-1) @ F.normalize(text, dim=-1).T
+        relation_cosine = torch.stack(relation_rows)
+        support = torch.stack(supports)
+        scores = self.calibration(global_cosine, relation_cosine, coordination_support=support)
+        return ContinuousScoreOutput(
+            scores, global_cosine, relation_cosine, support, receipt_sha, keys
+        )
+
+    def forward(
+        self,
+        views: tuple[ContinuousTrainingView, ...],
+        text_batch: FrozenClipTextBatch,
+        *,
+        motion_positive_keys: tuple[str, ...],
+        text_positive_keys: tuple[str, ...],
+    ) -> ContinuousRetrievalOutput:
+        output = self.score(views, text_batch)
+        if len(set(output.capture_keys)) != len(output.capture_keys):
+            raise ValueError("training views must be distinct source captures")
+        if (
+            type(motion_positive_keys) is not tuple
+            or len(motion_positive_keys) != output.scores.shape[0]
+            or type(text_positive_keys) is not tuple
+            or len(text_positive_keys) != output.scores.shape[1]
+        ):
+            raise ValueError("admitted positive family keys must cover every motion/text row")
+        positives = capture_family_positive_mask(
+            motion_positive_keys, text_positive_keys, device=output.scores.device
+        )
+        return ContinuousRetrievalOutput(
+            output.scores,
+            positives,
+            output.global_cosine,
+            output.coordination_cosine,
+            output.periodic_support,
+            output.text_receipt_sha256,
+            output.capture_keys,
+        )
+
+    @staticmethod
+    def objective(
+        output: ContinuousRetrievalOutput,
+        *,
+        cf_positive_scores: Tensor,
+        cf_negative_scores: Tensor,
+        verified_negative_mask: Tensor,
+        cf_weight: float = 0.2,
+        margin: float = 0.2,
+    ) -> Tensor:
+        """No implicit false labels; empty explicit CF tensors are only no-CF use.
+
+        The full-training host must supply genuinely verified-false relation
+        examples with their provenance. This function cannot certify their
+        human truth. Margin/weight/sampling are frozen by the bounded pilot.
+        """
+        return coordination_objective(
+            output.scores,
+            output.positive_mask,
+            cf_positive_scores=cf_positive_scores,
+            cf_negative_scores=cf_negative_scores,
+            verified_negative_mask=verified_negative_mask,
+            cf_weight=cf_weight,
+            margin=margin,
+        )
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
index 1422a6f..32eb71c 100644
--- a/src/phaseset_core/temporal_coordination.py
+++ b/src/phaseset_core/temporal_coordination.py
@@ -79,6 +79,13 @@ class TemporalCoordinationOutput:
     valid_pair_count: Tensor
 
 
+@dataclass(frozen=True)
+class TemporalRelationScores:
+    coordination: TemporalCoordinationOutput
+    cosine: Tensor
+    periodic_support: Tensor
+
+
 class TemporalIncidenceEncoder(nn.Module):
     """Shared endpoint-bound relations followed along their time trajectories."""
 
@@ -211,7 +218,7 @@ def _run_block(self, function, *inputs):
             return checkpoint(function, *inputs, use_reentrant=False)
         return function(*inputs)
 
-    def forward(self, field: DirectionalPhaseField) -> TemporalCoordinationOutput:
+    def _encode_context(self, field: DirectionalPhaseField) -> tuple[Tensor, Tensor, Tensor]:
         device = next(self.parameters()).device
         actor_values = torch.tensor(field.actor_features.copy(), dtype=torch.float32, device=device)
         actor_mask = torch.tensor(field.actor_patch_mask.copy(), device=device)
@@ -257,6 +264,12 @@ def node_block(value, block_start=start, block_stop=stop):
         nodes = torch.where(topology_mask[..., None], nodes, torch.zeros_like(nodes))
         nodes, _ = self.node_temporal(nodes, topology_mask)
         nodes = torch.where(topology_mask[..., None], nodes, torch.zeros_like(nodes))
+        return hidden, nodes, topology_mask
+
+    def _readout(
+        self, field: DirectionalPhaseField, hidden: Tensor, nodes: Tensor, topology_mask: Tensor
+    ) -> TemporalCoordinationOutput:
+        device = hidden.device
         patch_sum = torch.zeros((field.patch_count, self.width), dtype=torch.float64, device=device)
         pair_count = torch.zeros(field.patch_count, dtype=torch.int64, device=device)
         for start in range(0, field.pair_count, 64):
@@ -281,6 +294,92 @@ def edge_block(value, node_value, block_start=start, block_stop=stop):
             pair_count,
         )
 
+    def forward(self, field: DirectionalPhaseField) -> TemporalCoordinationOutput:
+        return self._readout(field, *self._encode_context(field))
+
+    def _directed_text_block(
+        self,
+        field: DirectionalPhaseField,
+        hidden: Tensor,
+        nodes: Tensor,
+        topology_mask: Tensor,
+        text: Tensor,
+        start: int,
+        stop: int,
+    ) -> tuple[Tensor, Tensor]:
+        a, b, endpoints, track_support = self._half_edges(field, hidden, start, stop)
+        left, right = endpoints[:, 0], endpoints[:, 1]
+        eligible = topology_mask[left] | topology_mask[right]
+        # Unlike the unordered pair readout, language packets retain ordered
+        # endpoints: a(i->j), n_i, n_j and b(j->i), n_j, n_i. Both directions
+        # share the same update and temporal weights. No actor IDs enter them.
+        delta_a = self.edge_delta(torch.cat((a, nodes[left], nodes[right]), dim=-1))
+        delta_b = self.edge_delta(torch.cat((b, nodes[right], nodes[left]), dim=-1))
+        delta_a = torch.where(eligible[..., None], delta_a, torch.zeros_like(delta_a))
+        delta_b = torch.where(eligible[..., None], delta_b, torch.zeros_like(delta_b))
+        packets = torch.cat(
+            (a + self.topology_scale.tanh() * delta_a, b + self.topology_scale.tanh() * delta_b)
+        )
+        histories, _ = self.edge_temporal(packets, torch.cat((track_support, track_support)))
+        # Zero-energy observations can carry track context but are not reliable
+        # periodic evidence. Coherence is a feature, never a threshold filter.
+        physical = local_pair_chunk(field, start, stop)
+        observable = torch.tensor(physical.phase_mask.any(axis=-1).copy(), device=hidden.device)
+        observable = observable & track_support
+        language_mask = torch.cat((observable, observable))
+        similarities = F.normalize(histories, dim=-1) @ text.T
+        best = similarities.masked_fill(~language_mask[..., None], -torch.inf).amax(dim=(0, 1))
+        return best, language_mask.any()
+
+    def score_text(
+        self, field: DirectionalPhaseField, text_embeddings: Tensor
+    ) -> TemporalRelationScores:
+        """Stream whole directed packets against whole captions on the full timeline.
+
+        The fixed pre-pilot rule is half ordered-capture cosine, half maximum
+        directed-packet cosine. Both consume the same adapted sentence vector;
+        subject/action/object maxima are never recombined. The maximum covers
+        both endpoint orientations and all observable patches in 64-edge
+        blocks, with backward recomputation. Text/gallery batching is explicit;
+        excessive block score storage fails rather than dropping edges/time.
+        """
+        device = next(self.parameters()).device
+        if (
+            text_embeddings.ndim != 2
+            or text_embeddings.shape[0] == 0
+            or text_embeddings.shape[1] != self.width
+            or text_embeddings.device != device
+            or text_embeddings.dtype != torch.float32
+            or not bool(torch.isfinite(text_embeddings).all())
+        ):
+            raise ValueError("text requires nonempty finite float32[Q,width] on the model device")
+        projected_bytes = (
+            2 * min(64, field.pair_count) * field.patch_count * len(text_embeddings) * 4
+        )
+        if projected_bytes > 512 * 1024**2:
+            raise MemoryError(
+                "RESOURCE_LIMIT: directed relation score block; batch the text gallery"
+            )
+        hidden, nodes, topology_mask = self._encode_context(field)
+        output = self._readout(field, hidden, nodes, topology_mask)
+        text = F.normalize(text_embeddings, dim=-1)
+        best = text.new_full((len(text),), -torch.inf)
+        support = torch.zeros((), dtype=torch.bool, device=device)
+        for start in range(0, field.pair_count, 64):
+            stop = min(field.pair_count, start + 64)
+
+            def text_block(value, node_value, text_value, block_start=start, block_stop=stop):
+                return self._directed_text_block(
+                    field, value, node_value, topology_mask, text_value, block_start, block_stop
+                )
+
+            local_best, local_support = self._run_block(text_block, hidden, nodes, text)
+            best = torch.maximum(best, local_best)
+            support = support | local_support
+        whole_capture = output.embedding @ text.T
+        cosine = torch.where(support, 0.5 * (whole_capture + best), torch.zeros_like(best))
+        return TemporalRelationScores(output, cosine, support)
+
 
 class OrderedCaptureReadout(nn.Module):
     """Sort by physical window start, then encode the complete ordered capture."""
diff --git a/tests/test_continuous_retrieval.py b/tests/test_continuous_retrieval.py
new file mode 100644
index 0000000..6b10430
--- /dev/null
+++ b/tests/test_continuous_retrieval.py
@@ -0,0 +1,301 @@
+"""Data-free directed-packet and capture/text training seam checks."""
+
+from __future__ import annotations
+
+import copy
+from dataclasses import replace
+import json
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from phaseset_core.continuous_retrieval import (
+    ContinuousRetrievalSystem,
+    capture_family_positive_mask,
+    human_holistic_captions,
+)
+from phaseset_core.models import GroupBaseOutput
+from phaseset_core.temporal_coordination import TemporalIncidenceEncoder
+from phaseset_core.training import (
+    BaseRetrievalSystem,
+    _atomic_torch_save,
+    _capture_rng,
+    _load_torch_checkpoint,
+    _restore_rng,
+)
+from test_continuous_training_input import _input
+from test_phaseset_frozen_clip_text import _commitment, _fixture
+from test_phaseset_v2_coordination import field, motion
+
+
+def _directed_oracle(model, physical, text):
+    """Unblocked small-graph oracle retaining both ordered endpoints and states."""
+    hidden, nodes, topology = model._encode_context(physical)
+    output = model._readout(physical, hidden, nodes, topology)
+    a, b, endpoints, track = model._half_edges(physical, hidden, 0, physical.pair_count)
+    left, right = endpoints[:, 0], endpoints[:, 1]
+    eligible = (topology[left] | topology[right])[..., None]
+    values = []
+    for half, source, target in ((a, left, right), (b, right, left)):
+        delta = model.edge_delta(torch.cat((half, nodes[source], nodes[target]), dim=-1))
+        delta = torch.where(eligible, delta, torch.zeros_like(delta))
+        encoded, _ = model.edge_temporal(half + model.topology_scale.tanh() * delta, track)
+        values.append(encoded)
+    from phaseset_core.directional_phase import local_pair_chunk
+
+    observed = torch.from_numpy(
+        local_pair_chunk(physical, 0, physical.pair_count).phase_mask.any(-1)
+    )
+    mask = torch.cat((observed & track, observed & track))
+    cosines = F.normalize(torch.cat(values), dim=-1) @ F.normalize(text, dim=-1).T
+    best = cosines.masked_fill(~mask[..., None], -torch.inf).amax((0, 1))
+    return 0.5 * (output.embedding @ F.normalize(text, dim=-1).T + best)
+
+
+def test_directed_stream_matches_complete_packet_oracle_and_preserves_old_readout():
+    torch.manual_seed(1729)
+    physical = field()
+    model = TemporalIncidenceEncoder(width=8).eval()
+    text = torch.randn(4, 8, requires_grad=True)
+    result = model.score_text(physical, text)
+    old_output = model(physical)
+    for name in ("embedding", "patch_tokens", "topology_nodes", "valid_pair_count"):
+        assert torch.equal(getattr(result.coordination, name), getattr(old_output, name))
+    torch.testing.assert_close(
+        result.cosine, _directed_oracle(model, physical, text), rtol=0, atol=0
+    )
+    assert bool(result.periodic_support) and bool((result.cosine.abs() <= 1.00001).all())
+    result.cosine.sum().backward()
+    assert bool(torch.isfinite(text.grad).all())
+    for module in (
+        model.actor_temporal,
+        model.half_edge,
+        model.node_mlp,
+        model.node_temporal,
+        model.edge_delta,
+        model.edge_temporal,
+        model.group_temporal,
+    ):
+        assert any(p.grad is not None and bool(p.grad.abs().sum() > 0) for p in module.parameters())
+
+
+def test_directed_score_k2_full_equals_pair_only_and_topology_gradients_exact_zero():
+    torch.manual_seed(2718)
+    physical = field(motion((0.0, 0.9)))
+    full = TemporalIncidenceEncoder(width=8)
+    pair = copy.deepcopy(full)
+    pair.use_topology = False
+    text = torch.randn(3, 8)
+    a, b = full.score_text(physical, text), pair.score_text(physical, text)
+    assert torch.equal(a.cosine, b.cosine)
+    a.cosine.sum().backward()
+    for name, p in full.named_parameters():
+        if name.startswith(("node_mlp", "node_temporal", "edge_delta", "topology_scale")):
+            assert p.grad is not None
+            assert not bool(p.grad.view(torch.int32).any())
+
+
+def test_directed_checkpoint_backward_matches_uncheckpointed_across_64_edges():
+    torch.manual_seed(31415)
+    physical = field(motion(tuple(np.arange(13) * 0.37)))
+    a = TemporalIncidenceEncoder(width=8)
+    b = copy.deepcopy(a)
+    b.checkpoint_blocks = False
+    text_a = torch.randn(2, 8, requires_grad=True)
+    text_b = text_a.detach().clone().requires_grad_()
+    x, y = a.score_text(physical, text_a), b.score_text(physical, text_b)
+    assert torch.equal(x.cosine, y.cosine)
+    x.cosine.sum().backward()
+    y.cosine.sum().backward()
+    torch.testing.assert_close(text_a.grad, text_b.grad, rtol=0, atol=0)
+    for p, q in zip(a.parameters(), b.parameters(), strict=True):
+        torch.testing.assert_close(p.grad, q.grad, rtol=0, atol=0)
+
+
+def test_directed_scores_actor_permutation_and_padding_invariant():
+    torch.manual_seed(1729)
+    model = TemporalIncidenceEncoder(width=8).eval()
+    text = torch.randn(3, 8)
+    expected = model.score_text(field(), text)
+    for order in ((2, 0, 1), (1, 2, 0)):
+        actual = model.score_text(field(motion(order=order, padding=5)), text)
+        assert torch.equal(actual.cosine, expected.cosine)
+
+
+def test_observed_stationary_support_is_not_periodic_evidence():
+    batch = motion()
+    from phaseset_core.contracts import PreparedGroupBatch
+
+    stationary = PreparedGroupBatch(
+        np.zeros_like(batch.skeletons),
+        batch.actor_mask,
+        batch.frame_mask,
+        batch.track_mask,
+        batch.actor_commitments,
+        batch.group_commitments,
+    )
+    model = TemporalIncidenceEncoder(width=8)
+    result = model.score_text(field(stationary), torch.randn(3, 8))
+    assert bool(result.coordination.patch_mask.any())
+    assert not bool(result.periodic_support)
+    assert not bool(result.cosine.detach().view(torch.int32).any())
+    result.cosine.sum().backward()
+    for p in model.parameters():
+        if p.grad is not None:
+            # A masked score has zero derivative; products with signed weights
+            # may represent that derivative as -0. This is not the distinct
+            # K=2 topology +0 bit-pattern contract tested above.
+            assert bool(torch.isfinite(p.grad).all())
+            assert not bool(torch.count_nonzero(p.grad))
+
+
+def test_directed_score_resource_limit_and_text_boundary_are_explicit():
+    model = TemporalIncidenceEncoder(width=8)
+    physical = field()
+    huge = replace(physical, intervals=physical.intervals * 10000)
+    with pytest.raises(MemoryError, match="RESOURCE_LIMIT"):
+        model.score_text(huge, torch.ones(1000, 8))
+    for text in (torch.ones(3, 7), torch.ones(0, 8), torch.ones(1, 8, dtype=torch.float64)):
+        with pytest.raises(ValueError, match="text requires"):
+            model.score_text(physical, text)
+
+
+class _MockB2(nn.Module):
+    """Mock only; never a qualified real B2 checkpoint or literature baseline."""
+
+    system_id = "B2"
+
+    def __init__(self):
+        super().__init__()
+        self.projection = nn.Linear(1, 512)
+        self.dropout = nn.Dropout(0.5)
+
+    def forward(self, groups):
+        signal = torch.tensor(groups.skeletons.copy()).mean((1, 2, 3, 4))[:, None]
+        embedding = self.dropout(self.projection(signal))
+        return GroupBaseOutput(
+            embedding, embedding[:, None], torch.ones(len(signal), 1, dtype=torch.bool)
+        )
+
+
+def test_capture_text_variable_positives_frozen_base_and_optimizer_resume(tmp_path):
+    torch.manual_seed(1729)
+    (tmp_path / "input").mkdir()
+    (tmp_path / "clip").mkdir()
+    source, _, _, _ = _input(tmp_path / "input")
+    first = source.view(allow_shared_yaw=False)
+    second = replace(first, capture=replace(first.capture, source_sha256="b" * 64))
+    assert first.capture.group_commitment == second.capture.group_commitment
+    clip, _, _, _ = _fixture(tmp_path / "clip")
+    captions = clip.encode(
+        ("walking", "moving", "turning"),
+        caption_commitments=tuple(_commitment(x) for x in ("a", "b", "c")),
+        batch_size=3,
+    )
+    model = ContinuousRetrievalSystem(BaseRetrievalSystem(_MockB2(), embedding_dim=512)).train()
+    assert not model.frozen_b2.training and not any(
+        p.requires_grad for p in model.frozen_b2.parameters()
+    )
+    base_before = copy.deepcopy(model.frozen_b2.state_dict())
+    optimizer = torch.optim.AdamW([p for p in model.parameters() if p.requires_grad], lr=3e-4)
+    with torch.no_grad():
+        arbitrary_gallery = model.score((first,), captions)
+    assert arbitrary_gallery.scores.shape == (1, 3)
+    assert not hasattr(arbitrary_gallery, "positive_mask")
+
+    def step(system, opt):
+        opt.zero_grad(set_to_none=True)
+        output = system(
+            (first, second),
+            captions,
+            motion_positive_keys=("c" * 64, "d" * 64),
+            text_positive_keys=("c" * 64, "c" * 64, "d" * 64),
+        )
+        assert output.positive_mask.tolist() == [[True, True, False], [False, False, True]]
+        empty = output.scores.new_empty((0,))
+        loss = system.objective(
+            output,
+            cf_positive_scores=empty,
+            cf_negative_scores=empty,
+            verified_negative_mask=torch.empty(0, dtype=torch.bool),
+        )
+        assert bool(torch.isfinite(loss))
+        loss.backward()
+        assert all(p.grad is None for p in system.frozen_b2.parameters())
+        assert any(
+            p.grad is not None and bool(p.grad.abs().sum() > 0)
+            for p in system.text_adapter.parameters()
+        )
+        torch.nn.utils.clip_grad_norm_([p for p in system.parameters() if p.requires_grad], 1.0)
+        opt.step()
+        return loss.detach()
+
+    step(model, optimizer)
+    saved = _atomic_torch_save(
+        tmp_path / "step1.pt",
+        {
+            "global_step": 1,
+            "model": model.state_dict(),
+            "optimizer": optimizer.state_dict(),
+            "rng": _capture_rng(),
+        },
+    )
+    expected = step(model, optimizer)
+    resumed = copy.deepcopy(model)
+    resumed_opt = torch.optim.AdamW([p for p in resumed.parameters() if p.requires_grad], lr=3e-4)
+    payload, _ = _load_torch_checkpoint(saved.path, expected_sha256=saved.sha256)
+    resumed.load_state_dict(payload["model"])
+    resumed_opt.load_state_dict(payload["optimizer"])
+    _restore_rng(payload["rng"])
+    actual = step(resumed, resumed_opt)
+    assert torch.equal(actual, expected)
+    for a, b in zip(model.parameters(), resumed.parameters(), strict=True):
+        assert torch.equal(a, b)
+    for name, value in model.frozen_b2.state_dict().items():
+        assert torch.equal(value, base_before[name])
+
+
+def test_target_families_include_siblings_and_reject_unmatched_rows():
+    keys = ("a" * 64, "b" * 64)
+    assert capture_family_positive_mask(
+        keys, (keys[0], keys[0], keys[1]), device=torch.device("cpu")
+    ).tolist() == [[True, True, False], [False, False, True]]
+    assert capture_family_positive_mask(
+        (keys[0], keys[0], keys[1]), keys, device=torch.device("cpu")
+    ).tolist() == [[True, False], [True, False], [False, True]]
+    for motions, captions in ((keys, (keys[0],)), (keys, ("invalid",))):
+        with pytest.raises(ValueError):
+            capture_family_positive_mask(motions, captions, device=torch.device("cpu"))
+
+
+def test_official_holistic_schema_keeps_variable_human_rows_without_mood_fusion():
+    assert human_holistic_captions(
+        {
+            "scene_explained": ["They move together.", "They walk."],
+            "scene_mood_explained": ["They feel happy."],
+        }
+    ) == ("They move together.", "They walk.")
+    for value in ({}, {"scene_explained": []}, {"scene_explained": [""]}, []):
+        with pytest.raises(ValueError):
+            human_holistic_captions(value)
+
+
+def test_pre_pilot_language_rule_is_recorded_without_changing_the_87_stage_plan():
+    matrix = json.loads(
+        (Path(__file__).parents[1] / "configs/phaseset_v2_experiment_matrix.json").read_text()
+    )
+    contract = matrix["mechanism_contract"]
+    assert (
+        contract["relation_language_readout"]
+        == "0.5 * ordered_capture_cosine + 0.5 * max_whole_directed_packet_cosine"
+    )
+    assert contract["relation_language_edge_block"] == 64
+    assert (
+        contract["human_holistic_caption_field"]
+        == "all_scene_explained_rows_no_questionnaire_fusion"
+    )
+    assert len(matrix["runs"]) == 87 and matrix["status"] == "PLANNED_NOT_LAUNCHABLE"

From 1e8999e917214f7737eee0a70b0df050147bec2c Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 22:46:13 -0500
Subject: [PATCH 34/95] feat: assemble native parent timelines without
 compressing gaps

---
 RELEASE_FILES.sha256                      |   6 +-
 docs/PHASESET_V2_CALIBRATION_AND_CACHE.md |  49 +++++++
 src/phaseset_core/parent_capture.py       | 134 ++++++++++++++++++
 tests/test_continuous_retrieval.py        |   7 +-
 tests/test_parent_capture.py              | 162 ++++++++++++++++++++++
 5 files changed, 355 insertions(+), 3 deletions(-)
 create mode 100644 src/phaseset_core/parent_capture.py
 create mode 100644 tests/test_parent_capture.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 279b244..650c958 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -67,7 +67,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
-d7f297d1c028f49e840a0e965c07195ee9a06e67503bac78832c4eaf925c759f	17902	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
+9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
@@ -170,6 +170,7 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 9e7fa84554d5676f8ef75049b17b53580395c3c60be0f75fcb55996ccc8ef36d	37720	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
+0f0309c54a5b48a0493bb24f97eda39ff93be657295e1b0ebcc3dd6e9a8ecd76	6565	src/phaseset_core/parent_capture.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 e69b0d27d479b4499bae73c96f7854f9d07539043a0b66f0f75f6bc3e23c1f51	55415	src/phaseset_core/periodic_cache_execution.py
 21ab159e434cc504867d22a22894a8e6a82afa77dde88a466773b5549a004850	42190	src/phaseset_core/periodic_capture_training_cache.py
@@ -194,11 +195,12 @@ ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 b0f9a8d920640d3c5455e9eacdeb87feecbaca87a8b1b06affe8ca297c78d075	3593	tests/test_continuous_capture_io.py
 5ca49ce282fc8cff3654af8af56ade66cfa6b732afd7dcca3e92ffe06d4ce64a	6030	tests/test_continuous_phase_cache.py
-8c11f2e5f37055f9abd3e8c691d81582a79ba702b1e9398da2a29ac688c71f61	12362	tests/test_continuous_retrieval.py
+e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/test_continuous_retrieval.py
 2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
+3b23c2b08ed81029ca6239cbb63b98093077786163aaf5bb47ccfefb21c6ef24	7117	tests/test_parent_capture.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
 9a05b2a025a694f94e158d2de74242d2a820efbc8973920001895114e7a894fb	14880	tests/test_periodic_cache_source_lineage.py
 8dccac5551ec4d519f98b4b81d8f10ba47bd1f19ece6168ce137c51dc2ca2f0e	18170	tests/test_periodic_capture_training_cache.py
diff --git a/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md b/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
index 6c23367..c03781d 100644
--- a/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
+++ b/docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
@@ -284,3 +284,52 @@ this two-family check. It used one-capture QA floors and random untrained B2:
 zero optimizer, pilot, formal, verified-CF and final-test runs. Complete parent
 task admission, true human relation evidence and the 87-stage study remain
 unfinished; their pending state must not be replaced by this qualification.
+
+## Raw parent assembly, not concatenated preprocessed children
+
+`parent_capture.ParentCaptureEpisode` accepts caller-licensed raw world body-22
+tracks with native source provenance and their actual half-open 30 Hz frame
+intervals. `assemble_parent_capture` aligns the same real actors across releases
+and preserves the complete parent timeline. Unreleased intervals retain false
+masks and exact positive-zero coordinates; they are not stationary motion,
+interpolated poses or deleted time. Overlap or changed actor membership needs
+an explicit data-contract resolution. Lineage is not a neural input.
+
+Only after assembly is the existing capture-wide origin, shared yaw, FIR and
+30-to-20 Hz grid applied once. Individually recentered/resampled child caches
+cannot reproduce this raw-input operation. The assembly budget is explicit
+and linear in actors and frames, not a claim about combined neural peak memory.
+The existing complete-10-second window policy is unchanged: a shorter final
+source tail is declared separately, not hidden or padded with invented poses.
+
+The expanded registered-server CPU regression passed **170 tests, one CUDA-only
+skip in 44.21 seconds**. It covers actor/episode permutation, direct full-world
+preprocessing, original absolute intervals, 15/150-second holes, short native
+tracking loss, tail accounting and input/resource rejection. Exact K=2 topology,
+legacy pair checks and same-runtime bitwise invariance remain strict. An
+independent directed-packet oracle uses frozen `rtol=2e-6, atol=2e-7`: separately
+executed and batched GRUs produced at most 2.98e-8 observed score rounding
+differences on Windows. This is not tolerance for actor permutation or old
+readout equality, and no model/runtime guard was changed.
+
+A preselected real development parent then passed raw licensed geometry,
+assembly, physical-cache loading, native frozen CLIP and untrained 512D
+forward/backward in **248.26 seconds**. Its three native releases contain
+**7,650 observed frames** within an **8,550-frame (285-second)** parent span,
+including **900 missing frames (30 seconds)**. The existing policy retains
+**5,600 target frames**, declares the remaining 150 source frames, accepts
+24 ten-second windows and rejects four. All 28 gap-only patches have no
+actor/edge physical evidence. Raw actor/episode permutation is bitwise equal;
+all 55 gradient tensors are finite and frozen random B2 remains unchanged.
+Five real human sentence rows have no CLIP truncation. Forward/backward took
+35.31/48.90 seconds; peak RSS was 1,969,516 KiB.
+
+This is a CPU execution qualification using one-parent QA floors and scalar
+score differentiation, **not** an all-positive one-parent InfoNCE experiment,
+trained checkpoint, GPU profile, pilot, verified-counterfactual dataset or
+formal run. Complete development-parent artifacts, five full train-only
+calibrations, the unified parent gallery/sampling/statistics contract, faithful
+literature controls, human relation verification and all 87 stages are still
+required before final research delivery. The original failed invocation is
+preserved; its existing SMPL-X library-path omission was corrected without
+installing packages or changing the qualified model source.
diff --git a/src/phaseset_core/parent_capture.py b/src/phaseset_core/parent_capture.py
new file mode 100644
index 0000000..25ab37c
--- /dev/null
+++ b/src/phaseset_core/parent_capture.py
@@ -0,0 +1,134 @@
+"""Place native raw body22 episodes on their actual parent 30 Hz timeline.
+
+The caller admits official parent/actor lineage before supplying episodes.
+These are licensed-format raw ``PrivateCaptureArrays`` (world coordinates),
+never independently centered/resampled child ``PreparedContinuousCapture``s.
+Unreleased intervals are explicitly unobserved, not stationary or interpolated
+motion. One subsequent ``prepare_continuous_capture`` applies a common origin,
+yaw, FIR, target sampling grid and the unchanged ten-second acceptance rules.
+This is a numeric input seam, not parent task/split/gallery/statistical admission.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+import hashlib
+
+import numpy as np
+
+from .pipeline import PrivateCaptureArrays, PreparationResourceLimit, _capture_lineage_sha256
+from .preprocessing import PreprocessingError
+
+
+@dataclass(frozen=True)
+class ParentCaptureEpisode:
+    """A raw released episode with verified absolute, half-open 30 Hz bounds.
+
+    Official inclusive filename ends must be converted to ``stop=end+1`` by
+    the private data adapter and checked against the actual array length.
+    One actor commitment follows the same real track across all episodes.
+    """
+
+    capture: PrivateCaptureArrays = field(repr=False)
+    source_start_frame: int
+    source_stop_frame: int
+
+    def __post_init__(self) -> None:
+        if type(self.capture) is not PrivateCaptureArrays:
+            raise TypeError("parent episodes require raw PrivateCaptureArrays")
+        if self.capture.source_sha256 is None:
+            raise PreprocessingError("parent episodes require explicit native source provenance")
+        if (
+            type(self.source_start_frame) is not int
+            or type(self.source_stop_frame) is not int
+            or not 0 <= self.source_start_frame < self.source_stop_frame
+            or self.source_stop_frame - self.source_start_frame != self.capture.frame_count
+        ):
+            raise PreprocessingError("episode absolute interval must match its real raw frames")
+
+
+@dataclass(frozen=True)
+class ParentCaptureAssembly:
+    capture: PrivateCaptureArrays = field(repr=False)
+    source_start_frame: int
+    source_stop_frame: int
+    episode_intervals: tuple[tuple[int, int], ...]
+    missing_intervals: tuple[tuple[int, int], ...]
+    episode_source_sha256s: tuple[str, ...]
+
+
+def assemble_parent_capture(
+    episodes: tuple[ParentCaptureEpisode, ...], *, max_assembly_bytes: int = 1_073_741_824
+) -> ParentCaptureAssembly:
+    """Align all native actors and episodes without compacting time or motion.
+
+    Array time zero is the first released episode's original start; the exact
+    absolute bounds remain on the assembly. Gaps and native missing tracks
+    keep false masks and exact +0 coordinates. No one is removed, no pose is
+    extrapolated, and no child is recentered. Overlapping releases or changed
+    actor sets require explicit data-contract resolution rather than guessing.
+    Memory grows linearly with the true parent span and actor count; an explicit
+    resource limit precedes assembly allocation. This does not assert the
+    combined assembly/preprocessing/phase/neural peak fits the same budget.
+    """
+    if (
+        type(episodes) is not tuple
+        or not episodes
+        or any(type(episode) is not ParentCaptureEpisode for episode in episodes)
+    ):
+        raise TypeError("episodes must be a nonempty exact ParentCaptureEpisode tuple")
+    if type(max_assembly_bytes) is not int or max_assembly_bytes < 1:
+        raise ValueError("max_assembly_bytes must be a positive exact int")
+    ordered = tuple(sorted(episodes, key=lambda episode: episode.source_start_frame))
+    actors = tuple(sorted(ordered[0].capture.actor_commitments))
+    for index, episode in enumerate(ordered):
+        if tuple(sorted(episode.capture.actor_commitments)) != actors:
+            raise PreprocessingError("parent episode actor membership changed")
+        if index and episode.source_start_frame < ordered[index - 1].source_stop_frame:
+            raise PreprocessingError("overlapping parent episodes require data-contract resolution")
+        track = episode.capture.track_mask[:, :, :22]
+        if not np.array_equal(track, np.broadcast_to(track[..., :1], track.shape)):
+            raise PreprocessingError("parent body22 tracking must be uniform per actor-frame")
+
+    first, stop = ordered[0].source_start_frame, ordered[-1].source_stop_frame
+    frames = stop - first
+    dtype = np.result_type(*(episode.capture.joints.dtype for episode in ordered))
+    # Assembly and PrivateCaptureArrays each own a numeric/mask copy; include
+    # conservative boolean validation/assignment scratch without K*K storage.
+    projected_bytes = frames * len(actors) * 22 * (2 * 3 * dtype.itemsize + 16)
+    if projected_bytes > max_assembly_bytes:
+        raise PreparationResourceLimit(
+            "RESOURCE_LIMIT: parent raw timeline assembly exceeds budget"
+        )
+    joints = np.zeros((frames, len(actors), 22, 3), dtype=dtype)
+    tracking = np.zeros(joints.shape[:-1], dtype=np.bool_)
+    missing, source_digests = [], []
+    lineage = hashlib.sha256(b"phaseset-native-parent-timeline-v1\x00")
+    for actor in actors:
+        lineage.update(actor)
+    previous_stop = first
+    for episode in ordered:
+        raw = episode.capture
+        order = [raw.actor_commitments.index(actor) for actor in actors]
+        start = episode.source_start_frame - first
+        end = episode.source_stop_frame - first
+        mask = raw.track_mask[:, order, :22]
+        joints[start:end] = np.where(mask[..., None], raw.joints[:, order, :22], 0.0)
+        tracking[start:end] = mask
+        if episode.source_start_frame > previous_stop:
+            missing.append((previous_stop, episode.source_start_frame))
+        previous_stop = episode.source_stop_frame
+        digest = _capture_lineage_sha256(raw)
+        source_digests.append(digest)
+        lineage.update(episode.source_start_frame.to_bytes(8, "big"))
+        lineage.update(episode.source_stop_frame.to_bytes(8, "big"))
+        lineage.update(digest.encode("ascii"))
+    joints[joints == 0.0] = 0.0
+    return ParentCaptureAssembly(
+        capture=PrivateCaptureArrays(joints, tracking, actors, lineage.hexdigest()),
+        source_start_frame=first,
+        source_stop_frame=stop,
+        episode_intervals=tuple((e.source_start_frame, e.source_stop_frame) for e in ordered),
+        missing_intervals=tuple(missing),
+        episode_source_sha256s=tuple(source_digests),
+    )
diff --git a/tests/test_continuous_retrieval.py b/tests/test_continuous_retrieval.py
index 6b10430..dd3577d 100644
--- a/tests/test_continuous_retrieval.py
+++ b/tests/test_continuous_retrieval.py
@@ -65,8 +65,13 @@ def test_directed_stream_matches_complete_packet_oracle_and_preserves_old_readou
     old_output = model(physical)
     for name in ("embedding", "patch_tokens", "topology_nodes", "valid_pair_count"):
         assert torch.equal(getattr(result.coordination, name), getattr(old_output, name))
+    # The independent oracle runs each orientation separately; production
+    # batches both through the same GRU/GEMM. Those different matrix shapes
+    # differ by a float32 rounding unit on the observed Windows backend.
+    # This oracle has a frozen numerical tolerance, not the bitwise contract
+    # for actor permutations, K=2, checkpoint replay or the old group readout.
     torch.testing.assert_close(
-        result.cosine, _directed_oracle(model, physical, text), rtol=0, atol=0
+        result.cosine, _directed_oracle(model, physical, text), rtol=2e-6, atol=2e-7
     )
     assert bool(result.periodic_support) and bool((result.cosine.abs() <= 1.00001).all())
     result.cosine.sum().backward()
diff --git a/tests/test_parent_capture.py b/tests/test_parent_capture.py
new file mode 100644
index 0000000..4184f8d
--- /dev/null
+++ b/tests/test_parent_capture.py
@@ -0,0 +1,162 @@
+"""Native-episode timeline assembly; all fixtures are data-free world body22."""
+
+from dataclasses import replace
+
+import numpy as np
+import pytest
+
+from phaseset_core.continuous_capture import prepare_continuous_capture
+from phaseset_core.directional_phase import continuous_directional_phase_field, local_pair_chunk
+from phaseset_core.parent_capture import ParentCaptureEpisode, assemble_parent_capture
+from phaseset_core.pipeline import PrivateCaptureArrays, PreparationResourceLimit
+from phaseset_core.preprocessing import PreprocessingError
+from test_phaseset_continuous_capture import _capture
+
+
+def _episodes(gap=300, tail=0):
+    full = _capture(600 + gap + tail)
+    episodes = tuple(
+        ParentCaptureEpisode(
+            PrivateCaptureArrays(
+                full.joints[start:stop],
+                full.track_mask[start:stop],
+                full.actor_commitments,
+                digest * 64,
+            ),
+            1000 + start,
+            1000 + stop,
+        )
+        for start, stop, digest in ((0, 300, "a"), (300 + gap, 600 + gap + tail, "b"))
+    )
+    return full, episodes
+
+
+def test_real_gap_retains_raw_world_coordinates_and_shared_parent_preprocessing():
+    full, episodes = _episodes()
+    assembly = assemble_parent_capture(episodes)
+    raw = assembly.capture
+    assert raw.frame_count == 900 and assembly.missing_intervals == ((1300, 1600),)
+    assert assembly.episode_intervals == ((1000, 1300), (1600, 1900))
+    np.testing.assert_array_equal(raw.joints[600:], full.joints[600:])
+    assert not raw.track_mask[300:600].any()
+    assert not raw.joints[300:600].view(np.uint32).any()
+    prepared = prepare_continuous_capture(raw)
+    assert prepared.frame_count == 600
+    assert [d.accepted for d in prepared.decisions] == [True, False, True]
+    assert not prepared.track_mask[:, 200:400].any()
+    assert not prepared.skeletons[:, 200:400].view(np.uint32).any()
+    assert prepared.skeletons[:, 410, 0, 0].mean() > 20
+    child = prepare_continuous_capture(episodes[1].capture)
+    assert abs(float(child.skeletons[:, 10, 0, 0].mean())) < 1
+    # Independent reference: mark the same hole on one true world trajectory,
+    # then use the existing, audited full-capture preprocessing once.
+    mask = full.track_mask.copy()
+    mask[300:600] = False
+    oracle = prepare_continuous_capture(replace(full, track_mask=mask))
+    np.testing.assert_array_equal(
+        prepared.skeletons.view(np.uint32), oracle.skeletons.view(np.uint32)
+    )
+    np.testing.assert_array_equal(prepared.track_mask, oracle.track_mask)
+    assert [assembly.source_start_frame + w.source_start_frame for w in prepared.windows()] == [
+        1000,
+        1600,
+    ]
+
+
+def test_episode_order_and_each_episodes_actor_permutation_are_bitwise_identical():
+    _, episodes = _episodes()
+    shuffled = []
+    for episode, order in zip(episodes, ([2, 0, 1], [1, 2, 0]), strict=True):
+        raw = episode.capture
+        shuffled.append(
+            replace(
+                episode,
+                capture=PrivateCaptureArrays(
+                    raw.joints[:, order],
+                    raw.track_mask[:, order],
+                    tuple(raw.actor_commitments[i] for i in order),
+                    raw.source_sha256,
+                ),
+            )
+        )
+    a, b = assemble_parent_capture(episodes), assemble_parent_capture(tuple(reversed(shuffled)))
+    assert a.capture.source_sha256 == b.capture.source_sha256
+    assert a.episode_source_sha256s == b.episode_source_sha256s
+    np.testing.assert_array_equal(
+        a.capture.joints.view(np.uint32), b.capture.joints.view(np.uint32)
+    )
+    np.testing.assert_array_equal(a.capture.track_mask, b.capture.track_mask)
+
+
+def test_fifteen_second_and_150_second_gaps_do_not_create_physical_phase_evidence():
+    for gap in (450, 4500):
+        _, episodes = _episodes(gap=gap)
+        assembled = assemble_parent_capture(episodes)
+        prepared = prepare_continuous_capture(assembled.capture)
+        assert prepared.frame_count == (600 + gap) // 300 * 200
+        field = continuous_directional_phase_field(prepared, energy_floors=np.zeros(6))
+        interior = [
+            p
+            for p, (start, stop) in enumerate(field.intervals)
+            if start >= 220 and stop <= (300 + gap) * 2 // 3 - 20
+        ]
+        assert interior
+        assert not field.actor_patch_mask[:, interior].any()
+        pair = local_pair_chunk(field, 0, field.pair_count)
+        assert not pair.support_mask[:, interior].any()
+        assert not pair.phase_mask[:, interior].any()
+
+
+def test_native_missing_values_are_masked_not_promoted_to_observed_zeros():
+    _, episodes = _episodes()
+    raw = episodes[0].capture
+    values, mask = raw.joints.copy(), raw.track_mask.copy()
+    values[100:107, 1] = np.nan
+    mask[100:107, 1] = False
+    changed = (
+        replace(episodes[0], capture=replace(raw, joints=values, track_mask=mask)),
+        episodes[1],
+    )
+    assembled = assemble_parent_capture(changed)
+    assert not assembled.capture.track_mask[100:107, 1].any()
+    assert not assembled.capture.joints[100:107, 1].view(np.uint32).any()
+    prepared = prepare_continuous_capture(assembled.capture)
+    assert prepared.decisions[0].accepted and prepared.decisions[0].missing_actor_frames == 7
+
+
+def test_absolute_frame_lineage_and_declared_partial_tail_are_preserved():
+    _, episodes = _episodes(tail=13)
+    a = assemble_parent_capture(episodes)
+    shifted = tuple(
+        replace(
+            e,
+            source_start_frame=e.source_start_frame + 300,
+            source_stop_frame=e.source_stop_frame + 300,
+        )
+        for e in episodes
+    )
+    b = assemble_parent_capture(shifted)
+    assert a.capture.source_sha256 != b.capture.source_sha256
+    np.testing.assert_array_equal(a.capture.joints, b.capture.joints)
+    assert a.capture.frame_count == 913 and a.source_stop_frame == 1913
+    assert prepare_continuous_capture(a.capture).trailing_source_frames == 13
+
+
+def test_overlap_actor_change_and_resource_limits_require_explicit_resolution():
+    _, episodes = _episodes()
+    with pytest.raises(PreprocessingError, match="overlapping"):
+        assemble_parent_capture(
+            (episodes[0], replace(episodes[1], source_start_frame=1200, source_stop_frame=1500))
+        )
+    raw = episodes[1].capture
+    changed = replace(raw, actor_commitments=(bytes([9]) * 32,) + raw.actor_commitments[1:])
+    with pytest.raises(PreprocessingError, match="membership"):
+        assemble_parent_capture((episodes[0], replace(episodes[1], capture=changed)))
+    with pytest.raises(PreparationResourceLimit, match="RESOURCE_LIMIT"):
+        assemble_parent_capture(episodes, max_assembly_bytes=1)
+    with pytest.raises(TypeError, match="raw"):
+        ParentCaptureEpisode(prepare_continuous_capture(raw), 1600, 1900)
+    with pytest.raises(PreprocessingError, match="real raw frames"):
+        replace(episodes[0], source_stop_frame=1299)
+    with pytest.raises(PreprocessingError, match="native source provenance"):
+        replace(episodes[0], capture=replace(episodes[0].capture, source_sha256=None))

From 65d68ebabdb1a60bef0a463b66df961e3f9f8405 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 23:23:09 -0500
Subject: [PATCH 35/95] ci: bound CPU thread pools and record backend
 diagnostics

---
 .github/workflows/ci.yml | 10 +++++++++-
 RELEASE_FILES.sha256     |  2 +-
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 9447747..267c0ae 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -12,6 +12,12 @@ jobs:
   verify:
     name: Windows Python ${{ matrix.python }}
     runs-on: windows-latest
+    # Freeze native CPU pools before Torch/NumPy import, as in server witnesses.
+    env:
+      OMP_NUM_THREADS: "1"
+      OPENBLAS_NUM_THREADS: "1"
+      MKL_NUM_THREADS: "1"
+      PYTHONHASHSEED: "0"
     strategy:
       fail-fast: false
       matrix:
@@ -44,7 +50,9 @@ jobs:
       - name: Lint
         run: python -m ruff check src tests scripts
       - name: Test
-        run: python -B -m pytest -p no:cacheprovider -q ${{ matrix.pytest_args }}
+        run: |
+          python -B -c "import torch; print({'torch': torch.__version__, 'cpu_capability': torch.backends.cpu.get_cpu_capability(), 'num_threads': torch.get_num_threads(), 'num_interop_threads': torch.get_num_interop_threads(), 'mkldnn_available': torch.backends.mkldnn.is_available(), 'mkldnn_enabled': torch.backends.mkldnn.enabled}); assert torch.get_num_threads() == 1"
+          python -B -m pytest -p no:cacheprovider -q ${{ matrix.pytest_args }}
       - name: Build wheel
         run: python -m pip wheel --no-deps --wheel-dir dist .
       - name: Audit built wheel bytes
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 650c958..be39426 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -1,7 +1,7 @@
 # sha256\tbytes\tgit_index_path; self excluded
 ca4262d34e1553bf562d8ef5d3ef15b666b8f12214d5ff9345a8573baf9dd0f3	327	.gitattributes
 2c5a644e7b812eb516fedeee66218e87a3ee9a0e6206d0e37a383b24eb1f1522	129	.github/CODEOWNERS
-6e6cf3cba1ad55fb25e1aff2a0037d9791d45f0ca757c33f4f2335d75ec061c7	3019	.github/workflows/ci.yml
+582a8f85592c529b6638397936cf0b7d1cad32f5192d90e6812db0bdb4e04d07	3607	.github/workflows/ci.yml
 e9938bfe4bbd1ee2357396d1a6a8788e511389e297c28d7247d9e16a9a8137fc	392	.gitignore
 4c20bbdbd002e6f52d43e0d4e5b2dd826a577cf45c817582ae3ed896e6d9f075	6957	CHANGELOG.md
 34ad6f564d314e436426109fb8e7e67a2473e8dedf200d45a766d27d030cd080	780	CITATION.cff

From 2139438effe66ae7f614b4dec4f8a3a36794352a Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 23:33:59 -0500
Subject: [PATCH 36/95] feat: index complete-parent galleries and variable
 human positives

---
 RELEASE_FILES.sha256                       |   3 +
 docs/PHASESET_V2_PARENT_TASK.md            |  59 +++++++
 src/phaseset_core/parent_retrieval_task.py | 178 +++++++++++++++++++++
 tests/test_parent_retrieval_task.py        | 123 ++++++++++++++
 4 files changed, 363 insertions(+)
 create mode 100644 docs/PHASESET_V2_PARENT_TASK.md
 create mode 100644 src/phaseset_core/parent_retrieval_task.py
 create mode 100644 tests/test_parent_retrieval_task.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index be39426..ef8f8be 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -70,6 +70,7 @@ cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHAS
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
+a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
@@ -171,6 +172,7 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 0f0309c54a5b48a0493bb24f97eda39ff93be657295e1b0ebcc3dd6e9a8ecd76	6565	src/phaseset_core/parent_capture.py
+be883315aa773e6c2d535ea981853ae92a791187338fd2b310b574f956174a82	7326	src/phaseset_core/parent_retrieval_task.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 e69b0d27d479b4499bae73c96f7854f9d07539043a0b66f0f75f6bc3e23c1f51	55415	src/phaseset_core/periodic_cache_execution.py
 21ab159e434cc504867d22a22894a8e6a82afa77dde88a466773b5549a004850	42190	src/phaseset_core/periodic_capture_training_cache.py
@@ -201,6 +203,7 @@ e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/tes
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 3b23c2b08ed81029ca6239cbb63b98093077786163aaf5bb47ccfefb21c6ef24	7117	tests/test_parent_capture.py
+32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
 9a05b2a025a694f94e158d2de74242d2a820efbc8973920001895114e7a894fb	14880	tests/test_periodic_cache_source_lineage.py
 8dccac5551ec4d519f98b4b81d8f10ba47bd1f19ece6168ce137c51dc2ca2f0e	18170	tests/test_periodic_capture_training_cache.py
diff --git a/docs/PHASESET_V2_PARENT_TASK.md b/docs/PHASESET_V2_PARENT_TASK.md
new file mode 100644
index 0000000..86539f7
--- /dev/null
+++ b/docs/PHASESET_V2_PARENT_TASK.md
@@ -0,0 +1,59 @@
+# Complete-parent retrieval labels
+
+`phaseset_core.parent_retrieval_task` is a provenance/label index, not a data
+license, physical admission gate, final task seal, or trained model. It reads
+no paths and opens no test data on its own.
+
+## Unit of retrieval
+
+Released episodes may share one native parent and one human holistic annotation
+file. They must first be placed on the actual parent world-coordinate timeline,
+including unreleased gaps, and preprocessed once. They are not independent
+motion rows or negative examples of each other. The host separately admits
+the complete body/cache, declared tail, source lineage, and population-specific
+training-only physical floors.
+
+The development annotation audit found 494 eligible released episodes belonging
+to 253 native parent/holistic-file families: 202 training and 51 validation
+parents. Reading each official `scene_explained` list once yields 1,334 human
+answer rows. An exact UTF-8 text audit found no repeated rows within or across
+these families. This is not semantic-equivalence or relation-truth verification;
+test annotations were not read by that diagnostic.
+
+## Interface
+
+The private host supplies `ParentCaptionRecord.from_official_bytes(...)` with
+the complete-parent source identity, admitted annotation-family digest,
+component, split, original annotation bytes and expected native human row
+count. Every `scene_explained` row is retained verbatim. Other questionnaire
+fields are not fused into motion captions. A digest cannot by itself prove that
+an annotation is official, licensed or skeleton-observable.
+
+`ParentRetrievalTask(records, expected_family_keys=...)` verifies that the
+explicit complete population is present before selecting any subset. Repeated
+source/family records, missing or extra families, and a participant component
+crossing splits are errors. It does not silently deduplicate released siblings
+or infer which parent should be retained.
+
+`gallery(split=...)` returns one motion row per admitted parent and every human
+caption row for that split. `batch(family_keys)` preserves the supplied parent
+order and rejects repeated or unadmitted parents. Positive keys use the parent
+annotation family; source keys remain separate. The resulting rectangular
+variable-positive mask has no fixed three-caption assumption. Text commitments
+follow the existing source/family/row-ordinal CLIP receipt convention and are
+lineage only, never tokenizer or neural features.
+
+Sampling schedules and full-population physical admission remain the training
+host's responsibility. All bases, literature controls and V2 variants must use
+the same admitted parent task. Capture-cluster statistics use independent
+parents, not released episodes, windows or individual captions as replicates.
+
+## Evidence boundary
+
+Data-free metadata tests are not human truth. A two-real-parent frozen-random-B2
+forward/loss/backward witness, if passed, establishes execution and label
+plumbing only; it is not an optimizer run, pilot, formal stage or retrieval
+result. Two real blinded human annotators are still required for verified
+relation counterfactuals. Complete-population floors, task freeze, faithful
+literature baselines, the fixed 87-stage matrix and sealed evaluation remain
+separate gates. No final-test discovery or unsealing is performed by this API.
diff --git a/src/phaseset_core/parent_retrieval_task.py b/src/phaseset_core/parent_retrieval_task.py
new file mode 100644
index 0000000..e26eb46
--- /dev/null
+++ b/src/phaseset_core/parent_retrieval_task.py
@@ -0,0 +1,178 @@
+"""One complete native parent per retrieval row, with every human holistic row.
+
+This is a provenance/label index, not permission, physical-floor admission or a
+task seal. The private host supplies complete-parent source identities and an
+explicit expected population. No data discovery, test opening, semantic label
+generation, motion encoding or caption-count assumption takes place here.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from dataclasses import dataclass, field
+
+import torch
+from torch import Tensor
+
+from .continuous_retrieval import capture_family_positive_mask, human_holistic_captions
+
+
+def _sha_key(value: str) -> None:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or any(char not in "0123456789abcdef" for char in value)
+    ):
+        raise ValueError("parent/source/family identity must be a lowercase SHA-256 key")
+
+
+@dataclass(frozen=True)
+class ParentCaptionRecord:
+    source_sha256: str
+    annotation_family_sha256: str
+    component: str
+    split: str
+    captions: tuple[str, ...] = field(repr=False)
+
+    def __post_init__(self) -> None:
+        _sha_key(self.source_sha256)
+        _sha_key(self.annotation_family_sha256)
+        if type(self.component) is not str or not self.component:
+            raise ValueError("parent component must be explicit")
+        if self.split not in ("train", "validation", "test"):
+            raise ValueError("parent split must be explicit")
+        if (
+            type(self.captions) is not tuple
+            or not self.captions
+            or any(type(row) is not str or not row.strip() for row in self.captions)
+        ):
+            raise ValueError("every official human holistic row must be nonempty")
+
+    @classmethod
+    def from_official_bytes(
+        cls,
+        *,
+        source_sha256: str,
+        annotation_family_sha256: str,
+        component: str,
+        split: str,
+        annotation_bytes: bytes,
+        expected_human_rows: int,
+    ) -> ParentCaptionRecord:
+        """Bind the native file and all scene_explained rows, without fusion.
+
+        The private host verifies official provenance and rights. A digest does
+        not make an arbitrary file official or verify skeleton-observable truth.
+        """
+        if type(annotation_bytes) is not bytes or (
+            hashlib.sha256(annotation_bytes).hexdigest() != annotation_family_sha256
+        ):
+            raise ValueError("official annotation bytes disagree with admitted family")
+        captions = human_holistic_captions(json.loads(annotation_bytes))
+        if type(expected_human_rows) is not int or expected_human_rows != len(captions):
+            raise ValueError("official human row count disagrees with admitted census")
+        return cls(source_sha256, annotation_family_sha256, component, split, captions)
+
+
+@dataclass(frozen=True)
+class ParentRetrievalBatch:
+    parents: tuple[ParentCaptionRecord, ...]
+
+    @property
+    def motion_source_keys(self) -> tuple[str, ...]:
+        return tuple(parent.source_sha256 for parent in self.parents)
+
+    @property
+    def motion_positive_keys(self) -> tuple[str, ...]:
+        return tuple(parent.annotation_family_sha256 for parent in self.parents)
+
+    @property
+    def captions(self) -> tuple[str, ...]:
+        return tuple(row for parent in self.parents for row in parent.captions)
+
+    @property
+    def text_positive_keys(self) -> tuple[str, ...]:
+        return tuple(
+            parent.annotation_family_sha256 for parent in self.parents for _ in parent.captions
+        )
+
+    @property
+    def caption_commitments(self) -> tuple[bytes, ...]:
+        # Lineage only: not inputs to the text adapter or motion network.
+        return tuple(
+            hashlib.sha256(
+                bytes.fromhex(parent.source_sha256)
+                + bytes.fromhex(parent.annotation_family_sha256)
+                + ordinal.to_bytes(8, "big")
+            ).digest()
+            for parent in self.parents
+            for ordinal in range(len(parent.captions))
+        )
+
+    def positive_mask(self, *, device: torch.device) -> Tensor:
+        return capture_family_positive_mask(
+            self.motion_positive_keys, self.text_positive_keys, device=device
+        )
+
+
+class ParentRetrievalTask:
+    """Validate a complete caller-admitted population before selecting batches.
+
+    Released siblings are not separate motion rows. Duplicate source/family
+    records are rejected, not silently dropped. Component split consistency is
+    checked on the complete population, before selecting a training/gallery
+    subset. Captions are never deduplicated, normalized, or sampled.
+    """
+
+    def __init__(
+        self,
+        parents: tuple[ParentCaptionRecord, ...],
+        *,
+        expected_family_keys: tuple[str, ...],
+    ) -> None:
+        if (
+            type(parents) is not tuple
+            or not parents
+            or any(type(parent) is not ParentCaptionRecord for parent in parents)
+        ):
+            raise ValueError("task requires a nonempty exact parent record tuple")
+        if type(expected_family_keys) is not tuple or not expected_family_keys:
+            raise ValueError("the complete expected parent population must be explicit")
+        for key in expected_family_keys:
+            _sha_key(key)
+        families = tuple(parent.annotation_family_sha256 for parent in parents)
+        sources = tuple(parent.source_sha256 for parent in parents)
+        if len(set(families)) != len(families) or len(set(sources)) != len(sources):
+            raise ValueError("released siblings or repeated parents cannot be gallery rows")
+        if len(set(expected_family_keys)) != len(expected_family_keys) or (
+            set(families) != set(expected_family_keys)
+        ):
+            raise ValueError("parent population is incomplete or contains unexpected families")
+        component_splits = {}
+        for parent in parents:
+            previous = component_splits.setdefault(parent.component, parent.split)
+            if previous != parent.split:
+                raise ValueError("a participant component cannot cross task splits")
+        self.parents = tuple(sorted(parents, key=lambda parent: parent.annotation_family_sha256))
+        self._by_family = {parent.annotation_family_sha256: parent for parent in self.parents}
+
+    def batch(self, family_keys: tuple[str, ...]) -> ParentRetrievalBatch:
+        """Preserve caller batch order and include every row of each parent once."""
+        if type(family_keys) is not tuple or not family_keys:
+            raise ValueError("batch parent identities must be explicit and nonempty")
+        for key in family_keys:
+            _sha_key(key)
+        if len(set(family_keys)) != len(family_keys) or any(
+            key not in self._by_family for key in family_keys
+        ):
+            raise ValueError("batch contains duplicate or unadmitted parent families")
+        return ParentRetrievalBatch(tuple(self._by_family[key] for key in family_keys))
+
+    def gallery(self, *, split: str) -> ParentRetrievalBatch:
+        """Use the complete requested split, not a released-window gallery."""
+        return self.batch(
+            tuple(
+                parent.annotation_family_sha256 for parent in self.parents if parent.split == split
+            )
+        )
diff --git a/tests/test_parent_retrieval_task.py b/tests/test_parent_retrieval_task.py
new file mode 100644
index 0000000..86cd90c
--- /dev/null
+++ b/tests/test_parent_retrieval_task.py
@@ -0,0 +1,123 @@
+"""Data-free provenance/label fixtures, not human relation ground truth."""
+
+import hashlib
+import json
+
+import pytest
+import torch
+from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+
+
+def key(value):
+    return hashlib.sha256(value.encode()).hexdigest()
+
+
+def record(name, rows=2, component="C01", split="train"):
+    raw = json.dumps(
+        {"scene_explained": [f"{name} fixture row {i}" for i in range(rows)], "mood": ["ignored"]}
+    ).encode()
+    return ParentCaptionRecord.from_official_bytes(
+        source_sha256=key(f"source-{name}"),
+        annotation_family_sha256=hashlib.sha256(raw).hexdigest(),
+        component=component,
+        split=split,
+        annotation_bytes=raw,
+        expected_human_rows=rows,
+    )
+
+
+def task(*parents):
+    return ParentRetrievalTask(
+        tuple(parents), expected_family_keys=tuple(p.annotation_family_sha256 for p in parents)
+    )
+
+
+def test_complete_gallery_uses_one_parent_and_all_variable_human_rows():
+    a, b, c = record("a", 2), record("b", 5), record("c", 1, "C00", "validation")
+    index = task(b, c, a)
+    batch = index.gallery(split="train")
+    assert len(batch.parents) == 2 and len(batch.captions) == 7
+    assert len(set(batch.motion_source_keys)) == 2
+    mask = batch.positive_mask(device=torch.device("cpu"))
+    assert mask.shape == (2, 7) and mask.sum() == 7
+    assert sorted(mask.sum(dim=1).tolist()) == [2, 5]
+    assert bool(mask.any(dim=0).all())
+    assert index.gallery(split="validation").captions == c.captions
+    assert "ignored" not in batch.captions
+
+
+def test_batch_order_and_caption_lineage_match_existing_native_clip_contract():
+    a, b = record("a"), record("b", 3)
+    batch = task(a, b).batch((b.annotation_family_sha256, a.annotation_family_sha256))
+    assert batch.motion_source_keys == (b.source_sha256, a.source_sha256)
+    assert batch.captions == b.captions + a.captions
+    expected = hashlib.sha256(
+        bytes.fromhex(b.source_sha256) + bytes.fromhex(b.annotation_family_sha256) + bytes(8)
+    ).digest()
+    assert batch.caption_commitments[0] == expected
+    assert len(set(batch.caption_commitments)) == 5
+    assert (
+        batch.text_positive_keys
+        == (b.annotation_family_sha256,) * 3 + (a.annotation_family_sha256,) * 2
+    )
+
+
+def test_task_input_reordering_has_same_canonical_gallery():
+    a, b = record("a"), record("b")
+    assert task(a, b).gallery(split="train") == task(b, a).gallery(split="train")
+
+
+def test_released_sibling_and_source_alias_are_not_independent_gallery_rows():
+    a, b = record("a"), record("b")
+    with pytest.raises(ValueError, match="siblings"):
+        task(a, a)
+    alias = ParentCaptionRecord(
+        a.source_sha256, b.annotation_family_sha256, "C01", "train", b.captions
+    )
+    with pytest.raises(ValueError, match="siblings"):
+        task(a, alias)
+
+
+def test_expected_population_rejects_partial_and_extra_parent_inputs():
+    a, b = record("a"), record("b")
+    with pytest.raises(ValueError, match="population"):
+        ParentRetrievalTask(
+            (a,), expected_family_keys=(a.annotation_family_sha256, b.annotation_family_sha256)
+        )
+    with pytest.raises(ValueError, match="population"):
+        ParentRetrievalTask((a, b), expected_family_keys=(a.annotation_family_sha256,))
+
+
+def test_component_cross_split_is_rejected_before_subset_selection():
+    a, b = record("a"), record("b", split="validation")
+    with pytest.raises(ValueError, match="component"):
+        task(a, b)
+
+
+def test_batch_rejects_repetition_unknown_and_empty_gallery():
+    a = record("a")
+    index = task(a)
+    for keys in ((a.annotation_family_sha256,) * 2, (key("unknown"),), ()):
+        with pytest.raises(ValueError):
+            index.batch(keys)
+    with pytest.raises(ValueError):
+        index.gallery(split="validation")
+
+
+def test_native_bytes_and_row_census_are_not_replaced_or_normalized():
+    raw = b'{"scene_explained": ["  unnormalized human fixture  "]}'
+    kwargs = dict(
+        source_sha256=key("source"),
+        annotation_family_sha256=hashlib.sha256(raw).hexdigest(),
+        component="C01",
+        split="train",
+        annotation_bytes=raw,
+        expected_human_rows=1,
+    )
+    assert ParentCaptionRecord.from_official_bytes(**kwargs).captions == (
+        "  unnormalized human fixture  ",
+    )
+    with pytest.raises(ValueError, match="bytes"):
+        ParentCaptionRecord.from_official_bytes(**{**kwargs, "annotation_bytes": raw + b" "})
+    with pytest.raises(ValueError, match="census"):
+        ParentCaptionRecord.from_official_bytes(**{**kwargs, "expected_human_rows": 3})

From 9f627aa1a8922def9c4ed24a9d9aadb8daf31415 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sat, 26 Sep 2026 23:55:42 -0500
Subject: [PATCH 37/95] test: isolate portable CPU BF16 autocast replay from
 native backend

---
 RELEASE_FILES.sha256                      |  2 +-
 tests/test_phaseset_streaming_autograd.py | 19 +++++++++++++++++++
 2 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index ef8f8be..7e5132d 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -284,7 +284,7 @@ a1fbdfe55e50f67bb6568d5eba276e24beeb5a44373c516f0bd7758bd5742127	12486	tests/tes
 3f7e4d75e8bd29e6b90c8742d8b5aeccdbe08f454257c43a05c14165ae01d327	16439	tests/test_phaseset_scaling.py
 9147a58a884062ca0b549e33aa7141c9e3d8729db1132f328673d59e93b13177	6762	tests/test_phaseset_split_audit.py
 d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/test_phaseset_statistics.py
-e6e9600a244af88f731e9cc981f19478b5760db8c254dfd8455960ffe789ef1a	12321	tests/test_phaseset_streaming_autograd.py
+11727c78e08d02dd433abf66ca75e7f3aafd77c10a98d94ea98f17560d6e86ba	13200	tests/test_phaseset_streaming_autograd.py
 ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test_phaseset_torch_periodic.py
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
 a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
diff --git a/tests/test_phaseset_streaming_autograd.py b/tests/test_phaseset_streaming_autograd.py
index d34417b..32b35e0 100644
--- a/tests/test_phaseset_streaming_autograd.py
+++ b/tests/test_phaseset_streaming_autograd.py
@@ -235,12 +235,30 @@ def test_public_full_forward_and_all_parameter_gradients_match_eager_oracle(
         torch.testing.assert_close(actual.grad, expected.grad, rtol=5e-5, atol=5e-6)
 
 
+@torch.backends.mkldnn.flags(enabled=False)
 def test_cpu_bfloat16_autocast_is_replayed_during_streaming_backward() -> None:
+    # Exercise autocast replay with portable ATen BF16 kernels, not an ISA-specific
+    # oneDNN primitive. The scoped decorator restores the backend even on failure;
+    # other tests and the server's native BF16 qualification keep their backend.
+    assert not torch.backends.mkldnn.enabled
     batch = _activity_batch(4)
     torch.manual_seed(1729)
     oracle_model = PhaseSetEncoder(embedding_dim=8, hidden_dim=12).train()
     streamed_model = PhaseSetEncoder(embedding_dim=8, hidden_dim=12).train()
     streamed_model.load_state_dict(oracle_model.state_dict())
+    observed_dtypes: list[torch.dtype] = []
+
+    def observe_bf16(
+        module: torch.nn.Module,
+        inputs: tuple[torch.Tensor, ...],
+        output: torch.Tensor,
+    ) -> None:
+        observed_dtypes.append(output.dtype)
+        assert output.dtype == torch.bfloat16
+
+    for model in (oracle_model, streamed_model):
+        model.half_edge_encoder.register_forward_hook(observe_bf16)
+        model.pair_encoder.register_forward_hook(observe_bf16)
     with torch.autocast("cpu", dtype=torch.bfloat16):
         oracle = eager_edge_summaries(
             oracle_model,
@@ -263,6 +281,7 @@ def test_cpu_bfloat16_autocast_is_replayed_during_streaming_backward() -> None:
     assert torch.equal(oracle.node_statistics, streamed.node_statistics)
     oracle_loss.backward()
     streamed_loss.backward()
+    assert len(observed_dtypes) >= 12  # Both forwards and both streaming replays.
     for (expected_name, expected), (actual_name, actual) in zip(
         oracle_model.named_parameters(),
         streamed_model.named_parameters(),

From 1063ca42d605a7d065bdd6ef4e568e7ed44c7534 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 00:38:55 -0500
Subject: [PATCH 38/95] feat: add complete-parent sampler and full-gallery
 gradient replay

---
 RELEASE_FILES.sha256                          |   3 +
 docs/PHASESET_V2_PARENT_TRAINING.md           |  37 +++
 .../continuous_parent_training.py             | 245 ++++++++++++++
 tests/test_continuous_parent_training.py      | 300 ++++++++++++++++++
 4 files changed, 585 insertions(+)
 create mode 100644 docs/PHASESET_V2_PARENT_TRAINING.md
 create mode 100644 src/phaseset_core/continuous_parent_training.py
 create mode 100644 tests/test_continuous_parent_training.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 7e5132d..2202366 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -71,6 +71,7 @@ cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHAS
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
+3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
@@ -153,6 +154,7 @@ d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phases
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
+7367f4664b72ed011806beb60c5bd0763b7b95e1f67eec76882f3f79115a331e	11090	src/phaseset_core/continuous_parent_training.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
 38f74724372e276257c1014e50e26eb8b2cab482728a6b143799118ad2f47ca9	10545	src/phaseset_core/continuous_retrieval.py
 a46004b4c5a94dc84029a6cf5a54631e614e67f680c506feb0b92b435702160b	6873	src/phaseset_core/continuous_training_input.py
@@ -196,6 +198,7 @@ d5d69092cc271059400773ef68447e06068aa802d5e9c82193704c6b65c71f59	18827	tests/tes
 ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test_capture_validation_source_lineage_v3.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 b0f9a8d920640d3c5455e9eacdeb87feecbaca87a8b1b06affe8ca297c78d075	3593	tests/test_continuous_capture_io.py
+8b91250de082536ab1d8b0118977e1d569e4f2698e5073ebce44c6ab4bd8324f	11609	tests/test_continuous_parent_training.py
 5ca49ce282fc8cff3654af8af56ade66cfa6b732afd7dcca3e92ffe06d4ce64a	6030	tests/test_continuous_phase_cache.py
 e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/test_continuous_retrieval.py
 2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
diff --git a/docs/PHASESET_V2_PARENT_TRAINING.md b/docs/PHASESET_V2_PARENT_TRAINING.md
new file mode 100644
index 0000000..0b3d58a
--- /dev/null
+++ b/docs/PHASESET_V2_PARENT_TRAINING.md
@@ -0,0 +1,37 @@
+# Complete-parent numerical training seam
+
+This module is the numerical seam used by the private V2 host, not a scientific
+task seal, production attempt scheduler, dataset download or human-CF verifier.
+It does not launch an optimizer, choose checkpoints, open final-test data or
+declare that the 87-stage plan is launchable.
+
+`parent_epoch_batches` visits every parent in the explicit learning components
+once, uses one of seeds 1729/2718/31415, and includes every human caption row.
+Its batch-size unit is **complete parents**, not released segments or windows.
+The common batch/epoch/max-step settings are still to be frozen by the bounded
+pilot. A singleton tail is rebalanced without dropping/repeating a parent;
+an impossible ceiling fails explicitly. Historical main validation C00 may be
+a learning component in the appropriate independent outer fold; final-test
+parents may not. The host takes the precise component sets from the dated study
+matrix, not from a sampler-selected score or fallback split.
+
+`backward_parent_batch` binds complete physical source order, annotation-family
+positives, all official human caption commitments and exact text bytes to the
+frozen CLIP pool. CF sentences may follow the human rows without acquiring fake
+retrieval positives. Explicit source/column/verified masks select their losses;
+only real blind-human provenance admitted by the private host can establish a
+true `verified_false` bit. Synthetic test bits are fixtures, not human evidence.
+
+The full B-by-Q objective includes cross-microbatch negatives and variable
+positives. A no-grad score pass plus a leaf-score VJP replays one complete
+capture graph at a time, including the shared text adapter and calibration.
+When extra CF columns follow the human prefix, that prefix is made contiguous
+at the legacy loss boundary; the copy remains differentiable into all scores.
+Within each capture, the existing ordered temporal/edge checkpointing remains.
+This bounds live *multi-capture neural activation graphs*; it does not make
+all-pair compute linear, eliminate physical-cache storage or bound all host RSS.
+RNG snapshots preserve replay and the next update's stochastic state. B2 is
+frozen, and gradients must be finite before the caller clips/steps/checkpoints.
+This seam is FP32-qualified; hardware BF16 needs separate qualification and
+integration. The complete private run host, registered budget accounting,
+faithful TMR/WaMo/MIME, validation selection and science freeze remain required.
diff --git a/src/phaseset_core/continuous_parent_training.py b/src/phaseset_core/continuous_parent_training.py
new file mode 100644
index 0000000..59d5180
--- /dev/null
+++ b/src/phaseset_core/continuous_parent_training.py
@@ -0,0 +1,245 @@
+"""Parent-unit sampling and memory-bounded complete-capture loss backward.
+
+This is the numerical seam for the private training host, not its scientific
+freeze, budget ledger, scheduler or checkpoint selector. It uses all human
+rows once per admitted parent, preserves a full effective-batch negative
+gallery, and replays one complete capture graph at a time. No optimizer is
+started here. Human verification of counterfactuals remains external.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+import random
+
+import torch
+from torch import Tensor
+
+from .capture_validation import _validate_text_batch
+from .continuous_retrieval import ContinuousRetrievalSystem
+from .continuous_training_input import ContinuousTrainingView
+from .frozen_clip_text import FrozenClipTextBatch
+from .parent_retrieval_task import ParentRetrievalBatch, ParentRetrievalTask
+from .temporal_coordination import coordination_objective
+from .training import OFFICIAL_SEEDS, _capture_rng, _restore_rng
+
+
+def parent_epoch_batches(
+    task: ParentRetrievalTask,
+    *,
+    train_components: tuple[str, ...],
+    batch_size: int,
+    seed: int,
+    epoch: int,
+) -> tuple[ParentRetrievalBatch, ...]:
+    """Visit every parent of the explicit learning components exactly once.
+
+    Components, not the historical main-split name, select the population:
+    an outer fold can train on C00 while keeping its held-out groups separate.
+    The private host freezes those components from the dated study matrix.
+    This does not open test data or choose epoch/step counts. Batch size is in
+    parents, not windows, and still awaits the common bounded-pilot freeze.
+    """
+    if type(seed) is not int or seed not in OFFICIAL_SEEDS:
+        raise ValueError("parent epochs require one of the three fixed seeds")
+    if type(epoch) is not int or epoch < 0:
+        raise ValueError("epoch must be a nonnegative integer")
+    if type(batch_size) is not int or batch_size < 2:
+        raise ValueError("contrastive parent batch size must be at least two")
+    if (
+        type(train_components) is not tuple
+        or not train_components
+        or len(set(train_components)) != len(train_components)
+        or any(type(component) is not str or not component for component in train_components)
+    ):
+        raise ValueError("the learning components must be explicit and unique")
+    selected = tuple(parent for parent in task.parents if parent.component in train_components)
+    if {parent.component for parent in selected} != set(train_components):
+        raise ValueError("the admitted task is missing a requested learning component")
+    if any(parent.split == "test" for parent in selected):
+        raise ValueError("final-test parents cannot enter a training epoch")
+    if len(selected) < 2:
+        raise ValueError("contrastive learning needs two distinct admitted parents")
+    keys = [parent.annotation_family_sha256 for parent in selected]
+    random.Random((seed << 32) + epoch).shuffle(keys)
+    batches = [keys[start : start + batch_size] for start in range(0, len(keys), batch_size)]
+    # Keep the full census, rather than dropping/repeating a singleton. Move
+    # one parent from the previous batch only if both still have negatives.
+    if len(batches[-1]) == 1:
+        if len(batches[-2]) < 3:
+            raise ValueError("batch ceiling cannot cover this census without a singleton")
+        batches[-1].insert(0, batches[-2].pop())
+    return tuple(task.batch(tuple(batch)) for batch in batches)
+
+
+@dataclass(frozen=True)
+class ParentCounterfactualRows:
+    """Explicit score columns and caller-admitted human-false masks.
+
+    Extra positive/negative CF sentences may follow the human retrieval rows
+    in the same CLIP pool. They never receive fabricated retrieval positives.
+    The private host supplies blind two-human provenance; these indices or a
+    True bit are not proof of that provenance. Unverified pairs are excluded.
+    """
+
+    source_keys: tuple[str, ...]
+    positive_columns: tuple[int, ...]
+    negative_columns: tuple[int, ...]
+    verified_false: tuple[bool, ...]
+
+    def indices(
+        self, labels: ParentRetrievalBatch, *, text_count: int, device: torch.device
+    ) -> tuple[Tensor, Tensor, Tensor, Tensor]:
+        count = len(self.source_keys)
+        if any(
+            type(value) is not tuple or len(value) != count
+            for value in (
+                self.source_keys,
+                self.positive_columns,
+                self.negative_columns,
+                self.verified_false,
+            )
+        ):
+            raise ValueError("CF source, column and verified tuples must align")
+        by_source = {key: index for index, key in enumerate(labels.motion_source_keys)}
+        for source, positive, negative, verified in zip(
+            self.source_keys,
+            self.positive_columns,
+            self.negative_columns,
+            self.verified_false,
+            strict=True,
+        ):
+            if source not in by_source or type(verified) is not bool:
+                raise ValueError("CF rows need an admitted source and explicit bool verification")
+            if (
+                any(
+                    type(column) is not int or not 0 <= column < text_count
+                    for column in (positive, negative)
+                )
+                or positive == negative
+            ):
+                raise ValueError("CF positive/negative columns must be distinct and in the pool")
+            if (
+                verified
+                and negative < len(labels.text_positive_keys)
+                and (
+                    labels.text_positive_keys[negative]
+                    == labels.motion_positive_keys[by_source[source]]
+                )
+            ):
+                raise ValueError("a retrieval positive cannot also be verified-false CF text")
+        return (
+            torch.tensor(
+                [by_source[key] for key in self.source_keys], dtype=torch.int64, device=device
+            ),
+            torch.tensor(self.positive_columns, dtype=torch.int64, device=device),
+            torch.tensor(self.negative_columns, dtype=torch.int64, device=device),
+            torch.tensor(self.verified_false, dtype=torch.bool, device=device),
+        )
+
+
+@dataclass(frozen=True)
+class ParentBackwardResult:
+    loss: float
+    scores: Tensor
+    parent_count: int
+    retrieval_caption_count: int
+    verified_counterfactual_count: int
+    replayed_captures: int
+
+
+def backward_parent_batch(
+    system: ContinuousRetrievalSystem,
+    views: tuple[ContinuousTrainingView, ...],
+    text_batch: FrozenClipTextBatch,
+    labels: ParentRetrievalBatch,
+    *,
+    counterfactuals: ParentCounterfactualRows,
+    cf_weight: float,
+    margin: float,
+) -> ParentBackwardResult:
+    """Accumulate the exact full-gallery objective, with one capture graph live.
+
+    The no-grad pass stores only B-by-Q scores and RNG snapshots; a leaf-score
+    objective supplies the VJP for each complete parent. The replay includes
+    calibration and the shared text adapter, so their gradients are not lost.
+    The existing per-capture time/edge checkpointing remains in use. Caller
+    clips/steps/checkpoints only after this succeeds. FP32 qualification only;
+    native BF16 must be qualified and integrated separately before use.
+    """
+    if not isinstance(system, ContinuousRetrievalSystem):
+        raise TypeError("complete parent training requires the V2 retrieval system")
+    if not views or len(views) != len(labels.parents):
+        raise ValueError("one complete input view is required per parent label")
+    if tuple(view.positive_capture_key for view in views) != labels.motion_source_keys:
+        raise ValueError("complete physical source order differs from parent label order")
+    if len(set(labels.motion_source_keys)) != len(views) or len(
+        set(labels.motion_positive_keys)
+    ) != len(views):
+        raise ValueError("released siblings cannot enter a parent batch twice")
+    text, commitments, _ = _validate_text_batch(text_batch)
+    retrieval_count = len(labels.captions)
+    if commitments[:retrieval_count] != labels.caption_commitments:
+        raise ValueError("CLIP pool must start with every admitted human row in label order")
+    for caption, row in zip(labels.captions, text_batch.receipt.caption_rows, strict=False):
+        if hashlib.sha256(caption.encode("utf-8")).hexdigest() != row[2]:
+            raise ValueError("CLIP human text differs from the official parent caption")
+    device = next(system.parameters()).device
+    indices = counterfactuals.indices(labels, text_count=len(text), device=device)
+    positive_mask = labels.positive_mask(device=device)
+    was_training = system.training
+    system.train()
+    system.zero_grad(set_to_none=True)
+    cached, states = [], []
+    try:
+        for view in views:
+            states.append(_capture_rng())
+            with torch.no_grad():
+                cached.append(system.score((view,), text_batch).scores.detach())
+        after = _capture_rng()
+        scores = torch.cat(cached).detach().requires_grad_(True)
+        if not bool(torch.isfinite(scores).all()):
+            raise ValueError("complete parent score cache is nonfinite")
+        motion, pos, neg, verified = indices
+        loss = coordination_objective(
+            scores[:, :retrieval_count].contiguous(),
+            positive_mask,
+            cf_positive_scores=scores[motion, pos],
+            cf_negative_scores=scores[motion, neg],
+            verified_negative_mask=verified,
+            cf_weight=cf_weight,
+            margin=margin,
+        )
+        if not bool(torch.isfinite(loss)):
+            raise ValueError("complete parent objective is nonfinite")
+        loss.backward()
+        if scores.grad is None:
+            raise RuntimeError("parent score cache has no objective gradient")
+        try:
+            for index, (view, state) in enumerate(zip(views, states, strict=True)):
+                _restore_rng(state)
+                replay = system.score((view,), text_batch)
+                if not torch.equal(replay.scores.detach(), cached[index]):
+                    raise RuntimeError("complete parent replay changed its score row")
+                torch.autograd.backward(replay.scores, scores.grad[index : index + 1])
+                del replay
+        finally:
+            _restore_rng(after)
+        if any(parameter.grad is not None for parameter in system.frozen_b2.parameters()):
+            raise RuntimeError("the shared B2 anchor must remain frozen")
+        if any(
+            parameter.grad is not None and not bool(torch.isfinite(parameter.grad).all())
+            for parameter in system.parameters()
+        ):
+            raise ValueError("complete parent gradients are nonfinite")
+        return ParentBackwardResult(
+            float(loss.detach().cpu()),
+            scores.detach().cpu(),
+            len(views),
+            retrieval_count,
+            int(verified.sum()),
+            len(views),
+        )
+    finally:
+        system.train(was_training)
diff --git a/tests/test_continuous_parent_training.py b/tests/test_continuous_parent_training.py
new file mode 100644
index 0000000..31eb135
--- /dev/null
+++ b/tests/test_continuous_parent_training.py
@@ -0,0 +1,300 @@
+"""Data-free qualifications; no native training or human-CF truth is claimed."""
+
+from __future__ import annotations
+
+import copy
+from dataclasses import replace
+import hashlib
+
+import pytest
+import torch
+
+from phaseset_core.continuous_parent_training import (
+    ParentCounterfactualRows,
+    backward_parent_batch,
+    parent_epoch_batches,
+)
+from phaseset_core.continuous_retrieval import ContinuousRetrievalSystem
+from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from phaseset_core.training import (
+    BaseRetrievalSystem,
+    _atomic_torch_save,
+    _capture_rng,
+    _load_torch_checkpoint,
+    _restore_rng,
+)
+from test_continuous_retrieval import _MockB2
+from test_continuous_training_input import _input
+from test_phaseset_frozen_clip_text import _fixture
+
+
+def _key(text):
+    return hashlib.sha256(text.encode()).hexdigest()
+
+
+def _population(count=202, component="C01", split="train"):
+    parents = tuple(
+        ParentCaptionRecord(
+            _key(f"source/{index}"),
+            _key(f"family/{index}"),
+            component,
+            split,
+            (f"human row {index}",),
+        )
+        for index in range(count)
+    )
+    return ParentRetrievalTask(
+        parents, expected_family_keys=tuple(parent.annotation_family_sha256 for parent in parents)
+    )
+
+
+def test_parent_epoch_visits_every_family_once_with_no_window_weighting():
+    task = _population()
+    kwargs = dict(train_components=("C01",), batch_size=128, seed=1729, epoch=0)
+    batches = parent_epoch_batches(task, **kwargs)
+    assert tuple(len(batch.parents) for batch in batches) == (128, 74)
+    keys = tuple(key for batch in batches for key in batch.motion_positive_keys)
+    assert len(keys) == len(set(keys)) == 202
+    assert set(keys) == {parent.annotation_family_sha256 for parent in task.parents}
+    assert parent_epoch_batches(task, **kwargs) == batches
+    for seed, epoch in ((2718, 0), (1729, 1)):
+        other = parent_epoch_batches(task, **(kwargs | dict(seed=seed, epoch=epoch)))
+        assert other != batches
+        assert {key for batch in other for key in batch.motion_positive_keys} == set(keys)
+
+
+def test_singleton_tail_is_rebalanced_without_dropping_or_repeating_parents():
+    task = _population(129)
+    batches = parent_epoch_batches(
+        task, train_components=("C01",), batch_size=128, seed=31415, epoch=2
+    )
+    assert tuple(len(batch.parents) for batch in batches) == (127, 2)
+    assert len({key for batch in batches for key in batch.motion_positive_keys}) == 129
+
+
+def test_fold_component_selection_does_not_confuse_old_main_validation_with_test():
+    task = _population(4, component="C00", split="validation")
+    assert (
+        len(parent_epoch_batches(task, train_components=("C00",), batch_size=4, seed=1729, epoch=0))
+        == 1
+    )
+    for bad_task, components, size in (
+        (_population(4, split="test"), ("C01",), 4),
+        (_population(1), ("C01",), 4),
+        (_population(3), ("C01",), 2),
+        (task, ("C00", "C00"), 4),
+        (task, ("C02",), 4),
+    ):
+        with pytest.raises(ValueError):
+            parent_epoch_batches(
+                bad_task, train_components=components, batch_size=size, seed=1729, epoch=0
+            )
+
+
+def _case(tmp_path, *, extra_cf=False):
+    (tmp_path / "input").mkdir()
+    (tmp_path / "clip").mkdir()
+    source, _, _, _ = _input(tmp_path / "input")
+    first = source.view(allow_shared_yaw=False)
+    second = replace(first, capture=replace(first.capture, source_sha256=_key("other-source")))
+    # Analytic duplicated motion is deliberately a fixture, not native data or
+    # an independent real capture, and cannot support paper performance claims.
+    records = (
+        ParentCaptionRecord(
+            first.positive_capture_key, _key("first-family"), "C01", "train", ("walking", "moving")
+        ),
+        ParentCaptionRecord(
+            second.positive_capture_key, _key("second-family"), "C02", "train", ("turning",)
+        ),
+    )
+    task = ParentRetrievalTask(
+        records, expected_family_keys=tuple(row.annotation_family_sha256 for row in records)
+    )
+    labels = task.batch(tuple(row.annotation_family_sha256 for row in records))
+    clip, _, _, _ = _fixture(tmp_path / "clip")
+    captions = labels.captions
+    commitments = labels.caption_commitments
+    if extra_cf:
+        captions += ("first leads", "first follows")
+        commitments += (bytes.fromhex(_key("cf-positive")), bytes.fromhex(_key("cf-negative")))
+    text = clip.encode(captions, caption_commitments=commitments, batch_size=3)
+    torch.manual_seed(1729)
+    system = ContinuousRetrievalSystem(BaseRetrievalSystem(_MockB2(), embedding_dim=512))
+    return system, (first, second), labels, text, clip
+
+
+def _empty_cf():
+    return ParentCounterfactualRows((), (), (), ())
+
+
+@pytest.mark.parametrize("verified", [None, False, True])
+def test_complete_gallery_replay_matches_dense_loss_and_all_parameter_gradients(tmp_path, verified):
+    system, views, labels, text, _ = _case(tmp_path, extra_cf=verified is not None)
+    dense = copy.deepcopy(system)
+    base = copy.deepcopy(system.frozen_b2.state_dict())
+    cf = (
+        _empty_cf()
+        if verified is None
+        else ParentCounterfactualRows((views[0].positive_capture_key,), (3,), (4,), (verified,))
+    )
+    output = (
+        dense(
+            views,
+            text,
+            motion_positive_keys=labels.motion_positive_keys,
+            text_positive_keys=labels.text_positive_keys,
+        )
+        if verified is None
+        else None
+    )
+    if output is None:
+        scores = dense.score(views, text).scores
+        from phaseset_core.temporal_coordination import coordination_objective
+
+        expected_loss = coordination_objective(
+            scores[:, :3].contiguous(),
+            labels.positive_mask(device=scores.device),
+            cf_positive_scores=scores[0, 3:4],
+            cf_negative_scores=scores[0, 4:5],
+            verified_negative_mask=torch.tensor([verified]),
+            cf_weight=0.2,
+            margin=0.2,
+        )
+    else:
+        scores = output.scores
+        empty = scores.new_empty(0)
+        expected_loss = dense.objective(
+            output,
+            cf_positive_scores=empty,
+            cf_negative_scores=empty,
+            verified_negative_mask=torch.empty(0, dtype=torch.bool),
+            cf_weight=0.2,
+            margin=0.2,
+        )
+    expected_loss.backward()
+    result = backward_parent_batch(
+        system, views, text, labels, counterfactuals=cf, cf_weight=0.2, margin=0.2
+    )
+    # Independent dense and parent-row GEMMs can round differently. Preserve
+    # bitwise checks for same-shape replay and checkpoint/resume, not this oracle.
+    torch.testing.assert_close(
+        torch.tensor(result.loss), expected_loss.detach(), rtol=2e-6, atol=2e-7
+    )
+    torch.testing.assert_close(result.scores, scores.detach(), rtol=2e-6, atol=2e-7)
+    assert result.replayed_captures == result.parent_count == 2
+    assert result.retrieval_caption_count == 3
+    assert result.verified_counterfactual_count == (1 if verified is True else 0)
+    for (name, actual), (expected_name, expected) in zip(
+        system.named_parameters(), dense.named_parameters(), strict=True
+    ):
+        assert name == expected_name
+        if name.startswith("frozen_b2."):
+            assert actual.grad is expected.grad is None
+        else:
+            assert actual.grad is not None and expected.grad is not None
+            torch.testing.assert_close(actual.grad, expected.grad, rtol=3e-5, atol=3e-6)
+    for name, value in system.frozen_b2.state_dict().items():
+        assert torch.equal(value, base[name])
+
+
+def test_parent_sources_and_actual_human_text_are_checked_before_backward(tmp_path):
+    system, views, labels, text, clip = _case(tmp_path)
+    for wrong_views, wrong_text in (
+        (tuple(reversed(views)), text),
+        (
+            views,
+            clip.encode(
+                ("not walking", "moving", "turning"),
+                caption_commitments=labels.caption_commitments,
+                batch_size=3,
+            ),
+        ),
+    ):
+        with pytest.raises(ValueError):
+            backward_parent_batch(
+                system,
+                wrong_views,
+                wrong_text,
+                labels,
+                counterfactuals=_empty_cf(),
+                cf_weight=0.2,
+                margin=0.2,
+            )
+
+
+def test_cf_rows_cannot_fabricate_retrieval_positives_or_use_unadmitted_sources(tmp_path):
+    _, views, labels, text, _ = _case(tmp_path)
+    for cf in (
+        ParentCounterfactualRows((views[0].positive_capture_key,), (1,), (0,), (True,)),
+        ParentCounterfactualRows((_key("unadmitted"),), (0,), (2,), (True,)),
+        ParentCounterfactualRows((views[0].positive_capture_key,), (0,), (0,), (False,)),
+        ParentCounterfactualRows((views[0].positive_capture_key,), (0,), (3,), (True,)),
+    ):
+        with pytest.raises(ValueError):
+            cf.indices(labels, text_count=len(text.caption_commitments), device=torch.device("cpu"))
+
+
+def test_rng_advances_once_and_mode_restores_after_complete_capture_replays(tmp_path):
+    system, views, labels, text, _ = _case(tmp_path)
+    # Stochastic fixture probes RNG replay; no production architecture change.
+    system.text_adapter[1] = torch.nn.Dropout(0.25)
+    system.eval()
+    torch.manual_seed(31415)
+    initial = _capture_rng()
+    system.train()
+    with torch.no_grad():
+        for view in views:
+            system.score((view,), text)
+    expected = torch.get_rng_state().clone()
+    system.eval()
+    _restore_rng(initial)
+    backward_parent_batch(
+        system, views, text, labels, counterfactuals=_empty_cf(), cf_weight=0.2, margin=0.2
+    )
+    assert not system.training and not system.frozen_b2.training
+    assert torch.equal(torch.get_rng_state(), expected)
+
+
+def test_parent_replay_optimizer_checkpoint_resume_is_bitwise_exact(tmp_path):
+    system, views, labels, text, _ = _case(tmp_path)
+    optimizer = torch.optim.AdamW(
+        [p for p in system.parameters() if p.requires_grad], lr=3e-4, weight_decay=0.01
+    )
+
+    def step(model, opt):
+        result = backward_parent_batch(
+            model, views, text, labels, counterfactuals=_empty_cf(), cf_weight=0.2, margin=0.2
+        )
+        torch.nn.utils.clip_grad_norm_([p for p in model.parameters() if p.requires_grad], 1.0)
+        opt.step()
+        return result.loss
+
+    step(system, optimizer)
+    saved = _atomic_torch_save(
+        tmp_path / "step1.pt",
+        {
+            "model": system.state_dict(),
+            "optimizer": optimizer.state_dict(),
+            "rng": _capture_rng(),
+            "parent_keys": labels.motion_positive_keys,
+            "epoch": 0,
+            "parent_offset": 2,
+            "global_step": 1,
+        },
+    )
+    expected = step(system, optimizer)
+    resumed = copy.deepcopy(system)
+    resumed_opt = torch.optim.AdamW(
+        [p for p in resumed.parameters() if p.requires_grad], lr=3e-4, weight_decay=0.01
+    )
+    payload, _ = _load_torch_checkpoint(saved.path, expected_sha256=saved.sha256)
+    assert payload["parent_keys"] == labels.motion_positive_keys
+    resumed.load_state_dict(payload["model"])
+    resumed_opt.load_state_dict(payload["optimizer"])
+    _restore_rng(payload["rng"])
+    actual = step(resumed, resumed_opt)
+    assert actual == expected
+    for actual_parameter, expected_parameter in zip(
+        resumed.parameters(), system.parameters(), strict=True
+    ):
+        assert torch.equal(actual_parameter, expected_parameter)

From 3dacca98476031360467001022aeb95779783d23 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 00:46:37 -0500
Subject: [PATCH 39/95] feat: add training-only MIME parent curriculum
 component

---
 RELEASE_FILES.sha256                        |   3 +
 docs/PHASESET_V2_MIME_CURRICULUM.md         |  27 ++++
 src/phaseset_core/mime_parent_curriculum.py | 167 ++++++++++++++++++++
 tests/test_mime_parent_curriculum.py        | 147 +++++++++++++++++
 4 files changed, 344 insertions(+)
 create mode 100644 docs/PHASESET_V2_MIME_CURRICULUM.md
 create mode 100644 src/phaseset_core/mime_parent_curriculum.py
 create mode 100644 tests/test_mime_parent_curriculum.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 2202366..b4c7607 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -70,6 +70,7 @@ cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHAS
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
+36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
@@ -170,6 +171,7 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 877302cdb2ad891eb064d5bb228d124b66042865b78fa3826f66cf7bacf01166	68520	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
+f7cb1c0893b0b504359e34e39dd24651aaa86c1b65e98d2ed0469c6d5ce84d4d	6920	src/phaseset_core/mime_parent_curriculum.py
 9e7fa84554d5676f8ef75049b17b53580395c3c60be0f75fcb55996ccc8ef36d	37720	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
@@ -204,6 +206,7 @@ e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/tes
 2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
+b65a2178d7faf25372adeccdfcd19ee7804544665c6059f2d684801ccff83e11	6050	tests/test_mime_parent_curriculum.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 3b23c2b08ed81029ca6239cbb63b98093077786163aaf5bb47ccfefb21c6ef24	7117	tests/test_parent_capture.py
 32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
diff --git a/docs/PHASESET_V2_MIME_CURRICULUM.md b/docs/PHASESET_V2_MIME_CURRICULUM.md
new file mode 100644
index 0000000..18bcda6
--- /dev/null
+++ b/docs/PHASESET_V2_MIME_CURRICULUM.md
@@ -0,0 +1,27 @@
+# MIME-Set curriculum component
+
+This is one paper-defined sampler component, **not a qualified MIME baseline**.
+It does not implement original role-aware/co-attention motion encoding, original
+fine-tuned CLIP, original Inter-X evaluation, training or scientific task seal.
+
+The [MIME v1 paper](https://arxiv.org/html/2607.22702v1) specifies uniform warmup,
+training-anchor cosine neighbors, no-replacement batch coverage and a cosine
+hardness ramp. This component uses three uniform epochs and a ten-epoch ramp
+to 0.25. The zero-based convention is explicit: epochs 0..2 are uniform, epoch
+3 starts at zero, epoch 12 reaches maximum. For descending-cosine eligible
+neighbors, the window center is `floor((1-hardness)*(N_i-1))`.
+
+Adaptations to the complete-parent group task are explicit: one anchor is the
+normalized mean of **all** admitted frozen CLIP human sentence embeddings of
+that parent, not a chosen caption or validation/held-out neighbor. This is not
+original MIME's fine-tuned language tower. The private bounded pilot freezes
+the disclosed neighbor-window width, which the paper does not specify, and
+the same effective-parent batch policy as the other systems. A singleton tail
+is rebalanced without dropping/repeating parents; all human rows remain
+retrieval positives. Historical C00 can enter an outer fold's learning set,
+but final-test parents cannot. The host supplies exact learning components.
+
+Tests use analytic text/motion-free fixtures; no resulting number is baseline
+performance or human verification. Original dyadic MIME and the shared group
+adapter, original auxiliary/input fidelity checks, actual profile, convergence,
+fair pilot selection and the registered formal matrix remain required.
diff --git a/src/phaseset_core/mime_parent_curriculum.py b/src/phaseset_core/mime_parent_curriculum.py
new file mode 100644
index 0000000..0bebce2
--- /dev/null
+++ b/src/phaseset_core/mime_parent_curriculum.py
@@ -0,0 +1,167 @@
+"""MIME paper-defined training-only semantic parent curriculum.
+
+One sampler component of the explicitly adapted MIME-Set, not the original
+MIME architecture, its original task replication or a qualified baseline.
+Anchors use the mean of all admitted frozen CLIP human rows per complete
+parent. The private study host freezes this adaptation and the explicit
+neighbor-window width during its bounded pilot; the paper gives no width.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+import math
+import random
+
+import torch
+from torch import Tensor
+from torch.nn import functional as F
+
+from .capture_validation import _validate_text_batch
+from .continuous_parent_training import parent_epoch_batches
+from .frozen_clip_text import FrozenClipTextBatch
+from .parent_retrieval_task import ParentRetrievalBatch, ParentRetrievalTask
+from .training import OFFICIAL_SEEDS
+
+
+@dataclass(frozen=True)
+class MimeParentAnchors:
+    family_keys: tuple[str, ...]
+    embeddings: Tensor
+
+
+def _learning_batch(
+    task: ParentRetrievalTask,
+    components: tuple[str, ...],
+    *,
+    batch_size: int,
+    seed: int,
+    epoch: int,
+) -> ParentRetrievalBatch:
+    # Reuse the qualified sampler's full-population/fold/test admission, not
+    # historical main train/validation labels as an outer-fold fallback.
+    parent_epoch_batches(
+        task, train_components=components, batch_size=batch_size, seed=seed, epoch=epoch
+    )
+    return task.batch(
+        tuple(p.annotation_family_sha256 for p in task.parents if p.component in components)
+    )
+
+
+@torch.no_grad()
+def mime_training_parent_anchors(
+    task: ParentRetrievalTask,
+    text_batch: FrozenClipTextBatch,
+    *,
+    train_components: tuple[str, ...],
+) -> MimeParentAnchors:
+    """Only this run's learning components and their complete human rows.
+
+    No validation/held-out/test text or CF sentences enter the neighbor graph.
+    Original MIME text finetuning is not claimed by this frozen-CLIP adaptation.
+    Private rights and official-file provenance still precede this interface.
+    """
+    labels = _learning_batch(
+        task, train_components, batch_size=len(task.parents), seed=1729, epoch=0
+    )
+    text, commitments, _ = _validate_text_batch(text_batch)
+    if commitments != labels.caption_commitments:
+        raise ValueError("MIME anchors need all and only learning-parent human rows in order")
+    for caption, row in zip(labels.captions, text_batch.receipt.caption_rows, strict=True):
+        if hashlib.sha256(caption.encode("utf-8")).hexdigest() != row[2]:
+            raise ValueError("MIME anchor text differs from the admitted official human row")
+    rows, start = [], 0
+    for parent in labels.parents:
+        stop = start + len(parent.captions)
+        mean = text[start:stop].to(device="cpu", dtype=torch.float64).mean(dim=0).float()
+        if not bool(torch.isfinite(mean).all()) or float(mean.norm()) == 0:
+            raise ValueError("MIME parent anchor must have a finite nonzero human-row mean")
+        rows.append(F.normalize(mean, dim=0))
+        start = stop
+    return MimeParentAnchors(labels.motion_positive_keys, torch.stack(rows).contiguous())
+
+
+def mime_curriculum_hardness(epoch: int) -> float:
+    """Paper v1: three uniform epochs, ten-epoch cosine ramp, maximum 0.25.
+
+    Epochs are zero based: 0..2 uniform, 3 starts ramp at zero, 12 reaches
+    maximum. This explicit convention is part of the disclosed adaptation.
+    """
+    if type(epoch) is not int or epoch < 0:
+        raise ValueError("MIME curriculum epoch must be a nonnegative integer")
+    if epoch < 3:
+        return 0.0
+    progress = min((epoch - 3) / 9, 1.0)
+    return 0.25 * (1 - math.cos(math.pi * progress)) / 2
+
+
+def mime_parent_epoch_batches(
+    task: ParentRetrievalTask,
+    anchors: MimeParentAnchors,
+    *,
+    train_components: tuple[str, ...],
+    batch_size: int,
+    neighbor_window: int,
+    seed: int,
+    epoch: int,
+) -> tuple[ParentRetrievalBatch, ...]:
+    """No-replacement anchor/window batches; exactly one visit per parent.
+
+    Eligible neighbors are sorted by decreasing cosine. The window center is
+    floor((1-hardness)*(N_i-1)), moving from far to closer neighbors as in MIME
+    Eq.22. Ties use canonical family order, never actor ordinals. Window width
+    is explicit, not represented as an undocumented author default. With a
+    small final pool it shrinks; a singleton tail is rebalanced without losing
+    or repeating observations. Returns all human positives of each parent.
+    """
+    if type(seed) is not int or seed not in OFFICIAL_SEEDS:
+        raise ValueError("MIME sampler requires a fixed study seed")
+    labels = _learning_batch(task, train_components, batch_size=batch_size, seed=seed, epoch=epoch)
+    if type(neighbor_window) is not int or neighbor_window < batch_size - 1:
+        raise ValueError("explicit MIME neighbor window must cover the batch's other parents")
+    values = anchors.embeddings
+    if (
+        type(anchors) is not MimeParentAnchors
+        or anchors.family_keys != labels.motion_positive_keys
+        or type(values) is not Tensor
+        or values.device.type != "cpu"
+        or values.dtype != torch.float32
+        or values.ndim != 2
+        or values.shape != (len(labels.parents), 512)
+        or not values.is_contiguous()
+        or values.requires_grad
+        or not bool(torch.isfinite(values).all())
+        or bool((values.norm(dim=1) == 0).any())
+    ):
+        raise ValueError("MIME anchors must cover the exact learning population as frozen CPU rows")
+    if epoch < 3:
+        return parent_epoch_batches(
+            task, train_components=train_components, batch_size=batch_size, seed=seed, epoch=epoch
+        )
+    similarity = F.normalize(values, dim=1) @ F.normalize(values, dim=1).T
+    rng = random.Random((seed << 32) + epoch)
+    remaining = list(range(len(labels.parents)))
+    batches = []
+    hardness = mime_curriculum_hardness(epoch)
+    while remaining:
+        anchor = rng.choice(remaining)
+        remaining.remove(anchor)
+        take = min(batch_size - 1, len(remaining))
+        if take:
+            ranked = sorted(remaining, key=lambda i: (-float(similarity[anchor, i]), i))
+            width = min(neighbor_window, len(ranked))
+            center = math.floor((1 - hardness) * (len(ranked) - 1))
+            start = max(0, min(center - width // 2, len(ranked) - width))
+            selected = rng.sample(ranked[start : start + width], take)
+            for index in selected:
+                remaining.remove(index)
+        else:
+            selected = []
+        batches.append([anchor, *selected])
+    if len(batches[-1]) == 1:
+        # The qualified common sampler already rejected impossible ceilings.
+        batches[-1].insert(0, batches[-2].pop())
+    return tuple(
+        task.batch(tuple(labels.motion_positive_keys[i] for i in batch)) for batch in batches
+    )
diff --git a/tests/test_mime_parent_curriculum.py b/tests/test_mime_parent_curriculum.py
new file mode 100644
index 0000000..3d0fca9
--- /dev/null
+++ b/tests/test_mime_parent_curriculum.py
@@ -0,0 +1,147 @@
+"""Analytic sampler/component checks, not original MIME reproduction."""
+
+from dataclasses import replace
+import hashlib
+import math
+import random
+
+import pytest
+import torch
+
+from phaseset_core.continuous_parent_training import parent_epoch_batches
+from phaseset_core.mime_parent_curriculum import (
+    MimeParentAnchors,
+    mime_curriculum_hardness,
+    mime_parent_epoch_batches,
+    mime_training_parent_anchors,
+)
+from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from test_phaseset_frozen_clip_text import _fixture
+
+
+def _key(text):
+    return hashlib.sha256(text.encode()).hexdigest()
+
+
+def _population(count=202, *, component="C01", split="train"):
+    parents = tuple(
+        ParentCaptionRecord(
+            _key(f"source/{i}"),
+            _key(f"family/{i}"),
+            component,
+            split,
+            (f"human row {i}", f"second human row {i}"),
+        )
+        for i in range(count)
+    )
+    return ParentRetrievalTask(
+        parents, expected_family_keys=tuple(p.annotation_family_sha256 for p in parents)
+    )
+
+
+def _anchors(task):
+    # Nonuniform analytic angles avoid intentionally tied neighbor ranks in
+    # the independent scalar-versus-matrix cosine ordering oracle.
+    angles = torch.linspace(0, 1, len(task.parents)).pow(3) * math.pi
+    embeddings = torch.zeros(len(task.parents), 512)
+    embeddings[:, 0], embeddings[:, 1] = angles.cos(), angles.sin()
+    return MimeParentAnchors(
+        tuple(p.annotation_family_sha256 for p in task.parents), embeddings.contiguous()
+    )
+
+
+def test_paper_cosine_ramp_has_explicit_zero_based_epoch_convention():
+    assert [mime_curriculum_hardness(i) for i in range(4)] == [0, 0, 0, 0]
+    ramp = [mime_curriculum_hardness(i) for i in range(3, 13)]
+    assert all(a < b for a, b in zip(ramp, ramp[1:]))
+    assert ramp[-1] == mime_curriculum_hardness(25) == 0.25
+    for invalid in (-1, True, 1.2):
+        with pytest.raises(ValueError):
+            mime_curriculum_hardness(invalid)
+
+
+@pytest.mark.parametrize("seed", [1729, 2718, 31415])
+def test_curriculum_never_repeats_or_drops_parent_or_human_rows(seed):
+    task = _population()
+    anchors = _anchors(task)
+    for epoch in (0, 3, 7, 12):
+        kwargs = dict(
+            train_components=("C01",), batch_size=128, neighbor_window=128, seed=seed, epoch=epoch
+        )
+        rng_before = random.getstate()
+        batches = mime_parent_epoch_batches(task, anchors, **kwargs)
+        assert random.getstate() == rng_before
+        assert batches == mime_parent_epoch_batches(task, anchors, **kwargs)
+        keys = tuple(k for batch in batches for k in batch.motion_positive_keys)
+        assert len(keys) == len(set(keys)) == 202
+        assert set(keys) == set(anchors.family_keys)
+        assert tuple(len(b.parents) for b in batches) == (128, 74)
+        assert sum(len(b.captions) for b in batches) == 404
+        if epoch < 3:
+            assert batches == parent_epoch_batches(
+                task, **{k: v for k, v in kwargs.items() if k != "neighbor_window"}
+            )
+
+
+def test_window_sampling_agrees_with_independent_paper_equation():
+    task = _population(20)
+    anchors = _anchors(task)
+    seed, epoch, size, window = 1729, 12, 4, 5
+    batches = mime_parent_epoch_batches(
+        task,
+        anchors,
+        train_components=("C01",),
+        batch_size=size,
+        neighbor_window=window,
+        seed=seed,
+        epoch=epoch,
+    )
+    rng = random.Random((seed << 32) + epoch)
+    anchor = rng.choice(list(range(20)))
+    vectors = anchors.embeddings
+    neighbors = [i for i in range(20) if i != anchor]
+    # Independent scalar Euclidean cosine and Eq.22, not sampler's matrix.
+    neighbors.sort(
+        key=lambda i: (-sum(float(a) * float(b) for a, b in zip(vectors[anchor], vectors[i])), i)
+    )
+    center = math.floor((1 - 0.25) * 18)
+    start = max(0, min(center - window // 2, 19 - window))
+    selected = [anchor, *rng.sample(neighbors[start : start + window], size - 1)]
+    assert batches[0].motion_positive_keys == tuple(anchors.family_keys[i] for i in selected)
+
+
+def test_tail_rebalances_and_test_or_wrong_learning_anchors_are_rejected():
+    task = _population(129, component="C00", split="validation")
+    anchors = _anchors(task)
+    kwargs = dict(
+        train_components=("C00",), batch_size=128, neighbor_window=128, seed=1729, epoch=12
+    )
+    batches = mime_parent_epoch_batches(task, anchors, **kwargs)
+    assert tuple(len(b.parents) for b in batches) == (127, 2)
+    assert len({k for b in batches for k in b.motion_positive_keys}) == 129
+    for bad_task, bad_anchors, bad_kwargs in (
+        (_population(4, component="C00", split="test"), anchors, kwargs),
+        (task, replace(anchors, family_keys=tuple(reversed(anchors.family_keys))), kwargs),
+        (task, anchors, kwargs | dict(neighbor_window=126)),
+        (task, anchors, kwargs | dict(train_components=("C01",))),
+    ):
+        with pytest.raises(ValueError):
+            mime_parent_epoch_batches(bad_task, bad_anchors, **bad_kwargs)
+
+
+def test_human_anchor_pool_uses_every_learning_row_not_validation_or_changed_text(tmp_path):
+    task = _population(4)
+    labels = task.batch(tuple(p.annotation_family_sha256 for p in task.parents))
+    clip, _, _, _ = _fixture(tmp_path)
+    text = clip.encode(labels.captions, labels.caption_commitments, batch_size=3)
+    anchors = mime_training_parent_anchors(task, text, train_components=("C01",))
+    assert anchors.family_keys == labels.motion_positive_keys
+    assert anchors.embeddings.shape == (4, 512) and not anchors.embeddings.requires_grad
+    assert torch.allclose(anchors.embeddings.norm(dim=1), torch.ones(4))
+    wrong_text = clip.encode(
+        ("changed human text", *labels.captions[1:]), labels.caption_commitments, batch_size=3
+    )
+    with pytest.raises(ValueError):
+        mime_training_parent_anchors(task, wrong_text, train_components=("C01",))
+    with pytest.raises(ValueError):
+        mime_training_parent_anchors(task, text, train_components=("C00",))

From 4fcd0c5dfd521d55f385177c4412a9e73fdd1955 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 01:34:31 -0500
Subject: [PATCH 40/95] feat: add complete-parent residual optimizer and resume
 host

---
 RELEASE_FILES.sha256                          |   5 +-
 docs/PHASESET_V2_PARENT_HOST.md               |  93 +++
 src/phaseset_core/continuous_parent_host.py   | 551 ++++++++++++++++++
 .../continuous_parent_training.py             |  73 ++-
 tests/test_continuous_parent_host.py          | 348 +++++++++++
 5 files changed, 1063 insertions(+), 7 deletions(-)
 create mode 100644 docs/PHASESET_V2_PARENT_HOST.md
 create mode 100644 src/phaseset_core/continuous_parent_host.py
 create mode 100644 tests/test_continuous_parent_host.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index b4c7607..4ac887a 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -71,6 +71,7 @@ cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHAS
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
+099c6c09a5a1779ed83b0de9b8e0528e7d75323af2912ba62814f6597259cf4e	5577	docs/PHASESET_V2_PARENT_HOST.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
@@ -155,7 +156,8 @@ d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phases
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
-7367f4664b72ed011806beb60c5bd0763b7b95e1f67eec76882f3f79115a331e	11090	src/phaseset_core/continuous_parent_training.py
+d187e4ea8040f4967822033f06c375106304f3bf9fceaa4f1bb9732bcdec72e5	24818	src/phaseset_core/continuous_parent_host.py
+5195c0326df704e7dd1b39500481c28ea7b74dc3e4ad92312e5133561f6fad17	13720	src/phaseset_core/continuous_parent_training.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
 38f74724372e276257c1014e50e26eb8b2cab482728a6b143799118ad2f47ca9	10545	src/phaseset_core/continuous_retrieval.py
 a46004b4c5a94dc84029a6cf5a54631e614e67f680c506feb0b92b435702160b	6873	src/phaseset_core/continuous_training_input.py
@@ -200,6 +202,7 @@ d5d69092cc271059400773ef68447e06068aa802d5e9c82193704c6b65c71f59	18827	tests/tes
 ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test_capture_validation_source_lineage_v3.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 b0f9a8d920640d3c5455e9eacdeb87feecbaca87a8b1b06affe8ca297c78d075	3593	tests/test_continuous_capture_io.py
+1996c74bc514251d6213079416cd5d6dccc80914349d4bb61c25b83a8b789961	15517	tests/test_continuous_parent_host.py
 8b91250de082536ab1d8b0118977e1d569e4f2698e5073ebce44c6ab4bd8324f	11609	tests/test_continuous_parent_training.py
 5ca49ce282fc8cff3654af8af56ade66cfa6b732afd7dcca3e92ffe06d4ce64a	6030	tests/test_continuous_phase_cache.py
 e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/test_continuous_retrieval.py
diff --git a/docs/PHASESET_V2_PARENT_HOST.md b/docs/PHASESET_V2_PARENT_HOST.md
new file mode 100644
index 0000000..91a000b
--- /dev/null
+++ b/docs/PHASESET_V2_PARENT_HOST.md
@@ -0,0 +1,93 @@
+# Complete-parent residual training host
+
+`continuous_parent_host.py` connects the qualified full-gallery gradient replay
+to real FP32 AdamW updates, checkpoint selection and restart. It is not the old
+window-unit training recipe, a formal-study authorization, a trained B2 anchor,
+or a literature-baseline implementation. A software qualification is not a
+native learning result. The formal study still requires the common bounded
+pilot, explicit parent-unit amendment, floors, hardware profile and 87-stage
+freeze. The two-human relationship audit cannot be supplied by this code.
+
+## Inputs and updates
+
+The private `ParentTrainingSource` loads one admitted **whole** capture at a
+time from disk. Every accepted window remains in the frozen global branch;
+every physical timeline interval remains in the ordered coordination branch.
+The source must reproduce the same shared-yaw transform during replay, and
+respect the pre-admitted language/yaw rule. No shorter timeline or physical
+batch materialization is introduced by the host.
+
+The effective batch is selected by explicit learning components and includes
+all official human holistic rows of every parent. Only the B-by-Q score matrix
+and RNG snapshots are cached. The full variable-positive symmetric objective
+is differentiated once; each complete capture graph is replayed separately.
+The physical loader is called separately for cache and replay. It must not
+silently keep the entire learning population in memory. Loading and neural
+activation storage are separate from the total disk-cache footprint.
+
+Optional CF sentences follow the human retrieval prefix. Their columns have no
+fabricated retrieval positive. A verified-false bit is not human provenance:
+the private manifest must bind the actual blind two-human records. Unverified
+CFs contribute no CF term. No label generation or data discovery occurs here.
+
+Optimizer: AdamW, weight decay 0.01, clipping 1.0, 5% warmup followed by cosine
+decay, default residual learning rate 3e-4 and 20 complete epochs. Seeds remain
+1729, 2718 and 31415. Batch size is explicitly **parents**, not 128 windows.
+The short qualification schedules are not the formal/pilot freeze. There is
+no automatic BF16 fallback; FP32 is the only supported path in this host.
+
+## Validation and selection
+
+Learning and checkpoint-selection components are explicit, disjoint and
+non-test. Historical main-validation components can be learning components in
+an authorized outer fold; held-out components are never opened by the host.
+Validation loads every parent and every exact human row of its complete
+gallery, with no CF extras or augmentation. Scores are computed against the
+same full text pool, one whole motion at a time. Evaluation consumes no training
+RNG and restores the previous model mode.
+
+The reused endpoint is capture-macro bidirectional R@1: motion-to-text counts
+any positive; text-to-motion is averaged first within each capture family,
+then equally across captures. Ties are broken by fixed lineage commitments,
+not input order. This is explicitly not caption-count-weighted R@1. Only a
+strictly better validation endpoint replaces the selected checkpoint; exact
+ties preserve the first. There is no final-test method or test-based selector.
+
+## Checkpoints and attempts
+
+Each `fit` owns a fresh attempt directory with an immutable run manifest,
+numbered progress events, atomic write-once checkpoints and terminal evidence.
+The private run manifest contains the full **active learning/selection**
+parent/text census and must remain private. Unused held-out/test rows of a
+larger supplied task are not serialized or made part of this host's resume
+identity. It binds configuration, input/physical-floor/CLIP/CF
+manifest, code including the source adapter, qualified runtime and frozen
+base identity. This caller-provided binding is not a substitute for real
+qualification or permission.
+
+Checkpoints contain model, AdamW, scheduler, Python/NumPy/Torch RNG, complete
+epoch/batch cursor, parents seen, validation history and selected artifact.
+Resume verifies the exact predecessor digest, payload, same execution inputs,
+cursor/census, frozen anchor and optimizer/scheduler progress. A resumed host
+must be built from the same seed-bound **initial** model. Resume uses a new
+attempt; it never overwrites or mutates the predecessor. Interruption at an
+epoch's last update still completes that epoch's validation before returning.
+Zero-update and already-completed resume are explicit cases.
+
+These event/terminal records intentionally carry no formal-study authority.
+The private production adapter remains responsible for resource admission,
+locks, budget accounting, attempt-ledger linkage, signal/error classification,
+long-operation heartbeat and publication-safe result export. Failed disk or
+process writes cannot guarantee an on-disk terminal; the remote supervisor
+must close that failure from its own evidence. Never publish private host
+manifests, checkpoints, caption text or licensed physical cache files.
+
+## Qualification scope
+
+Tests exercise the real host optimizer loop on clearly identified analytic
+fixtures, full gallery/caption census, first-best selection, bitwise
+uninterrupted-versus-resumed model/optimizer/scheduler/RNG, zero/mid/end/completed
+resume, concrete input drift, truncated checkpoints and wrong progress cursor.
+They also retain the existing independent dense-versus-replay gradient tests.
+None is evidence of real native learning, a paper metric, GPU cost, human CF
+truth, or successful completion of any of the 87 formal stages.
diff --git a/src/phaseset_core/continuous_parent_host.py b/src/phaseset_core/continuous_parent_host.py
new file mode 100644
index 0000000..a5dfdec
--- /dev/null
+++ b/src/phaseset_core/continuous_parent_host.py
@@ -0,0 +1,551 @@
+"""Complete-parent FP32 residual optimizer, validation and restart host.
+
+This runs real updates, not the superseded window-unit training recipe. The
+private operator still admits rights, physical floors, human CF provenance,
+hardware/budget and the dated 87-stage matrix. Constructing this host supplies
+none of those authorities. Base-model training and literature baselines are
+separate implementations. No final-test interface exists here.
+"""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+from datetime import datetime, timezone
+import json
+import math
+from pathlib import Path
+import random
+from typing import Protocol
+
+import numpy as np
+import torch
+
+from .continuous_parent_training import (
+    ParentCounterfactualRows,
+    backward_loaded_parent_batch,
+    parent_epoch_batches,
+    validate_parent_text_batch,
+)
+from .continuous_retrieval import ContinuousRetrievalSystem
+from .continuous_training_input import ContinuousTrainingView
+from .frozen_clip_text import FrozenClipTextBatch
+from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalBatch, ParentRetrievalTask
+from .training import (
+    OFFICIAL_SEEDS,
+    CheckpointArtifact,
+    TrainingCheckpointError,
+    _atomic_torch_save,
+    _capture_macro_bidirectional_r1,
+    _capture_rng,
+    _frozen_numerical_runtime,
+    _learning_rate_multiplier,
+    _load_torch_checkpoint,
+    _restore_rng,
+    _stable_hash,
+)
+
+
+class ParentTrainingSource(Protocol):
+    """Admitted disk-backed inputs; no private data discovery in public code.
+
+    ``view`` must return the entire source and reproduce its augmentation on
+    replay. Evaluation views must be unaugmented. ``text`` starts with all
+    exact human rows; training-only, genuinely verified CFs may follow. The
+    two-human truth records are bound by the private input manifest, not by
+    the returned bool bits. No test or held-out rows may be loaded incidentally.
+    """
+
+    def view(
+        self, parent: ParentCaptionRecord, *, seed: int, epoch: int, training: bool
+    ) -> ContinuousTrainingView: ...
+
+    def text(
+        self, labels: ParentRetrievalBatch, *, training: bool
+    ) -> tuple[FrozenClipTextBatch, ParentCounterfactualRows]: ...
+
+
+@dataclass(frozen=True)
+class ParentHostConfig:
+    seed: int
+    train_components: tuple[str, ...]
+    validation_components: tuple[str, ...]
+    epochs: int = 20
+    parent_batch_size: int = 128
+    learning_rate: float = 3e-4
+    cf_weight: float = 0.2
+    cf_margin: float = 0.2
+
+    def __post_init__(self):
+        if type(self.seed) is not int or self.seed not in OFFICIAL_SEEDS:
+            raise ValueError("host requires one of the three fixed seeds")
+        for components in (self.train_components, self.validation_components):
+            if (
+                type(components) is not tuple
+                or not components
+                or len(set(components)) != len(components)
+                or any(type(value) is not str or not value for value in components)
+            ):
+                raise ValueError("learning/validation components must be explicit and unique")
+        if set(self.train_components) & set(self.validation_components):
+            raise ValueError("learning and checkpoint-selection components cannot overlap")
+        if type(self.epochs) is not int or self.epochs < 1:
+            raise ValueError("epoch count must be a positive integer")
+        if type(self.parent_batch_size) is not int or self.parent_batch_size < 2:
+            raise ValueError("parent batch size must be at least two")
+        for value in (self.learning_rate, self.cf_weight, self.cf_margin):
+            if type(value) not in (int, float) or not math.isfinite(value) or value < 0:
+                raise ValueError("loss/optimizer scalars must be finite nonnegative numbers")
+        if self.learning_rate == 0:
+            raise ValueError("learning rate must be positive")
+
+
+@dataclass(frozen=True)
+class ParentHostBindings:
+    """Digests of the already admitted private execution inputs, not authority.
+
+    The input manifest includes physical source/cache/floor/CLIP identities,
+    complete parent census, yaw rules and human verification provenance. The
+    code manifest covers this host, the full scorer and private source adapter.
+    Runtime binding comes from the real frozen environment qualification.
+    """
+
+    input_manifest_sha256: str
+    code_manifest_sha256: str
+    runtime_sha256: str
+    frozen_base_checkpoint_sha256: str
+
+    def __post_init__(self):
+        for value in asdict(self).values():
+            if (
+                type(value) is not str
+                or len(value) != 64
+                or any(char not in "0123456789abcdef" for char in value)
+            ):
+                raise ValueError("execution bindings must be lowercase SHA-256 keys")
+
+
+@dataclass(frozen=True)
+class ParentHostReport:
+    outcome: str
+    global_step: int
+    completed_epochs: int
+    parents_seen: int
+    latest_checkpoint: CheckpointArtifact
+    best_checkpoint: CheckpointArtifact | None
+    best_validation_r1: float | None
+    validation_history: tuple[tuple[int, float], ...]
+
+
+def _write_json_once(path: Path, payload: dict) -> None:
+    raw = json.dumps(payload, sort_keys=True, separators=(",", ":"), allow_nan=False) + "\n"
+    with path.open("x", encoding="utf-8", newline="\n") as stream:
+        stream.write(raw)
+        stream.flush()
+
+
+class ContinuousParentTrainingHost:
+    """AdamW/5%-warmup/cosine, clip1, full-gallery validation, immutable resume.
+
+    FP32 only; no automatic BF16, shorter timeline, dropped tail, sampled
+    negatives or toy-model fallback. One effective update is one complete
+    parent batch; the same full-gallery score VJP is replayed per parent.
+    Validation R@1 is capture-macro in both directions, with any-positive
+    M2T and deterministic lineage tie breaks, using the existing endpoint.
+    Strict improvement selects checkpoints; exact ties retain the first.
+    """
+
+    def __init__(
+        self,
+        system: ContinuousRetrievalSystem,
+        task: ParentRetrievalTask,
+        source: ParentTrainingSource,
+        config: ParentHostConfig,
+        bindings: ParentHostBindings,
+    ):
+        if not isinstance(system, ContinuousRetrievalSystem):
+            raise TypeError("this host is for the complete-parent V2 residual scorer")
+        if any(parameter.dtype != torch.float32 for parameter in system.parameters()):
+            raise ValueError("this host has only been qualified for FP32")
+        self.system, self.task, self.source = system, task, source
+        self.config, self.bindings = config, bindings
+        # Establish the complete component census before any physical/text load.
+        self._steps_per_epoch = len(self._batches(0))
+        validation = tuple(
+            row for row in task.parents if row.component in config.validation_components
+        )
+        if {row.component for row in validation} != set(config.validation_components) or any(
+            row.split == "test" for row in validation
+        ):
+            raise ValueError("validation components must be fully admitted development parents")
+        self._gallery = task.batch(tuple(row.annotation_family_sha256 for row in validation))
+        self._total_steps = self._steps_per_epoch * config.epochs
+        self._parameters = [
+            parameter for parameter in system.parameters() if parameter.requires_grad
+        ]
+        if not self._parameters or any(p.requires_grad for p in system.frozen_b2.parameters()):
+            raise ValueError("the residual must be trainable and its B2 anchor frozen")
+        self._base_state = _stable_hash(system.frozen_b2.state_dict())
+        self._manifest = {
+            "schema": "phaseset-complete-parent-host-v1",
+            "bindings": asdict(bindings),
+            "config": asdict(config),
+            "task": [
+                asdict(parent)
+                for parent in task.parents
+                if parent.component in set(config.train_components + config.validation_components)
+            ],
+            "initial_model_sha256": _stable_hash(system.state_dict()),
+            # State tensors omit actual scorer controls and dropout settings.
+            # Store the scalar configuration/module tree in the same manifest.
+            "scorer_configuration": [
+                {
+                    "name": name,
+                    "class": f"{type(module).__module__}.{type(module).__qualname__}",
+                    "scalars": {
+                        key: value
+                        for key, value in vars(module).items()
+                        if not key.startswith("_")
+                        and key not in ("training", "dump_patches", "call_super_init")
+                        and (value is None or type(value) in (bool, int, float, str))
+                    },
+                }
+                for name, module in system.named_modules()
+            ],
+            "frozen_base_state_sha256": self._base_state,
+            "steps_per_epoch": self._steps_per_epoch,
+            "total_steps": self._total_steps,
+        }
+        self._optimizer = torch.optim.AdamW(
+            self._parameters, lr=config.learning_rate, weight_decay=0.01
+        )
+        self._scheduler = torch.optim.lr_scheduler.LambdaLR(
+            self._optimizer, lambda step: _learning_rate_multiplier(step, self._total_steps)
+        )
+        self._epoch = self._offset = self._step = self._parents_seen = self._sequence = 0
+        self._history: list[tuple[int, float]] = []
+        self._best: CheckpointArtifact | None = None
+        self._best_value: float | None = None
+        self._latest: CheckpointArtifact | None = None
+        self._root: Path | None = None
+        self._used = False
+        self._event_sequence = 0
+
+    def _batches(self, epoch: int):
+        return parent_epoch_batches(
+            self.task,
+            train_components=self.config.train_components,
+            batch_size=self.config.parent_batch_size,
+            seed=self.config.seed,
+            epoch=epoch,
+        )
+
+    def _event(self, phase: str, **fields):
+        assert self._root is not None
+        _write_json_once(
+            self._root / f"event-{self._event_sequence:08d}.json",
+            {
+                "phase": phase,
+                "observed_at_utc": datetime.now(timezone.utc).isoformat(),
+                "global_step": self._step,
+                "epoch": self._epoch,
+                "parent_batch_offset": self._offset,
+                **fields,
+            },
+        )
+        self._event_sequence += 1
+
+    @torch.no_grad()
+    def _validate(self) -> float:
+        labels = self._gallery
+        previous_rng, was_training = _capture_rng(), self.system.training
+        scores = []
+        self.system.eval()
+        try:
+            text, cf = self.source.text(labels, training=False)
+            if validate_parent_text_batch(labels, text) != len(labels.captions) or cf.source_keys:
+                raise ValueError(
+                    "validation pool must contain only all official human retrieval rows"
+                )
+            for index, parent in enumerate(labels.parents):
+                self._event("validation_capture", capture_index=index)
+                view = self.source.view(
+                    parent, seed=self.config.seed, epoch=self._epoch, training=False
+                )
+                if view.positive_capture_key != parent.source_sha256 or (
+                    view.capture.augmentation_yaw != 0.0
+                ):
+                    raise ValueError("validation requires the admitted unaugmented whole source")
+                scores.append(self.system.score((view,), text).scores.detach().cpu())
+                del view
+            logits = torch.cat(scores).contiguous()
+            if logits.shape != (len(labels.parents), len(labels.captions)) or not bool(
+                torch.isfinite(logits).all()
+            ):
+                raise ValueError("validation full-gallery score census is malformed/nonfinite")
+            return _capture_macro_bidirectional_r1(
+                logits,
+                labels.positive_mask(device=torch.device("cpu")),
+                tuple(bytes.fromhex(key) for key in labels.motion_positive_keys),
+                tuple(bytes.fromhex(key) for key in labels.text_positive_keys),
+                tuple(bytes.fromhex(key) for key in labels.motion_source_keys),
+                labels.caption_commitments,
+            )
+        finally:
+            self.system.train(was_training)
+            _restore_rng(previous_rng)
+
+    def _checkpoint(self, *, select: bool = False) -> CheckpointArtifact:
+        assert self._root is not None
+        self._sequence += 1
+        path = self._root / f"checkpoint-{self._step:012d}-{self._sequence:06d}.pt"
+        best = (
+            (str(path.resolve()), None)
+            if select
+            else (None if self._best is None else (str(self._best.path), self._best.sha256))
+        )
+        payload = {
+            "manifest": self._manifest,
+            "global_step": self._step,
+            "epoch": self._epoch,
+            "parent_batch_offset": self._offset,
+            "parents_seen": self._parents_seen,
+            "checkpoint_sequence": self._sequence,
+            "validation_history": self._history,
+            "best": best,
+            "best_validation_r1": self._best_value,
+            "model": self.system.state_dict(),
+            "optimizer": self._optimizer.state_dict(),
+            "scheduler": self._scheduler.state_dict(),
+            "rng": _capture_rng(),
+        }
+        payload["state_sha256"] = _stable_hash(payload)
+        self._latest = _atomic_torch_save(path, payload)
+        if select:
+            self._best = self._latest
+        return self._latest
+
+    def _resume(self, path: Path, digest: str):
+        payload, artifact = _load_torch_checkpoint(path, expected_sha256=digest)
+        saved_digest = payload.pop("state_sha256", None)
+        if _stable_hash(payload) != saved_digest or payload["manifest"] != self._manifest:
+            raise TrainingCheckpointError("parent checkpoint state or execution inputs drifted")
+        epoch, offset, step = (
+            payload[key] for key in ("epoch", "parent_batch_offset", "global_step")
+        )
+        if any(type(value) is not int for value in (epoch, offset, step)) or not (
+            0 <= epoch <= self.config.epochs
+            and 0 <= offset <= self._steps_per_epoch
+            and step == epoch * self._steps_per_epoch + offset
+            and step <= self._total_steps
+            and (epoch < self.config.epochs or offset == 0)
+        ):
+            raise TrainingCheckpointError("parent checkpoint cursor is inconsistent")
+        batches = self._batches(0)
+        expected_parents = epoch * sum(len(batch.parents) for batch in batches) + sum(
+            len(batch.parents) for batch in self._batches(epoch)[:offset]
+        )
+        history = payload["validation_history"]
+        if payload["parents_seen"] != expected_parents or (
+            type(history) is not list
+            or len(history) != epoch
+            or any(
+                type(row) is not tuple
+                or len(row) != 2
+                or row[0] != index
+                or not (type(row[1]) is float and math.isfinite(row[1]) and 0 <= row[1] <= 1)
+                for index, row in enumerate(history)
+            )
+        ):
+            raise TrainingCheckpointError("parent checkpoint census/validation history drifted")
+        model = payload["model"]
+        base = {
+            key.removeprefix("frozen_b2."): value
+            for key, value in model.items()
+            if key.startswith("frozen_b2.")
+        }
+        if _stable_hash(base) != self._base_state or any(
+            not bool(torch.isfinite(value).all()) for value in model.values()
+        ):
+            raise TrainingCheckpointError("parent checkpoint changed the anchor or is nonfinite")
+        self.system.load_state_dict(model, strict=True)
+        self._optimizer.load_state_dict(payload["optimizer"])
+        self._scheduler.load_state_dict(payload["scheduler"])
+        expected_lr = self.config.learning_rate * _learning_rate_multiplier(step, self._total_steps)
+        if (
+            self._scheduler.last_epoch != step
+            or any(
+                group["lr"] != expected_lr or group["weight_decay"] != 0.01
+                for group in self._optimizer.param_groups
+            )
+            or any(
+                int(state["step"]) != step
+                or not bool(torch.isfinite(state["exp_avg"]).all())
+                or not bool(torch.isfinite(state["exp_avg_sq"]).all())
+                for state in self._optimizer.state.values()
+            )
+        ):
+            raise TrainingCheckpointError("parent checkpoint optimizer/scheduler drifted")
+        self._epoch, self._offset, self._step = epoch, offset, step
+        self._parents_seen = payload["parents_seen"]
+        self._sequence = payload["checkpoint_sequence"]
+        if type(self._sequence) is not int or self._sequence < 1:
+            raise TrainingCheckpointError("checkpoint sequence must be a positive integer")
+        self._history = history
+        self._best_value = payload["best_validation_r1"]
+        best = payload["best"]
+        if best is not None:
+            best_path, best_digest = best
+            if best_digest is None:
+                if Path(best_path) != artifact.path:
+                    raise TrainingCheckpointError("selected checkpoint self-reference differs")
+                self._best = artifact
+            else:
+                _, self._best = _load_torch_checkpoint(Path(best_path), expected_sha256=best_digest)
+        if (self._best is None) != (not history) or self._best_value != (
+            max(row[1] for row in history) if history else None
+        ):
+            raise TrainingCheckpointError(
+                "parent checkpoint best selector disagrees with validation"
+            )
+        self._latest = artifact
+        _restore_rng(payload["rng"])
+
+    def fit(
+        self,
+        run_directory: str | Path,
+        *,
+        resume_checkpoint: str | Path | None = None,
+        resume_sha256: str | None = None,
+        stop_after_steps: int | None = None,
+    ) -> ParentHostReport:
+        """Run or resume into a distinct immutable attempt directory.
+
+        A deliberate interruption ends at an update boundary. If that boundary
+        is the end of an epoch, its validation is still performed before stop;
+        the selected checkpoint cannot be bypassed by resume. Terminal records
+        never claim research completion or grant a formal start/pilot budget.
+        """
+        if self._used:
+            raise RuntimeError("a host instance may own only one attempt")
+        if (resume_checkpoint is None) != (resume_sha256 is None):
+            raise ValueError("resume requires the exact predecessor artifact digest")
+        if stop_after_steps is not None and (
+            type(stop_after_steps) is not int or not 0 <= stop_after_steps <= self._total_steps
+        ):
+            raise ValueError("stop point must be an update boundary within this schedule")
+        self._used = True
+        self._root = Path(run_directory).resolve()
+        self._root.mkdir(parents=True, exist_ok=False, mode=0o700)
+        _write_json_once(
+            self._root / "run.json",
+            {
+                "manifest": self._manifest,
+                "predecessor_checkpoint": None
+                if resume_checkpoint is None
+                else str(Path(resume_checkpoint).resolve()),
+                "predecessor_sha256": resume_sha256,
+                "formal_authority": False,
+            },
+        )
+        outcome, failure = "FAILED", None
+        try:
+            with _frozen_numerical_runtime(next(self.system.parameters()).device):
+                if resume_checkpoint is None:
+                    random.seed(self.config.seed)
+                    np.random.seed(self.config.seed)
+                    torch.manual_seed(self.config.seed)
+                    if torch.cuda.is_available():
+                        torch.cuda.manual_seed_all(self.config.seed)
+                else:
+                    self._resume(Path(resume_checkpoint), resume_sha256)
+                if stop_after_steps is not None and stop_after_steps < self._step:
+                    raise ValueError("stop point precedes resumed progress")
+                self._event("start")
+                while self._epoch < self.config.epochs:
+                    batches = self._batches(self._epoch)
+                    if self._offset == len(batches):
+                        value = self._validate()
+                        selected = self._best_value is None or value > self._best_value
+                        if selected:
+                            self._best_value = value
+                        self._history.append((self._epoch, value))
+                        self._epoch += 1
+                        self._offset = 0
+                        self._checkpoint(select=selected)
+                        self._event(
+                            "validation", capture_macro_bidirectional_r1=value, selected=selected
+                        )
+                        continue
+                    if stop_after_steps is not None and self._step == stop_after_steps:
+                        break
+                    labels = batches[self._offset]
+                    by_source = {row.source_sha256: row for row in labels.parents}
+                    text, cf = self.source.text(labels, training=True)
+                    result = backward_loaded_parent_batch(
+                        self.system,
+                        labels,
+                        text,
+                        load_view=lambda key: self.source.view(
+                            by_source[key], seed=self.config.seed, epoch=self._epoch, training=True
+                        ),
+                        counterfactuals=cf,
+                        cf_weight=self.config.cf_weight,
+                        margin=self.config.cf_margin,
+                        progress=lambda phase, index: self._event(phase, capture_index=index),
+                    )
+                    norm = torch.nn.utils.clip_grad_norm_(
+                        self._parameters, 1.0, error_if_nonfinite=True
+                    )
+                    lr = self._optimizer.param_groups[0]["lr"]
+                    self._optimizer.step()
+                    self._scheduler.step()
+                    if _stable_hash(self.system.frozen_b2.state_dict()) != self._base_state:
+                        raise RuntimeError("training mutated the shared frozen B2 anchor")
+                    self._step += 1
+                    self._offset += 1
+                    self._parents_seen += len(labels.parents)
+                    self._checkpoint()
+                    self._event(
+                        "update",
+                        loss=result.loss,
+                        parents=len(labels.parents),
+                        human_rows=result.retrieval_caption_count,
+                        verified_cf=result.verified_counterfactual_count,
+                        gradient_norm=float(norm),
+                        learning_rate=lr,
+                    )
+                outcome = "COMPLETED" if self._epoch == self.config.epochs else "INTERRUPTED"
+                if self._latest is None:
+                    self._checkpoint()
+                assert self._latest is not None
+                return ParentHostReport(
+                    outcome,
+                    self._step,
+                    self._epoch,
+                    self._parents_seen,
+                    self._latest,
+                    self._best,
+                    self._best_value,
+                    tuple(self._history),
+                )
+        except BaseException as error:
+            failure = type(error).__name__
+            raise
+        finally:
+            self._event("terminal", outcome=outcome, failure_class=failure)
+            _write_json_once(
+                self._root / "terminal.json",
+                {
+                    "outcome": outcome,
+                    "failure_class": failure,
+                    "global_step": self._step,
+                    "completed_epochs": self._epoch,
+                    "parents_seen": self._parents_seen,
+                    "latest_checkpoint": None if self._latest is None else str(self._latest.path),
+                    "latest_checkpoint_sha256": None
+                    if self._latest is None
+                    else self._latest.sha256,
+                    "best_validation_r1": self._best_value,
+                    "formal_authority": False,
+                },
+            )
diff --git a/src/phaseset_core/continuous_parent_training.py b/src/phaseset_core/continuous_parent_training.py
index 59d5180..f001b6b 100644
--- a/src/phaseset_core/continuous_parent_training.py
+++ b/src/phaseset_core/continuous_parent_training.py
@@ -10,6 +10,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+from collections.abc import Callable
 import hashlib
 import random
 
@@ -178,6 +179,22 @@ def backward_parent_batch(
         set(labels.motion_positive_keys)
     ) != len(views):
         raise ValueError("released siblings cannot enter a parent batch twice")
+    by_source = {view.positive_capture_key: view for view in views}
+    return backward_loaded_parent_batch(
+        system,
+        labels,
+        text_batch,
+        load_view=lambda source: by_source[source],
+        counterfactuals=counterfactuals,
+        cf_weight=cf_weight,
+        margin=margin,
+    )
+
+
+def validate_parent_text_batch(
+    labels: ParentRetrievalBatch, text_batch: FrozenClipTextBatch
+) -> int:
+    """Bind the human prefix; any appended CF rows remain outside retrieval."""
     text, commitments, _ = _validate_text_batch(text_batch)
     retrieval_count = len(labels.captions)
     if commitments[:retrieval_count] != labels.caption_commitments:
@@ -185,18 +202,55 @@ def backward_parent_batch(
     for caption, row in zip(labels.captions, text_batch.receipt.caption_rows, strict=False):
         if hashlib.sha256(caption.encode("utf-8")).hexdigest() != row[2]:
             raise ValueError("CLIP human text differs from the official parent caption")
+    return len(text)
+
+
+def backward_loaded_parent_batch(
+    system: ContinuousRetrievalSystem,
+    labels: ParentRetrievalBatch,
+    text_batch: FrozenClipTextBatch,
+    *,
+    load_view: Callable[[str], ContinuousTrainingView],
+    counterfactuals: ParentCounterfactualRows,
+    cf_weight: float,
+    margin: float,
+    progress: Callable[[str, int], None] | None = None,
+) -> ParentBackwardResult:
+    """The same VJP replay with physical inputs loaded one parent at a time.
+
+    The loader must return the admitted complete source, with the same physical
+    transform on cache/replay. RNG is captured before loading and restored for
+    replay; a deterministic private source may instead derive yaw from epoch
+    and source. No physical batch tuple or all-capture graph is retained here.
+    """
+    if not isinstance(system, ContinuousRetrievalSystem):
+        raise TypeError("complete parent training requires the V2 retrieval system")
+    if (
+        not labels.parents
+        or len(set(labels.motion_source_keys)) != len(labels.parents)
+        or len(set(labels.motion_positive_keys)) != len(labels.parents)
+    ):
+        raise ValueError("complete parent labels must contain distinct admitted sources/families")
+    text_count = validate_parent_text_batch(labels, text_batch)
+    retrieval_count = len(labels.captions)
     device = next(system.parameters()).device
-    indices = counterfactuals.indices(labels, text_count=len(text), device=device)
+    indices = counterfactuals.indices(labels, text_count=text_count, device=device)
     positive_mask = labels.positive_mask(device=device)
     was_training = system.training
     system.train()
     system.zero_grad(set_to_none=True)
     cached, states = [], []
     try:
-        for view in views:
+        for index, source in enumerate(labels.motion_source_keys):
+            if progress is not None:
+                progress("cache", index)
             states.append(_capture_rng())
+            view = load_view(source)
+            if type(view) is not ContinuousTrainingView or view.positive_capture_key != source:
+                raise ValueError("parent loader returned a different physical source")
             with torch.no_grad():
                 cached.append(system.score((view,), text_batch).scores.detach())
+            del view
         after = _capture_rng()
         scores = torch.cat(cached).detach().requires_grad_(True)
         if not bool(torch.isfinite(scores).all()):
@@ -217,13 +271,20 @@ def backward_parent_batch(
         if scores.grad is None:
             raise RuntimeError("parent score cache has no objective gradient")
         try:
-            for index, (view, state) in enumerate(zip(views, states, strict=True)):
+            for index, (source, state) in enumerate(
+                zip(labels.motion_source_keys, states, strict=True)
+            ):
+                if progress is not None:
+                    progress("replay", index)
                 _restore_rng(state)
+                view = load_view(source)
+                if type(view) is not ContinuousTrainingView or view.positive_capture_key != source:
+                    raise ValueError("parent loader returned a different physical source")
                 replay = system.score((view,), text_batch)
                 if not torch.equal(replay.scores.detach(), cached[index]):
                     raise RuntimeError("complete parent replay changed its score row")
                 torch.autograd.backward(replay.scores, scores.grad[index : index + 1])
-                del replay
+                del replay, view
         finally:
             _restore_rng(after)
         if any(parameter.grad is not None for parameter in system.frozen_b2.parameters()):
@@ -236,10 +297,10 @@ def backward_parent_batch(
         return ParentBackwardResult(
             float(loss.detach().cpu()),
             scores.detach().cpu(),
-            len(views),
+            len(labels.parents),
             retrieval_count,
             int(verified.sum()),
-            len(views),
+            len(labels.parents),
         )
     finally:
         system.train(was_training)
diff --git a/tests/test_continuous_parent_host.py b/tests/test_continuous_parent_host.py
new file mode 100644
index 0000000..e77e3af
--- /dev/null
+++ b/tests/test_continuous_parent_host.py
@@ -0,0 +1,348 @@
+"""Actual host-loop qualification on analytic data, not native training results."""
+
+from __future__ import annotations
+
+import copy
+from dataclasses import replace
+import hashlib
+import json
+import math
+
+import pytest
+import torch
+
+from phaseset_core.continuous_parent_host import (
+    ContinuousParentTrainingHost,
+    ParentHostBindings,
+    ParentHostConfig,
+)
+from phaseset_core.continuous_parent_training import backward_loaded_parent_batch
+from phaseset_core.continuous_parent_training import ParentBackwardResult
+from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from phaseset_core.training import (
+    TrainingCheckpointError,
+    _atomic_torch_save,
+    _capture_macro_bidirectional_r1,
+    _load_torch_checkpoint,
+    _stable_hash,
+)
+from test_continuous_parent_training import _case, _empty_cf, _key, _population
+
+
+class _Source:
+    def __init__(self, views, clip):
+        self.views = {view.positive_capture_key: view for view in views}
+        self.clip = clip
+        self.loaded = []
+        self.text_loaded = []
+
+    def view(self, parent, *, seed, epoch, training):
+        self.loaded.append((parent.source_sha256, epoch, training))
+        return self.views[parent.source_sha256]
+
+    def text(self, labels, *, training):
+        self.text_loaded.append((labels.motion_source_keys, training))
+        return self.clip.encode(
+            labels.captions, caption_commitments=labels.caption_commitments, batch_size=3
+        ), _empty_cf()
+
+
+def _setup(tmp_path, *, batch_size=4):
+    system, views, labels, _, clip = _case(tmp_path)
+    # All duplicated motions/identities here are analytic fixtures, explicitly
+    # not separate real captures or an empirical validation population.
+    first = views[0]
+    learning = list(labels.parents)
+    all_views = list(views)
+    for index in range(2, 4):
+        view = replace(first, capture=replace(first.capture, source_sha256=_key(f"train/{index}")))
+        learning.append(
+            ParentCaptionRecord(
+                view.positive_capture_key,
+                _key(f"trainfamily/{index}"),
+                "C01",
+                "train",
+                (f"learning {index}",),
+            )
+        )
+        all_views.append(view)
+    for index in range(2):
+        view = replace(first, capture=replace(first.capture, source_sha256=_key(f"val/{index}")))
+        learning.append(
+            ParentCaptionRecord(
+                view.positive_capture_key,
+                _key(f"valfamily/{index}"),
+                "C03",
+                "validation",
+                (f"validation {index}",),
+            )
+        )
+        all_views.append(view)
+    task = ParentRetrievalTask(
+        tuple(learning),
+        expected_family_keys=tuple(row.annotation_family_sha256 for row in learning),
+    )
+    config = ParentHostConfig(
+        1729, ("C01", "C02"), ("C03",), epochs=2, parent_batch_size=batch_size
+    )
+    bindings = ParentHostBindings(*(_key(name) for name in ("inputs", "code", "runtime", "base")))
+    return system, task, _Source(all_views, clip), config, bindings
+
+
+def _events(path):
+    return [json.loads(item.read_text()) for item in sorted(path.glob("event-*.json"))]
+
+
+def test_host_runs_optimizer_selects_validation_and_preserves_full_parent_census(tmp_path):
+    system, task, source, config, bindings = _setup(tmp_path)
+    initial_base = copy.deepcopy(system.frozen_b2.state_dict())
+    initial = copy.deepcopy(system.state_dict())
+    host = ContinuousParentTrainingHost(system, task, source, config, bindings)
+    result = host.fit(tmp_path / "run")
+    assert result.outcome == "COMPLETED" and result.global_step == 2
+    assert result.completed_epochs == 2 and result.parents_seen == 8
+    assert result.best_checkpoint is not None and len(result.validation_history) == 2
+    assert result.best_validation_r1 == max(value for _, value in result.validation_history)
+    assert any(not torch.equal(value, initial[name]) for name, value in system.state_dict().items())
+    for name, value in system.frozen_b2.state_dict().items():
+        assert torch.equal(value, initial_base[name])
+    # Cache + replay per training parent; exactly one full view per validation
+    # parent/epoch. No physical input is skipped because its caption count differs.
+    assert len(source.loaded) == 20
+    assert len([row for row in source.loaded if row[2]]) == 16
+    events = _events(tmp_path / "run")
+    updates = [row for row in events if row["phase"] == "update"]
+    assert [row["human_rows"] for row in updates] == [5, 5]
+    assert all(row["verified_cf"] == 0 for row in updates)
+    assert [row["learning_rate"] for row in updates] == [3e-4, 3e-4]
+    assert json.loads((tmp_path / "run" / "terminal.json").read_text())["outcome"] == "COMPLETED"
+    best_payload, _ = _load_torch_checkpoint(
+        result.best_checkpoint.path, expected_sha256=result.best_checkpoint.sha256
+    )
+    assert best_payload["validation_history"][-1][1] == result.best_validation_r1
+    # Identical fixture motions yield a provable 1/2 endpoint regardless of
+    # which caption is maximal; both epochs tie and the first must remain best.
+    assert result.validation_history == ((0, 0.5), (1, 0.5))
+    assert best_payload["validation_history"] == [(0, 0.5)]
+    with pytest.raises(RuntimeError, match="only one attempt"):
+        host.fit(tmp_path / "second")
+
+
+@pytest.mark.parametrize("stop", [0, 1, 2, 4])
+def test_host_interruption_resume_matches_uninterrupted_bitwise(tmp_path, stop):
+    system, task, source, config, bindings = _setup(tmp_path, batch_size=2)
+    # Dropout tests replay RNG and validation's save/restore, not a new model.
+    system.text_adapter[1] = torch.nn.Dropout(0.2)
+    initial = copy.deepcopy(system)
+    full = ContinuousParentTrainingHost(system, task, source, config, bindings).fit(
+        tmp_path / "full"
+    )
+    interrupted_model = copy.deepcopy(initial)
+    interrupted = ContinuousParentTrainingHost(
+        interrupted_model, task, source, config, bindings
+    ).fit(tmp_path / "interrupted", stop_after_steps=stop)
+    assert interrupted.global_step == stop
+    # Construct from the same seed-bound initial model, not from post-training weights.
+    resumed_model = copy.deepcopy(initial)
+    resumed = ContinuousParentTrainingHost(resumed_model, task, source, config, bindings).fit(
+        tmp_path / "resumed",
+        resume_checkpoint=interrupted.latest_checkpoint.path,
+        resume_sha256=interrupted.latest_checkpoint.sha256,
+    )
+    assert resumed.outcome == full.outcome == "COMPLETED"
+    assert resumed.validation_history == full.validation_history
+    assert resumed.parents_seen == full.parents_seen == 8
+    for name, value in system.state_dict().items():
+        assert torch.equal(value, resumed_model.state_dict()[name])
+    expected, _ = _load_torch_checkpoint(full.latest_checkpoint.path)
+    actual, _ = _load_torch_checkpoint(resumed.latest_checkpoint.path)
+    for key in ("model", "optimizer", "scheduler", "rng"):
+        assert _stable_hash(actual[key]) == _stable_hash(expected[key])
+
+
+@pytest.mark.parametrize("drift", ["inputs", "code", "runtime", "base", "config", "text", "phase"])
+def test_resume_rejects_actual_execution_input_drift(tmp_path, drift):
+    system, task, source, config, bindings = _setup(tmp_path)
+    initial = copy.deepcopy(system)
+    result = ContinuousParentTrainingHost(system, task, source, config, bindings).fit(
+        tmp_path / "first", stop_after_steps=0
+    )
+    if drift in ("inputs", "code", "runtime", "base"):
+        field = {
+            "inputs": "input_manifest_sha256",
+            "code": "code_manifest_sha256",
+            "runtime": "runtime_sha256",
+            "base": "frozen_base_checkpoint_sha256",
+        }[drift]
+        bindings = replace(bindings, **{field: _key("drift")})
+    elif drift == "config":
+        config = replace(config, learning_rate=2e-4)
+    elif drift == "phase":
+        initial.coordination.strip_phase = True
+    else:
+        changed = list(task.parents)
+        changed[0] = replace(changed[0], captions=("changed exact human row",))
+        task = ParentRetrievalTask(
+            tuple(changed),
+            expected_family_keys=tuple(row.annotation_family_sha256 for row in changed),
+        )
+    with pytest.raises(TrainingCheckpointError, match="drifted"):
+        ContinuousParentTrainingHost(initial, task, source, config, bindings).fit(
+            tmp_path / "drift",
+            resume_checkpoint=result.latest_checkpoint.path,
+            resume_sha256=result.latest_checkpoint.sha256,
+        )
+    assert json.loads((tmp_path / "drift" / "terminal.json").read_text())["outcome"] == "FAILED"
+
+
+def test_truncated_checkpoint_and_rehashed_wrong_cursor_fail_with_terminal_evidence(tmp_path):
+    system, task, source, config, bindings = _setup(tmp_path)
+    initial = copy.deepcopy(system)
+    result = ContinuousParentTrainingHost(system, task, source, config, bindings).fit(
+        tmp_path / "first", stop_after_steps=0
+    )
+    raw = result.latest_checkpoint.path.read_bytes()[:80]
+    truncated = tmp_path / "truncated.pt"
+    truncated.write_bytes(raw)
+    with pytest.raises(TrainingCheckpointError, match="truncated"):
+        ContinuousParentTrainingHost(copy.deepcopy(initial), task, source, config, bindings).fit(
+            tmp_path / "truncated-run",
+            resume_checkpoint=truncated,
+            resume_sha256=hashlib.sha256(raw).hexdigest(),
+        )
+    payload, _ = _load_torch_checkpoint(result.latest_checkpoint.path)
+    payload.pop("state_sha256")
+    payload["parent_batch_offset"] = 1
+    payload["state_sha256"] = _stable_hash(payload)
+    wrong = _atomic_torch_save(tmp_path / "wrong-cursor.pt", payload)
+    with pytest.raises(TrainingCheckpointError, match="cursor"):
+        ContinuousParentTrainingHost(copy.deepcopy(initial), task, source, config, bindings).fit(
+            tmp_path / "wrong-run", resume_checkpoint=wrong.path, resume_sha256=wrong.sha256
+        )
+
+
+def test_full_validation_metric_uses_any_positive_capture_macro_and_lineage_ties():
+    # Three captions for capture A, one for B: caption-weighted T2M would be
+    # 3/4, but capture-macro T2M is 1/2. M2T=1, endpoint=(1+1/2)/2=3/4.
+    logits = torch.tensor([[5.0, 4.0, 3.0, 2.0], [1.0, 0.0, -1.0, 1.0]])
+    positives = torch.tensor([[True, True, True, False], [False, False, False, True]])
+    a, b = bytes(32), bytes([1]) * 32
+    value = _capture_macro_bidirectional_r1(
+        logits, positives, (a, b), (a, a, a, b), (a, b), tuple(bytes([i]) * 32 for i in range(4))
+    )
+    # Motion B has an exact tie for A/B; lineage selects caption0 => miss.
+    assert value == 0.5
+    logits[1, 3] = 1.5
+    assert (
+        _capture_macro_bidirectional_r1(
+            logits,
+            positives,
+            (a, b),
+            (a, a, a, b),
+            (a, b),
+            tuple(bytes([i]) * 32 for i in range(4)),
+        )
+        == 0.75
+    )
+
+
+def test_no_test_or_held_out_rows_loaded_when_outer_fold_learns_old_main_validation(tmp_path):
+    system, _, _, _, clip = _case(tmp_path)
+    task = _population(4, component="C00", split="validation")
+    val = _population(2, component="C03", split="train")
+    # Give fixtures distinct physical/family identities; no native data claim.
+    val_rows = tuple(
+        replace(
+            row, source_sha256=_key(f"valsrc/{i}"), annotation_family_sha256=_key(f"valfamily/{i}")
+        )
+        for i, row in enumerate(val.parents)
+    )
+    test = ParentCaptionRecord(_key("testsource"), _key("testfamily"), "C09", "test", ("sealed",))
+    rows = task.parents + val_rows + (test,)
+    full = ParentRetrievalTask(
+        rows, expected_family_keys=tuple(row.annotation_family_sha256 for row in rows)
+    )
+    config = ParentHostConfig(1729, ("C00",), ("C03",), epochs=1, parent_batch_size=4)
+    source = _Source((), clip)
+    host = ContinuousParentTrainingHost(
+        system, full, source, config, ParentHostBindings(*([_key("binding")] * 4))
+    )
+    assert host._steps_per_epoch == 1
+    assert {row.component for row in host._gallery.parents} == {"C03"}
+    assert source.loaded == source.text_loaded == []
+    assert all(row["component"] in {"C00", "C03"} for row in host._manifest["task"])
+    host.fit(tmp_path / "dev-only", stop_after_steps=0)
+    assert "sealed" not in (tmp_path / "dev-only" / "run.json").read_text()
+    assert source.loaded == source.text_loaded == []
+    for wrong in (
+        replace(config, train_components=("C09",)),
+        replace(config, validation_components=("C09",)),
+    ):
+        with pytest.raises(ValueError):
+            ContinuousParentTrainingHost(system, full, source, wrong, host.bindings)
+
+
+def test_loader_replay_rejects_wrong_native_source_before_scoring(tmp_path):
+    system, views, labels, text, _ = _case(tmp_path)
+    with pytest.raises(ValueError, match="different physical source"):
+        backward_loaded_parent_batch(
+            system,
+            labels,
+            text,
+            load_view=lambda _: views[1],
+            counterfactuals=_empty_cf(),
+            cf_weight=0.2,
+            margin=0.2,
+        )
+
+
+def test_nontrivial_warmup_cosine_schedule_has_independent_scalar_oracle(tmp_path):
+    system, task, source, config, bindings = _setup(tmp_path)
+    host = ContinuousParentTrainingHost(system, task, source, replace(config, epochs=40), bindings)
+    # No motion/CLIP or training run here: isolate the scheduler's 40-step
+    # schedule, including two warmup steps and interior/final cosine points.
+    for step in range(41):
+        expected = (
+            (step + 1) / 2
+            if step < 2
+            else 0.5 * (1 + math.cos(math.pi * min(1.0, (step - 2) / 38)))
+        )
+        assert host._optimizer.param_groups[0]["lr"] == pytest.approx(3e-4 * expected, abs=1e-15)
+        if step < 40:
+            host._optimizer.step()
+            host._scheduler.step()
+
+
+def test_actual_host_clips_before_adamw_with_fixed_decay_independent_scalar_oracle(
+    tmp_path, monkeypatch
+):
+    system, task, source, config, bindings = _setup(tmp_path)
+    config = replace(config, epochs=1)
+    old = float(system.calibration.log_scale.detach())
+
+    def forced_gradient(model, labels, text, **kwargs):
+        # This unit fixture isolates optimizer ordering; the actual full
+        # loss/backward is exercised independently by the preceding tests.
+        model.zero_grad(set_to_none=True)
+        model.calibration.log_scale.grad = torch.tensor(10.0)
+        return ParentBackwardResult(
+            1.0,
+            torch.zeros(len(labels.parents), len(labels.captions)),
+            len(labels.parents),
+            len(labels.captions),
+            0,
+            0,
+        )
+
+    monkeypatch.setattr(
+        "phaseset_core.continuous_parent_host.backward_loaded_parent_batch", forced_gradient
+    )
+    ContinuousParentTrainingHost(system, task, source, config, bindings).fit(tmp_path / "clip-run")
+    assert 0.99999 <= float(system.calibration.log_scale.grad) <= 1.0
+    # First-step AdamW: bias-corrected moments make the normalized positive
+    # gradient ~1, with epsilon 1e-8; decoupled WD acts on the old parameter.
+    gradient = 10 / (10 + 1e-6)
+    expected = old * (1 - 3e-4 * 0.01) - 3e-4 * gradient / (gradient + 1e-8)
+    assert float(system.calibration.log_scale.detach()) == pytest.approx(expected, abs=5e-7)
+    update = next(row for row in _events(tmp_path / "clip-run") if row["phase"] == "update")
+    assert update["gradient_norm"] == 10.0

From 269efbf2cef6fe5b5f10be67c6c6e0af6dd2f37c Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 02:42:40 -0500
Subject: [PATCH 41/95] feat: train registered bases on complete parent
 captures

---
 RELEASE_FILES.sha256                          |   8 +-
 docs/PHASESET_V2_PARENT_HOST.md               |  31 +-
 .../continuous_base_retrieval.py              | 126 +++++++
 src/phaseset_core/continuous_parent_host.py   |  78 ++++-
 .../continuous_parent_training.py             |  62 +++-
 tests/test_continuous_base_retrieval.py       | 310 ++++++++++++++++++
 6 files changed, 576 insertions(+), 39 deletions(-)
 create mode 100644 src/phaseset_core/continuous_base_retrieval.py
 create mode 100644 tests/test_continuous_base_retrieval.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 4ac887a..b093bdd 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -71,7 +71,7 @@ cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHAS
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
-099c6c09a5a1779ed83b0de9b8e0528e7d75323af2912ba62814f6597259cf4e	5577	docs/PHASESET_V2_PARENT_HOST.md
+3eb5f4237db4af02d49e8eb62a318ef68ac706bf14cc1b405bf9fa837e8d5705	7431	docs/PHASESET_V2_PARENT_HOST.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
@@ -154,10 +154,11 @@ d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phases
 0eaf0057ccf6ab6a4b83c1fa6e649399c4f783a79fab301e0778bafb5699daa9	47086	src/phaseset_core/capture_prepared_storage.py
 7a146f3c81122ed6492fb6dc30194f68e6e3b86e2a25d29a5313607eb18470b6	42026	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
+be4638d0ce9057468eb557db6e73afef1e2ce688833309991ce5f5818b0ad575	5577	src/phaseset_core/continuous_base_retrieval.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
-d187e4ea8040f4967822033f06c375106304f3bf9fceaa4f1bb9732bcdec72e5	24818	src/phaseset_core/continuous_parent_host.py
-5195c0326df704e7dd1b39500481c28ea7b74dc3e4ad92312e5133561f6fad17	13720	src/phaseset_core/continuous_parent_training.py
+60cb00e377324add06c1a539b2f80c37a2711589989cb3bb5c2b711d4d2394b6	26842	src/phaseset_core/continuous_parent_host.py
+19798b164958b1e65be4353d26ca4cbe167ebaa26c9639b488bcb3e63a00296c	15174	src/phaseset_core/continuous_parent_training.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
 38f74724372e276257c1014e50e26eb8b2cab482728a6b143799118ad2f47ca9	10545	src/phaseset_core/continuous_retrieval.py
 a46004b4c5a94dc84029a6cf5a54631e614e67f680c506feb0b92b435702160b	6873	src/phaseset_core/continuous_training_input.py
@@ -201,6 +202,7 @@ ae334bee8e2762f022020942141cb52f3285d3d9cb07d53574b6c37f57b2a556	20274	tests/tes
 d5d69092cc271059400773ef68447e06068aa802d5e9c82193704c6b65c71f59	18827	tests/test_caption_role_provenance.py
 ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test_capture_validation_source_lineage_v3.py
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
+9a6d69be46d3df730acabdb7e2b7fd23a9f4a10ecb6895c013a6fc9d712f14e5	13066	tests/test_continuous_base_retrieval.py
 b0f9a8d920640d3c5455e9eacdeb87feecbaca87a8b1b06affe8ca297c78d075	3593	tests/test_continuous_capture_io.py
 1996c74bc514251d6213079416cd5d6dccc80914349d4bb61c25b83a8b789961	15517	tests/test_continuous_parent_host.py
 8b91250de082536ab1d8b0118977e1d569e4f2698e5073ebce44c6ab4bd8324f	11609	tests/test_continuous_parent_training.py
diff --git a/docs/PHASESET_V2_PARENT_HOST.md b/docs/PHASESET_V2_PARENT_HOST.md
index 91a000b..1968596 100644
--- a/docs/PHASESET_V2_PARENT_HOST.md
+++ b/docs/PHASESET_V2_PARENT_HOST.md
@@ -1,4 +1,4 @@
-# Complete-parent residual training host
+# Complete-parent base and residual training host
 
 `continuous_parent_host.py` connects the qualified full-gallery gradient replay
 to real FP32 AdamW updates, checkpoint selection and restart. It is not the old
@@ -17,6 +17,23 @@ The source must reproduce the same shared-yaw transform during replay, and
 respect the pre-admitted language/yaw rule. No shorter timeline or physical
 batch materialization is introduced by the host.
 
+For a **base** stage, the source returns `PreparedContinuousCapture` directly,
+not a fabricated residual view. `ContinuousBaseRetrievalSystem` reuses B0/B1/B2
+without shrinking their registered architectures. Every accepted window is
+encoded in absolute order, then pooled with the same fixed float64 mean and
+float32 L2 normalization as the residual's global anchor. The base encoder and
+its bounded logit temperature are trainable; no text adapter, phase cache,
+physical floor, coordination evidence or learned window pool is added.
+Non-reentrant window checkpointing preserves dropout RNG and binds each actual
+window to its own replay closure. The checkpoint invocation includes an
+explicit tensor on the model's device so
+CUDA RNG is discoverable; closure-only parameters would not provide that.
+CPU invocation checks do not qualify CUDA numerical gradients. It bounds
+stored neural activations, not the
+size of the complete physical skeleton input. Native memory/cost qualification
+is still required. The outer full-gallery parent-score replay is shared by
+base and residual stages; the architectures and input types are not conflated.
+
 The effective batch is selected by explicit learning components and includes
 all official human holistic rows of every parent. Only the B-by-Q score matrix
 and RNG snapshots are cached. The full variable-positive symmetric objective
@@ -29,12 +46,19 @@ Optional CF sentences follow the human retrieval prefix. Their columns have no
 fabricated retrieval positive. A verified-false bit is not human provenance:
 the private manifest must bind the actual blind two-human records. Unverified
 CFs contribute no CF term. No label generation or data discovery occurs here.
+Base stages reject CF rows, extra text columns and any CF weight. They optimize
+only the existing variable-positive symmetric retrieval InfoNCE.
 
 Optimizer: AdamW, weight decay 0.01, clipping 1.0, 5% warmup followed by cosine
 decay, default residual learning rate 3e-4 and 20 complete epochs. Seeds remain
 1729, 2718 and 31415. Batch size is explicitly **parents**, not 128 windows.
 The short qualification schedules are not the formal/pilot freeze. There is
 no automatic BF16 fallback; FP32 is the only supported path in this host.
+`ParentHostConfig.for_base` explicitly sets the distinct base defaults:
+30 epochs, learning rate 2e-4, zero CF loss and `stage="base"`. The stage must
+match the actual scorer. A base has no frozen-anchor checkpoint binding;
+residual stages still require that exact frozen B2 binding and enforce its
+unchanged state. No checkpoint produced by a fixture is a qualified base.
 
 ## Validation and selection
 
@@ -84,7 +108,10 @@ manifests, checkpoints, caption text or licensed physical cache files.
 
 ## Qualification scope
 
-Tests exercise the real host optimizer loop on clearly identified analytic
+Tests exercise actual registered 512D B0/B1/B2 forward and all-parameter
+checkpointed backward with active dropout, plus an independent per-window
+pooling oracle. Separate, clearly marked analytic fixtures exercise the real
+base/residual host optimizer loop,
 fixtures, full gallery/caption census, first-best selection, bitwise
 uninterrupted-versus-resumed model/optimizer/scheduler/RNG, zero/mid/end/completed
 resume, concrete input drift, truncated checkpoints and wrong progress cursor.
diff --git a/src/phaseset_core/continuous_base_retrieval.py b/src/phaseset_core/continuous_base_retrieval.py
new file mode 100644
index 0000000..0d47115
--- /dev/null
+++ b/src/phaseset_core/continuous_base_retrieval.py
@@ -0,0 +1,126 @@
+"""Train registered group bases on whole parents, not independent windows.
+
+The input is the admitted prepared skeleton timeline, without an unused
+Morlet cache or physical floor. This is the trainable version of V2's fixed
+global-branch readout: every accepted window, fixed float64 mean, float32 L2.
+It does not grant data rights, a formal start, or a qualified base checkpoint.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+from torch.utils.checkpoint import checkpoint
+
+from .capture_validation import _validate_text_batch
+from .continuous_capture import PreparedContinuousCapture
+from .frozen_clip_text import FrozenClipTextBatch
+from .pipeline import collate_group_samples, edge_budget_batches
+from .training import BaseRetrievalSystem
+
+
+@dataclass(frozen=True)
+class ContinuousBaseScoreOutput:
+    scores: Tensor
+    motion_embeddings: Tensor
+    text_receipt_sha256: str
+    capture_keys: tuple[str, ...]
+
+
+class ContinuousBaseRetrievalSystem(nn.Module):
+    """Trainable B0/B1/B2, complete-parent pooling and base logit temperature.
+
+    Shared-actor architectures are reused without shrinking their registered
+    512D configuration. Window checkpointing recomputes neural activations in
+    backward; it does not pretend the complete physical input occupies O(1)
+    memory. The outer parent-score replay keeps only one parent graph alive.
+    No text adapter, CF term, coordination branch or trainable window pool is
+    added to a base. Window batching is an explicit, resume-bound setting.
+    """
+
+    def __init__(
+        self,
+        base: BaseRetrievalSystem,
+        *,
+        checkpoint_windows: bool = True,
+        base_window_batch_size: int = 1,
+        base_edge_budget: int = 32768,
+    ):
+        super().__init__()
+        if (
+            not isinstance(base, BaseRetrievalSystem)
+            or base.system_id not in ("B0", "B1", "B2")
+            or base.embedding_dim != 512
+        ):
+            raise ValueError("whole-parent bases require registered B0/B1/B2 with 512D output")
+        if any(not parameter.requires_grad for parameter in base.parameters()):
+            raise ValueError("a base-learning system cannot contain frozen base parameters")
+        if type(checkpoint_windows) is not bool:
+            raise ValueError("checkpoint_windows must be an explicit bool")
+        if type(base_window_batch_size) is not int or base_window_batch_size < 1:
+            raise ValueError("base_window_batch_size must be a positive integer")
+        if type(base_edge_budget) is not int or base_edge_budget < 1:
+            raise ValueError("base_edge_budget must be a positive integer")
+        self.base = base
+        self.checkpoint_windows = checkpoint_windows
+        self.base_window_batch_size = base_window_batch_size
+        self.base_edge_budget = base_edge_budget
+
+    @property
+    def system_id(self) -> str:
+        return self.base.system_id
+
+    def encode_capture(self, capture: PreparedContinuousCapture) -> Tensor:
+        if type(capture) is not PreparedContinuousCapture:
+            raise ValueError("base input must be an admitted complete prepared capture")
+        windows = capture.windows()
+        if not windows:
+            raise ValueError("base capture needs at least one accepted window")
+        batches = edge_budget_batches(
+            windows,
+            max_total_edges=self.base_edge_budget,
+            max_batch_size=self.base_window_batch_size,
+        )
+        embeddings = []
+        for window_batch in batches:
+            # Bind each batch now: late-binding a loop variable would replay
+            # the last window for every checkpoint during backward.
+            def encode(_device_witness, window_batch=window_batch):
+                return self.base.encode_trainable(collate_group_samples(window_batch))
+
+            embeddings.append(
+                # Checkpoint discovers CUDA RNG devices from explicit tensor
+                # inputs, not parameters captured only inside a closure. The
+                # scalar is a device witness, not a new encoder input/feature.
+                checkpoint(
+                    encode, self.base.logit_scale, use_reentrant=False, preserve_rng_state=True
+                )
+                if self.checkpoint_windows and torch.is_grad_enabled()
+                else encode(self.base.logit_scale)
+            )
+        rows = torch.cat(embeddings)
+        if rows.shape != (len(windows), 512):
+            raise RuntimeError("whole-parent base encoding omitted or malformed a window")
+        return F.normalize(rows.to(torch.float64).mean(dim=0).to(torch.float32), dim=0)
+
+    def score(
+        self,
+        captures: tuple[PreparedContinuousCapture, ...],
+        text_batch: FrozenClipTextBatch,
+    ) -> ContinuousBaseScoreOutput:
+        if (
+            type(captures) is not tuple
+            or not captures
+            or any(type(capture) is not PreparedContinuousCapture for capture in captures)
+        ):
+            raise ValueError("base scoring requires complete prepared capture rows")
+        text, _, receipt_sha = _validate_text_batch(text_batch)
+        device = next(self.base.parameters()).device
+        motion = torch.stack([self.encode_capture(capture) for capture in captures])
+        scores = self.base.scores(motion.contiguous(), text.to(device).contiguous())
+        return ContinuousBaseScoreOutput(
+            scores, motion, receipt_sha, tuple(capture.source_sha256 for capture in captures)
+        )
diff --git a/src/phaseset_core/continuous_parent_host.py b/src/phaseset_core/continuous_parent_host.py
index a5dfdec..290b1ef 100644
--- a/src/phaseset_core/continuous_parent_host.py
+++ b/src/phaseset_core/continuous_parent_host.py
@@ -1,10 +1,10 @@
-"""Complete-parent FP32 residual optimizer, validation and restart host.
+"""Complete-parent FP32 base/residual optimizer, validation and restart host.
 
 This runs real updates, not the superseded window-unit training recipe. The
 private operator still admits rights, physical floors, human CF provenance,
 hardware/budget and the dated 87-stage matrix. Constructing this host supplies
-none of those authorities. Base-model training and literature baselines are
-separate implementations. No final-test interface exists here.
+none of those authorities. Literature baselines remain separate implementations.
+No final-test interface exists here.
 """
 
 from __future__ import annotations
@@ -20,10 +20,13 @@
 import numpy as np
 import torch
 
+from .continuous_base_retrieval import ContinuousBaseRetrievalSystem
+from .continuous_capture import PreparedContinuousCapture
 from .continuous_parent_training import (
     ParentCounterfactualRows,
     backward_loaded_parent_batch,
     parent_epoch_batches,
+    parent_input_capture,
     validate_parent_text_batch,
 )
 from .continuous_retrieval import ContinuousRetrievalSystem
@@ -57,7 +60,7 @@ class ParentTrainingSource(Protocol):
 
     def view(
         self, parent: ParentCaptionRecord, *, seed: int, epoch: int, training: bool
-    ) -> ContinuousTrainingView: ...
+    ) -> ContinuousTrainingView | PreparedContinuousCapture: ...
 
     def text(
         self, labels: ParentRetrievalBatch, *, training: bool
@@ -74,6 +77,30 @@ class ParentHostConfig:
     learning_rate: float = 3e-4
     cf_weight: float = 0.2
     cf_margin: float = 0.2
+    stage: str = "residual"
+
+    @classmethod
+    def for_base(
+        cls,
+        seed: int,
+        train_components: tuple[str, ...],
+        validation_components: tuple[str, ...],
+        **settings,
+    ):
+        """Explicit base defaults; short qualifications are not a study freeze."""
+        return cls(
+            seed,
+            train_components,
+            validation_components,
+            **{
+                "epochs": 30,
+                "learning_rate": 2e-4,
+                "cf_weight": 0.0,
+                "cf_margin": 0.0,
+                "stage": "base",
+                **settings,
+            },
+        )
 
     def __post_init__(self):
         if type(self.seed) is not int or self.seed not in OFFICIAL_SEEDS:
@@ -97,6 +124,10 @@ def __post_init__(self):
                 raise ValueError("loss/optimizer scalars must be finite nonnegative numbers")
         if self.learning_rate == 0:
             raise ValueError("learning rate must be positive")
+        if self.stage not in ("base", "residual"):
+            raise ValueError("host stage must explicitly be base or residual")
+        if self.stage == "base" and (self.cf_weight != 0 or self.cf_margin != 0):
+            raise ValueError("a base stage has no CF loss")
 
 
 @dataclass(frozen=True)
@@ -112,10 +143,12 @@ class ParentHostBindings:
     input_manifest_sha256: str
     code_manifest_sha256: str
     runtime_sha256: str
-    frozen_base_checkpoint_sha256: str
+    frozen_base_checkpoint_sha256: str | None
 
     def __post_init__(self):
-        for value in asdict(self).values():
+        for name, value in asdict(self).items():
+            if name == "frozen_base_checkpoint_sha256" and value is None:
+                continue
             if (
                 type(value) is not str
                 or len(value) != 64
@@ -156,14 +189,19 @@ class ContinuousParentTrainingHost:
 
     def __init__(
         self,
-        system: ContinuousRetrievalSystem,
+        system: ContinuousRetrievalSystem | ContinuousBaseRetrievalSystem,
         task: ParentRetrievalTask,
         source: ParentTrainingSource,
         config: ParentHostConfig,
         bindings: ParentHostBindings,
     ):
-        if not isinstance(system, ContinuousRetrievalSystem):
-            raise TypeError("this host is for the complete-parent V2 residual scorer")
+        if not isinstance(system, (ContinuousRetrievalSystem, ContinuousBaseRetrievalSystem)):
+            raise TypeError("this host requires an actual complete-parent base or V2 scorer")
+        self._is_base = isinstance(system, ContinuousBaseRetrievalSystem)
+        if config.stage != ("base" if self._is_base else "residual"):
+            raise ValueError("host stage does not match the actual scorer")
+        if (bindings.frozen_base_checkpoint_sha256 is None) != self._is_base:
+            raise ValueError("only a residual stage binds a frozen B2 checkpoint")
         if any(parameter.dtype != torch.float32 for parameter in system.parameters()):
             raise ValueError("this host has only been qualified for FP32")
         self.system, self.task, self.source = system, task, source
@@ -182,9 +220,13 @@ def __init__(
         self._parameters = [
             parameter for parameter in system.parameters() if parameter.requires_grad
         ]
-        if not self._parameters or any(p.requires_grad for p in system.frozen_b2.parameters()):
+        if not self._parameters or (
+            not self._is_base and any(p.requires_grad for p in system.frozen_b2.parameters())
+        ):
             raise ValueError("the residual must be trainable and its B2 anchor frozen")
-        self._base_state = _stable_hash(system.frozen_b2.state_dict())
+        if self._is_base and any(not p.requires_grad for p in system.parameters()):
+            raise ValueError("base-learning parameters must all remain trainable")
+        self._base_state = None if self._is_base else _stable_hash(system.frozen_b2.state_dict())
         self._manifest = {
             "schema": "phaseset-complete-parent-host-v1",
             "bindings": asdict(bindings),
@@ -271,12 +313,11 @@ def _validate(self) -> float:
                 view = self.source.view(
                     parent, seed=self.config.seed, epoch=self._epoch, training=False
                 )
-                if view.positive_capture_key != parent.source_sha256 or (
-                    view.capture.augmentation_yaw != 0.0
-                ):
+                capture = parent_input_capture(self.system, view)
+                if capture.source_sha256 != parent.source_sha256 or capture.augmentation_yaw != 0.0:
                     raise ValueError("validation requires the admitted unaugmented whole source")
                 scores.append(self.system.score((view,), text).scores.detach().cpu())
-                del view
+                del view, capture
             logits = torch.cat(scores).contiguous()
             if logits.shape != (len(labels.parents), len(labels.captions)) or not bool(
                 torch.isfinite(logits).all()
@@ -363,7 +404,7 @@ def _resume(self, path: Path, digest: str):
             for key, value in model.items()
             if key.startswith("frozen_b2.")
         }
-        if _stable_hash(base) != self._base_state or any(
+        if (not self._is_base and _stable_hash(base) != self._base_state) or any(
             not bool(torch.isfinite(value).all()) for value in model.values()
         ):
             raise TrainingCheckpointError("parent checkpoint changed the anchor or is nonfinite")
@@ -499,7 +540,10 @@ def fit(
                     lr = self._optimizer.param_groups[0]["lr"]
                     self._optimizer.step()
                     self._scheduler.step()
-                    if _stable_hash(self.system.frozen_b2.state_dict()) != self._base_state:
+                    if (
+                        not self._is_base
+                        and _stable_hash(self.system.frozen_b2.state_dict()) != self._base_state
+                    ):
                         raise RuntimeError("training mutated the shared frozen B2 anchor")
                     self._step += 1
                     self._offset += 1
diff --git a/src/phaseset_core/continuous_parent_training.py b/src/phaseset_core/continuous_parent_training.py
index f001b6b..43f25b0 100644
--- a/src/phaseset_core/continuous_parent_training.py
+++ b/src/phaseset_core/continuous_parent_training.py
@@ -18,9 +18,12 @@
 from torch import Tensor
 
 from .capture_validation import _validate_text_batch
+from .continuous_base_retrieval import ContinuousBaseRetrievalSystem
+from .continuous_capture import PreparedContinuousCapture
 from .continuous_retrieval import ContinuousRetrievalSystem
 from .continuous_training_input import ContinuousTrainingView
 from .frozen_clip_text import FrozenClipTextBatch
+from .objectives import variable_positive_symmetric_infonce
 from .parent_retrieval_task import ParentRetrievalBatch, ParentRetrievalTask
 from .temporal_coordination import coordination_objective
 from .training import OFFICIAL_SEEDS, _capture_rng, _restore_rng
@@ -206,11 +209,11 @@ def validate_parent_text_batch(
 
 
 def backward_loaded_parent_batch(
-    system: ContinuousRetrievalSystem,
+    system: ContinuousRetrievalSystem | ContinuousBaseRetrievalSystem,
     labels: ParentRetrievalBatch,
     text_batch: FrozenClipTextBatch,
     *,
-    load_view: Callable[[str], ContinuousTrainingView],
+    load_view: Callable[[str], ContinuousTrainingView | PreparedContinuousCapture],
     counterfactuals: ParentCounterfactualRows,
     cf_weight: float,
     margin: float,
@@ -223,8 +226,8 @@ def backward_loaded_parent_batch(
     replay; a deterministic private source may instead derive yaw from epoch
     and source. No physical batch tuple or all-capture graph is retained here.
     """
-    if not isinstance(system, ContinuousRetrievalSystem):
-        raise TypeError("complete parent training requires the V2 retrieval system")
+    if not isinstance(system, (ContinuousRetrievalSystem, ContinuousBaseRetrievalSystem)):
+        raise TypeError("complete parent training requires a base or V2 retrieval system")
     if (
         not labels.parents
         or len(set(labels.motion_source_keys)) != len(labels.parents)
@@ -233,6 +236,9 @@ def backward_loaded_parent_batch(
         raise ValueError("complete parent labels must contain distinct admitted sources/families")
     text_count = validate_parent_text_batch(labels, text_batch)
     retrieval_count = len(labels.captions)
+    is_base = isinstance(system, ContinuousBaseRetrievalSystem)
+    if is_base and (counterfactuals.source_keys or text_count != retrieval_count or cf_weight != 0):
+        raise ValueError("base training uses only human retrieval rows and no CF objective")
     device = next(system.parameters()).device
     indices = counterfactuals.indices(labels, text_count=text_count, device=device)
     positive_mask = labels.positive_mask(device=device)
@@ -246,24 +252,29 @@ def backward_loaded_parent_batch(
                 progress("cache", index)
             states.append(_capture_rng())
             view = load_view(source)
-            if type(view) is not ContinuousTrainingView or view.positive_capture_key != source:
+            capture = parent_input_capture(system, view)
+            if capture.source_sha256 != source:
                 raise ValueError("parent loader returned a different physical source")
             with torch.no_grad():
                 cached.append(system.score((view,), text_batch).scores.detach())
-            del view
+            del view, capture
         after = _capture_rng()
         scores = torch.cat(cached).detach().requires_grad_(True)
         if not bool(torch.isfinite(scores).all()):
             raise ValueError("complete parent score cache is nonfinite")
         motion, pos, neg, verified = indices
-        loss = coordination_objective(
-            scores[:, :retrieval_count].contiguous(),
-            positive_mask,
-            cf_positive_scores=scores[motion, pos],
-            cf_negative_scores=scores[motion, neg],
-            verified_negative_mask=verified,
-            cf_weight=cf_weight,
-            margin=margin,
+        loss = (
+            variable_positive_symmetric_infonce(scores.contiguous(), positive_mask)[2]
+            if is_base
+            else coordination_objective(
+                scores[:, :retrieval_count].contiguous(),
+                positive_mask,
+                cf_positive_scores=scores[motion, pos],
+                cf_negative_scores=scores[motion, neg],
+                verified_negative_mask=verified,
+                cf_weight=cf_weight,
+                margin=margin,
+            )
         )
         if not bool(torch.isfinite(loss)):
             raise ValueError("complete parent objective is nonfinite")
@@ -278,16 +289,19 @@ def backward_loaded_parent_batch(
                     progress("replay", index)
                 _restore_rng(state)
                 view = load_view(source)
-                if type(view) is not ContinuousTrainingView or view.positive_capture_key != source:
+                capture = parent_input_capture(system, view)
+                if capture.source_sha256 != source:
                     raise ValueError("parent loader returned a different physical source")
                 replay = system.score((view,), text_batch)
                 if not torch.equal(replay.scores.detach(), cached[index]):
                     raise RuntimeError("complete parent replay changed its score row")
                 torch.autograd.backward(replay.scores, scores.grad[index : index + 1])
-                del replay, view
+                del replay, view, capture
         finally:
             _restore_rng(after)
-        if any(parameter.grad is not None for parameter in system.frozen_b2.parameters()):
+        if not is_base and any(
+            parameter.grad is not None for parameter in system.frozen_b2.parameters()
+        ):
             raise RuntimeError("the shared B2 anchor must remain frozen")
         if any(
             parameter.grad is not None and not bool(torch.isfinite(parameter.grad).all())
@@ -304,3 +318,17 @@ def backward_loaded_parent_batch(
         )
     finally:
         system.train(was_training)
+
+
+def parent_input_capture(
+    system: ContinuousRetrievalSystem | ContinuousBaseRetrievalSystem,
+    view: ContinuousTrainingView | PreparedContinuousCapture,
+) -> PreparedContinuousCapture:
+    """Keep the actual base and residual physical-input contracts distinct."""
+    if isinstance(system, ContinuousBaseRetrievalSystem):
+        if type(view) is not PreparedContinuousCapture:
+            raise ValueError("base loader must return a prepared capture, not a phase view")
+        return view
+    if type(view) is not ContinuousTrainingView:
+        raise ValueError("residual loader must return the complete phase training view")
+    return view.capture
diff --git a/tests/test_continuous_base_retrieval.py b/tests/test_continuous_base_retrieval.py
new file mode 100644
index 0000000..42c721a
--- /dev/null
+++ b/tests/test_continuous_base_retrieval.py
@@ -0,0 +1,310 @@
+"""Real registered architecture checks plus explicit analytic host fixtures.
+
+No native learning result, fitted base, pilot or formal stage is claimed.
+"""
+
+from __future__ import annotations
+
+import copy
+from dataclasses import replace
+import json
+
+import numpy as np
+import pytest
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from phaseset_core.continuous_base_retrieval import ContinuousBaseRetrievalSystem
+from phaseset_core.continuous_capture import prepare_continuous_capture
+from phaseset_core.continuous_parent_host import (
+    ContinuousParentTrainingHost,
+    ParentHostBindings,
+    ParentHostConfig,
+)
+from phaseset_core.continuous_parent_training import (
+    ParentCounterfactualRows,
+    backward_loaded_parent_batch,
+)
+from phaseset_core.models import GroupBaseOutput, build_group_base
+from phaseset_core.objectives import variable_positive_symmetric_infonce
+from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from phaseset_core.pipeline import PrivateCaptureArrays, collate_group_samples
+from phaseset_core.training import (
+    BaseRetrievalSystem,
+    TrainingCheckpointError,
+    _capture_rng,
+    _load_torch_checkpoint,
+    _restore_rng,
+    _stable_hash,
+)
+from test_continuous_parent_training import _empty_cf, _key
+from test_phaseset_frozen_clip_text import _fixture
+
+
+def _capture():
+    # Analytic three-actor input: two distinct accepted windows separated by
+    # a rejected interval. It is not a native capture or augmented benchmark.
+    t = np.arange(900, dtype=np.float32) / 30
+    joints = np.zeros((900, 3, 22, 3), np.float32)
+    for actor in range(3):
+        joints[:, actor, :, 0] = (actor + 0.02 * t + np.sin(t + actor))[:, None]
+        joints[:, actor, :, 2] = (np.cos(2 * t + actor) + 0.03 * t)[:, None]
+    track = np.ones(joints.shape[:-1], np.bool_)
+    track[300:360] = False
+    return prepare_continuous_capture(
+        PrivateCaptureArrays(
+            joints, track, tuple(bytes([i + 1]) * 32 for i in range(3)), _key("analytic")
+        )
+    )
+
+
+class _AnalyticWindowBase(nn.Module):
+    """Only the host/RNG fixture, never substituted for a registered model."""
+
+    system_id = "B2"
+
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(1, 512)
+        self.dropout = nn.Dropout(0.2)
+
+    def forward(self, batch):
+        signal = torch.from_numpy(batch.skeletons.copy()).mean((1, 2, 3, 4))[:, None]
+        embedding = self.dropout(self.linear(signal))
+        return GroupBaseOutput(
+            embedding, embedding[:, None], torch.ones(len(signal), 1, dtype=torch.bool)
+        )
+
+
+class _BaseSource:
+    def __init__(self, captures, clip):
+        self.captures = {capture.source_sha256: capture for capture in captures}
+        self.clip = clip
+        self.loaded = []
+
+    def view(self, parent, *, seed, epoch, training):
+        self.loaded.append((parent.source_sha256, epoch, training))
+        return self.captures[parent.source_sha256]
+
+    def text(self, labels, *, training):
+        return self.clip.encode(
+            labels.captions, caption_commitments=labels.caption_commitments, batch_size=3
+        ), _empty_cf()
+
+
+def _setup(tmp_path):
+    (tmp_path / "clip").mkdir()
+    clip, _, _, _ = _fixture(tmp_path / "clip")
+    capture = _capture()
+    captures, parents = [], []
+    for index in range(6):
+        key = _key(f"analytic-source/{index}")
+        captures.append(replace(capture, source_sha256=key))
+        parents.append(
+            ParentCaptionRecord(
+                key,
+                _key(f"analytic-family/{index}"),
+                "C01" if index < 2 else "C02" if index < 4 else "C03",
+                "train" if index < 4 else "validation",
+                (f"human {index}", f"extra {index}") if index == 0 else (f"human {index}",),
+            )
+        )
+    task = ParentRetrievalTask(
+        tuple(parents), expected_family_keys=tuple(row.annotation_family_sha256 for row in parents)
+    )
+    torch.manual_seed(1729)
+    model = ContinuousBaseRetrievalSystem(
+        BaseRetrievalSystem(_AnalyticWindowBase(), embedding_dim=512)
+    )
+    config = ParentHostConfig.for_base(
+        1729, ("C01", "C02"), ("C03",), epochs=2, parent_batch_size=2
+    )
+    bindings = ParentHostBindings(_key("inputs"), _key("code"), _key("runtime"), None)
+    return model, task, _BaseSource(captures, clip), config, bindings
+
+
+@pytest.mark.parametrize("system_id", ["B0", "B1", "B2"])
+def test_real_registered_bases_all_windows_checkpoint_scores_and_gradients(system_id, tmp_path):
+    capture = _capture()
+    windows = capture.windows()
+    assert len(windows) == 2 and windows[1].source_start_frame == 600
+    (tmp_path / "clip").mkdir()
+    clip, _, _, _ = _fixture(tmp_path / "clip")
+    text = clip.encode(
+        ("a group moves", "people interact"),
+        caption_commitments=(b"a" * 32, b"b" * 32),
+        batch_size=2,
+    )
+    torch.manual_seed(1729)
+    model = ContinuousBaseRetrievalSystem(
+        BaseRetrievalSystem(build_group_base(system_id), embedding_dim=512)
+    )
+    direct = copy.deepcopy(model)
+    direct.checkpoint_windows = False
+    # Training dropout is active: checkpoint must replay its exact RNG and
+    # each bound window, not merely agree in evaluation mode.
+    state = _capture_rng()
+    actual = model.score((capture,), text)
+    actual.scores.sum().backward()
+    after = _capture_rng()
+    _restore_rng(state)
+    expected = direct.score((capture,), text)
+    expected.scores.sum().backward()
+    assert torch.equal(actual.scores, expected.scores)
+    assert _stable_hash(_capture_rng()) == _stable_hash(after)
+    count = 0
+    for (name, p), (other, q) in zip(
+        model.named_parameters(), direct.named_parameters(), strict=True
+    ):
+        assert name == other and p.grad is not None and q.grad is not None
+        torch.testing.assert_close(p.grad, q.grad, rtol=0, atol=0)
+        count += int(bool(p.grad.abs().sum() > 0))
+    assert count > 1
+    model.eval()
+    with torch.no_grad():
+        rows = torch.cat(
+            [model.base.encode_trainable(collate_group_samples((window,))) for window in windows]
+        )
+        oracle = F.normalize(rows.double().mean(0).float(), dim=0)
+        assert torch.equal(model.encode_capture(capture), oracle)
+        # Readout is exactly the frozen residual global anchor's readout;
+        # constructing a residual is not needed just to obtain this oracle.
+        assert not torch.equal(oracle, F.normalize(rows[:1].double().mean(0).float(), dim=0))
+    assert actual.motion_embeddings.shape == (1, 512)
+    assert actual.capture_keys == (capture.source_sha256,)
+
+
+def test_base_full_gallery_replay_matches_dense_all_parameter_gradients(tmp_path):
+    model, task, source, _, _ = _setup(tmp_path)
+    labels = task.batch(tuple(row.annotation_family_sha256 for row in task.parents[:4]))
+    text, cf = source.text(labels, training=True)
+    captures = tuple(source.captures[key] for key in labels.motion_source_keys)
+    dense = copy.deepcopy(model)
+    state = _capture_rng()
+    scores = dense.score(captures, text).scores
+    loss = variable_positive_symmetric_infonce(scores, labels.positive_mask(device=scores.device))[
+        2
+    ]
+    loss.backward()
+    after = _capture_rng()
+    _restore_rng(state)
+    result = backward_loaded_parent_batch(
+        model,
+        labels,
+        text,
+        load_view=lambda key: source.captures[key],
+        counterfactuals=cf,
+        cf_weight=0,
+        margin=0,
+    )
+    # Dense B-row GEMM and single-row replay can differ by float32 rounding;
+    # cache vs its own replay remains bitwise inside the production helper.
+    torch.testing.assert_close(scores, result.scores, rtol=2e-6, atol=2e-7)
+    torch.testing.assert_close(torch.tensor(result.loss), loss.detach(), rtol=2e-6, atol=2e-7)
+    assert result.parent_count == 4 and result.retrieval_caption_count == 5
+    assert result.verified_counterfactual_count == 0 and result.replayed_captures == 4
+    assert _stable_hash(_capture_rng()) == _stable_hash(after)
+    for p, q in zip(model.parameters(), dense.parameters(), strict=True):
+        assert p.grad is not None and q.grad is not None
+        # The dense full-batch graph accumulates contributions in reverse
+        # graph order; replay accumulates canonical parent rows in forward
+        # order. Only this independent accumulation oracle has a tolerance.
+        torch.testing.assert_close(p.grad, q.grad, rtol=3e-5, atol=3e-6)
+
+
+def test_every_window_checkpoint_receives_explicit_model_device_tensor(monkeypatch):
+    import phaseset_core.continuous_base_retrieval as module
+
+    model = ContinuousBaseRetrievalSystem(
+        BaseRetrievalSystem(_AnalyticWindowBase(), embedding_dim=512)
+    )
+    actual_checkpoint, witnessed = module.checkpoint, []
+
+    def checked(function, *args, **settings):
+        # CPU qualification verifies the actual invocation contract, not CUDA
+        # numerical correctness. A later admitted CUDA all-grad check remains
+        # required; parameters hidden solely in a closure do not stash its RNG.
+        assert args and type(args[0]) is nn.Parameter
+        assert args[0] is model.base.logit_scale
+        assert args[0].device == next(model.parameters()).device
+        assert settings == {"use_reentrant": False, "preserve_rng_state": True}
+        witnessed.append(args[0].device)
+        return actual_checkpoint(function, *args, **settings)
+
+    monkeypatch.setattr(module, "checkpoint", checked)
+    model.encode_capture(_capture()).square().sum().backward()
+    assert len(witnessed) == 2
+
+
+@pytest.mark.parametrize("stop", [0, 1, 2, 4])
+def test_base_host_actual_updates_and_resume_are_bitwise(tmp_path, stop):
+    model, task, source, config, bindings = _setup(tmp_path)
+    initial = copy.deepcopy(model)
+    full = ContinuousParentTrainingHost(model, task, source, config, bindings).fit(
+        tmp_path / "full"
+    )
+    interrupted = ContinuousParentTrainingHost(
+        copy.deepcopy(initial), task, source, config, bindings
+    ).fit(tmp_path / "interrupted", stop_after_steps=stop)
+    resumed_model = copy.deepcopy(initial)
+    resumed = ContinuousParentTrainingHost(resumed_model, task, source, config, bindings).fit(
+        tmp_path / "resumed",
+        resume_checkpoint=interrupted.latest_checkpoint.path,
+        resume_sha256=interrupted.latest_checkpoint.sha256,
+    )
+    assert full.outcome == resumed.outcome == "COMPLETED"
+    assert (
+        full.parents_seen == resumed.parents_seen == 8
+        and full.global_step == resumed.global_step == 4
+    )
+    assert full.validation_history == resumed.validation_history
+    assert any(
+        not torch.equal(p, initial.state_dict()[name]) for name, p in model.state_dict().items()
+    )
+    expected, _ = _load_torch_checkpoint(full.latest_checkpoint.path)
+    actual, _ = _load_torch_checkpoint(resumed.latest_checkpoint.path)
+    for key in ("model", "optimizer", "scheduler", "rng"):
+        assert _stable_hash(actual[key]) == _stable_hash(expected[key])
+    assert expected["manifest"]["frozen_base_state_sha256"] is None
+    assert expected["manifest"]["config"]["stage"] == "base"
+    assert (
+        json.loads((tmp_path / "full" / "terminal.json").read_text())["formal_authority"] is False
+    )
+
+
+def test_base_host_stage_checkpoint_and_cf_contracts_are_explicit(tmp_path):
+    model, task, source, config, bindings = _setup(tmp_path)
+    defaults = ParentHostConfig.for_base(1729, ("C01",), ("C03",))
+    assert defaults.epochs == 30 and defaults.learning_rate == 2e-4 and defaults.cf_weight == 0
+    for bad_config, bad_bindings in (
+        (replace(config, stage="residual"), bindings),
+        (config, replace(bindings, frozen_base_checkpoint_sha256=_key("unexpected"))),
+    ):
+        with pytest.raises(ValueError):
+            ContinuousParentTrainingHost(model, task, source, bad_config, bad_bindings)
+    labels = task.batch(tuple(row.annotation_family_sha256 for row in task.parents[:2]))
+    text, _ = source.text(labels, training=True)
+    cf = ParentCounterfactualRows((labels.motion_source_keys[0],), (0,), (2,), (False,))
+    for pair, weight in ((_empty_cf(), 0.2), (cf, 0)):
+        with pytest.raises(ValueError, match="no CF"):
+            backward_loaded_parent_batch(
+                model,
+                labels,
+                text,
+                load_view=lambda key: source.captures[key],
+                counterfactuals=pair,
+                cf_weight=weight,
+                margin=0,
+            )
+    initial = copy.deepcopy(model)
+    result = ContinuousParentTrainingHost(model, task, source, config, bindings).fit(
+        tmp_path / "first", stop_after_steps=0
+    )
+    initial.checkpoint_windows = False
+    with pytest.raises(TrainingCheckpointError, match="drifted"):
+        ContinuousParentTrainingHost(initial, task, source, config, bindings).fit(
+            tmp_path / "drift",
+            resume_checkpoint=result.latest_checkpoint.path,
+            resume_sha256=result.latest_checkpoint.sha256,
+        )

From 2b66e0795f8f224d90b32733c2ea7c043211c6da Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 03:24:21 -0500
Subject: [PATCH 42/95] feat: preserve complete TMR VAE and training objective

---
 LICENSE_TMR.md                      |  28 +++
 RELEASE_FILES.sha256                |   6 +-
 docs/TMR_COMPONENTS.md              |  44 ++++
 pyproject.toml                      |   1 +
 src/phaseset_core/tmr_components.py | 335 ++++++++++++++++++++++++++++
 tests/test_tmr_components.py        | 187 ++++++++++++++++
 6 files changed, 600 insertions(+), 1 deletion(-)
 create mode 100644 LICENSE_TMR.md
 create mode 100644 docs/TMR_COMPONENTS.md
 create mode 100644 src/phaseset_core/tmr_components.py
 create mode 100644 tests/test_tmr_components.py

diff --git a/LICENSE_TMR.md b/LICENSE_TMR.md
new file mode 100644
index 0000000..3b80d28
--- /dev/null
+++ b/LICENSE_TMR.md
@@ -0,0 +1,28 @@
+# TMR components attribution
+
+The ACTOR-style components and objective in
+`src/phaseset_core/tmr_components.py` are adapted from
+[Mathux/TMR](https://github.com/Mathux/TMR/tree/6d74688730d15d43b0a755ce2b0e1f2d76138fc1).
+No pretrained weights, datasets, or third-party paper PDFs are included.
+
+MIT License
+
+Copyright (c) 2023 Mathis Petrovich
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index b093bdd..1ce5a4f 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -7,6 +7,7 @@ e9938bfe4bbd1ee2357396d1a6a8788e511389e297c28d7247d9e16a9a8137fc	392	.gitignore
 34ad6f564d314e436426109fb8e7e67a2473e8dedf200d45a766d27d030cd080	780	CITATION.cff
 a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTING.md
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
+761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
 1cb23b08139df6d86e581e23468210a6edbc42da72d2498fd1448a48b7773619	10220	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
@@ -77,6 +78,7 @@ a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASE
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
+c2c47d122f77ff5976d4f148a1a39c3089e78c47213b071427fd203e638e676c	2807	docs/TMR_COMPONENTS.md
 8d689f49b2704ff7c5988eff8a7e3e062f209b85d18425ccdba1e1099581079b	10658	docs/phasepair_research_contract_20260824_165840.md
 8d4b03326029b2a06478295b7328d61181d022604b1465bccd3a6cfcc8ce9704	2155	figures/EDITABLE_FIGURES.json
 efb48515925d5412bc840a5ae07415cda4645591deaa1ab6aca90e1e4f528591	4208	figures/README.md
@@ -95,7 +97,7 @@ a43fbeb379f7357af12deddc8b837f81218716ea30428ca320bfd5467968ca6e	209581	figures/
 e6589c0e35da06b3b7b8f72c3fbacc99eaa44adf6be9c20465b4cb03fe3961a8	514458	paper/main.pdf
 21f99fb638bce1d6451bf58fa75ce01a7f0d081c8573c4d82aaf1ec502c33f50	21449	paper/main.tex
 7db5537b9c85545e3fff89b29e17438936f0bcfb3e7d1ee2cd8de24f4de1a279	8419	paper/references.bib
-a5b896aed4121191b56294a34d40fa25fcb70238f9d9596a6c12ad7d5ebc6b03	1317	pyproject.toml
+f19916a3def23408fe1dc7d2b47fc8409ca644332f600f4e62805825d1e15157	1363	pyproject.toml
 a54cab7df5f76e32387eba55757a4bd8fa18d9f2cdc8e5b0c6725ceb00af1e78	9580	scripts/audit_all_refs.py
 b4184b377fc1dbde1bf4a14ff002c5b13be09dbe6a545cee30eb878ee017fdd3	13507	scripts/audit_release_artifact.py
 f6447b2870b24c657e3ca6f18d7330834826b6e5ec284392d94a82d25f5b180e	4422	scripts/build_paper.py
@@ -194,6 +196,7 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
 5604397a65a0032d2a14124d34ca3d96a2d6d67e5f4534732bb82f7a0c7b2546	23163	src/phaseset_core/temporal_coordination.py
+47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
@@ -304,4 +307,5 @@ a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/tes
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
 8727e87f1a0d44db6a39b1e5cfd8e22e3bed05544ddaed07b66b759adee66d84	5587	tests/test_release_tree_batch.py
 eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
+96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
 3aef8f056ef38ecbe5af51c283285b132db97f175c181018d8a4278d82131915	8150	tests/test_wheel_index_binding.py
diff --git a/docs/TMR_COMPONENTS.md b/docs/TMR_COMPONENTS.md
new file mode 100644
index 0000000..d15498c
--- /dev/null
+++ b/docs/TMR_COMPONENTS.md
@@ -0,0 +1,44 @@
+# TMR implementation components — not a trained TMR-Set result
+
+The [component implementation](../src/phaseset_core/tmr_components.py) follows
+the [official pinned TMR source](https://github.com/Mathux/TMR/tree/6d74688730d15d43b0a755ce2b0e1f2d76138fc1),
+under its [MIT license](../LICENSE_TMR.md). Defaults are the actual
+training YAML: latent 256, FFN 1024, six layers, four heads, dropout 0.1, GELU.
+The complete objective keeps both SmoothL1 motion reconstructions, all four
+element-mean KL divergences, sampled latent SmoothL1 alignment and filtered
+contrastive loss. Weights are 1, 1e-5, 1e-5, 0.1; temperature is 0.1.
+
+The encoder prepends two learned distribution tokens. The decoder attends to
+one sampled latent using zero time queries plus sinusoidal positions. Retrieval
+uses distribution means. The caller supplies frozen DistilBERT token features
+(default dimension 768), and separate normalized MPNet sentence features for
+negative filtering; global CLIP vectors are not substituted for either.
+
+The original diagonal, one-caption objective is available through
+`TMRSinglePerson`. This is an architecture/loss equivalence tool, not evidence
+that the original dataset preprocessing or converged original-task replication
+has been completed. It does not implement the group decoder, language asset
+loader, complete-parent training host, sampler, or pilot admission.
+
+Two explicit extensions are provided for later adaptation:
+
+- `filtered_contrastive_loss` accepts a rectangular known-positive matrix. A
+  motion–text negative is filtered when that text exceeds cosine **0.6** against
+  any known caption of that motion (official threshold 0.8 maps to 2t−1).
+  Known positives are always preserved. Similarity never creates a positive.
+  With a diagonal positive matrix this reduces to the original filter and
+  symmetric cross-entropy, up to frozen floating-point tolerance.
+- Reconstruction defaults to the original mean over the padded target tensor.
+  An explicit feature-level `observed` mask instead computes the mean only over
+  observed values, preserving genuine capture gaps. This changes normalization
+  and must be disclosed/frozen for the group adapter; gaps are not zero-valued
+  reconstruction targets. No set reconstruction assignment is implied here.
+
+Position capacity defaults to the upstream 5000. Longer sequences require an
+explicit larger `max_len` using the same analytic encoding; there is no silent
+truncation. Encoder capacity includes its two prefix tokens. Dense attention
+over whole native parents has not been profiled or approved for training.
+
+Training-only pool selection remains the host's responsibility. Semantic
+filtering must not inspect held-out/test captions and must not be applied to the
+study's common human-only full-gallery retrieval metrics.
diff --git a/pyproject.toml b/pyproject.toml
index e4bcfbf..dc61913 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,7 @@ description = "Permutation-invariant periodic relation tokens for multi-person m
 readme = "README.md"
 requires-python = ">=3.12,<3.15"
 license = "MIT"
+license-files = ["LICENSE", "LICENSE_TMR.md"]
 authors = [{ name = "PhaseSet contributors" }]
 dependencies = [
   "numpy==2.4.6",
diff --git a/src/phaseset_core/tmr_components.py b/src/phaseset_core/tmr_components.py
new file mode 100644
index 0000000..f9f11fe
--- /dev/null
+++ b/src/phaseset_core/tmr_components.py
@@ -0,0 +1,335 @@
+"""TMR's ACTOR VAE and complete objective, not yet a TMR-Set baseline.
+
+Adapted from Mathux/TMR at 6d74688730d15d43b0a755ce2b0e1f2d76138fc1
+(MIT, copyright 2023 Mathis Petrovich; see LICENSE_TMR.md).
+Defaults follow the training YAML, not the upstream class defaults. Frozen
+DistilBERT token features and separate normalized MPNet sentence features must
+be supplied by the caller. No language weights, split access, or optimizer live
+here. The rectangular multi-positive/filter extension is explicitly documented
+in docs/TMR_COMPONENTS.md; it does not create new ground-truth positives.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import math
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+Gaussian = tuple[Tensor, Tensor]
+
+
+class PositionalEncoding(nn.Module):
+    """Original analytic sin/cos encoding with an explicit length bound."""
+
+    def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):
+        super().__init__()
+        if d_model < 2 or d_model % 2 or max_len < 1:
+            raise ValueError("an even positive dimension and positive max_len are required")
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        self.register_buffer("pe", pe.unsqueeze(1), persistent=False)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x: Tensor) -> Tensor:
+        if x.shape[1] > self.pe.shape[0]:
+            raise ValueError("sequence exceeds explicit positional bound; never truncate it")
+        return self.dropout(x + self.pe.permute(1, 0, 2)[:, : x.shape[1]])
+
+
+def _sequence(x: Tensor, mask: Tensor, nfeats: int) -> None:
+    if x.ndim != 3 or x.shape[-1] != nfeats or mask.shape != x.shape[:2]:
+        raise ValueError("expected [batch,time,features] and matching [batch,time] mask")
+    if mask.dtype != torch.bool or mask.device != x.device or not bool(mask.any(1).all()):
+        raise ValueError("every sequence needs at least one valid position on its device")
+
+
+class ACTORStyleEncoder(nn.Module):
+    """Two distribution tokens, six-layer temporal Transformer, shared weights."""
+
+    def __init__(
+        self,
+        nfeats: int,
+        *,
+        latent_dim: int = 256,
+        ff_size: int = 1024,
+        num_layers: int = 6,
+        num_heads: int = 4,
+        dropout: float = 0.1,
+        max_len: int = 5000,
+    ):
+        super().__init__()
+        self.nfeats = nfeats
+        self.projection = nn.Linear(nfeats, latent_dim)
+        self.tokens = nn.Parameter(torch.randn(2, latent_dim))
+        self.sequence_pos_encoding = PositionalEncoding(latent_dim, dropout, max_len)
+        layer = nn.TransformerEncoderLayer(
+            latent_dim,
+            num_heads,
+            ff_size,
+            dropout,
+            activation="gelu",
+            batch_first=True,
+        )
+        self.seqTransEncoder = nn.TransformerEncoder(layer, num_layers)
+
+    def forward(self, x: Tensor, mask: Tensor) -> Tensor:
+        _sequence(x, mask, self.nfeats)
+        x = self.projection(x)
+        tokens = self.tokens.unsqueeze(0).expand(len(x), -1, -1)
+        xseq = torch.cat((tokens, x), 1)
+        token_mask = torch.ones((len(x), 2), dtype=torch.bool, device=x.device)
+        aug_mask = torch.cat((token_mask, mask), 1)
+        encoded = self.seqTransEncoder(
+            self.sequence_pos_encoding(xseq),
+            src_key_padding_mask=~aug_mask,
+        )
+        return encoded[:, :2]
+
+
+class ACTORStyleDecoder(nn.Module):
+    """Single latent memory and zero time queries plus analytic positions."""
+
+    def __init__(
+        self,
+        nfeats: int,
+        *,
+        latent_dim: int = 256,
+        ff_size: int = 1024,
+        num_layers: int = 6,
+        num_heads: int = 4,
+        dropout: float = 0.1,
+        max_len: int = 5000,
+    ):
+        super().__init__()
+        self.nfeats = nfeats
+        self.sequence_pos_encoding = PositionalEncoding(latent_dim, dropout, max_len)
+        layer = nn.TransformerDecoderLayer(
+            latent_dim,
+            num_heads,
+            ff_size,
+            dropout,
+            activation="gelu",
+            batch_first=True,
+        )
+        self.seqTransDecoder = nn.TransformerDecoder(layer, num_layers)
+        self.final_layer = nn.Linear(latent_dim, nfeats)
+
+    def forward(self, z: Tensor, mask: Tensor) -> Tensor:
+        if z.ndim != 2 or mask.ndim != 2 or len(z) != len(mask):
+            raise ValueError("expected [batch,latent] and [batch,time] mask")
+        if mask.dtype != torch.bool or mask.device != z.device or not bool(mask.any(1).all()):
+            raise ValueError("every decoder sequence needs at least one valid position")
+        queries = torch.zeros(len(z), mask.shape[1], z.shape[1], device=z.device, dtype=z.dtype)
+        output = self.seqTransDecoder(
+            tgt=self.sequence_pos_encoding(queries),
+            memory=z[:, None],
+            tgt_key_padding_mask=~mask,
+        )
+        return self.final_layer(output).masked_fill(~mask[..., None], 0.0)
+
+
+def sample_diagonal_gaussian(distribution: Gaussian, *, sample_mean: bool = False) -> Tensor:
+    mu, logvar = distribution
+    if sample_mean:
+        return mu
+    # Keep upstream TEMOS's operation order, not the algebraically equivalent
+    # exp(logvar / 2): this also makes its RNG/gradient oracle meaningful.
+    std = logvar.exp().pow(0.5)
+    return mu + torch.empty_like(std).normal_() * std
+
+
+def diagonal_kl(q: Gaussian, p: Gaussian) -> Tensor:
+    """Upstream KLLoss's element mean, not a sum over latent coordinates."""
+    mu_q, logvar_q = q
+    mu_p, logvar_p = p
+    log_ratio = logvar_q - logvar_p
+    return (0.5 * (log_ratio.exp() + (mu_p - mu_q).pow(2) / logvar_p.exp() - 1 - log_ratio)).mean()
+
+
+def reconstruction_loss(
+    predicted: Tensor, target: Tensor, observed: Tensor | None = None
+) -> Tensor:
+    """Original padded mean by default; explicit observed-only extension for gaps."""
+    if predicted.shape != target.shape:
+        raise ValueError("reconstruction must have exactly the target shape")
+    if observed is None:
+        return F.smooth_l1_loss(predicted, target)
+    if (
+        observed.dtype != torch.bool
+        or observed.shape != target.shape
+        or observed.device != target.device
+    ):
+        raise ValueError("observed must be a feature-level boolean mask matching the target")
+    if not bool(observed.any()):
+        raise ValueError("cannot reconstruct a sequence without observed features")
+    return F.smooth_l1_loss(predicted[observed], target[observed])
+
+
+def tmr_auxiliary_losses(
+    *,
+    text_reconstruction: Tensor,
+    motion_reconstruction: Tensor,
+    target: Tensor,
+    text_latent: Tensor,
+    motion_latent: Tensor,
+    text_distribution: Gaussian,
+    motion_distribution: Gaussian,
+    observed: Tensor | None = None,
+) -> dict[str, Tensor]:
+    """Both reconstructions, all four KLs, and the sampled latent alignment."""
+    if text_latent.shape != motion_latent.shape:
+        raise ValueError("auxiliary pairs must be aligned before computing TMR losses")
+    reference = (torch.zeros_like(motion_distribution[0]), torch.zeros_like(motion_distribution[1]))
+    return {
+        "recons": reconstruction_loss(text_reconstruction, target, observed)
+        + reconstruction_loss(motion_reconstruction, target, observed),
+        "kl": diagonal_kl(text_distribution, motion_distribution)
+        + diagonal_kl(motion_distribution, text_distribution)
+        + diagonal_kl(motion_distribution, reference)
+        + diagonal_kl(text_distribution, reference),
+        "latent": F.smooth_l1_loss(text_latent, motion_latent),
+    }
+
+
+def semantic_negative_mask(
+    positive: Tensor, sentence_embeddings: Tensor, threshold: float
+) -> Tensor:
+    """Remove similar negatives only; never turn similarity into a positive label.
+
+    For a motion with multiple known captions, use maximum similarity against
+    those captions. Diagonal single-positive batches reduce to TMR's filter.
+    These frozen sentence features must come only from the active learning pool.
+    """
+    if not 0 < threshold <= 1:
+        raise ValueError("the enabled TMR threshold must be in (0,1]")
+    if sentence_embeddings.ndim != 2 or len(sentence_embeddings) != positive.shape[1]:
+        raise ValueError("need one separate sentence-similarity embedding per text row")
+    sentence_embeddings = sentence_embeddings.detach().to(device=positive.device)
+    if not bool(torch.isfinite(sentence_embeddings).all()):
+        raise ValueError("sentence embeddings must be finite")
+    norms = sentence_embeddings.norm(dim=1)
+    if not bool(torch.allclose(norms, torch.ones_like(norms), rtol=1e-4, atol=1e-5)):
+        raise ValueError("TMR sentence-similarity embeddings must already be normalized")
+    similar = sentence_embeddings @ sentence_embeddings.T > (2 * threshold - 1)
+    filtered = (positive.to(torch.float32) @ similar.to(torch.float32)) > 0
+    return filtered & ~positive
+
+
+def filtered_contrastive_loss(
+    motion_latent: Tensor,
+    text_latent: Tensor,
+    positive: Tensor,
+    *,
+    sentence_embeddings: Tensor | None = None,
+    temperature: float = 0.1,
+    threshold_selfsim: float = 0.8,
+) -> Tensor:
+    """Variable-positive symmetric InfoNCE with TMR negative filtering."""
+    if not math.isfinite(temperature) or temperature <= 0:
+        raise ValueError("temperature must be finite and positive")
+    if (
+        motion_latent.ndim != 2
+        or text_latent.ndim != 2
+        or motion_latent.shape[1] != text_latent.shape[1]
+    ):
+        raise ValueError("motion and text need compatible latent matrices")
+    if positive.dtype != torch.bool or positive.shape != (len(motion_latent), len(text_latent)):
+        raise ValueError("positive must be a [motion,text] boolean matrix")
+    if positive.device != motion_latent.device or text_latent.device != motion_latent.device:
+        raise ValueError("latents and positive matrix must share a device")
+    if not bool(positive.any(0).all()) or not bool(positive.any(1).all()):
+        raise ValueError("each motion and text needs at least one known positive")
+    logits = F.normalize(motion_latent, dim=-1) @ F.normalize(text_latent, dim=-1).T / temperature
+    if sentence_embeddings is not None and threshold_selfsim:
+        logits = logits.masked_fill(
+            semantic_negative_mask(positive, sentence_embeddings, threshold_selfsim),
+            -torch.inf,
+        )
+    positives = logits.masked_fill(~positive, -torch.inf)
+    return 0.5 * (
+        (torch.logsumexp(logits, 1) - torch.logsumexp(positives, 1)).mean()
+        + (torch.logsumexp(logits, 0) - torch.logsumexp(positives, 0)).mean()
+    )
+
+
+@dataclass(frozen=True)
+class TMRLossWeights:
+    recons: float = 1.0
+    latent: float = 1e-5
+    kl: float = 1e-5
+    contrastive: float = 0.1
+
+    def total(self, losses: dict[str, Tensor]) -> Tensor:
+        return sum(
+            getattr(self, key) * losses[key] for key in ("recons", "kl", "latent", "contrastive")
+        )
+
+
+class TMRSinglePerson(nn.Module):
+    """Full original-task VAE forward/objective, not the multi-person adapter.
+
+    Input features remain caller-defined (the original task's motion feature
+    preprocessing is not replaced with skeleton flattening here). Text input is
+    token-level [B,L,768] by default, not a CLIP sentence embedding. This module
+    is useful for implementation equivalence checks, not a completed replication.
+    """
+
+    def __init__(self, motion_nfeats: int, text_nfeats: int = 768, **actor_kwargs):
+        super().__init__()
+        self.motion_encoder = ACTORStyleEncoder(motion_nfeats, **actor_kwargs)
+        self.text_encoder = ACTORStyleEncoder(text_nfeats, **actor_kwargs)
+        self.motion_decoder = ACTORStyleDecoder(motion_nfeats, **actor_kwargs)
+        self.loss_weights = TMRLossWeights()
+
+    def encode(self, x: Tensor, mask: Tensor, *, modality: str, sample_mean: bool = True):
+        if modality not in ("text", "motion"):
+            raise ValueError("modality must be text or motion")
+        encoder = self.text_encoder if modality == "text" else self.motion_encoder
+        encoded = encoder(x, mask)
+        distribution = (encoded[:, 0], encoded[:, 1])
+        return sample_diagonal_gaussian(distribution, sample_mean=sample_mean), distribution
+
+    def compute_loss(
+        self,
+        motion: Tensor,
+        motion_mask: Tensor,
+        text_tokens: Tensor,
+        text_mask: Tensor,
+        sentence_embeddings: Tensor,
+        *,
+        observed: Tensor | None = None,
+    ) -> dict[str, Tensor]:
+        if len(motion) != len(text_tokens):
+            raise ValueError("original-task auxiliary pairs require equal batch sizes")
+        # Preserve upstream stochastic execution order: text first, motion next.
+        text_z, text_dist = self.encode(text_tokens, text_mask, modality="text", sample_mean=False)
+        text_motion = self.motion_decoder(text_z, motion_mask)
+        motion_z, motion_dist = self.encode(
+            motion, motion_mask, modality="motion", sample_mean=False
+        )
+        motion_motion = self.motion_decoder(motion_z, motion_mask)
+        losses = tmr_auxiliary_losses(
+            text_reconstruction=text_motion,
+            motion_reconstruction=motion_motion,
+            target=motion,
+            text_latent=text_z,
+            motion_latent=motion_z,
+            text_distribution=text_dist,
+            motion_distribution=motion_dist,
+            observed=observed,
+        )
+        positive = torch.eye(len(motion), dtype=torch.bool, device=motion.device)
+        losses["contrastive"] = filtered_contrastive_loss(
+            motion_z,
+            text_z,
+            positive,
+            sentence_embeddings=sentence_embeddings,
+        )
+        losses["loss"] = self.loss_weights.total(losses)
+        return losses
diff --git a/tests/test_tmr_components.py b/tests/test_tmr_components.py
new file mode 100644
index 0000000..a5a789a
--- /dev/null
+++ b/tests/test_tmr_components.py
@@ -0,0 +1,187 @@
+"""Data-free implementation checks; no dataset/replication/pilot authority."""
+
+import pytest
+import torch
+from torch.nn import functional as F
+
+from phaseset_core.tmr_components import (
+    ACTORStyleDecoder,
+    ACTORStyleEncoder,
+    PositionalEncoding,
+    TMRLossWeights,
+    TMRSinglePerson,
+    diagonal_kl,
+    filtered_contrastive_loss,
+    reconstruction_loss,
+    sample_diagonal_gaussian,
+    semantic_negative_mask,
+    tmr_auxiliary_losses,
+)
+
+
+def test_default_actual_yaml_architecture_and_all_gradients():
+    torch.manual_seed(1729)
+    model = TMRSinglePerson(12)
+    assert model.text_encoder.projection.in_features == 768
+    for encoder in (model.text_encoder, model.motion_encoder):
+        assert encoder.tokens.shape == (2, 256)
+        assert len(encoder.seqTransEncoder.layers) == 6
+        assert encoder.seqTransEncoder.layers[0].self_attn.num_heads == 4
+        assert encoder.seqTransEncoder.layers[0].linear1.out_features == 1024
+        assert encoder.seqTransEncoder.layers[0].dropout.p == 0.1
+    assert len(model.motion_decoder.seqTransDecoder.layers) == 6
+    motion = torch.randn(2, 7, 12)
+    mask = torch.ones(2, 7, dtype=torch.bool)
+    mask[1, -2:] = False
+    motion[~mask] = 0
+    text = torch.randn(2, 5, 768)
+    text_mask = torch.ones(2, 5, dtype=torch.bool)
+    sentence = torch.eye(2)
+    losses = model.compute_loss(motion, mask, text, text_mask, sentence)
+    assert set(losses) == {"recons", "kl", "latent", "contrastive", "loss"}
+    assert all(torch.isfinite(value) and value > 0 for value in losses.values())
+    losses["loss"].backward()
+    assert all(
+        parameter.grad is not None and torch.isfinite(parameter.grad).all()
+        for parameter in model.parameters()
+    )
+    assert model.motion_encoder.projection.weight.grad.abs().sum() > 0
+    assert model.text_encoder.projection.weight.grad.abs().sum() > 0
+    assert model.motion_decoder.final_layer.weight.grad.abs().sum() > 0
+
+
+def test_four_kls_two_reconstructions_and_weights_match_independent_formula():
+    torch.manual_seed(2718)
+    mu_m, mu_t = torch.randn(3, 8), torch.randn(3, 8)
+    lv_m, lv_t = torch.randn(3, 8) * 0.2, torch.randn(3, 8) * 0.2
+    z_m, z_t = torch.randn(3, 8), torch.randn(3, 8)
+    target, rec_m, rec_t = (torch.randn(3, 9, 4) for _ in range(3))
+    got = tmr_auxiliary_losses(
+        text_reconstruction=rec_t,
+        motion_reconstruction=rec_m,
+        target=target,
+        text_latent=z_t,
+        motion_latent=z_m,
+        text_distribution=(mu_t, lv_t),
+        motion_distribution=(mu_m, lv_m),
+    )
+    qm = torch.distributions.Normal(mu_m, (lv_m / 2).exp())
+    qt = torch.distributions.Normal(mu_t, (lv_t / 2).exp())
+    unit = torch.distributions.Normal(torch.zeros_like(mu_m), torch.ones_like(mu_m))
+    expected_kl = sum(
+        torch.distributions.kl_divergence(q, p).mean()
+        for q, p in ((qt, qm), (qm, qt), (qm, unit), (qt, unit))
+    )
+    torch.testing.assert_close(got["kl"], expected_kl, rtol=1e-6, atol=1e-6)
+    assert got["recons"] == F.smooth_l1_loss(rec_t, target) + F.smooth_l1_loss(rec_m, target)
+    assert got["latent"] == F.smooth_l1_loss(z_t, z_m)
+    got["contrastive"] = torch.tensor(3.0)
+    expected = got["recons"] + 1e-5 * got["kl"] + 1e-5 * got["latent"] + 0.1 * got["contrastive"]
+    assert TMRLossWeights().total(got) == expected
+    assert diagonal_kl((mu_m, lv_m), (mu_m, lv_m)) == 0
+
+
+def test_diagonal_filter_is_original_symmetric_cross_entropy_and_gradients():
+    torch.manual_seed(31415)
+    motion = torch.randn(3, 8, requires_grad=True)
+    text = torch.randn(3, 8, requires_grad=True)
+    # Off-diagonal cosine .7 is above the actual .6, but below misread .8.
+    sentences = torch.tensor([[1.0, 0, 0], [0.7, (1 - 0.7**2) ** 0.5, 0], [0, 0, 1.0]])
+    positive = torch.eye(3, dtype=torch.bool)
+    filtered = semantic_negative_mask(positive, sentences, 0.8)
+    assert filtered[0, 1] and filtered[1, 0] and not filtered.diagonal().any()
+    logits = F.normalize(motion, dim=1) @ F.normalize(text, dim=1).T / 0.1
+    logits = logits.masked_fill(filtered, -torch.inf)
+    expected = (
+        F.cross_entropy(logits, torch.arange(3)) + F.cross_entropy(logits.T, torch.arange(3))
+    ) / 2
+    got = filtered_contrastive_loss(motion, text, positive, sentence_embeddings=sentences)
+    torch.testing.assert_close(got, expected, rtol=1e-6, atol=1e-6)
+    actual_grad = torch.autograd.grad(got, (motion, text), retain_graph=True)
+    expected_grad = torch.autograd.grad(expected, (motion, text))
+    for actual, reference in zip(actual_grad, expected_grad, strict=True):
+        torch.testing.assert_close(actual, reference, rtol=2e-6, atol=1e-6)
+
+
+def test_rectangular_variable_positives_are_preserved_and_filter_is_negative_only():
+    motion = torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True)
+    text = torch.tensor([[1.0, 0.0], [0.9, 0.1], [0.0, 1.0]], requires_grad=True)
+    positive = torch.tensor([[True, True, False], [False, False, True]])
+    sentences = F.normalize(
+        torch.tensor([[1.0, 0.0], [0.9, 0.1], [0.8, 0.2]]), dim=1
+    ).requires_grad_()
+    filtered = semantic_negative_mask(positive, sentences, 0.8)
+    assert not (filtered & positive).any()
+    assert filtered[0, 2] and filtered[1, 0] and filtered[1, 1]
+    got = filtered_contrastive_loss(motion, text, positive, sentence_embeddings=sentences)
+    assert got == 0 and torch.isfinite(got)
+    got.backward()
+    assert torch.isfinite(motion.grad).all() and torch.isfinite(text.grad).all()
+    assert sentences.grad is None
+    unfiltered = filtered_contrastive_loss(motion.detach(), text.detach(), positive)
+    logits = F.normalize(motion.detach(), dim=1) @ F.normalize(text.detach(), dim=1).T / 0.1
+    expected = 0.5 * (
+        (
+            torch.logsumexp(logits, 1)
+            - torch.logsumexp(logits.masked_fill(~positive, -torch.inf), 1)
+        ).mean()
+        + (
+            torch.logsumexp(logits, 0)
+            - torch.logsumexp(logits.masked_fill(~positive, -torch.inf), 0)
+        ).mean()
+    )
+    assert unfiltered == expected and unfiltered > 0
+
+
+def test_gap_mask_excludes_unknown_targets_and_their_gradients():
+    prediction = torch.tensor([[[2.0, 999.0], [3.0, -999.0]]], requires_grad=True)
+    target = torch.tensor([[[1.0, float("nan")], [1.0, float("nan")]]])
+    observed = torch.tensor([[[True, False], [True, False]]])
+    got = reconstruction_loss(prediction, target, observed)
+    assert got == 1.0
+    got.backward()
+    assert torch.equal(prediction.grad[~observed], torch.zeros(2))
+    with pytest.raises(ValueError, match="without observed"):
+        reconstruction_loss(prediction, target, torch.zeros_like(observed))
+
+
+def test_extended_positions_keep_original_prefix_and_do_not_truncate():
+    short = PositionalEncoding(256, dropout=0)
+    extended = PositionalEncoding(256, dropout=0, max_len=6002)
+    assert torch.equal(short.pe, extended.pe[:5000])
+    assert extended(torch.zeros(1, 6002, 256)).shape == (1, 6002, 256)
+    with pytest.raises(ValueError, match="never truncate"):
+        short(torch.zeros(1, 5001, 256))
+    assert "pe" not in extended.state_dict()
+
+
+def test_decoder_padding_and_sample_mean_are_exact():
+    torch.manual_seed(1729)
+    decoder = ACTORStyleDecoder(4, latent_dim=16, ff_size=32, num_layers=1, dropout=0)
+    encoder = ACTORStyleEncoder(4, latent_dim=16, ff_size=32, num_layers=1, dropout=0)
+    mask = torch.tensor([[True, True, False]])
+    encoded = encoder(torch.randn(1, 3, 4), mask)
+    dist = (encoded[:, 0], encoded[:, 1])
+    state = torch.get_rng_state()
+    z = sample_diagonal_gaussian(dist, sample_mean=True)
+    assert z is dist[0] and torch.equal(state, torch.get_rng_state())
+    decoded = decoder(z, mask)
+    assert torch.equal(decoded[~mask], torch.zeros(1, 4))
+    assert not torch.signbit(decoded[~mask]).any()
+    with pytest.raises(ValueError, match="valid position"):
+        decoder(z, torch.zeros_like(mask))
+
+
+@pytest.mark.parametrize("failure", ["empty_positive", "unnormalized_sentence", "temperature"])
+def test_invalid_loss_inputs_are_not_silently_changed(failure):
+    x = torch.eye(2)
+    positive = torch.eye(2, dtype=torch.bool)
+    kwargs = {}
+    if failure == "empty_positive":
+        positive[0] = False
+    elif failure == "unnormalized_sentence":
+        kwargs["sentence_embeddings"] = x * 2
+    else:
+        kwargs["temperature"] = 0
+    with pytest.raises(ValueError):
+        filtered_contrastive_loss(x, x, positive, **kwargs)

From 1b885b7570e63f396e7c780e6c302f42b62c2951 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 04:20:42 -0500
Subject: [PATCH 43/95] feat: implement complete-parent TMR-Set VAE and pinned
 language

---
 README.md                         |  11 +-
 RELEASE_FILES.sha256              |  15 +-
 docs/TMR_SET_ADAPTATION.md        |  66 ++++++
 pyproject.toml                    |   3 +
 src/phaseset_core/production.py   |  11 +
 src/phaseset_core/tmr_language.py | 131 +++++++++++
 src/phaseset_core/tmr_set.py      | 360 ++++++++++++++++++++++++++++++
 tests/test_phaseset_host.py       |   4 +-
 tests/test_phaseset_production.py |  10 +
 tests/test_tmr_language.py        |  73 ++++++
 tests/test_tmr_set.py             | 232 +++++++++++++++++++
 11 files changed, 907 insertions(+), 9 deletions(-)
 create mode 100644 docs/TMR_SET_ADAPTATION.md
 create mode 100644 src/phaseset_core/tmr_language.py
 create mode 100644 src/phaseset_core/tmr_set.py
 create mode 100644 tests/test_tmr_language.py
 create mode 100644 tests/test_tmr_set.py

diff --git a/README.md b/README.md
index f435124..ca01e83 100644
--- a/README.md
+++ b/README.md
@@ -47,13 +47,18 @@ phase, time-varying actor–edge incidence, chronological capture readout, and
 verified counterfactual coordination supervision. The
 [87-stage matrix](configs/phaseset_v2_experiment_matrix.json) supersedes the
 affected legacy 33-run plan. New modules are independent research APIs; the
-formal V2 host runner and literature reproductions are not yet implemented.
+production V2 training-host integration and converged literature reproductions
+are not yet qualified. [TMR-Set](docs/TMR_SET_ADAPTATION.md) now implements the
+complete original VAE objectives with disclosed full-parent group adaptation;
+its pinned frozen language loader is not a substitute for native learning.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
 There are still **0/87 formal V2 stages** and no native-data retrieval result.
-Missing approved main data, licensed body-model assets, exact handoff fold
-assignments, and human relational verification are explicit dependencies.
+Approved main-data and licensed body-model assets have been obtained privately,
+and the handoff fold assignments have been recovered. Whole-parent preprocessing,
+run-specific admission, faithful baseline training, resource profiling, and real
+human relational verification still require actual execution evidence.
 The descriptions and score equation below document the preserved V1 core,
 not completed V2 experiments or a submission-ready paper.
 
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 1ce5a4f..372fdca 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-1cb23b08139df6d86e581e23468210a6edbc42da72d2498fd1448a48b7773619	10220	README.md
+a554d46f303322f7c1426f5017c6e3993737f8560016fbd4be310fbcf9f641ad	10621	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -79,6 +79,7 @@ a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPA
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
 c2c47d122f77ff5976d4f148a1a39c3089e78c47213b071427fd203e638e676c	2807	docs/TMR_COMPONENTS.md
+a83d6c241bce7e0adb2add20b4d48247be03a2dda7b3d2cd8b9a40171e5bb23f	4424	docs/TMR_SET_ADAPTATION.md
 8d689f49b2704ff7c5988eff8a7e3e062f209b85d18425ccdba1e1099581079b	10658	docs/phasepair_research_contract_20260824_165840.md
 8d4b03326029b2a06478295b7328d61181d022604b1465bccd3a6cfcc8ce9704	2155	figures/EDITABLE_FIGURES.json
 efb48515925d5412bc840a5ae07415cda4645591deaa1ab6aca90e1e4f528591	4208	figures/README.md
@@ -97,7 +98,7 @@ a43fbeb379f7357af12deddc8b837f81218716ea30428ca320bfd5467968ca6e	209581	figures/
 e6589c0e35da06b3b7b8f72c3fbacc99eaa44adf6be9c20465b4cb03fe3961a8	514458	paper/main.pdf
 21f99fb638bce1d6451bf58fa75ce01a7f0d081c8573c4d82aaf1ec502c33f50	21449	paper/main.tex
 7db5537b9c85545e3fff89b29e17438936f0bcfb3e7d1ee2cd8de24f4de1a279	8419	paper/references.bib
-f19916a3def23408fe1dc7d2b47fc8409ca644332f600f4e62805825d1e15157	1363	pyproject.toml
+3c86acb1ec972ef65b2dd2e950bd617a027802a91b44cc8a8ff64906ea6ac538	1408	pyproject.toml
 a54cab7df5f76e32387eba55757a4bd8fa18d9f2cdc8e5b0c6725ceb00af1e78	9580	scripts/audit_all_refs.py
 b4184b377fc1dbde1bf4a14ff002c5b13be09dbe6a545cee30eb878ee017fdd3	13507	scripts/audit_release_artifact.py
 f6447b2870b24c657e3ca6f18d7330834826b6e5ec284392d94a82d25f5b180e	4422	scripts/build_paper.py
@@ -190,13 +191,15 @@ e69b0d27d479b4499bae73c96f7854f9d07539043a0b66f0f75f6bc3e23c1f51	55415	src/phase
 a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phaseset_core/pipeline.py
 5bd46d884f0fa3b8f022d1684ab5ffa1799ded3b970649753b0dd0510350216d	47614	src/phaseset_core/prepared_data_v2.py
 273225e6696351ccb16a3a9e7a653b55af18c64b316a4e5d80c7b265625cc5c1	17929	src/phaseset_core/preprocessing.py
-044b381ba73e73f476918d353f29c7cbee38cdca32ee612826549fef60671bdc	73146	src/phaseset_core/production.py
+ad2ce56325f09d2efb17847ef3f947535f81f7099019275fdd074c12efeeb559	73588	src/phaseset_core/production.py
 6f60feada83ebf1a55635bb657a5a3998e9fc77fa85012aad46e4f8d90d344db	37785	src/phaseset_core/publication.py
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
 5604397a65a0032d2a14124d34ca3d96a2d6d67e5f4534732bb82f7a0c7b2546	23163	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
+cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phaseset_core/tmr_language.py
+b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
@@ -277,7 +280,7 @@ e79f2cb54fc18bccef9b94adeaf8c1811b913d8ba2a22f77e4f35ad9669ad6db	1324	tests/test
 dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/test_phaseset_experiments.py
 04df5b4a8d75bed006f16b56910e4082d4104a45cc8aeb11d6932ede645777cb	14111	tests/test_phaseset_frozen_clip_cache_rehydrate.py
 937a48b65ab6fa2e91239b823ef528edb0af143f30c9c1c5298c3fc01e3c5327	18396	tests/test_phaseset_frozen_clip_text.py
-4ce41d44f43de7278260ac1b44f032d99f58d2bc125255d2b70865d9f46d7e2c	18644	tests/test_phaseset_host.py
+6d738b5ab6f2bdda6d5bdca2604105c84a688d5adf4daa16cb761b96f2fe04cd	18798	tests/test_phaseset_host.py
 841bb5bd72b98885cdc50c95a92022bacd5d9ba63f9f3776ccaaf9df06ac6de6	62702	tests/test_phaseset_host_base_qualification.py
 14f6d4b54d78876822e6f96440a32360ea4d4654c1d18d8d8662fe0f4dc42bf8	23309	tests/test_phaseset_host_capture_storage.py
 6eb892149a9b3dee02f48e816acfe0026a0efaed8985a32eedf412bad1edd141	11008	tests/test_phaseset_host_intent_binding.py
@@ -291,7 +294,7 @@ e55318c9bb4b265a1c174f20dacf3590f2743ea74ca4f6f4694770eb249f8dc5	5351	tests/test
 82c15cd86cd1ac918d4cec678814ad61509ae7405173a73871c2f2172e82faeb	17328	tests/test_phaseset_prepared_data_v2.py
 aae917cd95bde322f4ec7cea693fd5d0c926d51f0a366e7be160426472ad7973	15770	tests/test_phaseset_prepared_host_integration.py
 f6837c3ac23f4209cb3ec3eadb899d0bdcd4b4259c901b021254ffe7b3688492	8482	tests/test_phaseset_preprocessing.py
-64016f9369540dae8b87654f43d09df9b9c07a3c4af06fa45567008b57140193	45251	tests/test_phaseset_production.py
+7b6fc1ff9ddab99a41fc38fd7a44b21bfb51ff6057b9d8b8c227244ba83de52b	45585	tests/test_phaseset_production.py
 a1fbdfe55e50f67bb6568d5eba276e24beeb5a44373c516f0bd7758bd5742127	12486	tests/test_phaseset_publication.py
 44552bf3de846bc0ad98e4e4185cc9a19ae2f3172ac8047f4875644afb1010d5	10587	tests/test_phaseset_residual_cached_encode.py
 005a91d472a715e98b3a733cde102ecffc70222e1572d6100bb18f87ce9e3361	33889	tests/test_phaseset_residual_resume.py
@@ -308,4 +311,6 @@ a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/tes
 8727e87f1a0d44db6a39b1e5cfd8e22e3bed05544ddaed07b66b759adee66d84	5587	tests/test_release_tree_batch.py
 eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
 96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
+726ee82009ecb29ce87208749fb1780365640a52f2e2d6e55399e22119838b89	2988	tests/test_tmr_language.py
+2ae860859c1f9282a02cea929e54fa950433ac8fba81c7510c2b900321d5ff29	9606	tests/test_tmr_set.py
 3aef8f056ef38ecbe5af51c283285b132db97f175c181018d8a4278d82131915	8150	tests/test_wheel_index_binding.py
diff --git a/docs/TMR_SET_ADAPTATION.md b/docs/TMR_SET_ADAPTATION.md
new file mode 100644
index 0000000..c870aef
--- /dev/null
+++ b/docs/TMR_SET_ADAPTATION.md
@@ -0,0 +1,66 @@
+# Full-parent TMR-Set adaptation
+
+[The implementation](../src/phaseset_core/tmr_set.py) retains the
+[qualified TMR components](TMR_COMPONENTS.md), including the actual six-layer
+ACTOR VAE, two reconstructions, four KL terms, sampled-latent alignment and
+semantic-negative-filtered contrastive training. It is explicitly a group
+adaptation, not the original single-person TMR or a converged replication.
+
+Each actor's body22 coordinates use the existing capture-wide shared origin.
+A shared ACTOR encoder processes each accepted window; unordered PMA pools its
+actor states. A second ACTOR encoder reads every window summary in chronological
+order, with its absolute start time. Real gaps remain gaps: later windows are
+not reassigned earlier timestamps. Text uses the original 768D DistilBERT token
+features and separate normalized MPNet sentence features, not sentence CLIP.
+Group, text and decoder defaults are 256D, FFN 1024, six layers, four heads,
+dropout 0.1. The capture hierarchy/PMA are additional disclosed capacity.
+
+[The language loader](../src/phaseset_core/tmr_language.py) checks every
+tokenizer/config/checkpoint file against pinned public digests before loading
+from private directories, with local-only safetensors and no remote code.
+DistilBERT is fixed at `12040accade4e8a0f71eabdb258fecc2e7e948be`; MPNet at
+`e8c3b32edf5434bc2275fc9bab85f82640a19130`. Both are Apache-2.0 pretrained
+assets. The latter uses the upstream attention-masked mean plus L2 normalization.
+Language models stay frozen/eval even when the trainable group model is trained.
+Any caption exceeding the actual token bound fails explicitly instead of being
+silently truncated. No dataset, model weights, download receipts or private
+asset paths are packaged. Asset download alone is not runtime qualification.
+
+Both sampled motion and text latents reconstruct the entire observed group.
+The original six-layer decoder is reused over bounded windows, conditioned on
+the global latent, absolute window time, and analytic output-slot codes. These
+are **prediction slots**, not embeddings of input actor ordinal/identity.
+There is no learned finite actor-slot bank or maximum semantic K. Known K sets
+the auxiliary output cardinality only; K is not appended to retrieval features.
+
+For each reconstruction, SmoothL1 costs are accumulated over every window and
+observed body coordinate. A single minimum-cost slot-to-track assignment is
+then solved over the entire parent. This one assignment prevents a decoder from
+changing a slot's actor identity at each window to lower its reconstruction
+loss. The selected summed loss is divided by the parent's total observed
+coordinate count, retaining both reconstruction branches and ignoring gaps.
+No first actor is chosen as the target. Matching uses a K-by-K scalar table and
+O(K^3) Hungarian solver; no dense K-by-K-by-time feature tensor is allocated.
+The matching cost computation is O(K^2 T); these costs must be profiled.
+
+Known captions remain variable in number. Reconstruction/KL/latent terms are
+averaged within each parent's captions, then across parents, so caption-rich
+parents do not multiply auxiliary weight. The full rectangular contrastive
+gallery preserves all known positives; sentence similarity only filters
+training negatives against that run's learning captions, not evaluation labels.
+Retrieval uses distribution means and the common unfiltered human-only gallery.
+
+Actor commitment bytes canonicalize calculation and tie-breaking order only;
+they are never embedded. Shared input encoders and invariant set pooling do
+not use actor ordinal. Whole-window encoder and cost checkpointing preserve
+dropout/sampling RNG and pass explicit on-device tensors for CUDA discovery.
+Capture inputs themselves are not O(1) memory; no such claim is made.
+
+`TMRGroupCapture.from_prepared` includes every accepted window of the supplied
+admitted complete capture. A hand-built window tuple is a software seam, not
+proof of native provenance/completeness. Runtime caller responsibilities still
+include fixed asset hashes, correct body/text provenance, split/budget/seed
+admission, full-parent optimizer/checkpoint/resume host, and actual hardware
+qualification. GPU/BF16, native learning, convergence, and fair bounded pilot
+evidence are not granted by component tests. Temporal decoder conditioning and
+whole-track assignment are adaptations, not a claim of upstream bitwise identity.
diff --git a/pyproject.toml b/pyproject.toml
index dc61913..a5ca605 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,6 +34,9 @@ clip = [
   "requests==2.34.2",
   "transformers==4.57.3",
 ]
+tmr-language = [
+  "transformers==4.57.3",
+]
 embody = [
   "smplx==0.1.28",
 ]
diff --git a/src/phaseset_core/production.py b/src/phaseset_core/production.py
index 018341e..ab65ec1 100644
--- a/src/phaseset_core/production.py
+++ b/src/phaseset_core/production.py
@@ -138,6 +138,17 @@
     for name, value in sorted(vars(module).items())
     if isinstance(value, types.FunctionType) and value.__module__ == module.__name__
 )
+# Python 3.12 lazily adds this interpreter metadata on ordinary annotation
+# inspection. Materialize it before the census so introspection is not mistaken
+# for an injected semantic global; its binding remains protected like all others.
+for _semantic_module in (
+    evaluation_module,
+    statistics_module,
+    experiments_module,
+    publication_module,
+):
+    vars(_semantic_module).setdefault("__annotations__", {})
+del _semantic_module
 _SEMANTIC_MODULE_GLOBAL_NAME_CENSUS = tuple(
     (module, frozenset(vars(module)))
     for module in (
diff --git a/src/phaseset_core/tmr_language.py b/src/phaseset_core/tmr_language.py
new file mode 100644
index 0000000..be8eca2
--- /dev/null
+++ b/src/phaseset_core/tmr_language.py
@@ -0,0 +1,131 @@
+"""Pinned, frozen, local-only language assets used by the TMR adaptation.
+
+Token features use DistilBERT; semantic negative filtering uses a separate
+masked-mean, normalized MPNet representation, as in the upstream TextToEmb.
+No CLIP substitution, remote code, network download, or quiet truncation.
+"""
+
+from __future__ import annotations
+
+import hashlib
+from pathlib import Path
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .tmr_set import TMRTextBatch
+
+DISTILBERT_REVISION = "12040accade4e8a0f71eabdb258fecc2e7e948be"
+MPNET_REVISION = "e8c3b32edf5434bc2275fc9bab85f82640a19130"
+# Public checkpoint/file digests independently obtained from the official Hub.
+# Model bytes remain in the caller's private asset directories, never the wheel.
+DISTILBERT_FILES = {
+    "LICENSE": "43070e2d4e532684de521b885f385d0841030efa2b1a20bafb76133a5e1379c1",
+    "README.md": "b925a22d8efd7a29608a0e10d3f2fb33d5d80df7de4ac6e34093853cd8e0274e",
+    "config.json": "69c94b0222d5d1f4b0ad027ca7416cdafb98378cbbb8305d0bf47c9365c60c83",
+    "model.safetensors": "5e3f1108e3cb34ee048634875d8482665b65ac713291a7e32396fb18f6ff0063",
+    "tokenizer.json": "ce64fce797c24f68df90b40a3f74f579b336a493db14bd583fd520ea0d8c9a98",
+    "tokenizer_config.json": "a025160ef0431f1a392f6f050c1310f4c5d9fb6f275932dbccba73c4d214bf10",
+    "vocab.txt": "07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3",
+}
+MPNET_FILES = {
+    "README.md": "89a1a9c3290fe58e76c939b578c48a14331dc7bfcaaf5a53102adb183da6f96a",
+    "config.json": "d46a3e04ded82bba22528424480697d394eeda6a27484e08c5bb2bdf5906cfa0",
+    "model.safetensors": "78c0197b6159d92658e319bc1d72e4c73a9a03dd03815e70e555c5ef05615658",
+    "special_tokens_map.json": "9ef40e9c160511bf3f46ceb71f1471dafa1e9473d5120bb816c36b2efa75f8ba",
+    "tokenizer.json": "b8be2c30ba5dd723a6d5ee26d013da103d5408d92ddcb23747622f9e48f1d842",
+    "tokenizer_config.json": "67f2ff7e223518e729869bb3a70f0caf8368fe549383fc11cfe2dfb42fffc268",
+    "vocab.txt": "dbd90cb94e2247bd4d4ccaecbf616d2290e66691d7d5e5bb81f063c2d0649ada",
+}
+
+
+def verify_asset_directory(directory: Path, expected: dict[str, str]) -> None:
+    """Validate the actual tokenizer/config/checkpoint bytes before loading."""
+    if not directory.is_dir():
+        raise ValueError("private pinned language directory is missing")
+    actual = {path.name for path in directory.iterdir() if path.is_file()}
+    if actual != set(expected):
+        raise ValueError("language asset file roster differs from the pinned snapshot")
+    for name, digest in expected.items():
+        with (directory / name).open("rb") as stream:
+            actual_digest = hashlib.file_digest(stream, "sha256").hexdigest()
+        if actual_digest != digest:
+            raise ValueError(f"pinned language asset digest mismatch: {name}")
+
+
+def tokenize_without_truncation(tokenizer, model, captions: tuple[str, ...]):
+    if not captions or any(type(text) is not str or not text.strip() for text in captions):
+        raise ValueError("language input needs nonempty, unmodified human text rows")
+    encoded = tokenizer(list(captions), return_tensors="pt", padding=True, truncation=False)
+    # MPNet has 514 positional entries but padding-offset positions leave only
+    # 512 usable tokens; the pinned tokenizer supplies that actual input bound.
+    bound = min(tokenizer.model_max_length, model.config.max_position_embeddings)
+    if encoded["input_ids"].shape[1] > bound:
+        raise ValueError("caption exceeds the pinned language token bound; never truncate")
+    return encoded
+
+
+class FrozenTMRLanguage(nn.Module):
+    """Actual frozen DistilBERT tokens plus MPNet, with an explicit asset factory."""
+
+    @classmethod
+    def from_private_assets(cls, distilbert: Path, mpnet: Path, *, device="cpu"):
+        verify_asset_directory(distilbert, DISTILBERT_FILES)
+        verify_asset_directory(mpnet, MPNET_FILES)
+        from transformers import AutoModel, AutoTokenizer
+
+        def load(path):
+            tokenizer = AutoTokenizer.from_pretrained(
+                path, local_files_only=True, trust_remote_code=False
+            )
+            model = AutoModel.from_pretrained(
+                path, local_files_only=True, trust_remote_code=False, use_safetensors=True
+            )
+            if model.config.hidden_size != 768:
+                raise ValueError("pinned TMR language width must remain 768")
+            return tokenizer, model
+
+        dt, dm = load(distilbert)
+        mt, mm = load(mpnet)
+        return cls(dt, dm, mt, mm).to(device)
+
+    def __init__(self, distilbert_tokenizer, distilbert_model, mpnet_tokenizer, mpnet_model):
+        super().__init__()
+        # Direct construction is a test seam, not evidence of pinned assets.
+        self.distilbert_tokenizer = distilbert_tokenizer
+        self.distilbert_model = distilbert_model
+        self.mpnet_tokenizer = mpnet_tokenizer
+        self.mpnet_model = mpnet_model
+        self.requires_grad_(False)
+        self.train(False)
+
+    def train(self, mode: bool = True):
+        return super().train(False)
+
+    @torch.no_grad()
+    def forward(self, captions: tuple[str, ...], *, microbatch: int = 16) -> TMRTextBatch:
+        if type(microbatch) is not int or microbatch < 1:
+            raise ValueError("language microbatch must be a positive integer")
+        distilbert = tokenize_without_truncation(
+            self.distilbert_tokenizer, self.distilbert_model, captions
+        )
+        mpnet = tokenize_without_truncation(self.mpnet_tokenizer, self.mpnet_model, captions)
+        device = next(self.distilbert_model.parameters()).device
+        tokens, sentences = [], []
+        for start in range(0, len(captions), microbatch):
+            di = {
+                key: value[start : start + microbatch].to(device)
+                for key, value in distilbert.items()
+            }
+            mi = {key: value[start : start + microbatch].to(device) for key, value in mpnet.items()}
+            tokens.append(self.distilbert_model(**di).last_hidden_state)
+            hidden = self.mpnet_model(**mi).last_hidden_state
+            expanded = mi["attention_mask"].unsqueeze(-1).expand(hidden.size()).float()
+            pooled = (hidden * expanded).sum(1) / expanded.sum(1).clamp(min=1e-9)
+            sentences.append(F.normalize(pooled, p=2, dim=1))
+        return TMRTextBatch(
+            torch.cat(tokens).to(torch.float32),
+            distilbert["attention_mask"].to(device=device, dtype=torch.bool),
+            torch.cat(sentences).to(torch.float32),
+        )
diff --git a/src/phaseset_core/tmr_set.py b/src/phaseset_core/tmr_set.py
new file mode 100644
index 0000000..18d4d16
--- /dev/null
+++ b/src/phaseset_core/tmr_set.py
@@ -0,0 +1,360 @@
+"""Explicit full-parent TMR-Set adaptation with track-consistent set reconstruction.
+
+TMR's actual VAE encoders/decoder and all auxiliary objectives are retained.
+Shared per-actor/window encoding, set PMA, full-window temporal hierarchy, and
+dynamic output slots are disclosed adaptations, not the original single-person
+architecture. Slot-to-track assignment is solved once over the entire parent,
+never independently at each window. No actor ordinal enters the encoder.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import math
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+from torch.utils.checkpoint import checkpoint
+
+from .continuous_capture import PreparedContinuousCapture
+from .models import PMAPool
+from .tmr_components import (
+    ACTORStyleDecoder,
+    ACTORStyleEncoder,
+    Gaussian,
+    TMRLossWeights,
+    diagonal_kl,
+    filtered_contrastive_loss,
+    sample_diagonal_gaussian,
+)
+
+
+@dataclass(frozen=True)
+class TMRGroupWindow:
+    features: Tensor  # [K,<=200,66], shared capture origin, not per-actor centering
+    observed: Tensor  # same shape, feature-level observation mask
+    actor_commitments: tuple[bytes, ...]  # lineage/reduction ordering only
+    start_seconds: float
+
+    def __post_init__(self):
+        x = self.features
+        if x.dtype != torch.float32 or x.ndim != 3 or x.shape[0] < 2 or x.shape[2] != 66:
+            raise ValueError("TMR-Set windows need float32 [K>=2,T,66] body22 coordinates")
+        if not 1 <= x.shape[1] <= 200 or not bool(torch.isfinite(x).all()):
+            raise ValueError("window must contain 1..200 finite frames; never truncate")
+        if (
+            self.observed.dtype != torch.bool
+            or self.observed.shape != x.shape
+            or self.observed.device != x.device
+        ):
+            raise ValueError("observed must match every body coordinate")
+        if not bool(self.observed.flatten(1).any(1).all()):
+            raise ValueError("every actor needs observed coordinates")
+        unobserved = x[~self.observed]
+        if bool((unobserved != 0).any()) or bool(torch.signbit(unobserved).any()):
+            raise ValueError("unobserved coordinates must be exact +0")
+        if len(self.actor_commitments) != len(x) or len(set(self.actor_commitments)) != len(x):
+            raise ValueError("actor lineage must uniquely cover every actor")
+        if any(type(key) is not bytes or len(key) != 32 for key in self.actor_commitments):
+            raise ValueError("actor commitments must be bytes32, not model features")
+        if not math.isfinite(self.start_seconds) or self.start_seconds < 0:
+            raise ValueError("absolute window start must be finite and nonnegative")
+
+    def canonical(self, device: torch.device) -> tuple[Tensor, Tensor]:
+        order = sorted(range(len(self.actor_commitments)), key=self.actor_commitments.__getitem__)
+        index = torch.tensor(order, dtype=torch.int64, device=self.features.device)
+        return (
+            self.features.index_select(0, index).to(device),
+            self.observed.index_select(0, index).to(device),
+        )
+
+
+@dataclass(frozen=True)
+class TMRGroupCapture:
+    windows: tuple[TMRGroupWindow, ...]
+
+    def __post_init__(self):
+        if not self.windows:
+            raise ValueError("a complete parent needs all its accepted windows")
+        actors = set(self.windows[0].actor_commitments)
+        starts = tuple(window.start_seconds for window in self.windows)
+        if starts != tuple(sorted(set(starts))):
+            raise ValueError("windows must preserve strict absolute chronology including gaps")
+        if any(set(window.actor_commitments) != actors for window in self.windows):
+            raise ValueError("track membership cannot change between windows")
+
+    @classmethod
+    def from_prepared(cls, capture: PreparedContinuousCapture) -> TMRGroupCapture:
+        if type(capture) is not PreparedContinuousCapture:
+            raise ValueError("need an admitted complete prepared parent")
+        return cls(
+            tuple(
+                TMRGroupWindow(
+                    torch.from_numpy(window.skeletons.copy()).flatten(2),
+                    torch.from_numpy(window.track_mask.copy())
+                    .unsqueeze(-1)
+                    .expand(-1, -1, -1, 3)
+                    .flatten(2),
+                    window.actor_commitments,
+                    window.source_start_frame / 30.0,
+                )
+                for window in capture.windows()
+            )
+        )
+
+
+@dataclass(frozen=True)
+class TMRTextBatch:
+    tokens: Tensor  # frozen DistilBERT [Q,L,768]
+    token_mask: Tensor
+    sentences: Tensor  # separate frozen normalized MPNet [Q,768]
+
+    def __post_init__(self):
+        if self.tokens.ndim != 3 or self.tokens.shape[2] != 768 or len(self.tokens) < 1:
+            raise ValueError("TMR requires token-level [Q,L,768] language features")
+        if self.token_mask.dtype != torch.bool or self.token_mask.shape != self.tokens.shape[:2]:
+            raise ValueError("text token mask must cover the token sequence")
+        if not bool(self.token_mask.any(1).all()):
+            raise ValueError("every text row needs a valid token")
+        if self.sentences.shape != (len(self.tokens), 768):
+            raise ValueError("one separate 768D MPNet sentence embedding is needed per row")
+        for values in (self.tokens, self.sentences):
+            if (
+                values.dtype != torch.float32
+                or values.requires_grad
+                or not bool(torch.isfinite(values).all())
+            ):
+                raise ValueError("pretrained language features must be frozen finite float32")
+
+
+def absolute_sincos(positions: Tensor, width: int) -> Tensor:
+    if width % 2:
+        raise ValueError("sin/cos width must be even")
+    scales = torch.exp(
+        torch.arange(0, width, 2, device=positions.device, dtype=torch.float32)
+        * (-math.log(10000.0) / width)
+    )
+    angles = positions.to(torch.float32)[:, None] * scales
+    return torch.stack((angles.sin(), angles.cos()), dim=-1).flatten(1)
+
+
+def minimum_assignment(cost: Tensor) -> tuple[int, ...]:
+    """Exact square Hungarian assignment (O(K^3)), lowest-index tie breaking.
+
+    Only a K-by-K scalar cost table is materialized; no K-by-K-by-time tensor.
+    The discrete choice uses detached costs. Gradients flow through selected
+    costs, as in a standard set reconstruction objective, not the argmin.
+    """
+    if cost.ndim != 2 or cost.shape[0] != cost.shape[1] or len(cost) < 1:
+        raise ValueError("matching needs one nonempty square cost table")
+    if not bool(torch.isfinite(cost).all()):
+        raise ValueError("nonfinite reconstruction costs cannot be assigned")
+    values = cost.detach().to(device="cpu", dtype=torch.float64).tolist()
+    count = len(values)
+    u, v = [0.0] * (count + 1), [0.0] * (count + 1)
+    p, way = [0] * (count + 1), [0] * (count + 1)
+    for row in range(1, count + 1):
+        p[0] = row
+        current = 0
+        minimum = [math.inf] * (count + 1)
+        used = [False] * (count + 1)
+        while True:
+            used[current] = True
+            active_row = p[current]
+            delta, following = math.inf, 0
+            for column in range(1, count + 1):
+                if not used[column]:
+                    reduced = values[active_row - 1][column - 1] - u[active_row] - v[column]
+                    if reduced < minimum[column]:
+                        minimum[column], way[column] = reduced, current
+                    if minimum[column] < delta:
+                        delta, following = minimum[column], column
+            for column in range(count + 1):
+                if used[column]:
+                    u[p[column]] += delta
+                    v[column] -= delta
+                else:
+                    minimum[column] -= delta
+            current = following
+            if p[current] == 0:
+                break
+        while current:
+            previous = way[current]
+            p[current] = p[previous]
+            current = previous
+    assignment = [0] * count
+    for column in range(1, count + 1):
+        assignment[p[column] - 1] = column - 1
+    return tuple(assignment)
+
+
+def track_costs(prediction: Tensor, target: Tensor, observed: Tensor) -> Tensor:
+    """Per-slot/track unnormalized loss sums, without dense pair/frame features."""
+    if prediction.shape != target.shape or observed.shape != target.shape:
+        raise ValueError("slots/targets/masks must share [K,T,66] shape")
+    columns = []
+    for actor in range(len(target)):
+        element = F.smooth_l1_loss(
+            prediction, target[actor].expand_as(prediction), reduction="none"
+        )
+        columns.append(element.masked_fill(~observed[actor][None], 0).to(torch.float64).sum((1, 2)))
+    return torch.stack(columns, dim=1)
+
+
+class TMRSet(nn.Module):
+    """Trainable full-parent set VAE, with meaningful text-to-group reconstruction.
+
+    Six-layer original ACTOR blocks remain 256D/1024FFN/4heads/.1. The extra
+    capture ACTOR hierarchy sees every accepted window and its absolute time.
+    Reconstructed slot count comes from observed K only for the auxiliary
+    decoder; no ordinal, commitment, or count feature is appended to retrieval.
+    Caller still owns split, asset/provenance, seed, and training-budget admission.
+    """
+
+    system_id = "TMR-Set"
+
+    def __init__(
+        self,
+        *,
+        latent_dim: int = 256,
+        ff_size: int = 1024,
+        num_layers: int = 6,
+        num_heads: int = 4,
+        dropout: float = 0.1,
+        checkpoint_windows: bool = True,
+    ):
+        super().__init__()
+        self.latent_dim = latent_dim
+        self.checkpoint_windows = checkpoint_windows
+        kwargs = dict(
+            latent_dim=latent_dim,
+            ff_size=ff_size,
+            num_layers=num_layers,
+            num_heads=num_heads,
+            dropout=dropout,
+        )
+        self.actor_encoder = ACTORStyleEncoder(66, **kwargs)
+        self.actor_pool = PMAPool(latent_dim, num_heads, ff_size, dropout)
+        self.capture_encoder = ACTORStyleEncoder(latent_dim, **kwargs)
+        self.text_encoder = ACTORStyleEncoder(768, **kwargs)
+        self.motion_decoder = ACTORStyleDecoder(66, **kwargs)
+        self.slot_projection = nn.Sequential(nn.Linear(latent_dim, latent_dim), nn.GELU())
+        self.time_projection = nn.Sequential(nn.Linear(latent_dim, latent_dim), nn.GELU())
+        self.loss_weights = TMRLossWeights()
+
+    def encode_capture(self, capture: TMRGroupCapture) -> Gaussian:
+        device = self.actor_encoder.tokens.device
+        summaries = []
+        for window in capture.windows:
+
+            def encode(witness, window=window):
+                x, observed = window.canonical(witness.device)
+                actors = self.actor_encoder(x, observed.any(-1))[:, 0]
+                return self.actor_pool(
+                    actors[None], torch.ones(1, len(actors), device=device, dtype=torch.bool)
+                )[0]
+
+            summaries.append(
+                checkpoint(
+                    encode, self.actor_encoder.tokens, use_reentrant=False, preserve_rng_state=True
+                )
+                if self.checkpoint_windows and torch.is_grad_enabled()
+                else encode(self.actor_encoder.tokens)
+            )
+        times = torch.tensor([window.start_seconds for window in capture.windows], device=device)
+        sequence = torch.stack(summaries) + absolute_sincos(times, self.latent_dim)
+        encoded = self.capture_encoder(
+            sequence[None], torch.ones(1, len(sequence), device=device, dtype=torch.bool)
+        )
+        return encoded[0, 0], encoded[0, 1]
+
+    def reconstruct_capture(self, z: Tensor, capture: TMRGroupCapture) -> Tensor:
+        actor_count = len(capture.windows[0].actor_commitments)
+        slot_positions = torch.arange(actor_count, dtype=torch.float32, device=z.device)
+        slot_context = self.slot_projection(absolute_sincos(slot_positions, self.latent_dim))
+        costs = torch.zeros(actor_count, actor_count, dtype=torch.float64, device=z.device)
+        observed_count = 0
+        for window in capture.windows:
+            # Bind the window and count now; checkpoint replay must not use the
+            # last loop iteration. Explicit z discovers CUDA's RNG device.
+            def decode(latent, slots, window=window):
+                target, observed = window.canonical(latent.device)
+                time = torch.tensor([window.start_seconds], device=latent.device)
+                memory = (
+                    latent[None]
+                    + slots
+                    + self.time_projection(absolute_sincos(time, self.latent_dim))
+                )
+                # Slot masks cannot borrow a particular target track's missing
+                # pattern before matching. Decode all positions, then mask cost.
+                mask = torch.ones(
+                    actor_count, target.shape[1], device=latent.device, dtype=torch.bool
+                )
+                return track_costs(self.motion_decoder(memory, mask), target, observed)
+
+            costs = costs + (
+                checkpoint(decode, z, slot_context, use_reentrant=False, preserve_rng_state=True)
+                if self.checkpoint_windows and torch.is_grad_enabled()
+                else decode(z, slot_context)
+            )
+            observed_count += int(window.observed.sum().item())
+        assignment = torch.tensor(minimum_assignment(costs), device=z.device, dtype=torch.int64)
+        selected = costs[torch.arange(actor_count, device=z.device), assignment].sum()
+        return (selected / observed_count).to(torch.float32)
+
+    def text_distributions(self, text: TMRTextBatch) -> Gaussian:
+        device = self.text_encoder.tokens.device
+        encoded = self.text_encoder(text.tokens.to(device), text.token_mask.to(device))
+        return encoded[:, 0], encoded[:, 1]
+
+    def score(self, captures: tuple[TMRGroupCapture, ...], text: TMRTextBatch) -> Tensor:
+        motion = torch.stack([self.encode_capture(capture)[0] for capture in captures])
+        text_mu, _ = self.text_distributions(text)
+        return F.normalize(motion, dim=1) @ F.normalize(text_mu, dim=1).T / 0.1
+
+    def compute_loss(
+        self, captures: tuple[TMRGroupCapture, ...], text: TMRTextBatch, positive: Tensor
+    ) -> dict[str, Tensor]:
+        device = self.text_encoder.tokens.device
+        positive = positive.to(device)
+        if positive.dtype != torch.bool or positive.shape != (len(captures), len(text.tokens)):
+            raise ValueError("known positives must match complete parents and all human text rows")
+        if not bool(positive.any(0).all()) or not bool(positive.any(1).all()):
+            raise ValueError("each parent/text must have a known positive")
+        text_dist = self.text_distributions(text)
+        text_z = sample_diagonal_gaussian(text_dist)
+        motion_rows = [self.encode_capture(capture) for capture in captures]
+        motion_dist = tuple(torch.stack([row[index] for row in motion_rows]) for index in (0, 1))
+        motion_z = sample_diagonal_gaussian(motion_dist)
+        recons, kls, latents = [], [], []
+        for row, capture in enumerate(captures):
+            indices = positive[row].nonzero().flatten()
+            # Each parent's captions receive equal weight within that parent;
+            # caption-rich parents do not multiply their auxiliary weight.
+            text_recons = torch.stack(
+                [self.reconstruct_capture(text_z[index], capture) for index in indices]
+            ).mean()
+            recons.append(text_recons + self.reconstruct_capture(motion_z[row], capture))
+            td = tuple(part[indices] for part in text_dist)
+            md = tuple(part[row].expand_as(td[index]) for index, part in enumerate(motion_dist))
+            unit = (torch.zeros_like(md[0]), torch.zeros_like(md[1]))
+            kls.append(
+                diagonal_kl(td, md)
+                + diagonal_kl(md, td)
+                + diagonal_kl(md, unit)
+                + diagonal_kl(td, unit)
+            )
+            latents.append(
+                F.smooth_l1_loss(text_z[indices], motion_z[row].expand_as(text_z[indices]))
+            )
+        losses = {
+            "recons": torch.stack(recons).mean(),
+            "kl": torch.stack(kls).mean(),
+            "latent": torch.stack(latents).mean(),
+            "contrastive": filtered_contrastive_loss(
+                motion_z, text_z, positive, sentence_embeddings=text.sentences.to(device)
+            ),
+        }
+        losses["loss"] = self.loss_weights.total(losses)
+        return losses
diff --git a/tests/test_phaseset_host.py b/tests/test_phaseset_host.py
index b57fe21..71fe168 100644
--- a/tests/test_phaseset_host.py
+++ b/tests/test_phaseset_host.py
@@ -366,7 +366,9 @@ def test_attempt_lease_releases_only_after_owned_child_exits(tmp_path: Path) ->
         text=True,
     )
     try:
-        deadline = time.monotonic() + 10.0
+        # A cold Torch import on a shared Windows runner can exceed 10s. This
+        # bounds process startup, not the live-lease exclusion check below.
+        deadline = time.monotonic() + 30.0
         while not ready.is_file() and time.monotonic() < deadline:
             if child.poll() is not None:
                 _, stderr = child.communicate(timeout=1.0)
diff --git a/tests/test_phaseset_production.py b/tests/test_phaseset_production.py
index 50f3185..a8d6ed0 100644
--- a/tests/test_phaseset_production.py
+++ b/tests/test_phaseset_production.py
@@ -1017,6 +1017,16 @@ def execute(self, intent: execution.CommandIntent) -> production.BackendExecutio
     assert backend.publication_auth_calls == 1
 
 
+def test_annotation_introspection_does_not_inject_a_semantic_global():
+    import inspect
+
+    for module in (evaluation, statistics, experiments, publication):
+        before = set(vars(module))
+        inspect.get_annotations(module)
+        assert set(vars(module)) == before
+    production._assert_semantic_function_integrity()
+
+
 def test_semantic_helper_monkeypatch_is_rejected_before_recomputation(
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
diff --git a/tests/test_tmr_language.py b/tests/test_tmr_language.py
new file mode 100644
index 0000000..4ad2a5e
--- /dev/null
+++ b/tests/test_tmr_language.py
@@ -0,0 +1,73 @@
+"""Software seams only; real pinned language outputs are qualified privately."""
+
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from phaseset_core.tmr_language import (
+    FrozenTMRLanguage,
+    tokenize_without_truncation,
+    verify_asset_directory,
+)
+
+
+class AnalyticTokenizer:
+    model_max_length = 8
+
+    def __call__(self, text, **kwargs):
+        assert kwargs == dict(return_tensors="pt", padding=True, truncation=False)
+        lengths = [len(item.split()) + 2 for item in text]
+        mask = torch.arange(max(lengths))[None] < torch.tensor(lengths)[:, None]
+        return {"input_ids": mask.to(torch.int64), "attention_mask": mask.to(torch.int64)}
+
+
+class AnalyticLanguage(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.arange(768).float())
+        self.dropout = torch.nn.Dropout(0.9)
+        self.config = SimpleNamespace(max_position_embeddings=10)
+
+    def forward(self, input_ids, attention_mask):
+        return SimpleNamespace(
+            last_hidden_state=self.dropout(input_ids[..., None] + self.weight[None, None])
+        )
+
+
+def test_actual_asset_roster_and_digest_are_required(tmp_path):
+    import hashlib
+
+    path = tmp_path / "config.json"
+    path.write_bytes(b"analytic software fixture, never a language model")
+    expected = {path.name: hashlib.sha256(path.read_bytes()).hexdigest()}
+    verify_asset_directory(tmp_path, expected)
+    with pytest.raises(ValueError, match="digest"):
+        verify_asset_directory(tmp_path, {path.name: "0" * 64})
+    with pytest.raises(ValueError, match="roster"):
+        verify_asset_directory(tmp_path, {})
+    with pytest.raises(ValueError, match="missing"):
+        verify_asset_directory(Path(tmp_path / "missing"), expected)
+
+
+def test_frozen_language_cannot_train_and_preserves_rows_masks_and_batching():
+    language = FrozenTMRLanguage(
+        AnalyticTokenizer(), AnalyticLanguage(), AnalyticTokenizer(), AnalyticLanguage()
+    ).train(True)
+    assert not any(module.training for module in language.modules())
+    assert not any(parameter.requires_grad for parameter in language.parameters())
+    text = ("group walks", "one follows another", "all turn")
+    a, b = language(text, microbatch=1), language(text, microbatch=16)
+    assert torch.equal(a.tokens, b.tokens) and torch.equal(a.sentences, b.sentences)
+    assert torch.equal(a.token_mask.sum(1), torch.tensor([4, 5, 4]))
+    torch.testing.assert_close(a.sentences.norm(dim=1), torch.ones(3))
+    assert not a.tokens.requires_grad and not a.sentences.requires_grad
+
+
+def test_never_quietly_truncate_or_encode_empty_caption():
+    tokenizer, model = AnalyticTokenizer(), AnalyticLanguage()
+    with pytest.raises(ValueError, match="never truncate"):
+        tokenize_without_truncation(tokenizer, model, ("word " * 8,))
+    with pytest.raises(ValueError, match="nonempty"):
+        tokenize_without_truncation(tokenizer, model, (" ",))
diff --git a/tests/test_tmr_set.py b/tests/test_tmr_set.py
new file mode 100644
index 0000000..000ee0b
--- /dev/null
+++ b/tests/test_tmr_set.py
@@ -0,0 +1,232 @@
+"""Group adaptation invariants, not a native learning/replication result."""
+
+import copy
+from dataclasses import replace
+from itertools import permutations
+
+import pytest
+import torch
+
+from phaseset_core.tmr_set import (
+    TMRGroupCapture,
+    TMRGroupWindow,
+    TMRSet,
+    TMRTextBatch,
+    absolute_sincos,
+    minimum_assignment,
+    track_costs,
+)
+
+
+def _capture(actors=3, frames=9, *, starts=(0.0, 30.0), input_grad=False):
+    windows = []
+    for start in starts:
+        values = torch.randn(actors, frames, 66)
+        observed = torch.ones_like(values, dtype=torch.bool)
+        observed[-1, 2, :3] = False
+        values[~observed] = 0
+        values.requires_grad_(input_grad)
+        windows.append(
+            TMRGroupWindow(
+                values, observed, tuple(bytes([i + 1]) * 32 for i in range(actors)), start
+            )
+        )
+    return TMRGroupCapture(tuple(windows))
+
+
+def _text(rows=3):
+    tokens = torch.randn(rows, 5, 768)
+    mask = torch.ones(rows, 5, dtype=torch.bool)
+    sentences = torch.eye(rows, 768)
+    return TMRTextBatch(tokens, mask, sentences)
+
+
+def _small_model(**kwargs):
+    # Analytic algorithm fixture only; never a registered TMR-Set experiment.
+    return TMRSet(latent_dim=16, ff_size=32, num_layers=1, num_heads=4, **kwargs)
+
+
+@pytest.mark.parametrize("size", range(1, 7))
+def test_hungarian_against_exhaustive_assignment(size):
+    torch.manual_seed(1729 + size)
+    cost = torch.randn(size, size, dtype=torch.float64, requires_grad=True)
+    assignment = minimum_assignment(cost)
+    actual = cost[torch.arange(size), torch.tensor(assignment)].sum()
+    expected = min(
+        sum(cost[row, column].item() for row, column in enumerate(candidate))
+        for candidate in permutations(range(size))
+    )
+    assert actual.item() == pytest.approx(expected, abs=1e-12)
+    actual.backward()
+    reference = torch.zeros_like(cost)
+    reference[torch.arange(size), torch.tensor(assignment)] = 1
+    assert torch.equal(cost.grad, reference)
+    assert minimum_assignment(torch.zeros(size, size)) == tuple(range(size))
+
+
+def test_parent_assignment_cannot_switch_identity_between_windows():
+    target = torch.stack((torch.zeros(7, 66), torch.ones(7, 66)))
+    observed = torch.ones_like(target, dtype=torch.bool)
+    first = track_costs(target, target, observed)
+    second = track_costs(target.flip(0), target, observed)
+    rows = torch.arange(2)
+    assert first[rows, torch.tensor(minimum_assignment(first))].sum() == 0
+    assert second[rows, torch.tensor(minimum_assignment(second))].sum() == 0
+    total = first + second
+    assert total[rows, torch.tensor(minimum_assignment(total))].sum() > 0
+
+
+def test_track_cost_observed_mean_matches_selected_dense_reference():
+    torch.manual_seed(2718)
+    target = torch.randn(3, 11, 66)
+    prediction = torch.randn_like(target, requires_grad=True)
+    observed = torch.ones_like(target, dtype=torch.bool)
+    observed[1, 3:6, :9] = False
+    costs = track_costs(prediction, target, observed)
+    assignment = minimum_assignment(costs)
+    loss = costs[torch.arange(3), torch.tensor(assignment)].sum() / observed.sum()
+    selected = target[list(assignment)]
+    selected_mask = observed[list(assignment)]
+    expected = torch.nn.functional.smooth_l1_loss(
+        prediction[selected_mask], selected[selected_mask]
+    )
+    torch.testing.assert_close(loss.to(torch.float32), expected, rtol=2e-6, atol=2e-7)
+    got_grad = torch.autograd.grad(loss, prediction, retain_graph=True)[0]
+    expected_grad = torch.autograd.grad(expected, prediction)[0]
+    torch.testing.assert_close(got_grad, expected_grad, rtol=3e-5, atol=3e-6)
+
+
+def test_group_permutation_scores_reconstruction_and_input_gradient_equivariance():
+    torch.manual_seed(31415)
+    model = _small_model(dropout=0, checkpoint_windows=False).eval()
+    capture = _capture(input_grad=True)
+    text = _text(2)
+    positive = torch.ones(1, 2, dtype=torch.bool)
+    rng = torch.get_rng_state()
+    score = model.score((capture,), text)
+    losses = model.compute_loss((capture,), text, positive)
+    original_grads = torch.autograd.grad(
+        losses["loss"], [window.features for window in capture.windows]
+    )
+    for order in permutations(range(3)):
+        changed = TMRGroupCapture(
+            tuple(
+                replace(
+                    window,
+                    features=window.features.detach()[list(order)].clone().requires_grad_(),
+                    observed=window.observed[list(order)],
+                    actor_commitments=tuple(window.actor_commitments[index] for index in order),
+                )
+                for window in capture.windows
+            )
+        )
+        torch.set_rng_state(rng)
+        got_score = model.score((changed,), text)
+        got = model.compute_loss((changed,), text, positive)
+        assert torch.equal(got_score, score)
+        for key in losses:
+            assert torch.equal(got[key], losses[key])
+        gradients = torch.autograd.grad(
+            got["loss"], [window.features for window in changed.windows]
+        )
+        for actual, expected in zip(gradients, original_grads, strict=True):
+            assert torch.equal(actual, expected[list(order)])
+
+
+def test_all_windows_absolute_gap_and_late_input_affect_group_encoding():
+    torch.manual_seed(1729)
+    model = _small_model(dropout=0, checkpoint_windows=False).eval()
+    capture = _capture(starts=(0.0, 40.0))
+    actual = model.encode_capture(capture)[0]
+    without_gap = TMRGroupCapture(
+        (capture.windows[0], replace(capture.windows[1], start_seconds=10.0))
+    )
+    assert not torch.equal(actual, model.encode_capture(without_gap)[0])
+    changed_features = capture.windows[-1].features.clone()
+    changed_features[:, -1] += 0.5
+    changed = TMRGroupCapture(
+        (capture.windows[0], replace(capture.windows[-1], features=changed_features))
+    )
+    assert not torch.equal(actual, model.encode_capture(changed)[0])
+    assert not torch.equal(actual, model.encode_capture(TMRGroupCapture(capture.windows[:1]))[0])
+
+
+def test_checkpointed_and_direct_full_losses_all_gradients_and_rng():
+    torch.manual_seed(2718)
+    direct = _small_model(dropout=0.1, checkpoint_windows=False).train()
+    replay = copy.deepcopy(direct)
+    replay.checkpoint_windows = True
+    captures = (_capture(3), _capture(4))
+    text = _text(3)
+    positive = torch.tensor([[True, True, False], [False, False, True]])
+    rng = torch.get_rng_state()
+    first = direct.compute_loss(captures, text, positive)
+    first_rng = torch.get_rng_state()
+    first["loss"].backward()
+    torch.set_rng_state(rng)
+    second = replay.compute_loss(captures, text, positive)
+    second["loss"].backward()
+    assert torch.equal(first_rng, torch.get_rng_state())
+    for key in first:
+        assert torch.equal(first[key], second[key])
+    for a, b in zip(direct.parameters(), replay.parameters(), strict=True):
+        assert a.grad is not None and b.grad is not None
+        assert torch.equal(a.grad, b.grad) and torch.isfinite(a.grad).all()
+
+
+def test_actual_default_set_vae_has_complete_objective_and_all_trainable_gradients():
+    torch.manual_seed(1729)
+    model = TMRSet(checkpoint_windows=True)
+    assert model.latent_dim == 256
+    for encoder in (model.actor_encoder, model.capture_encoder, model.text_encoder):
+        assert len(encoder.seqTransEncoder.layers) == 6
+        assert encoder.seqTransEncoder.layers[0].self_attn.num_heads == 4
+        assert encoder.seqTransEncoder.layers[0].linear1.out_features == 1024
+    assert len(model.motion_decoder.seqTransDecoder.layers) == 6
+    captures = (_capture(3, frames=7, starts=(0.0,)), _capture(4, frames=7, starts=(0.0,)))
+    losses = model.compute_loss(
+        captures, _text(3), torch.tensor([[True, True, False], [False, False, True]])
+    )
+    assert all(torch.isfinite(value) and value > 0 for value in losses.values())
+    losses["loss"].backward()
+    assert all(
+        parameter.requires_grad
+        and parameter.grad is not None
+        and torch.isfinite(parameter.grad).all()
+        for parameter in model.parameters()
+    )
+    for parameter in (
+        model.actor_encoder.projection.weight,
+        model.capture_encoder.projection.weight,
+        model.text_encoder.projection.weight,
+        model.motion_decoder.final_layer.weight,
+        model.slot_projection[0].weight,
+        model.time_projection[0].weight,
+    ):
+        assert parameter.grad.abs().sum() > 0
+
+
+def test_no_actor_count_semantic_limit_or_finite_slot_embedding_bank():
+    torch.manual_seed(31415)
+    model = _small_model(dropout=0, checkpoint_windows=False).eval()
+    capture = _capture(17, frames=3, starts=(0.0,))
+    with torch.no_grad():
+        mean, logvar = model.encode_capture(capture)
+        loss = model.reconstruct_capture(mean, capture)
+    assert mean.shape == logvar.shape == (16,) and torch.isfinite(loss)
+    assert not any(isinstance(module, torch.nn.Embedding) for module in model.modules())
+    codes = absolute_sincos(torch.arange(257), 16)
+    assert codes.shape == (257, 16)
+
+
+def test_invalid_chronology_unknown_coordinates_and_nonfinite_matching_fail_explicitly():
+    capture = _capture()
+    with pytest.raises(ValueError, match="chronology"):
+        TMRGroupCapture(tuple(reversed(capture.windows)))
+    window = capture.windows[0]
+    changed = window.features.clone()
+    changed[~window.observed] = 999
+    with pytest.raises(ValueError, match="exact"):
+        replace(window, features=changed)
+    with pytest.raises(ValueError, match="nonfinite"):
+        minimum_assignment(torch.full((2, 2), torch.inf))

From 313d02a45d26f12f57c588b50cbe25dd1363e290 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 05:02:58 -0500
Subject: [PATCH 44/95] feat: implement complete-parent WaMo stationary wavelet
 baseline

---
 README.md                     |   4 +
 RELEASE_FILES.sha256          |   5 +-
 docs/WAMO_SET_ADAPTATION.md   |  67 +++++++
 src/phaseset_core/wamo_set.py | 367 ++++++++++++++++++++++++++++++++++
 tests/test_wamo_set.py        | 275 +++++++++++++++++++++++++
 5 files changed, 717 insertions(+), 1 deletion(-)
 create mode 100644 docs/WAMO_SET_ADAPTATION.md
 create mode 100644 src/phaseset_core/wamo_set.py
 create mode 100644 tests/test_wamo_set.py

diff --git a/README.md b/README.md
index ca01e83..b42fcb9 100644
--- a/README.md
+++ b/README.md
@@ -51,6 +51,10 @@ production V2 training-host integration and converged literature reproductions
 are not yet qualified. [TMR-Set](docs/TMR_SET_ADAPTATION.md) now implements the
 complete original VAE objectives with disclosed full-parent group adaptation;
 its pinned frozen language loader is not a substitute for native learning.
+[WaMo-Set](docs/WAMO_SET_ADAPTATION.md) implements learnable stationary analysis
+and inverse wavelets, both reconstruction branches and original/shuffled-frame
+ordering, with explicit full-parent group adaptation and unpublished-detail
+choices. Its software qualification is not converged baseline training.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 372fdca..f6f8191 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-a554d46f303322f7c1426f5017c6e3993737f8560016fbd4be310fbcf9f641ad	10621	README.md
+d3bfa51632667d019e925cedd78d4caece158f87eb544d8aea3c3f7fe0f2d3d7	10929	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -80,6 +80,7 @@ a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPA
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
 c2c47d122f77ff5976d4f148a1a39c3089e78c47213b071427fd203e638e676c	2807	docs/TMR_COMPONENTS.md
 a83d6c241bce7e0adb2add20b4d48247be03a2dda7b3d2cd8b9a40171e5bb23f	4424	docs/TMR_SET_ADAPTATION.md
+0ff9f1c8e8301ca6b13f1ee51e694a8dd05d452e21de9f6e28939263b7910067	4467	docs/WAMO_SET_ADAPTATION.md
 8d689f49b2704ff7c5988eff8a7e3e062f209b85d18425ccdba1e1099581079b	10658	docs/phasepair_research_contract_20260824_165840.md
 8d4b03326029b2a06478295b7328d61181d022604b1465bccd3a6cfcc8ce9704	2155	figures/EDITABLE_FIGURES.json
 efb48515925d5412bc840a5ae07415cda4645591deaa1ab6aca90e1e4f528591	4208	figures/README.md
@@ -202,6 +203,7 @@ cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phases
 b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
+c1b145691b73cb3fe56925de75b114dac6ba8a84fcae6ffeec282010fc36fa77	15762	src/phaseset_core/wamo_set.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
 ae334bee8e2762f022020942141cb52f3285d3d9cb07d53574b6c37f57b2a556	20274	tests/test_cached_residual_lifecycle.py
 358c3ac4470be9c1199a85ab18cadfc977690359875f226fbb4c591b323997bf	2082	tests/test_calibration_populations.py
@@ -313,4 +315,5 @@ eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test
 96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
 726ee82009ecb29ce87208749fb1780365640a52f2e2d6e55399e22119838b89	2988	tests/test_tmr_language.py
 2ae860859c1f9282a02cea929e54fa950433ac8fba81c7510c2b900321d5ff29	9606	tests/test_tmr_set.py
+342cea54b04c8b6fe0faae0cf3ae84a1abac9142792acf5fd042ae44937c62f4	11334	tests/test_wamo_set.py
 3aef8f056ef38ecbe5af51c283285b132db97f175c181018d8a4278d82131915	8150	tests/test_wheel_index_binding.py
diff --git a/docs/WAMO_SET_ADAPTATION.md b/docs/WAMO_SET_ADAPTATION.md
new file mode 100644
index 0000000..c5e3219
--- /dev/null
+++ b/docs/WAMO_SET_ADAPTATION.md
@@ -0,0 +1,67 @@
+# WaMo-Set: paper-defined complete-parent adaptation
+
+This is an independently written implementation of the methods in
+[WaMo v2](https://arxiv.org/html/2508.03343v2), not released author code or a
+claim of original-benchmark reproduction. The audited
+[official repository](https://github.com/3DAgentWorld/WaMo) at
+`cef15e9c75e736e3351f052e39e0aee12b13ad0e` contains no implementation;
+the latest read-only tree check also found only README and attributes.
+
+The retained components are learnable, dilated stationary wavelet analysis
+without temporal decimation; separate intra-frequency temporal encoders;
+concatenation and inter-frequency temporal encoding; additive frame attention;
+independent learnable inverse SWT plus intra-band reconstruction MLPs;
+inter-feature reconstruction; and original/partially shuffled temporal-group
+classification. Both reconstructions use SmoothL1; both ordering branches use
+cross-entropy. Text is projected frozen DistilBERT CLS, not CLIP or an MPNet
+sentence replacement. The caller supplies verified local frozen language.
+
+Paper-specified settings: decomposition level 3, latent width 256, temperature
+0.07, 16 temporal groups, shuffled fraction 0.25, Haar/db1 initialization
+(Table 9), Adam learning rate 1e-4 and cosine schedule. The model implements the
+paper's *sum* of directional contrastive losses, not a halved objective.
+For variable numbers of human captions, known positives use logsumexp in the
+numerator and all gallery candidates in the denominator. No semantic filtering
+or pseudo-positive mining is introduced.
+
+Unpublished details are disclosed project choices: circular SWT boundaries;
+two-tap Haar analysis `[1,1]/sqrt(2)` and `[-1,1]/sqrt(2)`; independent synthesis
+initialized to half those taps to account for undecimated redundancy; depthwise
+coordinate convolutions of width 9 (low) and 3 (high); two Transformer layers,
+four heads, FFN 1024, GELU and dropout 0.1; two-layer projection/decoder MLPs;
+unit reconstruction and ordering weights. These are configurable choices,
+not purported author defaults. Learnable filters are not constrained to remain
+orthogonal or perfectly reconstructing. Initial reconstruction is tested.
+The existing three WaMo short-pilot slots, not an additional search, govern
+any permitted configuration selection before the common contract freezes.
+
+Multi-person/whole-parent extensions:
+
+- Reuse the admitted complete body22 capture contract; shared per-actor
+  processing of *every* accepted 200-frame window, unordered actor PMA,
+  followed by a capture temporal encoder and additive pooling.
+- The capture hierarchy receives actual absolute window starts; omitted
+  invalid windows leave their temporal gaps rather than being collapsed.
+  This bounded-window wavelet hierarchy differs from a single-person transform
+  across an entire continuous trajectory and must be reported as such.
+- Actor commitments choose a deterministic reduction order only; no ordinal,
+  actor-specific weights or commitment features enter the model. Dynamic K
+  has no fixed slot bank. Reconstruction is actor-aligned autoencoding, not
+  text-to-motion set reconstruction, so no Hungarian matching is appropriate.
+- DMSP groups are defined within each accepted window, approximately evenly
+  by `floor(frame_index * 16 / window_length)`. A uniform subset of
+  `floor(0.25*T)` frames is permuted; positions outside the subset are untouched.
+  The same permutation moves all actors, masks and original labels together.
+  This concrete frame-subset realization and window-local objective are
+  disclosed adaptations. Labels never enter the encoders; positional codes
+  refer to current sequence positions, not shuffled origin positions.
+- Unobserved coordinates remain zero and do not contribute reconstruction;
+  entirely unobserved actor frames do not contribute either ordering loss.
+  Window losses accumulate coordinate/frame sums, normalize over the complete
+  parent's observed coordinates/actor frames, then average parents, so a longer
+  parent or more captions do not multiply its auxiliary weight. Masks are
+  still visible through the input and must be covered by missingness controls.
+- The model is not a production trainer: provenance, split selection,
+  optimizer/schedule, full effective negative gallery, budgets and immutable
+  attempt admission remain the host's responsibility. Qualification alone
+  does not establish learning, original-task reproduction or paper results.
diff --git a/src/phaseset_core/wamo_set.py b/src/phaseset_core/wamo_set.py
new file mode 100644
index 0000000..77b52df
--- /dev/null
+++ b/src/phaseset_core/wamo_set.py
@@ -0,0 +1,367 @@
+"""Paper-defined WaMo, explicitly adapted to complete multi-person parents.
+
+No official implementation was available at the audited upstream revision.
+See docs/WAMO_SET_ADAPTATION.md for disclosed choices versus paper defaults.
+Learnable SWT/ISWT, intra/inter frequency features, both reconstructions and
+original/partially shuffled frame classification are all trainable here.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import math
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+from torch.utils.checkpoint import checkpoint
+
+from .models import PMAPool
+from .tmr_set import TMRGroupCapture, absolute_sincos
+
+
+@dataclass(frozen=True)
+class WaMoConfig:
+    # Paper-specified: width, levels, temperature, temporal groups, fraction.
+    width: int = 256
+    levels: int = 3
+    temperature: float = 0.07
+    temporal_groups: int = 16
+    shuffle_fraction: float = 0.25
+    # Disclosed implementation choices: not purported author-code defaults.
+    heads: int = 4
+    layers: int = 2
+    ff_size: int = 1024
+    dropout: float = 0.1
+    low_kernel: int = 9
+    high_kernel: int = 3
+    reconstruction_weight: float = 1.0
+    ordering_weight: float = 1.0
+
+    def __post_init__(self):
+        integers = (
+            self.width,
+            self.levels,
+            self.temporal_groups,
+            self.heads,
+            self.layers,
+            self.ff_size,
+            self.low_kernel,
+            self.high_kernel,
+        )
+        if any(type(value) is not int or value < 1 for value in integers):
+            raise ValueError("dimensions, layers and kernel sizes must be positive integers")
+        if self.width % 2 or self.width % self.heads:
+            raise ValueError("even width must be divisible by attention heads")
+        if not (
+            self.low_kernel > self.high_kernel and self.low_kernel % 2 and self.high_kernel % 2
+        ):
+            raise ValueError("odd low-frequency kernel must exceed high-frequency kernel")
+        if not (0 <= self.dropout < 1 and 0 <= self.shuffle_fraction <= 1):
+            raise ValueError("invalid dropout or shuffled frame fraction")
+        for value in (self.temperature, self.reconstruction_weight, self.ordering_weight):
+            if not math.isfinite(value) or value <= 0:
+                raise ValueError("temperature and objective weights must be finite positive")
+
+
+class LearnableSWT(nn.Module):
+    """Dilated undecimated per-coordinate analysis and independent synthesis.
+
+    Circular boundaries; Haar initialization. Synthesis has the redundant SWT
+    factor 1/2, so each initial analysis/synthesis stage reconstructs exactly.
+    Neither filter orthogonality nor perfect reconstruction is forced after
+    learning. Output order is d1,...,dS,aS. Never decimate or remove a level.
+    """
+
+    def __init__(self, levels: int = 3):
+        super().__init__()
+        if type(levels) is not int or levels < 1:
+            raise ValueError("SWT needs a positive decomposition level")
+        self.levels = levels
+        scale = 1 / math.sqrt(2)
+        self.analysis_low = nn.Parameter(torch.tensor([scale, scale]))
+        self.analysis_high = nn.Parameter(torch.tensor([-scale, scale]))
+        self.synthesis_low = nn.Parameter(torch.tensor([scale / 2, scale / 2]))
+        self.synthesis_high = nn.Parameter(torch.tensor([-scale / 2, scale / 2]))
+
+    @staticmethod
+    def _filter(x: Tensor, taps: Tensor, dilation: int, direction: int) -> Tensor:
+        return sum(
+            tap * torch.roll(x, direction * dilation * index, dims=1)
+            for index, tap in enumerate(taps)
+        )
+
+    def forward(self, x: Tensor) -> tuple[Tensor, ...]:
+        if x.ndim != 3 or x.shape[1] < 1:
+            raise ValueError("SWT expects [actors,time,coordinates]")
+        low, details = x, []
+        for level in range(self.levels):
+            details.append(self._filter(low, self.analysis_high, 2**level, -1))
+            low = self._filter(low, self.analysis_low, 2**level, -1)
+        return (*details, low)
+
+    def inverse(self, bands: tuple[Tensor, ...]) -> Tensor:
+        if len(bands) != self.levels + 1 or any(x.shape != bands[0].shape for x in bands):
+            raise ValueError("ISWT requires all same-length details plus final approximation")
+        low = bands[-1]
+        for level in reversed(range(self.levels)):
+            low = self._filter(low, self.synthesis_low, 2**level, 1) + self._filter(
+                bands[level], self.synthesis_high, 2**level, 1
+            )
+        return low
+
+
+def temporal_labels(length: int, groups: int, device: torch.device) -> Tensor:
+    """Consecutive approximately equal groups; no motion-dependent targets."""
+    return torch.arange(length, device=device, dtype=torch.int64) * groups // length
+
+
+def partial_frame_permutation(length: int, fraction: float, device: torch.device) -> Tensor:
+    """Shuffle a uniformly selected frame subset, not temporal blocks.
+
+    One permutation is shared by all actors in a window. Origin labels and
+    observation masks must follow it; labels are never encoder inputs.
+    """
+    if length < 1 or not 0 <= fraction <= 1:
+        raise ValueError("invalid frame subset")
+    order = torch.arange(length, device=device)
+    count = math.floor(length * fraction)
+    if count >= 2:
+        selected = torch.randperm(length, device=device)[:count]
+        order[selected] = selected[torch.randperm(count, device=device)]
+    return order
+
+
+class AdditivePool(nn.Module):
+    def __init__(self, width: int):
+        super().__init__()
+        self.hidden = nn.Linear(width, width)
+        # A common score bias cancels in softmax, so do not add a dead parameter.
+        self.score = nn.Linear(width, 1, bias=False)
+
+    def forward(self, x: Tensor, mask: Tensor) -> Tensor:
+        logits = self.score(torch.tanh(self.hidden(x))).squeeze(-1)
+        weights = logits.masked_fill(~mask, -torch.inf).softmax(dim=1)
+        return (x * weights[..., None]).sum(dim=1)
+
+
+class TemporalFeatures(nn.Module):
+    def __init__(self, config: WaMoConfig):
+        super().__init__()
+        layer = nn.TransformerEncoderLayer(
+            config.width,
+            config.heads,
+            config.ff_size,
+            config.dropout,
+            activation="gelu",
+            batch_first=True,
+        )
+        self.transformer = nn.TransformerEncoder(layer, config.layers, enable_nested_tensor=False)
+        self.width = config.width
+
+    def forward(self, x: Tensor, mask: Tensor, positions: Tensor) -> Tensor:
+        encoded = self.transformer(
+            x + absolute_sincos(positions, self.width)[None],
+            src_key_padding_mask=~mask,
+        )
+        return encoded.masked_fill(~mask[..., None], 0)
+
+
+def _mlp(source: int, target: int, hidden: int) -> nn.Sequential:
+    return nn.Sequential(nn.Linear(source, hidden), nn.GELU(), nn.Linear(hidden, target))
+
+
+def positive_contrastive(scores: Tensor, positive: Tensor) -> Tensor:
+    """WaMo bidirectional SUM (not half), rectangular known-positive extension."""
+    if positive.dtype != torch.bool or positive.shape != scores.shape:
+        raise ValueError("positive mask must match all motion/text scores")
+    if not bool(positive.any(0).all()) or not bool(positive.any(1).all()):
+        raise ValueError("every motion/text must have a known positive")
+    selected = scores.masked_fill(~positive, -torch.inf)
+    return (torch.logsumexp(scores, 1) - torch.logsumexp(selected, 1)).mean() + (
+        torch.logsumexp(scores, 0) - torch.logsumexp(selected, 0)
+    ).mean()
+
+
+class WaMoSet(nn.Module):
+    """Shared actor/window WaMo with unordered actor PMA and whole-parent time.
+
+    Reconstruction is an actor-aligned autoencoder (not a text-to-set decoder).
+    Every accepted window contributes motion features and both auxiliary tasks.
+    Temporal group labels are window-local under this disclosed hierarchy.
+    Actual absolute starts, including gaps, reach only the capture hierarchy.
+    """
+
+    system_id = "WaMo-Set"
+
+    def __init__(self, config: WaMoConfig = WaMoConfig(), *, checkpoint_windows: bool = True):
+        super().__init__()
+        self.config = config
+        self.checkpoint_windows = checkpoint_windows
+        self.wavelet = LearnableSWT(config.levels)
+        kernels = [config.high_kernel] * config.levels + [config.low_kernel]
+        self.convolutions = nn.ModuleList(
+            [nn.Conv1d(66, 66, kernel, groups=66) for kernel in kernels]
+        )
+        self.band_projections = nn.ModuleList(
+            [_mlp(66, config.width, config.width) for _ in kernels]
+        )
+        self.intra_transformers = nn.ModuleList([TemporalFeatures(config) for _ in kernels])
+        self.fusion = _mlp((config.levels + 1) * config.width, config.width, config.width)
+        self.inter_transformer = TemporalFeatures(config)
+        self.frame_pool = AdditivePool(config.width)
+        self.actor_pool = PMAPool(config.width, config.heads, config.ff_size, config.dropout)
+        self.capture_transformer = TemporalFeatures(config)
+        self.capture_pool = AdditivePool(config.width)
+        self.intra_decoders = nn.ModuleList([_mlp(config.width, 66, config.width) for _ in kernels])
+        self.inter_decoder = _mlp(config.width, 66, config.width)
+        self.temporal_classifier = nn.Linear(config.width, config.temporal_groups)
+        self.text_projection = nn.Linear(768, config.width)
+
+    def frame_features(self, x: Tensor, observed: Tensor) -> tuple[tuple[Tensor, ...], Tensor]:
+        mask = observed.any(-1)
+        times = torch.arange(x.shape[1], device=x.device, dtype=torch.float32)
+        bands = self.wavelet(x)
+
+        def convolve(conv, band):
+            # Modular extension works even for T < kernel/2; circular pad()
+            # cannot wrap more than once and would reject admitted short inputs.
+            half = conv.kernel_size[0] // 2
+            index = torch.arange(-half, x.shape[1] + half, device=x.device) % x.shape[1]
+            return conv(band[:, index].transpose(1, 2)).transpose(1, 2)
+
+        intra = tuple(
+            transformer(projection(convolve(conv, band)), mask, times)
+            for conv, projection, transformer, band in zip(
+                self.convolutions,
+                self.band_projections,
+                self.intra_transformers,
+                bands,
+                strict=True,
+            )
+        )
+        inter = self.inter_transformer(self.fusion(torch.cat(intra, -1)), mask, times)
+        return intra, inter
+
+    def _window(
+        self, x: Tensor, observed: Tensor, order: Tensor | None
+    ) -> tuple[Tensor, Tensor, Tensor]:
+        intra, inter = self.frame_features(x, observed)
+        frame_mask = observed.any(-1)
+        actors = self.frame_pool(inter, frame_mask)
+        summary = self.actor_pool(
+            actors[None], torch.ones(1, len(actors), device=x.device, dtype=torch.bool)
+        )[0]
+        if order is None:
+            zero = x.new_zeros((), dtype=torch.float64)
+            return summary, zero, zero
+        reconstructed = self.wavelet.inverse(
+            tuple(
+                decoder(features)
+                for decoder, features in zip(self.intra_decoders, intra, strict=True)
+            )
+        )
+        inter_reconstructed = self.inter_decoder(inter)
+        rec_sum = sum(
+            F.smooth_l1_loss(prediction, x, reduction="none")
+            .masked_fill(~observed, 0)
+            .to(torch.float64)
+            .sum()
+            for prediction in (reconstructed, inter_reconstructed)
+        )
+        labels = temporal_labels(x.shape[1], self.config.temporal_groups, x.device)
+        _, shuffled = self.frame_features(x[:, order], observed[:, order])
+        ce_sum = sum(
+            F.cross_entropy(
+                self.temporal_classifier(features).transpose(1, 2),
+                target[None].expand(len(x), -1),
+                reduction="none",
+            )
+            .masked_fill(~mask, 0)
+            .to(torch.float64)
+            .sum()
+            for features, target, mask in (
+                (inter, labels, frame_mask),
+                (shuffled, labels[order], frame_mask[:, order]),
+            )
+        )
+        return summary, rec_sum, ce_sum
+
+    def encode_capture(
+        self, capture: TMRGroupCapture, *, auxiliary: bool = False
+    ) -> tuple[Tensor, Tensor, Tensor]:
+        device = self.wavelet.analysis_low.device
+        summaries, reconstruction, ordering = [], [], []
+        observations, frames = 0, 0
+        for window in capture.windows:
+            x, observed = window.canonical(device)
+            order = (
+                partial_frame_permutation(x.shape[1], self.config.shuffle_fraction, device)
+                if auxiliary
+                else None
+            )
+
+            def encode(witness, x=x, observed=observed, order=order):
+                # Explicit witness tells checkpoint which device's RNG to replay.
+                return self._window(x, observed, order)
+
+            summary, rec, dmsp = (
+                checkpoint(
+                    encode, self.wavelet.analysis_low, use_reentrant=False, preserve_rng_state=True
+                )
+                if self.checkpoint_windows and torch.is_grad_enabled()
+                else encode(self.wavelet.analysis_low)
+            )
+            summaries.append(summary)
+            reconstruction.append(rec)
+            ordering.append(dmsp)
+            observations += int(observed.sum().item())
+            frames += int(observed.any(-1).sum().item())
+        sequence = torch.stack(summaries)[None]
+        times = torch.tensor([window.start_seconds for window in capture.windows], device=device)
+        mask = torch.ones(1, len(summaries), device=device, dtype=torch.bool)
+        full = self.capture_pool(self.capture_transformer(sequence, mask, times), mask)[0]
+        return (
+            full,
+            (torch.stack(reconstruction).sum() / observations).to(torch.float32),
+            (torch.stack(ordering).sum() / frames).to(torch.float32),
+        )
+
+    def encode_text(self, cls: Tensor) -> Tensor:
+        if (
+            cls.ndim != 2
+            or cls.shape[1] != 768
+            or len(cls) < 1
+            or cls.dtype != torch.float32
+            or cls.requires_grad
+            or not bool(torch.isfinite(cls).all())
+        ):
+            raise ValueError("need frozen finite float32 DistilBERT CLS [texts,768]")
+        return self.text_projection(cls.to(self.text_projection.weight.device))
+
+    def score(self, captures: tuple[TMRGroupCapture, ...], cls: Tensor) -> Tensor:
+        motion = torch.stack([self.encode_capture(capture)[0] for capture in captures])
+        return (
+            F.normalize(motion, dim=1)
+            @ F.normalize(self.encode_text(cls), dim=1).T
+            / self.config.temperature
+        )
+
+    def compute_loss(
+        self, captures: tuple[TMRGroupCapture, ...], cls: Tensor, positive: Tensor
+    ) -> dict[str, Tensor]:
+        rows = [self.encode_capture(capture, auxiliary=True) for capture in captures]
+        motion = torch.stack([row[0] for row in rows])
+        text = self.encode_text(cls)
+        scores = F.normalize(motion, dim=1) @ F.normalize(text, dim=1).T / self.config.temperature
+        losses = dict(
+            contrastive=positive_contrastive(scores, positive.to(scores.device)),
+            reconstruction=torch.stack([row[1] for row in rows]).mean(),
+            ordering=torch.stack([row[2] for row in rows]).mean(),
+        )
+        losses["loss"] = (
+            losses["contrastive"]
+            + self.config.reconstruction_weight * losses["reconstruction"]
+            + self.config.ordering_weight * losses["ordering"]
+        )
+        return losses
diff --git a/tests/test_wamo_set.py b/tests/test_wamo_set.py
new file mode 100644
index 0000000..bdc0a5e
--- /dev/null
+++ b/tests/test_wamo_set.py
@@ -0,0 +1,275 @@
+"""Analytic software checks; real-data qualification is a separate attempt."""
+
+from dataclasses import replace
+import itertools
+
+import pytest
+import torch
+from torch.nn import functional as F
+
+from phaseset_core.tmr_set import TMRGroupCapture, TMRGroupWindow
+from phaseset_core.wamo_set import (
+    LearnableSWT,
+    WaMoConfig,
+    WaMoSet,
+    partial_frame_permutation,
+    positive_contrastive,
+    temporal_labels,
+)
+
+
+def fixture_capture(*, k=3, length=19, starts=(0.0, 30.0), grad=False):
+    windows = []
+    keys = tuple(bytes([index + 1]) * 32 for index in range(k))
+    for start in starts:
+        x = torch.randn(k, length, 66)
+        mask = torch.ones_like(x, dtype=torch.bool)
+        mask[0, 2:4, :3] = False
+        if length > 4:
+            mask[0, 0, :] = False
+        x = x.masked_fill(~mask, 0).requires_grad_(grad)
+        windows.append(TMRGroupWindow(x, mask, keys, start))
+    return TMRGroupCapture(tuple(windows))
+
+
+def permute(capture, order):
+    return TMRGroupCapture(
+        tuple(
+            TMRGroupWindow(
+                window.features[list(order)],
+                window.observed[list(order)],
+                tuple(window.actor_commitments[i] for i in order),
+                window.start_seconds,
+            )
+            for window in capture.windows
+        )
+    )
+
+
+def small(*, dropout=0.0, checkpoint=True):
+    return WaMoSet(
+        WaMoConfig(width=16, ff_size=32, layers=1, dropout=dropout), checkpoint_windows=checkpoint
+    )
+
+
+def scalar_filter(x, taps, dilation, direction):
+    # Independent direct coordinate indexing of paper equations, no roll/conv.
+    return torch.stack(
+        [
+            sum(
+                taps[j] * x[:, (n - direction * dilation * j) % x.shape[1]]
+                for j in range(len(taps))
+            )
+            for n in range(x.shape[1])
+        ],
+        dim=1,
+    )
+
+
+@pytest.mark.parametrize("length", [1, 7, 32, 200])
+def test_swt_equations_no_decimation_haar_roundtrip_and_shift(length):
+    torch.manual_seed(1729)
+    wavelet = LearnableSWT()
+    x = torch.randn(3, length, 66, dtype=torch.float64)
+    wavelet = wavelet.double()
+    bands = wavelet(x)
+    low = x
+    oracle = []
+    for level in range(3):
+        oracle.append(scalar_filter(low, wavelet.analysis_high, 2**level, -1))
+        low = scalar_filter(low, wavelet.analysis_low, 2**level, -1)
+    oracle.append(low)
+    assert all(band.shape == x.shape for band in bands)
+    for actual, expected in zip(bands, oracle, strict=True):
+        torch.testing.assert_close(actual, expected, rtol=0, atol=0)
+    # Initialization was created in float32 then cast to float64.
+    torch.testing.assert_close(wavelet.inverse(bands), x, rtol=3e-7, atol=3e-7)
+    shifted = wavelet(torch.roll(x, 2, 1))
+    for actual, original in zip(shifted, bands, strict=True):
+        assert torch.equal(actual, torch.roll(original, 2, 1))
+
+
+def test_learned_synthesis_independent_oracle_and_all_filter_gradients():
+    torch.manual_seed(2718)
+    wavelet = LearnableSWT().double()
+    with torch.no_grad():
+        for parameter in wavelet.parameters():
+            parameter.add_(torch.randn_like(parameter) * 0.03)
+    x = torch.randn(2, 13, 5, dtype=torch.float64, requires_grad=True)
+    bands = wavelet(x)
+    low = bands[-1]
+    for level in reversed(range(3)):
+        low = scalar_filter(low, wavelet.synthesis_low, 2**level, 1) + scalar_filter(
+            bands[level], wavelet.synthesis_high, 2**level, 1
+        )
+    torch.testing.assert_close(wavelet.inverse(bands), low, rtol=0, atol=0)
+    wavelet.inverse(bands).square().mean().backward()
+    assert x.grad.isfinite().all() and x.grad.abs().sum() > 0
+    assert all(p.grad.isfinite().all() and p.grad.abs().sum() > 0 for p in wavelet.parameters())
+
+
+def test_partial_frame_shuffle_keeps_masks_origin_labels_and_unselected_frames():
+    torch.manual_seed(31415)
+    order = partial_frame_permutation(200, 0.25, torch.device("cpu"))
+    assert sorted(order.tolist()) == list(range(200))
+    assert 0 < int((order != torch.arange(200)).sum()) <= 50
+    x = torch.arange(200).expand(3, 200)
+    mask = x % 7 != 0
+    labels = temporal_labels(200, 16, x.device)
+    assert torch.equal(labels[order], x[:, order][0] * 16 // 200)
+    assert torch.equal(mask[:, order], x[:, order] % 7 != 0)
+    assert torch.equal(partial_frame_permutation(1, 0.25, x.device), torch.tensor([0]))
+
+
+def test_contrastive_is_original_directional_sum_and_known_multi_positive():
+    torch.manual_seed(1729)
+    scores = torch.randn(3, 3, requires_grad=True)
+    labels = torch.arange(3)
+    expected = F.cross_entropy(scores, labels) + F.cross_entropy(scores.T, labels)
+    torch.testing.assert_close(
+        positive_contrastive(scores, torch.eye(3, dtype=torch.bool)), expected
+    )
+    mask = torch.tensor([[True, True, False], [False, False, True]])
+    result = positive_contrastive(scores[:2], mask)
+    assert torch.isfinite(result)
+    result.backward()
+    assert scores.grad.abs().sum() > 0
+    with pytest.raises(ValueError, match="known positive"):
+        positive_contrastive(scores.detach(), torch.zeros(3, 3, dtype=torch.bool))
+
+
+def test_all_k3_permutations_loss_score_and_input_gradient_equivariance():
+    torch.manual_seed(1729)
+    model = small().eval()
+    capture = fixture_capture(grad=True)
+    other = fixture_capture()
+    text = torch.randn(3, 768)
+    positive = torch.tensor([[True, True, False], [False, False, True]])
+    torch.manual_seed(2718)
+    expected = model.compute_loss((capture, other), text, positive)
+    expected["loss"].backward()
+    gradients = [window.features.grad.clone() for window in capture.windows]
+    scores = model.score((capture, other), text).detach()
+    for order in itertools.permutations(range(3)):
+        permuted = permute(capture, order)
+        model.zero_grad(set_to_none=True)
+        torch.manual_seed(2718)
+        actual = model.compute_loss((permuted, other), text, positive)
+        assert all(torch.equal(actual[key], expected[key]) for key in expected)
+        assert torch.equal(model.score((permuted, other), text).detach(), scores)
+        # Nonleaf permuted tensors retain their own derivative for the check.
+        for window in permuted.windows:
+            window.features.retain_grad()
+        actual["loss"].backward()
+        for window, gradient in zip(permuted.windows, gradients, strict=True):
+            assert torch.equal(window.features.grad, gradient[list(order)])
+
+
+def test_checkpoint_dropout_loss_rng_and_every_parameter_gradient_equal():
+    torch.manual_seed(1729)
+    direct = small(dropout=0.1, checkpoint=False).train()
+    replay = small(dropout=0.1, checkpoint=True).train()
+    replay.load_state_dict(direct.state_dict())
+    captures = (fixture_capture(), fixture_capture())
+    text = torch.randn(3, 768)
+    positive = torch.tensor([[True, True, False], [False, False, True]])
+    gradients, losses, rngs = [], [], []
+    for model in (direct, replay):
+        torch.manual_seed(31415)
+        loss = model.compute_loss(captures, text, positive)
+        loss["loss"].backward()
+        losses.append(loss)
+        gradients.append({name: p.grad.clone() for name, p in model.named_parameters()})
+        rngs.append(torch.get_rng_state())
+    assert torch.equal(rngs[0], rngs[1])
+    assert all(torch.equal(losses[0][key], losses[1][key]) for key in losses[0])
+    for name in gradients[0]:
+        assert torch.equal(gradients[0][name], gradients[1][name]), name
+
+
+def test_default_complete_objective_all_trainable_parameter_gradients():
+    torch.manual_seed(1729)
+    model = WaMoSet().train()
+    assert model.config == WaMoConfig()
+    captures = (fixture_capture(length=32), fixture_capture(length=32))
+    text = torch.randn(3, 768)
+    positive = torch.tensor([[True, True, False], [False, False, True]])
+    loss = model.compute_loss(captures, text, positive)
+    assert set(loss) == {"loss", "contrastive", "reconstruction", "ordering"}
+    loss["loss"].backward()
+    for name, parameter in model.named_parameters():
+        assert parameter.grad is not None, name
+        assert parameter.grad.isfinite().all() and parameter.grad.abs().sum() > 0, name
+
+
+def test_all_windows_absolute_gaps_dynamic_k_and_short_windows():
+    torch.manual_seed(1729)
+    model = small().eval()
+    capture = fixture_capture(k=17, length=1)
+    text = torch.randn(2, 768)
+    score = model.score((capture,), text)
+    assert score.shape == (1, 2) and score.isfinite().all()
+    short = TMRGroupCapture((capture.windows[0],))
+    gap = TMRGroupCapture((capture.windows[0], replace(capture.windows[1], start_seconds=300.0)))
+    assert not torch.equal(model.score((short,), text), score)
+    assert not torch.equal(model.score((gap,), text), score)
+    late = capture.windows[1]
+    changed = TMRGroupCapture((capture.windows[0], replace(late, features=late.features + 1)))
+    assert not torch.equal(model.score((changed,), text), score)
+    assert not any(isinstance(module, torch.nn.Embedding) for module in model.modules())
+
+
+def test_observed_coordinate_and_frame_loss_normalization():
+    torch.manual_seed(1729)
+    model = small(checkpoint=False).eval()
+    capture = fixture_capture(starts=(0.0,))
+    window = capture.windows[0]
+    x, observed = window.canonical(torch.device("cpu"))
+    order = torch.arange(x.shape[1])
+    _, rec, ce = model._window(x, observed, order)
+    intra, inter = model.frame_features(x, observed)
+    predictions = (
+        model.wavelet.inverse(
+            tuple(
+                decoder(features)
+                for decoder, features in zip(model.intra_decoders, intra, strict=True)
+            )
+        ),
+        model.inter_decoder(inter),
+    )
+    expected_rec = sum(
+        F.smooth_l1_loss(prediction[observed], x[observed], reduction="sum")
+        for prediction in predictions
+    )
+    torch.testing.assert_close(rec.float(), expected_rec)
+    frame_mask = observed.any(-1)
+    labels = temporal_labels(x.shape[1], 16, x.device)[None].expand(len(x), -1)
+    expected_ce = 2 * F.cross_entropy(
+        model.temporal_classifier(inter)[frame_mask], labels[frame_mask], reduction="sum"
+    )
+    torch.testing.assert_close(ce.float(), expected_ce)
+    # Independent nonidentity shuffled CE oracle exercises the actual branch,
+    # not only the helper's mask/label indexing.
+    order = torch.arange(x.shape[1]).roll(3)
+    _, _, actual_ce = model._window(x, observed, order)
+    _, shuffled = model.frame_features(x[:, order], observed[:, order])
+    shuffled_mask = frame_mask[:, order]
+    independent = F.cross_entropy(
+        model.temporal_classifier(inter)[frame_mask], labels[frame_mask], reduction="sum"
+    ) + F.cross_entropy(
+        model.temporal_classifier(shuffled)[shuffled_mask],
+        labels[:, order][shuffled_mask],
+        reduction="sum",
+    )
+    torch.testing.assert_close(actual_ce.float(), independent)
+
+
+def test_invalid_config_and_language_rejected():
+    with pytest.raises(ValueError, match="expects"):
+        LearnableSWT()(torch.zeros(2, 0, 66))
+    for kwargs in ({"low_kernel": 3}, {"temperature": 0}, {"levels": 0}, {"width": 15}):
+        with pytest.raises(ValueError):
+            WaMoConfig(**kwargs)
+    model = small()
+    with pytest.raises(ValueError, match="frozen"):
+        model.encode_text(torch.randn(2, 768, requires_grad=True))

From 43a291d4d5b1a305af530dac4ad0045dfda9a7c7 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 05:41:04 -0500
Subject: [PATCH 45/95] feat: implement original dyadic and full-parent MIME
 with trainable CLIP

---
 README.md                          |   4 +
 RELEASE_FILES.sha256               |   6 +-
 docs/MIME_SET_ADAPTATION.md        |  57 ++++
 src/phaseset_core/mime_language.py | 161 ++++++++++++
 src/phaseset_core/mime_set.py      | 404 +++++++++++++++++++++++++++++
 tests/test_mime_set.py             | 309 ++++++++++++++++++++++
 6 files changed, 940 insertions(+), 1 deletion(-)
 create mode 100644 docs/MIME_SET_ADAPTATION.md
 create mode 100644 src/phaseset_core/mime_language.py
 create mode 100644 src/phaseset_core/mime_set.py
 create mode 100644 tests/test_mime_set.py

diff --git a/README.md b/README.md
index b42fcb9..c414e6c 100644
--- a/README.md
+++ b/README.md
@@ -55,6 +55,10 @@ its pinned frozen language loader is not a substitute for native learning.
 and inverse wavelets, both reconstruction branches and original/shuffled-frame
 ordering, with explicit full-parent group adaptation and unpublished-detail
 choices. Its software qualification is not converged baseline training.
+[MIME](docs/MIME_SET_ADAPTATION.md) keeps separate role-aware original dyadic
+and shared complete-parent group APIs, full-time bidirectional co-attention,
+and a genuinely trainable pinned CLIP text tower. Group XYZ/long-text adapters
+are disclosed; equation/software checks are not original-task convergence.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index f6f8191..792e183 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-d3bfa51632667d019e925cedd78d4caece158f87eb544d8aea3c3f7fe0f2d3d7	10929	README.md
+e29a56cc389fedddbe677b68dd288535203ba5e96869d5a2dd51bd6dbe883547	11238	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -35,6 +35,7 @@ d03a1569556beb1b3ad7f7f501d7bdc40e271f6912c173e403f75458fd6ed238	6158	docs/HOST_
 e38d6f254f71d0027a0b82e1dc3ec95d23a9041831d21d41e96acf10b81e155f	19223	docs/HOST_RUNTIME_INTEGRATION.md
 5dd898347d3646cde4a74e10afa8ba1d12703e3d0c6df51e5113659a31808086	3606	docs/LATENCY_PROGRESS_JOURNAL.md
 f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
+3475ce4b97f5e4bfe1746b4997130d8c5a4011641244f302c445e538f5cf8fb1	3896	docs/MIME_SET_ADAPTATION.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 1cbb857ba962b29eae75d206aa49657f481e559085e062a2ef51ab4a6d87a220	3440	docs/PERIODIC_CACHE_EXECUTION.md
 a9e215279d354999c1f949f776a131b8cf4cec850abd81459c5c0d8cee0c8b03	4356	docs/PERIODIC_CAPTURE_TRAINING_CACHE.md
@@ -178,7 +179,9 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 877302cdb2ad891eb064d5bb228d124b66042865b78fa3826f66cf7bacf01166	68520	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
+e21cfa4186b6cbaecf122d364b72004075cf9b15a35b96a78e000bd9a8809a3e	6805	src/phaseset_core/mime_language.py
 f7cb1c0893b0b504359e34e39dd24651aaa86c1b65e98d2ed0469c6d5ce84d4d	6920	src/phaseset_core/mime_parent_curriculum.py
+c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phaseset_core/mime_set.py
 9e7fa84554d5676f8ef75049b17b53580395c3c60be0f75fcb55996ccc8ef36d	37720	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
@@ -220,6 +223,7 @@ e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/tes
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 b65a2178d7faf25372adeccdfcd19ee7804544665c6059f2d684801ccff83e11	6050	tests/test_mime_parent_curriculum.py
+10e507b64ce40e1a98a93833d0496b8d38d07977bd4ec3b0196e0f07e81cd0d4	12916	tests/test_mime_set.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 3b23c2b08ed81029ca6239cbb63b98093077786163aaf5bb47ccfefb21c6ef24	7117	tests/test_parent_capture.py
 32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
diff --git a/docs/MIME_SET_ADAPTATION.md b/docs/MIME_SET_ADAPTATION.md
new file mode 100644
index 0000000..00fc687
--- /dev/null
+++ b/docs/MIME_SET_ADAPTATION.md
@@ -0,0 +1,57 @@
+# MIME: original dyadic and complete-parent group implementations
+
+Independent implementation of [MIME v1](https://arxiv.org/html/2607.22702v1),
+not released author code or a claim of converged original-task reproduction.
+The original dyadic API and the group adapter are distinct; neither converts
+XYZ into fictitious SMPL rotations or silently truncates a capture.
+
+`OriginalMIMEMotion` takes true 135D root-displacement/22-joint 6D-rotation
+features plus common-frame root positions. It retains distinct role projections,
+normalizations and type embeddings, root distance/signed displacement,
+relative configuration and root-motion magnitude ratio, early relation injection,
+four layers of pre-LN independent self-attention, simultaneous bidirectional
+full-time cross-attention and GELU FFNs, ordered frame fusion and learned-query
+temporal pooling. The original Inter-X host must enforce its 30Hz/300-frame
+protocol separately and supply real rotation inputs; body22 is not a substitute.
+
+`MIMESetMotion` explicitly adapts these interaction mechanisms to shared body22
+XYZ inputs. All actor weights and endpoint block weights are shared; input-role
+embeddings are absent. Each endpoint receives its own directed relation encoding.
+Sum/absolute-difference/product fusion makes the pair readout symmetric.
+Every unordered pair is visited, checkpointed independently, and accumulated
+in canonical commitment order as float64 before the window mean. No actor
+ordinal or commitment is a neural feature; no dense K-by-K-by-time activation
+is constructed and no edge is silently sampled. All accepted windows enter an
+extra four-layer temporal hierarchy with actual absolute starts, including gaps,
+and learned-query full-parent pooling. The per-window hierarchy and 20Hz XYZ
+feature adapter differ from the original single 30Hz dyadic sequence.
+
+Missing pelvis gates root-relative features, rather than inventing zero distance.
+Pair pooling uses jointly observed pelvis frames. Per-actor self-attention sees
+valid observed frames; root displacement does not bridge a missing frame and is
+zero at each window start. These mask/boundary conventions are disclosed choices.
+
+Both use *fine-tuned* local CLIP text, a learned projection and positive learned
+logit scale, then bidirectional-average variable-known-positive InfoNCE. This
+is not the immutable frozen CLIP feature adapter; that adapter remains unchanged.
+The existing official asset revision/hashes are verified before local-only loading.
+No vision module is instantiated or optimized. The original dyadic text API
+rejects captions exceeding 77 tokens. The complete-parent group adaptation
+preserves every content BPE token in consecutive 75-content-token segments,
+adds BOS/EOS to each, and averages trainable pooled segment features per caption.
+This long-text adaptation is disclosed, not purported original MIME behavior.
+
+Paper settings are width 512, four layers, four heads, dropout 0.1, AdamW 1e-4,
+weight decay 1e-4 and batch 128; curriculum is the separately implemented
+[training-only parent sampler](PHASESET_V2_MIME_CURRICULUM.md). FFN 2048,
+two-layer GELU frame fusion, ratio epsilon 1e-8 and initial logit scale 1/0.07
+are disclosed implementation choices where author code is not available.
+The existing three MIME short-pilot slots govern eligible choices, not added HPO.
+
+Tests include original-input dimensions, independent full-time attention
+equations, all parameter families, endpoint swap, all K=3 permutations and
+input-gradient equivariance, dropout/checkpoint/RNG equality, missing root steps,
+full timeline and complete text segmentation. Tiny language seams are labeled
+software fixtures; only a real pinned CLIP/native attempt proves that execution.
+Neither software nor native backward qualification proves original-task learning,
+production-host readiness, retrieval performance, scientific claims or release.
diff --git a/src/phaseset_core/mime_language.py b/src/phaseset_core/mime_language.py
new file mode 100644
index 0000000..d839b85
--- /dev/null
+++ b/src/phaseset_core/mime_language.py
@@ -0,0 +1,161 @@
+"""Verified offline CLIP text fine-tuning for MIME, with explicit long text.
+
+The frozen CLIP adapter remains unchanged. This is an independent trainable
+tower, never reuses/mutates its opaque frozen instance or embedding receipts.
+Original dyadic task rejects over-77 tokens; group adaptation preserves all BPE
+content via 75-content-token segments and equal segment pooling per caption.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+from pathlib import Path
+
+import torch
+from torch import Tensor, nn
+from torch.utils.checkpoint import checkpoint
+
+from .frozen_clip_text import PINNED_FILES, TOKEN_BOS_ID, TOKEN_EOS_ID, TOKEN_LENGTH
+
+
+@dataclass(frozen=True)
+class MIMETextTokens:
+    input_ids: Tensor  # [segments,77], CPU integers (not embeddings)
+    attention_mask: Tensor
+    segment_counts: tuple[int, ...]  # one positive count per complete caption
+
+    def __post_init__(self):
+        ids = self.input_ids
+        if ids.dtype != torch.int64 or ids.ndim != 2 or ids.shape[1] != TOKEN_LENGTH:
+            raise ValueError("MIME CLIP needs integer [segments,77] tokens")
+        if self.attention_mask.dtype != torch.bool or self.attention_mask.shape != ids.shape:
+            raise ValueError("token masks must match every segment")
+        if not self.segment_counts or any(type(n) is not int or n < 1 for n in self.segment_counts):
+            raise ValueError("each caption needs at least one segment")
+        if sum(self.segment_counts) != len(ids) or not bool(self.attention_mask.any(1).all()):
+            raise ValueError("segments must exactly cover every caption")
+
+
+class TrainableMIMECLIP(nn.Module):
+    def __init__(
+        self,
+        tower: nn.Module,
+        tokenizer,
+        *,
+        width: int = 512,
+        group_long_text: bool = True,
+        checkpoint_segments: bool = True,
+    ):
+        super().__init__()
+        self.tower = tower
+        self.tokenizer = tokenizer
+        self.projection = nn.Linear(512, width)
+        self.group_long_text = group_long_text
+        self.checkpoint_segments = checkpoint_segments
+        self.tower.requires_grad_(True)
+
+    @classmethod
+    def from_private_snapshot(
+        cls,
+        path: Path,
+        *,
+        width: int = 512,
+        group_long_text: bool = True,
+        checkpoint_segments: bool = True,
+    ):
+        # Verify existing official fixed assets before deserializing; never fetch.
+        for name, size, digest in PINNED_FILES:
+            entry = path / name
+            if entry.stat().st_size != size:
+                raise ValueError("CLIP asset size mismatch")
+            actual = hashlib.sha256()
+            with entry.open("rb") as handle:
+                while block := handle.read(8 * 1024**2):
+                    actual.update(block)
+            if actual.hexdigest() != digest:
+                raise ValueError("CLIP asset digest mismatch")
+        from transformers import CLIPTextModelWithProjection, CLIPTokenizerFast
+
+        tokenizer = CLIPTokenizerFast.from_pretrained(
+            path, local_files_only=True, trust_remote_code=False
+        )
+        with torch.random.fork_rng(devices=[]):
+            torch.manual_seed(0)
+            source, info = CLIPTextModelWithProjection.from_pretrained(
+                path,
+                local_files_only=True,
+                trust_remote_code=False,
+                output_loading_info=True,
+                use_safetensors=False,
+            )
+        if info["missing_keys"] or info["mismatched_keys"] or info["error_msgs"]:
+            raise ValueError("CLIP text initialization is incomplete")
+        # Vision/logit keys exist in the fixed full CLIP checkpoint but no vision
+        # model is instantiated. Drop pretrained projection: MIME learns its own.
+        if any(
+            not (
+                key.startswith("vision_model.")
+                or key in ("visual_projection.weight", "logit_scale")
+            )
+            for key in info["unexpected_keys"]
+        ):
+            raise ValueError("unexpected CLIP checkpoint key")
+        if source.config.hidden_size != 512 or source.config.max_position_embeddings != 77:
+            raise ValueError("wrong CLIP text configuration")
+        if tokenizer.bos_token_id != TOKEN_BOS_ID or tokenizer.eos_token_id != TOKEN_EOS_ID:
+            raise ValueError("wrong CLIP tokenizer")
+        return cls(
+            source.text_model,
+            tokenizer,
+            width=width,
+            group_long_text=group_long_text,
+            checkpoint_segments=checkpoint_segments,
+        )
+
+    def tokenize(self, captions: tuple[str, ...]) -> MIMETextTokens:
+        if not captions or any(type(c) is not str or not c.strip() for c in captions):
+            raise ValueError("MIME requires real nonempty caption rows")
+        raw = self.tokenizer(list(captions), add_special_tokens=False, truncation=False)[
+            "input_ids"
+        ]
+        segments, masks, counts = [], [], []
+        for content in raw:
+            if len(content) > 75 and not self.group_long_text:
+                raise ValueError("TEXT_CONTEXT_LIMIT: original MIME caption exceeds CLIP context")
+            pieces = [content[start : start + 75] for start in range(0, len(content), 75)] or [[]]
+            counts.append(len(pieces))
+            for piece in pieces:
+                values = [TOKEN_BOS_ID, *piece, TOKEN_EOS_ID]
+                segments.append(values + [TOKEN_EOS_ID] * (77 - len(values)))
+                masks.append([True] * len(values) + [False] * (77 - len(values)))
+        return MIMETextTokens(
+            torch.tensor(segments, dtype=torch.int64),
+            torch.tensor(masks, dtype=torch.bool),
+            tuple(counts),
+        )
+
+    def forward(self, tokens: MIMETextTokens) -> Tensor:
+        witness = self.tower.embeddings.token_embedding.weight
+        rows = []
+        for index in range(len(tokens.input_ids)):
+            ids = tokens.input_ids[index : index + 1].to(witness.device)
+            mask = tokens.attention_mask[index : index + 1].to(witness.device)
+
+            def encode(parameter, ids=ids, mask=mask):
+                return self.tower(
+                    input_ids=ids, attention_mask=mask, return_dict=True
+                ).pooler_output[0]
+
+            rows.append(
+                checkpoint(encode, witness, use_reentrant=False, preserve_rng_state=True)
+                if self.checkpoint_segments and torch.is_grad_enabled()
+                else encode(witness)
+            )
+        pooled, start = [], 0
+        for count in tokens.segment_counts:
+            pooled.append(
+                torch.stack(rows[start : start + count]).to(torch.float64).mean(0).float()
+            )
+            start += count
+        return self.projection(torch.stack(pooled))
diff --git a/src/phaseset_core/mime_set.py b/src/phaseset_core/mime_set.py
new file mode 100644
index 0000000..467a68d
--- /dev/null
+++ b/src/phaseset_core/mime_set.py
@@ -0,0 +1,404 @@
+"""MIME's dyadic equations and an explicit shared complete-parent extension.
+
+Independent paper-defined implementation, not released author code. Original
+135D SMPL inputs/role-specific streams and shared body22 multi-person inputs
+are distinct APIs. See docs/MIME_SET_ADAPTATION.md. Language is trainable CLIP
+in mime_language.py; a frozen sentence-vector replacement is not MIME here.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import math
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+from torch.utils.checkpoint import checkpoint
+
+from .tmr_set import TMRGroupCapture, absolute_sincos
+from .wamo_set import positive_contrastive
+
+
+@dataclass(frozen=True)
+class MIMEConfig:
+    width: int = 512
+    heads: int = 4
+    layers: int = 4
+    ff_size: int = 2048
+    dropout: float = 0.1
+    epsilon: float = 1e-8
+
+    def __post_init__(self):
+        if any(
+            type(x) is not int or x < 1 for x in (self.width, self.heads, self.layers, self.ff_size)
+        ):
+            raise ValueError("positive integer model dimensions required")
+        if self.width % 2 or self.width % self.heads:
+            raise ValueError("even width must be divisible by heads")
+        if not (0 <= self.dropout < 1 and math.isfinite(self.epsilon) and self.epsilon > 0):
+            raise ValueError("invalid dropout or root-energy epsilon")
+
+
+def pair_features(
+    a: Tensor,
+    b: Tensor,
+    roots_a: Tensor,
+    roots_b: Tensor,
+    root_delta_a: Tensor,
+    root_delta_b: Tensor,
+    joint_valid: Tensor,
+    epsilon: float = 1e-8,
+) -> tuple[Tensor, Tensor, Tensor]:
+    """Paper Eq.3..6; either original135 or disclosed body22-coordinate inputs."""
+    difference = roots_a - roots_b
+    distance = difference.norm(dim=-1, keepdim=True)
+    aa = torch.cat((a, distance, difference), -1)
+    bb = torch.cat((b, distance, -difference), -1)
+    energy_a = root_delta_a.square().sum(-1, keepdim=True)
+    energy_b = root_delta_b.square().sum(-1, keepdim=True)
+    rho = energy_a / (energy_a + energy_b + epsilon)
+    relation = torch.cat((aa, bb, bb - aa, rho), -1)
+    # Missing pelvis does not become an invented zero-distance interaction.
+    relation = relation.masked_fill(~joint_valid[..., None], 0)
+    root_augmentation = joint_valid[..., None]
+    aa = torch.cat((a, aa[..., a.shape[-1] :].masked_fill(~root_augmentation, 0)), -1)
+    bb = torch.cat((b, bb[..., b.shape[-1] :].masked_fill(~root_augmentation, 0)), -1)
+    return aa, bb, relation
+
+
+class QueryPool(nn.Module):
+    """Original dot-product query pooling, not mean or first-frame pooling."""
+
+    def __init__(self, width: int):
+        super().__init__()
+        self.query = nn.Parameter(torch.randn(width) / math.sqrt(width))
+
+    def forward(self, x: Tensor, valid: Tensor) -> Tensor:
+        if not bool(valid.any(-1).all()):
+            raise ValueError("query pooling needs a jointly observed frame")
+        scores = (x * self.query).sum(-1).masked_fill(~valid, -torch.inf)
+        return (scores.softmax(-1)[..., None] * x).sum(-2)
+
+
+class CoAttentionLayer(nn.Module):
+    """Pre-LN self attention, simultaneous full-time cross attention, GELU FFN.
+
+    Shared=True ties weights across endpoints, not across layers. Shared=False
+    retains original two-role distinct self/cross/FFN blocks. Neither path uses
+    updated A when computing B's cross attention. Padding keys are masked.
+    """
+
+    def __init__(self, config: MIMEConfig, *, shared: bool):
+        super().__init__()
+        self.shared = shared
+        count = 1 if shared else 2
+        self.self_norms = nn.ModuleList([nn.LayerNorm(config.width) for _ in range(count)])
+        self.cross_norms = nn.ModuleList([nn.LayerNorm(config.width) for _ in range(count)])
+        self.ff_norms = nn.ModuleList([nn.LayerNorm(config.width) for _ in range(count)])
+        self.self_attention = nn.ModuleList(
+            [
+                nn.MultiheadAttention(config.width, config.heads, config.dropout, batch_first=True)
+                for _ in range(count)
+            ]
+        )
+        self.cross_attention = nn.ModuleList(
+            [
+                nn.MultiheadAttention(config.width, config.heads, config.dropout, batch_first=True)
+                for _ in range(count)
+            ]
+        )
+        self.ffns = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.Linear(config.width, config.ff_size),
+                    nn.GELU(),
+                    nn.Dropout(config.dropout),
+                    nn.Linear(config.ff_size, config.width),
+                )
+                for _ in range(count)
+            ]
+        )
+        self.residual_dropout = nn.Dropout(config.dropout)
+
+    def forward(
+        self, a: Tensor, b: Tensor, mask_a: Tensor, mask_b: Tensor
+    ) -> tuple[Tensor, Tensor]:
+        masks = (mask_a, mask_b)
+        inputs = (a, b)
+        self_updated = []
+        for endpoint, (x, mask) in enumerate(zip(inputs, masks, strict=True)):
+            index = 0 if self.shared else endpoint
+            normalized = self.self_norms[index](x)
+            attended = self.self_attention[index](
+                normalized, normalized, normalized, key_padding_mask=~mask, need_weights=False
+            )[0]
+            self_updated.append(
+                (x + self.residual_dropout(attended)).masked_fill(~mask[..., None], 0)
+            )
+        cross_sources = tuple(
+            self.cross_norms[0 if self.shared else endpoint](x)
+            for endpoint, x in enumerate(self_updated)
+        )
+        outputs = []
+        for endpoint, (x, mask) in enumerate(zip(self_updated, masks, strict=True)):
+            index = 0 if self.shared else endpoint
+            other = 1 - endpoint
+            attended = self.cross_attention[index](
+                cross_sources[endpoint],
+                cross_sources[other],
+                cross_sources[other],
+                key_padding_mask=~masks[other],
+                need_weights=False,
+            )[0]
+            crossed = x + self.residual_dropout(attended)
+            output = crossed + self.residual_dropout(
+                self.ffns[index](self.ff_norms[index](crossed))
+            )
+            outputs.append(output.masked_fill(~mask[..., None], 0))
+        return outputs[0], outputs[1]
+
+
+class OriginalMIMEMotion(nn.Module):
+    """Original role-aware dyadic motion architecture; not the group adapter.
+
+    Inputs [B,2,T,135] must truly be root displacement+22 local6Drotations;
+    roots [B,2,T,3] supply absolute shared-frame pelvis positions. No conversion
+    of body22 XYZ to fictitious rotations happens here. No internal truncation.
+    Original Inter-X host separately enforces its disclosed <=300/30fps task.
+    """
+
+    def __init__(self, config: MIMEConfig = MIMEConfig()):
+        super().__init__()
+        self.config = config
+        self.projections = nn.ModuleList([nn.Linear(139, config.width) for _ in range(2)])
+        self.norms = nn.ModuleList([nn.LayerNorm(config.width) for _ in range(2)])
+        self.roles = nn.Parameter(torch.randn(2, config.width) / math.sqrt(config.width))
+        self.relation = nn.Linear(418, config.width)
+        self.relation_norm = nn.LayerNorm(config.width)
+        self.relation_type = nn.Parameter(torch.randn(config.width) / math.sqrt(config.width))
+        self.blocks = nn.ModuleList(
+            [CoAttentionLayer(config, shared=False) for _ in range(config.layers)]
+        )
+        self.fusion = nn.Sequential(
+            nn.Linear(2 * config.width, config.width),
+            nn.GELU(),
+            nn.Linear(config.width, config.width),
+        )
+        self.pool = QueryPool(config.width)
+
+    def forward(self, motion: Tensor, roots: Tensor, mask: Tensor) -> Tensor:
+        if motion.ndim != 4 or motion.shape[1] != 2 or motion.shape[-1] != 135:
+            raise ValueError("original MIME needs true [B,2,T,135] dyadic SMPL inputs")
+        if (
+            roots.shape != (*motion.shape[:3], 3)
+            or mask.shape != motion.shape[:3]
+            or mask.dtype != torch.bool
+        ):
+            raise ValueError("root positions and masks must match both motion streams")
+        if not bool(mask.any(-1).all()) or not bool((mask[:, 0] & mask[:, 1]).any(-1).all()):
+            raise ValueError("original dyad needs valid actor and jointly valid frames")
+        if not bool(torch.isfinite(motion).all()) or not bool(torch.isfinite(roots).all()):
+            raise ValueError("original dyad must be finite")
+        joint = mask[:, 0] & mask[:, 1]
+        aa, bb, relation = pair_features(
+            motion[:, 0],
+            motion[:, 1],
+            roots[:, 0],
+            roots[:, 1],
+            motion[:, 0, :, :3],
+            motion[:, 1, :, :3],
+            joint,
+            self.config.epsilon,
+        )
+        rel = (self.relation_norm(self.relation(relation)) + self.relation_type).masked_fill(
+            ~joint[..., None], 0
+        )
+        positions = absolute_sincos(
+            torch.arange(motion.shape[2], device=motion.device), self.config.width
+        )
+        streams = [
+            (self.norms[i](self.projections[i](x)) + self.roles[i] + positions + rel).masked_fill(
+                ~mask[:, i, :, None], 0
+            )
+            for i, x in enumerate((aa, bb))
+        ]
+        a, b = streams
+        for block in self.blocks:
+            a, b = block(a, b, mask[:, 0], mask[:, 1])
+        return self.pool(self.fusion(torch.cat((a, b), -1)), joint)
+
+
+class MIMESetMotion(nn.Module):
+    """Shared body22 endpoint/co-attention model over every unordered edge.
+
+    Directed relational encoding is injected into its own endpoint. Shared
+    stream weights plus symmetric sum/absolute-difference/product fusion give
+    an unordered pair embedding. Canonical lineage order fixes RNG/reductions;
+    lineage is not a neural input. Every window's all edges enter its mean,
+    followed by an absolute-gap-aware full-parent temporal readout.
+    """
+
+    system_id = "MIME-Set"
+
+    def __init__(self, config: MIMEConfig = MIMEConfig(), *, checkpoint_edges: bool = True):
+        super().__init__()
+        self.config = config
+        self.checkpoint_edges = checkpoint_edges
+        self.actor_projection = nn.Linear(70, config.width)
+        self.actor_norm = nn.LayerNorm(config.width)
+        self.relation_projection = nn.Linear(211, config.width)
+        self.relation_norm = nn.LayerNorm(config.width)
+        self.relation_type = nn.Parameter(torch.randn(config.width) / math.sqrt(config.width))
+        self.blocks = nn.ModuleList(
+            [CoAttentionLayer(config, shared=True) for _ in range(config.layers)]
+        )
+        self.fusion = nn.Sequential(
+            nn.Linear(3 * config.width, config.width),
+            nn.GELU(),
+            nn.Linear(config.width, config.width),
+        )
+        self.pool = QueryPool(config.width)
+        layer = nn.TransformerEncoderLayer(
+            config.width,
+            config.heads,
+            config.ff_size,
+            config.dropout,
+            activation="gelu",
+            batch_first=True,
+            norm_first=True,
+        )
+        self.capture_transformer = nn.TransformerEncoder(
+            layer, config.layers, enable_nested_tensor=False
+        )
+        self.capture_pool = QueryPool(config.width)
+
+    @staticmethod
+    def root_delta(x: Tensor, observed: Tensor) -> Tensor:
+        roots = x[..., :3]
+        result = torch.cat((torch.zeros_like(roots[:, :1]), roots[:, 1:] - roots[:, :-1]), 1)
+        valid = observed[..., :3].all(-1)
+        adjacent = torch.cat((torch.zeros_like(valid[:, :1]), valid[:, 1:] & valid[:, :-1]), 1)
+        return result.masked_fill(~adjacent[..., None], 0)
+
+    def pair(
+        self,
+        a: Tensor,
+        b: Tensor,
+        observed_a: Tensor,
+        observed_b: Tensor,
+        delta_a: Tensor,
+        delta_b: Tensor,
+    ) -> Tensor:
+        mask_a, mask_b = observed_a.any(-1), observed_b.any(-1)
+        joint = observed_a[..., :3].all(-1) & observed_b[..., :3].all(-1)
+        aa, bb, ab = pair_features(
+            a, b, a[..., :3], b[..., :3], delta_a, delta_b, joint, self.config.epsilon
+        )
+        _, _, ba = pair_features(
+            b, a, b[..., :3], a[..., :3], delta_b, delta_a, joint, self.config.epsilon
+        )
+        times = absolute_sincos(torch.arange(a.shape[1], device=a.device), self.config.width)
+        ra = (self.relation_norm(self.relation_projection(ab)) + self.relation_type).masked_fill(
+            ~joint[..., None], 0
+        )
+        rb = (self.relation_norm(self.relation_projection(ba)) + self.relation_type).masked_fill(
+            ~joint[..., None], 0
+        )
+        a = (self.actor_norm(self.actor_projection(aa)) + times + ra).masked_fill(
+            ~mask_a[..., None], 0
+        )
+        b = (self.actor_norm(self.actor_projection(bb)) + times + rb).masked_fill(
+            ~mask_b[..., None], 0
+        )
+        for block in self.blocks:
+            a, b = block(a, b, mask_a, mask_b)
+        symmetric = torch.cat((a + b, (a - b).abs(), a * b), -1)
+        return self.pool(self.fusion(symmetric), joint)
+
+    def forward(self, capture: TMRGroupCapture) -> Tensor:
+        device = self.actor_projection.weight.device
+        windows = []
+        for window in capture.windows:
+            x, observed = window.canonical(device)
+            deltas = self.root_delta(x, observed)
+            total = torch.zeros(self.config.width, device=device, dtype=torch.float64)
+            count = 0
+            for i in range(len(x)):
+                for j in range(i + 1, len(x)):
+
+                    def encode(witness, i=i, j=j, x=x, observed=observed, deltas=deltas):
+                        return self.pair(
+                            x[i : i + 1],
+                            x[j : j + 1],
+                            observed[i : i + 1],
+                            observed[j : j + 1],
+                            deltas[i : i + 1],
+                            deltas[j : j + 1],
+                        )[0]
+
+                    value = (
+                        checkpoint(
+                            encode,
+                            self.actor_projection.weight,
+                            use_reentrant=False,
+                            preserve_rng_state=True,
+                        )
+                        if self.checkpoint_edges and torch.is_grad_enabled()
+                        else encode(self.actor_projection.weight)
+                    )
+                    total = total + value.to(torch.float64)
+                    count += 1
+            windows.append((total / count).float())
+        times = torch.tensor([window.start_seconds for window in capture.windows], device=device)
+        sequence = torch.stack(windows)[None] + absolute_sincos(times, self.config.width)[None]
+        mask = torch.ones(1, len(windows), dtype=torch.bool, device=device)
+        encoded = self.capture_transformer(sequence, src_key_padding_mask=~mask)
+        return self.capture_pool(encoded, mask)[0]
+
+
+class MIMERetrieval(nn.Module):
+    """Motion + *trainable* CLIP language + learned positive logit scale.
+
+    Language module owns verified local tokenizer/tower and accepts text tokens,
+    not frozen sentence embeddings. Host owns curriculum/splits/optimizer.
+    """
+
+    def __init__(self, motion: nn.Module, language: nn.Module, *, initial_scale: float = 1 / 0.07):
+        super().__init__()
+        self.motion = motion
+        self.language = language
+        if not math.isfinite(initial_scale) or initial_scale <= 0:
+            raise ValueError("positive logit-scale initialization needed")
+        self.logit_scale = nn.Parameter(torch.tensor(math.log(initial_scale)))
+
+    def score(self, captures: tuple[TMRGroupCapture, ...], tokens) -> Tensor:
+        motion = torch.stack([self.motion(capture) for capture in captures])
+        text = self.language(tokens)
+        return self.logit_scale.exp() * (F.normalize(motion, dim=1) @ F.normalize(text, dim=1).T)
+
+    def compute_loss(
+        self, captures: tuple[TMRGroupCapture, ...], tokens, positive: Tensor
+    ) -> Tensor:
+        scores = self.score(captures, tokens)
+        # MIME original averages the two directions (unlike WaMo's sum).
+        return positive_contrastive(scores, positive.to(scores.device)) / 2
+
+    def score_dyads(self, motion: Tensor, roots: Tensor, mask: Tensor, tokens) -> Tensor:
+        if not isinstance(self.motion, OriginalMIMEMotion):
+            raise ValueError(
+                "original dyadic scoring requires original role-specific motion encoder"
+            )
+        if any(count != 1 for count in tokens.segment_counts):
+            raise ValueError(
+                "TEXT_CONTEXT_LIMIT: original MIME does not use group text segmentation"
+            )
+        encoded = self.motion(motion, roots, mask)
+        text = self.language(tokens)
+        return self.logit_scale.exp() * (F.normalize(encoded, dim=1) @ F.normalize(text, dim=1).T)
+
+    def compute_loss_dyads(
+        self, motion: Tensor, roots: Tensor, mask: Tensor, tokens, positive: Tensor
+    ) -> Tensor:
+        scores = self.score_dyads(motion, roots, mask, tokens)
+        return positive_contrastive(scores, positive.to(scores.device)) / 2
diff --git a/tests/test_mime_set.py b/tests/test_mime_set.py
new file mode 100644
index 0000000..789002c
--- /dev/null
+++ b/tests/test_mime_set.py
@@ -0,0 +1,309 @@
+"""Analytic software checks, not original-task or human-data performance."""
+
+from dataclasses import replace
+import itertools
+import math
+from types import SimpleNamespace
+
+import pytest
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from phaseset_core.mime_language import MIMETextTokens, TrainableMIMECLIP
+from phaseset_core.mime_set import (
+    CoAttentionLayer,
+    MIMEConfig,
+    MIMERetrieval,
+    MIMESetMotion,
+    OriginalMIMEMotion,
+    pair_features,
+)
+from phaseset_core.tmr_set import TMRGroupCapture, TMRGroupWindow
+
+
+def capture(*, k=3, length=13, grad=False):
+    keys = tuple(bytes([i + 1]) * 32 for i in range(k))
+    windows = []
+    for start in (0.0, 40.0):
+        x = torch.randn(k, length, 66)
+        mask = torch.ones_like(x, dtype=torch.bool)
+        if length > 1:
+            mask[0, 0] = False
+        windows.append(
+            TMRGroupWindow(x.masked_fill(~mask, 0).requires_grad_(grad), mask, keys, start)
+        )
+    return TMRGroupCapture(tuple(windows))
+
+
+def reordered(parent, order):
+    return TMRGroupCapture(
+        tuple(
+            replace(
+                window,
+                features=window.features[list(order)],
+                observed=window.observed[list(order)],
+                actor_commitments=tuple(window.actor_commitments[i] for i in order),
+            )
+            for window in parent.windows
+        )
+    )
+
+
+def config(dropout=0.0):
+    return MIMEConfig(width=16, layers=1, ff_size=32, dropout=dropout)
+
+
+class FixtureTokenizer:
+    """Deterministic content tokens for software-only segmentation tests."""
+
+    def __call__(self, captions, *, add_special_tokens, truncation):
+        assert not add_special_tokens and not truncation
+        return {"input_ids": [[i % 127 for i, _ in enumerate(c.split())] for c in captions]}
+
+
+class FixtureTower(nn.Module):
+    """Tiny differentiable seam, never claimed to be actual pretrained CLIP."""
+
+    def __init__(self):
+        super().__init__()
+        self.embeddings = nn.Module()
+        self.embeddings.token_embedding = nn.Embedding(128, 512)
+        self.mix = nn.Linear(512, 512)
+
+    def forward(self, input_ids, attention_mask, return_dict):
+        x = self.embeddings.token_embedding(input_ids % 128)
+        x = (x * attention_mask[..., None]).sum(1) / attention_mask.sum(1)[:, None]
+        return SimpleNamespace(pooler_output=self.mix(x))
+
+
+def language(width=16, checkpoint=True, group=True):
+    return TrainableMIMECLIP(
+        FixtureTower(),
+        FixtureTokenizer(),
+        width=width,
+        checkpoint_segments=checkpoint,
+        group_long_text=group,
+    )
+
+
+def manual_attention(module, query, key, value, key_mask):
+    width, heads = module.embed_dim, module.num_heads
+    projections = []
+    for index, x in enumerate((query, key, value)):
+        projected = F.linear(
+            x,
+            module.in_proj_weight[index * width : (index + 1) * width],
+            module.in_proj_bias[index * width : (index + 1) * width],
+        )
+        projections.append(
+            projected.reshape(len(x), x.shape[1], heads, width // heads).transpose(1, 2)
+        )
+    q, k, v = projections
+    scores = (q @ k.transpose(-1, -2)) / math.sqrt(width / heads)
+    probabilities = scores.masked_fill(~key_mask[:, None, None], -torch.inf).softmax(-1)
+    attended = (probabilities @ v).transpose(1, 2).reshape(len(query), query.shape[1], width)
+    return F.linear(attended, module.out_proj.weight, module.out_proj.bias)
+
+
+@pytest.mark.parametrize("shared", [False, True])
+def test_bidirectional_full_time_attention_matches_independent_equations(shared):
+    torch.manual_seed(1729)
+    block = CoAttentionLayer(config(), shared=shared).eval()
+    a, b = torch.randn(2, 9, 16), torch.randn(2, 9, 16)
+    ma, mb = torch.ones(2, 9, dtype=torch.bool), torch.ones(2, 9, dtype=torch.bool)
+    mb[0, -2:] = False
+    updates = []
+    for endpoint, (x, mask) in enumerate(((a, ma), (b, mb))):
+        index = 0 if shared else endpoint
+        z = block.self_norms[index](x)
+        updates.append(
+            (x + manual_attention(block.self_attention[index], z, z, z, mask)).masked_fill(
+                ~mask[..., None], 0
+            )
+        )
+    sources = [block.cross_norms[0 if shared else i](x) for i, x in enumerate(updates)]
+    expected = []
+    for endpoint, mask in enumerate((ma, mb)):
+        index = 0 if shared else endpoint
+        other = 1 - endpoint
+        crossed = updates[endpoint] + manual_attention(
+            block.cross_attention[index],
+            sources[endpoint],
+            sources[other],
+            sources[other],
+            (ma, mb)[other],
+        )
+        expected.append(
+            (crossed + block.ffns[index](block.ff_norms[index](crossed))).masked_fill(
+                ~mask[..., None], 0
+            )
+        )
+    for actual, oracle in zip(block(a, b, ma, mb), expected, strict=True):
+        torch.testing.assert_close(actual, oracle, rtol=2e-6, atol=3e-7)
+
+
+def test_original_root_distance_displacement_delta_ratio_and_dimensions():
+    torch.manual_seed(2718)
+    a, b = torch.randn(2, 7, 135), torch.randn(2, 7, 135)
+    ra, rb = torch.randn(2, 7, 3), torch.randn(2, 7, 3)
+    mask = torch.ones(2, 7, dtype=torch.bool)
+    aa, bb, r = pair_features(a, b, ra, rb, a[..., :3], b[..., :3], mask)
+    assert aa.shape[-1] == bb.shape[-1] == 139 and r.shape[-1] == 418
+    assert torch.equal(aa[..., :135], a) and torch.equal(bb[..., :135], b)
+    assert torch.equal(aa[..., -3:], ra - rb) and torch.equal(bb[..., -3:], rb - ra)
+    torch.testing.assert_close(
+        r[..., -1],
+        a[..., :3].square().sum(-1)
+        / (a[..., :3].square().sum(-1) + b[..., :3].square().sum(-1) + 1e-8),
+    )
+    aa, bb, r = pair_features(a, b, ra, rb, a[..., :3], b[..., :3], ~mask)
+    assert torch.count_nonzero(r) == 0
+    assert torch.count_nonzero(aa[..., -4:]) == torch.count_nonzero(bb[..., -4:]) == 0
+
+
+def test_original_default_roles_and_full_dyadic_loss_all_gradients():
+    torch.manual_seed(1729)
+    motion = OriginalMIMEMotion()
+    assert motion.config == MIMEConfig()
+    assert motion.projections[0].weight is not motion.projections[1].weight
+    model = MIMERetrieval(motion, language(512))
+    tokens = model.language.tokenize(
+        ("first software sentence", "second test sentence", "third fixture")
+    )
+    x, roots = torch.randn(2, 2, 13, 135), torch.randn(2, 2, 13, 3)
+    mask = torch.ones(2, 2, 13, dtype=torch.bool)
+    positive = torch.tensor([[True, True, False], [False, False, True]])
+    loss = model.compute_loss_dyads(x, roots, mask, tokens, positive)
+    loss.backward()
+    for name, parameter in model.named_parameters():
+        assert parameter.grad is not None and parameter.grad.isfinite().all(), name
+        assert parameter.grad.abs().sum() > 0, name
+    with pytest.raises(ValueError, match="135"):
+        motion(x[..., :66], roots, mask)
+    # Enforce rejection at the integrated original scoring/loss boundary even
+    # if a caller supplies tokens from the group-compatible language factory.
+    long_tokens = model.language.tokenize((" ".join("word" for _ in range(180)),))
+    assert long_tokens.segment_counts == (3,)
+    with pytest.raises(ValueError, match="TEXT_CONTEXT_LIMIT"):
+        model.score_dyads(x, roots, mask, long_tokens)
+    with pytest.raises(ValueError, match="TEXT_CONTEXT_LIMIT"):
+        model.compute_loss_dyads(x, roots, mask, long_tokens, torch.ones(2, 1, dtype=torch.bool))
+
+
+def test_group_all_permutations_full_loss_and_input_gradients():
+    torch.manual_seed(1729)
+    model = MIMERetrieval(MIMESetMotion(config()), language()).eval()
+    parent = capture(grad=True)
+    other = capture()
+    tokens = model.language.tokenize(("one software fixture", "two fixture", "three"))
+    positive = torch.tensor([[True, True, False], [False, False, True]])
+    score = model.score((parent, other), tokens).detach()
+    expected = model.compute_loss((parent, other), tokens, positive)
+    expected.backward()
+    gradients = [window.features.grad.clone() for window in parent.windows]
+    for order in itertools.permutations(range(3)):
+        moved = reordered(parent, order)
+        assert torch.equal(model.score((moved, other), tokens).detach(), score)
+        actual = model.compute_loss((moved, other), tokens, positive)
+        assert torch.equal(actual, expected)
+        for window in moved.windows:
+            window.features.retain_grad()
+        actual.backward()
+        for window, grad in zip(moved.windows, gradients, strict=True):
+            assert torch.equal(window.features.grad, grad[list(order)])
+
+
+def test_group_pair_endpoint_swap_is_structurally_symmetric():
+    torch.manual_seed(1729)
+    model = MIMESetMotion(config()).eval()
+    window = capture().windows[0]
+    x, observed = window.features, window.observed
+    delta = model.root_delta(x, observed)
+    ab = model.pair(x[0:1], x[1:2], observed[0:1], observed[1:2], delta[0:1], delta[1:2])
+    ba = model.pair(x[1:2], x[0:1], observed[1:2], observed[0:1], delta[1:2], delta[0:1])
+    assert torch.equal(ab, ba)
+
+
+def test_checkpoint_edge_and_trainable_language_loss_all_gradients_rng():
+    torch.manual_seed(1729)
+    direct = MIMERetrieval(
+        MIMESetMotion(config(0.1), checkpoint_edges=False), language(checkpoint=False)
+    )
+    replay = MIMERetrieval(
+        MIMESetMotion(config(0.1), checkpoint_edges=True), language(checkpoint=True)
+    )
+    replay.load_state_dict(direct.state_dict())
+    parents = (capture(), capture())
+    tokens = direct.language.tokenize(("one two three", "four", "five six"))
+    positive = torch.tensor([[True, True, False], [False, False, True]])
+    values = []
+    for model in (direct, replay):
+        torch.manual_seed(31415)
+        loss = model.compute_loss(parents, tokens, positive)
+        loss.backward()
+        values.append(
+            (loss, torch.get_rng_state(), {n: p.grad.clone() for n, p in model.named_parameters()})
+        )
+    assert torch.equal(values[0][0], values[1][0]) and torch.equal(values[0][1], values[1][1])
+    for name in values[0][2]:
+        assert torch.equal(values[0][2][name], values[1][2][name]), name
+
+
+def test_group_default_complete_objective_all_trainable_gradients():
+    torch.manual_seed(1729)
+    model = MIMERetrieval(MIMESetMotion(), language(512))
+    tokens = model.language.tokenize(("one software sentence", "two words", "three fixture"))
+    positive = torch.tensor([[True, True, False], [False, False, True]])
+    model.compute_loss((capture(), capture()), tokens, positive).backward()
+    for name, parameter in model.named_parameters():
+        assert parameter.grad is not None and parameter.grad.isfinite().all(), name
+        assert parameter.grad.abs().sum() > 0, name
+
+
+def test_all_windows_absolute_gaps_and_dynamic_k_without_ordinal_bank():
+    torch.manual_seed(1729)
+    model = MIMESetMotion(config()).eval()
+    parent = capture(k=8, length=1)
+    score = model(parent)
+    short = TMRGroupCapture((parent.windows[0],))
+    gap = TMRGroupCapture((parent.windows[0], replace(parent.windows[1], start_seconds=400)))
+    assert not torch.equal(score, model(short)) and not torch.equal(score, model(gap))
+    late = TMRGroupCapture(
+        (parent.windows[0], replace(parent.windows[1], features=parent.windows[1].features + 1))
+    )
+    assert not torch.equal(score, model(late))
+    assert not any(isinstance(module, nn.Embedding) for module in model.modules())
+
+
+def test_missing_root_steps_do_not_bridge_unobserved_frames():
+    x = torch.arange(6, dtype=torch.float32)[None, :, None].expand(2, 6, 66)
+    observed = torch.ones_like(x, dtype=torch.bool)
+    observed[:, 2] = False
+    delta = MIMESetMotion.root_delta(x.masked_fill(~observed, 0), observed)
+    assert torch.count_nonzero(delta[:, [0, 2, 3]]) == 0
+    assert torch.equal(delta[:, [1, 4, 5]], torch.ones(2, 3, 3))
+
+
+def test_complete_caption_segments_keep_every_content_token_and_original_rejects():
+    module = language()
+    short, long = "first short", " ".join("word" for _ in range(180))
+    tokens = module.tokenize((short, long))
+    assert tokens.segment_counts == (1, 3)
+    content = []
+    for ids, mask in zip(tokens.input_ids[1:], tokens.attention_mask[1:], strict=True):
+        valid = ids[mask]
+        assert valid[0] == 49406 and valid[-1] == 49407
+        content.extend(valid[1:-1].tolist())
+    assert content == [i % 127 for i in range(180)]
+    with pytest.raises(ValueError, match="TEXT_CONTEXT_LIMIT"):
+        language(group=False).tokenize((long,))
+    output = module(tokens)
+    assert output.shape == (2, 16)
+    output.square().mean().backward()
+    assert all(
+        p.requires_grad and p.grad is not None and p.grad.abs().sum() > 0
+        for p in module.tower.parameters()
+    )
+    with pytest.raises(ValueError, match="exactly cover"):
+        MIMETextTokens(tokens.input_ids, tokens.attention_mask, (1, 1))

From e76c4bdba89479de565c48aa765778d953e9fd21 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 06:02:18 -0500
Subject: [PATCH 46/95] fix: use strict extracted CLIP transformer API and
 actual component oracle

---
 RELEASE_FILES.sha256               |  6 ++--
 docs/MIME_SET_ADAPTATION.md        |  4 +++
 src/phaseset_core/mime_language.py |  7 ++--
 tests/test_mime_set.py             | 51 +++++++++++++++++++++++++++++-
 4 files changed, 61 insertions(+), 7 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 792e183..4b1c3ac 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -35,7 +35,7 @@ d03a1569556beb1b3ad7f7f501d7bdc40e271f6912c173e403f75458fd6ed238	6158	docs/HOST_
 e38d6f254f71d0027a0b82e1dc3ec95d23a9041831d21d41e96acf10b81e155f	19223	docs/HOST_RUNTIME_INTEGRATION.md
 5dd898347d3646cde4a74e10afa8ba1d12703e3d0c6df51e5113659a31808086	3606	docs/LATENCY_PROGRESS_JOURNAL.md
 f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
-3475ce4b97f5e4bfe1746b4997130d8c5a4011641244f302c445e538f5cf8fb1	3896	docs/MIME_SET_ADAPTATION.md
+8490b9076c14891cd93b5d91743f5ef58dd96d13d28d334cfa5d3372e1fd44b7	4208	docs/MIME_SET_ADAPTATION.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 1cbb857ba962b29eae75d206aa49657f481e559085e062a2ef51ab4a6d87a220	3440	docs/PERIODIC_CACHE_EXECUTION.md
 a9e215279d354999c1f949f776a131b8cf4cec850abd81459c5c0d8cee0c8b03	4356	docs/PERIODIC_CAPTURE_TRAINING_CACHE.md
@@ -179,7 +179,7 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 877302cdb2ad891eb064d5bb228d124b66042865b78fa3826f66cf7bacf01166	68520	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
-e21cfa4186b6cbaecf122d364b72004075cf9b15a35b96a78e000bd9a8809a3e	6805	src/phaseset_core/mime_language.py
+f0d7c36ecda4326ca2b1f9461fdc812039cae33739595c60d40a436f0051edb7	6976	src/phaseset_core/mime_language.py
 f7cb1c0893b0b504359e34e39dd24651aaa86c1b65e98d2ed0469c6d5ce84d4d	6920	src/phaseset_core/mime_parent_curriculum.py
 c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phaseset_core/mime_set.py
 9e7fa84554d5676f8ef75049b17b53580395c3c60be0f75fcb55996ccc8ef36d	37720	src/phaseset_core/models.py
@@ -223,7 +223,7 @@ e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/tes
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 b65a2178d7faf25372adeccdfcd19ee7804544665c6059f2d684801ccff83e11	6050	tests/test_mime_parent_curriculum.py
-10e507b64ce40e1a98a93833d0496b8d38d07977bd4ec3b0196e0f07e81cd0d4	12916	tests/test_mime_set.py
+773430e42927751a5b07ab20da7ddd716d13853f9496033476b2926198148ef8	14947	tests/test_mime_set.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 3b23c2b08ed81029ca6239cbb63b98093077786163aaf5bb47ccfefb21c6ef24	7117	tests/test_parent_capture.py
 32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
diff --git a/docs/MIME_SET_ADAPTATION.md b/docs/MIME_SET_ADAPTATION.md
index 00fc687..b73116a 100644
--- a/docs/MIME_SET_ADAPTATION.md
+++ b/docs/MIME_SET_ADAPTATION.md
@@ -53,5 +53,9 @@ equations, all parameter families, endpoint swap, all K=3 permutations and
 input-gradient equivariance, dropout/checkpoint/RNG equality, missing root steps,
 full timeline and complete text segmentation. Tiny language seams are labeled
 software fixtures; only a real pinned CLIP/native attempt proves that execution.
+When Transformers is installed, a random small actual CLIP component also
+checks the extracted-transformer API and checkpoint/direct gradient/RNG equality;
+its outer model constructor initializes the attention backend, as in asset loading.
+That component is not a pretrained-language or real-data qualification.
 Neither software nor native backward qualification proves original-task learning,
 production-host readiness, retrieval performance, scientific claims or release.
diff --git a/src/phaseset_core/mime_language.py b/src/phaseset_core/mime_language.py
index d839b85..c7a4ce4 100644
--- a/src/phaseset_core/mime_language.py
+++ b/src/phaseset_core/mime_language.py
@@ -143,9 +143,10 @@ def forward(self, tokens: MIMETextTokens) -> Tensor:
             mask = tokens.attention_mask[index : index + 1].to(witness.device)
 
             def encode(parameter, ids=ids, mask=mask):
-                return self.tower(
-                    input_ids=ids, attention_mask=mask, return_dict=True
-                ).pooler_output[0]
+                # The extracted CLIPTextTransformer returns a pooled ModelOutput
+                # directly; unlike the outer pretrained model it has no
+                # return_dict keyword in the frozen Transformers runtime.
+                return self.tower(input_ids=ids, attention_mask=mask).pooler_output[0]
 
             rows.append(
                 checkpoint(encode, witness, use_reentrant=False, preserve_rng_state=True)
diff --git a/tests/test_mime_set.py b/tests/test_mime_set.py
index 789002c..3628485 100644
--- a/tests/test_mime_set.py
+++ b/tests/test_mime_set.py
@@ -1,6 +1,7 @@
 """Analytic software checks, not original-task or human-data performance."""
 
 from dataclasses import replace
+import copy
 import itertools
 import math
 from types import SimpleNamespace
@@ -71,7 +72,7 @@ def __init__(self):
         self.embeddings.token_embedding = nn.Embedding(128, 512)
         self.mix = nn.Linear(512, 512)
 
-    def forward(self, input_ids, attention_mask, return_dict):
+    def forward(self, input_ids, attention_mask):
         x = self.embeddings.token_embedding(input_ids % 128)
         x = (x * attention_mask[..., None]).sum(1) / attention_mask.sum(1)[:, None]
         return SimpleNamespace(pooler_output=self.mix(x))
@@ -87,6 +88,54 @@ def language(width=16, checkpoint=True, group=True):
     )
 
 
+def test_actual_clip_transformer_api_and_checkpoint_gradients():
+    """Random tiny CLIP component, not pretrained assets or benchmark evidence.
+
+    Use the actual extracted transformer API, not just a permissive mock.
+    Transformers is optional in data-free installs; the fixed server has it.
+    """
+    module = pytest.importorskip("transformers.models.clip.modeling_clip")
+    from transformers import CLIPTextConfig
+
+    torch.manual_seed(1729)
+    # Match the asset factory: the pretrained-model constructor selects an
+    # actual attention backend before its extracted transformer is called.
+    # A naked internal transformer leaves config._attn_implementation=None.
+    tower = module.CLIPTextModel(
+        CLIPTextConfig(
+            vocab_size=128,
+            hidden_size=512,
+            intermediate_size=32,
+            num_hidden_layers=1,
+            num_attention_heads=4,
+            max_position_embeddings=77,
+            bos_token_id=126,
+            eos_token_id=127,
+            pad_token_id=127,
+            attention_dropout=0.1,
+        )
+    ).text_model
+    projected = TrainableMIMECLIP(tower, FixtureTokenizer(), width=16)
+    direct = copy.deepcopy(projected)
+    direct.checkpoint_segments = False
+    ids = torch.full((2, 77), 127, dtype=torch.int64)
+    ids[:, :4] = torch.tensor([[126, 5, 6, 127], [126, 8, 9, 127]])
+    masks = torch.zeros_like(ids, dtype=torch.bool)
+    masks[:, :4] = True
+    tokens = MIMETextTokens(ids, masks, (1, 1))
+    rng = torch.get_rng_state()
+    output = projected(tokens)
+    output.square().sum().backward()
+    after = torch.get_rng_state()
+    torch.set_rng_state(rng)
+    oracle = direct(tokens)
+    oracle.square().sum().backward()
+    assert torch.equal(output, oracle) and torch.equal(after, torch.get_rng_state())
+    for parameter, expected in zip(projected.parameters(), direct.parameters(), strict=True):
+        assert parameter.grad is not None and torch.isfinite(parameter.grad).all()
+        assert torch.equal(parameter.grad, expected.grad)
+
+
 def manual_attention(module, query, key, value, key_mask):
     width, heads = module.embed_dim, module.num_heads
     projections = []

From d26f8bde5696975d4db24ddb9cdcf773d6841edb Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 06:20:58 -0500
Subject: [PATCH 47/95] feat: replay full-negative WaMo and trainable MIME
 parent gradients

---
 README.md                                     |   4 +
 RELEASE_FILES.sha256                          |   5 +-
 .../PHASESET_V2_LITERATURE_PARENT_TRAINING.md |  38 +++
 .../literature_parent_training.py             | 219 ++++++++++++++++++
 tests/test_literature_parent_training.py      | 156 +++++++++++++
 5 files changed, 421 insertions(+), 1 deletion(-)
 create mode 100644 docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 create mode 100644 src/phaseset_core/literature_parent_training.py
 create mode 100644 tests/test_literature_parent_training.py

diff --git a/README.md b/README.md
index c414e6c..29343cd 100644
--- a/README.md
+++ b/README.md
@@ -59,6 +59,10 @@ choices. Its software qualification is not converged baseline training.
 and shared complete-parent group APIs, full-time bidirectional co-attention,
 and a genuinely trainable pinned CLIP text tower. Group XYZ/long-text adapters
 are disclosed; equation/software checks are not original-task convergence.
+Their [full-negative parent backward seam](docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md)
+replays one complete capture graph at a time without dropping original WaMo
+auxiliary objectives or freezing MIME text. Dense-gradient software oracles
+are not native optimizer, profile, checkpoint-host or convergence evidence.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 4b1c3ac..3e952e7 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-e29a56cc389fedddbe677b68dd288535203ba5e96869d5a2dd51bd6dbe883547	11238	README.md
+3670d7b895a047e39b91c95a9eb739859476d0f58fe8da0281f02ffe5d0579a8	11557	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -71,6 +71,7 @@ cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHAS
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
+719dbbd7fb6f9f0daa105e863fe8039ba94c162d1f3c623cb9ae6e92b9a85678	2547	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
 3eb5f4237db4af02d49e8eb62a318ef68ac706bf14cc1b405bf9fa837e8d5705	7431	docs/PHASESET_V2_PARENT_HOST.md
@@ -179,6 +180,7 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 877302cdb2ad891eb064d5bb228d124b66042865b78fa3826f66cf7bacf01166	68520	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
+02a11d20ac299c4886479b863f2f614512365a9f1fe800861b2f261dbdad1c7a	9101	src/phaseset_core/literature_parent_training.py
 f0d7c36ecda4326ca2b1f9461fdc812039cae33739595c60d40a436f0051edb7	6976	src/phaseset_core/mime_language.py
 f7cb1c0893b0b504359e34e39dd24651aaa86c1b65e98d2ed0469c6d5ce84d4d	6920	src/phaseset_core/mime_parent_curriculum.py
 c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phaseset_core/mime_set.py
@@ -222,6 +224,7 @@ e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/tes
 2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
+4b4193cc100b1076e31fe8c9f3fd1801b0270368afd80eca93c6cce0e671482a	6224	tests/test_literature_parent_training.py
 b65a2178d7faf25372adeccdfcd19ee7804544665c6059f2d684801ccff83e11	6050	tests/test_mime_parent_curriculum.py
 773430e42927751a5b07ab20da7ddd716d13853f9496033476b2926198148ef8	14947	tests/test_mime_set.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
diff --git a/docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md b/docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
new file mode 100644
index 0000000..6277806
--- /dev/null
+++ b/docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
@@ -0,0 +1,38 @@
+# Full-negative WaMo and MIME parent backward
+
+`literature_parent_training.py` preserves each model's complete batch objective
+while loading and replaying one entire prepared capture graph at a time.
+This is a numerical seam, not the production optimizer/supervisor, a study
+freeze, a converged baseline, or an authorization to open final-test data.
+TMR is deliberately absent: its VAE samples, global filtering and track-consistent
+set reconstruction cannot be replaced by either of these objectives.
+
+For WaMo, every capture's complete motion representation, both reconstruction
+branches and both original/shuffled ordering branches are cached without an
+activation graph. All exact human rows receive admitted frozen DistilBERT CLS
+features once. The original summed bidirectional variable-positive InfoNCE and
+equal-parent auxiliary means are differentiated over the entire B-by-Q gallery.
+Its VJPs replay every complete capture, including original auxiliary branches.
+
+For MIME, all complete motion rows are cached, then all exact human captions
+are tokenized once and encoded by the genuinely trainable CLIP tower. The
+original averaged bidirectional known-positive objective differentiates text,
+learned logit scale and motion leaves jointly. Motion VJPs are replayed one
+parent at a time. No text gradients are frozen and no microbatch negatives
+are dropped. Role-aware original dyadic Inter-X inputs are a separate protocol.
+
+Only explicit learning components are admitted; historical development C00 may
+be a learning component in its authorized outer fold, but final-test rows may
+never enter. Loaders must return the same admitted complete physical source
+on cache and replay. Exact representation/auxiliary equality is checked before
+backward. The private host still binds official data/asset identity, augmentation,
+component census, runtime, budget and real human provenance; callbacks do not
+invent those authorities. It must not retain the entire physical population.
+
+RNG snapshots include each physical load and stochastic encoding; replay restores
+the next update's post-text-forward state. Shared-parameter gradient accumulation
+order can differ from dense autograd, so independent FP32 gradient oracles use
+frozen tolerances while loss and cached representation equality remain exact.
+FP32 only; hardware/BF16/resource qualification and full optimizer/checkpoint/
+resume/native learning remain separate. Software fixtures are explicitly analytic,
+including rejected timeline gaps, and cannot populate any paper result table.
diff --git a/src/phaseset_core/literature_parent_training.py b/src/phaseset_core/literature_parent_training.py
new file mode 100644
index 0000000..23f4f63
--- /dev/null
+++ b/src/phaseset_core/literature_parent_training.py
@@ -0,0 +1,219 @@
+"""Full-negative, one-complete-parent-at-a-time WaMo/MIME backward.
+
+No optimizer, task seal, data discovery or TMR approximation. TMR's VAE and
+set reconstruction need a separate faithful seam; they are not routed here.
+Private hosts still admit native sources, language assets and learning folds.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+from .continuous_capture import PreparedContinuousCapture
+from .mime_language import TrainableMIMECLIP
+from .mime_set import MIMERetrieval, MIMESetMotion
+from .parent_retrieval_task import ParentRetrievalBatch
+from .tmr_set import TMRGroupCapture
+from .training import _capture_rng, _restore_rng
+from .wamo_set import WaMoSet, positive_contrastive
+
+
+@dataclass(frozen=True)
+class LiteratureParentBackward:
+    loss: float
+    loss_components: dict[str, float]
+    scores: Tensor
+    parent_count: int
+    text_count: int
+    replayed_parents: int
+
+
+def _validate_learning(labels: ParentRetrievalBatch, components: tuple[str, ...]) -> None:
+    if (
+        type(components) is not tuple
+        or not components
+        or len(set(components)) != len(components)
+        or any(type(component) is not str or not component for component in components)
+    ):
+        raise ValueError("explicit unique learning components are required")
+    if len(labels.parents) < 2 or any(
+        parent.component not in components or parent.split == "test" for parent in labels.parents
+    ):
+        raise ValueError("need two or more admitted learning parents, never final test")
+    if len(set(labels.motion_source_keys)) != len(labels.parents) or len(
+        set(labels.motion_positive_keys)
+    ) != len(labels.parents):
+        raise ValueError("released siblings/source aliases cannot be independent parents")
+
+
+def _group(load_capture, source: str) -> TMRGroupCapture:
+    capture = load_capture(source)
+    if type(capture) is not PreparedContinuousCapture or capture.source_sha256 != source:
+        raise ValueError("loader must return the admitted complete prepared source")
+    return TMRGroupCapture.from_prepared(capture)
+
+
+def _backward(
+    system: nn.Module,
+    labels: ParentRetrievalBatch,
+    load_capture: Callable[[str], PreparedContinuousCapture],
+    encode: Callable[[TMRGroupCapture], tuple[Tensor, ...]],
+    objective: Callable[[tuple[Tensor, ...]], tuple[dict[str, Tensor], Tensor]],
+    progress: Callable[[str, int], None] | None,
+) -> LiteratureParentBackward:
+    if not torch.is_grad_enabled():
+        raise ValueError("training backward cannot run under no_grad")
+    if any(p.dtype != torch.float32 for p in system.parameters()):
+        raise ValueError("this numerical seam is FP32 only; BF16 needs qualification")
+    was_training, after = system.training, None
+    system.train()
+    system.zero_grad(set_to_none=True)
+    states, cached = [], []
+    try:
+        for index, source in enumerate(labels.motion_source_keys):
+            if progress is not None:
+                progress("cache", index)
+            states.append(_capture_rng())
+            capture = _group(load_capture, source)
+            with torch.no_grad():
+                rows = tuple(value.detach() for value in encode(capture))
+            if any(not bool(torch.isfinite(value).all()) for value in rows):
+                raise ValueError("complete-parent representation/auxiliary cache is nonfinite")
+            cached.append(rows)
+            del capture
+        leaves = tuple(
+            torch.stack([rows[index] for rows in cached]).detach().requires_grad_(True)
+            for index in range(len(cached[0]))
+        )
+        losses, scores = objective(leaves)
+        if scores.shape != (len(labels.parents), len(labels.captions)) or any(
+            not bool(torch.isfinite(value).all()) for value in (*losses.values(), scores)
+        ):
+            raise ValueError("full human gallery/objective is malformed or nonfinite")
+        # Includes any stochastic trainable text encoding, just as the dense
+        # objective. Backward checkpoints preserve the post-forward RNG state.
+        after = _capture_rng()
+        losses["loss"].backward()
+        if any(value.grad is None for value in leaves):
+            raise RuntimeError("full objective did not differentiate every cached branch")
+        for index, (source, state) in enumerate(
+            zip(labels.motion_source_keys, states, strict=True)
+        ):
+            if progress is not None:
+                progress("replay", index)
+            _restore_rng(state)
+            capture = _group(load_capture, source)
+            outputs = encode(capture)
+            if len(outputs) != len(cached[index]) or any(
+                not torch.equal(value.detach(), expected)
+                for value, expected in zip(outputs, cached[index], strict=True)
+            ):
+                raise RuntimeError("complete-parent replay changed an encoded/auxiliary value")
+            torch.autograd.backward(outputs, tuple(value.grad[index] for value in leaves))
+            del outputs, capture
+        for name, parameter in system.named_parameters():
+            if parameter.requires_grad and (
+                parameter.grad is None or not bool(torch.isfinite(parameter.grad).all())
+            ):
+                raise ValueError(f"missing or nonfinite trainable gradient: {name}")
+        return LiteratureParentBackward(
+            float(losses["loss"].detach()),
+            {name: float(value.detach()) for name, value in losses.items()},
+            scores.detach().cpu(),
+            len(labels.parents),
+            len(labels.captions),
+            len(cached),
+        )
+    finally:
+        if after is not None:
+            _restore_rng(after)
+        system.train(was_training)
+
+
+def backward_loaded_wamo_parent_batch(
+    system: WaMoSet,
+    labels: ParentRetrievalBatch,
+    *,
+    learning_components: tuple[str, ...],
+    load_capture: Callable[[str], PreparedContinuousCapture],
+    load_cls: Callable[[tuple[str, ...]], Tensor],
+    progress: Callable[[str, int], None] | None = None,
+) -> LiteratureParentBackward:
+    """Exact WaMo SUM InfoNCE + both reconstructions + both ordering branches.
+
+    ``load_cls`` must use the admitted frozen DistilBERT assets and every
+    provided exact human row. Asset identity remains the private host's input
+    binding, not an authority invented by this callable interface.
+    """
+    if not isinstance(system, WaMoSet):
+        raise TypeError("need the actual complete-parent WaMo model")
+    _validate_learning(labels, learning_components)
+    cls = load_cls(labels.captions)
+    device = system.wavelet.analysis_low.device
+    positive = labels.positive_mask(device=device)
+
+    def objective(rows):
+        motion, reconstruction, ordering = rows
+        text = system.encode_text(cls)
+        scores = F.normalize(motion, dim=1) @ F.normalize(text, dim=1).T / system.config.temperature
+        losses = dict(
+            contrastive=positive_contrastive(scores, positive),
+            reconstruction=reconstruction.mean(),
+            ordering=ordering.mean(),
+        )
+        losses["loss"] = (
+            losses["contrastive"]
+            + system.config.reconstruction_weight * losses["reconstruction"]
+            + system.config.ordering_weight * losses["ordering"]
+        )
+        return losses, scores
+
+    return _backward(
+        system,
+        labels,
+        load_capture,
+        lambda capture: system.encode_capture(capture, auxiliary=True),
+        objective,
+        progress,
+    )
+
+
+def backward_loaded_mime_parent_batch(
+    system: MIMERetrieval,
+    labels: ParentRetrievalBatch,
+    *,
+    learning_components: tuple[str, ...],
+    load_capture: Callable[[str], PreparedContinuousCapture],
+    progress: Callable[[str, int], None] | None = None,
+) -> LiteratureParentBackward:
+    """Exact MIME averaged InfoNCE, full batch negatives and trainable CLIP.
+
+    The original role-aware dyadic Inter-X protocol is intentionally not
+    admitted here. Curriculum supplies labels upstream, never replaces loss.
+    All exact official rows are tokenized once; text gradients are not frozen.
+    """
+    if (
+        not isinstance(system, MIMERetrieval)
+        or not isinstance(system.motion, MIMESetMotion)
+        or not isinstance(system.language, TrainableMIMECLIP)
+    ):
+        raise TypeError("need shared full-parent MIME plus genuinely trainable CLIP module")
+    _validate_learning(labels, learning_components)
+    tokens = system.language.tokenize(labels.captions)
+    positive = labels.positive_mask(device=system.logit_scale.device)
+
+    def objective(rows):
+        text = system.language(tokens)
+        scores = system.logit_scale.exp() * (
+            F.normalize(rows[0], dim=1) @ F.normalize(text, dim=1).T
+        )
+        return {"loss": positive_contrastive(scores, positive) / 2}, scores
+
+    return _backward(
+        system, labels, load_capture, lambda capture: (system.motion(capture),), objective, progress
+    )
diff --git a/tests/test_literature_parent_training.py b/tests/test_literature_parent_training.py
new file mode 100644
index 0000000..3e3e8da
--- /dev/null
+++ b/tests/test_literature_parent_training.py
@@ -0,0 +1,156 @@
+"""Analytic VJP oracles, never native optimizer/pilot/performance evidence."""
+
+import copy
+from dataclasses import replace
+import hashlib
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.continuous_capture import prepare_continuous_capture
+from phaseset_core.literature_parent_training import (
+    backward_loaded_mime_parent_batch,
+    backward_loaded_wamo_parent_batch,
+)
+from phaseset_core.mime_set import MIMERetrieval, MIMESetMotion
+from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from phaseset_core.pipeline import PrivateCaptureArrays
+from phaseset_core.tmr_set import TMRGroupCapture
+from phaseset_core.training import _capture_rng, _restore_rng, _stable_hash
+from test_mime_set import config, language
+from test_wamo_set import small
+
+
+def fixture():
+    # Entire synthetic timelines, including a rejected middle interval. No
+    # released siblings are presented as independent real data.
+    rng = np.random.default_rng(1729)
+    captures, records = {}, []
+    for index, caption_count in enumerate((2, 1, 3)):
+        k = 3
+        x = rng.normal(0, 0.1, (900, k, 22, 3)).astype(np.float32)
+        x += np.arange(k, dtype=np.float32)[None, :, None, None] * 0.2
+        observed = np.ones(x.shape[:-1], dtype=np.bool_)
+        observed[300:600] = False
+        source = hashlib.sha256(f"analytic-source-{index}".encode()).hexdigest()
+        family = hashlib.sha256(f"analytic-family-{index}".encode()).hexdigest()
+        capture = prepare_continuous_capture(
+            PrivateCaptureArrays(x, observed, tuple(bytes([i + 1]) * 32 for i in range(k)), source)
+        )
+        assert len(capture.windows()) == 2
+        captures[source] = capture
+        records.append(
+            ParentCaptionRecord(
+                source,
+                family,
+                "C01",
+                "train",
+                tuple(f"analytic human-like row {index} {row}" for row in range(caption_count)),
+            )
+        )
+    task = ParentRetrievalTask(
+        tuple(records), expected_family_keys=tuple(row.annotation_family_sha256 for row in records)
+    )
+    return captures, task.batch(tuple(row.annotation_family_sha256 for row in records))
+
+
+@pytest.mark.parametrize("kind", ["wamo", "mime"])
+def test_full_negative_replay_matches_original_objective_all_gradients_and_rng(kind):
+    torch.manual_seed(1729)
+    captures, labels = fixture()
+    model = (
+        small(dropout=0.1)
+        if kind == "wamo"
+        else MIMERetrieval(MIMESetMotion(config(dropout=0.1)), language())
+    ).eval()
+    dense = copy.deepcopy(model).train()
+    physical = tuple(
+        TMRGroupCapture.from_prepared(captures[key]) for key in labels.motion_source_keys
+    )
+    cls = torch.randn(len(labels.captions), 768)
+    positive = labels.positive_mask(device=torch.device("cpu"))
+    state = _capture_rng()
+    if kind == "wamo":
+        expected = dense.compute_loss(physical, cls, positive)
+    else:
+        expected = {
+            "loss": dense.compute_loss(physical, dense.language.tokenize(labels.captions), positive)
+        }
+    expected["loss"].backward()
+    after = _stable_hash(_capture_rng())
+    _restore_rng(state)
+    loaded, text_loaded = [], []
+
+    def load(source):
+        loaded.append(source)
+        return captures[source]
+
+    def load_cls(captions):
+        text_loaded.append(captions)
+        return cls
+
+    args = dict(learning_components=("C01",), load_capture=load)
+    result = (
+        backward_loaded_wamo_parent_batch(model, labels, load_cls=load_cls, **args)
+        if kind == "wamo"
+        else backward_loaded_mime_parent_batch(model, labels, **args)
+    )
+    assert _stable_hash(_capture_rng()) == after and not model.training
+    assert loaded == list(labels.motion_source_keys) * 2
+    assert text_loaded == ([labels.captions] if kind == "wamo" else [])
+    assert result.scores.shape == (3, 6) and result.parent_count == result.replayed_parents == 3
+    assert result.text_count == 6
+    for name, value in expected.items():
+        assert result.loss_components[name] == float(value.detach())
+    for actual, oracle in zip(model.parameters(), dense.parameters(), strict=True):
+        assert actual.grad is not None and oracle.grad is not None
+        # Dense autograd sums shared capture gradients in reverse traversal;
+        # replay visits fixed source order. Only this FP32 accumulation differs.
+        torch.testing.assert_close(actual.grad, oracle.grad, rtol=3e-5, atol=3e-6)
+
+
+@pytest.mark.parametrize("kind", ["wamo", "mime"])
+def test_wrong_population_and_source_are_rejected_without_silent_substitution(kind):
+    captures, labels = fixture()
+    model = small() if kind == "wamo" else MIMERetrieval(MIMESetMotion(config()), language())
+    function = (
+        backward_loaded_wamo_parent_batch if kind == "wamo" else backward_loaded_mime_parent_batch
+    )
+    text = {"load_cls": lambda captions: torch.randn(len(captions), 768)} if kind == "wamo" else {}
+    calls = []
+
+    def load(source):
+        calls.append(source)
+        return replace(captures[source], source_sha256="b" * 64)
+
+    with pytest.raises(ValueError, match="learning parents"):
+        function(model, labels, learning_components=("C02",), load_capture=load, **text)
+    assert not calls
+    with pytest.raises(ValueError, match="complete prepared source"):
+        function(model, labels, learning_components=("C01",), load_capture=load, **text)
+
+
+def test_replay_detects_physical_drift_and_restores_model_mode():
+    captures, labels = fixture()
+    model = small().eval()
+    seen = set()
+
+    def load(source):
+        capture = captures[source]
+        if source in seen:
+            changed = capture.skeletons.copy()
+            changed[capture.track_mask] += np.float32(0.25)
+            return replace(capture, skeletons=changed)
+        seen.add(source)
+        return capture
+
+    with pytest.raises(RuntimeError, match="replay changed"):
+        backward_loaded_wamo_parent_batch(
+            model,
+            labels,
+            learning_components=("C01",),
+            load_capture=load,
+            load_cls=lambda captions: torch.ones(len(captions), 768),
+        )
+    assert not model.training

From 33ee1589e2dfe9de7aaf5f2b634a11402b0f5220 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 06:48:43 -0500
Subject: [PATCH 48/95] feat: replay complete TMR VAE parent gradients with
 full batch negatives

---
 README.md                                     |   4 +
 RELEASE_FILES.sha256                          |   7 +-
 .../PHASESET_V2_LITERATURE_PARENT_TRAINING.md |   6 +-
 docs/PHASESET_V2_TMR_PARENT_TRAINING.md       |  27 +++
 src/phaseset_core/tmr_parent_training.py      | 189 ++++++++++++++++++
 tests/test_tmr_parent_training.py             | 125 ++++++++++++
 6 files changed, 354 insertions(+), 4 deletions(-)
 create mode 100644 docs/PHASESET_V2_TMR_PARENT_TRAINING.md
 create mode 100644 src/phaseset_core/tmr_parent_training.py
 create mode 100644 tests/test_tmr_parent_training.py

diff --git a/README.md b/README.md
index 29343cd..6aec88b 100644
--- a/README.md
+++ b/README.md
@@ -63,6 +63,10 @@ Their [full-negative parent backward seam](docs/PHASESET_V2_LITERATURE_PARENT_TR
 replays one complete capture graph at a time without dropping original WaMo
 auxiliary objectives or freezing MIME text. Dense-gradient software oracles
 are not native optimizer, profile, checkpoint-host or convergence evidence.
+The separate [TMR VAE parent backward seam](docs/PHASESET_V2_TMR_PARENT_TRAINING.md)
+preserves the original stochastic order, both full-track reconstructions,
+all four KL terms, latent alignment and filtered full-batch negatives; it does
+not route TMR through a generic contrastive-only objective.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 3e952e7..a229423 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-3670d7b895a047e39b91c95a9eb739859476d0f58fe8da0281f02ffe5d0579a8	11557	README.md
+89b0d00533b4b9f83ab069cb7c1c6d295d2431e1f028b0def59bf9a53b08bcd0	11855	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -71,12 +71,13 @@ cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHAS
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
-719dbbd7fb6f9f0daa105e863fe8039ba94c162d1f3c623cb9ae6e92b9a85678	2547	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
+ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
 3eb5f4237db4af02d49e8eb62a318ef68ac706bf14cc1b405bf9fa837e8d5705	7431	docs/PHASESET_V2_PARENT_HOST.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
+81b47eff03531162dd2ab6b3d29a0ca9909326e7b13dec34776551d171b182c7	1727	docs/PHASESET_V2_TMR_PARENT_TRAINING.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
@@ -205,6 +206,7 @@ a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phase
 5604397a65a0032d2a14124d34ca3d96a2d6d67e5f4534732bb82f7a0c7b2546	23163	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phaseset_core/tmr_language.py
+d1b92f7b65d750572a8447a48cd2a09cf12d7ec0fc3edd2f33c4088d0d77a14d	9442	src/phaseset_core/tmr_parent_training.py
 b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
@@ -321,6 +323,7 @@ a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/tes
 eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
 96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
 726ee82009ecb29ce87208749fb1780365640a52f2e2d6e55399e22119838b89	2988	tests/test_tmr_language.py
+9441ab431bc210814bbc5c94a353fc5178d133e729ffb75c67456cf07ed38d79	4559	tests/test_tmr_parent_training.py
 2ae860859c1f9282a02cea929e54fa950433ac8fba81c7510c2b900321d5ff29	9606	tests/test_tmr_set.py
 342cea54b04c8b6fe0faae0cf3ae84a1abac9142792acf5fd042ae44937c62f4	11334	tests/test_wamo_set.py
 3aef8f056ef38ecbe5af51c283285b132db97f175c181018d8a4278d82131915	8150	tests/test_wheel_index_binding.py
diff --git a/docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md b/docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
index 6277806..f6ed871 100644
--- a/docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
+++ b/docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
@@ -4,8 +4,10 @@
 while loading and replaying one entire prepared capture graph at a time.
 This is a numerical seam, not the production optimizer/supervisor, a study
 freeze, a converged baseline, or an authorization to open final-test data.
-TMR is deliberately absent: its VAE samples, global filtering and track-consistent
-set reconstruction cannot be replaced by either of these objectives.
+TMR is deliberately absent from this callable: its VAE samples, global filtering
+and track-consistent set reconstruction cannot be replaced by either of these
+objectives. Its separate faithful path is described in
+[TMR parent backward](PHASESET_V2_TMR_PARENT_TRAINING.md).
 
 For WaMo, every capture's complete motion representation, both reconstruction
 branches and both original/shuffled ordering branches are cached without an
diff --git a/docs/PHASESET_V2_TMR_PARENT_TRAINING.md b/docs/PHASESET_V2_TMR_PARENT_TRAINING.md
new file mode 100644
index 0000000..99ec8ab
--- /dev/null
+++ b/docs/PHASESET_V2_TMR_PARENT_TRAINING.md
@@ -0,0 +1,27 @@
+# Full-negative whole-parent TMR VAE backward
+
+`tmr_parent_training.py` is a numerical seam, not a production experiment or
+completed original-task replication. It uses the actual `TMRSet`, frozen
+DistilBERT token features, separate MPNet negative-filter features, family-derived
+positives, and every accepted window with its absolute chronology and gaps.
+
+Stochastic order matches `TMRSet.compute_loss`: encode/sample all texts, encode
+all parents, draw all motion latents together, then reconstruct each parent's
+positive captions and motion. Reconstruction uses the full-parent Hungarian
+assignment, never independent per-window identities. Both reconstructions, four
+KL terms, sampled latent alignment, and filtered full-batch contrastive loss
+retain their coefficients and equal-parent weighting.
+
+Only Gaussian parameters, sampled latents and scalar reconstruction values are
+cached. Decoder replay differentiates both sampled latent paths and decoder
+parameters; the global objective then differentiates text and cached motion
+distributions, followed by one-parent-at-a-time encoder replay. This bounds the
+number of resident motion/decoder graphs, not the work within one complete
+parent or the full text batch. It does not promise arbitrary-size memory use.
+
+Tests compare the original dense VAE loss, every parameter gradient, RNG, full
+gallery, and physical drift rejection. Loss/RNG are exact in the frozen runtime;
+shared FP32 gradient accumulation order may differ (rtol 3e-5, atol 3e-6).
+These analytic oracles are not native optimizer, CUDA/BF16, convergence, pilot,
+profile, production-host, or scientific-result evidence. Those gates remain
+separate and require actual server receipts before formal training.
diff --git a/src/phaseset_core/tmr_parent_training.py b/src/phaseset_core/tmr_parent_training.py
new file mode 100644
index 0000000..56ad814
--- /dev/null
+++ b/src/phaseset_core/tmr_parent_training.py
@@ -0,0 +1,189 @@
+"""Full-negative whole-parent TMR VAE backward with faithful stochastic order.
+
+Text sampling, every motion distribution, batch motion sampling, and then
+every parent's text/motion set reconstruction retain TMRSet.compute_loss's
+order. All KL, latent, reconstruction, and filtered contrastive terms remain.
+This is a numerical seam, not an optimizer, asset admission or experiment seal.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+
+import torch
+from torch.nn import functional as F
+
+from .continuous_capture import PreparedContinuousCapture
+from .literature_parent_training import LiteratureParentBackward, _group, _validate_learning
+from .parent_retrieval_task import ParentRetrievalBatch
+from .tmr_components import diagonal_kl, filtered_contrastive_loss, sample_diagonal_gaussian
+from .tmr_set import TMRSet, TMRTextBatch
+from .training import _capture_rng, _restore_rng
+
+
+def backward_loaded_tmr_parent_batch(
+    system: TMRSet,
+    labels: ParentRetrievalBatch,
+    *,
+    learning_components: tuple[str, ...],
+    load_capture: Callable[[str], PreparedContinuousCapture],
+    load_text: Callable[[tuple[str, ...]], TMRTextBatch],
+    progress: Callable[[str, int], None] | None = None,
+) -> LiteratureParentBackward:
+    """Retain the complete TMR-Set loss and every full-batch negative.
+
+    The private host supplies exact frozen DistilBERT tokens and MPNet filter
+    features, not learned/generated positives. Only distributions, sampled
+    latents and scalar reconstruction values are cached. Decoder replay solves
+    the whole-parent slot/track assignment exactly as the dense model does.
+    """
+    if not isinstance(system, TMRSet):
+        raise TypeError("need the actual complete-parent TMR set VAE")
+    _validate_learning(labels, learning_components)
+    if not torch.is_grad_enabled() or any(p.dtype != torch.float32 for p in system.parameters()):
+        raise ValueError("TMR replay requires grad-enabled FP32; BF16 is not qualified here")
+    text = load_text(labels.captions)
+    if type(text) is not TMRTextBatch or len(text.tokens) != len(labels.captions):
+        raise ValueError("all admitted exact human rows need frozen TMR language features")
+    device = system.text_encoder.tokens.device
+    positive = labels.positive_mask(device=device)
+    was_training, after = system.training, None
+    system.train()
+    system.zero_grad(set_to_none=True)
+    encoder_states, decoder_states, cached, reconstructions = [], [], [], []
+
+    def announce(phase, index):
+        if progress is not None:
+            progress(phase, index)
+
+    def reconstruct(capture, text_latents, motion_latent):
+        # One full-track Hungarian assignment for each reconstruction over all
+        # windows; never independently match slots in individual windows.
+        return torch.stack(
+            [system.reconstruct_capture(latent, capture) for latent in text_latents]
+        ).mean() + system.reconstruct_capture(motion_latent, capture)
+
+    try:
+        text_dist = system.text_distributions(text)
+        text_z = sample_diagonal_gaussian(text_dist)
+        for index, source in enumerate(labels.motion_source_keys):
+            announce("encoder-cache", index)
+            encoder_states.append(_capture_rng())
+            capture = _group(load_capture, source)
+            with torch.no_grad():
+                distribution = tuple(value.detach() for value in system.encode_capture(capture))
+            if any(not bool(torch.isfinite(value).all()) for value in distribution):
+                raise ValueError("complete-parent TMR distribution is nonfinite")
+            cached.append(distribution)
+            del capture
+        motion_dist = tuple(
+            torch.stack([row[index] for row in cached]).detach().requires_grad_(True)
+            for index in (0, 1)
+        )
+        # A single B-by-D normal draw, after all encoders. Drawing separately
+        # inside a per-parent loop changes the dense model's stochastic order.
+        motion_z = sample_diagonal_gaussian(motion_dist)
+        kls, latents, caption_indices = [], [], []
+        for row, source in enumerate(labels.motion_source_keys):
+            announce("decoder-cache", row)
+            decoder_states.append(_capture_rng())
+            capture = _group(load_capture, source)
+            indices = positive[row].nonzero().flatten()
+            caption_indices.append(indices)
+            with torch.no_grad():
+                reconstructed = reconstruct(capture, text_z[indices], motion_z[row])
+            if not bool(torch.isfinite(reconstructed)):
+                raise ValueError("complete-parent set reconstruction is nonfinite")
+            reconstructions.append(reconstructed.detach())
+            td = tuple(part[indices] for part in text_dist)
+            md = tuple(part[row].expand_as(td[index]) for index, part in enumerate(motion_dist))
+            unit = (torch.zeros_like(md[0]), torch.zeros_like(md[1]))
+            kls.append(
+                diagonal_kl(td, md)
+                + diagonal_kl(md, td)
+                + diagonal_kl(md, unit)
+                + diagonal_kl(td, unit)
+            )
+            latents.append(
+                F.smooth_l1_loss(text_z[indices], motion_z[row].expand_as(text_z[indices]))
+            )
+            del capture
+        losses = dict(
+            recons=torch.stack(reconstructions).mean(),
+            kl=torch.stack(kls).mean(),
+            latent=torch.stack(latents).mean(),
+            contrastive=filtered_contrastive_loss(
+                motion_z, text_z, positive, sentence_embeddings=text.sentences.to(device)
+            ),
+        )
+        losses["loss"] = system.loss_weights.total(losses)
+        # Return raw full scores; semantic filtering changes loss denominators
+        # only, and must never invent positive labels or truncate the gallery.
+        scores = F.normalize(motion_z, dim=1) @ F.normalize(text_z, dim=1).T / 0.1
+        if any(not bool(torch.isfinite(value).all()) for value in (*losses.values(), scores)):
+            raise ValueError("full TMR gallery/objective is nonfinite")
+        after = _capture_rng()
+
+        text_reconstruction_vjp = torch.zeros_like(text_z)
+        motion_reconstruction_vjp = torch.zeros_like(motion_z)
+        for row, (source, state, indices) in enumerate(
+            zip(labels.motion_source_keys, decoder_states, caption_indices, strict=True)
+        ):
+            announce("decoder-replay", row)
+            _restore_rng(state)
+            capture = _group(load_capture, source)
+            tz = text_z[indices].detach().requires_grad_(True)
+            mz = motion_z[row].detach().requires_grad_(True)
+            reconstructed = reconstruct(capture, tz, mz)
+            if not torch.equal(reconstructed.detach(), reconstructions[row]):
+                raise RuntimeError("complete-parent TMR reconstruction replay changed a value")
+            # Identical derivative of the original equal-parent mean and its
+            # registered reconstruction coefficient; no auxiliary term omitted.
+            (reconstructed * (system.loss_weights.recons / len(labels.parents))).backward()
+            if tz.grad is None or mz.grad is None:
+                raise RuntimeError("set reconstruction did not differentiate both sampled latents")
+            text_reconstruction_vjp[indices] += tz.grad
+            motion_reconstruction_vjp[row] = mz.grad
+            del reconstructed, capture, tz, mz
+
+        _restore_rng(after)
+        # The detached reconstruction scalar reports the true dense loss.
+        # Its missing paths are supplied by both explicit sampled-latent VJPs;
+        # decoder parameter gradients were accumulated in the replay above.
+        torch.autograd.backward(
+            (losses["loss"], text_z, motion_z),
+            (None, text_reconstruction_vjp, motion_reconstruction_vjp),
+        )
+        if any(value.grad is None for value in motion_dist):
+            raise RuntimeError("full TMR objective did not differentiate both motion distributions")
+        for row, (source, state) in enumerate(
+            zip(labels.motion_source_keys, encoder_states, strict=True)
+        ):
+            announce("encoder-replay", row)
+            _restore_rng(state)
+            capture = _group(load_capture, source)
+            outputs = system.encode_capture(capture)
+            if any(
+                not torch.equal(actual.detach(), expected)
+                for actual, expected in zip(outputs, cached[row], strict=True)
+            ):
+                raise RuntimeError("complete-parent TMR encoder replay changed a value")
+            torch.autograd.backward(outputs, tuple(value.grad[row] for value in motion_dist))
+            del outputs, capture
+        for name, parameter in system.named_parameters():
+            if parameter.requires_grad and (
+                parameter.grad is None or not bool(torch.isfinite(parameter.grad).all())
+            ):
+                raise ValueError(f"missing or nonfinite TMR trainable gradient: {name}")
+        return LiteratureParentBackward(
+            float(losses["loss"].detach()),
+            {name: float(value.detach()) for name, value in losses.items()},
+            scores.detach().cpu(),
+            len(labels.parents),
+            len(labels.captions),
+            len(cached),
+        )
+    finally:
+        if after is not None:
+            _restore_rng(after)
+        system.train(was_training)
diff --git a/tests/test_tmr_parent_training.py b/tests/test_tmr_parent_training.py
new file mode 100644
index 0000000..cebe183
--- /dev/null
+++ b/tests/test_tmr_parent_training.py
@@ -0,0 +1,125 @@
+"""Analytic dense-VAE replay oracles, not native convergence/optimizer evidence."""
+
+import copy
+from dataclasses import replace
+
+import pytest
+import torch
+
+from phaseset_core.tmr_parent_training import backward_loaded_tmr_parent_batch
+from phaseset_core.tmr_set import TMRGroupCapture, TMRSet, TMRTextBatch
+from phaseset_core.training import _capture_rng, _restore_rng, _stable_hash
+from test_literature_parent_training import fixture
+
+
+def model():
+    return TMRSet(latent_dim=16, ff_size=32, num_layers=1, num_heads=4, dropout=0.1)
+
+
+def text_batch(labels):
+    rows = len(labels.captions)
+    tokens = torch.randn(rows, 5, 768)
+    # Enable a real off-diagonal semantic filter for two different parents;
+    # similarity never changes the registered family-derived positives.
+    sentences = torch.eye(rows, 768)
+    sentences[2] = sentences[0]
+    return TMRTextBatch(tokens, torch.ones(rows, 5, dtype=torch.bool), sentences)
+
+
+def test_all_vae_terms_dense_gradients_rng_and_full_parent_replay():
+    torch.manual_seed(1729)
+    captures, labels = fixture()
+    actual = model().eval()
+    dense = copy.deepcopy(actual).train()
+    text = text_batch(labels)
+    physical = tuple(
+        TMRGroupCapture.from_prepared(captures[key]) for key in labels.motion_source_keys
+    )
+    state = _capture_rng()
+    expected = dense.compute_loss(physical, text, labels.positive_mask(device=torch.device("cpu")))
+    expected["loss"].backward()
+    after = _stable_hash(_capture_rng())
+    _restore_rng(state)
+    loaded, texts, events = [], [], []
+
+    def load(source):
+        loaded.append(source)
+        return captures[source]
+
+    def load_text(captions):
+        texts.append(captions)
+        return text
+
+    result = backward_loaded_tmr_parent_batch(
+        actual,
+        labels,
+        learning_components=("C01",),
+        load_capture=load,
+        load_text=load_text,
+        progress=lambda phase, index: events.append((phase, index)),
+    )
+    assert not actual.training and _stable_hash(_capture_rng()) == after
+    assert texts == [labels.captions]
+    assert loaded == list(labels.motion_source_keys) * 4
+    assert events == [
+        (phase, index)
+        for phase in ("encoder-cache", "decoder-cache", "decoder-replay", "encoder-replay")
+        for index in range(3)
+    ]
+    assert result.scores.shape == (3, 6) and result.parent_count == result.replayed_parents == 3
+    assert result.text_count == 6
+    assert set(result.loss_components) == {"loss", "recons", "kl", "latent", "contrastive"}
+    for name, loss in expected.items():
+        assert result.loss_components[name] == float(loss.detach())
+    for (name, parameter), (_, reference) in zip(
+        actual.named_parameters(), dense.named_parameters(), strict=True
+    ):
+        assert parameter.grad is not None and reference.grad is not None, name
+        torch.testing.assert_close(parameter.grad, reference.grad, rtol=3e-5, atol=3e-6, msg=name)
+
+
+def test_wrong_population_and_wrong_physical_source_rejected():
+    captures, labels = fixture()
+    actual, text = model().eval(), text_batch(labels)
+    with pytest.raises(ValueError, match="learning parents"):
+        backward_loaded_tmr_parent_batch(
+            actual,
+            labels,
+            learning_components=("C02",),
+            load_capture=lambda key: captures[key],
+            load_text=lambda rows: text,
+        )
+    with pytest.raises(ValueError, match="complete prepared source"):
+        backward_loaded_tmr_parent_batch(
+            actual,
+            labels,
+            learning_components=("C01",),
+            load_capture=lambda key: replace(captures[key], source_sha256="b" * 64),
+            load_text=lambda rows: text,
+        )
+    assert not actual.training
+
+
+def test_decoder_cache_replay_drift_fails_and_restores_eval_mode():
+    captures, labels = fixture()
+    actual, text = model().eval(), text_batch(labels)
+    seen = {}
+
+    def load(key):
+        seen[key] = seen.get(key, 0) + 1
+        capture = captures[key]
+        if seen[key] == 3:  # encoder cache, decoder cache, then decoder replay
+            x = capture.skeletons.copy()
+            x[capture.track_mask] += 0.25
+            return replace(capture, skeletons=x)
+        return capture
+
+    with pytest.raises(RuntimeError, match="reconstruction replay changed"):
+        backward_loaded_tmr_parent_batch(
+            actual,
+            labels,
+            learning_components=("C01",),
+            load_capture=load,
+            load_text=lambda rows: text,
+        )
+    assert not actual.training

From 77eb87b2b75a7fa7bca49d0879f0e84cba91a3d1 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 07:36:10 -0500
Subject: [PATCH 49/95] feat: train faithful literature baselines with complete
 parent resume host

---
 README.md                                   |   5 +
 RELEASE_FILES.sha256                        |   7 +-
 docs/PHASESET_V2_LITERATURE_PARENT_HOST.md  |  69 ++++++
 src/phaseset_core/continuous_parent_host.py |  35 +--
 src/phaseset_core/literature_parent_host.py | 195 ++++++++++++++++
 tests/test_literature_parent_host.py        | 242 ++++++++++++++++++++
 6 files changed, 537 insertions(+), 16 deletions(-)
 create mode 100644 docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 create mode 100644 src/phaseset_core/literature_parent_host.py
 create mode 100644 tests/test_literature_parent_host.py

diff --git a/README.md b/README.md
index 6aec88b..2128ee2 100644
--- a/README.md
+++ b/README.md
@@ -67,6 +67,11 @@ The separate [TMR VAE parent backward seam](docs/PHASESET_V2_TMR_PARENT_TRAINING
 preserves the original stochastic order, both full-track reconstructions,
 all four KL terms, latent alignment and filtered full-batch negatives; it does
 not route TMR through a generic contrastive-only objective.
+The [literature optimizer host](docs/PHASESET_V2_LITERATURE_PARENT_HOST.md)
+shares the full-parent AdamW, validation selection and checkpoint/resume loop,
+while keeping each baseline's original objective. Analytic optimizer/resume
+checks and native dense-gradient replay are distinct from a native training
+pilot or converged baseline result.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index a229423..46a2760 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-89b0d00533b4b9f83ab069cb7c1c6d295d2431e1f028b0def59bf9a53b08bcd0	11855	README.md
+fb6b196b13bd4a8f93553f127f05974144ec4ad3bdb8e87905cf434c6335e0a5	12198	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -71,6 +71,7 @@ cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHAS
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
+004dfc49150cc66ba71f7c303dda5dd5f9ce16ca4a220e7558b2da4914e94336	3978	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
@@ -164,7 +165,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 be4638d0ce9057468eb557db6e73afef1e2ce688833309991ce5f5818b0ad575	5577	src/phaseset_core/continuous_base_retrieval.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
-60cb00e377324add06c1a539b2f80c37a2711589989cb3bb5c2b711d4d2394b6	26842	src/phaseset_core/continuous_parent_host.py
+2ef64b87f94fa635dce12fc5589088c64680b1816d640aa99f960a43ae9fe8a9	27008	src/phaseset_core/continuous_parent_host.py
 19798b164958b1e65be4353d26ca4cbe167ebaa26c9639b488bcb3e63a00296c	15174	src/phaseset_core/continuous_parent_training.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
 38f74724372e276257c1014e50e26eb8b2cab482728a6b143799118ad2f47ca9	10545	src/phaseset_core/continuous_retrieval.py
@@ -181,6 +182,7 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 877302cdb2ad891eb064d5bb228d124b66042865b78fa3826f66cf7bacf01166	68520	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
+50a6240c62827c89e62939863ef5e12580e886b80e1997c15bf1423d37610779	8555	src/phaseset_core/literature_parent_host.py
 02a11d20ac299c4886479b863f2f614512365a9f1fe800861b2f261dbdad1c7a	9101	src/phaseset_core/literature_parent_training.py
 f0d7c36ecda4326ca2b1f9461fdc812039cae33739595c60d40a436f0051edb7	6976	src/phaseset_core/mime_language.py
 f7cb1c0893b0b504359e34e39dd24651aaa86c1b65e98d2ed0469c6d5ce84d4d	6920	src/phaseset_core/mime_parent_curriculum.py
@@ -226,6 +228,7 @@ e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/tes
 2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
+ad4fb11844e43b26566803a0805dacf20e0a6b6cdf4e9741dcc98739141d6b2a	10614	tests/test_literature_parent_host.py
 4b4193cc100b1076e31fe8c9f3fd1801b0270368afd80eca93c6cce0e671482a	6224	tests/test_literature_parent_training.py
 b65a2178d7faf25372adeccdfcd19ee7804544665c6059f2d684801ccff83e11	6050	tests/test_mime_parent_curriculum.py
 773430e42927751a5b07ab20da7ddd716d13853f9496033476b2926198148ef8	14947	tests/test_mime_set.py
diff --git a/docs/PHASESET_V2_LITERATURE_PARENT_HOST.md b/docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
new file mode 100644
index 0000000..166f670
--- /dev/null
+++ b/docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
@@ -0,0 +1,69 @@
+# Complete-parent literature optimizer host
+
+`phaseset_core.literature_parent_host.LiteratureParentTrainingHost` uses the
+same AdamW, warmup/cosine, gradient clipping, validation checkpoint selector,
+immutable attempt and checkpoint/resume loop as the complete-parent base and
+residual host. The original constructor still admits only its actual base/V2
+scorers; literature models enter through their own strict constructor.
+
+## Objectives and inputs
+
+- TMR-Set: complete stochastic VAE objective, both full-track reconstructions,
+  four KL terms, latent alignment and MPNet-filtered full-batch contrastive loss.
+- WaMo-Set: both reconstructions, original/shuffled temporal classification,
+  and the original **sum** of contrastive directions.
+- MIME-Set: genuine trainable CLIP language tower and learned score scale;
+  the original **average** of contrastive directions. Original role-aware
+  Inter-X MIME is a distinct interface and is not admitted by this group host.
+
+The host dispatches the already-qualified per-parent cache/VJP/replay seams,
+not a common replacement loss. Every effective batch retains its entire
+rectangular motion-by-human-caption negative gallery, variable positives,
+and every accepted window at its absolute time, including windows after gaps.
+Source reload augmentation must depend on source/seed/epoch, independently of
+model dropout and VAE sampling RNG. It must reproduce the same arrays each time.
+
+`LiteratureParentSource` supplies entire `PreparedContinuousCapture` objects,
+frozen TMR token/sentence features, and frozen WaMo DistilBERT CLS features.
+MIME uses its own actual trainable tokenizer/tower. Language asset identities,
+source adapter, preprocessing and complete census belong in the operator's
+private input/code bindings; no private download URLs or captions are supplied
+by this public package.
+
+## Validation and restart
+
+All validation human rows are encoded once; motion parents are streamed one
+at a time without auxiliary loss or augmentation. Scores follow the methods'
+public `score` equations, with TMR distribution means at evaluation. The
+selector remains capture-macro bidirectional R@1 against registered family
+positives, deterministic lineage tie breaks, and first-checkpoint retention
+on exact ties. Model mode and dropout/sampling RNG are restored afterwards.
+
+The manifest additionally records the method and its loss configuration;
+resume rejects changed objective coefficients as well as execution bindings,
+initial state, task, scalar/module configuration and optimizer settings.
+The shared checkpoint loop retains optimizer/scheduler/RNG state, source census,
+cursor checks, predecessor evidence and terminal failures.
+
+## Qualification is not research completion
+
+The analytic tests exercise real optimizer updates, method-specific losses,
+full validation endpoint, dropout/VAE bitwise interrupted resume, objective
+drift refusal, and exclusion of held-out/test rows. They are expressly not
+native optimization, a pilot, original-task convergence or a hardware profile.
+The existing base/residual host and original model/seam tests must pass too;
+the real Transformers CLIP component must execute, not skip.
+
+Native default-model replay qualification separately uses complete real
+development captures, all human rows, and each original dense objective as
+the gradient oracle. It proves objective preservation, not baseline accuracy.
+
+No constructor, software test or code review authorizes formal training.
+The dated whole-parent training-unit amendment, hardware profile, bounded
+pilot, study/budget freeze and rights remain private admission steps. No
+final-test interface is provided here. Same-family semantic review remains
+provisional; research metrics, statistics and paper results are still pending.
+
+See the [WaMo/MIME replay contract](PHASESET_V2_LITERATURE_PARENT_TRAINING.md),
+[TMR replay contract](PHASESET_V2_TMR_PARENT_TRAINING.md), and
+[complete-parent host](PHASESET_V2_PARENT_HOST.md).
diff --git a/src/phaseset_core/continuous_parent_host.py b/src/phaseset_core/continuous_parent_host.py
index 290b1ef..169090b 100644
--- a/src/phaseset_core/continuous_parent_host.py
+++ b/src/phaseset_core/continuous_parent_host.py
@@ -202,6 +202,10 @@ def __init__(
             raise ValueError("host stage does not match the actual scorer")
         if (bindings.frozen_base_checkpoint_sha256 is None) != self._is_base:
             raise ValueError("only a residual stage binds a frozen B2 checkpoint")
+        self._initialize(system, task, source, config, bindings)
+
+    def _initialize(self, system, task, source, config, bindings):
+        """Shared optimizer/checkpoint loop; public constructors admit scorers."""
         if any(parameter.dtype != torch.float32 for parameter in system.parameters()):
             raise ValueError("this host has only been qualified for FP32")
         self.system, self.task, self.source = system, task, source
@@ -281,6 +285,22 @@ def _batches(self, epoch: int):
             epoch=epoch,
         )
 
+    def _backward_batch(self, labels: ParentRetrievalBatch):
+        by_source = {row.source_sha256: row for row in labels.parents}
+        text, cf = self.source.text(labels, training=True)
+        return backward_loaded_parent_batch(
+            self.system,
+            labels,
+            text,
+            load_view=lambda key: self.source.view(
+                by_source[key], seed=self.config.seed, epoch=self._epoch, training=True
+            ),
+            counterfactuals=cf,
+            cf_weight=self.config.cf_weight,
+            margin=self.config.cf_margin,
+            progress=lambda phase, index: self._event(phase, capture_index=index),
+        )
+
     def _event(self, phase: str, **fields):
         assert self._root is not None
         _write_json_once(
@@ -520,20 +540,7 @@ def fit(
                     if stop_after_steps is not None and self._step == stop_after_steps:
                         break
                     labels = batches[self._offset]
-                    by_source = {row.source_sha256: row for row in labels.parents}
-                    text, cf = self.source.text(labels, training=True)
-                    result = backward_loaded_parent_batch(
-                        self.system,
-                        labels,
-                        text,
-                        load_view=lambda key: self.source.view(
-                            by_source[key], seed=self.config.seed, epoch=self._epoch, training=True
-                        ),
-                        counterfactuals=cf,
-                        cf_weight=self.config.cf_weight,
-                        margin=self.config.cf_margin,
-                        progress=lambda phase, index: self._event(phase, capture_index=index),
-                    )
+                    result = self._backward_batch(labels)
                     norm = torch.nn.utils.clip_grad_norm_(
                         self._parameters, 1.0, error_if_nonfinite=True
                     )
diff --git a/src/phaseset_core/literature_parent_host.py b/src/phaseset_core/literature_parent_host.py
new file mode 100644
index 0000000..49ae378
--- /dev/null
+++ b/src/phaseset_core/literature_parent_host.py
@@ -0,0 +1,195 @@
+"""Faithful TMR/WaMo/MIME optimizer hosts using the shared parent loop.
+
+No final-test interface or generic contrastive substitute. Native data/assets,
+whole-parent amendment, hardware profile and bounded study/pilot admission
+remain private operator gates; this constructor cannot grant them.
+"""
+
+from __future__ import annotations
+
+from dataclasses import asdict
+from typing import Protocol
+
+import torch
+from torch import Tensor
+from torch.nn import functional as F
+
+from .continuous_capture import PreparedContinuousCapture
+from .continuous_parent_host import (
+    ContinuousParentTrainingHost,
+    ParentHostBindings,
+    ParentHostConfig,
+)
+from .continuous_parent_training import ParentBackwardResult
+from .literature_parent_training import (
+    _group,
+    backward_loaded_mime_parent_batch,
+    backward_loaded_wamo_parent_batch,
+)
+from .mime_language import TrainableMIMECLIP
+from .mime_set import MIMERetrieval, MIMESetMotion
+from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from .tmr_parent_training import backward_loaded_tmr_parent_batch
+from .tmr_set import TMRSet, TMRTextBatch
+from .training import _capture_macro_bidirectional_r1, _capture_rng, _restore_rng
+from .wamo_set import WaMoSet
+
+
+class LiteratureParentSource(Protocol):
+    """Entire disk-backed source, deterministic seed/epoch augmentation.
+
+    Every reload must return identical physical arrays for that source,
+    seed/epoch/training tuple, independently of model dropout/sampling RNG.
+    Validation returns unaugmented data. Language methods receive all exact
+    human rows in order and use the operator-admitted frozen asset snapshots.
+    MIME uses its own genuinely trainable tokenizer/tower, not these features.
+    """
+
+    def capture(
+        self, parent: ParentCaptionRecord, *, seed: int, epoch: int, training: bool
+    ) -> PreparedContinuousCapture: ...
+
+    def tmr_text(self, captions: tuple[str, ...]) -> TMRTextBatch: ...
+
+    def wamo_cls(self, captions: tuple[str, ...]) -> Tensor: ...
+
+
+class LiteratureParentTrainingHost(ContinuousParentTrainingHost):
+    """Same schedule, clipping, validation selector and resume as B0/B1/B2.
+
+    Each method keeps its own qualified full-negative objective/replay. Text
+    is encoded once per full validation gallery; complete motion parents are
+    streamed one at a time. Inter-X's original role-aware MIME is separate.
+    """
+
+    def __init__(
+        self,
+        system: TMRSet | WaMoSet | MIMERetrieval,
+        task: ParentRetrievalTask,
+        source: LiteratureParentSource,
+        config: ParentHostConfig,
+        bindings: ParentHostBindings,
+    ):
+        if isinstance(system, TMRSet):
+            method, objective = "TMR-Set", asdict(system.loss_weights)
+        elif isinstance(system, WaMoSet):
+            method, objective = "WaMo-Set", asdict(system.config)
+        elif (
+            isinstance(system, MIMERetrieval)
+            and isinstance(system.motion, MIMESetMotion)
+            and isinstance(system.language, TrainableMIMECLIP)
+        ):
+            method, objective = "MIME-Set", asdict(system.motion.config)
+        else:
+            raise TypeError("need actual TMR/WaMo/shared MIME models, not original dyadic MIME")
+        if config.stage != "base" or config.cf_weight != 0 or config.cf_margin != 0:
+            raise ValueError(
+                "literature baselines use their original losses, not the V2 CF residual"
+            )
+        if bindings.frozen_base_checkpoint_sha256 is not None:
+            raise ValueError("literature models train independently, not on a frozen B2 checkpoint")
+        self._is_base = True  # independent trainable model, shared loop has no anchor
+        self._method = method
+        self._initialize(system, task, source, config, bindings)
+        self._manifest.update(
+            schema="phaseset-literature-parent-host-v1",
+            literature_method=method,
+            objective_configuration=objective,
+        )
+
+    def _capture(self, parent, *, training):
+        capture = self.source.capture(
+            parent, seed=self.config.seed, epoch=self._epoch, training=training
+        )
+        if (
+            type(capture) is not PreparedContinuousCapture
+            or capture.source_sha256 != parent.source_sha256
+        ):
+            raise ValueError("loader returned a different complete physical source")
+        if not training and capture.augmentation_yaw != 0.0:
+            raise ValueError("validation must use the admitted unaugmented complete source")
+        return capture
+
+    def _backward_batch(self, labels):
+        by_source = {row.source_sha256: row for row in labels.parents}
+        args = dict(
+            learning_components=self.config.train_components,
+            load_capture=lambda key: self._capture(by_source[key], training=True),
+            progress=lambda phase, index: self._event(phase, capture_index=index),
+        )
+        if self._method == "TMR-Set":
+            result = backward_loaded_tmr_parent_batch(
+                self.system, labels, load_text=self.source.tmr_text, **args
+            )
+        elif self._method == "WaMo-Set":
+            result = backward_loaded_wamo_parent_batch(
+                self.system, labels, load_cls=self.source.wamo_cls, **args
+            )
+        else:
+            result = backward_loaded_mime_parent_batch(self.system, labels, **args)
+        self._event(
+            "literature_objective", method=self._method, loss_components=result.loss_components
+        )
+        return ParentBackwardResult(
+            result.loss,
+            result.scores,
+            result.parent_count,
+            result.text_count,
+            0,
+            result.replayed_parents,
+        )
+
+    @torch.no_grad()
+    def _validate(self):
+        labels = self._gallery
+        previous_rng, was_training = _capture_rng(), self.system.training
+        self.system.eval()
+        try:
+            if self._method == "TMR-Set":
+                text = self.source.tmr_text(labels.captions)
+                if type(text) is not TMRTextBatch or len(text.tokens) != len(labels.captions):
+                    raise ValueError("validation needs all exact frozen TMR human rows")
+                text_embedding = self.system.text_distributions(text)[0]
+                scale = 1 / 0.1
+            elif self._method == "WaMo-Set":
+                cls = self.source.wamo_cls(labels.captions)
+                if len(cls) != len(labels.captions):
+                    raise ValueError("validation needs all exact frozen DistilBERT CLS rows")
+                text_embedding = self.system.encode_text(cls)
+                scale = 1 / self.system.config.temperature
+            else:
+                text_embedding = self.system.language(
+                    self.system.language.tokenize(labels.captions)
+                )
+                scale = self.system.logit_scale.exp()
+            text_embedding = F.normalize(text_embedding, dim=1)
+            scores = []
+            for index, parent in enumerate(labels.parents):
+                self._event("validation_capture", capture_index=index)
+                capture = _group(
+                    lambda key: self._capture(parent, training=False), parent.source_sha256
+                )
+                if self._method == "TMR-Set":
+                    motion = self.system.encode_capture(capture)[0]
+                elif self._method == "WaMo-Set":
+                    motion = self.system.encode_capture(capture)[0]
+                else:
+                    motion = self.system.motion(capture)
+                scores.append((scale * (F.normalize(motion[None], dim=1) @ text_embedding.T)).cpu())
+                del capture, motion
+            logits = torch.cat(scores).contiguous()
+            if logits.shape != (len(labels.parents), len(labels.captions)) or not bool(
+                torch.isfinite(logits).all()
+            ):
+                raise ValueError("literature full human gallery is malformed or nonfinite")
+            return _capture_macro_bidirectional_r1(
+                logits,
+                labels.positive_mask(device=torch.device("cpu")),
+                tuple(bytes.fromhex(key) for key in labels.motion_positive_keys),
+                tuple(bytes.fromhex(key) for key in labels.text_positive_keys),
+                tuple(bytes.fromhex(key) for key in labels.motion_source_keys),
+                labels.caption_commitments,
+            )
+        finally:
+            self.system.train(was_training)
+            _restore_rng(previous_rng)
diff --git a/tests/test_literature_parent_host.py b/tests/test_literature_parent_host.py
new file mode 100644
index 0000000..ab376d8
--- /dev/null
+++ b/tests/test_literature_parent_host.py
@@ -0,0 +1,242 @@
+"""Analytic optimizer/resume checks; never native pilot/convergence evidence."""
+
+import copy
+from dataclasses import replace
+import hashlib
+import json
+
+import pytest
+import torch
+
+from phaseset_core.continuous_parent_host import ParentHostBindings, ParentHostConfig
+from phaseset_core.literature_parent_host import LiteratureParentTrainingHost
+from phaseset_core.mime_set import MIMERetrieval, MIMESetMotion
+from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from phaseset_core.tmr_set import TMRGroupCapture, TMRSet, TMRTextBatch
+from phaseset_core.training import (
+    TrainingCheckpointError,
+    _capture_macro_bidirectional_r1,
+    _capture_rng,
+    _load_torch_checkpoint,
+    _stable_hash,
+)
+from test_literature_parent_training import fixture
+from test_mime_set import config as mime_config, language
+from test_wamo_set import small
+
+
+def key(value):
+    return hashlib.sha256(value.encode()).hexdigest()
+
+
+class Source:
+    def __init__(self, captures):
+        self.captures = captures
+        self.loaded, self.texts = [], []
+
+    def capture(self, parent, *, seed, epoch, training):
+        self.loaded.append((parent.source_sha256, epoch, training))
+        assert seed == 1729
+        return self.captures[parent.source_sha256]
+
+    def _features(self, captions, shape):
+        # Independent per-row generators: identical rows/features under batching
+        # and replay, without consuming any model/dropout/sampling RNG.
+        return torch.stack(
+            [
+                torch.randn(shape, generator=torch.Generator().manual_seed(int(key(row)[:12], 16)))
+                for row in captions
+            ]
+        )
+
+    def tmr_text(self, captions):
+        self.texts.append(("tmr", captions))
+        tokens = self._features(captions, (5, 768))
+        sentence = torch.nn.functional.normalize(self._features(captions, (768,)), dim=1)
+        return TMRTextBatch(tokens, torch.ones(len(captions), 5, dtype=torch.bool), sentence)
+
+    def wamo_cls(self, captions):
+        self.texts.append(("wamo", captions))
+        return self._features(captions, (768,))
+
+
+def setup(kind):
+    torch.manual_seed(1729)
+    captures, labels = fixture()
+    records = list(labels.parents[:2])
+    validation = replace(labels.parents[2], component="C03", split="validation")
+    records.append(validation)
+    second = replace(
+        validation,
+        source_sha256=key("validation-source"),
+        annotation_family_sha256=key("validation-family"),
+        captions=("other validation a", "other b"),
+    )
+    records.append(second)
+    captures[second.source_sha256] = replace(
+        captures[validation.source_sha256], source_sha256=second.source_sha256
+    )
+    task = ParentRetrievalTask(
+        tuple(records), expected_family_keys=tuple(row.annotation_family_sha256 for row in records)
+    )
+    model = (
+        TMRSet(latent_dim=16, ff_size=32, num_layers=1, num_heads=4, dropout=0.1)
+        if kind == "tmr"
+        else small(dropout=0.1)
+        if kind == "wamo"
+        else MIMERetrieval(MIMESetMotion(mime_config(dropout=0.1)), language())
+    )
+    config = ParentHostConfig.for_base(1729, ("C01",), ("C03",), epochs=2, parent_batch_size=2)
+    bindings = ParentHostBindings(key("inputs"), key("code"), key("runtime"), None)
+    return model, task, Source(captures), config, bindings
+
+
+@pytest.mark.parametrize("kind", ["tmr", "wamo", "mime"])
+def test_real_objective_optimizer_and_full_gallery_endpoint(kind, tmp_path):
+    model, task, source, cfg, bindings = setup(kind)
+    initial = copy.deepcopy(model.state_dict())
+    host = LiteratureParentTrainingHost(model, task, source, cfg, bindings)
+    result = host.fit(tmp_path / "run")
+    assert result.outcome == "COMPLETED" and result.global_step == 2
+    assert result.parents_seen == 4 and result.completed_epochs == 2
+    assert len(result.validation_history) == 2
+    assert result.best_validation_r1 == max(value for _, value in result.validation_history)
+    assert any(not torch.equal(initial[name], value) for name, value in model.state_dict().items())
+    training_loads = [row for row in source.loaded if row[2]]
+    assert len(training_loads) == (16 if kind == "tmr" else 8)
+    assert len(source.loaded) == len(training_loads) + 4
+    events = [json.loads(p.read_text()) for p in sorted((tmp_path / "run").glob("event-*.json"))]
+    updates = [row for row in events if row["phase"] == "update"]
+    assert [row["human_rows"] for row in updates] == [3, 3]
+    assert all(row["verified_cf"] == 0 and row["learning_rate"] == 2e-4 for row in updates)
+    losses = [row["loss_components"] for row in events if row["phase"] == "literature_objective"]
+    expected = (
+        {"loss", "recons", "kl", "latent", "contrastive"}
+        if kind == "tmr"
+        else {"loss", "reconstruction", "ordering", "contrastive"}
+        if kind == "wamo"
+        else {"loss"}
+    )
+    assert len(losses) == 2 and all(set(row) == expected for row in losses)
+    # Independent public model.score against ground-truth family positives.
+    # No model-generated pseudo-label, caption-weighted endpoint or shortened timeline.
+    gallery = host._gallery
+    physical = tuple(
+        TMRGroupCapture.from_prepared(source.captures[p.source_sha256]) for p in gallery.parents
+    )
+    model.eval()
+    with torch.no_grad():
+        text = (
+            source.tmr_text(gallery.captions)
+            if kind == "tmr"
+            else source.wamo_cls(gallery.captions)
+            if kind == "wamo"
+            else model.language.tokenize(gallery.captions)
+        )
+        scores = model.score(physical, text)
+    assert scores.shape == (2, 5)
+    oracle = _capture_macro_bidirectional_r1(
+        scores.cpu(),
+        gallery.positive_mask(device=torch.device("cpu")),
+        tuple(bytes.fromhex(k) for k in gallery.motion_positive_keys),
+        tuple(bytes.fromhex(k) for k in gallery.text_positive_keys),
+        tuple(bytes.fromhex(k) for k in gallery.motion_source_keys),
+        gallery.caption_commitments,
+    )
+    model.train()
+    state = _stable_hash(_capture_rng())
+    assert host._validate() == oracle == result.validation_history[-1][1]
+    assert model.training and _stable_hash(_capture_rng()) == state
+    payload, _ = _load_torch_checkpoint(
+        result.best_checkpoint.path, expected_sha256=result.best_checkpoint.sha256
+    )
+    assert payload["validation_history"][-1][1] == result.best_validation_r1
+    assert (
+        host._manifest["literature_method"]
+        == {"tmr": "TMR-Set", "wamo": "WaMo-Set", "mime": "MIME-Set"}[kind]
+    )
+
+
+@pytest.mark.parametrize("kind", ["tmr", "wamo", "mime"])
+def test_optimizer_dropout_vae_interruption_resume_bitwise(kind, tmp_path):
+    model, task, source, cfg, bindings = setup(kind)
+    initial = copy.deepcopy(model)
+    full = LiteratureParentTrainingHost(model, task, source, cfg, bindings).fit(tmp_path / "full")
+    stopped = LiteratureParentTrainingHost(copy.deepcopy(initial), task, source, cfg, bindings).fit(
+        tmp_path / "stop", stop_after_steps=1
+    )
+    resumed_model = copy.deepcopy(initial)
+    resumed = LiteratureParentTrainingHost(resumed_model, task, source, cfg, bindings).fit(
+        tmp_path / "resumed",
+        resume_checkpoint=stopped.latest_checkpoint.path,
+        resume_sha256=stopped.latest_checkpoint.sha256,
+    )
+    assert resumed.outcome == full.outcome == "COMPLETED"
+    assert resumed.parents_seen == full.parents_seen == 4
+    assert resumed.validation_history == full.validation_history
+    for name, value in model.state_dict().items():
+        assert torch.equal(value, resumed_model.state_dict()[name]), name
+    expected, _ = _load_torch_checkpoint(full.latest_checkpoint.path)
+    actual, _ = _load_torch_checkpoint(resumed.latest_checkpoint.path)
+    for name in ("model", "optimizer", "scheduler", "rng"):
+        assert _stable_hash(actual[name]) == _stable_hash(expected[name]), name
+
+
+@pytest.mark.parametrize("kind", ["tmr", "wamo", "mime"])
+def test_loss_configuration_drift_rejected_on_resume(kind, tmp_path):
+    model, task, source, cfg, bindings = setup(kind)
+    initial = copy.deepcopy(model)
+    stopped = LiteratureParentTrainingHost(model, task, source, cfg, bindings).fit(
+        tmp_path / "stop", stop_after_steps=0
+    )
+    if kind == "tmr":
+        initial.loss_weights = replace(initial.loss_weights, latent=0.02)
+    elif kind == "wamo":
+        initial.config = replace(initial.config, reconstruction_weight=2.0)
+    else:
+        initial.motion.config = replace(initial.motion.config, epsilon=1e-6)
+    with pytest.raises(TrainingCheckpointError, match="drifted"):
+        LiteratureParentTrainingHost(initial, task, source, cfg, bindings).fit(
+            tmp_path / "drift",
+            resume_checkpoint=stopped.latest_checkpoint.path,
+            resume_sha256=stopped.latest_checkpoint.sha256,
+        )
+    assert json.loads((tmp_path / "drift" / "terminal.json").read_text())["outcome"] == "FAILED"
+
+
+def test_test_and_held_out_rows_never_loaded_or_persisted(tmp_path):
+    model, task, source, cfg, bindings = setup("wamo")
+    sealed = ParentCaptionRecord(
+        key("sealed-source"), key("sealed-family"), "C09", "test", ("sealed",)
+    )
+    held = replace(
+        sealed,
+        source_sha256=key("held-source"),
+        annotation_family_sha256=key("held-family"),
+        component="C04",
+        split="train",
+        captions=("held",),
+    )
+    rows = task.parents + (sealed, held)
+    task = ParentRetrievalTask(
+        rows, expected_family_keys=tuple(row.annotation_family_sha256 for row in rows)
+    )
+    host = LiteratureParentTrainingHost(model, task, source, cfg, bindings)
+    host.fit(tmp_path / "not-started", stop_after_steps=0)
+    assert not source.loaded and not source.texts
+    assert all(row["component"] in {"C01", "C03"} for row in host._manifest["task"])
+    assert "sealed" not in (tmp_path / "not-started" / "run.json").read_text()
+    for wrong in (
+        replace(cfg, train_components=("C09",)),
+        replace(cfg, validation_components=("C09",)),
+    ):
+        with pytest.raises(ValueError):
+            LiteratureParentTrainingHost(model, task, source, wrong, bindings)
+    with pytest.raises(ValueError, match="original losses"):
+        LiteratureParentTrainingHost(
+            model, task, source, replace(cfg, stage="residual", cf_weight=0.2), bindings
+        )
+    with pytest.raises(ValueError, match="independently"):
+        LiteratureParentTrainingHost(
+            model, task, source, cfg, replace(bindings, frozen_base_checkpoint_sha256=key("base"))
+        )

From ffbaa245fffe8ffa9a5498e5af9c628cd7477bb6 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 08:13:37 -0500
Subject: [PATCH 50/95] Add fixed human language row cache for full-parent TMR
 and WaMo

---
 README.md                                |  10 +-
 RELEASE_FILES.sha256                     |   5 +-
 docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md |  41 ++++++++
 src/phaseset_core/tmr_feature_cache.py   | 125 +++++++++++++++++++++++
 tests/test_tmr_feature_cache.py          |  90 ++++++++++++++++
 5 files changed, 267 insertions(+), 4 deletions(-)
 create mode 100644 docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
 create mode 100644 src/phaseset_core/tmr_feature_cache.py
 create mode 100644 tests/test_tmr_feature_cache.py

diff --git a/README.md b/README.md
index 2128ee2..59c4ce5 100644
--- a/README.md
+++ b/README.md
@@ -72,14 +72,18 @@ shares the full-parent AdamW, validation selection and checkpoint/resume loop,
 while keeping each baseline's original objective. Analytic optimizer/resume
 checks and native dense-gradient replay are distinct from a native training
 pilot or converged baseline result.
+The [fixed TMR/WaMo language-row cache](docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md)
+retains every human caption occurrence and content token, reusing only frozen
+features; MIME still trains its own genuine CLIP tower.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
 There are still **0/87 formal V2 stages** and no native-data retrieval result.
 Approved main-data and licensed body-model assets have been obtained privately,
-and the handoff fold assignments have been recovered. Whole-parent preprocessing,
-run-specific admission, faithful baseline training, resource profiling, and real
-human relational verification still require actual execution evidence.
+and the handoff fold assignments have been recovered. Complete development-parent
+preprocessing and five training-only physical calibrations have closed private
+receipts. Run-specific admission, faithful baseline training, resource profiling,
+and real human relational verification still require actual execution evidence.
 The descriptions and score equation below document the preserved V1 core,
 not completed V2 experiments or a submission-ready paper.
 
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 46a2760..2863b99 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-fb6b196b13bd4a8f93553f127f05974144ec4ad3bdb8e87905cf434c6335e0a5	12198	README.md
+755b760e3a0353c21e5623193ac8955877d74fc0d5732c93c44afbcd20423682	12503	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -70,6 +70,7 @@ f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHAS
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
+b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 004dfc49150cc66ba71f7c303dda5dd5f9ce16ca4a220e7558b2da4914e94336	3978	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
@@ -207,6 +208,7 @@ eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phase
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
 5604397a65a0032d2a14124d34ca3d96a2d6d67e5f4534732bb82f7a0c7b2546	23163	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
+974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
 cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phaseset_core/tmr_language.py
 d1b92f7b65d750572a8447a48cd2a09cf12d7ec0fc3edd2f33c4088d0d77a14d	9442	src/phaseset_core/tmr_parent_training.py
 b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
@@ -325,6 +327,7 @@ a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/tes
 8727e87f1a0d44db6a39b1e5cfd8e22e3bed05544ddaed07b66b759adee66d84	5587	tests/test_release_tree_batch.py
 eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
 96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
+18c5c10980d8e0d81eb1a6b351c0e0e770325c95bcca59e15763e5c86967ce78	4260	tests/test_tmr_feature_cache.py
 726ee82009ecb29ce87208749fb1780365640a52f2e2d6e55399e22119838b89	2988	tests/test_tmr_language.py
 9441ab431bc210814bbc5c94a353fc5178d133e729ffb75c67456cf07ed38d79	4559	tests/test_tmr_parent_training.py
 2ae860859c1f9282a02cea929e54fa950433ac8fba81c7510c2b900321d5ff29	9606	tests/test_tmr_set.py
diff --git a/docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md b/docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
new file mode 100644
index 0000000..97a0baa
--- /dev/null
+++ b/docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
@@ -0,0 +1,41 @@
+# Fixed TMR/WaMo human-language features
+
+`phaseset_core.tmr_feature_cache` stores actual frozen TMR language outputs
+for private complete-parent development data. It is not an encoder, a new
+annotation, a label filter, or evidence that arbitrary input files are official.
+The private preparation caller uses the admitted pinned DistilBERT and MPNet
+snapshots through `FrozenTMRLanguage`, without training or content truncation.
+WaMo reuses the actual DistilBERT CLS token, not a TMR projection or MPNet/CLIP
+substitute. MIME's own genuinely trainable CLIP is **not** cached here.
+
+Only contiguous masked right padding is removed. Every content token and
+special token is retained. Reload dynamically restores exact positive-zero
+padding and masks; valid tokens, CLS and sentence features match the stored
+preparation outputs exactly. The preparation batch shape and frozen runtime
+are part of that feature snapshot; this is not a universal bitwise claim
+across different Transformer batching layouts or hardware.
+
+Exact duplicate UTF-8 text can reuse a fixed feature file. Annotation
+occurrences, caption commitments, complete-parent sources, families and all
+variable positive rows remain distinct in `ParentRetrievalTask`. The cache
+never samples captions or changes their whitespace, wording or order.
+
+Files are written once into a fresh private directory. Loading verifies
+closed manifest entries, complete array headers, finite FP32 values, token
+lengths and file identity. Unknown rows or changed artifacts fail explicitly;
+they do not trigger a substitute model, online download or regeneration.
+The private caller additionally verifies the admitted development population,
+official human-file lineage, exact feature reload, unchanged model weights
+and RNG, and source/asset identity before and after preparation. The final
+test population is excluded, and no learned training statistic is fitted.
+
+Analytic tests cover ordering, repeated occurrences, masks, padding, CLS,
+file drift, unknown rows, and TMR text distribution/gradient preservation.
+They are not proof of pretrained assets. That requires the separate actual
+private generation receipt. Neither a cache, these tests, nor a same-family
+review is optimization, a pilot, convergence, a hardware profile, a retrieval
+result or completion of the research project. No data or model-derived
+feature files belong in this public repository.
+
+See the [literature optimizer host](PHASESET_V2_LITERATURE_PARENT_HOST.md)
+and [TMR adaptation](TMR_SET_ADAPTATION.md).
diff --git a/src/phaseset_core/tmr_feature_cache.py b/src/phaseset_core/tmr_feature_cache.py
new file mode 100644
index 0000000..8cc2676
--- /dev/null
+++ b/src/phaseset_core/tmr_feature_cache.py
@@ -0,0 +1,125 @@
+"""Private frozen-language rows for complete-parent TMR/WaMo training.
+
+This stores model-derived text features, not a new annotation or retrieval
+label. Every human caption occurrence remains in the batch; identical text
+may reuse frozen features. Files/manifest stay private. Cache generation uses
+the admitted actual frozen language tower, never this module as a mock encoder.
+"""
+
+from __future__ import annotations
+
+import hashlib
+from pathlib import Path
+from typing import Mapping
+
+import numpy as np
+import torch
+
+from .tmr_set import TMRTextBatch
+
+
+def caption_key(caption: str) -> str:
+    if type(caption) is not str or not caption.strip():
+        raise ValueError("cache rows require exact nonempty caption strings")
+    return hashlib.sha256(caption.encode("utf-8")).hexdigest()
+
+
+def file_sha256(path: Path) -> str:
+    with path.open("rb") as stream:
+        return hashlib.file_digest(stream, "sha256").hexdigest()
+
+
+def save_frozen_tmr_rows(
+    directory: str | Path, captions: tuple[str, ...], text: TMRTextBatch
+) -> tuple[dict, ...]:
+    """Write each unique input row once, stripping only masked right padding.
+
+    No content token, CLS/SEP, or sentence feature is removed. The caller
+    pre-admits a unique-text preparation batch but retains all annotation
+    occurrences and their positive family lineage in the task manifest.
+    """
+    if type(text) is not TMRTextBatch or len(captions) != len(text.tokens):
+        raise ValueError("all captions must have their actual frozen TMR features")
+    keys = tuple(caption_key(row) for row in captions)
+    if len(set(keys)) != len(keys):
+        raise ValueError("preparation writes each exact unique text once")
+    root = Path(directory)
+    root.mkdir(parents=True, exist_ok=True)
+    records = []
+    for index, key in enumerate(keys):
+        mask = text.token_mask[index].detach().cpu()
+        length = int(mask.sum())
+        if not bool(mask[:length].all()) or bool(mask[length:].any()):
+            raise ValueError("the admitted frozen tokenizer must use contiguous right padding")
+        path = root / f"{key}.npz"
+        tokens = text.tokens[index, :length].detach().cpu().contiguous().numpy()
+        sentence = text.sentences[index].detach().cpu().contiguous().numpy()
+        with path.open("xb") as stream:
+            np.savez(stream, tokens=tokens, sentence=sentence)
+        records.append(
+            dict(
+                caption_utf8_sha256=key,
+                file_sha256=file_sha256(path),
+                file_bytes=path.stat().st_size,
+                token_count=length,
+            )
+        )
+    return tuple(records)
+
+
+class FrozenTMRRowCache:
+    """Load exact rows in caller order, with no batch/caption multiplicity loss.
+
+    ``records`` comes from the closed private cache manifest bound to actual
+    source, frozen model, code, precision and runtime receipts. An arbitrary
+    matching file is not official provenance or permission. No final-test
+    discovery, learned statistic or row generation occurs here.
+    """
+
+    def __init__(self, directory: str | Path, records: tuple[Mapping, ...]):
+        self.directory = Path(directory)
+        self.records = {row["caption_utf8_sha256"]: dict(row) for row in records}
+        if not records or len(self.records) != len(records):
+            raise ValueError("cache manifest needs each unique exact text once")
+
+    def _row(self, caption):
+        key = caption_key(caption)
+        if key not in self.records:
+            raise ValueError("caption is outside the admitted frozen human feature cache")
+        record, path = self.records[key], self.directory / f"{key}.npz"
+        if (
+            file_sha256(path) != record["file_sha256"]
+            or path.stat().st_size != record["file_bytes"]
+        ):
+            raise ValueError("private frozen language artifact differs from its closed manifest")
+        with np.load(path, allow_pickle=False) as values:
+            if set(values.files) != {"tokens", "sentence"}:
+                raise ValueError("frozen language artifact has an unexpected array roster")
+            tokens, sentence = values["tokens"], values["sentence"]
+        if (
+            tokens.dtype != np.float32
+            or tokens.shape != (record["token_count"], 768)
+            or sentence.dtype != np.float32
+            or sentence.shape != (768,)
+            or not 1 <= len(tokens) <= 512
+            or not np.isfinite(tokens).all()
+            or not np.isfinite(sentence).all()
+        ):
+            raise ValueError("private frozen language arrays have invalid headers or values")
+        return torch.from_numpy(tokens), torch.from_numpy(sentence)
+
+    def tmr_text(self, captions: tuple[str, ...]) -> TMRTextBatch:
+        rows = [self._row(caption) for caption in captions]
+        if not rows:
+            raise ValueError("frozen text batch cannot be empty")
+        length = max(len(tokens) for tokens, _ in rows)
+        features = torch.zeros(len(rows), length, 768, dtype=torch.float32)
+        mask = torch.zeros(len(rows), length, dtype=torch.bool)
+        for index, (tokens, _) in enumerate(rows):
+            features[index, : len(tokens)] = tokens
+            mask[index, : len(tokens)] = True
+        return TMRTextBatch(features, mask, torch.stack([sentence for _, sentence in rows]))
+
+    def wamo_cls(self, captions: tuple[str, ...]) -> torch.Tensor:
+        # The actual frozen DistilBERT CLS, not MPNet/CLIP or the learned TMR projection.
+        return torch.stack([self._row(caption)[0][0] for caption in captions]).contiguous()
diff --git a/tests/test_tmr_feature_cache.py b/tests/test_tmr_feature_cache.py
new file mode 100644
index 0000000..4128c49
--- /dev/null
+++ b/tests/test_tmr_feature_cache.py
@@ -0,0 +1,90 @@
+"""Analytic cache IO checks, not proof of actual pretrained assets."""
+
+import copy
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.tmr_feature_cache import FrozenTMRRowCache, save_frozen_tmr_rows
+from phaseset_core.tmr_set import TMRSet, TMRTextBatch
+from phaseset_core.training import _capture_rng, _stable_hash
+
+
+def fixture():
+    torch.manual_seed(1729)
+    tokens = torch.randn(3, 8, 768)
+    mask = torch.arange(8)[None] < torch.tensor([3, 8, 5])[:, None]
+    sentences = torch.nn.functional.normalize(torch.randn(3, 768), dim=1)
+    return ("exact text", "中文  unchanged whitespace ", "a different row"), TMRTextBatch(
+        tokens, mask, sentences
+    )
+
+
+def test_complete_rows_padding_reordering_duplicates_cls_and_rng(tmp_path):
+    captions, text = fixture()
+    state = _stable_hash(_capture_rng())
+    records = save_frozen_tmr_rows(tmp_path / "rows", captions, text)
+    cache = FrozenTMRRowCache(tmp_path / "rows", records)
+    order = [2, 0, 1, 0]
+    actual = cache.tmr_text(tuple(captions[index] for index in order))
+    for i, j in enumerate(order):
+        valid = text.token_mask[j]
+        assert torch.equal(actual.tokens[i, : int(valid.sum())], text.tokens[j, valid])
+        assert torch.equal(actual.sentences[i], text.sentences[j])
+        assert torch.equal(actual.token_mask[i], text.token_mask[j])
+        assert (actual.tokens[i, ~actual.token_mask[i]].view(torch.int32) == 0).all()
+    assert torch.equal(cache.wamo_cls(tuple(captions[j] for j in order)), text.tokens[order, 0])
+    assert _stable_hash(_capture_rng()) == state
+    assert len(records) == 3 and len(actual.tokens) == 4
+
+
+def test_closed_files_and_unknown_human_rows_are_not_silently_replaced(tmp_path):
+    captions, text = fixture()
+    records = save_frozen_tmr_rows(tmp_path, captions, text)
+    cache = FrozenTMRRowCache(tmp_path, records)
+    with pytest.raises(ValueError, match="outside"):
+        cache.tmr_text(("new machine-fused row",))
+    with pytest.raises(FileExistsError):
+        save_frozen_tmr_rows(tmp_path, captions, text)
+    path = tmp_path / f"{records[0]['caption_utf8_sha256']}.npz"
+    with path.open("wb") as stream:
+        np.savez(stream, tokens=np.zeros((3, 768), np.float32), sentence=np.zeros(768, np.float32))
+    with pytest.raises(ValueError, match="differs"):
+        cache.wamo_cls(captions)
+
+
+def test_removing_only_right_padding_preserves_tmr_latents_and_all_text_gradients(tmp_path):
+    captions, text = fixture()
+    records = save_frozen_tmr_rows(tmp_path, captions, text)
+    cached = FrozenTMRRowCache(tmp_path, records).tmr_text(captions)
+    model = TMRSet(latent_dim=16, ff_size=32, num_layers=1, num_heads=4, dropout=0.0).eval()
+    oracle = copy.deepcopy(model)
+    actual = model.text_distributions(cached)
+    expected = oracle.text_distributions(text)
+    for left, right in zip(actual, expected, strict=True):
+        torch.testing.assert_close(left, right, rtol=1e-5, atol=1e-6)
+    sum(value.square().sum() for value in actual).backward()
+    sum(value.square().sum() for value in expected).backward()
+    for (name, parameter), (_, reference) in zip(
+        model.text_encoder.named_parameters(), oracle.text_encoder.named_parameters(), strict=True
+    ):
+        assert parameter.grad is not None and reference.grad is not None, name
+        torch.testing.assert_close(parameter.grad, reference.grad, rtol=1e-5, atol=1e-6, msg=name)
+
+
+def test_no_content_or_masked_interior_tokens_can_be_removed(tmp_path):
+    captions, text = fixture()
+    mask = text.token_mask.clone()
+    mask[0, 0], mask[0, 3] = False, True
+    with pytest.raises(ValueError, match="right padding"):
+        save_frozen_tmr_rows(tmp_path, captions, TMRTextBatch(text.tokens, mask, text.sentences))
+    with pytest.raises(ValueError, match="unique"):
+        save_frozen_tmr_rows(tmp_path, (captions[0],) * 3, text)
+    records = save_frozen_tmr_rows(tmp_path / "good", captions, text)
+    with pytest.raises(ValueError, match="unique"):
+        FrozenTMRRowCache(tmp_path / "good", records + records[:1])
+    changed = copy.deepcopy(records)
+    changed[0]["token_count"] = 2
+    with pytest.raises(ValueError, match="headers"):
+        FrozenTMRRowCache(tmp_path / "good", changed).tmr_text(captions)

From 7a0f7c5627c11b2eb1081d6b01272a0d33fe7192 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 08:26:34 -0500
Subject: [PATCH 51/95] Add full-parent disk source with independent
 deterministic group yaw

---
 README.md                               |   4 +
 RELEASE_FILES.sha256                    |   5 +-
 docs/PHASESET_V2_PARENT_DISK_SOURCE.md  |  46 +++++++
 src/phaseset_core/parent_disk_source.py | 159 ++++++++++++++++++++++++
 tests/test_parent_disk_source.py        | 122 ++++++++++++++++++
 5 files changed, 335 insertions(+), 1 deletion(-)
 create mode 100644 docs/PHASESET_V2_PARENT_DISK_SOURCE.md
 create mode 100644 src/phaseset_core/parent_disk_source.py
 create mode 100644 tests/test_parent_disk_source.py

diff --git a/README.md b/README.md
index 59c4ce5..2c075be 100644
--- a/README.md
+++ b/README.md
@@ -75,6 +75,10 @@ pilot or converged baseline result.
 The [fixed TMR/WaMo language-row cache](docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md)
 retains every human caption occurrence and content token, reusing only frozen
 features; MIME still trains its own genuine CLIP tower.
+The [development disk source](docs/PHASESET_V2_PARENT_DISK_SOURCE.md) streams
+complete body/phase timelines and uses source/seed/epoch group yaw independent
+of model RNG. Caption eligibility and run-specific floors remain explicit
+private inputs; the complete frozen-CLIP/verified-CF interface is still pending.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 2863b99..592690c 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-755b760e3a0353c21e5623193ac8955877d74fc0d5732c93c44afbcd20423682	12503	README.md
+0b46f9cb05b366c393d081a6591cfa73713eb3a8a4fd154281956e2b5001fa8e	12815	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -76,6 +76,7 @@ b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASE
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
+8d732afa31dd6d3f51628818df5254e79374d49671c6626fca4b936ffeaa09c1	2824	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
 3eb5f4237db4af02d49e8eb62a318ef68ac706bf14cc1b405bf9fa837e8d5705	7431	docs/PHASESET_V2_PARENT_HOST.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
@@ -192,6 +193,7 @@ c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phase
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 0f0309c54a5b48a0493bb24f97eda39ff93be657295e1b0ebcc3dd6e9a8ecd76	6565	src/phaseset_core/parent_capture.py
+b492696e83648095acca985d6e86f97570548bedaef8741aedb18bd96189be04	7464	src/phaseset_core/parent_disk_source.py
 be883315aa773e6c2d535ea981853ae92a791187338fd2b310b574f956174a82	7326	src/phaseset_core/parent_retrieval_task.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 e69b0d27d479b4499bae73c96f7854f9d07539043a0b66f0f75f6bc3e23c1f51	55415	src/phaseset_core/periodic_cache_execution.py
@@ -236,6 +238,7 @@ b65a2178d7faf25372adeccdfcd19ee7804544665c6059f2d684801ccff83e11	6050	tests/test
 773430e42927751a5b07ab20da7ddd716d13853f9496033476b2926198148ef8	14947	tests/test_mime_set.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 3b23c2b08ed81029ca6239cbb63b98093077786163aaf5bb47ccfefb21c6ef24	7117	tests/test_parent_capture.py
+487ee577859df867298041fe731166cd211506e5f211d3c508eeec5aa282640f	5825	tests/test_parent_disk_source.py
 32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
 9a05b2a025a694f94e158d2de74242d2a820efbc8973920001895114e7a894fb	14880	tests/test_periodic_cache_source_lineage.py
diff --git a/docs/PHASESET_V2_PARENT_DISK_SOURCE.md b/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
new file mode 100644
index 0000000..4d7c2fb
--- /dev/null
+++ b/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
@@ -0,0 +1,46 @@
+# Complete-parent disk source
+
+`DevelopmentParentDiskSource` consumes caller-admitted `ParentRetrievalTask`
+and `ParentDiskRecord` entries. Every family must have exactly one complete
+source and an explicit caption-yaw eligibility bit. Unknown or changed human
+records fail before loading. The development source rejects final-test rows;
+it does not discover datasets or infer rights from matching files.
+
+The literature interface supplies complete body timelines and fixed TMR/WaMo
+human-language rows. The body view loads verified prepared arrays without
+reading the physical cache. The phase view additionally loads the entire
+verified physical cache with the supplied training-only floors. It recomputes
+all motion-derived phase, actor and root fields for a nonzero rotation; it
+does not approximate a rotation of cached RMS vectors. Accepted windows,
+absolute positions on the timeline, rejected intervals, masks and trailing
+frame declarations stay intact. There is no dataset-sized in-memory cache.
+
+One shared group yaw is determined solely by source, one of the three fixed
+seeds, and epoch. A fixed SHA-to-angle mapping makes replay independent of
+model dropout, VAE sampling and global NumPy/Python/Torch RNG state. Every
+reload of that source/seed/epoch receives the same physical arrays. Validation
+always uses zero yaw. Disallowed captions also receive zero yaw at training;
+the private caller must bind a real, frozen text-eligibility rule and its
+population, not assume all world-direction captions allow arbitrary rotation.
+
+The physical floors must come from the correct run's learning population.
+This source does not fit them or authorize reusing main-experiment floors in
+a held-out fold. Source, annotation, floor, feature, augmentation-rule and
+runtime identities belong in the private execution inputs.
+
+This is the literature source interface and the body/phase loading seam,
+not the entire base/V2 text interface: that still needs its admitted frozen
+CLIP batches and genuinely two-human-verified counterfactual rows. The source
+also supplies no production lock, heartbeat, budget, retention, hardware,
+pilot, formal-stage or final-test authority.
+
+Analytic tests exercise full timelines with a rejected gap, RNG-independent
+replay, shared rotation, actual phase recomputation, exact-zero validation,
+caption multiplicity, task drift and final-test exclusion. Native validation
+must separately load the complete admitted development population and its
+closed fixed text features. An evaluation-only source qualification does not
+verify a training yaw policy or demonstrate retrieval performance.
+
+See the [fixed language rows](PHASESET_V2_FROZEN_LANGUAGE_ROWS.md),
+[literature host](PHASESET_V2_LITERATURE_PARENT_HOST.md) and
+[physical cache and calibration](PHASESET_V2_CALIBRATION_AND_CACHE.md).
diff --git a/src/phaseset_core/parent_disk_source.py b/src/phaseset_core/parent_disk_source.py
new file mode 100644
index 0000000..76b92f0
--- /dev/null
+++ b/src/phaseset_core/parent_disk_source.py
@@ -0,0 +1,159 @@
+"""Complete development-parent disk views with model-RNG-independent yaw.
+
+Private callers supply the admitted human records, exact preparation/cache
+records and explicit caption yaw eligibility. This module discovers no data,
+opens no final test, infers no semantic permission and holds no model weights.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+import math
+from pathlib import Path
+from typing import Mapping
+
+import numpy as np
+
+from .continuous_capture_io import load_prepared_continuous_capture
+from .continuous_training_input import ContinuousTrainingInput, shared_yaw_capture
+from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from .tmr_feature_cache import FrozenTMRRowCache, caption_key
+from .training import OFFICIAL_SEEDS
+
+
+@dataclass(frozen=True)
+class ParentDiskRecord:
+    parent: ParentCaptionRecord
+    body_directory: Path
+    prepared_record: dict
+    physical_directory: Path | None = None
+    physical_record: dict | None = None
+
+
+def deterministic_parent_yaw(source_sha256: str, *, seed: int, epoch: int) -> float:
+    """One group rotation per entire source/seed/epoch, no global RNG draws."""
+    if type(seed) is not int or seed not in OFFICIAL_SEEDS:
+        raise ValueError("augmentation requires an official fixed seed")
+    if type(epoch) is not int or not 0 <= epoch < 2**64:
+        raise ValueError("augmentation epoch must be a nonnegative integer")
+    value = hashlib.sha256(
+        b"phaseset-parent-shared-yaw-v1\0"
+        + bytes.fromhex(source_sha256)
+        + seed.to_bytes(8, "big")
+        + epoch.to_bytes(8, "big")
+    ).digest()
+    # Explicit fixed 53-bit mapping avoids platform RNG/version dependence.
+    fraction = (int.from_bytes(value[:8], "big") >> 11) / 2**53
+    return 2 * math.pi * fraction - math.pi
+
+
+class DevelopmentParentDiskSource:
+    """Stream exact full timelines; never keep a dataset-sized RAM cache.
+
+    TMR/WaMo and base ``capture`` views load body arrays only. Residual ``view``
+    additionally loads the complete physical cache and the operator-admitted
+    training-only floors, recomputing all physical features for nonzero yaw.
+    No stale zero-yaw approximation, timeline truncation or sampled window
+    fallback exists. Validation always uses the original unaugmented source.
+
+    ``yaw_eligibility`` must cover every admitted family explicitly. Its
+    human-text rule/provenance belongs in the private input manifest; a bool
+    here cannot establish that world-direction captions permit a rotation.
+    This is a source adapter, not the production lock/budget/pilot authority.
+    """
+
+    def __init__(
+        self,
+        task: ParentRetrievalTask,
+        records: tuple[ParentDiskRecord, ...],
+        *,
+        yaw_eligibility: Mapping[str, bool],
+        language_rows: FrozenTMRRowCache,
+        energy_floors: np.ndarray | None = None,
+    ):
+        if type(task) is not ParentRetrievalTask or any(
+            type(record) is not ParentDiskRecord for record in records
+        ):
+            raise TypeError("need the admitted complete task and exact disk records")
+        self.records = {record.parent.annotation_family_sha256: record for record in records}
+        families = {parent.annotation_family_sha256 for parent in task.parents}
+        if len(self.records) != len(records) or set(self.records) != families:
+            raise ValueError("disk source must cover the complete admitted task exactly once")
+        self.yaw_eligibility = dict(yaw_eligibility)
+        if set(self.yaw_eligibility) != families or any(
+            type(value) is not bool for value in self.yaw_eligibility.values()
+        ):
+            raise ValueError("every human-caption family needs explicit boolean yaw eligibility")
+        if type(language_rows) is not FrozenTMRRowCache:
+            raise TypeError("need the closed frozen TMR/WaMo human feature rows")
+        for parent in task.parents:
+            record = self.records[parent.annotation_family_sha256]
+            if parent.split == "test":
+                raise ValueError("final test is not admitted by a development source")
+            if record.parent != parent or (
+                record.prepared_record["source_sha256"] != parent.source_sha256
+                or record.prepared_record["augmentation_yaw"] != 0.0
+            ):
+                raise ValueError("disk preparation differs from the unaugmented parent task")
+            if any(
+                caption_key(caption) not in language_rows.records for caption in parent.captions
+            ):
+                raise ValueError("all official human rows must exist in the closed language cache")
+            if energy_floors is not None and (
+                record.physical_directory is None
+                or record.physical_record is None
+                or record.physical_record["prepared_record"] != record.prepared_record
+            ):
+                raise ValueError("phase views require complete matching physical records")
+        self.energy_floors = None
+        if energy_floors is not None:
+            floors = np.array(energy_floors, dtype=np.float64, copy=True)
+            if floors.shape != (6,) or not np.isfinite(floors).all() or (floors < 0).any():
+                raise ValueError("physical floors need six finite nonnegative values")
+            floors.setflags(write=False)
+            self.energy_floors = floors
+        self.language_rows = language_rows
+
+    def _record_and_yaw(self, parent, *, seed, epoch, training):
+        if type(parent) is not ParentCaptionRecord or (
+            parent.annotation_family_sha256 not in self.records
+        ):
+            raise ValueError("parent is outside the admitted development task")
+        record = self.records[parent.annotation_family_sha256]
+        if record.parent != parent:
+            raise ValueError("parent human/source lineage differs from the admitted task")
+        if type(training) is not bool:
+            raise ValueError("training view selection must be explicit")
+        angle = deterministic_parent_yaw(parent.source_sha256, seed=seed, epoch=epoch)
+        if not training or not self.yaw_eligibility[parent.annotation_family_sha256]:
+            angle = 0.0
+        return record, angle
+
+    def capture(self, parent, *, seed, epoch, training):
+        record, angle = self._record_and_yaw(parent, seed=seed, epoch=epoch, training=training)
+        capture = load_prepared_continuous_capture(
+            record.body_directory, expected_metadata=record.prepared_record
+        )
+        return shared_yaw_capture(capture, yaw_delta=angle)
+
+    def view(self, parent, *, seed, epoch, training):
+        if self.energy_floors is None:
+            return self.capture(parent, seed=seed, epoch=epoch, training=training)
+        record, angle = self._record_and_yaw(parent, seed=seed, epoch=epoch, training=training)
+        complete = ContinuousTrainingInput.load(
+            record.body_directory,
+            record.physical_directory,
+            cache_record=record.physical_record,
+            energy_floors=self.energy_floors,
+        )
+        return complete.view(
+            yaw_delta=angle,
+            allow_shared_yaw=self.yaw_eligibility[parent.annotation_family_sha256],
+        )
+
+    def tmr_text(self, captions):
+        return self.language_rows.tmr_text(captions)
+
+    def wamo_cls(self, captions):
+        return self.language_rows.wamo_cls(captions)
diff --git a/tests/test_parent_disk_source.py b/tests/test_parent_disk_source.py
new file mode 100644
index 0000000..9e60f60
--- /dev/null
+++ b/tests/test_parent_disk_source.py
@@ -0,0 +1,122 @@
+"""Analytic disk source and independent replay checks, not native training."""
+
+from dataclasses import replace
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.parent_disk_source import (
+    DevelopmentParentDiskSource,
+    ParentDiskRecord,
+    deterministic_parent_yaw,
+)
+from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from phaseset_core.tmr_feature_cache import FrozenTMRRowCache, save_frozen_tmr_rows
+from phaseset_core.tmr_set import TMRTextBatch
+from phaseset_core.training import _capture_rng, _stable_hash
+from test_continuous_training_input import _input
+
+
+def _source(tmp_path, *, allowed=True, phase=True, split="train"):
+    inputs, body, physical, record = _input(tmp_path)
+    parent = ParentCaptionRecord("a" * 64, "b" * 64, "C01", split, ("group moves together",))
+    task = ParentRetrievalTask((parent,), expected_family_keys=("b" * 64,))
+    text = TMRTextBatch(
+        torch.ones(1, 4, 768), torch.ones(1, 4, dtype=torch.bool), torch.full((1, 768), 768**-0.5)
+    )
+    cached = save_frozen_tmr_rows(tmp_path / "language", parent.captions, text)
+    row = ParentDiskRecord(parent, body, record["prepared_record"], physical, record)
+    source = DevelopmentParentDiskSource(
+        task,
+        (row,),
+        yaw_eligibility={"b" * 64: allowed},
+        language_rows=FrozenTMRRowCache(tmp_path / "language", cached),
+        energy_floors=inputs.cached_field.energy_floors if phase else None,
+    )
+    return source, parent, inputs, task, row
+
+
+def test_replayed_complete_body_is_identical_after_model_rng_draws(tmp_path):
+    source, parent, original, _, _ = _source(tmp_path, phase=False)
+    args = dict(seed=1729, epoch=3, training=True)
+    before = _stable_hash(_capture_rng())
+    first = source.capture(parent, **args)
+    assert _stable_hash(_capture_rng()) == before
+    torch.randn(127)
+    np.random.random(127)
+    next_rng = _stable_hash(_capture_rng())
+    second = source.capture(parent, **args)
+    assert _stable_hash(_capture_rng()) == next_rng
+    np.testing.assert_array_equal(first.skeletons, second.skeletons)
+    np.testing.assert_array_equal(first.track_mask, original.capture.track_mask)
+    assert first.decisions == original.capture.decisions and len(first.windows()) == 2
+    assert first.frame_count == 600 and first.source_sha256 == parent.source_sha256
+    assert first.augmentation_yaw == deterministic_parent_yaw("a" * 64, seed=1729, epoch=3)
+    assert first.augmentation_yaw != source.capture(parent, **{**args, "epoch": 4}).augmentation_yaw
+    validation = source.capture(parent, **{**args, "training": False})
+    np.testing.assert_array_equal(validation.skeletons, original.capture.skeletons)
+    assert validation.augmentation_yaw == 0.0
+    assert not first.skeletons[~first.track_mask].view(np.uint32).any()
+    assert not first.skeletons.flags.writeable
+
+
+def test_phase_views_recompute_every_field_and_validation_reuses_cache(tmp_path):
+    source, parent, _, _, _ = _source(tmp_path)
+    args = dict(seed=2718, epoch=0, training=True)
+    first, second = source.view(parent, **args), source.view(parent, **args)
+    body = source.capture(parent, **args)
+    np.testing.assert_array_equal(first.capture.skeletons, body.skeletons)
+    assert not first.reused_physical_cache
+    for name in ("actor_features", "actor_patch_mask", "root_positions", "root_patch_mask"):
+        np.testing.assert_array_equal(
+            getattr(first.phase_field, name), getattr(second.phase_field, name)
+        )
+    for a, b in zip(first.phase_field.responses, second.phase_field.responses, strict=True):
+        np.testing.assert_array_equal(a, b)
+    assert source.view(parent, **{**args, "training": False}).reused_physical_cache
+    text = source.tmr_text(parent.captions * 2)
+    assert len(text.tokens) == 2 and torch.equal(text.tokens[0], text.tokens[1])
+    assert torch.equal(source.wamo_cls(parent.captions), text.tokens[:1, 0])
+
+
+def test_unapproved_caption_yaw_and_task_drift_do_not_get_silent_defaults(tmp_path):
+    source, parent, original, task, row = _source(tmp_path, allowed=False)
+    rotated = source.view(parent, seed=31415, epoch=0, training=True)
+    assert rotated.reused_physical_cache and rotated.capture.augmentation_yaw == 0.0
+    np.testing.assert_array_equal(rotated.capture.skeletons, original.capture.skeletons)
+    for changed in (
+        replace(parent, source_sha256="c" * 64),
+        replace(parent, captions=("different label",)),
+        replace(parent, annotation_family_sha256="c" * 64),
+    ):
+        with pytest.raises(ValueError, match="admitted"):
+            source.capture(changed, seed=1729, epoch=0, training=True)
+    for rules in ({}, {"b" * 64: 1}):
+        with pytest.raises(ValueError, match="eligibility"):
+            DevelopmentParentDiskSource(
+                task, (row,), yaw_eligibility=rules, language_rows=source.language_rows
+            )
+    with pytest.raises(ValueError, match="cover"):
+        DevelopmentParentDiskSource(
+            task, (), yaw_eligibility={}, language_rows=source.language_rows
+        )
+    with pytest.raises(ValueError, match="complete matching"):
+        DevelopmentParentDiskSource(
+            task,
+            (replace(row, physical_record=None),),
+            yaw_eligibility={"b" * 64: True},
+            language_rows=source.language_rows,
+            energy_floors=np.zeros(6),
+        )
+
+
+def test_final_test_is_never_opened_by_development_source(tmp_path):
+    with pytest.raises(ValueError, match="final test"):
+        _source(tmp_path, split="test")
+
+
+@pytest.mark.parametrize("seed,epoch", [(0, 0), (True, 0), (1729, -1), (1729, True)])
+def test_yaw_seed_and_epoch_are_explicit(seed, epoch):
+    with pytest.raises(ValueError):
+        deterministic_parent_yaw("a" * 64, seed=seed, epoch=epoch)

From d4d5096a586d19a55645fe768faf6d8bc3eb21bf Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 09:08:27 -0500
Subject: [PATCH 52/95] Connect frozen CLIP preparation rows to complete-parent
 hosts

---
 README.md                                     |   5 +-
 RELEASE_FILES.sha256                          |  19 +-
 docs/PHASESET_V2_FROZEN_CLIP_ROWS.md          |  54 +++++
 docs/PHASESET_V2_PARENT_DISK_SOURCE.md        |   7 +-
 src/phaseset_core/capture_validation.py       | 186 ++++++++-------
 .../continuous_base_retrieval.py              |   2 +-
 .../continuous_parent_training.py             |   2 +-
 src/phaseset_core/continuous_retrieval.py     |   2 +-
 src/phaseset_core/frozen_clip_text.py         | 166 ++++++++++----
 src/phaseset_core/parent_clip_rows.py         |  63 ++++++
 src/phaseset_core/parent_disk_source.py       |  17 ++
 tests/test_parent_clip_rows.py                | 212 ++++++++++++++++++
 12 files changed, 597 insertions(+), 138 deletions(-)
 create mode 100644 docs/PHASESET_V2_FROZEN_CLIP_ROWS.md
 create mode 100644 src/phaseset_core/parent_clip_rows.py
 create mode 100644 tests/test_parent_clip_rows.py

diff --git a/README.md b/README.md
index 2c075be..de18f95 100644
--- a/README.md
+++ b/README.md
@@ -78,7 +78,10 @@ features; MIME still trains its own genuine CLIP tower.
 The [development disk source](docs/PHASESET_V2_PARENT_DISK_SOURCE.md) streams
 complete body/phase timelines and uses source/seed/epoch group yaw independent
 of model RNG. Caption eligibility and run-specific floors remain explicit
-private inputs; the complete frozen-CLIP/verified-CF interface is still pending.
+private inputs. The [frozen human CLIP row source](docs/PHASESET_V2_FROZEN_CLIP_ROWS.md)
+connects base/V2 human-only text using exact cached preparation rows with a
+distinct derived receipt, not a new encoder invocation. The genuine verified-CF
+pool and production admission remain pending.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 592690c..4d85d63 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-0b46f9cb05b366c393d081a6591cfa73713eb3a8a4fd154281956e2b5001fa8e	12815	README.md
+7cc6876398afdb06926697a4dd707192a42c4c4c29211d1e3b4cbdea295534b1	13025	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -70,13 +70,14 @@ f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHAS
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
 55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
+6d50263826636377615082911b0de5afe871b8887f9419f95bbe4fb4db7f914d	3439	docs/PHASESET_V2_FROZEN_CLIP_ROWS.md
 b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 004dfc49150cc66ba71f7c303dda5dd5f9ce16ca4a220e7558b2da4914e94336	3978	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
-8d732afa31dd6d3f51628818df5254e79374d49671c6626fca4b936ffeaa09c1	2824	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
+c94c1538acb0e892c91c54ad1c6740073c9d75623465aa665ab1ea81b9b1ead3	2904	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
 3eb5f4237db4af02d49e8eb62a318ef68ac706bf14cc1b405bf9fa837e8d5705	7431	docs/PHASESET_V2_PARENT_HOST.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
@@ -162,15 +163,15 @@ b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phase
 121dcb6b948b9a3a4878ed376b3692dcbbd4b19f7ea147a7d28341c64b77d296	36926	src/phaseset_core/caption_role_provenance.py
 d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
 0eaf0057ccf6ab6a4b83c1fa6e649399c4f783a79fab301e0778bafb5699daa9	47086	src/phaseset_core/capture_prepared_storage.py
-7a146f3c81122ed6492fb6dc30194f68e6e3b86e2a25d29a5313607eb18470b6	42026	src/phaseset_core/capture_validation.py
+501a70f3f772fa99538f516e586d2d0810f86a21d15f5042b6a6cb30e63280c7	43337	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
-be4638d0ce9057468eb557db6e73afef1e2ce688833309991ce5f5818b0ad575	5577	src/phaseset_core/continuous_base_retrieval.py
+80e6d497ff9bb4354664cb1a054cdb550ab222e92c841813afc27af6d3759495	5603	src/phaseset_core/continuous_base_retrieval.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
 2ef64b87f94fa635dce12fc5589088c64680b1816d640aa99f960a43ae9fe8a9	27008	src/phaseset_core/continuous_parent_host.py
-19798b164958b1e65be4353d26ca4cbe167ebaa26c9639b488bcb3e63a00296c	15174	src/phaseset_core/continuous_parent_training.py
+cd9b85e1b3e576d23c04f15c5b6dad853110c2a7733b030ec2c6a3b1e6d874ef	15200	src/phaseset_core/continuous_parent_training.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
-38f74724372e276257c1014e50e26eb8b2cab482728a6b143799118ad2f47ca9	10545	src/phaseset_core/continuous_retrieval.py
+6af5ecfcadc139acfdb3fe2d2e00995a40e90522a821172bc44e7a3604028dbc	10571	src/phaseset_core/continuous_retrieval.py
 a46004b4c5a94dc84029a6cf5a54631e614e67f680c506feb0b92b435702160b	6873	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
@@ -181,7 +182,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
-877302cdb2ad891eb064d5bb228d124b66042865b78fa3826f66cf7bacf01166	68520	src/phaseset_core/frozen_clip_text.py
+793a975242b014b87a8f9b4ac1ba0c05f25f46a3ddb702a895f477446ec6b784	72698	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
 50a6240c62827c89e62939863ef5e12580e886b80e1997c15bf1423d37610779	8555	src/phaseset_core/literature_parent_host.py
@@ -193,7 +194,8 @@ c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phase
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 0f0309c54a5b48a0493bb24f97eda39ff93be657295e1b0ebcc3dd6e9a8ecd76	6565	src/phaseset_core/parent_capture.py
-b492696e83648095acca985d6e86f97570548bedaef8741aedb18bd96189be04	7464	src/phaseset_core/parent_disk_source.py
+fb417f794ee32b7e7777933ee2ef4ceeda9868ea82da62245beec53994560f77	3293	src/phaseset_core/parent_clip_rows.py
+f8e257a1cff56e93ff76229c2a4c47b1a85902a64b76338758178d111a2d3ecb	8322	src/phaseset_core/parent_disk_source.py
 be883315aa773e6c2d535ea981853ae92a791187338fd2b310b574f956174a82	7326	src/phaseset_core/parent_retrieval_task.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 e69b0d27d479b4499bae73c96f7854f9d07539043a0b66f0f75f6bc3e23c1f51	55415	src/phaseset_core/periodic_cache_execution.py
@@ -238,6 +240,7 @@ b65a2178d7faf25372adeccdfcd19ee7804544665c6059f2d684801ccff83e11	6050	tests/test
 773430e42927751a5b07ab20da7ddd716d13853f9496033476b2926198148ef8	14947	tests/test_mime_set.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 3b23c2b08ed81029ca6239cbb63b98093077786163aaf5bb47ccfefb21c6ef24	7117	tests/test_parent_capture.py
+fc0c27fb68c7d119d1cc7a48afeeff5a707cab9ec206eb5ad85c606e31656490	9443	tests/test_parent_clip_rows.py
 487ee577859df867298041fe731166cd211506e5f211d3c508eeec5aa282640f	5825	tests/test_parent_disk_source.py
 32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
diff --git a/docs/PHASESET_V2_FROZEN_CLIP_ROWS.md b/docs/PHASESET_V2_FROZEN_CLIP_ROWS.md
new file mode 100644
index 0000000..c4855df
--- /dev/null
+++ b/docs/PHASESET_V2_FROZEN_CLIP_ROWS.md
@@ -0,0 +1,54 @@
+# Frozen human CLIP rows for complete-parent training
+
+`ParentHumanClipRows` takes one original sealed CLIP preparation batch and the
+complete caller-admitted development `ParentRetrievalTask`. All official human
+occurrences must be present, in canonical task order, with their exact UTF-8
+text digest and source/family/ordinal commitment. Equal sentence strings are
+not deduplicated. No motion-derived information or fitted learning statistics
+enter these frozen text features. Final-test parents are rejected.
+
+Prepare with the existing pinned offline CPU-only CLIP adapter, save its
+original receipt and raw projected FP32 embeddings, and authenticate them with
+the existing original-batch rehydrator. Select rows in the actual parent batch
+order using `select_frozen_clip_text_rows`. This does not construct a CLIP tower
+in the training process or relax the preparation runtime contract.
+
+Each selection has the distinct `phaseset-frozen-clip-row-selection-v1` schema
+and `ROW_SELECTION_AUTHORITY0` status. It records original row indices, the
+original receipt identity and unchanged token/embedding/commitment provenance.
+It is explicitly a cached-row operation, not a new encoder invocation. Original
+runtime/model/source identities describe preparation, not the selection host.
+No caption or commitment can be supplied anew to the selection factory.
+Nested selection and duplicate indices are rejected. Persist the original
+batch; the original rehydrator deliberately rejects derived selection receipts.
+
+Only the continuous base/V2 scoring and parent-training validators opt into
+this derived schema. Legacy prepared-capture validation/storage admission is
+unchanged. Returned embeddings remain snapshot-owned contiguous frozen CPU
+FP32; the scoring module moves these numbers to its model device as usual.
+This promises exact reuse of the stored preparation rows, not universally
+bitwise-identical Transformer outputs under different inference batch shapes
+or hardware.
+
+The existing CLIP-native 77-token truncation policy is retained, with each
+original token count and truncation flag preserved. Actual private preparation
+reports how many captions exceed it. This differs openly from the untruncated
+[TMR/WaMo language rows](PHASESET_V2_FROZEN_LANGUAGE_ROWS.md). MIME still trains
+its own genuine CLIP tower; this cache is not its text training path.
+
+`DevelopmentParentDiskSource(..., human_clip_rows=...)` supplies its human-only
+`text(labels, training=...)` interface with empty counterfactual indices. Empty
+CF rows mean no evidence: neither a negative truth label nor permission to run
+the formal V2 CF objective without the two real blind human verifications.
+The full human-verified CF pool, semantic yaw policy, run-specific floors,
+production locks, heartbeat, budget, hardware profile and study freeze remain
+separate inputs and obligations. No pilot or formal stage is authorized here.
+
+Analytic tests cover ordered exact reuse, ownership, RNG preservation, altered
+ancestry/text/task rejection, original-schema exclusion and base/V2 optimizer,
+full validation and bitwise interruption/resume. Actual native preparation must
+separately verify every admitted human occurrence and all three seed orderings.
+Neither software tests nor feature preparation are native learning results.
+
+See the [development disk source](PHASESET_V2_PARENT_DISK_SOURCE.md) and
+[complete-parent training host](PHASESET_V2_PARENT_HOST.md).
diff --git a/docs/PHASESET_V2_PARENT_DISK_SOURCE.md b/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
index 4d7c2fb..27deb1a 100644
--- a/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
+++ b/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
@@ -28,9 +28,10 @@ This source does not fit them or authorize reusing main-experiment floors in
 a held-out fold. Source, annotation, floor, feature, augmentation-rule and
 runtime identities belong in the private execution inputs.
 
-This is the literature source interface and the body/phase loading seam,
-not the entire base/V2 text interface: that still needs its admitted frozen
-CLIP batches and genuinely two-human-verified counterfactual rows. The source
+The optional [frozen human CLIP rows](PHASESET_V2_FROZEN_CLIP_ROWS.md) connect
+the base/V2 human-only text interface without loading a tower at training.
+This is not the entire verified-CF interface: that still needs genuinely
+two-human-verified counterfactual rows and their separate admission. The source
 also supplies no production lock, heartbeat, budget, retention, hardware,
 pilot, formal-stage or final-test authority.
 
diff --git a/src/phaseset_core/capture_validation.py b/src/phaseset_core/capture_validation.py
index e44eca5..46246d2 100644
--- a/src/phaseset_core/capture_validation.py
+++ b/src/phaseset_core/capture_validation.py
@@ -34,7 +34,11 @@
     capture_r1_contributions,
     validate_retrieval_dataset,
 )
-from .frozen_clip_text import FrozenClipTextBatch, FrozenClipTextReceipt
+from .frozen_clip_text import (
+    FrozenClipTextBatch,
+    FrozenClipTextReceipt,
+    FrozenClipTextRowSelectionReceipt,
+)
 from .objectives import variable_positive_symmetric_infonce
 from .periodic_descriptor_cache_v2 import DescriptorWindowContext
 from .training import (
@@ -107,14 +111,7 @@ def _array_sha256(value: np.ndarray) -> str:
 
 
 def _tensor_bytes(value: Tensor) -> bytes:
-    return (
-        value.detach()
-        .to(device="cpu")
-        .contiguous()
-        .view(torch.uint8)
-        .numpy()
-        .tobytes(order="C")
-    )
+    return value.detach().to(device="cpu").contiguous().view(torch.uint8).numpy().tobytes(order="C")
 
 
 def _positive_int(value: object, label: str) -> int:
@@ -125,6 +122,8 @@ def _positive_int(value: object, label: str) -> int:
 
 def _validate_text_batch(
     value: object,
+    *,
+    allow_row_selection: bool = False,
 ) -> tuple[Tensor, tuple[bytes, ...], str]:
     """Rebind a sealed CLIP batch to its receipt and return a CPU snapshot."""
 
@@ -152,17 +151,44 @@ def _validate_text_batch(
     if type(lineage) is not tuple or len(lineage) != count:
         raise CaptureValidationError("frozen text lineage does not match its rows")
     commitments = tuple(
-        _raw32(item, f"caption_commitments[{index}]")
-        for index, item in enumerate(lineage)
+        _raw32(item, f"caption_commitments[{index}]") for index, item in enumerate(lineage)
     )
     if len(set(commitments)) != count:
         raise CaptureValidationError("caption commitments must be unique within a capture")
-    if type(receipt) is not FrozenClipTextReceipt:
-        raise CaptureValidationError("frozen text receipt type is invalid")
-    if receipt.schema != "phaseset-frozen-clip-text-receipt-v1":
-        raise CaptureValidationError("frozen text receipt schema is invalid")
-    if receipt.status != "ENCODED_AUTHORITY0":
-        raise CaptureValidationError("frozen text receipt status is invalid")
+    selected = type(receipt) is FrozenClipTextRowSelectionReceipt
+    if selected:
+        if not allow_row_selection or (
+            receipt.schema != "phaseset-frozen-clip-row-selection-v1"
+            or receipt.status != "ROW_SELECTION_AUTHORITY0"
+        ):
+            raise CaptureValidationError("derived frozen text selection is not admitted here")
+        origin = receipt.origin_receipt
+        indices = receipt.selected_indices
+        if (
+            type(origin) is not FrozenClipTextReceipt
+            or origin.schema != "phaseset-frozen-clip-text-receipt-v1"
+            or origin.status != "ENCODED_AUTHORITY0"
+            or type(indices) is not tuple
+            or len(indices) != count
+            or any(
+                type(index) is not int or not 0 <= index < len(origin.caption_rows)
+                for index in indices
+            )
+            or len(set(indices)) != count
+            or receipt.caption_rows
+            != tuple(
+                (position, *origin.caption_rows[index][1:])
+                for position, index in enumerate(indices)
+            )
+        ):
+            raise CaptureValidationError("derived frozen text selection ancestry differs")
+    else:
+        if type(receipt) is not FrozenClipTextReceipt:
+            raise CaptureValidationError("frozen text receipt type is invalid")
+        if receipt.schema != "phaseset-frozen-clip-text-receipt-v1":
+            raise CaptureValidationError("frozen text receipt schema is invalid")
+        if receipt.status != "ENCODED_AUTHORITY0":
+            raise CaptureValidationError("frozen text receipt status is invalid")
     raw = _tensor_bytes(embeddings)
     if (
         receipt.output_shape != tuple(embeddings.shape)
@@ -171,13 +197,11 @@ def _validate_text_batch(
         or receipt.output_sha256 != _sha256(raw)
     ):
         raise CaptureValidationError("frozen text output differs from its receipt")
-    if type(receipt.batch_size) is not int or receipt.batch_size < 1:
+    if not selected and (type(receipt.batch_size) is not int or receipt.batch_size < 1):
         raise CaptureValidationError("frozen text receipt batch size is invalid")
     if type(receipt.caption_rows) is not tuple or len(receipt.caption_rows) != count:
         raise CaptureValidationError("frozen text receipt caption census is invalid")
-    for index, (commitment, row) in enumerate(
-        zip(commitments, receipt.caption_rows, strict=True)
-    ):
+    for index, (commitment, row) in enumerate(zip(commitments, receipt.caption_rows, strict=True)):
         if type(row) is not tuple or len(row) != 9:
             raise CaptureValidationError("frozen text receipt caption row is invalid")
         if row[0] != index or row[1] != commitment.hex():
@@ -185,10 +209,10 @@ def _validate_text_batch(
         row_raw = _tensor_bytes(embeddings[index : index + 1].contiguous())
         if row[8] != _sha256(row_raw):
             raise CaptureValidationError("frozen text receipt row digest mismatch")
-    if type(receipt.chunk_ranges) is not tuple or not receipt.chunk_ranges:
+    if not selected and (type(receipt.chunk_ranges) is not tuple or not receipt.chunk_ranges):
         raise CaptureValidationError("frozen text receipt chunk census is invalid")
     position = 0
-    for chunk in receipt.chunk_ranges:
+    for chunk in () if selected else receipt.chunk_ranges:
         if (
             type(chunk) is not tuple
             or len(chunk) != 2
@@ -200,7 +224,7 @@ def _validate_text_batch(
         ):
             raise CaptureValidationError("frozen text receipt chunks do not partition rows")
         position = chunk[1]
-    if position != count:
+    if not selected and position != count:
         raise CaptureValidationError("frozen text receipt chunks do not cover all rows")
     try:
         receipt_sha256 = _lower_sha256(receipt.sha256, "frozen text receipt SHA-256")
@@ -259,7 +283,10 @@ def __post_init__(self) -> None:
         if groups.skeletons.shape[2] != 200 or not bool(groups.frame_mask.all()):
             raise CaptureValidationError("capture validation requires complete 200-frame windows")
         descriptor_source = self.descriptor_source
-        if descriptor_source is not None and type(descriptor_source) is not CaptureDescriptorWindowSource:
+        if (
+            descriptor_source is not None
+            and type(descriptor_source) is not CaptureDescriptorWindowSource
+        ):
             raise CaptureValidationError(
                 "descriptor_source must be None or exact CaptureDescriptorWindowSource"
             )
@@ -354,9 +381,7 @@ def __post_init__(self) -> None:
         reference_k = reference.actor_counts[0]
         for window in canonical[1:]:
             groups = window.groups
-            actors = frozenset(
-                item for item in groups.actor_commitments[0] if item is not None
-            )
+            actors = frozenset(item for item in groups.actor_commitments[0] if item is not None)
             if (
                 actors != reference_actors
                 or groups.group_commitments[0] != reference_group
@@ -382,9 +407,7 @@ def group_commitment(self) -> bytes:
     @property
     def actor_commitment_set(self) -> frozenset[bytes]:
         return frozenset(
-            item
-            for item in self.windows[0].groups.actor_commitments[0]
-            if item is not None
+            item for item in self.windows[0].groups.actor_commitments[0] if item is not None
         )
 
     @property
@@ -492,20 +515,13 @@ def __post_init__(self) -> None:
             seen_captions.update(caption_set)
         if descriptor_rows:
             if len(descriptor_rows) != window_count:
-                raise CaptureValidationError(
-                    "capture source cannot mix v1 and v2 window lineage"
-                )
+                raise CaptureValidationError("capture source cannot mix v1 and v2 window lineage")
             descriptor_manifest = descriptor_rows[0].storage_manifest_sha256
-            if any(
-                row.storage_manifest_sha256 != descriptor_manifest
-                for row in descriptor_rows
-            ):
+            if any(row.storage_manifest_sha256 != descriptor_manifest for row in descriptor_rows):
                 raise CaptureValidationError(
                     "capture descriptor rows must share one storage manifest"
                 )
-            if tuple(row.window_ordinal for row in descriptor_rows) != tuple(
-                range(window_count)
-            ):
+            if tuple(row.window_ordinal for row in descriptor_rows) != tuple(range(window_count)):
                 raise CaptureValidationError(
                     "capture descriptor ordinals must equal canonical global order"
                 )
@@ -634,17 +650,20 @@ def _capture_fractions(
 ) -> tuple[Fraction, Fraction, Fraction]:
     rows = capture_r1_contributions(dataset)
     count = len(rows)
-    text = sum(
-        (Fraction(sum(row.text_to_motion_hits), len(row.text_to_motion_hits)) for row in rows),
-        start=Fraction(0, 1),
-    ) / count
-    motion = sum(
-        (
-            Fraction(sum(row.motion_to_text_hits), len(row.motion_to_text_hits))
-            for row in rows
-        ),
-        start=Fraction(0, 1),
-    ) / count
+    text = (
+        sum(
+            (Fraction(sum(row.text_to_motion_hits), len(row.text_to_motion_hits)) for row in rows),
+            start=Fraction(0, 1),
+        )
+        / count
+    )
+    motion = (
+        sum(
+            (Fraction(sum(row.motion_to_text_hits), len(row.motion_to_text_hits)) for row in rows),
+            start=Fraction(0, 1),
+        )
+        / count
+    )
     return text, motion, (text + motion) / 2
 
 
@@ -803,11 +822,9 @@ def _run_capture_validation_core(
                             if descriptor_plan is None:
                                 tokens, band_mask = system.encode_trainable(window.groups)
                             else:
-                                descriptor_stream = (
-                                    descriptor_plan.open_capture_validation_window(
-                                        capture,
-                                        window_position,
-                                    )
+                                descriptor_stream = descriptor_plan.open_capture_validation_window(
+                                    capture,
+                                    window_position,
                                 )
                                 tokens, band_mask = system.encode_trainable_cached(
                                     window.groups,
@@ -855,9 +872,7 @@ def _run_capture_validation_core(
                         window_commitments=tuple(
                             window.window_commitment for window in capture.windows
                         ),
-                        base_embeddings=np.ascontiguousarray(
-                            np.stack(base_rows), dtype=np.float32
-                        ),
+                        base_embeddings=np.ascontiguousarray(np.stack(base_rows), dtype=np.float32),
                         tokens=(
                             np.ascontiguousarray(np.stack(token_rows), dtype=np.float32)
                             if residual
@@ -873,7 +888,9 @@ def _run_capture_validation_core(
                 except CapturePoolingResourceLimit as error:
                     raise CaptureValidationResourceLimit(str(error)) from error
                 except CapturePoolingError as error:
-                    raise CaptureValidationError("capture pooling rejected encoder output") from error
+                    raise CaptureValidationError(
+                        "capture pooling rejected encoder output"
+                    ) from error
                 pooled_base.append(pooled.base_embedding)
                 if residual:
                     if pooled.tokens is None or pooled.band_mask is None:
@@ -886,9 +903,11 @@ def _run_capture_validation_core(
                 caption_commitments.extend(commitments[index] for index in order)
                 positive_motion_indices.extend((motion_index,) for _ in order)
 
-            motion = torch.from_numpy(
-                np.ascontiguousarray(np.stack(pooled_base), dtype=np.float32)
-            ).to(device=device).contiguous()
+            motion = (
+                torch.from_numpy(np.ascontiguousarray(np.stack(pooled_base), dtype=np.float32))
+                .to(device=device)
+                .contiguous()
+            )
             text_gallery = torch.cat(text_rows).to(device=device).contiguous()
             positive = torch.zeros(
                 (len(checked_source.captures), len(caption_commitments)),
@@ -899,12 +918,18 @@ def _run_capture_validation_core(
                 positive[row[0], caption_index] = True
             if residual:
                 assert isinstance(system, ResidualRetrievalSystem)
-                tokens_gallery = torch.from_numpy(
-                    np.ascontiguousarray(np.stack(pooled_tokens), dtype=np.float32)
-                ).to(device=device).contiguous()
-                masks_gallery = torch.from_numpy(
-                    np.ascontiguousarray(np.stack(pooled_masks), dtype=np.bool_)
-                ).to(device=device).contiguous()
+                tokens_gallery = (
+                    torch.from_numpy(
+                        np.ascontiguousarray(np.stack(pooled_tokens), dtype=np.float32)
+                    )
+                    .to(device=device)
+                    .contiguous()
+                )
+                masks_gallery = (
+                    torch.from_numpy(np.ascontiguousarray(np.stack(pooled_masks), dtype=np.bool_))
+                    .to(device=device)
+                    .contiguous()
+                )
                 logits = system.scores(
                     tokens_gallery,
                     masks_gallery,
@@ -918,8 +943,7 @@ def _run_capture_validation_core(
                 type(logits) is not Tensor
                 or logits.dtype != torch.float32
                 or logits.device != device
-                or tuple(logits.shape)
-                != (len(checked_source.captures), len(caption_commitments))
+                or tuple(logits.shape) != (len(checked_source.captures), len(caption_commitments))
                 or not logits.is_contiguous()
                 or logits.requires_grad
                 or not bool(torch.isfinite(logits).all().item())
@@ -956,9 +980,7 @@ def _run_capture_validation_core(
                 [capture.group_size for capture in checked_source.captures],
                 dtype=np.int64,
             ),
-            component_labels=tuple(
-                capture.component_label for capture in checked_source.captures
-            ),
+            component_labels=tuple(capture.component_label for capture in checked_source.captures),
         )
     )
     text_r1, motion_r1, primary_r1 = _capture_fractions(dataset)
@@ -984,10 +1006,16 @@ def _run_capture_validation_core(
 _CAPTURE_RUNTIME_BINDINGS = tuple(
     (name, globals()[name])
     for name in (
-        "CaptureValidationSource", "run_capture_validation",
-        "run_capture_validation_cached", "_run_capture_validation_core", "_snapshot_source",
-        "_capture_fractions", "pool_capture_windows", "capture_r1_contributions",
-        "validate_retrieval_dataset", "variable_positive_symmetric_infonce",
+        "CaptureValidationSource",
+        "run_capture_validation",
+        "run_capture_validation_cached",
+        "_run_capture_validation_core",
+        "_snapshot_source",
+        "_capture_fractions",
+        "pool_capture_windows",
+        "capture_r1_contributions",
+        "validate_retrieval_dataset",
+        "variable_positive_symmetric_infonce",
     )
 )
 
diff --git a/src/phaseset_core/continuous_base_retrieval.py b/src/phaseset_core/continuous_base_retrieval.py
index 0d47115..41b027b 100644
--- a/src/phaseset_core/continuous_base_retrieval.py
+++ b/src/phaseset_core/continuous_base_retrieval.py
@@ -117,7 +117,7 @@ def score(
             or any(type(capture) is not PreparedContinuousCapture for capture in captures)
         ):
             raise ValueError("base scoring requires complete prepared capture rows")
-        text, _, receipt_sha = _validate_text_batch(text_batch)
+        text, _, receipt_sha = _validate_text_batch(text_batch, allow_row_selection=True)
         device = next(self.base.parameters()).device
         motion = torch.stack([self.encode_capture(capture) for capture in captures])
         scores = self.base.scores(motion.contiguous(), text.to(device).contiguous())
diff --git a/src/phaseset_core/continuous_parent_training.py b/src/phaseset_core/continuous_parent_training.py
index 43f25b0..e82d978 100644
--- a/src/phaseset_core/continuous_parent_training.py
+++ b/src/phaseset_core/continuous_parent_training.py
@@ -198,7 +198,7 @@ def validate_parent_text_batch(
     labels: ParentRetrievalBatch, text_batch: FrozenClipTextBatch
 ) -> int:
     """Bind the human prefix; any appended CF rows remain outside retrieval."""
-    text, commitments, _ = _validate_text_batch(text_batch)
+    text, commitments, _ = _validate_text_batch(text_batch, allow_row_selection=True)
     retrieval_count = len(labels.captions)
     if commitments[:retrieval_count] != labels.caption_commitments:
         raise ValueError("CLIP pool must start with every admitted human row in label order")
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
index 763c279..5bea986 100644
--- a/src/phaseset_core/continuous_retrieval.py
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -176,7 +176,7 @@ def score(
             or any(type(view) is not ContinuousTrainingView for view in views)
         ):
             raise ValueError("views must be a nonempty tuple of admitted continuous inputs")
-        text, _, receipt_sha = _validate_text_batch(text_batch)
+        text, _, receipt_sha = _validate_text_batch(text_batch, allow_row_selection=True)
         device = next(self.coordination.parameters()).device
         keys = tuple(view.positive_capture_key for view in views)
         text = text.to(device)
diff --git a/src/phaseset_core/frozen_clip_text.py b/src/phaseset_core/frozen_clip_text.py
index ae2fb10..24594ec 100644
--- a/src/phaseset_core/frozen_clip_text.py
+++ b/src/phaseset_core/frozen_clip_text.py
@@ -266,10 +266,7 @@ def _scan_snapshot(
 
 def _snapshot_manifest_sha256(rows: tuple[tuple[str, int, str], ...]) -> str:
     payload = {
-        "files": [
-            {"bytes": size, "name": name, "sha256": digest}
-            for name, size, digest in rows
-        ],
+        "files": [{"bytes": size, "name": name, "sha256": digest} for name, size, digest in rows],
         "model_id": MODEL_ID,
         "revision": REVISION,
         "schema": "phaseset-frozen-clip-snapshot-v1",
@@ -297,9 +294,8 @@ def _source_row(
     ):
         _hold("HOLD_SOURCE_IDENTITY")
     digest = _sha256_file(path)
-    if (
-        (expected_bytes is not None and metadata.st_size != expected_bytes)
-        or (expected_sha256 is not None and digest != expected_sha256)
+    if (expected_bytes is not None and metadata.st_size != expected_bytes) or (
+        expected_sha256 is not None and digest != expected_sha256
     ):
         _hold("HOLD_SOURCE_IDENTITY")
     return label, metadata.st_size, digest
@@ -345,17 +341,13 @@ def _runtime_facts(
             {
                 "cuda_available": str(bool(torch.cuda.is_available())).lower(),
                 "cuda_visible_devices": (
-                    "hidden"
-                    if os.environ.get("CUDA_VISIBLE_DEVICES") == ""
-                    else "not-hidden"
+                    "hidden" if os.environ.get("CUDA_VISIBLE_DEVICES") == "" else "not-hidden"
                 ),
                 "deterministic_algorithms": str(
                     bool(torch.are_deterministic_algorithms_enabled())
                 ).lower(),
                 "device": "cpu",
-                "float32_matmul_precision": str(
-                    torch.get_float32_matmul_precision()
-                ),
+                "float32_matmul_precision": str(torch.get_float32_matmul_precision()),
                 "huggingface_hub": str(huggingface_hub.__version__),
                 "interop_threads": str(torch.get_num_interop_threads()),
                 "intraop_threads": str(torch.get_num_threads()),
@@ -663,10 +655,7 @@ def _live_model_manifest(model: object, backend: _Backend) -> str:
 def _source_manifest_sha256(rows: tuple[tuple[str, int, str], ...]) -> str:
     return hashlib.sha256(
         _canonical_json(
-            [
-                {"bytes": size, "label": label, "sha256": digest}
-                for label, size, digest in rows
-            ]
+            [{"bytes": size, "label": label, "sha256": digest} for label, size, digest in rows]
         )
     ).hexdigest()
 
@@ -784,9 +773,8 @@ def _validate_token_batch(value: object, count: int, backend: _Backend) -> None:
             _hold("HOLD_TOKEN_BATCH")
     input_ids = value["input_ids"]
     attention_mask = value["attention_mask"]
-    if (
-        not bool(((input_ids >= 0) & (input_ids < TOKEN_VOCAB_SIZE)).all().item())
-        or not bool(((attention_mask == 0) | (attention_mask == 1)).all().item())
+    if not bool(((input_ids >= 0) & (input_ids < TOKEN_VOCAB_SIZE)).all().item()) or not bool(
+        ((attention_mask == 0) | (attention_mask == 1)).all().item()
     ):
         _hold("HOLD_TOKEN_BATCH")
 
@@ -808,10 +796,7 @@ def _untruncated_token_rows(
             or not row
             or row[0] != TOKEN_BOS_ID
             or row[-1] != TOKEN_EOS_ID
-            or any(
-                type(token) is not int or not 0 <= token < TOKEN_VOCAB_SIZE
-                for token in row
-            )
+            or any(type(token) is not int or not 0 <= token < TOKEN_VOCAB_SIZE for token in row)
         ):
             _hold("HOLD_TOKEN_LENGTH_PROBE")
         checked_rows.append(tuple(row))
@@ -955,7 +940,7 @@ def __init__(
         self,
         embeddings: Any,
         lineage: tuple[bytes, ...],
-        receipt: FrozenClipTextReceipt,
+        receipt: FrozenClipTextReceipt | FrozenClipTextRowSelectionReceipt,
         *,
         _seal: object,
     ) -> None:
@@ -974,7 +959,7 @@ def caption_commitments(self) -> tuple[bytes, ...]:
         return self.__lineage
 
     @property
-    def receipt(self) -> FrozenClipTextReceipt:
+    def receipt(self) -> FrozenClipTextReceipt | FrozenClipTextRowSelectionReceipt:
         return self.__receipt
 
     def __repr__(self) -> str:
@@ -984,6 +969,109 @@ def __repr__(self) -> str:
         )
 
 
+@dataclass(frozen=True, slots=True)
+class FrozenClipTextRowSelectionReceipt:
+    """Derived cached rows, explicitly NOT a new encoder invocation.
+
+    The immutable original receipt remains available in memory. Its encoding
+    chunks/runtime/model identities describe preparation, not the runtime or
+    batching of this selection. Persist the original batch and select again;
+    the original receipt rehydrator deliberately rejects this derived schema.
+    """
+
+    origin_receipt: FrozenClipTextReceipt
+    selected_indices: tuple[int, ...]
+    caption_rows: tuple[tuple[int, str, str, int, int, bool, str, str, str], ...]
+    output_bytes: int
+    output_sha256: str
+    output_shape: tuple[int, int]
+    output_stride: tuple[int, int]
+    schema: str = "phaseset-frozen-clip-row-selection-v1"
+    status: str = "ROW_SELECTION_AUTHORITY0"
+
+    def to_public_dict(self) -> dict[str, object]:
+        origin = self.origin_receipt
+        return {
+            "authority": AUTHORITY,
+            "production": PRODUCTION,
+            "training_authorized": TRAINING_AUTHORIZED,
+            "schema": self.schema,
+            "status": self.status,
+            "operation": "ordered_exact_cached_row_selection_no_encoder_call",
+            "origin_receipt_sha256": origin.sha256,
+            "origin_frozen_embedding_cache_key_sha256": origin.frozen_embedding_cache_key_sha256,
+            "origin_source_manifest_sha256": origin.source_manifest_sha256,
+            "origin_runtime_manifest_sha256": origin.runtime_manifest_sha256,
+            "origin_snapshot_manifest_sha256": origin.snapshot_manifest_sha256,
+            "origin_live_model_manifest_sha256": origin.live_model_manifest_sha256,
+            "model_id": MODEL_ID,
+            "revision": REVISION,
+            "method_id": METHOD_ID,
+            "selected_indices": list(self.selected_indices),
+            "caption_rows": [list(row) for row in self.caption_rows],
+            "output": {
+                "bytes": self.output_bytes,
+                "sha256": self.output_sha256,
+                "shape": list(self.output_shape),
+                "stride": list(self.output_stride),
+                "dtype": "torch.float32",
+                "device": "cpu",
+                "requires_grad": False,
+            },
+        }
+
+    def canonical_json_bytes(self) -> bytes:
+        return _canonical_json(self.to_public_dict()) + b"\n"
+
+    @property
+    def sha256(self) -> str:
+        return hashlib.sha256(self.canonical_json_bytes()).hexdigest()
+
+
+def select_frozen_clip_text_rows(
+    original: FrozenClipTextBatch, indices: tuple[int, ...]
+) -> FrozenClipTextBatch:
+    """Gather from one genuine encoded/rehydrated batch without relabeling.
+
+    Captions and commitments cannot be supplied or rebound. No model, runtime
+    probe or RNG call occurs. Nested selections and repeated commitments are
+    rejected: callers always address the saved original preparation census.
+    This supplies cached numbers, never rights, CF truth or training authority.
+    """
+    from .capture_validation import _validate_text_batch
+
+    if (
+        type(original) is not FrozenClipTextBatch
+        or type(original.receipt) is not FrozenClipTextReceipt
+    ):
+        raise ValueError("row selection requires the original sealed encoded batch")
+    embeddings, commitments, _ = _validate_text_batch(original)
+    if (
+        type(indices) is not tuple
+        or not indices
+        or any(type(index) is not int or not 0 <= index < len(commitments) for index in indices)
+        or len(set(indices)) != len(indices)
+    ):
+        raise ValueError("selection requires unique in-range exact integer row indices")
+    selected = embeddings[list(indices)].contiguous()
+    raw = _tensor_bytes(selected)
+    receipt = FrozenClipTextRowSelectionReceipt(
+        origin_receipt=original.receipt,
+        selected_indices=indices,
+        caption_rows=tuple(
+            (position, *original.receipt.caption_rows[index][1:])
+            for position, index in enumerate(indices)
+        ),
+        output_bytes=len(raw),
+        output_sha256=hashlib.sha256(raw).hexdigest(),
+        output_shape=tuple(selected.shape),
+        output_stride=tuple(selected.stride()),
+    )
+    return FrozenClipTextBatch(
+        selected, tuple(commitments[index] for index in indices), receipt, _seal=_CONSTRUCTION_SEAL
+    )
+
+
 def _rehydration_unique_object(
     pairs: list[tuple[str, object]],
 ) -> dict[str, object]:
@@ -1136,8 +1224,7 @@ def _rehydration_chunk_ranges(
     if type(value) is not list:
         _hold("HOLD_REHYDRATION_CHUNKS")
     expected = tuple(
-        (start, min(start + batch_size, count))
-        for start in range(0, count, batch_size)
+        (start, min(start + batch_size, count)) for start in range(0, count, batch_size)
     )
     checked: list[tuple[int, int]] = []
     for item in value:
@@ -1265,9 +1352,7 @@ def _rehydration_runtime(value: object) -> tuple[tuple[str, str], ...]:
 def _rehydration_cache_key(
     *,
     batch_size: int,
-    caption_rows: tuple[
-        tuple[int, str, str, int, int, bool, str, str, str], ...
-    ],
+    caption_rows: tuple[tuple[int, str, str, int, int, bool, str, str, str], ...],
     chunk_ranges: tuple[tuple[int, int], ...],
     runtime_manifest_sha256: str,
     snapshot_manifest_sha256: str,
@@ -1392,9 +1477,7 @@ def _rehydrate_frozen_clip_text_batch(
     count = len(caption_rows_value)
     if not 1 <= count <= MAX_CAPTIONS:
         _hold("HOLD_REHYDRATION_CAPTION_ROWS")
-    if type(receipt_value["caption_count"]) is not int or receipt_value[
-        "caption_count"
-    ] != count:
+    if type(receipt_value["caption_count"]) is not int or receipt_value["caption_count"] != count:
         _hold("HOLD_REHYDRATION_CAPTION_ROWS")
     batch_size = _rehydration_positive_int(
         receipt_value["batch_size"],
@@ -1477,9 +1560,7 @@ def _rehydrate_frozen_clip_text_batch(
         _hold("HOLD_REHYDRATION_OUTPUT")
     row_bytes = EMBEDDING_DIM * 4
     if any(
-        hashlib.sha256(
-            output_bytes[index * row_bytes : (index + 1) * row_bytes]
-        ).hexdigest()
+        hashlib.sha256(output_bytes[index * row_bytes : (index + 1) * row_bytes]).hexdigest()
         != row[8]
         for index, row in enumerate(caption_rows)
     ):
@@ -1542,10 +1623,7 @@ def _rehydrate_frozen_clip_text_batch(
         source_files=source_files,
         source_manifest_sha256=source_manifest,
     )
-    if (
-        receipt.canonical_json_bytes() != receipt_json_bytes
-        or receipt.sha256 != expected_digest
-    ):
+    if receipt.canonical_json_bytes() != receipt_json_bytes or receipt.sha256 != expected_digest:
         _hold("HOLD_REHYDRATION_RECEIPT_REBUILD")
     return FrozenClipTextBatch(
         frozen_embeddings,
@@ -1796,9 +1874,7 @@ def _encode(
                     attention_mask_digest,
                 ),
                 output_digest,
-            ) in enumerate(
-                zip(lineage, text_digests, token_rows, row_digests, strict=True)
-            )
+            ) in enumerate(zip(lineage, text_digests, token_rows, row_digests, strict=True))
         )
         snapshot_manifest = _snapshot_manifest_sha256(self._snapshot_rows)
         source_manifest = _source_manifest_sha256(self._source_rows)
@@ -1922,6 +1998,7 @@ def load_frozen_clip_text_adapter(
     "FrozenClipTextAdapterError",
     "FrozenClipTextBatch",
     "FrozenClipTextReceipt",
+    "FrozenClipTextRowSelectionReceipt",
     "MAX_BATCH_SIZE",
     "MAX_CAPTIONS",
     "METHOD_ID",
@@ -1931,4 +2008,5 @@ def load_frozen_clip_text_adapter(
     "TRAINING_AUTHORIZED",
     "load_frozen_clip_text_adapter",
     "rehydrate_frozen_clip_text_batch",
+    "select_frozen_clip_text_rows",
 ]
diff --git a/src/phaseset_core/parent_clip_rows.py b/src/phaseset_core/parent_clip_rows.py
new file mode 100644
index 0000000..ffbbd26
--- /dev/null
+++ b/src/phaseset_core/parent_clip_rows.py
@@ -0,0 +1,63 @@
+"""Exact human CLIP rows for a caller-admitted complete development task."""
+
+from __future__ import annotations
+
+import hashlib
+
+from .capture_validation import _validate_text_batch
+from .continuous_parent_training import ParentCounterfactualRows, validate_parent_text_batch
+from .frozen_clip_text import FrozenClipTextBatch, select_frozen_clip_text_rows
+from .parent_retrieval_task import ParentRetrievalBatch, ParentRetrievalTask
+
+
+class ParentHumanClipRows:
+    """Keep one authenticated preparation batch; select without a CLIP tower.
+
+    Every official human occurrence retains its own source/family/ordinal
+    commitment. Equal sentence strings are not deduplicated. No motion or
+    learning statistics enter this cache. The complete task must contain only
+    development parents. No test rows or arbitrary additional text are loaded.
+    Returned empty CF rows mean *no CF evidence*, not verified negative truth.
+    This serves base training and human-only evaluation; full CF training still
+    requires the actual blind two-human records and a separate admitted pool.
+    """
+
+    def __init__(self, task: ParentRetrievalTask, original: FrozenClipTextBatch):
+        if type(task) is not ParentRetrievalTask or any(
+            parent.split == "test" for parent in task.parents
+        ):
+            raise ValueError("human CLIP rows require the complete admitted development task")
+        all_labels = task.batch(tuple(parent.annotation_family_sha256 for parent in task.parents))
+        _validate_text_batch(original)  # Original encoded/rehydrated schema only.
+        if len(original.caption_commitments) != len(all_labels.captions):
+            raise ValueError("original CLIP batch must cover every human occurrence exactly once")
+        validate_parent_text_batch(all_labels, original)
+        self.original = original
+        self.parents = {parent.annotation_family_sha256: parent for parent in task.parents}
+        self.indices = {
+            commitment: index for index, commitment in enumerate(original.caption_commitments)
+        }
+
+    def text(self, labels: ParentRetrievalBatch, *, training: bool):
+        if (
+            type(training) is not bool
+            or type(labels) is not ParentRetrievalBatch
+            or not labels.parents
+        ):
+            raise ValueError("human text selection requires explicit labels and training bool")
+        families = tuple(parent.annotation_family_sha256 for parent in labels.parents)
+        if len(set(families)) != len(families) or any(
+            self.parents.get(parent.annotation_family_sha256) != parent for parent in labels.parents
+        ):
+            raise ValueError("human text labels differ from the admitted development population")
+        rows = select_frozen_clip_text_rows(
+            self.original,
+            tuple(self.indices[commitment] for commitment in labels.caption_commitments),
+        )
+        validate_parent_text_batch(labels, rows)
+        if any(
+            hashlib.sha256(caption.encode("utf-8")).hexdigest() != row[2]
+            for caption, row in zip(labels.captions, rows.receipt.caption_rows, strict=True)
+        ):
+            raise ValueError("selected human text changed its official bytes")
+        return rows, ParentCounterfactualRows((), (), (), ())
diff --git a/src/phaseset_core/parent_disk_source.py b/src/phaseset_core/parent_disk_source.py
index 76b92f0..e09435b 100644
--- a/src/phaseset_core/parent_disk_source.py
+++ b/src/phaseset_core/parent_disk_source.py
@@ -18,6 +18,7 @@
 from .continuous_capture_io import load_prepared_continuous_capture
 from .continuous_training_input import ContinuousTrainingInput, shared_yaw_capture
 from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from .parent_clip_rows import ParentHumanClipRows
 from .tmr_feature_cache import FrozenTMRRowCache, caption_key
 from .training import OFFICIAL_SEEDS
 
@@ -71,6 +72,7 @@ def __init__(
         yaw_eligibility: Mapping[str, bool],
         language_rows: FrozenTMRRowCache,
         energy_floors: np.ndarray | None = None,
+        human_clip_rows: ParentHumanClipRows | None = None,
     ):
         if type(task) is not ParentRetrievalTask or any(
             type(record) is not ParentDiskRecord for record in records
@@ -114,6 +116,13 @@ def __init__(
             floors.setflags(write=False)
             self.energy_floors = floors
         self.language_rows = language_rows
+        if human_clip_rows is not None and (
+            type(human_clip_rows) is not ParentHumanClipRows
+            or human_clip_rows.parents
+            != {parent.annotation_family_sha256: parent for parent in task.parents}
+        ):
+            raise ValueError("human CLIP rows must cover the same complete development task")
+        self.human_clip_rows = human_clip_rows
 
     def _record_and_yaw(self, parent, *, seed, epoch, training):
         if type(parent) is not ParentCaptionRecord or (
@@ -157,3 +166,11 @@ def tmr_text(self, captions):
 
     def wamo_cls(self, captions):
         return self.language_rows.wamo_cls(captions)
+
+    def text(self, labels, *, training):
+        """Human-only source seam; never turn absent CF annotations into truth."""
+        if self.human_clip_rows is None:
+            raise ValueError(
+                "base/V2 human text requires the closed original CLIP preparation batch"
+            )
+        return self.human_clip_rows.text(labels, training=training)
diff --git a/tests/test_parent_clip_rows.py b/tests/test_parent_clip_rows.py
new file mode 100644
index 0000000..6283053
--- /dev/null
+++ b/tests/test_parent_clip_rows.py
@@ -0,0 +1,212 @@
+"""Cached row/host integrity on analytic fixtures, not native learning."""
+
+import copy
+from dataclasses import replace
+import hashlib
+
+import pytest
+import torch
+
+from phaseset_core import frozen_clip_text as clip
+from phaseset_core.capture_validation import CaptureValidationError, _validate_text_batch
+from phaseset_core.continuous_parent_host import ContinuousParentTrainingHost
+from phaseset_core.parent_clip_rows import ParentHumanClipRows
+from phaseset_core.parent_disk_source import DevelopmentParentDiskSource
+from phaseset_core.parent_retrieval_task import (
+    ParentCaptionRecord,
+    ParentRetrievalBatch,
+    ParentRetrievalTask,
+)
+from phaseset_core.training import _capture_rng, _load_torch_checkpoint, _stable_hash
+from test_phaseset_frozen_clip_cache_rehydrate import _fixture
+
+
+def _task():
+    parents = tuple(
+        ParentCaptionRecord(
+            hashlib.sha256(f"source/{index}".encode()).hexdigest(),
+            hashlib.sha256(f"family/{index}".encode()).hexdigest(),
+            "C01" if index < 2 else "C03",
+            "train" if index < 2 else "validation",
+            ("same exact sentence", "additional human row")
+            if index == 0
+            else ("same exact sentence",),
+        )
+        for index in range(3)
+    )
+    return ParentRetrievalTask(
+        parents, expected_family_keys=tuple(p.annotation_family_sha256 for p in parents)
+    )
+
+
+def _original(task):
+    labels = task.batch(tuple(parent.annotation_family_sha256 for parent in task.parents))
+    embeddings, receipt = _fixture(len(labels.captions), batch_size=2)
+    rows = tuple(
+        (index, commitment.hex(), hashlib.sha256(caption.encode()).hexdigest(), *old[3:])
+        for index, (commitment, caption, old) in enumerate(
+            zip(labels.caption_commitments, labels.captions, receipt.caption_rows, strict=True)
+        )
+    )
+    receipt = replace(receipt, caption_rows=rows)
+    receipt = replace(
+        receipt,
+        frozen_embedding_cache_key_sha256=clip._rehydration_cache_key(
+            batch_size=receipt.batch_size,
+            caption_rows=rows,
+            chunk_ranges=receipt.chunk_ranges,
+            runtime_manifest_sha256=receipt.runtime_manifest_sha256,
+            snapshot_manifest_sha256=receipt.snapshot_manifest_sha256,
+            source_manifest_sha256=receipt.source_manifest_sha256,
+        ),
+    )
+    return clip.rehydrate_frozen_clip_text_batch(
+        embeddings,
+        receipt_json_bytes=receipt.canonical_json_bytes(),
+        expected_receipt_sha256=receipt.sha256,
+    )
+
+
+def test_selection_is_exact_owned_and_honest_without_encoder_runtime_or_rng(monkeypatch):
+    original = _original(_task())
+    before = _stable_hash(_capture_rng())
+
+    def forbidden(*args, **kwargs):
+        raise AssertionError("cached selection attempted a model/runtime probe")
+
+    monkeypatch.setattr(clip, "_real_backend", forbidden)
+    monkeypatch.setattr(clip, "_load_components", forbidden)
+    selected = clip.select_frozen_clip_text_rows(original, (3, 1, 0))
+    rows, commitments, digest = _validate_text_batch(selected, allow_row_selection=True)
+    assert torch.equal(rows, original.embeddings[[3, 1, 0]])
+    assert commitments == tuple(original.caption_commitments[i] for i in (3, 1, 0))
+    assert selected.receipt.origin_receipt is original.receipt
+    assert selected.receipt.selected_indices == (3, 1, 0)
+    public = selected.receipt.to_public_dict()
+    assert public["origin_receipt_sha256"] == original.receipt.sha256
+    assert public["status"] == "ROW_SELECTION_AUTHORITY0" and public["authority"] == 0
+    assert public["training_authorized"] is False and public["production"] is False
+    assert digest == selected.receipt.sha256 and digest != original.receipt.sha256
+    assert b"same exact sentence" not in selected.receipt.canonical_json_bytes()
+    leaked = selected.embeddings
+    leaked.zero_()
+    assert not torch.equal(leaked, selected.embeddings)
+    assert _stable_hash(_capture_rng()) == before
+    with pytest.raises(CaptureValidationError, match="not admitted"):
+        _validate_text_batch(selected)
+    with pytest.raises(clip.FrozenClipTextAdapterError):
+        clip.rehydrate_frozen_clip_text_batch(
+            selected.embeddings,
+            receipt_json_bytes=selected.receipt.canonical_json_bytes(),
+            expected_receipt_sha256=selected.receipt.sha256,
+        )
+
+
+@pytest.mark.parametrize("indices", [(), (True,), (-1,), (4,), (0, 0), [0]])
+def test_row_selection_rejects_ambiguous_or_duplicate_lineage(indices):
+    with pytest.raises(ValueError, match="indices"):
+        clip.select_frozen_clip_text_rows(_original(_task()), indices)
+
+
+def test_selection_does_not_accept_nested_gathers_or_changed_ancestry():
+    selected = clip.select_frozen_clip_text_rows(_original(_task()), (0, 2))
+    with pytest.raises(ValueError, match="original"):
+        clip.select_frozen_clip_text_rows(selected, (0,))
+    object.__setattr__(
+        selected,
+        "_FrozenClipTextBatch__receipt",
+        replace(selected.receipt, selected_indices=(2, 0)),
+    )
+    with pytest.raises(CaptureValidationError, match="ancestry"):
+        _validate_text_batch(selected, allow_row_selection=True)
+
+
+def test_every_human_occurrence_and_reordered_parent_batch_are_preserved():
+    task = _task()
+    original = _original(task)
+    rows = ParentHumanClipRows(task, original)
+    labels = task.batch(tuple(parent.annotation_family_sha256 for parent in reversed(task.parents)))
+    selected, cf = rows.text(labels, training=True)
+    assert selected.caption_commitments == labels.caption_commitments
+    assert len(selected.embeddings) == 4  # Equal text is not deduplicated.
+    assert not cf.source_keys and not cf.verified_false
+    again, _ = rows.text(labels, training=False)
+    assert selected.receipt.sha256 == again.receipt.sha256
+    assert torch.equal(selected.embeddings, again.embeddings)
+    changed = replace(labels.parents[0], captions=("altered human bytes",))
+    with pytest.raises(ValueError, match="population"):
+        rows.text(ParentRetrievalBatch((changed,)), training=True)
+    with pytest.raises(ValueError, match="population"):
+        rows.text(ParentRetrievalBatch((labels.parents[0], labels.parents[0])), training=False)
+    with pytest.raises(ValueError, match="bool"):
+        rows.text(labels, training=1)
+    with pytest.raises(ValueError):
+        ParentHumanClipRows(task, selected)
+    wrong = replace(task.parents[0], split="test", component="C09")
+    test_task = ParentRetrievalTask(
+        (wrong,), expected_family_keys=(wrong.annotation_family_sha256,)
+    )
+    with pytest.raises(ValueError, match="development"):
+        ParentHumanClipRows(test_task, original)
+
+
+def test_disk_source_binds_the_same_task_and_returns_explicitly_no_cf(tmp_path):
+    from test_parent_disk_source import _source
+
+    source, _, _, task, record = _source(tmp_path)
+    rows = ParentHumanClipRows(task, _original(task))
+    connected = DevelopmentParentDiskSource(
+        task,
+        (record,),
+        yaw_eligibility=source.yaw_eligibility,
+        language_rows=source.language_rows,
+        human_clip_rows=rows,
+    )
+    labels = task.batch(tuple(rows.parents))
+    text, cf = connected.text(labels, training=False)
+    assert text.caption_commitments == labels.caption_commitments and not cf.verified_false
+    with pytest.raises(ValueError, match="closed original"):
+        source.text(labels, training=True)
+    other = _task()
+    with pytest.raises(ValueError, match="same complete"):
+        DevelopmentParentDiskSource(
+            task,
+            (record,),
+            yaw_eligibility=source.yaw_eligibility,
+            language_rows=source.language_rows,
+            human_clip_rows=ParentHumanClipRows(other, _original(other)),
+        )
+
+
+@pytest.mark.parametrize("stage", ["base", "residual"])
+def test_cached_text_host_optimizer_validation_resume_is_bitwise(stage, tmp_path):
+    if stage == "base":
+        from test_continuous_base_retrieval import _setup
+    else:
+        from test_continuous_parent_host import _setup
+    system, task, source, config, bindings = _setup(tmp_path)
+    labels = task.batch(tuple(parent.annotation_family_sha256 for parent in task.parents))
+    original = source.clip.encode(
+        labels.captions, caption_commitments=labels.caption_commitments, batch_size=3
+    )
+    cached = ParentHumanClipRows(task, original)
+    source.text = cached.text
+    initial = copy.deepcopy(system)
+    full = ContinuousParentTrainingHost(system, task, source, config, bindings).fit(
+        tmp_path / "full"
+    )
+    interrupted = ContinuousParentTrainingHost(
+        copy.deepcopy(initial), task, source, config, bindings
+    ).fit(tmp_path / "interrupted", stop_after_steps=1)
+    resumed_model = copy.deepcopy(initial)
+    resumed = ContinuousParentTrainingHost(resumed_model, task, source, config, bindings).fit(
+        tmp_path / "resumed",
+        resume_checkpoint=interrupted.latest_checkpoint.path,
+        resume_sha256=interrupted.latest_checkpoint.sha256,
+    )
+    assert full.outcome == resumed.outcome == "COMPLETED"
+    assert full.validation_history == resumed.validation_history
+    expected, _ = _load_torch_checkpoint(full.latest_checkpoint.path)
+    actual, _ = _load_torch_checkpoint(resumed.latest_checkpoint.path)
+    for key in ("model", "optimizer", "scheduler", "rng"):
+        assert _stable_hash(actual[key]) == _stable_hash(expected[key])

From 371170cec2cd207dce1e5c9cb93d93d80d9f12ec Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 09:50:37 -0500
Subject: [PATCH 53/95] Add leased parent operation heartbeats and checkpoint
 retention

---
 README.md                                    |   4 +
 RELEASE_FILES.sha256                         |   7 +-
 docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md |  41 +++
 src/phaseset_core/continuous_parent_host.py  | 123 +++++---
 src/phaseset_core/parent_run_monitor.py      | 219 +++++++++++++
 tests/test_parent_run_monitor.py             | 315 +++++++++++++++++++
 6 files changed, 670 insertions(+), 39 deletions(-)
 create mode 100644 docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 create mode 100644 src/phaseset_core/parent_run_monitor.py
 create mode 100644 tests/test_parent_run_monitor.py

diff --git a/README.md b/README.md
index de18f95..ca20b61 100644
--- a/README.md
+++ b/README.md
@@ -82,6 +82,10 @@ private inputs. The [frozen human CLIP row source](docs/PHASESET_V2_FROZEN_CLIP_
 connects base/V2 human-only text using exact cached preparation rows with a
 distinct derived receipt, not a new encoder invocation. The genuine verified-CF
 pool and production admission remain pending.
+The [parent operation monitor](docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md)
+holds a process-released lease, emits independent long-operation heartbeats and
+supports explicit recent/best checkpoint retention without deleting predecessor
+or historical evidence. It does not grant formal starts or global compute budget.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 4d85d63..f38d328 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-7cc6876398afdb06926697a4dd707192a42c4c4c29211d1e3b4cbdea295534b1	13025	README.md
+e2e84266cfa826f80d00a25a07938222b10a84ad110987b83815f2011bea6ec5	13344	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -79,6 +79,7 @@ ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASE
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
 c94c1538acb0e892c91c54ad1c6740073c9d75623465aa665ab1ea81b9b1ead3	2904	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
 3eb5f4237db4af02d49e8eb62a318ef68ac706bf14cc1b405bf9fa837e8d5705	7431	docs/PHASESET_V2_PARENT_HOST.md
+1eef1de33e2f1d193a6a02a0a982df0c9dea7b1f9406b4cbd155d22ef7378f51	3314	docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
 81b47eff03531162dd2ab6b3d29a0ca9909326e7b13dec34776551d171b182c7	1727	docs/PHASESET_V2_TMR_PARENT_TRAINING.md
@@ -168,7 +169,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 80e6d497ff9bb4354664cb1a054cdb550ab222e92c841813afc27af6d3759495	5603	src/phaseset_core/continuous_base_retrieval.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
-2ef64b87f94fa635dce12fc5589088c64680b1816d640aa99f960a43ae9fe8a9	27008	src/phaseset_core/continuous_parent_host.py
+f53d85531385a5c8e8fcf4b27f0c62bd02e08b295283332493cd86a0b000e647	29579	src/phaseset_core/continuous_parent_host.py
 cd9b85e1b3e576d23c04f15c5b6dad853110c2a7733b030ec2c6a3b1e6d874ef	15200	src/phaseset_core/continuous_parent_training.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
 6af5ecfcadc139acfdb3fe2d2e00995a40e90522a821172bc44e7a3604028dbc	10571	src/phaseset_core/continuous_retrieval.py
@@ -197,6 +198,7 @@ c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phase
 fb417f794ee32b7e7777933ee2ef4ceeda9868ea82da62245beec53994560f77	3293	src/phaseset_core/parent_clip_rows.py
 f8e257a1cff56e93ff76229c2a4c47b1a85902a64b76338758178d111a2d3ecb	8322	src/phaseset_core/parent_disk_source.py
 be883315aa773e6c2d535ea981853ae92a791187338fd2b310b574f956174a82	7326	src/phaseset_core/parent_retrieval_task.py
+e0c9d77e9ccaf277785beb148a54afbdf542c607a6298e6b20db458828a32fe7	9331	src/phaseset_core/parent_run_monitor.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 e69b0d27d479b4499bae73c96f7854f9d07539043a0b66f0f75f6bc3e23c1f51	55415	src/phaseset_core/periodic_cache_execution.py
 21ab159e434cc504867d22a22894a8e6a82afa77dde88a466773b5549a004850	42190	src/phaseset_core/periodic_capture_training_cache.py
@@ -243,6 +245,7 @@ d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test
 fc0c27fb68c7d119d1cc7a48afeeff5a707cab9ec206eb5ad85c606e31656490	9443	tests/test_parent_clip_rows.py
 487ee577859df867298041fe731166cd211506e5f211d3c508eeec5aa282640f	5825	tests/test_parent_disk_source.py
 32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
+cdf4d6dcea5c29e44ebb3f2f91945fd944a8026a7abe91eaa47beb59428f159c	12935	tests/test_parent_run_monitor.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
 9a05b2a025a694f94e158d2de74242d2a820efbc8973920001895114e7a894fb	14880	tests/test_periodic_cache_source_lineage.py
 8dccac5551ec4d519f98b4b81d8f10ba47bd1f19ece6168ce137c51dc2ca2f0e	18170	tests/test_periodic_capture_training_cache.py
diff --git a/docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md b/docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
new file mode 100644
index 0000000..b77583c
--- /dev/null
+++ b/docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
@@ -0,0 +1,41 @@
+# 完整 parent host 的运行监测和 checkpoint 保留
+
+`parent_run_monitor.py` 复用现有 `_AttemptLease`，不改旧 33-run 的
+`AttemptStore`/run-ID 合同。`ContinuousParentTrainingHost.fit`（包含继承它的
+TMR/WaMo/MIME host）在数值循环前持有 lifetime lease；终止/异常均释放。
+每 30 秒写一次独立线程 heartbeat，包含当前操作、step、epoch、最近已发布
+checkpoint SHA、单进程 monotonic elapsed。线程只读 scalar snapshot，不碰
+模型、张量或 RNG，因此完整 capture forward/backward、数据读取和保存期间
+没有新 update，也仍可判断进程存活。它不是每 30 秒必须完成一步的 watchdog。
+
+现有不可变 run/events/checkpoint/resume 继续使用，新增不可变 heartbeat、
+checkpoint metadata receipts 和 retention receipts。终止记录补充失败分类：
+磁盘不足、checkpoint 无效、资源上限、进程中断、输入/合同错误或未分类。
+原始 traceback 应留在私有日志；分类不授予自动重试。监测/终止写入同时失败时
+尽量保留主异常并追加 note，不用清理错误替换原始训练根因。突然 kill/power loss
+可能没有 terminal，最后 heartbeat 和 shell exit 收据需由操作员据实核对。
+
+生产调用显式使用 `fit(..., checkpoint_keep_recent=2)`。默认 `None` 保留原先
+keep-all 行为，不回收旧资格目录。保留最新两个 payload（回滚候选）、它们
+各自依赖的 selected-best 以及当前 best；resume predecessor 在其他目录，不删除。
+仅从本 attempt 自己发布并已记录的 checkpoint 中回收旧且不再被保留候选引用的
+payload，逐文件确认目录/regular file/原摘要，先写 planned、删除后写 retired。
+事件、摘要、历史 checkpoint receipts、旧失败目录均永久保留。不递归、不 glob
+删除，不按未验证的磁盘占用猜测删除资产。已回收 payload 不能直接恢复，只能从
+保留候选重新计算；planned 无 retired 明确是中途中断，不冒充已完成回收。
+
+该监测器不批准正式实验、不实现全局预算或硬墙钟 timeout，也不把单进程 elapsed
+冒充多 GPU-hours。私有执行器仍须对所有分配设备（包括失败计算）累计 GPU 时间，
+在冻结的 87 阶段、最多 90 starts/12 pilots/3 rooted restarts/2 structural reworks/
+300 GPU-hours 内预留和结算预算，并用有界 shell timeout 处理卡死操作。
+监测器的存在不关闭真实 GPU profile、正式 freeze、逐 family yaw 或双人类 CF 证据。
+
+资格测试只在登记服务器执行。分析性软件 optimizer/resume 通过不是 native
+学习结果、短程 pilot 或论文效果；本实现也不修改 loss、优化器、调度器、
+完整 parent/gallery 单位、验证选择或冻结 base 的算法。
+
+登记服务器 CPU-only 资格于 2026-09-27 关闭：**19 PASS / 0 skip / 0 xfail**，
+103.72 秒；包含新 16 case（base/residual 实际分析性 optimizer + bitwise resume、
+初始化/heartbeat/terminal/release 故障、保留 best 依赖）及 3 个直接相关回归。
+所有 numerical/source/invocation 检查 exit0，stderr 空；旧失败、旧资格不重启。
+它不证明 GPU training profile、真实数据学习、正式预算或研究假设成立。
diff --git a/src/phaseset_core/continuous_parent_host.py b/src/phaseset_core/continuous_parent_host.py
index 169090b..5331203 100644
--- a/src/phaseset_core/continuous_parent_host.py
+++ b/src/phaseset_core/continuous_parent_host.py
@@ -33,6 +33,7 @@
 from .continuous_training_input import ContinuousTrainingView
 from .frozen_clip_text import FrozenClipTextBatch
 from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalBatch, ParentRetrievalTask
+from .parent_run_monitor import ParentRunMonitor, parent_failure_code
 from .training import (
     OFFICIAL_SEEDS,
     CheckpointArtifact,
@@ -275,6 +276,7 @@ def _initialize(self, system, task, source, config, bindings):
         self._root: Path | None = None
         self._used = False
         self._event_sequence = 0
+        self._monitor: ParentRunMonitor | None = None
 
     def _batches(self, epoch: int):
         return parent_epoch_batches(
@@ -303,18 +305,21 @@ def _backward_batch(self, labels: ParentRetrievalBatch):
 
     def _event(self, phase: str, **fields):
         assert self._root is not None
+        event = {
+            "phase": phase,
+            "observed_at_utc": datetime.now(timezone.utc).isoformat(),
+            "global_step": self._step,
+            "epoch": self._epoch,
+            "parent_batch_offset": self._offset,
+            **fields,
+        }
         _write_json_once(
             self._root / f"event-{self._event_sequence:08d}.json",
-            {
-                "phase": phase,
-                "observed_at_utc": datetime.now(timezone.utc).isoformat(),
-                "global_step": self._step,
-                "epoch": self._epoch,
-                "parent_batch_offset": self._offset,
-                **fields,
-            },
+            event,
         )
         self._event_sequence += 1
+        if self._monitor is not None and phase != "terminal":
+            self._monitor.observe(event)
 
     @torch.no_grad()
     def _validate(self) -> float:
@@ -357,6 +362,7 @@ def _validate(self) -> float:
 
     def _checkpoint(self, *, select: bool = False) -> CheckpointArtifact:
         assert self._root is not None
+        self._event("checkpoint_write")
         self._sequence += 1
         path = self._root / f"checkpoint-{self._step:012d}-{self._sequence:06d}.pt"
         best = (
@@ -383,6 +389,8 @@ def _checkpoint(self, *, select: bool = False) -> CheckpointArtifact:
         self._latest = _atomic_torch_save(path, payload)
         if select:
             self._best = self._latest
+        if self._monitor is not None:
+            self._monitor.checkpoint(self._latest, best=self._best)
         return self._latest
 
     def _resume(self, path: Path, digest: str):
@@ -478,6 +486,7 @@ def fit(
         resume_checkpoint: str | Path | None = None,
         resume_sha256: str | None = None,
         stop_after_steps: int | None = None,
+        checkpoint_keep_recent: int | None = None,
     ) -> ParentHostReport:
         """Run or resume into a distinct immutable attempt directory.
 
@@ -485,6 +494,8 @@ def fit(
         is the end of an epoch, its validation is still performed before stop;
         the selected checkpoint cannot be bypassed by resume. Terminal records
         never claim research completion or grant a formal start/pilot budget.
+        ``checkpoint_keep_recent>=2`` explicitly retires this attempt's older
+        unreferenced payloads only; None preserves the legacy keep-all policy.
         """
         if self._used:
             raise RuntimeError("a host instance may own only one attempt")
@@ -494,22 +505,29 @@ def fit(
             type(stop_after_steps) is not int or not 0 <= stop_after_steps <= self._total_steps
         ):
             raise ValueError("stop point must be an update boundary within this schedule")
+        if checkpoint_keep_recent is not None and (
+            type(checkpoint_keep_recent) is not int or checkpoint_keep_recent < 2
+        ):
+            raise ValueError("checkpoint retention must keep at least two recent payloads")
         self._used = True
         self._root = Path(run_directory).resolve()
         self._root.mkdir(parents=True, exist_ok=False, mode=0o700)
-        _write_json_once(
-            self._root / "run.json",
-            {
-                "manifest": self._manifest,
-                "predecessor_checkpoint": None
-                if resume_checkpoint is None
-                else str(Path(resume_checkpoint).resolve()),
-                "predecessor_sha256": resume_sha256,
-                "formal_authority": False,
-            },
-        )
-        outcome, failure = "FAILED", None
+        self._monitor = ParentRunMonitor(self._root, keep_recent=checkpoint_keep_recent)
+        outcome, failure, primary_error = "FAILED", None, None
         try:
+            self._monitor.start()
+            _write_json_once(
+                self._root / "run.json",
+                {
+                    "manifest": self._manifest,
+                    "predecessor_checkpoint": None
+                    if resume_checkpoint is None
+                    else str(Path(resume_checkpoint).resolve()),
+                    "predecessor_sha256": resume_sha256,
+                    "checkpoint_keep_recent": checkpoint_keep_recent,
+                    "formal_authority": False,
+                },
+            )
             with _frozen_numerical_runtime(next(self.system.parameters()).device):
                 if resume_checkpoint is None:
                     random.seed(self.config.seed)
@@ -540,7 +558,9 @@ def fit(
                     if stop_after_steps is not None and self._step == stop_after_steps:
                         break
                     labels = batches[self._offset]
+                    self._event("training_batch")
                     result = self._backward_batch(labels)
+                    self._monitor.check()
                     norm = torch.nn.utils.clip_grad_norm_(
                         self._parameters, 1.0, error_if_nonfinite=True
                     )
@@ -581,22 +601,51 @@ def fit(
                 )
         except BaseException as error:
             failure = type(error).__name__
+            primary_error = error
             raise
         finally:
-            self._event("terminal", outcome=outcome, failure_class=failure)
-            _write_json_once(
-                self._root / "terminal.json",
-                {
-                    "outcome": outcome,
-                    "failure_class": failure,
-                    "global_step": self._step,
-                    "completed_epochs": self._epoch,
-                    "parents_seen": self._parents_seen,
-                    "latest_checkpoint": None if self._latest is None else str(self._latest.path),
-                    "latest_checkpoint_sha256": None
-                    if self._latest is None
-                    else self._latest.sha256,
-                    "best_validation_r1": self._best_value,
-                    "formal_authority": False,
-                },
-            )
+            cleanup_error = None
+            try:
+                self._monitor.finish(outcome=outcome, failure=primary_error)
+            except BaseException as error:
+                cleanup_error = error
+                if primary_error is None:
+                    outcome, failure, primary_error = "FAILED", type(error).__name__, error
+                else:
+                    primary_error.add_note(f"monitor close also failed: {error!r}")
+            try:
+                self._event("terminal", outcome=outcome, failure_class=failure)
+                _write_json_once(
+                    self._root / "terminal.json",
+                    {
+                        "outcome": outcome,
+                        "failure_class": failure,
+                        "failure_code": parent_failure_code(primary_error),
+                        "global_step": self._step,
+                        "completed_epochs": self._epoch,
+                        "parents_seen": self._parents_seen,
+                        "latest_checkpoint": None
+                        if self._latest is None
+                        else str(self._latest.path),
+                        "latest_checkpoint_sha256": None
+                        if self._latest is None
+                        else self._latest.sha256,
+                        "best_validation_r1": self._best_value,
+                        "monitor": self._monitor.summary(),
+                        "formal_authority": False,
+                    },
+                )
+            except BaseException as error:
+                if primary_error is None:
+                    primary_error = error
+                    raise
+                primary_error.add_note(f"terminal evidence write also failed: {error!r}")
+            finally:
+                try:
+                    self._monitor.release()
+                except BaseException as error:
+                    if primary_error is None:
+                        raise
+                    primary_error.add_note(f"lifetime lease release also failed: {error!r}")
+            if cleanup_error is not None and primary_error is cleanup_error:
+                raise cleanup_error
diff --git a/src/phaseset_core/parent_run_monitor.py b/src/phaseset_core/parent_run_monitor.py
new file mode 100644
index 0000000..fedbf8b
--- /dev/null
+++ b/src/phaseset_core/parent_run_monitor.py
@@ -0,0 +1,219 @@
+"""Lifetime lease, operation heartbeat and bounded checkpoint retention.
+
+This is execution evidence, not a study launch grant. It deliberately does not
+route the new parent study through the legacy 33-run AttemptStore parser.
+"""
+
+from __future__ import annotations
+
+import errno
+import hashlib
+import json
+from pathlib import Path
+import threading
+import time
+from datetime import datetime, timezone
+
+from .execution import _write_once, artifact_sha256
+from .host import _AttemptLease
+from .training import CheckpointArtifact, TrainingCheckpointError
+
+
+def parent_failure_code(error: BaseException | None) -> str | None:
+    """Descriptive failure classes; never an automatic restart decision."""
+    if error is None:
+        return None
+    if isinstance(error, OSError) and error.errno == errno.ENOSPC:
+        return "DISK_FULL"
+    if isinstance(error, TrainingCheckpointError):
+        return "CHECKPOINT_INVALID"
+    if isinstance(error, (KeyboardInterrupt, SystemExit)):
+        return "PROCESS_INTERRUPTED"
+    if isinstance(error, MemoryError) or type(error).__name__ == "OutOfMemoryError":
+        return "RESOURCE_LIMIT"
+    if isinstance(error, (ValueError, TypeError)):
+        return "INPUT_OR_CONTRACT"
+    return "UNCLASSIFIED"
+
+
+def _record(path: Path, payload: dict) -> str:
+    raw = (
+        json.dumps(payload, sort_keys=True, separators=(",", ":"), allow_nan=False) + "\n"
+    ).encode("utf-8")
+    _write_once(path, raw)
+    return artifact_sha256(raw)
+
+
+def _file_digest(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+class ParentRunMonitor:
+    """One fresh host attempt; heartbeats continue inside long native calls.
+
+    Retention is explicit: None preserves every payload, integer >=2 keeps the
+    newest N plus their selected-best dependencies and the current best. Only
+    payloads produced by this monitor in this directory can be retired. Resume
+    predecessors, failed attempts, events and receipts are never deleted.
+    The worker reads scalar snapshots only, not tensors or model/RNG state.
+    """
+
+    HEARTBEAT_SECONDS = 30.0
+
+    def __init__(self, root: Path, *, keep_recent: int | None = None):
+        if keep_recent is not None and (type(keep_recent) is not int or keep_recent < 2):
+            raise ValueError("checkpoint retention must keep at least two recent payloads")
+        self.root = root.resolve()
+        self.keep_recent = keep_recent
+        self._lease = None
+        self._lock = threading.Lock()
+        self._stop = threading.Event()
+        self._thread = None
+        self._error: BaseException | None = None
+        self._started = None
+        self._elapsed = 0.0
+        self._heartbeat_sequence = 0
+        self._last_heartbeat_sha256 = None
+        self._latest_checkpoint_sha256 = None
+        self._snapshot = {"phase": "STARTING", "global_step": 0}
+        self._checkpoints: list[tuple[CheckpointArtifact, CheckpointArtifact | None]] = []
+        self._retired: set[Path] = set()
+
+    def start(self):
+        if self._started is not None:
+            raise RuntimeError("monitor may start only once")
+        self._lease = _AttemptLease.acquire(self.root)
+        # After acquisition, even initialization failure belongs to the host's
+        # terminal/finally path. Do not make an unfinished attempt acquirable.
+        for name in ("heartbeats", "checkpoint-receipts", "retention"):
+            (self.root / name).mkdir(mode=0o700)
+        self._started = time.monotonic()
+        with self._lock:
+            self._heartbeat()
+        self._thread = threading.Thread(target=self._worker, daemon=True)
+        self._thread.start()
+
+    def _heartbeat(self):
+        assert self._started is not None
+        self._elapsed = time.monotonic() - self._started
+        self._last_heartbeat_sha256 = _record(
+            self.root / "heartbeats" / f"heartbeat-{self._heartbeat_sequence:08d}.json",
+            {
+                "schema": "phaseset-parent-operation-heartbeat-v1",
+                "sequence": self._heartbeat_sequence,
+                "process_elapsed_seconds": self._elapsed,
+                "previous_heartbeat_sha256": self._last_heartbeat_sha256,
+                "latest_checkpoint_sha256": self._latest_checkpoint_sha256,
+                **self._snapshot,
+                "observed_at_utc": datetime.now(timezone.utc).isoformat(),
+                "formal_authority": False,
+            },
+        )
+        self._heartbeat_sequence += 1
+
+    def _worker(self):
+        while not self._stop.wait(self.HEARTBEAT_SECONDS):
+            try:
+                with self._lock:
+                    self._heartbeat()
+            except BaseException as error:
+                self._error = error
+                self._stop.set()
+
+    def check(self):
+        if self._error is not None:
+            raise self._error
+
+    def observe(self, event: dict):
+        self.check()
+        with self._lock:
+            # An event is a freshly constructed scalar host record. Make our
+            # own snapshot so the background writer never observes mutation.
+            self._snapshot = dict(event)
+
+    def checkpoint(self, artifact: CheckpointArtifact, *, best: CheckpointArtifact | None):
+        self.check()
+        if artifact.path.parent != self.root or artifact.path.is_symlink():
+            raise ValueError("retention accepts only this attempt's own checkpoint payloads")
+        index = len(self._checkpoints)
+        _record(
+            self.root / "checkpoint-receipts" / f"checkpoint-{index:08d}.json",
+            {
+                "schema": "phaseset-parent-checkpoint-receipt-v1",
+                "sequence": index,
+                "global_step": artifact.global_step,
+                "payload": artifact.path.name,
+                "payload_sha256": artifact.sha256,
+                "payload_bytes": artifact.path.stat().st_size,
+                "best_path": None if best is None else str(best.path),
+                "best_sha256": None if best is None else best.sha256,
+            },
+        )
+        self._checkpoints.append((artifact, best))
+        with self._lock:
+            self._latest_checkpoint_sha256 = artifact.sha256
+        if self.keep_recent is None:
+            return
+        recent = self._checkpoints[-self.keep_recent :]
+        protected = {item.path for item, _ in recent}
+        protected.update(selected.path for _, selected in recent if selected is not None)
+        if best is not None:
+            protected.add(best.path)
+        for old_index, (old, _) in enumerate(self._checkpoints):
+            if old.path in protected or old.path in self._retired:
+                continue
+            if old.path.parent != self.root or old.path.is_symlink() or not old.path.is_file():
+                raise TrainingCheckpointError(
+                    "retention payload is no longer an owned regular file"
+                )
+            if _file_digest(old.path) != old.sha256:
+                raise TrainingCheckpointError("retention payload digest changed; preserve evidence")
+            receipt = {
+                "payload": old.path.name,
+                "payload_sha256": old.sha256,
+                "payload_bytes": old.path.stat().st_size,
+                "protected_paths": sorted(str(path) for path in protected),
+                "reason": "superseded_unreferenced_payload_keep_recent_and_best",
+            }
+            # A crash between these records is explicitly a pending retirement,
+            # not a claim that deletion finished. No recursive/glob deletion.
+            _record(self.root / "retention" / f"planned-{old_index:08d}.json", receipt)
+            old.path.unlink()
+            _record(self.root / "retention" / f"retired-{old_index:08d}.json", receipt)
+            self._retired.add(old.path)
+
+    def finish(self, *, outcome: str, failure: BaseException | None):
+        """Stop heartbeat before writing host terminal; lease remains held."""
+        self._stop.set()
+        if self._thread is not None and self._thread.ident is not None:
+            self._thread.join(timeout=self.HEARTBEAT_SECONDS + 5)
+            if self._thread.is_alive():
+                raise RuntimeError("heartbeat worker did not terminate")
+        self.check()
+        if self._started is not None:
+            with self._lock:
+                self._snapshot.update(
+                    phase="TERMINAL", outcome=outcome, failure_code=parent_failure_code(failure)
+                )
+                self._heartbeat()
+
+    def release(self):
+        """Release only after the host's terminal write (including its failure)."""
+        if self._lease is not None:
+            self._lease.release()
+            self._lease = None
+
+    def summary(self) -> dict:
+        return {
+            "latest_heartbeat_sha256": self._last_heartbeat_sha256,
+            "heartbeat_count": self._heartbeat_sequence,
+            "process_elapsed_seconds": self._elapsed,
+            "checkpoint_keep_recent": self.keep_recent,
+            "retired_payload_count": len(self._retired),
+            "monitor_failure_class": None if self._error is None else type(self._error).__name__,
+            "budget_or_study_authority": False,
+        }
diff --git a/tests/test_parent_run_monitor.py b/tests/test_parent_run_monitor.py
new file mode 100644
index 0000000..ec48e70
--- /dev/null
+++ b/tests/test_parent_run_monitor.py
@@ -0,0 +1,315 @@
+"""Execution qualifications; analytic checkpoints are not empirical runs."""
+
+import copy
+import errno
+import hashlib
+import json
+from pathlib import Path
+import time
+
+import pytest
+import torch
+
+from phaseset_core.continuous_parent_host import ContinuousParentTrainingHost
+from phaseset_core.host import HostConfigurationError, _AttemptLease
+from phaseset_core.parent_run_monitor import ParentRunMonitor, parent_failure_code
+from phaseset_core.training import (
+    CheckpointArtifact,
+    TrainingCheckpointError,
+    _load_torch_checkpoint,
+    _stable_hash,
+)
+
+
+def _payload(root, index):
+    path = root / f"checkpoint-{index:012d}-{index + 1:06d}.pt"
+    raw = f"analytic payload {index}".encode()
+    path.write_bytes(raw)
+    return CheckpointArtifact(path, hashlib.sha256(raw).hexdigest(), index)
+
+
+def test_long_operation_heartbeat_is_chained_and_lease_is_process_released(tmp_path, monkeypatch):
+    monkeypatch.setattr(ParentRunMonitor, "HEARTBEAT_SECONDS", 0.01)
+    root = tmp_path / "attempt"
+    root.mkdir()
+    monitor = ParentRunMonitor(root)
+    monitor.start()
+    try:
+        with pytest.raises(HostConfigurationError, match="live process"):
+            _AttemptLease.acquire(root)
+        monitor.observe(
+            {
+                "phase": "native_backward",
+                "global_step": 3,
+                "epoch": 0,
+                "observed_at_utc": "2000-01-01T00:00:00+00:00",
+            }
+        )
+        # No event or update occurs in this interval: the worker is independent.
+        deadline = time.monotonic() + 5
+        while not (root / "heartbeats" / "heartbeat-00000003.json").exists():
+            assert time.monotonic() < deadline
+            time.sleep(0.01)
+    finally:
+        monitor.finish(outcome="INTERRUPTED", failure=None)
+        with pytest.raises(HostConfigurationError, match="live process"):
+            _AttemptLease.acquire(root)
+        monitor.release()
+    with _AttemptLease.acquire(root):
+        pass
+    previous, elapsed = None, -1.0
+    heartbeats = sorted((root / "heartbeats").glob("*.json"))
+    for index, path in enumerate(heartbeats):
+        raw = path.read_bytes()
+        row = json.loads(raw)
+        assert row["sequence"] == index and row["previous_heartbeat_sha256"] == previous
+        assert row["process_elapsed_seconds"] >= elapsed
+        assert row["formal_authority"] is False
+        assert not row["observed_at_utc"].startswith("2000-")
+        previous, elapsed = hashlib.sha256(raw).hexdigest(), row["process_elapsed_seconds"]
+    assert (
+        sum(json.loads(path.read_text())["phase"] == "native_backward" for path in heartbeats) >= 2
+    )
+    assert json.loads(heartbeats[-1].read_text())["outcome"] == "INTERRUPTED"
+    assert monitor.summary()["latest_heartbeat_sha256"] == previous
+
+
+@pytest.mark.parametrize("bad", [0, 1, True, 2.0])
+def test_retention_requires_two_rollback_candidates(bad, tmp_path):
+    with pytest.raises(ValueError, match="at least two"):
+        ParentRunMonitor(tmp_path, keep_recent=bad)
+
+
+@pytest.mark.parametrize("phase", ["mkdir", "heartbeat", "thread"])
+def test_initialization_failure_keeps_lease_until_terminal_owner_releases(
+    tmp_path, monkeypatch, phase
+):
+    import phaseset_core.parent_run_monitor as module
+
+    root = tmp_path / "attempt"
+    root.mkdir()
+    monitor = ParentRunMonitor(root)
+    error = OSError("actual monitor initialization failure")
+    original_mkdir = Path.mkdir
+    original_record = module._record
+
+    def failing_mkdir(path, *args, **kwargs):
+        if path.name == "checkpoint-receipts":
+            raise error
+        return original_mkdir(path, *args, **kwargs)
+
+    def failing_record(*args, **kwargs):
+        raise error
+
+    def failing_thread(*args, **kwargs):
+        raise error
+
+    if phase == "mkdir":
+        monkeypatch.setattr(Path, "mkdir", failing_mkdir)
+    elif phase == "heartbeat":
+        monkeypatch.setattr(module, "_record", failing_record)
+    else:
+        monkeypatch.setattr(module.threading.Thread, "start", failing_thread)
+    try:
+        with pytest.raises(OSError, match="initialization failure") as raised:
+            monitor.start()
+        assert raised.value is error
+        with pytest.raises(HostConfigurationError, match="live process"):
+            _AttemptLease.acquire(root)
+    finally:
+        monkeypatch.setattr(module, "_record", original_record)
+        monitor.release()
+    with _AttemptLease.acquire(root):
+        pass
+
+
+def test_retention_protects_recent_best_dependencies_and_external_predecessor(tmp_path):
+    root = tmp_path / "attempt"
+    root.mkdir()
+    predecessor = _payload(tmp_path, 99)
+    monitor = ParentRunMonitor(root, keep_recent=2)
+    monitor.start()
+    try:
+        first, second, third, fourth = [_payload(root, index) for index in range(4)]
+        monitor.checkpoint(first, best=first)
+        monitor.checkpoint(second, best=first)
+        monitor.checkpoint(third, best=third)
+        assert first.path.exists()  # The second/rollback candidate still names it.
+        monitor.checkpoint(fourth, best=third)
+        assert not first.path.exists() and not second.path.exists()
+        assert third.path.exists() and fourth.path.exists()
+        fifth = _payload(root, 4)
+        monitor.checkpoint(fifth, best=predecessor)
+        assert third.path.exists()  # The fourth still names it as best.
+        assert predecessor.path.exists()
+        with pytest.raises(ValueError, match="own checkpoint"):
+            monitor.checkpoint(predecessor, best=None)
+    finally:
+        monitor.finish(outcome="COMPLETED", failure=None)
+        monitor.release()
+    assert len(list((root / "checkpoint-receipts").glob("*.json"))) == 5
+    assert len(list((root / "retention").glob("retired-*.json"))) == 2
+    assert monitor.summary()["retired_payload_count"] == 2
+
+
+def test_changed_payload_is_preserved_before_retirement(tmp_path):
+    root = tmp_path / "attempt"
+    root.mkdir()
+    monitor = ParentRunMonitor(root, keep_recent=2)
+    monitor.start()
+    first, second, third = [_payload(root, index) for index in range(3)]
+    try:
+        monitor.checkpoint(first, best=None)
+        monitor.checkpoint(second, best=None)
+        first.path.write_bytes(b"actual corruption")
+        with pytest.raises(TrainingCheckpointError, match="digest changed"):
+            monitor.checkpoint(third, best=None)
+        assert first.path.read_bytes() == b"actual corruption"
+        assert not list((root / "retention").glob("*.json"))
+    finally:
+        monitor.finish(outcome="FAILED", failure=TrainingCheckpointError("corruption"))
+        monitor.release()
+
+
+def test_keep_all_and_close_never_prune_failed_or_predecessor_attempt(tmp_path):
+    root = tmp_path / "attempt"
+    root.mkdir()
+    monitor = ParentRunMonitor(root)
+    monitor.start()
+    artifacts = [_payload(root, index) for index in range(4)]
+    for item in artifacts:
+        monitor.checkpoint(item, best=artifacts[0])
+    monitor.finish(outcome="FAILED", failure=OSError(errno.ENOSPC, "full"))
+    monitor.release()
+    assert all(item.path.exists() for item in artifacts)
+    assert parent_failure_code(OSError(errno.ENOSPC, "full")) == "DISK_FULL"
+    assert parent_failure_code(TrainingCheckpointError("drift")) == "CHECKPOINT_INVALID"
+    assert parent_failure_code(KeyboardInterrupt()) == "PROCESS_INTERRUPTED"
+    assert parent_failure_code(MemoryError()) == "RESOURCE_LIMIT"
+
+
+def test_worker_io_failure_surfaces_without_false_completion(tmp_path, monkeypatch):
+    import phaseset_core.parent_run_monitor as module
+
+    monkeypatch.setattr(ParentRunMonitor, "HEARTBEAT_SECONDS", 0.01)
+    root = tmp_path / "attempt"
+    root.mkdir()
+    original = module._record
+    error = OSError(errno.ENOSPC, "actual heartbeat disk full")
+
+    def disk_full(path, payload):
+        if payload.get("sequence") == 1:
+            raise error
+        return original(path, payload)
+
+    monkeypatch.setattr(module, "_record", disk_full)
+    monitor = ParentRunMonitor(root)
+    monitor.start()
+    try:
+        assert monitor._stop.wait(5)
+        with pytest.raises(OSError, match="heartbeat disk full") as raised:
+            monitor.check()
+        assert raised.value is error
+        with pytest.raises(OSError, match="heartbeat disk full"):
+            monitor.finish(outcome="COMPLETED", failure=None)
+    finally:
+        monitor.release()
+    with _AttemptLease.acquire(root):
+        pass
+    assert len(list((root / "heartbeats").glob("*.json"))) == 1
+    assert monitor.summary()["monitor_failure_class"] == "OSError"
+
+
+@pytest.mark.parametrize("terminal_write_bad", [False, True])
+def test_monitor_failure_releases_lease_and_preserves_primary_training_error(
+    tmp_path, monkeypatch, terminal_write_bad
+):
+    from test_continuous_base_retrieval import _setup
+
+    model, task, source, config, bindings = _setup(tmp_path)
+    error = ValueError("actual source failure")
+
+    def broken_source(*args, **kwargs):
+        raise error
+
+    source.text = broken_source
+    original_finish = ParentRunMonitor.finish
+
+    def broken_finish(monitor, **kwargs):
+        original_finish(monitor, **kwargs)
+        raise OSError("monitor close failed")
+
+    monkeypatch.setattr(ParentRunMonitor, "finish", broken_finish)
+    original_release = ParentRunMonitor.release
+
+    def broken_release(monitor):
+        original_release(monitor)
+        raise OSError("lease release failed")
+
+    monkeypatch.setattr(ParentRunMonitor, "release", broken_release)
+    if terminal_write_bad:
+        import phaseset_core.continuous_parent_host as host_module
+
+        original_write = host_module._write_json_once
+
+        def broken_terminal(path, payload):
+            if path.name == "terminal.json":
+                raise OSError(errno.ENOSPC, "terminal disk full")
+            return original_write(path, payload)
+
+        monkeypatch.setattr(host_module, "_write_json_once", broken_terminal)
+    with pytest.raises(ValueError, match="actual source failure") as raised:
+        ContinuousParentTrainingHost(model, task, source, config, bindings).fit(tmp_path / "failed")
+    assert raised.value is error and any("monitor close" in note for note in error.__notes__)
+    assert any("lease release" in note for note in error.__notes__)
+    if terminal_write_bad:
+        assert any("terminal evidence write" in note for note in error.__notes__)
+        assert not (tmp_path / "failed" / "terminal.json").exists()
+    else:
+        row = json.loads((tmp_path / "failed" / "terminal.json").read_text())
+        assert row["outcome"] == "FAILED" and row["failure_code"] == "INPUT_OR_CONTRACT"
+    with _AttemptLease.acquire(tmp_path / "failed"):
+        pass
+
+
+@pytest.mark.parametrize("kind", ["base", "residual"])
+def test_actual_host_retention_resume_is_bitwise_and_keeps_best(tmp_path, kind):
+    if kind == "base":
+        from test_continuous_base_retrieval import _setup
+    else:
+        from test_continuous_parent_host import _setup as residual_setup
+
+        def _setup(path):
+            return residual_setup(path, batch_size=2)
+
+    model, task, source, config, bindings = _setup(tmp_path)
+    initial = copy.deepcopy(model)
+    full = ContinuousParentTrainingHost(model, task, source, config, bindings).fit(
+        tmp_path / "full", checkpoint_keep_recent=2
+    )
+    stopped = ContinuousParentTrainingHost(
+        copy.deepcopy(initial), task, source, config, bindings
+    ).fit(tmp_path / "stopped", stop_after_steps=2, checkpoint_keep_recent=2)
+    resumed_model = copy.deepcopy(initial)
+    resumed = ContinuousParentTrainingHost(resumed_model, task, source, config, bindings).fit(
+        tmp_path / "resumed",
+        resume_checkpoint=stopped.latest_checkpoint.path,
+        resume_sha256=stopped.latest_checkpoint.sha256,
+        checkpoint_keep_recent=2,
+    )
+    assert full.outcome == resumed.outcome == "COMPLETED"
+    assert full.validation_history == resumed.validation_history
+    assert full.best_checkpoint.path.exists() and resumed.best_checkpoint.path.exists()
+    assert stopped.latest_checkpoint.path.exists() and stopped.best_checkpoint.path.exists()
+    for name, value in model.state_dict().items():
+        assert torch.equal(value, resumed_model.state_dict()[name])
+    expected, _ = _load_torch_checkpoint(full.latest_checkpoint.path)
+    actual, _ = _load_torch_checkpoint(resumed.latest_checkpoint.path)
+    for key in ("model", "optimizer", "scheduler", "rng"):
+        assert _stable_hash(actual[key]) == _stable_hash(expected[key])
+    terminal = json.loads((tmp_path / "full" / "terminal.json").read_text())
+    assert terminal["monitor"]["retired_payload_count"] >= 1
+    assert terminal["monitor"]["budget_or_study_authority"] is False
+    assert len(list((tmp_path / "full").glob("checkpoint-*.pt"))) <= 4
+    manifest = json.loads((tmp_path / "resumed" / "run.json").read_text())
+    assert Path(manifest["predecessor_checkpoint"]) == stopped.latest_checkpoint.path

From 144570e847ba26d79313b29b3a1ce1d954b12017 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 10:37:57 -0500
Subject: [PATCH 54/95] feat: persist V2 study budgets and rooted restart
 accounting

---
 README.md                         |   4 +
 RELEASE_FILES.sha256              |   5 +-
 docs/PHASESET_V2_STUDY_BUDGET.md  |  63 +++++
 src/phaseset_core/study_budget.py | 441 ++++++++++++++++++++++++++++++
 tests/test_study_budget.py        | 438 +++++++++++++++++++++++++++++
 5 files changed, 950 insertions(+), 1 deletion(-)
 create mode 100644 docs/PHASESET_V2_STUDY_BUDGET.md
 create mode 100644 src/phaseset_core/study_budget.py
 create mode 100644 tests/test_study_budget.py

diff --git a/README.md b/README.md
index ca20b61..a9fd8d3 100644
--- a/README.md
+++ b/README.md
@@ -86,6 +86,10 @@ The [parent operation monitor](docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md)
 holds a process-released lease, emits independent long-operation heartbeats and
 supports explicit recent/best checkpoint retention without deleting predecessor
 or historical evidence. It does not grant formal starts or global compute budget.
+The [persistent study budget](docs/PHASESET_V2_STUDY_BUDGET.md) preserves actual
+historical GPU charges, pending wall-time envelopes, fixed pilot allocations,
+and rooted formal predecessors. Bookkeeping qualification does not close the
+production spawn/timeout/settlement integration or scientific admission.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index f38d328..f4d6964 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-e2e84266cfa826f80d00a25a07938222b10a84ad110987b83815f2011bea6ec5	13344	README.md
+ea21f2d3927aa830c6bc4f7e77d3e96878e95f588fadca1abfe526bd106cffdc	13653	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -82,6 +82,7 @@ c94c1538acb0e892c91c54ad1c6740073c9d75623465aa665ab1ea81b9b1ead3	2904	docs/PHASE
 1eef1de33e2f1d193a6a02a0a982df0c9dea7b1f9406b4cbd155d22ef7378f51	3314	docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
+f403dc89f99772ec6911b0e14d0107299f6808ee39269f546acd59ece4e731e4	4995	docs/PHASESET_V2_STUDY_BUDGET.md
 81b47eff03531162dd2ab6b3d29a0ca9909326e7b13dec34776551d171b182c7	1727	docs/PHASESET_V2_TMR_PARENT_TRAINING.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
@@ -212,6 +213,7 @@ ad2ce56325f09d2efb17847ef3f947535f81f7099019275fdd074c12efeeb559	73588	src/phase
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
+d0dd93cf286224694abeeb082e7e9b8e91b02a6fa107303a2a526ef4176e6026	19341	src/phaseset_core/study_budget.py
 5604397a65a0032d2a14124d34ca3d96a2d6d67e5f4534732bb82f7a0c7b2546	23163	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
@@ -335,6 +337,7 @@ a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/tes
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
 8727e87f1a0d44db6a39b1e5cfd8e22e3bed05544ddaed07b66b759adee66d84	5587	tests/test_release_tree_batch.py
 eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
+771a4895ff2e62d99190e0b0226142ad4bec4b1e90b0f303b80d199e2662341b	15834	tests/test_study_budget.py
 96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
 18c5c10980d8e0d81eb1a6b351c0e0e770325c95bcca59e15763e5c86967ce78	4260	tests/test_tmr_feature_cache.py
 726ee82009ecb29ce87208749fb1780365640a52f2e2d6e55399e22119838b89	2988	tests/test_tmr_language.py
diff --git a/docs/PHASESET_V2_STUDY_BUDGET.md b/docs/PHASESET_V2_STUDY_BUDGET.md
new file mode 100644
index 0000000..41d041f
--- /dev/null
+++ b/docs/PHASESET_V2_STUDY_BUDGET.md
@@ -0,0 +1,63 @@
+# V2 持久全局预算账本
+
+`StudyBudget` 是单个研究的 append-only accounting journal，复用原 `_AttemptLease`
+做短事务锁，不启动模型、SSH、GPU 或授权系统。不改 legacy33run 解析器。所有
+生产任务必须使用同一个私有根目录；另建空账本或忽略历史违反研究合同。
+
+初始化必须显式提供完整87行/三seed矩阵、每一个历史计数器、历史证据及每个
+pilot家族对应的 nominal formal optimizer-step cap，不会默认历史为0。已有目录
+不能重新初始化。GPU预算300h、formal reservations90、pilot12、rooted formal
+restart3、结构返工2，以及五个pilot家族3/3/3/1/2的分配固定；不能通过传参放大。
+Reservations是实际启动数的保守上界，spawn失败也消费名额，不伪装成成功训练。
+当前研究尚无formal/pilot启动，因此bootstrap只允许显式确认的zero-training历史，
+另导入真实既往GPU检查用量；若找到已有训练，必须沿用详细原journal，不能把
+非零训练仅总结成几个计数后丢掉run/seed/predecessor身份重新初始化。
+
+启动子进程前预约所有分配GPU乘以完整wall envelope（包括timeout和kill grace）。
+未结算任务一直占用整份预约，进程缺失/过时heartbeat不自动返还。生产wrapper
+仍须实际设置相同有界timeout，并确认自己的子进程已终止/不再持GPU之后才结算。
+按实际分配wall time×GPU数收费，失败/中断也收费；早结束归还未用时长，但不
+归还pilot/formal名额。超过预估/300h的实际用量照实结算，不能为使数字好看拒记。
+Optimizer-step超cap也保留成本并标出，不能冒充合同通过；操作员须修复根因。
+账本不会自动kill进程、判断关系真值、选择seed或关闭科学acceptance gate。
+
+profile必须0 optimizer updates；真实训练不能藏在未计数profile中。若完整training
+profile执行真实优化器更新，就占既定pilot名额，同一次pilot可同时提供profile
+证据，不再偷偷额外训练。每pilot `5*planned_steps <= nominal_formal_steps`，不以
+epoch单位转换逃过20%限制。pilot编号唯一，失败名额不重置。正式run只匹配矩阵
+ID和seed；先记录**已在外部真实冻结**的全87个max_steps，只可记录一次。
+记录step limits本身不是研究freeze或训练许可；当前真实研究仍未完成该freeze。
+
+Formal重复启动必须关联同run最新已结算失败/中断前驱，写真实原始日志根因，
+全球只允许3次，不分配置额外给三次，也不从旧前驱分叉或重跑已完成seed。
+GPU收费、starts/restarts、结构返工跨重新打开账本保存，不因新host或中断而清零。
+达到三次formal rooted restart额度后，不再新增长formal任务；先交付实际故障证据
+和预算缺口，不扩大额度。90 reservation上限也独立保护，未用seed不因此删掉。
+写盘截断/gap等不完整journal会显式拒绝继续；保留证据，不能另建零账本重试。
+
+JSON中的`scientific_or_launch_authority:false`明确是成本证据，不替代数据权利、
+完整输入/环境/代码binding、true two-human CF、真实GPU空闲和dated科学合同。
+生产外部controller的实际spawn/timeout/settlement仍要有真实收据，软件模拟账本
+和测试中模拟90 reservations不等于启动了90个实验。所有数值/模型/资格仍只在
+登记服务器；公开只含源代码、合成bookkeeping测试和不敏感说明。
+
+## 本次实际限定资格
+
+Fresh ARIS GPT-5.6-Sol/xhigh发现首次建账的parent-directory durability缺口，
+已修正为已有parent、event-file fsync → journal-directory fsync → parent fsync。
+两个新增case验证POSIX顺序及sync失败时保留账本/拒绝重建；Windows不冒称同样
+directory-fsync保证。最终限定复核none，same-family仍provisional。
+
+登记服务器warmenv、CPU1/interop1/noCUDA/nice10/16GiB有界执行，新26项全部
+PASS/0skip/0xfail，1.79秒；numerical/source-check/exit全0，源/invocation前后相同。
+测试含并发最后额度保护、失败成本、pending保持、90合成reservation、三rooted
+restart/两rework、12pilot分配/20%steps、截断journal与目录同步故障，不运行模型。
+
+随后独立CPU-only历史导入真实核验四份既有CUDA收据：三个单卡mechanism检查
+整段秒级receipt wall各加1秒rounding，保守合计34GPU秒；另一个profile在Torch/
+CUDA分配前HOLD，计0。这是保守历史成本，不是精确allocation计时或新profile。
+唯一私有生产journal现在history34、pending0、formal0、pilot0、formalstepfreeze
+未记录。旧失败/资产/收据保留，未新增GPU分配/优化器步骤、重启或结构返工。
+
+这些软件测试和一次历史bootstrap不替代真实production controller资格、完整
+training profile、既定pilot/87阶段学习、two-human关系核实、结果统计或论文交付。
diff --git a/src/phaseset_core/study_budget.py b/src/phaseset_core/study_budget.py
new file mode 100644
index 0000000..8a8ba72
--- /dev/null
+++ b/src/phaseset_core/study_budget.py
@@ -0,0 +1,441 @@
+"""Persistent V2 study-wide reservations and observed resource accounting.
+
+Accounting is not scientific/rights/hardware admission. Existing attempt
+receipts prove those separately. No model, optimizer or SSH is launched here.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import os
+from pathlib import Path
+from datetime import datetime, timezone
+
+from .execution import _write_once
+from .host import _AttemptLease
+
+
+PILOT_ALLOCATION = {"TMR-Set": 3, "WaMo-Set": 3, "MIME-Set": 3, "B2": 1, "PhaseSet-V2-head": 2}
+LIMITS = {
+    "gpu_seconds": 300 * 3600,
+    "formal_reservations": 90,
+    "pilot_reservations": 12,
+    "formal_restarts": 3,
+    "architecture_reworks": 2,
+}
+
+
+class StudyBudgetError(ValueError):
+    """Invalid accounting input, exhausted allowance or unresolved predecessor."""
+
+
+def _integer(value, name, *, minimum=0):
+    if type(value) is not int or value < minimum:
+        raise StudyBudgetError(f"{name} must be an integer >= {minimum}")
+    return value
+
+
+def _seconds(value, name):
+    if type(value) not in (int, float) or not math.isfinite(value) or value < 0:
+        raise StudyBudgetError(f"{name} must be finite nonnegative seconds")
+    return float(value)
+
+
+def _text(value, name):
+    if type(value) is not str or not value.strip():
+        raise StudyBudgetError(f"{name} must be explicit nonempty text")
+    return value
+
+
+def _fsync_directory(path):
+    if os.name == "posix":
+        descriptor = os.open(path, os.O_RDONLY | os.O_DIRECTORY)
+        try:
+            os.fsync(descriptor)
+        finally:
+            os.close(descriptor)
+
+
+class StudyBudget:
+    """Append-only transactions under the already qualified lifetime lease.
+
+    Reserve before spawning; the full wall-time envelope remains charged until
+    verified settlement. Reservation counts are conservative upper bounds on
+    actual starts: failed spawning does NOT refund a pilot/formal slot. All
+    allocated GPUs multiply wall time, including failed and interrupted work.
+    Missing processes or old heartbeats never silently refund reservations.
+    """
+
+    def __init__(self, root: str | Path):
+        self.root = Path(root).resolve()
+        if not (self.root / "event-00000000.json").is_file():
+            raise StudyBudgetError("budget absent; import verified history explicitly, never reset")
+
+    @classmethod
+    def create(
+        cls,
+        root: str | Path,
+        *,
+        matrix: dict,
+        historical_usage: dict,
+        historical_evidence: tuple[str, ...],
+        pilot_formal_steps: dict[str, int],
+    ):
+        """One explicit bootstrap; no default-zero history or overwrite path."""
+        budget = matrix.get("budget", {})
+        expected = {
+            "gpu_hours": 300,
+            "formal_stages": 87,
+            "max_formal_starts": 90,
+            "max_pilot_stages": 12,
+            "max_root_cause_restarts": 3,
+            "max_architecture_revisions": 2,
+            "pilot_max_formal_step_fraction": 0.2,
+        }
+        if any(budget.get(key) != value for key, value in expected.items()):
+            raise StudyBudgetError("the fixed V2 study budget cannot be raised")
+        runs = matrix.get("runs", [])
+        run_seeds = {row["id"]: row["seed"] for row in runs}
+        if (
+            len(runs) != 87
+            or set(run_seeds) != {f"V2-{i:03d}" for i in range(1, 88)}
+            or any(
+                type(seed) is not int or seed not in (1729, 2718, 31415)
+                for seed in run_seeds.values()
+            )
+            or matrix.get("seeds") != [1729, 2718, 31415]
+        ):
+            raise StudyBudgetError("the complete 87-stage/three-seed matrix is required")
+        if set(historical_usage) != set(LIMITS) | {"pilot_families"}:
+            raise StudyBudgetError("all historical counters must be imported explicitly")
+        _seconds(historical_usage["gpu_seconds"], "historical GPU time")
+        for key in set(LIMITS) - {"gpu_seconds"}:
+            _integer(historical_usage[key], key)
+        families = historical_usage["pilot_families"]
+        if (
+            type(families) is not dict
+            or set(families) != set(PILOT_ALLOCATION)
+            or sum(_integer(value, "historical family count") for value in families.values())
+            != historical_usage["pilot_reservations"]
+        ):
+            raise StudyBudgetError("historical pilot family census must match the total")
+        if any(
+            historical_usage[key]
+            for key in ("formal_reservations", "pilot_reservations", "formal_restarts")
+        ):
+            raise StudyBudgetError(
+                "historical training needs its original detailed journal, not a new summary bootstrap"
+            )
+        if not historical_evidence or type(historical_evidence) is not tuple:
+            raise StudyBudgetError("explicit historical evidence is required")
+        for reference in historical_evidence:
+            _text(reference, "historical evidence")
+        if set(pilot_formal_steps) != set(PILOT_ALLOCATION):
+            raise StudyBudgetError("each pilot family needs its explicit nominal formal step cap")
+        for value in pilot_formal_steps.values():
+            _integer(value, "nominal formal steps", minimum=1)
+        path = Path(root).resolve()
+        path.mkdir(mode=0o700, exist_ok=False)
+        with _AttemptLease.acquire(path):
+            cls._append(
+                path,
+                0,
+                "IMPORT_HISTORY",
+                {
+                    "historical_usage": historical_usage,
+                    "historical_evidence": historical_evidence,
+                    "run_seeds": run_seeds,
+                    "pilot_formal_steps": pilot_formal_steps,
+                    "matrix_status_at_import": matrix.get("status"),
+                },
+            )
+            _fsync_directory(path.parent)
+        return cls(path)
+
+    @staticmethod
+    def _append(root, sequence, kind, fields):
+        raw = (
+            json.dumps(
+                {
+                    "schema": "phaseset-v2-study-budget-v1",
+                    "sequence": sequence,
+                    "observed_at_utc": datetime.now(timezone.utc).isoformat(),
+                    "kind": kind,
+                    **fields,
+                    "scientific_or_launch_authority": False,
+                },
+                sort_keys=True,
+                separators=(",", ":"),
+                allow_nan=False,
+            ).encode()
+            + b"\n"
+        )
+        _write_once(root / f"event-{sequence:08d}.json", raw)
+        _fsync_directory(root)
+
+    def _state(self):
+        events = []
+        for index, path in enumerate(sorted(self.root.glob("event-*.json"))):
+            if path.name != f"event-{index:08d}.json":
+                raise StudyBudgetError("budget journal sequence has a gap; preserve and inspect")
+            row = json.loads(path.read_bytes())
+            if row.get("sequence") != index or row.get("schema") != "phaseset-v2-study-budget-v1":
+                raise StudyBudgetError("budget journal metadata is inconsistent")
+            events.append(row)
+        if not events or events[0]["kind"] != "IMPORT_HISTORY":
+            raise StudyBudgetError("budget historical import is missing")
+        history = events[0]
+        usage = dict(history["historical_usage"])
+        usage["pilot_families"] = dict(usage["pilot_families"])
+        attempts, settled, step_limits = {}, {}, None
+        for row in events[1:]:
+            kind = row["kind"]
+            if kind == "RESERVE":
+                if row["attempt_id"] in attempts:
+                    raise StudyBudgetError("duplicate attempt in budget journal")
+                attempts[row["attempt_id"]] = row
+                if row["purpose"] != "profile":
+                    usage[row["purpose"] + "_reservations"] += 1
+                if row["purpose"] == "pilot":
+                    usage["pilot_families"][row["family"]] += 1
+                if row["purpose"] == "formal" and row["predecessor_attempt_id"] is not None:
+                    usage["formal_restarts"] += 1
+            elif kind == "SETTLE":
+                if row["attempt_id"] not in attempts or row["attempt_id"] in settled:
+                    raise StudyBudgetError("settlement is missing its unique reservation")
+                settled[row["attempt_id"]] = row
+                usage["gpu_seconds"] += row["actual_gpu_seconds"]
+            elif kind == "REWORK":
+                usage["architecture_reworks"] += 1
+            elif kind == "FORMAL_STEP_LIMITS":
+                if step_limits is not None:
+                    raise StudyBudgetError("formal step limits were rewritten")
+                step_limits = row["run_max_steps"]
+            else:
+                raise StudyBudgetError("unknown budget journal event; do not reset it")
+        pending = {key: value for key, value in attempts.items() if key not in settled}
+        reserved = sum(row["reserved_gpu_seconds"] for row in pending.values())
+        return {
+            "events": events,
+            "history": history,
+            "usage": usage,
+            "attempts": attempts,
+            "settled": settled,
+            "pending": pending,
+            "reserved_gpu_seconds": reserved,
+            "step_limits": step_limits,
+        }
+
+    def summary(self):
+        with _AttemptLease.acquire(self.root):
+            state = self._state()
+            return {
+                "usage": state["usage"],
+                "reserved_gpu_seconds": state["reserved_gpu_seconds"],
+                "available_gpu_seconds": max(
+                    0,
+                    LIMITS["gpu_seconds"]
+                    - state["usage"]["gpu_seconds"]
+                    - state["reserved_gpu_seconds"],
+                ),
+                "unsettled_attempt_ids": sorted(state["pending"]),
+                "formal_step_limits_recorded": state["step_limits"] is not None,
+                "optimizer_step_cap_exceeded_attempt_ids": sorted(
+                    key
+                    for key, row in state["settled"].items()
+                    if row["optimizer_step_cap_exceeded"]
+                ),
+                "scientific_or_launch_authority": False,
+            }
+
+    def record_formal_step_limits(self, run_max_steps: dict[str, int], *, evidence: str):
+        """Record the already frozen external schedule, not freeze the science."""
+        _text(evidence, "dated external freeze evidence")
+        with _AttemptLease.acquire(self.root):
+            state = self._state()
+            if state["step_limits"] is not None:
+                raise StudyBudgetError("formal step limits are immutable once recorded")
+            if set(run_max_steps) != set(state["history"]["run_seeds"]):
+                raise StudyBudgetError("formal step limits must cover all 87 registered stages")
+            for value in run_max_steps.values():
+                _integer(value, "formal max steps", minimum=1)
+            self._append(
+                self.root,
+                len(state["events"]),
+                "FORMAL_STEP_LIMITS",
+                {
+                    "run_max_steps": run_max_steps,
+                    "evidence": evidence,
+                },
+            )
+
+    def reserve(
+        self,
+        *,
+        attempt_id: str,
+        run_id: str,
+        purpose: str,
+        seed: int,
+        gpu_count: int,
+        wall_time_limit_seconds: int,
+        planned_steps: int,
+        evidence: str,
+        family: str | None = None,
+        predecessor_attempt_id: str | None = None,
+        root_cause: str | None = None,
+    ):
+        """Reserve the full timeout+kill-grace envelope BEFORE child spawning."""
+        for value, name in (
+            (attempt_id, "attempt ID"),
+            (run_id, "run ID"),
+            (evidence, "execution evidence"),
+        ):
+            _text(value, name)
+        if (
+            purpose not in ("profile", "pilot", "formal")
+            or type(seed) is not int
+            or seed not in (1729, 2718, 31415)
+        ):
+            raise StudyBudgetError("purpose and one fixed seed must be explicit")
+        _integer(gpu_count, "allocated GPU count")
+        _integer(wall_time_limit_seconds, "wall envelope", minimum=1)
+        _integer(planned_steps, "planned optimizer steps")
+        if purpose == "profile" and planned_steps != 0:
+            raise StudyBudgetError("optimizer training cannot be hidden as an uncounted profile")
+        if purpose != "profile" and (gpu_count < 1 or planned_steps < 1):
+            raise StudyBudgetError("training reserves actual GPU allocation and positive step caps")
+        cost = gpu_count * wall_time_limit_seconds
+        with _AttemptLease.acquire(self.root):
+            state = self._state()
+            if attempt_id in state["attempts"]:
+                raise StudyBudgetError("attempt IDs are immutable, including failed attempts")
+            usage = state["usage"]
+            if usage["gpu_seconds"] + state["reserved_gpu_seconds"] + cost > LIMITS["gpu_seconds"]:
+                raise StudyBudgetError("study GPU budget exhausted or fully reserved")
+            if purpose != "formal" and (
+                predecessor_attempt_id is not None or root_cause is not None
+            ):
+                raise StudyBudgetError("only a formal rooted restart has a predecessor")
+            if purpose != "pilot" and family is not None:
+                raise StudyBudgetError("pilot family must not disguise another purpose")
+            same_run = [row for row in state["attempts"].values() if row["run_id"] == run_id]
+            if purpose != "formal" and run_id in state["history"]["run_seeds"]:
+                raise StudyBudgetError(
+                    "profile/pilot IDs must not occupy registered formal run IDs"
+                )
+            if purpose == "pilot":
+                if same_run or family not in PILOT_ALLOCATION:
+                    raise StudyBudgetError(
+                        "each pilot stage is unique and uses a registered family"
+                    )
+                if (
+                    usage["pilot_reservations"] >= 12
+                    or usage["pilot_families"][family] >= PILOT_ALLOCATION[family]
+                ):
+                    raise StudyBudgetError("fixed pilot allocation exhausted")
+                if 5 * planned_steps > state["history"]["pilot_formal_steps"][family]:
+                    raise StudyBudgetError(
+                        "pilot exceeds 20% of its registered nominal formal steps"
+                    )
+            if purpose == "formal":
+                if state["step_limits"] is None:
+                    raise StudyBudgetError(
+                        "dated external formal step freeze has not been recorded"
+                    )
+                if state["history"]["run_seeds"].get(run_id) != seed or planned_steps != state[
+                    "step_limits"
+                ].get(run_id):
+                    raise StudyBudgetError(
+                        "formal run/seed/step cap differs from the registered schedule"
+                    )
+                if usage["formal_reservations"] >= 90:
+                    raise StudyBudgetError("formal start reservation cap exhausted")
+                if usage["formal_restarts"] >= 3:
+                    raise StudyBudgetError("global three rooted formal restart allowance exhausted")
+                if same_run:
+                    previous = same_run[-1]
+                    terminal = state["settled"].get(previous["attempt_id"])
+                    if (
+                        previous["purpose"] != "formal"
+                        or predecessor_attempt_id != previous["attempt_id"]
+                        or terminal is None
+                        or terminal["outcome"] not in ("FAILED", "INTERRUPTED", "NOT_STARTED")
+                    ):
+                        raise StudyBudgetError(
+                            "restart needs the latest failed/interrupted, settled predecessor"
+                        )
+                    _text(root_cause, "root cause from original failure evidence")
+                elif predecessor_attempt_id is not None or root_cause is not None:
+                    raise StudyBudgetError("a first formal start has no invented predecessor")
+            self._append(
+                self.root,
+                len(state["events"]),
+                "RESERVE",
+                {
+                    "attempt_id": attempt_id,
+                    "run_id": run_id,
+                    "purpose": purpose,
+                    "seed": seed,
+                    "gpu_count": gpu_count,
+                    "wall_time_limit_seconds": wall_time_limit_seconds,
+                    "reserved_gpu_seconds": cost,
+                    "planned_steps": planned_steps,
+                    "family": family,
+                    "predecessor_attempt_id": predecessor_attempt_id,
+                    "root_cause": root_cause,
+                    "evidence": evidence,
+                },
+            )
+
+    def settle(
+        self,
+        attempt_id: str,
+        *,
+        actual_wall_seconds: float,
+        completed_steps: int,
+        outcome: str,
+        evidence: str,
+    ):
+        """Observed child termination; overspend is recorded, never suppressed."""
+        _seconds(actual_wall_seconds, "actual allocated wall time")
+        _integer(completed_steps, "completed optimizer cursor")
+        _text(evidence, "actual process/terminal evidence")
+        if outcome not in ("COMPLETED", "FAILED", "INTERRUPTED", "NOT_STARTED"):
+            raise StudyBudgetError("explicit terminal outcome is required")
+        with _AttemptLease.acquire(self.root):
+            state = self._state()
+            reservation = state["pending"].get(attempt_id)
+            if reservation is None:
+                raise StudyBudgetError(
+                    "settlement needs one unresolved reservation, not a duplicate"
+                )
+            if outcome == "NOT_STARTED" and (actual_wall_seconds != 0 or completed_steps != 0):
+                raise StudyBudgetError(
+                    "unstarted requires verified zero allocation and zero updates"
+                )
+            self._append(
+                self.root,
+                len(state["events"]),
+                "SETTLE",
+                {
+                    "attempt_id": attempt_id,
+                    "outcome": outcome,
+                    "optimizer_step_cap_exceeded": completed_steps > reservation["planned_steps"],
+                    "completed_steps": completed_steps,
+                    "actual_wall_seconds": actual_wall_seconds,
+                    "actual_gpu_seconds": actual_wall_seconds * reservation["gpu_count"],
+                    "evidence": evidence,
+                },
+            )
+
+    def record_rework(self, *, reason: str, evidence: str):
+        _text(reason, "structural rework reason")
+        _text(evidence, "structural rework evidence")
+        with _AttemptLease.acquire(self.root):
+            state = self._state()
+            if state["usage"]["architecture_reworks"] >= 2:
+                raise StudyBudgetError("two structural rework cap exhausted")
+            self._append(
+                self.root, len(state["events"]), "REWORK", {"reason": reason, "evidence": evidence}
+            )
diff --git a/tests/test_study_budget.py b/tests/test_study_budget.py
new file mode 100644
index 0000000..365169c
--- /dev/null
+++ b/tests/test_study_budget.py
@@ -0,0 +1,438 @@
+"""Synthetic bookkeeping only; none of these reservations launches a model."""
+
+import copy
+import json
+import os
+from pathlib import Path
+import stat
+import threading
+
+import pytest
+
+from phaseset_core.host import HostConfigurationError
+from phaseset_core.study_budget import PILOT_ALLOCATION, StudyBudget, StudyBudgetError
+
+
+def matrix():
+    return json.loads(
+        (
+            Path(__file__).resolve().parents[1] / "configs" / "phaseset_v2_experiment_matrix.json"
+        ).read_text()
+    )
+
+
+def history():
+    return {
+        "gpu_seconds": 34.0,
+        "formal_reservations": 0,
+        "pilot_reservations": 0,
+        "formal_restarts": 0,
+        "architecture_reworks": 0,
+        "pilot_families": {name: 0 for name in PILOT_ALLOCATION},
+    }
+
+
+def create(tmp_path, **kwargs):
+    return StudyBudget.create(
+        tmp_path / "budget",
+        **{
+            "matrix": matrix(),
+            "historical_usage": history(),
+            "historical_evidence": ("analytic import fixture, not actual GPU accounting",),
+            "pilot_formal_steps": {
+                name: 40 if name == "PhaseSet-V2-head" else 60 for name in PILOT_ALLOCATION
+            },
+            **kwargs,
+        },
+    )
+
+
+def reserve(budget, attempt="analytic-1", **kwargs):
+    return budget.reserve(
+        **{
+            "attempt_id": attempt,
+            "run_id": "analytic-profile-1",
+            "purpose": "profile",
+            "seed": 1729,
+            "gpu_count": 2,
+            "wall_time_limit_seconds": 40,
+            "planned_steps": 0,
+            "evidence": "analytic metadata only; no child/model launched",
+            **kwargs,
+        }
+    )
+
+
+def settle(budget, attempt="analytic-1", **kwargs):
+    return budget.settle(
+        attempt,
+        **{
+            "actual_wall_seconds": 10.5,
+            "completed_steps": 0,
+            "outcome": "FAILED",
+            "evidence": "analytic terminal fixture only; no actual job result",
+            **kwargs,
+        },
+    )
+
+
+def formal_steps():
+    return {row["id"]: 60 for row in matrix()["runs"]}
+
+
+def freeze(budget):
+    budget.record_formal_step_limits(
+        formal_steps(), evidence="synthetic schedule; NOT scientific freeze"
+    )
+
+
+def formal(budget, attempt="analytic-formal-1", run="V2-001", **kwargs):
+    seed = next(row["seed"] for row in matrix()["runs"] if row["id"] == run)
+    return reserve(
+        budget,
+        attempt,
+        **{
+            "run_id": run,
+            "purpose": "formal",
+            "seed": seed,
+            "gpu_count": 1,
+            "planned_steps": 60,
+            **kwargs,
+        },
+    )
+
+
+def test_history_is_explicit_immutable_and_not_reset_on_reopen(tmp_path):
+    initial = history()
+    budget = create(tmp_path, historical_usage=initial)
+    initial["gpu_seconds"] = 0
+    assert budget.summary()["usage"]["gpu_seconds"] == 34
+    assert StudyBudget(budget.root).summary() == budget.summary()
+    assert budget.summary()["scientific_or_launch_authority"] is False
+    with pytest.raises(FileExistsError):
+        create(tmp_path)
+    with pytest.raises(StudyBudgetError, match="budget absent"):
+        StudyBudget(tmp_path / "new-root")
+
+
+@pytest.mark.parametrize("fail_parent_sync", [False, True])
+def test_bootstrap_syncs_event_then_journal_then_parent_and_preserves_failure(
+    tmp_path, monkeypatch, fail_parent_sync
+):
+    original = os.fsync
+    calls = []
+
+    def observed_sync(descriptor):
+        observed = os.fstat(descriptor)
+        if stat.S_ISDIR(observed.st_mode):
+            parent = tmp_path.stat()
+            label = (
+                "parent"
+                if (observed.st_dev, observed.st_ino) == (parent.st_dev, parent.st_ino)
+                else "journal"
+            )
+        else:
+            label = "event"
+        calls.append(label)
+        if label == "parent" and fail_parent_sync:
+            raise OSError("parent directory sync failure fixture")
+        return original(descriptor)
+
+    monkeypatch.setattr(os, "fsync", observed_sync)
+    if os.name == "posix" and fail_parent_sync:
+        with pytest.raises(OSError, match="parent directory sync"):
+            create(tmp_path)
+        assert (tmp_path / "budget" / "event-00000000.json").is_file()
+        with pytest.raises(FileExistsError):
+            create(tmp_path)
+        assert StudyBudget(tmp_path / "budget").summary()["usage"]["gpu_seconds"] == 34
+    else:
+        assert create(tmp_path).summary()["usage"]["gpu_seconds"] == 34
+    assert calls == (["event", "journal", "parent"] if os.name == "posix" else ["event"])
+
+
+@pytest.mark.parametrize(
+    "missing", ["gpu_seconds", "formal_reservations", "pilot_families", "architecture_reworks"]
+)
+def test_missing_historical_counter_is_not_assumed_zero(tmp_path, missing):
+    incomplete = history()
+    del incomplete[missing]
+    with pytest.raises(StudyBudgetError, match="explicitly"):
+        create(tmp_path, historical_usage=incomplete)
+    assert not (tmp_path / "budget").exists()
+
+
+def test_history_evidence_and_fixed_caps_cannot_be_omitted_or_raised(tmp_path):
+    with pytest.raises(StudyBudgetError, match="evidence"):
+        create(tmp_path, historical_evidence=())
+    changed = matrix()
+    changed["budget"]["gpu_hours"] = 301
+    with pytest.raises(StudyBudgetError, match="cannot be raised"):
+        create(tmp_path, matrix=changed)
+    bad = history()
+    bad["pilot_reservations"] = 1
+    with pytest.raises(StudyBudgetError, match="census"):
+        create(tmp_path, historical_usage=bad)
+    trained = history()
+    trained["formal_reservations"] = 1
+    with pytest.raises(StudyBudgetError, match="original detailed journal"):
+        create(tmp_path, historical_usage=trained)
+
+
+def test_multigpu_failed_wall_time_and_pending_envelope_are_persistent(tmp_path):
+    budget = create(tmp_path)
+    reserve(budget)
+    row = StudyBudget(budget.root).summary()
+    assert row["usage"]["gpu_seconds"] == 34 and row["reserved_gpu_seconds"] == 80
+    assert row["available_gpu_seconds"] == 300 * 3600 - 114
+    assert row["unsettled_attempt_ids"] == ["analytic-1"]
+    settle(budget)
+    final = StudyBudget(budget.root).summary()
+    assert final["usage"]["gpu_seconds"] == 55
+    assert final["reserved_gpu_seconds"] == 0 and final["unsettled_attempt_ids"] == []
+    assert final["usage"]["formal_reservations"] == final["usage"]["pilot_reservations"] == 0
+    with pytest.raises(StudyBudgetError, match="duplicate"):
+        settle(budget)
+    with pytest.raises(StudyBudgetError, match="immutable"):
+        reserve(budget)
+
+
+def test_missing_process_does_not_release_reservation_and_overspend_is_recorded(tmp_path):
+    budget = create(tmp_path)
+    reserve(budget, gpu_count=1, wall_time_limit_seconds=300 * 3600 - 34)
+    with pytest.raises(StudyBudgetError, match="budget exhausted"):
+        reserve(StudyBudget(budget.root), "analytic-2", gpu_count=1, wall_time_limit_seconds=1)
+    # Actual allocated wall-time overshoot is evidence, not rejected input.
+    settle(budget, actual_wall_seconds=300 * 3600, outcome="INTERRUPTED")
+    assert budget.summary()["usage"]["gpu_seconds"] == 300 * 3600 + 34
+    assert budget.summary()["available_gpu_seconds"] == 0
+    with pytest.raises(StudyBudgetError, match="budget exhausted"):
+        reserve(budget, "analytic-3", gpu_count=1, wall_time_limit_seconds=1)
+
+
+@pytest.mark.parametrize(
+    "field,value",
+    [
+        ("seed", 42),
+        ("gpu_count", True),
+        ("gpu_count", -1),
+        ("wall_time_limit_seconds", 0),
+        ("planned_steps", 1),
+        ("purpose", "extra_training"),
+        ("run_id", "V2-001"),
+    ],
+)
+def test_profiles_cannot_hide_optimizer_steps_or_formal_ids(tmp_path, field, value):
+    budget = create(tmp_path)
+    with pytest.raises(StudyBudgetError):
+        reserve(budget, **{field: value})
+    assert budget.summary()["unsettled_attempt_ids"] == []
+
+
+def test_all_twelve_pilot_slots_and_family_allocation_include_spawn_failures(tmp_path):
+    budget = create(tmp_path)
+    for family, allowance in PILOT_ALLOCATION.items():
+        for index in range(allowance):
+            attempt = f"analytic-{family}-{index}"
+            reserve(
+                budget,
+                attempt,
+                run_id=attempt,
+                purpose="pilot",
+                family=family,
+                gpu_count=1,
+                planned_steps=8 if family == "PhaseSet-V2-head" else 12,
+            )
+            settle(budget, attempt, actual_wall_seconds=0, outcome="NOT_STARTED")
+    counts = budget.summary()["usage"]
+    assert counts["pilot_reservations"] == 12 and counts["pilot_families"] == PILOT_ALLOCATION
+    assert counts["gpu_seconds"] == 34  # Simulated spawning failures had no allocation.
+    with pytest.raises(StudyBudgetError, match="allocation exhausted"):
+        reserve(
+            budget,
+            "analytic-thirteenth",
+            run_id="analytic-thirteenth",
+            purpose="pilot",
+            family="TMR-Set",
+            gpu_count=1,
+            planned_steps=12,
+        )
+
+
+def test_pilot_step_fraction_is_exact_and_unsupported_family_is_rejected(tmp_path):
+    budget = create(tmp_path)
+    with pytest.raises(StudyBudgetError, match="20%"):
+        reserve(budget, purpose="pilot", family="TMR-Set", planned_steps=13)
+    with pytest.raises(StudyBudgetError, match="registered family"):
+        reserve(budget, purpose="pilot", family="extra-family", planned_steps=1)
+    assert budget.summary()["usage"]["pilot_reservations"] == 0
+
+
+def test_formal_needs_all_87_step_limits_once_and_exact_seed_schedule(tmp_path):
+    budget = create(tmp_path)
+    with pytest.raises(StudyBudgetError, match="freeze"):
+        formal(budget)
+    incomplete = formal_steps()
+    incomplete.pop("V2-087")
+    with pytest.raises(StudyBudgetError, match="all 87"):
+        budget.record_formal_step_limits(incomplete, evidence="analytic incomplete schedule")
+    freeze(budget)
+    with pytest.raises(StudyBudgetError, match="immutable"):
+        freeze(budget)
+    for change in ({"seed": 2718}, {"planned_steps": 61}):
+        with pytest.raises(StudyBudgetError, match="differs"):
+            formal(budget, **change)
+    formal(budget)
+    settle(budget, "analytic-formal-1", completed_steps=60, outcome="COMPLETED")
+    with pytest.raises(StudyBudgetError, match="predecessor"):
+        formal(
+            budget,
+            "analytic-formal-2",
+            predecessor_attempt_id="analytic-formal-1",
+            root_cause="cannot rerun completed seed",
+        )
+
+
+def test_rooted_restarts_need_latest_settled_failure_and_stop_at_global_three(tmp_path):
+    budget = create(tmp_path)
+    freeze(budget)
+    formal(budget)
+    with pytest.raises(StudyBudgetError, match="settled predecessor"):
+        formal(
+            budget,
+            "analytic-early",
+            predecessor_attempt_id="analytic-formal-1",
+            root_cause="not actually terminal",
+        )
+    settle(budget, "analytic-formal-1")
+    with pytest.raises(StudyBudgetError, match="root cause"):
+        formal(budget, "analytic-unrooted", predecessor_attempt_id="analytic-formal-1")
+    previous = "analytic-formal-1"
+    for index in range(3):
+        attempt = f"analytic-restart-{index}"
+        formal(
+            budget,
+            attempt,
+            predecessor_attempt_id=previous,
+            root_cause="synthetic infrastructure failure fixture",
+        )
+        settle(budget, attempt)
+        previous = attempt
+        if index == 0:
+            with pytest.raises(StudyBudgetError, match="latest"):
+                formal(
+                    budget,
+                    "analytic-fork-old",
+                    predecessor_attempt_id="analytic-formal-1",
+                    root_cause="old branch fork forbidden",
+                )
+    assert budget.summary()["usage"]["formal_reservations"] == 4
+    assert budget.summary()["usage"]["formal_restarts"] == 3
+    with pytest.raises(StudyBudgetError, match="three rooted"):
+        formal(
+            budget,
+            "analytic-fourth-restart",
+            predecessor_attempt_id=previous,
+            root_cause="cap must hold",
+        )
+    with pytest.raises(StudyBudgetError, match="three rooted"):
+        formal(budget, "analytic-new-after-cap", "V2-004")
+
+
+def test_complete_87_matrix_plus_three_restarts_cannot_launch_91st(tmp_path):
+    budget = create(tmp_path)
+    freeze(budget)
+    for row in matrix()["runs"]:
+        attempt = "analytic-" + row["id"]
+        formal(budget, attempt, row["id"])
+        settle(budget, attempt, actual_wall_seconds=1)
+    for index in range(1, 4):
+        run_id = f"V2-{index:03d}"
+        attempt = "analytic-restart-" + run_id
+        formal(
+            budget,
+            attempt,
+            run_id,
+            predecessor_attempt_id="analytic-" + run_id,
+            root_cause="synthetic fixture only",
+        )
+        settle(budget, attempt, actual_wall_seconds=1)
+    assert budget.summary()["usage"]["formal_reservations"] == 90
+    with pytest.raises(StudyBudgetError, match="start reservation cap"):
+        formal(
+            budget,
+            "analytic-91",
+            "V2-004",
+            predecessor_attempt_id="analytic-V2-004",
+            root_cause="must not add run",
+        )
+
+
+def test_structural_reworks_do_not_reset_pilots_or_allow_a_third(tmp_path):
+    budget = create(tmp_path)
+    for index in range(2):
+        budget.record_rework(reason=f"analytic rework {index}", evidence="synthetic only")
+    with pytest.raises(StudyBudgetError, match="two structural"):
+        StudyBudget(budget.root).record_rework(reason="third", evidence="must reject")
+    assert budget.summary()["usage"]["architecture_reworks"] == 2
+    assert budget.summary()["usage"]["pilot_reservations"] == 0
+
+
+def test_step_cap_violation_cannot_hide_actual_gpu_charge(tmp_path):
+    budget = create(tmp_path)
+    reserve(budget)
+    settle(budget, actual_wall_seconds=41, completed_steps=1)
+    summary = budget.summary()
+    assert summary["usage"]["gpu_seconds"] == 116
+    assert summary["optimizer_step_cap_exceeded_attempt_ids"] == ["analytic-1"]
+
+
+def test_concurrent_reservations_cannot_double_spend_last_envelope(tmp_path):
+    budget = create(tmp_path)
+    barrier = threading.Barrier(3)
+    outcomes = []
+
+    def worker(index):
+        barrier.wait()
+        try:
+            reserve(
+                StudyBudget(budget.root),
+                f"analytic-{index}",
+                gpu_count=1,
+                wall_time_limit_seconds=300 * 3600 - 34,
+            )
+            outcomes.append("reserved")
+        except (StudyBudgetError, HostConfigurationError):
+            outcomes.append("held")
+
+    workers = [threading.Thread(target=worker, args=(index,)) for index in range(2)]
+    for item in workers:
+        item.start()
+    barrier.wait()
+    for item in workers:
+        item.join(timeout=5)
+        assert not item.is_alive()
+    assert sorted(outcomes) == ["held", "reserved"]
+    assert len(budget.summary()["unsettled_attempt_ids"]) == 1
+    assert budget.summary()["available_gpu_seconds"] == 0
+
+
+def test_partial_disk_write_is_preserved_and_not_replaced_by_fresh_history(tmp_path, monkeypatch):
+    import phaseset_core.study_budget as module
+
+    budget = create(tmp_path)
+    snapshot = copy.deepcopy(budget.summary())
+
+    def partial_write(path, raw):
+        path.write_bytes(raw[:10])
+        raise OSError("actual partial disk write fixture")
+
+    monkeypatch.setattr(module, "_write_once", partial_write)
+    with pytest.raises(OSError, match="partial disk"):
+        reserve(budget)
+    assert (budget.root / "event-00000001.json").read_bytes()
+    with pytest.raises(json.JSONDecodeError):
+        budget.summary()
+    with pytest.raises(FileExistsError):
+        create(tmp_path)
+    assert snapshot["usage"]["gpu_seconds"] == 34

From a34b6655bed005d3b71b61d1d5f195adb7f0955d Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 11:28:16 -0500
Subject: [PATCH 55/95] Add owned-process study execution and user-selected
 ICMR target

---
 README.md                          |  14 +-
 RELEASE_FILES.sha256               |  13 +-
 configs/phaseset_v2_venue.json     |  22 ++
 docs/PHASESET_V2_ICMR_TARGET.md    |  16 ++
 docs/PHASESET_V2_STUDY_BUDGET.md   |  11 +-
 docs/PHASESET_V2_STUDY_PROCESS.md  |  50 ++++
 src/phaseset_core/study_budget.py  |  13 +-
 src/phaseset_core/study_process.py | 428 ++++++++++++++++++++++++++++
 tests/test_study_budget.py         |  21 ++
 tests/test_study_process.py        | 440 +++++++++++++++++++++++++++++
 10 files changed, 1017 insertions(+), 11 deletions(-)
 create mode 100644 configs/phaseset_v2_venue.json
 create mode 100644 docs/PHASESET_V2_ICMR_TARGET.md
 create mode 100644 docs/PHASESET_V2_STUDY_PROCESS.md
 create mode 100644 src/phaseset_core/study_process.py
 create mode 100644 tests/test_study_process.py

diff --git a/README.md b/README.md
index a9fd8d3..f6a4fdb 100644
--- a/README.md
+++ b/README.md
@@ -42,6 +42,11 @@ multi-person skeleton sequence
 
 ## PhaseSet-V2 research upgrade — 2026-09-26
 
+The user-selected target is now [ACM ICMR 2027](docs/PHASESET_V2_ICMR_TARGET.md).
+The venue amendment changes submission format, not the scientific matrix or
+compute budget. The existing ICASSP manuscript is historical, not an ICMR-ready
+paper or a submitted result.
+
 The new [V2 amendment](docs/PHASESET_V2_AMENDMENT.md) introduces signed local
 phase, time-varying actor–edge incidence, chronological capture readout, and
 verified counterfactual coordination supervision. The
@@ -88,8 +93,13 @@ supports explicit recent/best checkpoint retention without deleting predecessor
 or historical evidence. It does not grant formal starts or global compute budget.
 The [persistent study budget](docs/PHASESET_V2_STUDY_BUDGET.md) preserves actual
 historical GPU charges, pending wall-time envelopes, fixed pilot allocations,
-and rooted formal predecessors. Bookkeeping qualification does not close the
-production spawn/timeout/settlement integration or scientific admission.
+and rooted formal predecessors. Bookkeeping alone does not qualify process
+execution or scientific admission.
+The [Linux process controller](docs/PHASESET_V2_STUDY_PROCESS.md) connects
+reservations to owned child sessions, verified exit and actual wall-time charges.
+Its 23 focused tests ran on the registered Linux server with actual stdlib
+children, not native training. Native GPU integration and scientific admission
+still need their own evidence.
 
 [Server mechanism verification](docs/PHASESET_V2_MECHANISM_VERIFICATION.md)
 records 117 focused CPU tests and one full-width CUDA forward/backward witness.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index f4d6964..fe12c7d 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-ea21f2d3927aa830c6bc4f7e77d3e96878e95f588fadca1abfe526bd106cffdc	13653	README.md
+99425351d401759961327e5285444c2d8025544d7cbc861e22274c12658e61fa	14223	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -17,6 +17,7 @@ e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
 cd6fb3868996ab909d8a382cfc62a35fa5510e465ca697aae0a142afc50b9498	26169	configs/phaseset_v2_experiment_matrix.json
+59916f216450b4d79817f6c7f0574243e1092496e53a07a4f1a518047e427acd	994	configs/phaseset_v2_venue.json
 2970469ad0787e74ab4fabce2275486eb50c30c38f3cb7cf8f2800551d16991f	9540	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
 e32c97a223c2b04278b9ce3c5d3b25590b390bc82dbf29e99b06d08d6c96b268	3054	docs/CACHED_RESIDUAL_TRAINING.md
@@ -73,6 +74,7 @@ cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHAS
 6d50263826636377615082911b0de5afe871b8887f9419f95bbe4fb4db7f914d	3439	docs/PHASESET_V2_FROZEN_CLIP_ROWS.md
 b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
+795e9fd4fed59fdb05186cce6a0c146f1007846a6d4a2a7d43944bcea82c58ee	1183	docs/PHASESET_V2_ICMR_TARGET.md
 004dfc49150cc66ba71f7c303dda5dd5f9ce16ca4a220e7558b2da4914e94336	3978	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
@@ -82,7 +84,8 @@ c94c1538acb0e892c91c54ad1c6740073c9d75623465aa665ab1ea81b9b1ead3	2904	docs/PHASE
 1eef1de33e2f1d193a6a02a0a982df0c9dea7b1f9406b4cbd155d22ef7378f51	3314	docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
-f403dc89f99772ec6911b0e14d0107299f6808ee39269f546acd59ece4e731e4	4995	docs/PHASESET_V2_STUDY_BUDGET.md
+8944faef4cfe50dfdccf0443abf739a510a94291184b2374392e0c039762f93a	5607	docs/PHASESET_V2_STUDY_BUDGET.md
+3bd7f8091f6673023fb41cafc6eb64be7a4ef5d39d117c9098e9379640d67be7	3882	docs/PHASESET_V2_STUDY_PROCESS.md
 81b47eff03531162dd2ab6b3d29a0ca9909326e7b13dec34776551d171b182c7	1727	docs/PHASESET_V2_TMR_PARENT_TRAINING.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
@@ -213,7 +216,8 @@ ad2ce56325f09d2efb17847ef3f947535f81f7099019275fdd074c12efeeb559	73588	src/phase
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
-d0dd93cf286224694abeeb082e7e9b8e91b02a6fa107303a2a526ef4176e6026	19341	src/phaseset_core/study_budget.py
+b3c69e0c6b5b79adc0bc15472486e38df483ec4698ebe5b4fb064cd7a8f0968b	19796	src/phaseset_core/study_budget.py
+a984350a2838101fde448eee44d32f22eec5dfa8487637fa0d7941f99de82aec	17205	src/phaseset_core/study_process.py
 5604397a65a0032d2a14124d34ca3d96a2d6d67e5f4534732bb82f7a0c7b2546	23163	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
@@ -337,7 +341,8 @@ a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/tes
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
 8727e87f1a0d44db6a39b1e5cfd8e22e3bed05544ddaed07b66b759adee66d84	5587	tests/test_release_tree_batch.py
 eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
-771a4895ff2e62d99190e0b0226142ad4bec4b1e90b0f303b80d199e2662341b	15834	tests/test_study_budget.py
+1ede4189b31a502ced40838b9aa08b6ce0e726ecd34a8dc73adf6c56fd59c519	16744	tests/test_study_budget.py
+67b2e0ebb76d7def996bc3ce6242a7373fac4e1e7334dde5fb17bbfc995fce87	17565	tests/test_study_process.py
 96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
 18c5c10980d8e0d81eb1a6b351c0e0e770325c95bcca59e15763e5c86967ce78	4260	tests/test_tmr_feature_cache.py
 726ee82009ecb29ce87208749fb1780365640a52f2e2d6e55399e22119838b89	2988	tests/test_tmr_language.py
diff --git a/configs/phaseset_v2_venue.json b/configs/phaseset_v2_venue.json
new file mode 100644
index 0000000..4fdb4c2
--- /dev/null
+++ b/configs/phaseset_v2_venue.json
@@ -0,0 +1,22 @@
+{
+  "schema": "phaseset-v2-venue-amendment-v1",
+  "amendment_date": "2026-09-27",
+  "user_selected_venue": "ACM ICMR 2027",
+  "track": "Regular long research paper",
+  "status": "PLANNED_NOT_SUBMITTED",
+  "deadline": "2027-01-31T23:59:59-12:00",
+  "review": "double-blind",
+  "latex_class": "acmart",
+  "latex_options": ["sigconf", "review", "anonymous"],
+  "maximum_body_pages": 8,
+  "maximum_reference_only_pages": 2,
+  "appendices_allowed": false,
+  "supplementary_material_allowed": false,
+  "official_submission_portal_status_at_check": "More details will be announced soon",
+  "checked_on": "2026-09-27",
+  "official_sources": ["https://www.icmr2027.org/date", "https://www.icmr2027.org/cfc"],
+  "scientific_matrix_changed": false,
+  "compute_budget_changed": false,
+  "public_repository_anonymity": "Public development continues; the anonymous submission must not identify authors or link to identifying websites. Keep a separate submission artifact.",
+  "submission_authority": false
+}
diff --git a/docs/PHASESET_V2_ICMR_TARGET.md b/docs/PHASESET_V2_ICMR_TARGET.md
new file mode 100644
index 0000000..8621d9a
--- /dev/null
+++ b/docs/PHASESET_V2_ICMR_TARGET.md
@@ -0,0 +1,16 @@
+# 用户选择的 ICMR 2027 目标
+
+2026-09-27 用户将目标从 ICASSP 改为 **ACM ICMR 2027 regular long paper**。
+这改变投稿包装，不修改三seed/87阶段/12pilot/300GPUh及科学主张/negative branch。
+旧ICASSP模板/记录保留为历史，不移动旧tag，不宣称曾提交或已获录用。
+
+当前官方[日期](https://www.icmr2027.org/date)：regular paper截止2027-01-31
+23:59:59 AoE。官方[征稿](https://www.icmr2027.org/cfc)要求ACM双栏、double-blind，
+`acmart`选项`sigconf,review,anonymous`，long paper正文≤8页、纯参考文献≤2页；
+不允许附录或supplementary。投稿入口细节尚待官方公布，提交前须再次核验。
+
+公开开发仓库继续保留；匿名投稿稿件不可直接链接识别作者的网站或公开owner。
+实验完整证据/可复现release继续作为项目交付，但不冒充大会允许的投稿附件。
+真人关系核实与数据权利、真实训练/统计仍须完成；不为换venue伪造或缩小研究。
+新ICMR论文须由同一冻结aggregate生成，尚无真实结果时不填写结果数字。
+机器可读目标见[venue contract](../configs/phaseset_v2_venue.json)。
diff --git a/docs/PHASESET_V2_STUDY_BUDGET.md b/docs/PHASESET_V2_STUDY_BUDGET.md
index 41d041f..7c5260e 100644
--- a/docs/PHASESET_V2_STUDY_BUDGET.md
+++ b/docs/PHASESET_V2_STUDY_BUDGET.md
@@ -19,6 +19,9 @@ Reservations是实际启动数的保守上界，spawn失败也消费名额，不
 按实际分配wall time×GPU数收费，失败/中断也收费；早结束归还未用时长，但不
 归还pilot/formal名额。超过预估/300h的实际用量照实结算，不能为使数字好看拒记。
 Optimizer-step超cap也保留成本并标出，不能冒充合同通过；操作员须修复根因。
+实际硬中断未留下完整terminal时，FAILED/INTERRUPTED可显式记录未知cursor=null，
+成本仍照实计入；单列unknown cursor，不伪造0步，也不允许以未知cursor称COMPLETED
+或NOT_STARTED。原整数cursor接口不变，重启仍需实际原始证据和checkpoint恢复。
 账本不会自动kill进程、判断关系真值、选择seed或关闭科学acceptance gate。
 
 profile必须0 optimizer updates；真实训练不能藏在未计数profile中。若完整training
@@ -40,6 +43,8 @@ JSON中的`scientific_or_launch_authority:false`明确是成本证据，不替
 生产外部controller的实际spawn/timeout/settlement仍要有真实收据，软件模拟账本
 和测试中模拟90 reservations不等于启动了90个实验。所有数值/模型/资格仍只在
 登记服务器；公开只含源代码、合成bookkeeping测试和不敏感说明。
+新[Linux process controller](PHASESET_V2_STUDY_PROCESS.md)提供实际执行接入；
+其本身不授科学admission，真实nativeGPU训练还需独立资格和数据/关系合同。
 
 ## 本次实际限定资格
 
@@ -59,5 +64,7 @@ CUDA分配前HOLD，计0。这是保守历史成本，不是精确allocation计
 唯一私有生产journal现在history34、pending0、formal0、pilot0、formalstepfreeze
 未记录。旧失败/资产/收据保留，未新增GPU分配/优化器步骤、重启或结构返工。
 
-这些软件测试和一次历史bootstrap不替代真实production controller资格、完整
-training profile、既定pilot/87阶段学习、two-human关系核实、结果统计或论文交付。
+这些bookkeeping测试和一次历史bootstrap本身不替代production controller资格。
+新controller另已完成[23项实际Linux软件资格](PHASESET_V2_STUDY_PROCESS.md)，
+仍不替代native GPU接入、完整training profile、既定pilot/87阶段学习、two-human
+关系核实、结果统计或论文交付。
diff --git a/docs/PHASESET_V2_STUDY_PROCESS.md b/docs/PHASESET_V2_STUDY_PROCESS.md
new file mode 100644
index 0000000..2884770
--- /dev/null
+++ b/docs/PHASESET_V2_STUDY_PROCESS.md
@@ -0,0 +1,50 @@
+# Linux owned-process执行与真实成本结算
+
+`run_budgeted_process`把已实现的单一StudyBudget接到实际子进程生命周期：先读
+GPU占用/其他compute，取得本研究per-GPU lease，再检查；完整wall envelope预约
+后spawn前又查一次。不启动SSH、不使用shell=True、不自动重试，也不授科学许可。
+UUID、visible cards、GPU数量和timeout+两份TERM grace+exit-verification envelope必须
+相同；CPU1/PYTHONHASHSEED=0固定。CPU profile可无GPU；训练仍需真实外部admission。
+
+每child新POSIX session，只有这个session被TERM/KILL。Linux WNOWAIT保留leader
+PID直到所有同组live进程/CUDA持有者退出验证后再reap，避免PID过早回收；他人的
+进程不终止。已登记训练程序不得daemonize/setsid逃出session；这是当前实际Python
+parent host调用约定，不声称支持任意daemon或Windows生产服务。
+子session由已存在的GNU timeout独立计时/TERM/KILL，父controller被SIGKILL也
+不留下无限运行的child；账本仍保留其pending，不能因父死亡自动退款。controller
+自身SIGINT/SIGTERM由main-thread暂存，拿到childhandle后先清理/结算，再恢复
+原handler并报告中断；不在spawn中途因signal故意丢失handle。
+
+正常退出后也检查遗留descendant；有遗留则清理并FAILED，不假称成功。忽略TERM
+会在固定grace后KILL；不能证实退出、startup handle丢失或证据写盘失败时保留整
+份pending预约，不猜PID/不自动退款。未来repair必须读原日志和实际状态，不盲重启。
+只有确认termination之后，实测monotonic分配wall×全GPU数结算，失败/中断也收费。
+
+`process-terminal.json`保存实际child exit、cleanup、host cursor和成本；单独
+`settlement.json`只能在journal真的settle后出现。若结算后第二份收据写失败，
+必须以实际journal为准，不重复收费。原primary错误不被cleanup/terminal secondary
+覆盖。stderr/stdout和所有失败目录保留；没有动作/数据/模型资产公开。
+
+`ContinuousParentTrainingHost`及继承它的`LiteratureParentTrainingHost`的
+terminal `outcome/global_step`直接消费，不兼容被替代的legacy `Host` schema，不加载Torch
+checkpoint猜optimizer进度。无/截断terminal记录cursor=null而非0、不得COMPLETED；
+StudyBudget仍记录已知实际成本，并单列unknown cursor。它不把未知进度转换成
+真实结果，也不为restart挑seed、设置floor/yaw或自动构造两真人CF许可。
+
+软件验证只用真实stdlib子进程和合成journal；fake-card参数只测试账务，非实际
+CUDA分配/硬件资格。实际GPU profile、完整native host/pilot、87datedfreeze、真人
+关系核实及真实论文结果仍需要各自外部证据。当前没有新增正式/pilot训练。
+
+## 登记服务器实际软件资格
+
+2026-09-27一次CPU1/interop1/noCUDA/nice10/16GiB/warmenv有界执行，
+23项全部PASS，10.90秒，0skip/0xfail；numeric/source-check/exit全0、stderr0。
+19项新process cases及4项定向budget cases，真实stdlib child和信号，不是
+CUDA训练或模拟日志替代真实进程。覆盖父controller被SIGKILL后的独立watchdog、
+自身SIGTERM、忽略TERM、残留descendant、WNOWAIT、缺失/截断terminal、
+三次GPU gate/lease碰撞、spawn歧义、PID/terminal/settle写盘及cleanup失败。
+两个已有实际base/residual host terminal均固定SHA并成功读出真实global_step=4；
+未重跑旧模型。整个资格前后同一生产journal摘要/原event SHA一致，无新增预约、
+成本或native optimizer；所有故障仅发生于隔离合成fixture，原失败证据保留。
+Fresh ARIS限定Sol/xhigh复核none，same-family仍provisional。Linux生命周期软件
+资格已闭合；实际native训练仍0/87、pilot0/12，其接入/科学资格不因此闭合。
diff --git a/src/phaseset_core/study_budget.py b/src/phaseset_core/study_budget.py
index 8a8ba72..b893932 100644
--- a/src/phaseset_core/study_budget.py
+++ b/src/phaseset_core/study_budget.py
@@ -246,6 +246,9 @@ def summary(self):
                     for key, row in state["settled"].items()
                     if row["optimizer_step_cap_exceeded"]
                 ),
+                "unknown_optimizer_cursor_attempt_ids": sorted(
+                    key for key, row in state["settled"].items() if row["completed_steps"] is None
+                ),
                 "scientific_or_launch_authority": False,
             }
 
@@ -393,16 +396,19 @@ def settle(
         attempt_id: str,
         *,
         actual_wall_seconds: float,
-        completed_steps: int,
+        completed_steps: int | None,
         outcome: str,
         evidence: str,
     ):
         """Observed child termination; overspend is recorded, never suppressed."""
         _seconds(actual_wall_seconds, "actual allocated wall time")
-        _integer(completed_steps, "completed optimizer cursor")
+        if completed_steps is not None:
+            _integer(completed_steps, "completed optimizer cursor")
         _text(evidence, "actual process/terminal evidence")
         if outcome not in ("COMPLETED", "FAILED", "INTERRUPTED", "NOT_STARTED"):
             raise StudyBudgetError("explicit terminal outcome is required")
+        if completed_steps is None and outcome in ("COMPLETED", "NOT_STARTED"):
+            raise StudyBudgetError("success or unstarted needs a known optimizer cursor")
         with _AttemptLease.acquire(self.root):
             state = self._state()
             reservation = state["pending"].get(attempt_id)
@@ -421,7 +427,8 @@ def settle(
                 {
                     "attempt_id": attempt_id,
                     "outcome": outcome,
-                    "optimizer_step_cap_exceeded": completed_steps > reservation["planned_steps"],
+                    "optimizer_step_cap_exceeded": completed_steps is not None
+                    and completed_steps > reservation["planned_steps"],
                     "completed_steps": completed_steps,
                     "actual_wall_seconds": actual_wall_seconds,
                     "actual_gpu_seconds": actual_wall_seconds * reservation["gpu_count"],
diff --git a/src/phaseset_core/study_process.py b/src/phaseset_core/study_process.py
new file mode 100644
index 0000000..8abb8ca
--- /dev/null
+++ b/src/phaseset_core/study_process.py
@@ -0,0 +1,428 @@
+"""Linux owned-process timeout/cleanup and persistent study-cost settlement.
+
+No SSH, shell evaluation, model or automatic retry. Scientific admission stays
+with the existing private study operator; accounting never grants it.
+"""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+import errno
+import json
+import os
+from pathlib import Path
+import re
+import signal
+import subprocess
+import sys
+import threading
+import time
+
+from .host import _AttemptLease, _write_once_json
+from .study_budget import StudyBudget
+
+
+class StudyProcessError(RuntimeError):
+    """Execution/cleanup evidence could not close; never retry automatically."""
+
+
+class StudyResourceHold(StudyProcessError):
+    """A requested card is not actually idle; do not preempt others."""
+
+
+@contextmanager
+def _termination_guard():
+    received = []
+    previous = {sig: signal.getsignal(sig) for sig in (signal.SIGINT, signal.SIGTERM)}
+
+    def record(signum, frame):
+        received.append(signum)
+
+    try:
+        for sig in previous:
+            signal.signal(sig, record)
+        yield received
+    finally:
+        for sig, handler in previous.items():
+            signal.signal(sig, handler)
+
+
+def check_gpu_free(uuids: tuple[str, ...]):
+    if not uuids:
+        return
+    rows = subprocess.check_output(
+        ["nvidia-smi", "--query-gpu=uuid,memory.used", "--format=csv,noheader,nounits"],
+        text=True,
+        timeout=10,
+    ).splitlines()
+    memory = {row.split(",")[0].strip(): int(row.split(",")[1]) for row in rows}
+    compute = subprocess.check_output(
+        ["nvidia-smi", "--query-compute-apps=gpu_uuid,pid", "--format=csv,noheader,nounits"],
+        text=True,
+        timeout=10,
+    ).splitlines()
+    if any(uuid not in memory or memory[uuid] >= 500 for uuid in uuids) or any(
+        row.split(",")[0].strip() in uuids for row in compute if row.strip()
+    ):
+        raise StudyResourceHold("requested GPU is not idle; no foreign process is stopped")
+
+
+@contextmanager
+def _gpu_leases(budget, uuids):
+    leases = []
+    try:
+        for uuid in sorted(uuids):
+            root = budget.root / "gpu-leases" / uuid
+            root.mkdir(parents=True, exist_ok=True, mode=0o700)
+            leases.append(_AttemptLease.acquire(root))
+        yield
+    finally:
+        primary = sys.exception()
+        first_error = None
+        for lease in reversed(leases):
+            try:
+                lease.release()
+            except BaseException as error:
+                if primary is not None:
+                    primary.add_note(f"GPU lease release also failed: {error!r}")
+                elif first_error is None:
+                    first_error = error
+        if first_error is not None:
+            raise first_error
+
+
+def _live_group(group_id):
+    live = set()
+    for path in Path("/proc").glob("[0-9]*/stat"):
+        try:
+            raw = path.read_text()
+        except (FileNotFoundError, ProcessLookupError):
+            continue
+        fields = raw[raw.rfind(")") + 2 :].split()
+        if int(fields[2]) == group_id and fields[0] != "Z":
+            live.add(int(path.parent.name))
+    return live
+
+
+def _owned_gpu_clear(uuids, seen, *, timeout):
+    if not uuids:
+        return True
+    rows = subprocess.check_output(
+        ["nvidia-smi", "--query-compute-apps=gpu_uuid,pid", "--format=csv,noheader,nounits"],
+        text=True,
+        timeout=timeout,
+    ).splitlines()
+    return not any(
+        row.split(",")[0].strip() in uuids and int(row.split(",")[1]) in seen
+        for row in rows
+        if row.strip()
+    )
+
+
+def _leader_exited(process):
+    # Keep the leader unreaped until group cleanup is verified: its PID/PGID
+    # cannot be recycled into somebody else's process group while we signal it.
+    return os.waitid(os.P_PID, process.pid, os.WEXITED | os.WNOHANG | os.WNOWAIT) is not None
+
+
+def _close_owned_group(process, uuids, grace, cleanup_seconds):
+    """TERM then KILL this fresh session only; verify group and CUDA exit."""
+    started = time.monotonic()
+    force_at = started + grace
+    deadline = force_at + cleanup_seconds
+    seen = {process.pid}
+    live = _live_group(process.pid)
+    seen.update(live)
+    descendants_after_leader_exit = _leader_exited(process) and bool(live)
+    if live:
+        try:
+            os.killpg(process.pid, signal.SIGTERM)
+        except ProcessLookupError:
+            pass
+    forced = False
+    while time.monotonic() < deadline:
+        leader_exited = _leader_exited(process)
+        live = _live_group(process.pid)
+        seen.update(live)
+        if not live and leader_exited:
+            remaining = deadline - time.monotonic()
+            if remaining > 0 and _owned_gpu_clear(uuids, seen, timeout=min(2, remaining)):
+                process.wait(timeout=0)
+                return {
+                    "observed_owned_pids": sorted(seen),
+                    "owned_group_and_cuda_exit_verified": True,
+                    "forced_kill": forced,
+                    "descendants_after_leader_exit": descendants_after_leader_exit,
+                }
+        if live and not forced and time.monotonic() >= force_at:
+            try:
+                os.killpg(process.pid, signal.SIGKILL)
+            except ProcessLookupError:
+                pass
+            forced = True
+        time.sleep(0.05)
+    raise StudyProcessError("owned group/CUDA exit unverified; keep the full budget reservation")
+
+
+def _cursor(terminal_path):
+    try:
+        value = json.loads(terminal_path.read_bytes())
+        step, outcome = value["global_step"], value["outcome"]
+        if (
+            type(step) is not int
+            or step < 0
+            or outcome not in ("COMPLETED", "FAILED", "INTERRUPTED")
+        ):
+            raise ValueError("invalid host terminal cursor/outcome")
+        return step, outcome, None
+    except (OSError, ValueError, KeyError, TypeError) as error:
+        return None, None, f"{type(error).__name__}: {error}"
+
+
+def run_budgeted_process(
+    budget: StudyBudget,
+    *,
+    process_directory: Path,
+    argv: tuple[str, ...],
+    working_directory: Path,
+    environment: dict[str, str],
+    reservation: dict,
+    gpu_uuids: tuple[str, ...],
+    timeout_seconds: int,
+    kill_grace_seconds: int,
+    cleanup_seconds: int,
+    host_terminal_path: Path,
+    bindings: dict,
+):
+    """One immutable process attempt, never a retry or a scientific grant.
+
+    Registered children must remain in their fresh POSIX session (no daemon/
+    setsid escape). ContinuousParentTrainingHost and its literature subclass
+    produce the outcome/global_step terminal cursor consumed here, not the
+    superseded legacy Host terminal schema. Missing/truncated evidence means
+    FAILED and unknown cursor,
+    not zero updates or successful training; observed cost is still charged.
+    """
+    if sys.platform != "linux":
+        raise StudyProcessError("production owned-group controller requires Linux /proc")
+    if threading.current_thread() is not threading.main_thread():
+        raise StudyProcessError("each production controller runs in its own main-thread process")
+    for value in (timeout_seconds, kill_grace_seconds, cleanup_seconds):
+        if type(value) is not int or value < 1:
+            raise ValueError(
+                "timeout, TERM grace and exit-verification envelope are positive integers"
+            )
+    if (
+        type(argv) is not tuple
+        or not argv
+        or any(type(arg) is not str or not arg for arg in argv)
+        or not Path(argv[0]).is_absolute()
+        or type(gpu_uuids) is not tuple
+        or len(set(gpu_uuids)) != len(gpu_uuids)
+        or any(
+            type(uuid) is not str or not re.fullmatch(r"GPU-[A-Za-z0-9-]+", uuid)
+            for uuid in gpu_uuids
+        )
+        or reservation.get("gpu_count") != len(gpu_uuids)
+        or reservation.get("wall_time_limit_seconds")
+        != timeout_seconds + 2 * kill_grace_seconds + cleanup_seconds
+    ):
+        raise ValueError("exact argv/card-count/full timeout envelope required")
+    if (
+        environment.get("CUDA_VISIBLE_DEVICES") != ",".join(gpu_uuids)
+        or any(
+            environment.get(key) != "1"
+            for key in ("OMP_NUM_THREADS", "MKL_NUM_THREADS", "OPENBLAS_NUM_THREADS")
+        )
+        or environment.get("PYTHONHASHSEED") != "0"
+    ):
+        raise ValueError("fixed visible cards/CPU1/PYTHONHASHSEED=0 required")
+    root = Path(process_directory).resolve()
+    cwd = Path(working_directory).resolve()
+    terminal = Path(host_terminal_path).resolve()
+    if not cwd.is_dir() or terminal.exists():
+        raise ValueError("existing working directory and fresh host terminal required")
+    check_gpu_free(gpu_uuids)
+    with _gpu_leases(budget, gpu_uuids), _termination_guard() as received_signals:
+        check_gpu_free(gpu_uuids)
+        root.mkdir(mode=0o700, exist_ok=False)
+        lease = _AttemptLease.acquire(root)
+        primary = None
+        try:
+            _write_once_json(
+                root / "launch.json",
+                {
+                    "argv": argv,
+                    "working_directory": str(cwd),
+                    "reservation": reservation,
+                    "gpu_uuids": gpu_uuids,
+                    "bindings": bindings,
+                    "host_terminal_path": str(terminal),
+                    "scientific_or_launch_authority": False,
+                },
+            )
+            with (
+                (root / "stdout.log").open("xb") as stdout,
+                (root / "stderr.log").open("xb") as stderr,
+            ):
+                budget.reserve(**reservation)
+                process, started, timed_out, cleanup = None, None, False, None
+                try:
+                    check_gpu_free(gpu_uuids)
+                    if received_signals:
+                        raise KeyboardInterrupt("controller terminated before child spawn")
+                    started = time.monotonic()
+                    process = subprocess.Popen(
+                        (
+                            "/usr/bin/timeout",
+                            f"--kill-after={kill_grace_seconds}s",
+                            f"{timeout_seconds}s",
+                            *argv,
+                        ),
+                        cwd=cwd,
+                        env=environment,
+                        stdin=subprocess.DEVNULL,
+                        stdout=stdout,
+                        stderr=stderr,
+                        start_new_session=True,
+                        shell=False,
+                    )
+                    _write_once_json(
+                        root / "process.json", {"pid": process.pid, "group_id": process.pid}
+                    )
+                    while not _leader_exited(process):
+                        if received_signals:
+                            raise KeyboardInterrupt("controller received termination signal")
+                        if time.monotonic() - started >= timeout_seconds + kill_grace_seconds:
+                            timed_out = True
+                            break
+                        time.sleep(0.05)
+                except BaseException as error:
+                    primary = error
+                if process is not None:
+                    try:
+                        cleanup = _close_owned_group(
+                            process, gpu_uuids, kill_grace_seconds, cleanup_seconds
+                        )
+                    except BaseException as error:
+                        if primary is not None:
+                            primary.add_note(f"owned cleanup also failed: {error!r}")
+                        else:
+                            primary = error
+                        try:
+                            _write_once_json(
+                                root / "cleanup-unverified.json",
+                                {
+                                    "pid": process.pid,
+                                    "reservation_retained": True,
+                                    "error": repr(primary),
+                                    "scientific_or_launch_authority": False,
+                                },
+                            )
+                        except BaseException as record_error:
+                            primary.add_note(
+                                f"cleanup evidence write also failed: {record_error!r}"
+                            )
+                        raise primary
+                elif started is not None and not (
+                    isinstance(primary, OSError)
+                    and primary.errno in (errno.ENOENT, errno.EACCES, errno.ENOEXEC, errno.ENOTDIR)
+                ):
+                    # Interrupted Popen may have created a child without returning
+                    # its handle. Do not invent zero allocation or kill guessed PIDs.
+                    try:
+                        _write_once_json(
+                            root / "cleanup-unverified.json",
+                            {
+                                "pid": None,
+                                "reservation_retained": True,
+                                "error": repr(primary),
+                                "scientific_or_launch_authority": False,
+                            },
+                        )
+                    except BaseException as record_error:
+                        primary.add_note(f"startup evidence write also failed: {record_error!r}")
+                    raise primary
+                wall = 0.0 if process is None else time.monotonic() - started
+                if received_signals and primary is None:
+                    primary = KeyboardInterrupt("controller received termination signal")
+                watchdog_forced_kill = (
+                    process is not None
+                    and process.returncode in (-9, 137)
+                    and wall >= timeout_seconds
+                )
+                timed_out = (
+                    timed_out
+                    or (process is not None and process.returncode == 124)
+                    or watchdog_forced_kill
+                )
+                step, host_outcome, cursor_error = (
+                    (0, None, None) if process is None else _cursor(terminal)
+                )
+                outcome = (
+                    "NOT_STARTED"
+                    if process is None
+                    else (
+                        "INTERRUPTED"
+                        if timed_out or isinstance(primary, (KeyboardInterrupt, SystemExit))
+                        else "FAILED"
+                        if primary is not None
+                        or process.returncode != 0
+                        or cursor_error is not None
+                        or cleanup["descendants_after_leader_exit"]
+                        or step > reservation["planned_steps"]
+                        else host_outcome
+                    )
+                )
+                report = {
+                    "attempt_id": reservation["attempt_id"],
+                    "outcome": outcome,
+                    "returncode": None if process is None else process.returncode,
+                    "timed_out": timed_out,
+                    "watchdog_forced_kill": watchdog_forced_kill,
+                    "received_termination_signals": received_signals,
+                    "global_step": step,
+                    "cursor_error": cursor_error,
+                    "actual_wall_seconds": wall,
+                    "actual_gpu_seconds": wall * reservation["gpu_count"],
+                    "cleanup": cleanup,
+                    "primary_error": None if primary is None else repr(primary),
+                    "scientific_or_launch_authority": False,
+                }
+                try:
+                    _write_once_json(root / "process-terminal.json", report)
+                    budget.settle(
+                        reservation["attempt_id"],
+                        actual_wall_seconds=wall,
+                        completed_steps=step,
+                        outcome=outcome,
+                        evidence=str(root / "process-terminal.json"),
+                    )
+                    _write_once_json(
+                        root / "settlement.json",
+                        {
+                            "actual_settlement_completed": True,
+                            "summary": budget.summary(),
+                        },
+                    )
+                except BaseException as record_error:
+                    if primary is not None:
+                        primary.add_note(
+                            f"terminal/budget evidence write also failed: {record_error!r}"
+                        )
+                        raise primary
+                    raise
+                if primary is not None:
+                    raise primary
+                return report
+        except BaseException as error:
+            primary = error
+            raise
+        finally:
+            try:
+                lease.release()
+            except BaseException as error:
+                if primary is not None:
+                    primary.add_note(f"process lease release also failed: {error!r}")
+                else:
+                    raise
diff --git a/tests/test_study_budget.py b/tests/test_study_budget.py
index 365169c..c31a804 100644
--- a/tests/test_study_budget.py
+++ b/tests/test_study_budget.py
@@ -387,6 +387,27 @@ def test_step_cap_violation_cannot_hide_actual_gpu_charge(tmp_path):
     assert summary["optimizer_step_cap_exceeded_attempt_ids"] == ["analytic-1"]
 
 
+def test_missing_optimizer_cursor_does_not_invent_zero_or_hide_failed_cost(tmp_path):
+    budget = create(tmp_path)
+    reserve(budget)
+    settle(budget, completed_steps=None)
+    summary = budget.summary()
+    assert summary["usage"]["gpu_seconds"] == 55
+    assert summary["unknown_optimizer_cursor_attempt_ids"] == ["analytic-1"]
+    assert summary["unsettled_attempt_ids"] == []
+    row = json.loads((budget.root / "event-00000002.json").read_text())
+    assert row["completed_steps"] is None
+
+
+def test_unknown_cursor_cannot_claim_completed_or_unstarted(tmp_path):
+    budget = create(tmp_path)
+    reserve(budget)
+    for outcome in ("COMPLETED", "NOT_STARTED"):
+        with pytest.raises(StudyBudgetError, match="known optimizer cursor"):
+            settle(budget, completed_steps=None, outcome=outcome, actual_wall_seconds=0)
+    assert budget.summary()["unsettled_attempt_ids"] == ["analytic-1"]
+
+
 def test_concurrent_reservations_cannot_double_spend_last_envelope(tmp_path):
     budget = create(tmp_path)
     barrier = threading.Barrier(3)
diff --git a/tests/test_study_process.py b/tests/test_study_process.py
new file mode 100644
index 0000000..a5ba33e
--- /dev/null
+++ b/tests/test_study_process.py
@@ -0,0 +1,440 @@
+"""Real Linux stdlib children; no model, native training or GPU is launched."""
+
+import errno
+import json
+import os
+from pathlib import Path
+import sys
+import signal
+import subprocess
+import threading
+import time
+
+import pytest
+
+from phaseset_core.host import _AttemptLease, HostConfigurationError
+import phaseset_core.study_process as module
+from phaseset_core.study_process import StudyProcessError, StudyResourceHold, run_budgeted_process
+from test_study_budget import create as create_budget
+
+
+linux = pytest.mark.skipif(
+    sys.platform != "linux", reason="registered production controller is Linux-only"
+)
+
+
+def inputs(tmp_path, *, timeout=2, body=None):
+    work = tmp_path / "work"
+    work.mkdir()
+    terminal = work / "host-terminal.json"
+    body = (
+        body
+        or "terminal.write_text(json.dumps({'global_step':0,'outcome':'COMPLETED'})); print('STDLIB_CHILD_EXECUTED')"
+    )
+    code = (
+        "import json,time,signal,subprocess,sys; from pathlib import Path; terminal=Path(sys.argv[1]); "
+        + body
+    )
+    env = dict(
+        os.environ,
+        CUDA_VISIBLE_DEVICES="",
+        OMP_NUM_THREADS="1",
+        MKL_NUM_THREADS="1",
+        OPENBLAS_NUM_THREADS="1",
+        PYTHONHASHSEED="0",
+        PYTHONDONTWRITEBYTECODE="1",
+    )
+    return {
+        "process_directory": work / "controller",
+        "working_directory": work,
+        "argv": (sys.executable, "-B", "-c", code, str(terminal)),
+        "environment": env,
+        "reservation": {
+            "attempt_id": "analytic-process-1",
+            "run_id": "analytic-process-profile-1",
+            "purpose": "profile",
+            "seed": 1729,
+            "gpu_count": 0,
+            "wall_time_limit_seconds": timeout + 5,
+            "planned_steps": 0,
+            "evidence": "stdlib process qualification only; no training/GPU",
+        },
+        "gpu_uuids": (),
+        "timeout_seconds": timeout,
+        "kill_grace_seconds": 1,
+        "cleanup_seconds": 3,
+        "host_terminal_path": terminal,
+        "bindings": {"stage": "analytic stdlib child; not native optimizer evidence"},
+    }
+
+
+def report(args):
+    return json.loads((args["process_directory"] / "process-terminal.json").read_text())
+
+
+def simulate_card(args, monkeypatch):
+    # Pure accounting/gate injection. The stdlib child never imports a CUDA library.
+    args["gpu_uuids"] = ("GPU-analytic-not-real-hardware",)
+    args["environment"]["CUDA_VISIBLE_DEVICES"] = args["gpu_uuids"][0]
+    args["reservation"]["gpu_count"] = 1
+    monkeypatch.setattr(module, "check_gpu_free", lambda uuids: None)
+    monkeypatch.setattr(module, "_owned_gpu_clear", lambda *args, **kwargs: True)
+
+
+def test_non_linux_rejects_before_any_launch(tmp_path, monkeypatch):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    monkeypatch.setattr(module.sys, "platform", "unsupported-qualification-platform")
+    with pytest.raises(StudyProcessError, match="requires Linux"):
+        run_budgeted_process(budget, **args)
+    assert not args["process_directory"].exists()
+    assert budget.summary()["unsettled_attempt_ids"] == []
+
+
+@linux
+def test_real_completed_child_logs_terminal_and_settlement_are_distinct(tmp_path):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    result = run_budgeted_process(budget, **args)
+    assert result["outcome"] == "COMPLETED" and result["global_step"] == 0
+    assert result["returncode"] == 0 and result["actual_wall_seconds"] > 0
+    assert (
+        result["actual_gpu_seconds"] == 0
+        and result["cleanup"]["owned_group_and_cuda_exit_verified"]
+    )
+    assert "STDLIB_CHILD_EXECUTED" in (args["process_directory"] / "stdout.log").read_text()
+    assert (args["process_directory"] / "stderr.log").stat().st_size == 0
+    assert json.loads((args["process_directory"] / "settlement.json").read_text())[
+        "actual_settlement_completed"
+    ]
+    assert budget.summary()["usage"]["gpu_seconds"] == 34
+    assert budget.summary()["unsettled_attempt_ids"] == []
+    with pytest.raises(ValueError, match="fresh host terminal"):
+        run_budgeted_process(budget, **args)
+
+
+@linux
+def test_real_failed_child_keeps_logs_and_simulated_allocated_card_wall_cost(tmp_path, monkeypatch):
+    budget = create_budget(tmp_path)
+    args = inputs(
+        tmp_path,
+        body="terminal.write_text(json.dumps({'global_step':0,'outcome':'FAILED'})); print('actual child failure',file=sys.stderr); sys.exit(7)",
+    )
+    simulate_card(args, monkeypatch)
+    result = run_budgeted_process(budget, **args)
+    assert result["outcome"] == "FAILED" and result["returncode"] == 7
+    assert result["actual_gpu_seconds"] == result["actual_wall_seconds"] > 0
+    assert budget.summary()["usage"]["gpu_seconds"] == 34 + result["actual_wall_seconds"]
+    assert "actual child failure" in (args["process_directory"] / "stderr.log").read_text()
+
+
+@linux
+def test_timeout_closes_real_child_and_does_not_invent_optimizer_cursor(tmp_path):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path, timeout=1, body="time.sleep(60)")
+    result = run_budgeted_process(budget, **args)
+    assert result["timed_out"] and result["outcome"] == "INTERRUPTED"
+    assert result["global_step"] is None and result["cleanup"]["owned_group_and_cuda_exit_verified"]
+    assert budget.summary()["unknown_optimizer_cursor_attempt_ids"] == ["analytic-process-1"]
+    pid = json.loads((args["process_directory"] / "process.json").read_text())["pid"]
+    assert module._live_group(pid) == set()
+
+
+@linux
+def test_ignored_term_is_forced_killed_and_exit_is_actually_verified(tmp_path):
+    budget = create_budget(tmp_path)
+    args = inputs(
+        tmp_path, timeout=1, body="signal.signal(signal.SIGTERM,signal.SIG_IGN); time.sleep(60)"
+    )
+    result = run_budgeted_process(budget, **args)
+    assert result["timed_out"] and (
+        result["cleanup"]["forced_kill"] or result["watchdog_forced_kill"]
+    )
+    assert result["returncode"] == -9
+
+
+@linux
+def test_exited_leader_does_not_leave_own_descendant_or_claim_completed(tmp_path, monkeypatch):
+    budget = create_budget(tmp_path)
+    args = inputs(
+        tmp_path,
+        body="subprocess.Popen([sys.executable,'-c','import time; time.sleep(60)']); terminal.write_text(json.dumps({'global_step':0,'outcome':'COMPLETED'}))",
+    )
+    original_kill = module.os.killpg
+    observed = []
+
+    def checked_kill(group, sig):
+        raw = (Path("/proc") / str(group) / "stat").read_text()
+        state = raw[raw.rfind(")") + 2 :].split()[0]
+        assert state == "Z"  # Leader PID retained, not available for foreign reuse.
+        observed.append(group)
+        return original_kill(group, sig)
+
+    monkeypatch.setattr(module.os, "killpg", checked_kill)
+    result = run_budgeted_process(budget, **args)
+    assert result["returncode"] == 0 and result["outcome"] == "FAILED"
+    assert result["cleanup"]["descendants_after_leader_exit"]
+    assert len(result["cleanup"]["observed_owned_pids"]) == 2
+    assert observed
+    assert module._live_group(result["cleanup"]["observed_owned_pids"][0]) == set()
+
+
+@linux
+@pytest.mark.parametrize("body", ["pass", "terminal.write_text('{')"])
+def test_missing_or_truncated_host_terminal_is_not_success_or_zero_updates(tmp_path, body):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path, body=body)
+    result = run_budgeted_process(budget, **args)
+    assert result["returncode"] == 0 and result["outcome"] == "FAILED"
+    assert result["global_step"] is None and result["cursor_error"]
+    assert budget.summary()["unsettled_attempt_ids"] == []
+
+
+@linux
+def test_busy_card_holds_before_creating_attempt_or_reserving(tmp_path, monkeypatch):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    simulate_card(args, monkeypatch)
+
+    def busy(uuids):
+        raise StudyResourceHold("injected other compute, no foreign process stopped")
+
+    monkeypatch.setattr(module, "check_gpu_free", busy)
+    with pytest.raises(StudyResourceHold, match="other compute"):
+        run_budgeted_process(budget, **args)
+    assert not args["process_directory"].exists()
+    assert budget.summary()["unsettled_attempt_ids"] == []
+
+
+@linux
+def test_card_busy_after_reservation_settles_only_verified_unstarted_zero(tmp_path, monkeypatch):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    simulate_card(args, monkeypatch)
+    calls = []
+
+    def race(uuids):
+        calls.append(uuids)
+        if len(calls) == 3:
+            raise StudyResourceHold("actual pre-spawn gate fixture")
+
+    monkeypatch.setattr(module, "check_gpu_free", race)
+    with pytest.raises(StudyResourceHold, match="pre-spawn"):
+        run_budgeted_process(budget, **args)
+    assert report(args)["outcome"] == "NOT_STARTED" and report(args)["actual_wall_seconds"] == 0
+    assert budget.summary()["unsettled_attempt_ids"] == []
+    assert not (args["process_directory"] / "process.json").exists()
+
+
+@linux
+def test_known_exec_failure_is_recorded_without_refunding_reservation_identity(
+    tmp_path, monkeypatch
+):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+
+    def missing_watchdog(*args, **kwargs):
+        raise FileNotFoundError(errno.ENOENT, "injected missing watchdog executable")
+
+    monkeypatch.setattr(module.subprocess, "Popen", missing_watchdog)
+    with pytest.raises(FileNotFoundError):
+        run_budgeted_process(budget, **args)
+    assert report(args)["outcome"] == "NOT_STARTED" and report(args)["global_step"] == 0
+    assert budget.summary()["unsettled_attempt_ids"] == []
+    assert len(list(budget.root.glob("event-*.json"))) == 3
+
+
+@linux
+def test_interrupted_spawn_without_returned_handle_does_not_invent_unstarted_refund(
+    tmp_path, monkeypatch
+):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+
+    def ambiguous(*args, **kwargs):
+        raise KeyboardInterrupt("injected Popen handle ambiguity; no real child in this case")
+
+    monkeypatch.setattr(module.subprocess, "Popen", ambiguous)
+    with pytest.raises(KeyboardInterrupt, match="handle ambiguity"):
+        run_budgeted_process(budget, **args)
+    assert budget.summary()["unsettled_attempt_ids"] == ["analytic-process-1"]
+    assert (
+        json.loads((args["process_directory"] / "cleanup-unverified.json").read_text())["pid"]
+        is None
+    )
+    assert not (args["process_directory"] / "settlement.json").exists()
+
+
+@linux
+def test_own_controller_sigterm_closes_child_settles_and_restores_handler(tmp_path):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path, timeout=10, body="time.sleep(60)")
+    old_handler = signal.getsignal(signal.SIGTERM)
+    emitted = []
+
+    def send_own_signal():
+        deadline = time.monotonic() + 5
+        while time.monotonic() < deadline:
+            if (args["process_directory"] / "process.json").exists():
+                emitted.append(True)
+                os.kill(os.getpid(), signal.SIGTERM)
+                return
+            time.sleep(0.02)
+
+    sender = threading.Thread(target=send_own_signal)
+    sender.start()
+    try:
+        with pytest.raises(KeyboardInterrupt, match="termination signal"):
+            run_budgeted_process(budget, **args)
+    finally:
+        sender.join(timeout=6)
+    assert emitted and not sender.is_alive()
+    assert signal.getsignal(signal.SIGTERM) == old_handler
+    assert report(args)["outcome"] == "INTERRUPTED"
+    assert report(args)["received_termination_signals"] == [signal.SIGTERM]
+    assert budget.summary()["unsettled_attempt_ids"] == []
+
+
+@linux
+def test_autonomous_watchdog_stops_child_after_controller_sigkill(tmp_path):
+    args = inputs(tmp_path, timeout=1, body="time.sleep(60)")
+    encoded = json.dumps(
+        {key: str(value) if isinstance(value, Path) else value for key, value in args.items()}
+    )
+    code = (
+        "import json,sys; from pathlib import Path; from test_study_budget import create; "
+        "from phaseset_core.study_process import run_budgeted_process; "
+        "args=json.loads(sys.argv[1]); "
+        "args.update({k:Path(args[k]) for k in ('process_directory','working_directory','host_terminal_path')}); "
+        "args['argv']=tuple(args['argv']); args['gpu_uuids']=tuple(args['gpu_uuids']); "
+        "run_budgeted_process(create(Path(sys.argv[2])),**args)"
+    )
+    controller = subprocess.Popen(
+        (sys.executable, "-B", "-c", code, encoded, str(tmp_path)),
+        env=args["environment"],
+        stdout=subprocess.DEVNULL,
+        stderr=subprocess.DEVNULL,
+        start_new_session=True,
+    )
+    watchdog_pid, start_ticks = None, None
+    try:
+        deadline = time.monotonic() + 20
+        pid_path = args["process_directory"] / "process.json"
+        while not pid_path.exists() and time.monotonic() < deadline:
+            assert controller.poll() is None
+            time.sleep(0.02)
+        assert pid_path.exists()
+        watchdog_pid = json.loads(pid_path.read_text())["pid"]
+        raw = (Path("/proc") / str(watchdog_pid) / "stat").read_text()
+        start_ticks = raw[raw.rfind(")") + 2 :].split()[19]
+        controller.kill()
+        assert controller.wait(timeout=5) == -9
+        deadline = time.monotonic() + 5
+        while module._live_group(watchdog_pid) and time.monotonic() < deadline:
+            time.sleep(0.05)
+        assert module._live_group(watchdog_pid) == set()
+        from phaseset_core.study_budget import StudyBudget
+
+        assert StudyBudget(tmp_path / "budget").summary()["unsettled_attempt_ids"] == [
+            "analytic-process-1"
+        ]
+        assert not (args["process_directory"] / "settlement.json").exists()
+    finally:
+        if controller.poll() is None:
+            controller.kill()
+            controller.wait(timeout=5)
+        if watchdog_pid is not None and module._live_group(watchdog_pid):
+            path = Path("/proc") / str(watchdog_pid) / "stat"
+            if path.exists():
+                raw = path.read_text()
+                if raw[raw.rfind(")") + 2 :].split()[19] == start_ticks:
+                    os.killpg(watchdog_pid, signal.SIGKILL)
+
+
+@linux
+def test_pid_receipt_write_failure_still_closes_real_owned_child_and_preserves_primary(
+    tmp_path, monkeypatch
+):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path, body="time.sleep(60)")
+    original = module._write_once_json
+
+    def broken(path, value):
+        if path.name == "process.json":
+            raise OSError(errno.ENOSPC, "primary PID write disk fixture")
+        return original(path, value)
+
+    monkeypatch.setattr(module, "_write_once_json", broken)
+    with pytest.raises(OSError, match="primary PID write"):
+        run_budgeted_process(budget, **args)
+    assert report(args)["cleanup"]["owned_group_and_cuda_exit_verified"]
+    assert budget.summary()["unsettled_attempt_ids"] == []
+
+
+@linux
+def test_terminal_write_failure_preserves_reservation_after_child_exit(tmp_path, monkeypatch):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    original = module._write_once_json
+
+    def broken(path, value):
+        if path.name == "process-terminal.json":
+            raise OSError(errno.ENOSPC, "terminal disk fixture")
+        return original(path, value)
+
+    monkeypatch.setattr(module, "_write_once_json", broken)
+    with pytest.raises(OSError, match="terminal disk"):
+        run_budgeted_process(budget, **args)
+    pid = json.loads((args["process_directory"] / "process.json").read_text())["pid"]
+    assert module._live_group(pid) == set()
+    assert budget.summary()["unsettled_attempt_ids"] == ["analytic-process-1"]
+    assert not (args["process_directory"] / "settlement.json").exists()
+
+
+@linux
+def test_settlement_write_failure_is_not_falsely_reported_as_settled(tmp_path, monkeypatch):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+
+    def broken(*args, **kwargs):
+        raise OSError(errno.ENOSPC, "budget event disk fixture")
+
+    monkeypatch.setattr(budget, "settle", broken)
+    with pytest.raises(OSError, match="budget event disk"):
+        run_budgeted_process(budget, **args)
+    assert (args["process_directory"] / "process-terminal.json").exists()
+    assert not (args["process_directory"] / "settlement.json").exists()
+    assert budget.summary()["unsettled_attempt_ids"] == ["analytic-process-1"]
+
+
+@linux
+def test_cleanup_uncertainty_never_refunds_even_if_test_child_really_exited(tmp_path, monkeypatch):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    original = module._close_owned_group
+
+    def uncertain(*args, **kwargs):
+        original(*args, **kwargs)
+        raise StudyProcessError("injected uncertain exit proof")
+
+    monkeypatch.setattr(module, "_close_owned_group", uncertain)
+    with pytest.raises(StudyProcessError, match="uncertain exit"):
+        run_budgeted_process(budget, **args)
+    assert json.loads((args["process_directory"] / "cleanup-unverified.json").read_text())[
+        "reservation_retained"
+    ]
+    assert budget.summary()["unsettled_attempt_ids"] == ["analytic-process-1"]
+
+
+@linux
+def test_cooperating_controller_gpu_lease_collision_cannot_double_launch(tmp_path, monkeypatch):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    simulate_card(args, monkeypatch)
+    gpu_root = budget.root / "gpu-leases" / args["gpu_uuids"][0]
+    gpu_root.mkdir(parents=True)
+    with _AttemptLease.acquire(gpu_root):
+        with pytest.raises(HostConfigurationError, match="held"):
+            run_budgeted_process(budget, **args)
+    assert not args["process_directory"].exists()
+    assert budget.summary()["unsettled_attempt_ids"] == []

From 4484c9d2562ebbbc88118cb2da918180bf7f74d0 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 12:13:03 -0500
Subject: [PATCH 56/95] Document native CPU lifecycle proof and amended
 relation supervision

---
 README.md                                     | 12 ++++
 RELEASE_FILES.sha256                          | 11 +--
 configs/phaseset_v2_relation_supervision.json | 48 +++++++++++++
 docs/PHASESET_V2_AMENDMENT.md                 |  5 ++
 docs/PHASESET_V2_ICMR_TARGET.md               |  3 +-
 docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md    | 67 +++++++++++++++++++
 ...SESET_V2_RELATION_SUPERVISION_AMENDMENT.md | 48 +++++++++++++
 docs/PHASESET_V2_STUDY_PROCESS.md             |  3 +
 8 files changed, 192 insertions(+), 5 deletions(-)
 create mode 100644 configs/phaseset_v2_relation_supervision.json
 create mode 100644 docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
 create mode 100644 docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md

diff --git a/README.md b/README.md
index f6a4fdb..d709161 100644
--- a/README.md
+++ b/README.md
@@ -43,6 +43,18 @@ multi-person skeleton sequence
 ## PhaseSet-V2 research upgrade — 2026-09-26
 
 The user-selected target is now [ACM ICMR 2027](docs/PHASESET_V2_ICMR_TARGET.md).
+
+The subsequent [relation-supervision amendment](docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md)
+uses existing official human retrieval annotations and permits separately
+disclosed weak machine supervision; no new human annotators are awaited.
+Machine labels are not independent motion ground truth or human-challenge
+success. The weak-CF implementation is still pending, not a completed run.
+
+A [complete-native-parent CPU profile](docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md)
+has now passed through the production lifecycle and journal: both 512-D systems,
+all 55 accepted windows and 10 official human rows, full-negative backward
+replay, unchanged weights, zero optimizer/GPU use. This is not formal training,
+GPU throughput or a paper retrieval result; formal stages remain **0/87**.
 The venue amendment changes submission format, not the scientific matrix or
 compute budget. The existing ICASSP manuscript is historical, not an ICMR-ready
 paper or a submitted result.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index fe12c7d..91bf9d4 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-99425351d401759961327e5285444c2d8025544d7cbc861e22274c12658e61fa	14223	README.md
+7f24570125dc7f904b982d3bfae4f66e277f9643cbc1cddba44f0300abc07b66	15015	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -17,6 +17,7 @@ e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
 cd6fb3868996ab909d8a382cfc62a35fa5510e465ca697aae0a142afc50b9498	26169	configs/phaseset_v2_experiment_matrix.json
+4d48ae2afe90b6954b5aafdd6afaa4037326e74a4b38c53ef844fc5c8f357793	2685	configs/phaseset_v2_relation_supervision.json
 59916f216450b4d79817f6c7f0574243e1092496e53a07a4f1a518047e427acd	994	configs/phaseset_v2_venue.json
 2970469ad0787e74ab4fabce2275486eb50c30c38f3cb7cf8f2800551d16991f	9540	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
@@ -69,23 +70,25 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
-55f261d2491bfdf5946bc7c640a551e8cb1f10c393203ea1fcf30ef85340d376	6116	docs/PHASESET_V2_AMENDMENT.md
+4f6373298ef558489e33a48ee7cf31090095cf19508c92b40ace682f461518bc	6458	docs/PHASESET_V2_AMENDMENT.md
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 6d50263826636377615082911b0de5afe871b8887f9419f95bbe4fb4db7f914d	3439	docs/PHASESET_V2_FROZEN_CLIP_ROWS.md
 b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
-795e9fd4fed59fdb05186cce6a0c146f1007846a6d4a2a7d43944bcea82c58ee	1183	docs/PHASESET_V2_ICMR_TARGET.md
+1e0d2fb0b35a6900dfb03af4b9bfe1701eb2c843f566c0f842b47b03b11b645e	1276	docs/PHASESET_V2_ICMR_TARGET.md
 004dfc49150cc66ba71f7c303dda5dd5f9ce16ca4a220e7558b2da4914e94336	3978	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
+9ead6b89eacf034b52dcc15544e25be56e225c1b0432475b66c1a300adfa562d	3961	docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
 c94c1538acb0e892c91c54ad1c6740073c9d75623465aa665ab1ea81b9b1ead3	2904	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
 3eb5f4237db4af02d49e8eb62a318ef68ac706bf14cc1b405bf9fa837e8d5705	7431	docs/PHASESET_V2_PARENT_HOST.md
 1eef1de33e2f1d193a6a02a0a982df0c9dea7b1f9406b4cbd155d22ef7378f51	3314	docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
+0075ca91defe63ecdc1050b560ee3ba2ff44fd0e95cef332f2ed22e4797fdfab	3386	docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
 8944faef4cfe50dfdccf0443abf739a510a94291184b2374392e0c039762f93a	5607	docs/PHASESET_V2_STUDY_BUDGET.md
-3bd7f8091f6673023fb41cafc6eb64be7a4ef5d39d117c9098e9379640d67be7	3882	docs/PHASESET_V2_STUDY_PROCESS.md
+0e7c300498c28e83f8df3c3d78e2be44e8d9270e0005e79f4ec9847cbfc8cc16	4079	docs/PHASESET_V2_STUDY_PROCESS.md
 81b47eff03531162dd2ab6b3d29a0ca9909326e7b13dec34776551d171b182c7	1727	docs/PHASESET_V2_TMR_PARENT_TRAINING.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
diff --git a/configs/phaseset_v2_relation_supervision.json b/configs/phaseset_v2_relation_supervision.json
new file mode 100644
index 0000000..ad4a0af
--- /dev/null
+++ b/configs/phaseset_v2_relation_supervision.json
@@ -0,0 +1,48 @@
+{
+  "schema": "phaseset-v2-relation-supervision-amendment-v1",
+  "amendment_date": "2026-09-27",
+  "status": "DECLARED_IMPLEMENTATION_PENDING_NOT_TRAINING_ADMISSION",
+  "reason": "No two new human annotators are available; the project owner requested existing annotations or LLM assistance instead of waiting.",
+  "supersedes": "The requirement to obtain two new human annotators before independent development or training preparation can proceed.",
+  "primary_retrieval": {
+    "positive_source": "original_official_human_holistic_rows",
+    "machine_text_in_primary_gallery": false,
+    "identity": "admitted_parent_annotation_family",
+    "sealed_test_unchanged": true
+  },
+  "counterfactual_training": {
+    "preferred_source": "existing_explicit_relation_annotations_if_their_label_semantics_and_rights_match",
+    "fallback_source": "machine_caption_contradiction",
+    "fallback_role": "weak_training_supervision_not_verified_motion_truth",
+    "model": "gpt-5.6-sol",
+    "inputs": "official_human_text_of_the_same_training_parent_only",
+    "forbidden_inputs": ["model_scores", "motion_features", "phase", "held_out_or_final_test_text"],
+    "same_pool_for_full_and_applicable_controls": true,
+    "no_new_data_or_training_slots": true,
+    "human_verified_false_from_machine_labels": false,
+    "implementation_requirement": "Separate weak-label admission and counts; never set the existing human-only verified_false bit merely because an LLM supplies a label.",
+    "prompt_and_selection_freeze": "before_the_relevant_pilot_or_training_start"
+  },
+  "relation_evaluation": {
+    "two_new_human_annotators_required_to_continue": false,
+    "machine_assisted_challenge_role": "exploratory_separate_from_primary_retrieval",
+    "old_200_pair_100_capture_8_component_target_met_by_llm": false,
+    "old_human_challenge_5pp_criterion": "UNASSESSABLE_WITHOUT_MATCHING_INDEPENDENT_LABELS",
+    "existing_positive_caption_is_proof_of_false_counterfactual": false,
+    "machine_accuracy_is_independent_ground_truth": false
+  },
+  "claim_limits": [
+    "Report primary retrieval against the unchanged official human labels.",
+    "CF-versus-no-CF retrieval effects may support the utility of weak training supervision, not verified relation understanding.",
+    "Do not claim human-verified relational challenge success or grounding from machine labels.",
+    "Do not mark the historical all-mechanism success target achieved by substituting an LLM for the missing humans."
+  ],
+  "unchanged": {
+    "formal_stages": 87,
+    "max_formal_starts": 90,
+    "pilot_stages": 12,
+    "seeds": [1729, 2718, 31415],
+    "gpu_hours": 300,
+    "component_splits_and_budget_history": true
+  }
+}
diff --git a/docs/PHASESET_V2_AMENDMENT.md b/docs/PHASESET_V2_AMENDMENT.md
index 64f936b..edb3d81 100644
--- a/docs/PHASESET_V2_AMENDMENT.md
+++ b/docs/PHASESET_V2_AMENDMENT.md
@@ -1,5 +1,10 @@
 # PhaseSet-V2 研究修订 — 2026-09-26
 
+2026-09-27 后续用户指令已形成
+[关系监督修订](PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md)：不再等待两位新
+标注者，已有人工主检索标签保持不变；机器关系监督只作披露的弱监督/探索性
+证据，不能替代下文历史的人类挑战成功条件。其接入仍须实际实现和资格。
+
 此修订执行用户指定的“升级至顶会程度”聊天。研究目标是区分
 “个体动作相近、协同关系不同”的原生多人场景。旧协议和历史证据保留；
 受影响的正式训练使用本修订的87阶段矩阵，不再叠加旧33-run矩阵。
diff --git a/docs/PHASESET_V2_ICMR_TARGET.md b/docs/PHASESET_V2_ICMR_TARGET.md
index 8621d9a..77ec1b9 100644
--- a/docs/PHASESET_V2_ICMR_TARGET.md
+++ b/docs/PHASESET_V2_ICMR_TARGET.md
@@ -11,6 +11,7 @@
 
 公开开发仓库继续保留；匿名投稿稿件不可直接链接识别作者的网站或公开owner。
 实验完整证据/可复现release继续作为项目交付，但不冒充大会允许的投稿附件。
-真人关系核实与数据权利、真实训练/统计仍须完成；不为换venue伪造或缩小研究。
+关系证据按后续[监督修订](PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md)披露：
+不等两位新标注者，机器不冒充真人挑战；数据权利、真实训练/统计不以此省略。
 新ICMR论文须由同一冻结aggregate生成，尚无真实结果时不填写结果数字。
 机器可读目标见[venue contract](../configs/phaseset_v2_venue.json)。
diff --git a/docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md b/docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
new file mode 100644
index 0000000..37a37dd
--- /dev/null
+++ b/docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
@@ -0,0 +1,67 @@
+# Complete native parent CPU profile through the production journal
+
+One real, bounded CPU invocation completed on 2026-09-27, 16:54:29–17:03:37 UTC.
+It joined the already qualified [complete-parent backward interface](PHASESET_V2_PARENT_TRAINING.md),
+development disk source, original human CLIP rows and
+[StudyProcess](PHASESET_V2_STUDY_PROCESS.md) to the existing production budget.
+This is **native CPU seam qualification**, not a trained result or GPU profile.
+
+## Actual input and scope
+
+Two native training parents were selected in immutable byte-key order before
+model scores, without timeline cropping or window sampling. They contain four
+actors each, 5,800 and 6,000 frames, all 26 and 29 accepted windows, and all five
+official human holistic rows each. The other development parents remained in
+the admitted task metadata; final-test inputs were absent. Main-training-only
+physical floors and the already closed body/physical/language caches were used.
+
+Full-width 512-D B2 and V2 performed a complete rectangular 2-motion × 10-text
+negative-gallery cache followed by RNG replay and vector–Jacobian backward.
+Each system loaded each entire parent once for caching and again for replay.
+The untrained seeded B2 state was reused; it was frozen for the V2 head.
+There was no optimizer, weight update, CF pair or training-yaw admission.
+
+| System | Complete cache + VJP seconds | Trainable gradient tensors |
+|---|---:|---:|
+| B2 | 264.5908735031262 | 88 |
+| V2 | 266.6414572298527 | 55 |
+
+Every trainable gradient was present and finite; at least one gradient element
+per system was nonzero. This does **not** assert every individual parameter
+tensor had a nonzero element. All model state bytes remained unchanged and the
+V2 frozen base received no gradients. The recorded peak process RSS was
+4,113,476 KiB. These single complete-batch CPU timings are not pure forward
+latencies, p50/p95, GPU throughput, a 128-parent batch cost, inference ratio or
+a prediction that all stages fit the 300-GPU-hour envelope.
+
+## Production lifecycle and costs
+
+FP32, seed 1729, CPU/interop one thread, no visible CUDA, offline warm environment,
+nice 10 and a 16-GiB address-space bound were fixed. The child had a 1,800-second
+watchdog and the production reservation included its full cleanup envelope.
+It exited normally: all three wrapper/numerical/source-check exits were zero,
+both stderr files empty, and source/invocation bytes unchanged. The controller
+verified its own process group had exited; no timeout or forced kill occurred.
+
+The existing production journal appended one real `RESERVE` and `SETTLE`,
+without rebootstrap or alteration of its original historical import. Actual
+controller wall time was 543.8673342310358 seconds, GPU cost zero, optimizer
+cursor zero, and no pending/unknown-cursor attempt remained. Historical cost
+stayed 34 conservatively accounted GPU seconds. Pilot and formal counts stayed
+**0/12 and 0/87**; dated formal step limits remain unfrozen.
+
+The private metadata-only archive was independently downloaded and verified:
+28 hashed files plus the receipt itself, 29 flat members total. Raw inputs,
+caption text, arrays, model assets and checkpoints were not in that archive or
+the public repository.
+
+- Receipt SHA-256: `d243b5a8547f97636d04d0ade3b521aa3b16573ee0ba824381d5bed766271a0b`.
+- Archive SHA-256: `43df64c735d5dca3fbdd3bac8a8201ea13c17937ba410cde5f03174ab7808e5a`.
+
+Fresh GPT-5.6-Sol/xhigh review of the three new private callers found no blocking
+or non-blocking issues; same-family semantic review remains provisional. The
+actual server evidence closes only the scope above. Real GPU profile, admitted
+training transforms, bounded pilot, 87-stage learning, sealed evaluation,
+statistics and paper delivery still require their own actual execution.
+The later [label-source amendment](PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md)
+cancels waiting for new annotators, but does not fabricate human CF evidence.
diff --git a/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md b/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
new file mode 100644
index 0000000..8438a4d
--- /dev/null
+++ b/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
@@ -0,0 +1,48 @@
+# PhaseSet-V2：已有标注优先，机器关系监督单独披露
+
+2026-09-27，项目负责人明确表示没有两位新标注者，要求使用 LLM 或已有标注
+继续研究。本修订取代“必须等到两位新标注者才能继续”的执行要求，不追溯
+改写原 handoff，不伪造人工记录，也不改变当前 87 阶段、12 pilot、三 seed、
+300 GPU-hours 上限或已有成本。对应机器可读规则见
+[supervision contract](../configs/phaseset_v2_relation_supervision.json)。
+
+## 主结果不换 ground truth
+
+确认性 full-gallery motion–language retrieval 继续使用官方原始人工 holistic
+文本、既定 parent 正例身份和组件划分。机器改写、机器融合、伪负例不进入
+human-only 主 gallery。已有人工正例不自动证明一个反事实句子为假：同一长
+capture 内可能同时发生它们，缺少一句描述也不等于事件不存在。
+
+优先复用已有、许可适配且语义确实匹配的显式关系标注；本修订不声称官方
+holistic 描述已经提供两人独立的真/假关系标签、actor/time grounding，或已经
+找到额外资产。不得为补标签无声增加数据集或训练阶段。
+
+## LLM 可提供弱监督，不是独立真值
+
+可用 GPT-5.6-Sol 读取同一训练 parent 的官方人工文本，提出带来源的最小
+关系对比。它不读取模型分数、骨架/频谱/phase、held-out 或最终 test 文本。
+固定 prompt、模型与解码参数、候选数及不依赖分数的纳入/拒绝规则，在相应
+pilot/训练前一次冻结；保留机器来源、原人工文本依据、输出及排除记录。
+无法判断的项不强行打真/假标签。不通过反复改写刷挑战集数量或模型成绩。
+
+若实施机器 CF 训练，其标签身份为 `machine_caption_contradiction`，只提供
+明确披露的弱训练监督。full 与适用的机制对照使用相同弱监督池和预算，A8
+仍关闭 CF 目标；不可将机器推断塞进原 human-only `verified_false=True`
+接口。需要独立的弱标签接入和计数，原真实人工接口与旧测试保留。当前修订
+只是输入/结论合同，**弱 CF 生成与训练接入尚未实现或资格通过**，不能据此
+宣称正式训练已启动。无 CF 的基础模型与独立开发工作不再等新真人标注。
+
+## 评估与结论边界
+
+机器辅助关系挑战只能单独标为探索性：报告其标签来源和覆盖/排除情况，
+不能称为人工核实的关系准确率，更不能以模型对机器标签的一致性证明模型
+理解真实动作。具有真正匹配的已有独立标注时，才按其真实适用范围另报结果。
+
+历史“200 对、100 capture、8 组件、两名盲人类审核”和挑战提升 5pp 目标不由
+机器数量替代；没有匹配独立标签时记为 **UNASSESSABLE**。这取消继续等待
+真人的执行阻碍，不把原全部机制成功标准偷偷降格为已通过。官方人工检索
+结果、预登记的结构/方向消融仍可支持各自的实证结论；CF 对 A8 的检索增益
+只能支持弱监督训练的效用，不单独支持人类核实的关系理解或 grounding。
+
+最终论文必须反映实际证据：弱关系监督和没有独立真人挑战是明确限制。
+负结果和证据不足仍交付完整代码、真实结果、统计与论文，不刷 seed/test。
diff --git a/docs/PHASESET_V2_STUDY_PROCESS.md b/docs/PHASESET_V2_STUDY_PROCESS.md
index 2884770..8b3da03 100644
--- a/docs/PHASESET_V2_STUDY_PROCESS.md
+++ b/docs/PHASESET_V2_STUDY_PROCESS.md
@@ -1,5 +1,8 @@
 # Linux owned-process执行与真实成本结算
 
+后续 [完整 native CPU profile](PHASESET_V2_NATIVE_PROCESS_PROFILE.md) 已实际接入
+同一生产 journal 并闭合；下文旧23项软件资格记录保留，不能代替GPU/训练结果。
+
 `run_budgeted_process`把已实现的单一StudyBudget接到实际子进程生命周期：先读
 GPU占用/其他compute，取得本研究per-GPU lease，再检查；完整wall envelope预约
 后spawn前又查一次。不启动SSH、不使用shell=True、不自动重试，也不授科学许可。

From c20ce8836393a6eb7e406a5228d97e849e4782bf Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 12:58:12 -0500
Subject: [PATCH 57/95] feat: separate weak counterfactual loss and
 complete-parent counts

---
 README.md                                     |   4 +-
 RELEASE_FILES.sha256                          |  16 +-
 configs/phaseset_v2_relation_supervision.json |   2 +-
 ...SESET_V2_RELATION_SUPERVISION_AMENDMENT.md |   7 +-
 docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md     |  72 ++++
 src/phaseset_core/continuous_parent_host.py   |  15 +-
 .../continuous_parent_training.py             | 111 +++++-
 src/phaseset_core/parent_clip_rows.py         |   5 +-
 src/phaseset_core/temporal_coordination.py    |  50 +++
 tests/test_parent_weak_cf.py                  | 325 ++++++++++++++++++
 10 files changed, 575 insertions(+), 32 deletions(-)
 create mode 100644 docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md
 create mode 100644 tests/test_parent_weak_cf.py

diff --git a/README.md b/README.md
index d709161..7449951 100644
--- a/README.md
+++ b/README.md
@@ -48,7 +48,9 @@ The subsequent [relation-supervision amendment](docs/PHASESET_V2_RELATION_SUPERV
 uses existing official human retrieval annotations and permits separately
 disclosed weak machine supervision; no new human annotators are awaited.
 Machine labels are not independent motion ground truth or human-challenge
-success. The weak-CF implementation is still pending, not a completed run.
+success. The [separate weak-CF loss/backward seam](docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md)
+has passed 37 scoped server checks; the actual generated training pool and its
+provenance are still pending. This is not a completed native training run.
 
 A [complete-native-parent CPU profile](docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md)
 has now passed through the production lifecycle and journal: both 512-D systems,
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 91bf9d4..5c91e12 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-7f24570125dc7f904b982d3bfae4f66e277f9643cbc1cddba44f0300abc07b66	15015	README.md
+7b8bdaefeef8d88d33368ac475b35b1981c5e4c44b9dd1d287caaed3f4645616	15188	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -17,7 +17,7 @@ e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
 cd6fb3868996ab909d8a382cfc62a35fa5510e465ca697aae0a142afc50b9498	26169	configs/phaseset_v2_experiment_matrix.json
-4d48ae2afe90b6954b5aafdd6afaa4037326e74a4b38c53ef844fc5c8f357793	2685	configs/phaseset_v2_relation_supervision.json
+a78e2cebaa50abd68d9f185bd5ad11d4619ea8603b80772c0f3ac433f4e9dfff	2691	configs/phaseset_v2_relation_supervision.json
 59916f216450b4d79817f6c7f0574243e1092496e53a07a4f1a518047e427acd	994	configs/phaseset_v2_venue.json
 2970469ad0787e74ab4fabce2275486eb50c30c38f3cb7cf8f2800551d16991f	9540	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
@@ -86,10 +86,11 @@ c94c1538acb0e892c91c54ad1c6740073c9d75623465aa665ab1ea81b9b1ead3	2904	docs/PHASE
 1eef1de33e2f1d193a6a02a0a982df0c9dea7b1f9406b4cbd155d22ef7378f51	3314	docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
-0075ca91defe63ecdc1050b560ee3ba2ff44fd0e95cef332f2ed22e4797fdfab	3386	docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
+bc2abbcded41e6a56c7302de6ef85139c9fb438e0637983a88ff0f67628babab	3472	docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
 8944faef4cfe50dfdccf0443abf739a510a94291184b2374392e0c039762f93a	5607	docs/PHASESET_V2_STUDY_BUDGET.md
 0e7c300498c28e83f8df3c3d78e2be44e8d9270e0005e79f4ec9847cbfc8cc16	4079	docs/PHASESET_V2_STUDY_PROCESS.md
 81b47eff03531162dd2ab6b3d29a0ca9909326e7b13dec34776551d171b182c7	1727	docs/PHASESET_V2_TMR_PARENT_TRAINING.md
+8ed656e8f4c6e1c9513bf69795275cba9948643cdc0c3ba47043602b30ece251	4370	docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
@@ -176,8 +177,8 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 80e6d497ff9bb4354664cb1a054cdb550ab222e92c841813afc27af6d3759495	5603	src/phaseset_core/continuous_base_retrieval.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
-f53d85531385a5c8e8fcf4b27f0c62bd02e08b295283332493cd86a0b000e647	29579	src/phaseset_core/continuous_parent_host.py
-cd9b85e1b3e576d23c04f15c5b6dad853110c2a7733b030ec2c6a3b1e6d874ef	15200	src/phaseset_core/continuous_parent_training.py
+55813c17f038b9999c136160da54d3f307fe6d59fe75648cddc5ce608941808a	29782	src/phaseset_core/continuous_parent_host.py
+296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
 6af5ecfcadc139acfdb3fe2d2e00995a40e90522a821172bc44e7a3604028dbc	10571	src/phaseset_core/continuous_retrieval.py
 a46004b4c5a94dc84029a6cf5a54631e614e67f680c506feb0b92b435702160b	6873	src/phaseset_core/continuous_training_input.py
@@ -202,7 +203,7 @@ c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phase
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 0f0309c54a5b48a0493bb24f97eda39ff93be657295e1b0ebcc3dd6e9a8ecd76	6565	src/phaseset_core/parent_capture.py
-fb417f794ee32b7e7777933ee2ef4ceeda9868ea82da62245beec53994560f77	3293	src/phaseset_core/parent_clip_rows.py
+be88afc2d543a9ceb227eda07046cc8e9221faf7af85888c1b303632da642dc8	3366	src/phaseset_core/parent_clip_rows.py
 f8e257a1cff56e93ff76229c2a4c47b1a85902a64b76338758178d111a2d3ecb	8322	src/phaseset_core/parent_disk_source.py
 be883315aa773e6c2d535ea981853ae92a791187338fd2b310b574f956174a82	7326	src/phaseset_core/parent_retrieval_task.py
 e0c9d77e9ccaf277785beb148a54afbdf542c607a6298e6b20db458828a32fe7	9331	src/phaseset_core/parent_run_monitor.py
@@ -221,7 +222,7 @@ eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phase
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
 b3c69e0c6b5b79adc0bc15472486e38df483ec4698ebe5b4fb064cd7a8f0968b	19796	src/phaseset_core/study_budget.py
 a984350a2838101fde448eee44d32f22eec5dfa8487637fa0d7941f99de82aec	17205	src/phaseset_core/study_process.py
-5604397a65a0032d2a14124d34ca3d96a2d6d67e5f4534732bb82f7a0c7b2546	23163	src/phaseset_core/temporal_coordination.py
+febde5f65ea132a9921973be393a6fbea715c6f9b1b1d9f5f8df3654cf007696	25153	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
 cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phaseset_core/tmr_language.py
@@ -255,6 +256,7 @@ fc0c27fb68c7d119d1cc7a48afeeff5a707cab9ec206eb5ad85c606e31656490	9443	tests/test
 487ee577859df867298041fe731166cd211506e5f211d3c508eeec5aa282640f	5825	tests/test_parent_disk_source.py
 32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
 cdf4d6dcea5c29e44ebb3f2f91945fd944a8026a7abe91eaa47beb59428f159c	12935	tests/test_parent_run_monitor.py
+d189e29ef4ef81b9604a352a7828ff97c789a79cafda18e8928056faf08a7825	13424	tests/test_parent_weak_cf.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
 9a05b2a025a694f94e158d2de74242d2a820efbc8973920001895114e7a894fb	14880	tests/test_periodic_cache_source_lineage.py
 8dccac5551ec4d519f98b4b81d8f10ba47bd1f19ece6168ce137c51dc2ca2f0e	18170	tests/test_periodic_capture_training_cache.py
diff --git a/configs/phaseset_v2_relation_supervision.json b/configs/phaseset_v2_relation_supervision.json
index ad4a0af..ef24d9c 100644
--- a/configs/phaseset_v2_relation_supervision.json
+++ b/configs/phaseset_v2_relation_supervision.json
@@ -1,7 +1,7 @@
 {
   "schema": "phaseset-v2-relation-supervision-amendment-v1",
   "amendment_date": "2026-09-27",
-  "status": "DECLARED_IMPLEMENTATION_PENDING_NOT_TRAINING_ADMISSION",
+  "status": "WEAK_LOSS_SEAM_QUALIFIED_POOL_PENDING_NOT_TRAINING_ADMISSION",
   "reason": "No two new human annotators are available; the project owner requested existing annotations or LLM assistance instead of waiting.",
   "supersedes": "The requirement to obtain two new human annotators before independent development or training preparation can proceed.",
   "primary_retrieval": {
diff --git a/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md b/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
index 8438a4d..54b15d7 100644
--- a/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
+++ b/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
@@ -28,9 +28,10 @@ pilot/训练前一次冻结；保留机器来源、原人工文本依据、输
 若实施机器 CF 训练，其标签身份为 `machine_caption_contradiction`，只提供
 明确披露的弱训练监督。full 与适用的机制对照使用相同弱监督池和预算，A8
 仍关闭 CF 目标；不可将机器推断塞进原 human-only `verified_false=True`
-接口。需要独立的弱标签接入和计数，原真实人工接口与旧测试保留。当前修订
-只是输入/结论合同，**弱 CF 生成与训练接入尚未实现或资格通过**，不能据此
-宣称正式训练已启动。无 CF 的基础模型与独立开发工作不再等新真人标注。
+接口。独立弱标签类型、loss、完整 parent backward 及事件计数已经实现，
+并通过 [37 项服务器软件检查](PHASESET_V2_WEAK_CF_TRAINING_SEAM.md)；原真实
+人工接口与旧测试保留。**实际弱 CF 生成、冻结训练池和来源准入仍待完成**，
+不能据此宣称正式训练已启动。无 CF 的基础模型与独立开发工作不再等新真人。
 
 ## 评估与结论边界
 
diff --git a/docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md b/docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md
new file mode 100644
index 0000000..4da6c42
--- /dev/null
+++ b/docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md
@@ -0,0 +1,72 @@
+# PhaseSet-V2：独立弱监督训练接口资格
+
+2026-09-27。按 [用户关系监督修订](PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md)
+实现软件接口，不再等待两位新标注者。不改变官方人工主检索 gallery、最终
+test、87 阶段矩阵、三 seed 或 300 GPU-hours 上限。
+
+## 已实现的边界
+
+`ParentWeakCounterfactualRows` 使用独立 `included_weak` mask，没有
+`verified_false` 字段。正例必须是同一 parent 的原始人工行，负例必须追加在
+训练 text pool 的人工 gallery 前缀之后；与该 parent 原人工正例完全相同的
+句子不能作为弱负例。索引和 mask 检查不证明一个句子在真实视频中为假。
+
+`weak_counterfactual_loss` 保留已登记 softplus margin 公式，独立于原人工
+verified-false 入口。`weak_coordination_objective` 的 variable-positive
+symmetric InfoNCE 只使用原人工前缀；额外弱句子不成为检索正例或主 gallery。
+基础模型仍拒绝 CF 训练输入。
+
+完整 parent cache/VJP/backward 和原 replay/RNG 路径支持独立弱行类型。
+`ParentBackwardResult.weak_counterfactual_count` 和主机 `weak_cf` 事件与原
+`verified_cf` 分开。它统计纳入的弱行数，不是独立真值数量；A8 的 CF weight
+为 0 时仍记录相同输入池的纳入数量，但不贡献 CF loss。旧六字段位置构造
+保持兼容，TMR/WaMo/MIME 的新 weak count 默认 0。
+
+## 一次真实服务器软件检查
+
+Fresh ARIS GPT-5.6-Sol/xhigh 受限复核无 blocking/non-blocking，仅同家族
+provisional。随后只运行一次隔离尝试，2026-09-27 17:48:16–17:51:30 UTC：
+CPU FP32、单计算线程/单 interop、nice 10、离线、无 CUDA、16 GiB 上限和
+900 秒 timeout。warm environment 没有重建，不分配或抢占其他 GPU。
+
+**37 passed、0 skipped、0 failures/errors，pytest 189.61 秒。** 31 项新测试
+加 6 项显式原接口检查，覆盖：
+
+- margin/mask 公式和梯度、全排除 exact zero、错误 source/column/type 拒绝；
+- 原人工前缀 InfoNCE、弱句子隔离、完整 dense-vs-replay 参数梯度一致；
+- 真正 analytic AdamW 主机更新、中断/resume 的 state/optimizer/scheduler/RNG
+  一致、弱与人工计数分离，以及 validation gallery 污染产生 FAILED terminal；
+- TMR/WaMo/MIME 既有 analytic 主机端点对新增事件字段的兼容性。
+
+包含一次 Torch nested-tensor prototype 警告，无异常；三 exit code 均为 0，
+stderr 空，source/invocation 前后逐字一致。19 个 hashed metadata 成员及
+receipt 共 20 个 flat 文件双端核验；仅元数据私有留存，没有公开数据或权重。
+Receipt SHA-256：
+`37ec17cb43d3c7706942cf7861c4a3753a415205dd9b9ab2789e30346e7f4ac7`。
+
+生产预算三个原事件及 summary 前后不变。正式 **0/87**、pilot **0/12**，历史
+GPU 使用仍 34 秒，本次新增 0 秒。测试中的 analytic optimizer 是实际执行，
+不是 native 训练、合格 pilot、收敛或论文检索成绩。
+
+## 未完成与不能声称的内容
+
+该接口不生成弱句子、不验证它们的来源/语义，也不把现有人工正例当成 false
+CF 的证明。实际训练池、固定生成 prompt/选择规则、私有生成记录和 CLIP
+编码准入仍待完成；当前 `ParentHumanClipRows` 继续只返回原人工行和空 human
+CF，没有偷偷注入机器标签。
+
+这些检查不关闭真实 native CF/yaw、GPU training profile、预算内 pilot 和
+87 阶段冻结/训练 gate，不证明任何关系理解或原人类挑战成功。
+
+公开重现本页的 37 项选择（在合适的独立 CPU 环境中）：
+
+```bash
+OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 CUDA_VISIBLE_DEVICES='' \
+PYTHONPATH=src:tests python -m pytest -q tests/test_parent_weak_cf.py \
+  tests/test_continuous_parent_training.py::test_complete_gallery_replay_matches_dense_loss_and_all_parameter_gradients \
+  tests/test_continuous_parent_training.py::test_rng_advances_once_and_mode_restores_after_complete_capture_replays \
+  tests/test_continuous_parent_host.py::test_host_runs_optimizer_selects_validation_and_preserves_full_parent_census \
+  tests/test_continuous_base_retrieval.py::test_base_host_stage_checkpoint_and_cf_contracts_are_explicit
+```
+
+上述命令不包含私有预算 caller，不会重新建立真实训练准入或数据来源证据。
diff --git a/src/phaseset_core/continuous_parent_host.py b/src/phaseset_core/continuous_parent_host.py
index 5331203..7b09b83 100644
--- a/src/phaseset_core/continuous_parent_host.py
+++ b/src/phaseset_core/continuous_parent_host.py
@@ -1,7 +1,7 @@
 """Complete-parent FP32 base/residual optimizer, validation and restart host.
 
 This runs real updates, not the superseded window-unit training recipe. The
-private operator still admits rights, physical floors, human CF provenance,
+private operator still admits rights, physical floors, human or weak CF provenance,
 hardware/budget and the dated 87-stage matrix. Constructing this host supplies
 none of those authorities. Literature baselines remain separate implementations.
 No final-test interface exists here.
@@ -24,6 +24,7 @@
 from .continuous_capture import PreparedContinuousCapture
 from .continuous_parent_training import (
     ParentCounterfactualRows,
+    ParentWeakCounterfactualRows,
     backward_loaded_parent_batch,
     parent_epoch_batches,
     parent_input_capture,
@@ -54,9 +55,10 @@ class ParentTrainingSource(Protocol):
 
     ``view`` must return the entire source and reproduce its augmentation on
     replay. Evaluation views must be unaugmented. ``text`` starts with all
-    exact human rows; training-only, genuinely verified CFs may follow. The
-    two-human truth records are bound by the private input manifest, not by
-    the returned bool bits. No test or held-out rows may be loaded incidentally.
+    exact human rows; training-only verified or explicitly weak CFs may follow
+    through distinct row types. The input manifest binds their actual sources,
+    not bool bits. Weak labels are never human truth or evaluation ground truth.
+    No test or held-out rows may be loaded incidentally.
     """
 
     def view(
@@ -65,7 +67,7 @@ def view(
 
     def text(
         self, labels: ParentRetrievalBatch, *, training: bool
-    ) -> tuple[FrozenClipTextBatch, ParentCounterfactualRows]: ...
+    ) -> tuple[FrozenClipTextBatch, ParentCounterfactualRows | ParentWeakCounterfactualRows]: ...
 
 
 @dataclass(frozen=True)
@@ -136,7 +138,7 @@ class ParentHostBindings:
     """Digests of the already admitted private execution inputs, not authority.
 
     The input manifest includes physical source/cache/floor/CLIP identities,
-    complete parent census, yaw rules and human verification provenance. The
+    complete parent census, yaw rules and human or weak-label provenance. The
     code manifest covers this host, the full scorer and private source adapter.
     Runtime binding comes from the real frozen environment qualification.
     """
@@ -582,6 +584,7 @@ def fit(
                         parents=len(labels.parents),
                         human_rows=result.retrieval_caption_count,
                         verified_cf=result.verified_counterfactual_count,
+                        weak_cf=result.weak_counterfactual_count,
                         gradient_norm=float(norm),
                         learning_rate=lr,
                     )
diff --git a/src/phaseset_core/continuous_parent_training.py b/src/phaseset_core/continuous_parent_training.py
index e82d978..8ba3e7a 100644
--- a/src/phaseset_core/continuous_parent_training.py
+++ b/src/phaseset_core/continuous_parent_training.py
@@ -4,7 +4,8 @@
 freeze, budget ledger, scheduler or checkpoint selector. It uses all human
 rows once per admitted parent, preserves a full effective-batch negative
 gallery, and replays one complete capture graph at a time. No optimizer is
-started here. Human verification of counterfactuals remains external.
+started here. Human and weak CFs have distinct types; neither bit proves its
+external provenance. Machine labels never acquire human verification here.
 """
 
 from __future__ import annotations
@@ -25,7 +26,7 @@
 from .frozen_clip_text import FrozenClipTextBatch
 from .objectives import variable_positive_symmetric_infonce
 from .parent_retrieval_task import ParentRetrievalBatch, ParentRetrievalTask
-from .temporal_coordination import coordination_objective
+from .temporal_coordination import coordination_objective, weak_coordination_objective
 from .training import OFFICIAL_SEEDS, _capture_rng, _restore_rng
 
 
@@ -143,6 +144,66 @@ def indices(
         )
 
 
+@dataclass(frozen=True)
+class ParentWeakCounterfactualRows:
+    """Machine-caption training targets, separate from human verified-false rows.
+
+    Positives must be original human rows of the same parent; negatives must
+    be appended training-only columns, never a gallery row. Inclusion is a
+    weak supervision decision, not motion truth. The private input manifest
+    binds the frozen generation policy, original text and machine provenance.
+    No generation, relevance judgement or human verification happens here.
+    """
+
+    source_keys: tuple[str, ...]
+    positive_columns: tuple[int, ...]
+    negative_columns: tuple[int, ...]
+    included_weak: tuple[bool, ...]
+
+    def indices(
+        self, labels: ParentRetrievalBatch, *, text_count: int, device: torch.device
+    ) -> tuple[Tensor, Tensor, Tensor, Tensor]:
+        count = len(self.source_keys)
+        if any(
+            type(value) is not tuple or len(value) != count
+            for value in (
+                self.source_keys,
+                self.positive_columns,
+                self.negative_columns,
+                self.included_weak,
+            )
+        ):
+            raise ValueError("weak CF source, column and inclusion tuples must align")
+        by_source = {key: index for index, key in enumerate(labels.motion_source_keys)}
+        human_count = len(labels.text_positive_keys)
+        for source, positive, negative, included in zip(
+            self.source_keys,
+            self.positive_columns,
+            self.negative_columns,
+            self.included_weak,
+            strict=True,
+        ):
+            if source not in by_source or type(included) is not bool:
+                raise ValueError("weak CF needs an admitted source and explicit bool inclusion")
+            if (
+                type(positive) is not int
+                or not 0 <= positive < human_count
+                or labels.text_positive_keys[positive]
+                != labels.motion_positive_keys[by_source[source]]
+            ):
+                raise ValueError("weak CF positive must be the same parent's original human row")
+            if type(negative) is not int or not human_count <= negative < text_count:
+                raise ValueError("weak CF negative must be appended outside the human gallery")
+        return (
+            torch.tensor(
+                [by_source[key] for key in self.source_keys], dtype=torch.int64, device=device
+            ),
+            torch.tensor(self.positive_columns, dtype=torch.int64, device=device),
+            torch.tensor(self.negative_columns, dtype=torch.int64, device=device),
+            torch.tensor(self.included_weak, dtype=torch.bool, device=device),
+        )
+
+
 @dataclass(frozen=True)
 class ParentBackwardResult:
     loss: float
@@ -151,6 +212,7 @@ class ParentBackwardResult:
     retrieval_caption_count: int
     verified_counterfactual_count: int
     replayed_captures: int
+    weak_counterfactual_count: int = 0
 
 
 def backward_parent_batch(
@@ -159,7 +221,7 @@ def backward_parent_batch(
     text_batch: FrozenClipTextBatch,
     labels: ParentRetrievalBatch,
     *,
-    counterfactuals: ParentCounterfactualRows,
+    counterfactuals: ParentCounterfactualRows | ParentWeakCounterfactualRows,
     cf_weight: float,
     margin: float,
 ) -> ParentBackwardResult:
@@ -214,7 +276,7 @@ def backward_loaded_parent_batch(
     text_batch: FrozenClipTextBatch,
     *,
     load_view: Callable[[str], ContinuousTrainingView | PreparedContinuousCapture],
-    counterfactuals: ParentCounterfactualRows,
+    counterfactuals: ParentCounterfactualRows | ParentWeakCounterfactualRows,
     cf_weight: float,
     margin: float,
     progress: Callable[[str, int], None] | None = None,
@@ -236,11 +298,26 @@ def backward_loaded_parent_batch(
         raise ValueError("complete parent labels must contain distinct admitted sources/families")
     text_count = validate_parent_text_batch(labels, text_batch)
     retrieval_count = len(labels.captions)
+    if type(counterfactuals) not in (ParentCounterfactualRows, ParentWeakCounterfactualRows):
+        raise TypeError("CF rows must declare human-verified or weak supervision explicitly")
+    is_weak = type(counterfactuals) is ParentWeakCounterfactualRows
     is_base = isinstance(system, ContinuousBaseRetrievalSystem)
     if is_base and (counterfactuals.source_keys or text_count != retrieval_count or cf_weight != 0):
         raise ValueError("base training uses only human retrieval rows and no CF objective")
     device = next(system.parameters()).device
     indices = counterfactuals.indices(labels, text_count=text_count, device=device)
+    if is_weak:
+        human_digests = {
+            parent.source_sha256: {
+                hashlib.sha256(text.encode("utf-8")).hexdigest() for text in parent.captions
+            }
+            for parent in labels.parents
+        }
+        for source, negative in zip(
+            counterfactuals.source_keys, counterfactuals.negative_columns, strict=True
+        ):
+            if text_batch.receipt.caption_rows[negative][2] in human_digests[source]:
+                raise ValueError("weak negative repeats an original human positive of its parent")
     positive_mask = labels.positive_mask(device=device)
     was_training = system.training
     system.train()
@@ -262,20 +339,29 @@ def backward_loaded_parent_batch(
         scores = torch.cat(cached).detach().requires_grad_(True)
         if not bool(torch.isfinite(scores).all()):
             raise ValueError("complete parent score cache is nonfinite")
-        motion, pos, neg, verified = indices
-        loss = (
-            variable_positive_symmetric_infonce(scores.contiguous(), positive_mask)[2]
-            if is_base
-            else coordination_objective(
+        motion, pos, neg, included = indices
+        if is_base:
+            loss = variable_positive_symmetric_infonce(scores.contiguous(), positive_mask)[2]
+        elif is_weak:
+            loss = weak_coordination_objective(
                 scores[:, :retrieval_count].contiguous(),
                 positive_mask,
                 cf_positive_scores=scores[motion, pos],
                 cf_negative_scores=scores[motion, neg],
-                verified_negative_mask=verified,
+                included_weak_mask=included,
+                cf_weight=cf_weight,
+                margin=margin,
+            )
+        else:
+            loss = coordination_objective(
+                scores[:, :retrieval_count].contiguous(),
+                positive_mask,
+                cf_positive_scores=scores[motion, pos],
+                cf_negative_scores=scores[motion, neg],
+                verified_negative_mask=included,
                 cf_weight=cf_weight,
                 margin=margin,
             )
-        )
         if not bool(torch.isfinite(loss)):
             raise ValueError("complete parent objective is nonfinite")
         loss.backward()
@@ -313,8 +399,9 @@ def backward_loaded_parent_batch(
             scores.detach().cpu(),
             len(labels.parents),
             retrieval_count,
-            int(verified.sum()),
+            0 if is_weak else int(included.sum()),
             len(labels.parents),
+            int(included.sum()) if is_weak else 0,
         )
     finally:
         system.train(was_training)
diff --git a/src/phaseset_core/parent_clip_rows.py b/src/phaseset_core/parent_clip_rows.py
index ffbbd26..107b9b7 100644
--- a/src/phaseset_core/parent_clip_rows.py
+++ b/src/phaseset_core/parent_clip_rows.py
@@ -18,8 +18,9 @@ class ParentHumanClipRows:
     learning statistics enter this cache. The complete task must contain only
     development parents. No test rows or arbitrary additional text are loaded.
     Returned empty CF rows mean *no CF evidence*, not verified negative truth.
-    This serves base training and human-only evaluation; full CF training still
-    requires the actual blind two-human records and a separate admitted pool.
+    This serves base training and human-only evaluation. Residual CF training
+    requires a separate admitted pool: human-false evidence or explicitly weak
+    machine targets, never fabricated verification from these empty rows.
     """
 
     def __init__(self, task: ParentRetrievalTask, original: FrozenClipTextBatch):
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
index 32eb71c..8bd5227 100644
--- a/src/phaseset_core/temporal_coordination.py
+++ b/src/phaseset_core/temporal_coordination.py
@@ -504,3 +504,53 @@ def coordination_objective(
         cf_positive_scores, cf_negative_scores, verified_negative_mask, margin=margin
     )
     return contrastive + cf_weight * counterfactual
+
+
+def weak_counterfactual_loss(
+    positive_scores: Tensor,
+    negative_scores: Tensor,
+    included_weak_mask: Tensor,
+    *,
+    margin: float = 0.2,
+) -> Tensor:
+    """Disclosed caption-derived training targets, never human-verified truth.
+
+    This uses the same registered margin formula but a distinct weak-label
+    entry point. An included bit admits a weak training target only; it is
+    not a false-event judgement, independent evaluation label or provenance.
+    """
+    if (
+        positive_scores.ndim != 1
+        or positive_scores.shape != negative_scores.shape
+        or included_weak_mask.shape != positive_scores.shape
+        or included_weak_mask.dtype != torch.bool
+        or positive_scores.device != negative_scores.device
+        or positive_scores.device != included_weak_mask.device
+    ):
+        raise ValueError("weak scores and bool inclusion mask need matching vectors/devices")
+    if not math.isfinite(margin) or margin < 0:
+        raise ValueError("margin must be finite and nonnegative")
+    penalties = F.softplus(margin + negative_scores - positive_scores)
+    if not bool(included_weak_mask.any()):
+        return (positive_scores.sum() + negative_scores.sum()) * 0.0
+    return penalties[included_weak_mask].mean()
+
+
+def weak_coordination_objective(
+    scores: Tensor,
+    positive_mask: Tensor,
+    *,
+    cf_positive_scores: Tensor,
+    cf_negative_scores: Tensor,
+    included_weak_mask: Tensor,
+    cf_weight: float = 0.2,
+    margin: float = 0.2,
+) -> Tensor:
+    """Human-only retrieval plus explicitly weak, training-only CF supervision."""
+    if not math.isfinite(cf_weight) or cf_weight < 0:
+        raise ValueError("cf_weight must be finite and nonnegative")
+    _, _, contrastive = variable_positive_symmetric_infonce(scores, positive_mask)
+    counterfactual = weak_counterfactual_loss(
+        cf_positive_scores, cf_negative_scores, included_weak_mask, margin=margin
+    )
+    return contrastive + cf_weight * counterfactual
diff --git a/tests/test_parent_weak_cf.py b/tests/test_parent_weak_cf.py
new file mode 100644
index 0000000..bb94be4
--- /dev/null
+++ b/tests/test_parent_weak_cf.py
@@ -0,0 +1,325 @@
+"""Analytic weak-supervision interface checks; not native labels or results."""
+
+from __future__ import annotations
+
+import copy
+from dataclasses import replace
+import json
+from types import SimpleNamespace
+
+import pytest
+import torch
+from torch.nn import functional as F
+
+from phaseset_core.continuous_parent_host import ContinuousParentTrainingHost
+from phaseset_core.continuous_base_retrieval import ContinuousBaseRetrievalSystem
+from phaseset_core.continuous_parent_training import (
+    ParentCounterfactualRows,
+    ParentWeakCounterfactualRows,
+    backward_loaded_parent_batch,
+    backward_parent_batch,
+)
+from phaseset_core.objectives import variable_positive_symmetric_infonce
+from phaseset_core.temporal_coordination import (
+    weak_coordination_objective,
+    weak_counterfactual_loss,
+)
+from phaseset_core.training import _load_torch_checkpoint, _stable_hash
+from test_continuous_parent_host import _Source, _setup
+from test_continuous_parent_training import _case, _key, _population
+
+
+def test_weak_margin_has_independent_formula_and_no_human_mask():
+    positive = torch.tensor([1.0, 2.0, -0.5], requires_grad=True)
+    negative = torch.tensor([0.0, 3.0, 0.5], requires_grad=True)
+    mask = torch.tensor([True, False, True])
+    actual = weak_counterfactual_loss(positive, negative, mask, margin=0.3)
+    expected = (
+        F.softplus(0.3 + negative[0] - positive[0]) + F.softplus(0.3 + negative[2] - positive[2])
+    ) / 2
+    assert torch.equal(actual, expected)
+    actual.backward()
+    assert positive.grad[1] == negative.grad[1] == 0
+    assert positive.grad[0] < 0 and negative.grad[0] > 0
+    row = ParentWeakCounterfactualRows((), (), (), ())
+    assert not hasattr(row, "verified_false")
+
+
+@pytest.mark.parametrize("size", [0, 3])
+def test_excluded_weak_targets_have_zero_loss_and_gradient(size):
+    pos = torch.arange(size, dtype=torch.float32, requires_grad=True)
+    neg = torch.arange(size, dtype=torch.float32, requires_grad=True)
+    value = weak_counterfactual_loss(pos, neg, torch.zeros(size, dtype=torch.bool))
+    value.backward()
+    assert value == 0 and torch.count_nonzero(pos.grad) == torch.count_nonzero(neg.grad) == 0
+
+
+@pytest.mark.parametrize("bad", ["shape", "mask", "rank", "negative_margin", "nan_margin"])
+def test_weak_margin_rejects_malformed_inputs(bad):
+    pos, neg, mask = torch.ones(2), torch.zeros(2), torch.ones(2, dtype=torch.bool)
+    margin = 0.2
+    if bad == "shape":
+        neg = torch.zeros(1)
+    elif bad == "mask":
+        mask = mask.to(torch.float32)
+    elif bad == "rank":
+        pos, neg, mask = pos[None], neg[None], mask[None]
+    else:
+        margin = -0.1 if bad == "negative_margin" else float("nan")
+    with pytest.raises(ValueError):
+        weak_counterfactual_loss(pos, neg, mask, margin=margin)
+
+
+def test_weak_objective_keeps_original_retrieval_prefix_and_weight_zero_control():
+    scores = torch.tensor([[2.0, 1.0, -1.0], [0.0, 1.0, 3.0]], requires_grad=True)
+    mask = torch.tensor([[True, True, False], [False, False, True]])
+    positive, negative = scores[0, :1], scores[0, 2:3]
+    contrastive = variable_positive_symmetric_infonce(scores, mask)[2]
+    for weight in (0.0, 0.2):
+        actual = weak_coordination_objective(
+            scores,
+            mask,
+            cf_positive_scores=positive,
+            cf_negative_scores=negative,
+            included_weak_mask=torch.tensor([True]),
+            cf_weight=weight,
+            margin=0.1,
+        )
+        expected = contrastive + weight * F.softplus(0.1 + negative - positive).mean()
+        assert torch.equal(actual, expected)
+    with pytest.raises(ValueError, match="cf_weight"):
+        weak_coordination_objective(
+            scores,
+            mask,
+            cf_positive_scores=positive,
+            cf_negative_scores=negative,
+            included_weak_mask=torch.tensor([True]),
+            cf_weight=-1,
+        )
+
+
+def _labels():
+    task = _population(2)
+    return task.batch(tuple(row.annotation_family_sha256 for row in task.parents))
+
+
+@pytest.mark.parametrize("included", [False, True])
+def test_weak_columns_bind_same_parent_human_evidence_outside_gallery(included):
+    labels = _labels()
+    rows = ParentWeakCounterfactualRows((labels.motion_source_keys[0],), (0,), (2,), (included,))
+    indices = rows.indices(labels, text_count=3, device=torch.device("cpu"))
+    assert [value.tolist() for value in indices] == [[0], [0], [2], [included]]
+
+
+@pytest.mark.parametrize(
+    "bad",
+    [
+        "unknown",
+        "other_positive",
+        "generated_positive",
+        "gallery_negative",
+        "outside_pool",
+        "bool_column",
+        "nonbool",
+        "length",
+        "not_tuple",
+    ],
+)
+def test_weak_columns_reject_fake_positive_labels_and_invalid_admission(bad):
+    labels = _labels()
+    rows = ParentWeakCounterfactualRows((labels.motion_source_keys[0],), (0,), (2,), (True,))
+    settings = {
+        "unknown": dict(source_keys=(_key("unknown"),)),
+        "other_positive": dict(positive_columns=(1,)),
+        "generated_positive": dict(positive_columns=(2,)),
+        "gallery_negative": dict(negative_columns=(1,)),
+        "outside_pool": dict(negative_columns=(3,)),
+        "bool_column": dict(positive_columns=(False,)),
+        "nonbool": dict(included_weak=(1,)),
+        "length": dict(included_weak=()),
+        "not_tuple": dict(source_keys=[labels.motion_source_keys[0]]),
+    }
+    with pytest.raises(ValueError):
+        replace(rows, **settings[bad]).indices(labels, text_count=3, device=torch.device("cpu"))
+
+
+@pytest.mark.parametrize("included,weight", [(False, 0.2), (True, 0.2), (True, 0.0)])
+def test_weak_complete_parent_replay_matches_independent_dense_gradients(
+    tmp_path, included, weight
+):
+    system, views, labels, text, _ = _case(tmp_path, extra_cf=True)
+    dense = copy.deepcopy(system)
+    cf = ParentWeakCounterfactualRows((views[0].positive_capture_key,), (0,), (4,), (included,))
+    scores = dense.score(views, text).scores
+    expected = variable_positive_symmetric_infonce(
+        scores[:, :3].contiguous(), labels.positive_mask(device=scores.device)
+    )[2]
+    if included:
+        expected = expected + weight * F.softplus(0.2 + scores[0, 4] - scores[0, 0])
+    expected.backward()
+    result = backward_parent_batch(
+        system, views, text, labels, counterfactuals=cf, cf_weight=weight, margin=0.2
+    )
+    torch.testing.assert_close(torch.tensor(result.loss), expected.detach(), rtol=2e-6, atol=2e-7)
+    torch.testing.assert_close(result.scores, scores.detach(), rtol=2e-6, atol=2e-7)
+    assert result.verified_counterfactual_count == 0
+    assert result.weak_counterfactual_count == int(included)
+    assert result.retrieval_caption_count == 3 and result.replayed_captures == 2
+    for (name, actual), (other_name, oracle) in zip(
+        system.named_parameters(), dense.named_parameters(), strict=True
+    ):
+        assert name == other_name
+        if name.startswith("frozen_b2."):
+            assert actual.grad is oracle.grad is None
+        else:
+            torch.testing.assert_close(actual.grad, oracle.grad, rtol=3e-5, atol=3e-6)
+
+
+def test_weak_negative_cannot_alias_known_original_positive_and_kind_is_explicit(tmp_path):
+    system, views, labels, _, clip = _case(tmp_path)
+    text = clip.encode(
+        labels.captions + ("walking",),
+        caption_commitments=labels.caption_commitments + (bytes.fromhex(_key("alias")),),
+        batch_size=3,
+    )
+    for rows, error in (
+        (
+            ParentWeakCounterfactualRows((views[0].positive_capture_key,), (0,), (3,), (True,)),
+            ValueError,
+        ),
+        (SimpleNamespace(source_keys=()), TypeError),
+    ):
+        with pytest.raises(error):
+            backward_parent_batch(
+                system, views, text, labels, counterfactuals=rows, cf_weight=0.2, margin=0.2
+            )
+
+
+class _WeakSource(_Source):
+    """Synthetic generation for tests only; no actual label or language model."""
+
+    def text(self, labels, *, training):
+        if not training:
+            return super().text(labels, training=False)
+        self.text_loaded.append((labels.motion_source_keys, training))
+        extras = tuple(
+            f"synthetic weak alternative {index}" for index in range(len(labels.parents))
+        )
+        commits = tuple(
+            bytes.fromhex(_key("weak/" + source)) for source in labels.motion_source_keys
+        )
+        offsets, offset = [], 0
+        for parent in labels.parents:
+            offsets.append(offset)
+            offset += len(parent.captions)
+        text = self.clip.encode(
+            labels.captions + extras,
+            caption_commitments=labels.caption_commitments + commits,
+            batch_size=3,
+        )
+        rows = ParentWeakCounterfactualRows(
+            labels.motion_source_keys,
+            tuple(offsets),
+            tuple(range(offset, offset + len(extras))),
+            (True,) * len(extras),
+        )
+        return text, rows
+
+
+def test_weak_host_actual_updates_resume_and_disjoint_counts_are_bitwise(tmp_path):
+    system, task, source, config, bindings = _setup(tmp_path)
+    initial = copy.deepcopy(system)
+
+    def make_source():
+        return _WeakSource(tuple(source.views.values()), source.clip)
+
+    full = ContinuousParentTrainingHost(system, task, make_source(), config, bindings).fit(
+        tmp_path / "full"
+    )
+    stopped = ContinuousParentTrainingHost(
+        copy.deepcopy(initial), task, make_source(), config, bindings
+    ).fit(tmp_path / "stopped", stop_after_steps=1)
+    resumed_model = copy.deepcopy(initial)
+    resumed_source = make_source()
+    resumed = ContinuousParentTrainingHost(
+        resumed_model, task, resumed_source, config, bindings
+    ).fit(
+        tmp_path / "resumed",
+        resume_checkpoint=stopped.latest_checkpoint.path,
+        resume_sha256=stopped.latest_checkpoint.sha256,
+    )
+    assert full.outcome == resumed.outcome == "COMPLETED"
+    assert full.global_step == resumed.global_step == 2
+    for name, value in system.state_dict().items():
+        assert torch.equal(value, resumed_model.state_dict()[name])
+    a, _ = _load_torch_checkpoint(full.latest_checkpoint.path)
+    b, _ = _load_torch_checkpoint(resumed.latest_checkpoint.path)
+    for key in ("model", "optimizer", "scheduler", "rng"):
+        assert _stable_hash(a[key]) == _stable_hash(b[key])
+    events = [
+        json.loads(path.read_text()) for path in sorted((tmp_path / "full").glob("event-*.json"))
+    ]
+    updates = [event for event in events if event["phase"] == "update"]
+    assert len(updates) == 2
+    assert all(
+        event["weak_cf"] == 4 and event["verified_cf"] == 0 and event["human_rows"] == 5
+        for event in updates
+    )
+    assert any(not training for _, training in resumed_source.text_loaded)
+    assert not any(parent.split == "test" for parent in task.parents)
+    # Actual AdamW/resume on analytic fixtures is software evidence only.
+
+
+def test_original_human_row_type_retains_its_distinct_verification_field():
+    original = ParentCounterfactualRows((), (), (), ())
+    assert hasattr(original, "verified_false") and not hasattr(original, "included_weak")
+
+
+@pytest.mark.parametrize("kind", ["tmr", "wamo", "mime"])
+def test_new_shared_event_counts_preserve_literature_hosts_without_weak_labels(kind, tmp_path):
+    from test_literature_parent_host import test_real_objective_optimizer_and_full_gallery_endpoint
+
+    # Only the new shared event/result field motivates this existing analytic
+    # optimizer call. It is not a repeated native comparator quality review.
+    test_real_objective_optimizer_and_full_gallery_endpoint(kind, tmp_path)
+    events = [
+        json.loads(path.read_text()) for path in sorted((tmp_path / "run").glob("event-*.json"))
+    ]
+    updates = [event for event in events if event["phase"] == "update"]
+    assert len(updates) == 2 and all(
+        event["weak_cf"] == event["verified_cf"] == 0 for event in updates
+    )
+
+
+def test_base_training_rejects_weak_cf_before_any_capture_load(tmp_path):
+    residual, views, labels, text, _ = _case(tmp_path, extra_cf=True)
+    base = ContinuousBaseRetrievalSystem(copy.deepcopy(residual.frozen_b2).requires_grad_(True))
+    cf = ParentWeakCounterfactualRows((views[0].positive_capture_key,), (0,), (4,), (True,))
+    loaded = []
+
+    def load(key):
+        loaded.append(key)
+        raise AssertionError("weak CF must be rejected before loading a base input")
+
+    with pytest.raises(ValueError, match="base training"):
+        backward_loaded_parent_batch(
+            base, labels, text, load_view=load, counterfactuals=cf, cf_weight=0, margin=0
+        )
+    assert not loaded
+
+
+def test_validation_rejects_weak_cf_gallery_leak_with_original_failure_evidence(tmp_path):
+    system, task, source, config, bindings = _setup(tmp_path)
+
+    class LeakyWeakSource(_WeakSource):
+        def text(self, labels, *, training):
+            return super().text(labels, training=True)
+
+    bad_source = LeakyWeakSource(tuple(source.views.values()), source.clip)
+    host = ContinuousParentTrainingHost(
+        system, task, bad_source, replace(config, epochs=1), bindings
+    )
+    with pytest.raises(ValueError, match="only all official human"):
+        host.fit(tmp_path / "failure")
+    terminal = json.loads((tmp_path / "failure/terminal.json").read_text())
+    assert terminal["outcome"] == "FAILED" and terminal["global_step"] == 1

From 479b57cdebdf7fb2e28b88377a70bae914bdd117 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 13:46:56 -0500
Subject: [PATCH 58/95] feat: qualify stage-isolated cached weak CLIP pools

---
 README.md                                     |   6 +-
 RELEASE_FILES.sha256                          |  15 +-
 configs/phaseset_v2_relation_supervision.json |   2 +-
 ...SESET_V2_RELATION_SUPERVISION_AMENDMENT.md |   5 +-
 docs/PHASESET_V2_WEAK_CLIP_POOL.md            |  58 +++
 src/phaseset_core/capture_validation.py       |  53 ++-
 src/phaseset_core/frozen_clip_text.py         | 110 ++++-
 src/phaseset_core/parent_disk_source.py       |  13 +-
 src/phaseset_core/parent_weak_clip_rows.py    | 162 ++++++++
 tests/test_parent_weak_clip_rows.py           | 381 ++++++++++++++++++
 10 files changed, 786 insertions(+), 19 deletions(-)
 create mode 100644 docs/PHASESET_V2_WEAK_CLIP_POOL.md
 create mode 100644 src/phaseset_core/parent_weak_clip_rows.py
 create mode 100644 tests/test_parent_weak_clip_rows.py

diff --git a/README.md b/README.md
index 7449951..518c430 100644
--- a/README.md
+++ b/README.md
@@ -49,8 +49,10 @@ uses existing official human retrieval annotations and permits separately
 disclosed weak machine supervision; no new human annotators are awaited.
 Machine labels are not independent motion ground truth or human-challenge
 success. The [separate weak-CF loss/backward seam](docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md)
-has passed 37 scoped server checks; the actual generated training pool and its
-provenance are still pending. This is not a completed native training run.
+has passed 37 scoped server checks, and the
+[cached training-only weak text pool](docs/PHASESET_V2_WEAK_CLIP_POOL.md) has passed
+24 additional scoped checks. Actual generation, encoded weak text and private
+provenance admission remain pending. Neither is a completed native training run.
 
 A [complete-native-parent CPU profile](docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md)
 has now passed through the production lifecycle and journal: both 512-D systems,
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 5c91e12..36e2bce 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-7b8bdaefeef8d88d33368ac475b35b1981c5e4c44b9dd1d287caaed3f4645616	15188	README.md
+ddb5009755af5a219159df8fafbc3bf5e5de5737c6dcc6c17cc72169a1df1513	15322	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -17,7 +17,7 @@ e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
 cd6fb3868996ab909d8a382cfc62a35fa5510e465ca697aae0a142afc50b9498	26169	configs/phaseset_v2_experiment_matrix.json
-a78e2cebaa50abd68d9f185bd5ad11d4619ea8603b80772c0f3ac433f4e9dfff	2691	configs/phaseset_v2_relation_supervision.json
+97a0296e84dfdf3255a52041e50e873664e3493edc2e47bb3f787accc3442911	2710	configs/phaseset_v2_relation_supervision.json
 59916f216450b4d79817f6c7f0574243e1092496e53a07a4f1a518047e427acd	994	configs/phaseset_v2_venue.json
 2970469ad0787e74ab4fabce2275486eb50c30c38f3cb7cf8f2800551d16991f	9540	docs/BASE_COHORT_QUALIFICATION.md
 3824afe584e31b5765ebcedfae64b26d2b12a3b20ee9936e0cd5bda4e57493d4	5573	docs/BASE_COHORT_VALIDATION.md
@@ -86,11 +86,12 @@ c94c1538acb0e892c91c54ad1c6740073c9d75623465aa665ab1ea81b9b1ead3	2904	docs/PHASE
 1eef1de33e2f1d193a6a02a0a982df0c9dea7b1f9406b4cbd155d22ef7378f51	3314	docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
-bc2abbcded41e6a56c7302de6ef85139c9fb438e0637983a88ff0f67628babab	3472	docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
+bab08ac05ce49d686cf19b64cf19b2dc05a4928d24e9e5b19ef122ac5c732632	3567	docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
 8944faef4cfe50dfdccf0443abf739a510a94291184b2374392e0c039762f93a	5607	docs/PHASESET_V2_STUDY_BUDGET.md
 0e7c300498c28e83f8df3c3d78e2be44e8d9270e0005e79f4ec9847cbfc8cc16	4079	docs/PHASESET_V2_STUDY_PROCESS.md
 81b47eff03531162dd2ab6b3d29a0ca9909326e7b13dec34776551d171b182c7	1727	docs/PHASESET_V2_TMR_PARENT_TRAINING.md
 8ed656e8f4c6e1c9513bf69795275cba9948643cdc0c3ba47043602b30ece251	4370	docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md
+b17d807c2ef5df818dbb99c274186bad5617a14903576524f6e76bebb1783bd9	3619	docs/PHASESET_V2_WEAK_CLIP_POOL.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
@@ -172,7 +173,7 @@ b8c239437873367ce2880d460c17f51f58bf94d800b8087aa1a811fc0d9e678d	37144	src/phase
 121dcb6b948b9a3a4878ed376b3692dcbbd4b19f7ea147a7d28341c64b77d296	36926	src/phaseset_core/caption_role_provenance.py
 d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phaseset_core/capture_pooling.py
 0eaf0057ccf6ab6a4b83c1fa6e649399c4f783a79fab301e0778bafb5699daa9	47086	src/phaseset_core/capture_prepared_storage.py
-501a70f3f772fa99538f516e586d2d0810f86a21d15f5042b6a6cb30e63280c7	43337	src/phaseset_core/capture_validation.py
+213df61614f99afc5936dc42f897466945f1979339f7008fc23414e026b89324	45428	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 80e6d497ff9bb4354664cb1a054cdb550ab222e92c841813afc27af6d3759495	5603	src/phaseset_core/continuous_base_retrieval.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
@@ -191,7 +192,7 @@ e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phase
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
 05175adc59f341f4f59da3b18f8e834e8ac537424c9df3d8aa78ba6253e5b235	34026	src/phaseset_core/experiments.py
-793a975242b014b87a8f9b4ac1ba0c05f25f46a3ddb702a895f477446ec6b784	72698	src/phaseset_core/frozen_clip_text.py
+7c491ef7d65cddffa264d68d0a9fdd9216180bb741f85dd662cb570ca6e872f1	77027	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
 50a6240c62827c89e62939863ef5e12580e886b80e1997c15bf1423d37610779	8555	src/phaseset_core/literature_parent_host.py
@@ -204,9 +205,10 @@ c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phase
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 0f0309c54a5b48a0493bb24f97eda39ff93be657295e1b0ebcc3dd6e9a8ecd76	6565	src/phaseset_core/parent_capture.py
 be88afc2d543a9ceb227eda07046cc8e9221faf7af85888c1b303632da642dc8	3366	src/phaseset_core/parent_clip_rows.py
-f8e257a1cff56e93ff76229c2a4c47b1a85902a64b76338758178d111a2d3ecb	8322	src/phaseset_core/parent_disk_source.py
+0d91c16503f8704916eecd6c72e0b943dd06cf251ba6f7b847a32d5c009ab6dd	8826	src/phaseset_core/parent_disk_source.py
 be883315aa773e6c2d535ea981853ae92a791187338fd2b310b574f956174a82	7326	src/phaseset_core/parent_retrieval_task.py
 e0c9d77e9ccaf277785beb148a54afbdf542c607a6298e6b20db458828a32fe7	9331	src/phaseset_core/parent_run_monitor.py
+52b22af181cb52cf3c9c3584d15ea02a604e5c29d8abeaa0de217baf07d8af06	7027	src/phaseset_core/parent_weak_clip_rows.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 e69b0d27d479b4499bae73c96f7854f9d07539043a0b66f0f75f6bc3e23c1f51	55415	src/phaseset_core/periodic_cache_execution.py
 21ab159e434cc504867d22a22894a8e6a82afa77dde88a466773b5549a004850	42190	src/phaseset_core/periodic_capture_training_cache.py
@@ -257,6 +259,7 @@ fc0c27fb68c7d119d1cc7a48afeeff5a707cab9ec206eb5ad85c606e31656490	9443	tests/test
 32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
 cdf4d6dcea5c29e44ebb3f2f91945fd944a8026a7abe91eaa47beb59428f159c	12935	tests/test_parent_run_monitor.py
 d189e29ef4ef81b9604a352a7828ff97c789a79cafda18e8928056faf08a7825	13424	tests/test_parent_weak_cf.py
+d61fcc5a3f5ca3428300e49bcdc138a6483edd82fed11aac9d06de79293bfd65	15534	tests/test_parent_weak_clip_rows.py
 deb4200c8161fefd5c9b0d992fa81cb2448545b419bf60f8f238f9a3efdcac8b	18014	tests/test_periodic_cache_model_integration.py
 9a05b2a025a694f94e158d2de74242d2a820efbc8973920001895114e7a894fb	14880	tests/test_periodic_cache_source_lineage.py
 8dccac5551ec4d519f98b4b81d8f10ba47bd1f19ece6168ce137c51dc2ca2f0e	18170	tests/test_periodic_capture_training_cache.py
diff --git a/configs/phaseset_v2_relation_supervision.json b/configs/phaseset_v2_relation_supervision.json
index ef24d9c..7ee6df1 100644
--- a/configs/phaseset_v2_relation_supervision.json
+++ b/configs/phaseset_v2_relation_supervision.json
@@ -1,7 +1,7 @@
 {
   "schema": "phaseset-v2-relation-supervision-amendment-v1",
   "amendment_date": "2026-09-27",
-  "status": "WEAK_LOSS_SEAM_QUALIFIED_POOL_PENDING_NOT_TRAINING_ADMISSION",
+  "status": "WEAK_LOSS_AND_POOL_SOFTWARE_QUALIFIED_GENERATION_PENDING_NOT_TRAINING_ADMISSION",
   "reason": "No two new human annotators are available; the project owner requested existing annotations or LLM assistance instead of waiting.",
   "supersedes": "The requirement to obtain two new human annotators before independent development or training preparation can proceed.",
   "primary_retrieval": {
diff --git a/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md b/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
index 54b15d7..b4aaf1a 100644
--- a/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
+++ b/docs/PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md
@@ -29,8 +29,9 @@ pilot/训练前一次冻结；保留机器来源、原人工文本依据、输
 明确披露的弱训练监督。full 与适用的机制对照使用相同弱监督池和预算，A8
 仍关闭 CF 目标；不可将机器推断塞进原 human-only `verified_false=True`
 接口。独立弱标签类型、loss、完整 parent backward 及事件计数已经实现，
-并通过 [37 项服务器软件检查](PHASESET_V2_WEAK_CF_TRAINING_SEAM.md)；原真实
-人工接口与旧测试保留。**实际弱 CF 生成、冻结训练池和来源准入仍待完成**，
+并通过 [37 项服务器软件检查](PHASESET_V2_WEAK_CF_TRAINING_SEAM.md)；缓存
+训练-only文本池通过 [另外 24 项检查](PHASESET_V2_WEAK_CLIP_POOL.md)，原真实
+人工接口与旧测试保留。**实际弱 CF 生成、真实文本编码池和来源准入仍待完成**，
 不能据此宣称正式训练已启动。无 CF 的基础模型与独立开发工作不再等新真人。
 
 ## 评估与结论边界
diff --git a/docs/PHASESET_V2_WEAK_CLIP_POOL.md b/docs/PHASESET_V2_WEAK_CLIP_POOL.md
new file mode 100644
index 0000000..13855c5
--- /dev/null
+++ b/docs/PHASESET_V2_WEAK_CLIP_POOL.md
@@ -0,0 +1,58 @@
+# PhaseSet-V2：训练-only缓存弱文本池
+
+2026-09-27。按 [关系监督修订](PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md)
+继续使用原官方人工主检索文本，机器句子只能作为单独披露的弱训练监督。
+
+## 缓存与阶段隔离
+
+`pool_frozen_clip_text_rows` 精确选择两个 original CLIP batch，再追加机器
+向量。人工前缀按位不变，不重新编码，不消耗 RNG。派生 receipt 保存两个
+original receipts、各自原行索引及人工前缀长度，明确不是一次新的 CLIP 编码。
+不同模型/snapshot/runtime、重复 commitment 和篡改 ancestry 被拒绝；原始
+rehydrator 不接收派生池。两个 original 的 source provenance 均保留，不
+虚构共同 encoder source。commitment 只用于谱系，不进入模型。
+
+`ParentWeakClipRows` 绑定同一完整开发任务的人工 provider，显式接收当前
+阶段 training components、原机器编码 receipt 和每句来源记录。检查 source/
+family/人工 ordinal、政策/生成记录摘要及编码文本相符；机器文本不能是同一
+parent 的原人工正例。阶段 held-out 不进入训练，外层 fold 的训练组件可以
+包含旧主验证组件，不混淆这些不同任务。**这些检查不证明机器语义正确。**
+
+训练只选择当前 batch 对应的弱句子并追加于全部原人工 occurrence 后。
+没有候选的 parent 返回空 weak rows，不补造标签。evaluation 直接走原
+`ParentHumanClipRows`，不读机器池；基础模型在读取 motion 前拒绝 weak CF。
+既有 disk source 接入为显式可选项，原 human-only 路径保留。
+
+## 一次真实服务器软件资格
+
+Fresh ARIS GPT-5.6-Sol/xhigh 部署前受限复核 PASS/no findings，同家族
+provisional。2026-09-27 18:32:25–18:33:47 UTC，仅运行一次：单线程 CPU
+FP32/interop 1、nice 10、离线/无 CUDA、16 GiB/900 秒限额，warm env 未改变。
+
+**24 passed，0 skipped/failures/errors，76.80 秒。** 21 项新用例加 3 项
+相关原人工接口用例，覆盖缓存前缀、honest receipts、阶段过滤、独立计数、
+全人工 occurrence、重排、排除 mask、空候选、disk source、validation
+不污染，以及 analytic AdamW 完整主机中断/resume bitwise 一致。
+
+三 exit code 均 0、stderr 空、source/invocation 前后不变。20 个 flat
+metadata 文件（19 hashed 加 receipt）双端核验，receipt SHA-256：
+`6074d028be82ab5564f3577044190f9f0496769ae1d657f33e028d5c34b464a0`。
+原生产三个预算事件与 summary 不变；正式 **0/87**、pilot **0/12**，本次
+GPU 0 秒。analytic optimizer 用例不是原生学习或论文结果。
+
+## 准入仍待真实生成与训练
+
+本资格没有生成真实机器标签、没有编码真实弱文本，不关闭私有来源/语义、
+训练 yaw、native CF backward、GPU profile 或正式实验 gate。实际生成
+prompt/选择及原始输出需在训练前冻结并披露；机器标签永不升级为人工
+`verified_false`、独立运动真值或历史两人挑战成功。
+
+公开选择重现（独立合适 CPU 环境，不含私有预算 caller）：
+
+```bash
+OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 CUDA_VISIBLE_DEVICES='' \
+PYTHONPATH=src:tests python -m pytest -q tests/test_parent_weak_clip_rows.py \
+  tests/test_parent_clip_rows.py::test_selection_is_exact_owned_and_honest_without_encoder_runtime_or_rng \
+  tests/test_parent_clip_rows.py::test_every_human_occurrence_and_reordered_parent_batch_are_preserved \
+  tests/test_parent_clip_rows.py::test_disk_source_binds_the_same_task_and_returns_explicitly_no_cf
+```
diff --git a/src/phaseset_core/capture_validation.py b/src/phaseset_core/capture_validation.py
index 46246d2..fb8c7ae 100644
--- a/src/phaseset_core/capture_validation.py
+++ b/src/phaseset_core/capture_validation.py
@@ -38,6 +38,7 @@
     FrozenClipTextBatch,
     FrozenClipTextReceipt,
     FrozenClipTextRowSelectionReceipt,
+    FrozenClipTextRowPoolReceipt,
 )
 from .objectives import variable_positive_symmetric_infonce
 from .periodic_descriptor_cache_v2 import DescriptorWindowContext
@@ -156,6 +157,8 @@ def _validate_text_batch(
     if len(set(commitments)) != count:
         raise CaptureValidationError("caption commitments must be unique within a capture")
     selected = type(receipt) is FrozenClipTextRowSelectionReceipt
+    pooled = type(receipt) is FrozenClipTextRowPoolReceipt
+    derived = selected or pooled
     if selected:
         if not allow_row_selection or (
             receipt.schema != "phaseset-frozen-clip-row-selection-v1"
@@ -182,6 +185,48 @@ def _validate_text_batch(
             )
         ):
             raise CaptureValidationError("derived frozen text selection ancestry differs")
+    elif pooled:
+        if not allow_row_selection or (
+            receipt.schema != "phaseset-frozen-clip-row-pool-v1"
+            or receipt.status != "ROW_POOL_AUTHORITY0"
+        ):
+            raise CaptureValidationError("derived frozen text pool is not admitted here")
+        origins, selections = receipt.origin_receipts, receipt.selected_indices
+        if (
+            type(origins) is not tuple
+            or len(origins) != 2
+            or type(selections) is not tuple
+            or len(selections) != 2
+        ):
+            raise CaptureValidationError("derived frozen text pool ancestry differs")
+        rows = []
+        for origin, indices in zip(origins, selections, strict=True):
+            if (
+                type(origin) is not FrozenClipTextReceipt
+                or origin.schema != "phaseset-frozen-clip-text-receipt-v1"
+                or origin.status != "ENCODED_AUTHORITY0"
+                or type(indices) is not tuple
+                or not indices
+                or any(type(i) is not int or not 0 <= i < len(origin.caption_rows) for i in indices)
+                or len(set(indices)) != len(indices)
+            ):
+                raise CaptureValidationError("derived frozen text pool ancestry differs")
+            rows.extend(origin.caption_rows[i] for i in indices)
+        if (
+            type(receipt.prefix_count) is not int
+            or receipt.prefix_count != len(selections[0])
+            or len(rows) != count
+            or receipt.caption_rows != tuple((i, *row[1:]) for i, row in enumerate(rows))
+            or any(
+                getattr(origins[0], identity) != getattr(origins[1], identity)
+                for identity in (
+                    "snapshot_manifest_sha256",
+                    "live_model_manifest_sha256",
+                    "runtime_manifest_sha256",
+                )
+            )
+        ):
+            raise CaptureValidationError("derived frozen text pool ancestry differs")
     else:
         if type(receipt) is not FrozenClipTextReceipt:
             raise CaptureValidationError("frozen text receipt type is invalid")
@@ -197,7 +242,7 @@ def _validate_text_batch(
         or receipt.output_sha256 != _sha256(raw)
     ):
         raise CaptureValidationError("frozen text output differs from its receipt")
-    if not selected and (type(receipt.batch_size) is not int or receipt.batch_size < 1):
+    if not derived and (type(receipt.batch_size) is not int or receipt.batch_size < 1):
         raise CaptureValidationError("frozen text receipt batch size is invalid")
     if type(receipt.caption_rows) is not tuple or len(receipt.caption_rows) != count:
         raise CaptureValidationError("frozen text receipt caption census is invalid")
@@ -209,10 +254,10 @@ def _validate_text_batch(
         row_raw = _tensor_bytes(embeddings[index : index + 1].contiguous())
         if row[8] != _sha256(row_raw):
             raise CaptureValidationError("frozen text receipt row digest mismatch")
-    if not selected and (type(receipt.chunk_ranges) is not tuple or not receipt.chunk_ranges):
+    if not derived and (type(receipt.chunk_ranges) is not tuple or not receipt.chunk_ranges):
         raise CaptureValidationError("frozen text receipt chunk census is invalid")
     position = 0
-    for chunk in () if selected else receipt.chunk_ranges:
+    for chunk in () if derived else receipt.chunk_ranges:
         if (
             type(chunk) is not tuple
             or len(chunk) != 2
@@ -224,7 +269,7 @@ def _validate_text_batch(
         ):
             raise CaptureValidationError("frozen text receipt chunks do not partition rows")
         position = chunk[1]
-    if not selected and position != count:
+    if not derived and position != count:
         raise CaptureValidationError("frozen text receipt chunks do not cover all rows")
     try:
         receipt_sha256 = _lower_sha256(receipt.sha256, "frozen text receipt SHA-256")
diff --git a/src/phaseset_core/frozen_clip_text.py b/src/phaseset_core/frozen_clip_text.py
index 24594ec..03705d2 100644
--- a/src/phaseset_core/frozen_clip_text.py
+++ b/src/phaseset_core/frozen_clip_text.py
@@ -940,7 +940,9 @@ def __init__(
         self,
         embeddings: Any,
         lineage: tuple[bytes, ...],
-        receipt: FrozenClipTextReceipt | FrozenClipTextRowSelectionReceipt,
+        receipt: FrozenClipTextReceipt
+        | FrozenClipTextRowSelectionReceipt
+        | FrozenClipTextRowPoolReceipt,
         *,
         _seal: object,
     ) -> None:
@@ -959,7 +961,9 @@ def caption_commitments(self) -> tuple[bytes, ...]:
         return self.__lineage
 
     @property
-    def receipt(self) -> FrozenClipTextReceipt | FrozenClipTextRowSelectionReceipt:
+    def receipt(
+        self,
+    ) -> FrozenClipTextReceipt | FrozenClipTextRowSelectionReceipt | FrozenClipTextRowPoolReceipt:
         return self.__receipt
 
     def __repr__(self) -> str:
@@ -1028,6 +1032,106 @@ def sha256(self) -> str:
         return hashlib.sha256(self.canonical_json_bytes()).hexdigest()
 
 
+@dataclass(frozen=True, slots=True)
+class FrozenClipTextRowPoolReceipt:
+    """Two original cached row selections, never a new encoder invocation.
+
+    The prefix stays bitwise unchanged. Persist both originals and reselect;
+    the original-batch rehydrator deliberately rejects this derived schema.
+    Prefix/extra roles are supplied by the caller, not ground-truth authority.
+    """
+
+    origin_receipts: tuple[FrozenClipTextReceipt, FrozenClipTextReceipt]
+    selected_indices: tuple[tuple[int, ...], tuple[int, ...]]
+    prefix_count: int
+    caption_rows: tuple[tuple[int, str, str, int, int, bool, str, str, str], ...]
+    output_bytes: int
+    output_sha256: str
+    output_shape: tuple[int, int]
+    output_stride: tuple[int, int]
+    schema: str = "phaseset-frozen-clip-row-pool-v1"
+    status: str = "ROW_POOL_AUTHORITY0"
+
+    def to_public_dict(self) -> dict[str, object]:
+        return {
+            "authority": AUTHORITY,
+            "production": PRODUCTION,
+            "training_authorized": TRAINING_AUTHORIZED,
+            "schema": self.schema,
+            "status": self.status,
+            "operation": "ordered_cached_rows_from_two_originals_no_encoder_call",
+            "origin_receipt_sha256": [origin.sha256 for origin in self.origin_receipts],
+            "selected_indices": [list(indices) for indices in self.selected_indices],
+            "prefix_count": self.prefix_count,
+            "caption_rows": [list(row) for row in self.caption_rows],
+            "model_id": MODEL_ID,
+            "revision": REVISION,
+            "method_id": METHOD_ID,
+            "output": {
+                "bytes": self.output_bytes,
+                "sha256": self.output_sha256,
+                "shape": list(self.output_shape),
+                "stride": list(self.output_stride),
+                "dtype": "torch.float32",
+                "device": "cpu",
+                "requires_grad": False,
+            },
+        }
+
+    def canonical_json_bytes(self) -> bytes:
+        return _canonical_json(self.to_public_dict()) + b"\n"
+
+    @property
+    def sha256(self) -> str:
+        return hashlib.sha256(self.canonical_json_bytes()).hexdigest()
+
+
+def pool_frozen_clip_text_rows(
+    prefix_original: FrozenClipTextBatch,
+    extra_original: FrozenClipTextBatch,
+    *,
+    prefix_indices: tuple[int, ...],
+    extra_indices: tuple[int, ...],
+) -> FrozenClipTextBatch:
+    """Append authenticated cached rows without re-encoding the frozen prefix.
+
+    Both inputs must be original encoded/rehydrated batches of the same live
+    model, snapshot and numerical runtime. Their separate source/encoding
+    receipts are retained; no synthetic combined encoder receipt is invented.
+    """
+    import torch
+
+    prefix = select_frozen_clip_text_rows(prefix_original, prefix_indices)
+    extra = select_frozen_clip_text_rows(extra_original, extra_indices)
+    origins = (prefix_original.receipt, extra_original.receipt)
+    for identity in (
+        "snapshot_manifest_sha256",
+        "live_model_manifest_sha256",
+        "runtime_manifest_sha256",
+    ):
+        if getattr(origins[0], identity) != getattr(origins[1], identity):
+            raise ValueError("cached text pool requires the same model, snapshot and runtime")
+    lineage = prefix.caption_commitments + extra.caption_commitments
+    if len(set(lineage)) != len(lineage):
+        raise ValueError("cached text pool cannot repeat a caption commitment")
+    embeddings = torch.cat((prefix.embeddings, extra.embeddings)).contiguous()
+    raw = _tensor_bytes(embeddings)
+    receipt = FrozenClipTextRowPoolReceipt(
+        origin_receipts=origins,
+        selected_indices=(prefix_indices, extra_indices),
+        prefix_count=len(prefix_indices),
+        caption_rows=tuple(
+            (index, *row[1:])
+            for index, row in enumerate(prefix.receipt.caption_rows + extra.receipt.caption_rows)
+        ),
+        output_bytes=len(raw),
+        output_sha256=hashlib.sha256(raw).hexdigest(),
+        output_shape=tuple(embeddings.shape),
+        output_stride=tuple(embeddings.stride()),
+    )
+    return FrozenClipTextBatch(embeddings, lineage, receipt, _seal=_CONSTRUCTION_SEAL)
+
+
 def select_frozen_clip_text_rows(
     original: FrozenClipTextBatch, indices: tuple[int, ...]
 ) -> FrozenClipTextBatch:
@@ -1999,6 +2103,7 @@ def load_frozen_clip_text_adapter(
     "FrozenClipTextBatch",
     "FrozenClipTextReceipt",
     "FrozenClipTextRowSelectionReceipt",
+    "FrozenClipTextRowPoolReceipt",
     "MAX_BATCH_SIZE",
     "MAX_CAPTIONS",
     "METHOD_ID",
@@ -2009,4 +2114,5 @@ def load_frozen_clip_text_adapter(
     "load_frozen_clip_text_adapter",
     "rehydrate_frozen_clip_text_batch",
     "select_frozen_clip_text_rows",
+    "pool_frozen_clip_text_rows",
 ]
diff --git a/src/phaseset_core/parent_disk_source.py b/src/phaseset_core/parent_disk_source.py
index e09435b..04faa3a 100644
--- a/src/phaseset_core/parent_disk_source.py
+++ b/src/phaseset_core/parent_disk_source.py
@@ -19,6 +19,7 @@
 from .continuous_training_input import ContinuousTrainingInput, shared_yaw_capture
 from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
 from .parent_clip_rows import ParentHumanClipRows
+from .parent_weak_clip_rows import ParentWeakClipRows
 from .tmr_feature_cache import FrozenTMRRowCache, caption_key
 from .training import OFFICIAL_SEEDS
 
@@ -73,6 +74,7 @@ def __init__(
         language_rows: FrozenTMRRowCache,
         energy_floors: np.ndarray | None = None,
         human_clip_rows: ParentHumanClipRows | None = None,
+        weak_clip_rows: ParentWeakClipRows | None = None,
     ):
         if type(task) is not ParentRetrievalTask or any(
             type(record) is not ParentDiskRecord for record in records
@@ -123,6 +125,12 @@ def __init__(
         ):
             raise ValueError("human CLIP rows must cover the same complete development task")
         self.human_clip_rows = human_clip_rows
+        if weak_clip_rows is not None and (
+            type(weak_clip_rows) is not ParentWeakClipRows
+            or weak_clip_rows.human is not human_clip_rows
+        ):
+            raise ValueError("weak rows must use the same admitted human row provider")
+        self.weak_clip_rows = weak_clip_rows
 
     def _record_and_yaw(self, parent, *, seed, epoch, training):
         if type(parent) is not ParentCaptionRecord or (
@@ -168,9 +176,10 @@ def wamo_cls(self, captions):
         return self.language_rows.wamo_cls(captions)
 
     def text(self, labels, *, training):
-        """Human-only source seam; never turn absent CF annotations into truth."""
+        """Original human evaluation; separately admitted weak training pool."""
         if self.human_clip_rows is None:
             raise ValueError(
                 "base/V2 human text requires the closed original CLIP preparation batch"
             )
-        return self.human_clip_rows.text(labels, training=training)
+        provider = self.weak_clip_rows if self.weak_clip_rows is not None else self.human_clip_rows
+        return provider.text(labels, training=training)
diff --git a/src/phaseset_core/parent_weak_clip_rows.py b/src/phaseset_core/parent_weak_clip_rows.py
new file mode 100644
index 0000000..79e4064
--- /dev/null
+++ b/src/phaseset_core/parent_weak_clip_rows.py
@@ -0,0 +1,162 @@
+"""Disclosed machine-caption training rows, never human motion truth."""
+
+from dataclasses import dataclass, field
+import hashlib
+
+from .capture_validation import _validate_text_batch
+from .continuous_parent_training import ParentWeakCounterfactualRows
+from .frozen_clip_text import FrozenClipTextBatch, pool_frozen_clip_text_rows
+from .parent_clip_rows import ParentHumanClipRows
+from .parent_retrieval_task import _sha_key
+
+
+@dataclass(frozen=True)
+class WeakCaptionRecord:
+    """One caller-admitted machine output and its original human occurrence.
+
+    The private caller verifies the actual frozen generation record/policy.
+    A digest or inclusion bit here is not proof of its semantic correctness.
+    Rejected generation outputs remain in the private log, not fabricated rows.
+    """
+
+    source_sha256: str
+    annotation_family_sha256: str
+    positive_ordinal: int
+    negative_caption: str = field(repr=False)
+    generation_record_sha256: str
+    policy_sha256: str
+    included_weak: bool
+    label_source: str = "machine_caption_contradiction"
+    generator_model: str = "gpt-5.6-sol"
+
+    def __post_init__(self):
+        for key in (
+            self.source_sha256,
+            self.annotation_family_sha256,
+            self.generation_record_sha256,
+            self.policy_sha256,
+        ):
+            _sha_key(key)
+        if (
+            type(self.positive_ordinal) is not int
+            or not 0 <= self.positive_ordinal < 2**64
+            or type(self.negative_caption) is not str
+            or not self.negative_caption.strip()
+            or type(self.included_weak) is not bool
+            or self.label_source != "machine_caption_contradiction"
+            or self.generator_model != "gpt-5.6-sol"
+        ):
+            raise ValueError("weak rows require explicit occurrence and machine provenance")
+
+    @property
+    def caption_commitment(self) -> bytes:
+        return hashlib.sha256(
+            b"phaseset-parent-machine-caption-v1\0"
+            + bytes.fromhex(self.source_sha256)
+            + bytes.fromhex(self.annotation_family_sha256)
+            + self.positive_ordinal.to_bytes(8, "big")
+            + hashlib.sha256(self.negative_caption.encode("utf-8")).digest()
+            + bytes.fromhex(self.generation_record_sha256)
+            + bytes.fromhex(self.policy_sha256)
+        ).digest()
+
+
+class ParentWeakClipRows:
+    """Stage-specific frozen weak pool, with an unchanged original human prefix.
+
+    Stage training components are explicit: an outer fold may learn an old
+    main-validation component, but this pool must not contain that stage's
+    held-out or final-test rows. Evaluation delegates to human-only selection.
+    No language model, CLIP tower, motion, score, or RNG is accessed here.
+    """
+
+    def __init__(
+        self,
+        human: ParentHumanClipRows,
+        extra_original: FrozenClipTextBatch,
+        records: tuple[WeakCaptionRecord, ...],
+        *,
+        train_components: tuple[str, ...],
+        policy_sha256: str,
+    ):
+        _sha_key(policy_sha256)
+        if type(human) is not ParentHumanClipRows:
+            raise TypeError("need the original admitted human row provider")
+        if (
+            type(train_components) is not tuple
+            or not train_components
+            or len(set(train_components)) != len(train_components)
+            or any(type(c) is not str or not c for c in train_components)
+            or not set(train_components) <= {p.component for p in human.parents.values()}
+        ):
+            raise ValueError("weak pool needs explicit stage training components")
+        if (
+            type(records) is not tuple
+            or not records
+            or any(type(r) is not WeakCaptionRecord for r in records)
+        ):
+            raise ValueError("weak pool needs an exact nonempty record tuple")
+        _validate_text_batch(extra_original)  # No derived or relabeled pool.
+        if len(records) != len(extra_original.caption_commitments):
+            raise ValueError("weak machine encoding must cover exactly its admitted records")
+        for record, commitment, row in zip(
+            records,
+            extra_original.caption_commitments,
+            extra_original.receipt.caption_rows,
+            strict=True,
+        ):
+            parent = human.parents.get(record.annotation_family_sha256)
+            if (
+                parent is None
+                or parent.source_sha256 != record.source_sha256
+                or parent.component not in train_components
+            ):
+                raise ValueError("weak row is outside this stage's admitted training parents")
+            if (
+                record.positive_ordinal >= len(parent.captions)
+                or record.policy_sha256 != policy_sha256
+            ):
+                raise ValueError("weak positive occurrence or frozen policy differs")
+            if record.negative_caption in parent.captions:
+                raise ValueError("weak negative repeats an original human positive")
+            if (
+                commitment != record.caption_commitment
+                or row[2] != hashlib.sha256(record.negative_caption.encode("utf-8")).hexdigest()
+            ):
+                raise ValueError("weak cached sentence differs from the admitted machine record")
+        self.human, self.extra_original, self.records = human, extra_original, records
+        self.train_components, self.policy_sha256 = train_components, policy_sha256
+        self.by_family = {
+            family: tuple(i for i, r in enumerate(records) if r.annotation_family_sha256 == family)
+            for family in human.parents
+        }
+
+    def text(self, labels, *, training):
+        human_rows, human_cf = self.human.text(labels, training=training)
+        if not training:
+            return human_rows, human_cf
+        if any(p.component not in self.train_components for p in labels.parents):
+            raise ValueError("weak training cannot read stage-held-out labels")
+        indices = tuple(
+            i for p in labels.parents for i in self.by_family[p.annotation_family_sha256]
+        )
+        if not indices:
+            return human_rows, ParentWeakCounterfactualRows((), (), (), ())
+        rows = pool_frozen_clip_text_rows(
+            self.human.original,
+            self.extra_original,
+            prefix_indices=tuple(self.human.indices[c] for c in labels.caption_commitments),
+            extra_indices=indices,
+        )
+        offsets, cursor = {}, 0
+        for parent in labels.parents:
+            offsets[parent.annotation_family_sha256] = cursor
+            cursor += len(parent.captions)
+        records = tuple(self.records[i] for i in indices)
+        cf = ParentWeakCounterfactualRows(
+            tuple(r.source_sha256 for r in records),
+            tuple(offsets[r.annotation_family_sha256] + r.positive_ordinal for r in records),
+            tuple(range(cursor, cursor + len(records))),
+            tuple(r.included_weak for r in records),
+        )
+        return rows, cf
diff --git a/tests/test_parent_weak_clip_rows.py b/tests/test_parent_weak_clip_rows.py
new file mode 100644
index 0000000..6404535
--- /dev/null
+++ b/tests/test_parent_weak_clip_rows.py
@@ -0,0 +1,381 @@
+"""Analytic cached weak inputs; not generated native labels or convergence."""
+
+import copy
+from dataclasses import replace
+import hashlib
+
+import pytest
+import torch
+
+from phaseset_core import frozen_clip_text as clip
+from phaseset_core.capture_validation import CaptureValidationError, _validate_text_batch
+from phaseset_core.continuous_parent_host import ContinuousParentTrainingHost
+from phaseset_core.continuous_parent_training import ParentWeakCounterfactualRows
+from phaseset_core.parent_clip_rows import ParentHumanClipRows
+from phaseset_core.parent_disk_source import DevelopmentParentDiskSource
+from phaseset_core.parent_weak_clip_rows import ParentWeakClipRows, WeakCaptionRecord
+from phaseset_core.training import _capture_rng, _load_torch_checkpoint, _stable_hash
+from test_parent_clip_rows import _original, _task
+from test_phaseset_frozen_clip_cache_rehydrate import _fixture
+
+
+POLICY = "c" * 64
+
+
+def _encoded(captions, commitments):
+    embeddings, receipt = _fixture(len(captions), batch_size=2)
+    rows = tuple(
+        (i, c.hex(), hashlib.sha256(text.encode()).hexdigest(), *old[3:])
+        for i, (c, text, old) in enumerate(
+            zip(commitments, captions, receipt.caption_rows, strict=True)
+        )
+    )
+    receipt = replace(receipt, caption_rows=rows)
+    receipt = replace(
+        receipt,
+        frozen_embedding_cache_key_sha256=clip._rehydration_cache_key(
+            batch_size=receipt.batch_size,
+            caption_rows=rows,
+            chunk_ranges=receipt.chunk_ranges,
+            runtime_manifest_sha256=receipt.runtime_manifest_sha256,
+            snapshot_manifest_sha256=receipt.snapshot_manifest_sha256,
+            source_manifest_sha256=receipt.source_manifest_sha256,
+        ),
+    )
+    return clip.rehydrate_frozen_clip_text_batch(
+        embeddings,
+        receipt_json_bytes=receipt.canonical_json_bytes(),
+        expected_receipt_sha256=receipt.sha256,
+    )
+
+
+def _record(parent, ordinal=0, *, included=True):
+    return WeakCaptionRecord(
+        parent.source_sha256,
+        parent.annotation_family_sha256,
+        ordinal,
+        f"analytic machine alternative {ordinal} for {parent.source_sha256[:8]}",
+        "d" * 64,
+        POLICY,
+        included,
+    )
+
+
+def _extra(records):
+    return _encoded(
+        tuple(r.negative_caption for r in records), tuple(r.caption_commitment for r in records)
+    )
+
+
+def _pool(task=None):
+    task = _task() if task is None else task
+    human = ParentHumanClipRows(task, _original(task))
+    records = tuple(_record(p) for p in task.parents if p.component == "C01")
+    return ParentWeakClipRows(
+        human, _extra(records), records, train_components=("C01",), policy_sha256=POLICY
+    )
+
+
+def test_two_original_pool_keeps_prefix_bytes_without_encoder_or_rng(monkeypatch):
+    pool = _pool()
+    before = _stable_hash(_capture_rng())
+
+    def forbidden(*args, **kwargs):
+        raise AssertionError("cached pool attempted model/runtime loading")
+
+    monkeypatch.setattr(clip, "_real_backend", forbidden)
+    monkeypatch.setattr(clip, "_load_components", forbidden)
+    result = clip.pool_frozen_clip_text_rows(
+        pool.human.original, pool.extra_original, prefix_indices=(3, 1, 0), extra_indices=(1, 0)
+    )
+    embeddings, lineage, digest = _validate_text_batch(result, allow_row_selection=True)
+    assert torch.equal(embeddings[:3], pool.human.original.embeddings[[3, 1, 0]])
+    assert torch.equal(embeddings[3:], pool.extra_original.embeddings[[1, 0]])
+    assert lineage == tuple(pool.human.original.caption_commitments[i] for i in (3, 1, 0)) + tuple(
+        pool.extra_original.caption_commitments[i] for i in (1, 0)
+    )
+    assert result.receipt.origin_receipts == (
+        pool.human.original.receipt,
+        pool.extra_original.receipt,
+    )
+    assert result.receipt.prefix_count == 3 and digest == result.receipt.sha256
+    public = result.receipt.to_public_dict()
+    assert public["training_authorized"] is False and public["authority"] == 0
+    assert public["operation"].endswith("no_encoder_call")
+    assert b"analytic machine" not in result.receipt.canonical_json_bytes()
+    snapshot = result.embeddings
+    snapshot.zero_()
+    assert not torch.equal(snapshot, result.embeddings)
+    assert _stable_hash(_capture_rng()) == before
+    with pytest.raises(CaptureValidationError, match="not admitted"):
+        _validate_text_batch(result)
+    with pytest.raises(clip.FrozenClipTextAdapterError):
+        clip.rehydrate_frozen_clip_text_batch(
+            result.embeddings,
+            receipt_json_bytes=result.receipt.canonical_json_bytes(),
+            expected_receipt_sha256=result.receipt.sha256,
+        )
+    with pytest.raises(ValueError, match="original"):
+        clip.select_frozen_clip_text_rows(result, (0,))
+
+
+@pytest.mark.parametrize(
+    "identity",
+    ["snapshot_manifest_sha256", "live_model_manifest_sha256", "runtime_manifest_sha256"],
+)
+def test_pool_rejects_mismatched_encoding_identity(identity):
+    pool = _pool()
+    changed = copy.deepcopy(pool.extra_original)
+    object.__setattr__(
+        changed, "_FrozenClipTextBatch__receipt", replace(changed.receipt, **{identity: "f" * 64})
+    )
+    with pytest.raises(ValueError, match="same model"):
+        clip.pool_frozen_clip_text_rows(
+            pool.human.original, changed, prefix_indices=(0,), extra_indices=(0,)
+        )
+
+
+@pytest.mark.parametrize("change", ["prefix", "indices", "runtime"])
+def test_pool_receipt_ancestry_is_bound_to_original_rows(change):
+    pool = _pool()
+    result = clip.pool_frozen_clip_text_rows(
+        pool.human.original, pool.extra_original, prefix_indices=(0, 2), extra_indices=(1, 0)
+    )
+    receipt = result.receipt
+    if change == "prefix":
+        receipt = replace(receipt, prefix_count=1)
+    elif change == "indices":
+        receipt = replace(receipt, selected_indices=((0, 2), (0, 1)))
+    else:
+        receipt = replace(
+            receipt,
+            origin_receipts=(
+                receipt.origin_receipts[0],
+                replace(receipt.origin_receipts[1], runtime_manifest_sha256="f" * 64),
+            ),
+        )
+    object.__setattr__(result, "_FrozenClipTextBatch__receipt", receipt)
+    with pytest.raises(CaptureValidationError, match="ancestry"):
+        _validate_text_batch(result, allow_row_selection=True)
+
+
+def test_pool_rejects_repeated_commitments_between_originals():
+    pool = _pool()
+    with pytest.raises(ValueError, match="repeat"):
+        clip.pool_frozen_clip_text_rows(
+            pool.human.original, pool.human.original, prefix_indices=(0,), extra_indices=(0,)
+        )
+
+
+def test_stage_pool_parent_reordering_preserves_human_occurrences_and_cf_columns():
+    pool = _pool()
+    parents = tuple(p for p in reversed(tuple(pool.human.parents.values())) if p.component == "C01")
+    from phaseset_core.parent_retrieval_task import ParentRetrievalBatch
+
+    labels = ParentRetrievalBatch(parents)
+    rows, cf = pool.text(labels, training=True)
+    expected, _ = pool.human.text(labels, training=True)
+    assert rows.caption_commitments[: len(labels.captions)] == labels.caption_commitments
+    assert torch.equal(rows.embeddings[: len(labels.captions)], expected.embeddings)
+    assert len(labels.captions) == 3  # Equal human strings remain separate occurrences.
+    assert type(cf) is ParentWeakCounterfactualRows and not hasattr(cf, "verified_false")
+    assert cf.source_keys == labels.motion_source_keys
+    assert cf.negative_columns == (3, 4)
+    assert cf.positive_columns == (0, len(parents[0].captions))
+    assert cf.included_weak == (True, True)
+
+
+def test_evaluation_never_reads_machine_rows_or_changes_original_gallery():
+    pool = _pool()
+    from phaseset_core.parent_retrieval_task import ParentRetrievalBatch
+
+    labels = ParentRetrievalBatch(tuple(pool.human.parents.values()))
+    expected, _ = pool.human.text(labels, training=False)
+    pool.extra_original = None  # An unusable machine store must be irrelevant to eval.
+    rows, cf = pool.text(labels, training=False)
+    assert torch.equal(rows.embeddings, expected.embeddings)
+    assert rows.receipt.sha256 == expected.receipt.sha256
+    assert not cf.verified_false and len(rows.embeddings) == len(labels.captions)
+    with pytest.raises(ValueError, match="held-out"):
+        pool.text(labels, training=True)
+
+
+@pytest.mark.parametrize(
+    "drift", ["source", "policy", "ordinal", "positive_alias", "encoded_text", "commitment"]
+)
+def test_weak_records_reject_stage_and_cached_text_drift(drift):
+    pool = _pool()
+    records = list(pool.records)
+    extra = pool.extra_original
+    if drift == "source":
+        records[0] = replace(records[0], source_sha256="e" * 64)
+    elif drift == "policy":
+        records[0] = replace(records[0], policy_sha256="e" * 64)
+    elif drift == "ordinal":
+        records[0] = replace(records[0], positive_ordinal=100)
+    elif drift == "positive_alias":
+        records[0] = replace(
+            records[0],
+            negative_caption=pool.human.parents[records[0].annotation_family_sha256].captions[0],
+        )
+    elif drift == "encoded_text":
+        extra = _encoded(
+            ("changed text", records[1].negative_caption),
+            tuple(r.caption_commitment for r in records),
+        )
+    else:
+        extra = _encoded(
+            tuple(r.negative_caption for r in records), (b"x" * 32, records[1].caption_commitment)
+        )
+    with pytest.raises(ValueError):
+        ParentWeakClipRows(
+            pool.human, extra, tuple(records), train_components=("C01",), policy_sha256=POLICY
+        )
+
+
+def test_component_admission_allows_outer_fold_learning_but_not_held_out():
+    pool = _pool()
+    parent = next(p for p in pool.human.parents.values() if p.component == "C03")
+    record = _record(parent)
+    with pytest.raises(ValueError, match="training parents"):
+        ParentWeakClipRows(
+            pool.human,
+            _extra((record,)),
+            (record,),
+            train_components=("C01",),
+            policy_sha256=POLICY,
+        )
+    outer = ParentWeakClipRows(
+        pool.human, _extra((record,)), (record,), train_components=("C03",), policy_sha256=POLICY
+    )
+    from phaseset_core.parent_retrieval_task import ParentRetrievalBatch
+
+    rows, cf = outer.text(ParentRetrievalBatch((parent,)), training=True)
+    assert len(rows.embeddings) == 2 and cf.negative_columns == (1,)
+
+
+def test_no_candidate_parent_returns_weak_empty_without_fabricating_negatives():
+    pool = _pool()
+    record = pool.records[0]
+    pool = ParentWeakClipRows(
+        pool.human, _extra((record,)), (record,), train_components=("C01",), policy_sha256=POLICY
+    )
+    parent = next(
+        p
+        for p in pool.human.parents.values()
+        if p.component == "C01" and p.annotation_family_sha256 != record.annotation_family_sha256
+    )
+    from phaseset_core.parent_retrieval_task import ParentRetrievalBatch
+
+    rows, cf = pool.text(ParentRetrievalBatch((parent,)), training=True)
+    assert len(rows.embeddings) == len(parent.captions) and not cf.source_keys
+    assert type(cf) is ParentWeakCounterfactualRows
+
+
+def test_existing_disk_source_uses_separate_pool_only_for_training(tmp_path):
+    from test_parent_disk_source import _source
+
+    original, _, inputs, task, disk_record = _source(tmp_path)
+    human = ParentHumanClipRows(task, _original(task))
+    record = _record(task.parents[0], included=False)
+    pool = ParentWeakClipRows(
+        human, _extra((record,)), (record,), train_components=("C01",), policy_sha256=POLICY
+    )
+    source = DevelopmentParentDiskSource(
+        task,
+        (disk_record,),
+        yaw_eligibility=original.yaw_eligibility,
+        language_rows=original.language_rows,
+        human_clip_rows=human,
+        weak_clip_rows=pool,
+    )
+    labels = task.batch(tuple(human.parents))
+    text, cf = source.text(labels, training=True)
+    assert len(text.embeddings) == 2 and cf.included_weak == (False,)
+    text, cf = source.text(labels, training=False)
+    assert len(text.embeddings) == 1 and not cf.verified_false
+    assert (
+        source.capture(task.parents[0], seed=1729, epoch=0, training=False).frame_count
+        == inputs.capture.frame_count
+    )
+    with pytest.raises(ValueError, match="same admitted human"):
+        DevelopmentParentDiskSource(
+            task,
+            (disk_record,),
+            yaw_eligibility=original.yaw_eligibility,
+            language_rows=original.language_rows,
+            weak_clip_rows=pool,
+        )
+
+
+def test_cached_pool_actual_optimizer_and_resume_preserve_human_validation(tmp_path):
+    from test_continuous_parent_host import _setup, _events
+
+    system, task, source, config, bindings = _setup(tmp_path)
+    human = ParentHumanClipRows(task, _original(task))
+    records = tuple(_record(p) for p in task.parents if p.component in config.train_components)
+    pool = ParentWeakClipRows(
+        human,
+        _extra(records),
+        records,
+        train_components=config.train_components,
+        policy_sha256=POLICY,
+    )
+    source.text = pool.text
+    initial = copy.deepcopy(system)
+    full = ContinuousParentTrainingHost(system, task, source, config, bindings).fit(
+        tmp_path / "full"
+    )
+    stopped = ContinuousParentTrainingHost(
+        copy.deepcopy(initial), task, source, config, bindings
+    ).fit(tmp_path / "stopped", stop_after_steps=1)
+    resumed_model = copy.deepcopy(initial)
+    resumed = ContinuousParentTrainingHost(resumed_model, task, source, config, bindings).fit(
+        tmp_path / "resumed",
+        resume_checkpoint=stopped.latest_checkpoint.path,
+        resume_sha256=stopped.latest_checkpoint.sha256,
+    )
+    assert full.outcome == resumed.outcome == "COMPLETED"
+    assert full.validation_history == resumed.validation_history
+    expected, _ = _load_torch_checkpoint(full.latest_checkpoint.path)
+    actual, _ = _load_torch_checkpoint(resumed.latest_checkpoint.path)
+    for key in ("model", "optimizer", "scheduler", "rng"):
+        assert _stable_hash(actual[key]) == _stable_hash(expected[key])
+    updates = [event for event in _events(tmp_path / "full") if event["phase"] == "update"]
+    assert len(updates) == 2 and all(
+        event["weak_cf"] == 4 and event["verified_cf"] == 0 and event["human_rows"] == 5
+        for event in updates
+    )
+    # Real analytic optimizer execution is not native learning or label evidence.
+
+
+def test_base_stage_rejects_cached_weak_pool_before_loading_motion(tmp_path):
+    from test_continuous_base_retrieval import _setup
+    from phaseset_core.continuous_parent_training import backward_loaded_parent_batch
+
+    system, task, source, config, _ = _setup(tmp_path)
+    human = ParentHumanClipRows(task, _original(task))
+    records = tuple(_record(p) for p in task.parents if p.component in config.train_components)
+    pool = ParentWeakClipRows(
+        human,
+        _extra(records),
+        records,
+        train_components=config.train_components,
+        policy_sha256=POLICY,
+    )
+    labels = task.batch(
+        tuple(
+            p.annotation_family_sha256
+            for p in task.parents
+            if p.component in config.train_components
+        )
+    )
+    rows, cf = pool.text(labels, training=True)
+
+    def forbidden(*args):
+        raise AssertionError("base loaded motion despite inadmissible weak inputs")
+
+    with pytest.raises(ValueError, match="base"):
+        backward_loaded_parent_batch(
+            system, labels, rows, load_view=forbidden, counterfactuals=cf, cf_weight=0.0, margin=0.0
+        )

From 9ee2ec239143b5d32cddb55246b92ec58fdff887 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 14:57:32 -0500
Subject: [PATCH 59/95] docs: record actual weak-caption and CLIP preparation

---
 RELEASE_FILES.sha256                   |  6 +++---
 docs/PHASESET_V2_PARENT_DISK_SOURCE.md |  9 ++++++---
 docs/PHASESET_V2_PARENT_HOST.md        |  9 +++++++--
 docs/PHASESET_V2_WEAK_CLIP_POOL.md     | 21 +++++++++++++++++----
 4 files changed, 33 insertions(+), 12 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 36e2bce..b8be8e9 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -81,8 +81,8 @@ ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASE
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
 9ead6b89eacf034b52dcc15544e25be56e225c1b0432475b66c1a300adfa562d	3961	docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
-c94c1538acb0e892c91c54ad1c6740073c9d75623465aa665ab1ea81b9b1ead3	2904	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
-3eb5f4237db4af02d49e8eb62a318ef68ac706bf14cc1b405bf9fa837e8d5705	7431	docs/PHASESET_V2_PARENT_HOST.md
+5a106355600b7223e516d5f270d81697a53e9c267930d34189860803b32c3fab	3116	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
+429cb14d5054df0f2f1beef3b02af3d222705933567ddd3e2079f69a104920f8	7849	docs/PHASESET_V2_PARENT_HOST.md
 1eef1de33e2f1d193a6a02a0a982df0c9dea7b1f9406b4cbd155d22ef7378f51	3314	docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
@@ -91,7 +91,7 @@ bab08ac05ce49d686cf19b64cf19b2dc05a4928d24e9e5b19ef122ac5c732632	3567	docs/PHASE
 0e7c300498c28e83f8df3c3d78e2be44e8d9270e0005e79f4ec9847cbfc8cc16	4079	docs/PHASESET_V2_STUDY_PROCESS.md
 81b47eff03531162dd2ab6b3d29a0ca9909326e7b13dec34776551d171b182c7	1727	docs/PHASESET_V2_TMR_PARENT_TRAINING.md
 8ed656e8f4c6e1c9513bf69795275cba9948643cdc0c3ba47043602b30ece251	4370	docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md
-b17d807c2ef5df818dbb99c274186bad5617a14903576524f6e76bebb1783bd9	3619	docs/PHASESET_V2_WEAK_CLIP_POOL.md
+37664dd6c7a08ace013247c4b5bd7fe31e87a475e554d750056c811d7d89034e	4591	docs/PHASESET_V2_WEAK_CLIP_POOL.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
diff --git a/docs/PHASESET_V2_PARENT_DISK_SOURCE.md b/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
index 27deb1a..73b9c60 100644
--- a/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
+++ b/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
@@ -30,9 +30,12 @@ runtime identities belong in the private execution inputs.
 
 The optional [frozen human CLIP rows](PHASESET_V2_FROZEN_CLIP_ROWS.md) connect
 the base/V2 human-only text interface without loading a tower at training.
-This is not the entire verified-CF interface: that still needs genuinely
-two-human-verified counterfactual rows and their separate admission. The source
-also supplies no production lock, heartbeat, budget, retention, hardware,
+The legacy verified-CF branch still needs real human provenance. Under the
+user's [supervision amendment](PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md),
+the separate [weak CLIP pool](PHASESET_V2_WEAK_CLIP_POOL.md) supplies disclosed
+machine-caption training targets without granting human verification. Neither
+branch changes the human-only primary gallery. The source supplies no
+production lock, heartbeat, budget, retention, hardware,
 pilot, formal-stage or final-test authority.
 
 Analytic tests exercise full timelines with a rejected gap, RNG-independent
diff --git a/docs/PHASESET_V2_PARENT_HOST.md b/docs/PHASESET_V2_PARENT_HOST.md
index 1968596..f6fbe05 100644
--- a/docs/PHASESET_V2_PARENT_HOST.md
+++ b/docs/PHASESET_V2_PARENT_HOST.md
@@ -6,7 +6,9 @@ window-unit training recipe, a formal-study authorization, a trained B2 anchor,
 or a literature-baseline implementation. A software qualification is not a
 native learning result. The formal study still requires the common bounded
 pilot, explicit parent-unit amendment, floors, hardware profile and 87-stage
-freeze. The two-human relationship audit cannot be supplied by this code.
+freeze. The legacy human relationship audit cannot be supplied by this code;
+the [user amendment](PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md) now permits
+a separate weak-training branch, not replacement of independent motion truth.
 
 ## Inputs and updates
 
@@ -46,7 +48,10 @@ Optional CF sentences follow the human retrieval prefix. Their columns have no
 fabricated retrieval positive. A verified-false bit is not human provenance:
 the private manifest must bind the actual blind two-human records. Unverified
 CFs contribute no CF term. No label generation or data discovery occurs here.
-Base stages reject CF rows, extra text columns and any CF weight. They optimize
+`ParentWeakCounterfactualRows` instead uses `included_weak` and the separate
+weak loss/count fields. Its source is disclosed machine-caption contradiction,
+never human `verified_false`. The original retrieval prefix and all validation
+rows stay human-only. Base stages reject CF rows, extra text columns and any CF weight. They optimize
 only the existing variable-positive symmetric retrieval InfoNCE.
 
 Optimizer: AdamW, weight decay 0.01, clipping 1.0, 5% warmup followed by cosine
diff --git a/docs/PHASESET_V2_WEAK_CLIP_POOL.md b/docs/PHASESET_V2_WEAK_CLIP_POOL.md
index 13855c5..cc7ab10 100644
--- a/docs/PHASESET_V2_WEAK_CLIP_POOL.md
+++ b/docs/PHASESET_V2_WEAK_CLIP_POOL.md
@@ -40,11 +40,24 @@ metadata 文件（19 hashed 加 receipt）双端核验，receipt SHA-256：
 原生产三个预算事件与 summary 不变；正式 **0/87**、pilot **0/12**，本次
 GPU 0 秒。analytic optimizer 用例不是原生学习或论文结果。
 
-## 准入仍待真实生成与训练
+## 后续真实训练文本准备（不是训练结果）
 
-本资格没有生成真实机器标签、没有编码真实弱文本，不关闭私有来源/语义、
-训练 yaw、native CF backward、GPU profile 或正式实验 gate。实际生成
-prompt/选择及原始输出需在训练前冻结并披露；机器标签永不升级为人工
+同日完成一次固定 pilot-learning 文本批次：34 个训练 parent、169 条原官方
+人工 occurrence，GPT-5.6-Sol 纳入 57 条弱句子、排除 112 条。仅读取这些
+parent 的人工 holistic 文本；不读取 motion、phase、模型分数、held-out
+文本或 test。每 occurrence 只允许一次候选或排除，不按配额补齐，不做
+语义重生成。一次无语义输出的真实网络超时保留证据，修复传输后单次恢复；
+首个成功 job 在完整批次复用。原始私有生成记录和完整批次均已冻结。
+
+真实 CPU CLIP 准备于 19:40:37–19:42:15 UTC 完成：只编码这 57 条新增
+机器句子，未截断、最大未截断长度 71 tokens。原 1,334 条人工向量没有
+重新编码；当前 169 条训练人工前缀按位不变。全部 253 个开发 parent 的
+evaluation 仍只返回原人工文本，stage held-out 不进入 weak training。
+模型、RNG、源码、资产及生产预算前后不变，所有退出码 0、stderr 空。
+
+这是数据准备，不是 native learning、pilot 或检索增益。训练 yaw、实际
+native CF backward、GPU profile 及正式实验仍待；正式 **0/87**、pilot
+**0/12**。机器标签永不升级为人工
 `verified_false`、独立运动真值或历史两人挑战成功。
 
 公开选择重现（独立合适 CPU 环境，不含私有预算 caller）：

From 2a2543b80df625a228b586e9da39750c9061ba2b Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 15:35:37 -0500
Subject: [PATCH 60/95] docs: record native full-capture weak-supervision
 backward evidence

---
 RELEASE_FILES.sha256               |  4 ++--
 docs/PHASESET_V2_PARENT_HOST.md    |  7 +++++++
 docs/PHASESET_V2_WEAK_CLIP_POOL.md | 24 +++++++++++++++++++++---
 3 files changed, 30 insertions(+), 5 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index b8be8e9..2092c48 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -82,7 +82,7 @@ ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASE
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
 9ead6b89eacf034b52dcc15544e25be56e225c1b0432475b66c1a300adfa562d	3961	docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
 5a106355600b7223e516d5f270d81697a53e9c267930d34189860803b32c3fab	3116	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
-429cb14d5054df0f2f1beef3b02af3d222705933567ddd3e2079f69a104920f8	7849	docs/PHASESET_V2_PARENT_HOST.md
+5136d783b69b378cd40908c0fbd404e4048f9933c505f086f46288024cf217f3	8309	docs/PHASESET_V2_PARENT_HOST.md
 1eef1de33e2f1d193a6a02a0a982df0c9dea7b1f9406b4cbd155d22ef7378f51	3314	docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
@@ -91,7 +91,7 @@ bab08ac05ce49d686cf19b64cf19b2dc05a4928d24e9e5b19ef122ac5c732632	3567	docs/PHASE
 0e7c300498c28e83f8df3c3d78e2be44e8d9270e0005e79f4ec9847cbfc8cc16	4079	docs/PHASESET_V2_STUDY_PROCESS.md
 81b47eff03531162dd2ab6b3d29a0ca9909326e7b13dec34776551d171b182c7	1727	docs/PHASESET_V2_TMR_PARENT_TRAINING.md
 8ed656e8f4c6e1c9513bf69795275cba9948643cdc0c3ba47043602b30ece251	4370	docs/PHASESET_V2_WEAK_CF_TRAINING_SEAM.md
-37664dd6c7a08ace013247c4b5bd7fe31e87a475e554d750056c811d7d89034e	4591	docs/PHASESET_V2_WEAK_CLIP_POOL.md
+53f55d1554cc9e1262a11f0cbe69e2d41526f2aeca38e063082b0439346db8c9	5726	docs/PHASESET_V2_WEAK_CLIP_POOL.md
 a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPARED_DATA_V2.md
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
diff --git a/docs/PHASESET_V2_PARENT_HOST.md b/docs/PHASESET_V2_PARENT_HOST.md
index f6fbe05..6da4d58 100644
--- a/docs/PHASESET_V2_PARENT_HOST.md
+++ b/docs/PHASESET_V2_PARENT_HOST.md
@@ -123,3 +123,10 @@ resume, concrete input drift, truncated checkpoints and wrong progress cursor.
 They also retain the existing independent dense-versus-replay gradient tests.
 None is evidence of real native learning, a paper metric, GPU cost, human CF
 truth, or successful completion of any of the 87 formal stages.
+
+A separate native CPU qualification has now replayed two complete four-person
+captures through the actual 512D V2 score/loss backward with ten original human
+rows and six disclosed machine weak negatives. All 55 trainable gradient
+tensors were finite and weights stayed unchanged; no optimizer was created.
+See the [weak-pool qualification scope](PHASESET_V2_WEAK_CLIP_POOL.md).
+This closes that input/loss seam, not native learning or a hardware cost claim.
diff --git a/docs/PHASESET_V2_WEAK_CLIP_POOL.md b/docs/PHASESET_V2_WEAK_CLIP_POOL.md
index cc7ab10..f8654d7 100644
--- a/docs/PHASESET_V2_WEAK_CLIP_POOL.md
+++ b/docs/PHASESET_V2_WEAK_CLIP_POOL.md
@@ -55,9 +55,27 @@ parent 的人工 holistic 文本；不读取 motion、phase、模型分数、hel
 evaluation 仍只返回原人工文本，stage held-out 不进入 weak training。
 模型、RNG、源码、资产及生产预算前后不变，所有退出码 0、stderr 空。
 
-这是数据准备，不是 native learning、pilot 或检索增益。训练 yaw、实际
-native CF backward、GPU profile 及正式实验仍待；正式 **0/87**、pilot
-**0/12**。机器标签永不升级为人工
+这部分是数据准备，不是 native learning、pilot 或检索增益。
+
+## 完整原生 capture 的实际 weak-CF backward
+
+同日 19:54:32–19:59:08 UTC，一次 CPU FP32/单线程检查使用两个完整
+四人 capture（各 5,800 帧、共 54 个有效窗口），不裁短、不拼接。
+完整 512D V2、随机未训练且冻结的 B2，使用 10 条原人工正例和 6 条
+实际生成弱负例，进行全 capture score cache、RNG replay 和 VJP。
+55 个可训练梯度张量全部存在且有限，有非零梯度；模型权重按位未改变。
+cache/replay/VJP 耗时 264.08 秒；退出码均 0、stderr 空。
+receipt SHA-256：
+`5e5889cdf7666b2f592dde5c81db4f6e536d236187ae1018203b45fb54f4b50d`。
+
+没有 optimizer、GPU、非零训练 yaw、正式学习或检索成绩；这不是完整训练
+成本或性能结论。生产预算保留历史 GPU 用量，新增两个 CPU profile
+记账事件已闭合，正式 **0/87**、pilot **0/12**。
+
+一次训练文本 yaw 分类在固定时限内没有返回，失败证据保留，不重生成。
+该调用没有提供任何资格决定。后续诊断对未知资格显式使用零 yaw，并记录
+“资格未知”，不能称为语义否决、非零 yaw 验收或全关增强政策通过。
+GPU profile 和正式学习仍待实际可用资源。机器标签永不升级为人工
 `verified_false`、独立运动真值或历史两人挑战成功。
 
 公开选择重现（独立合适 CPU 环境，不含私有预算 caller）：

From 6e63f5ab51bd14d79da5715001674c18a5863663 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 16:14:45 -0500
Subject: [PATCH 61/95] fix: preserve literature optimizer settings across
 parent resume

---
 RELEASE_FILES.sha256                        |  13 +-
 docs/MIME_SET_ADAPTATION.md                 |   6 +
 docs/PHASESET_V2_PARENT_HOST.md             |  25 ++-
 docs/TMR_SET_ADAPTATION.md                  |   9 ++
 docs/WAMO_SET_ADAPTATION.md                 |   7 +
 src/phaseset_core/continuous_parent_host.py |  38 ++++-
 src/phaseset_core/literature_parent_host.py |   2 +
 tests/test_parent_optimizer_config.py       | 163 ++++++++++++++++++++
 8 files changed, 249 insertions(+), 14 deletions(-)
 create mode 100644 tests/test_parent_optimizer_config.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 2092c48..2d1f06e 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -37,7 +37,7 @@ d03a1569556beb1b3ad7f7f501d7bdc40e271f6912c173e403f75458fd6ed238	6158	docs/HOST_
 e38d6f254f71d0027a0b82e1dc3ec95d23a9041831d21d41e96acf10b81e155f	19223	docs/HOST_RUNTIME_INTEGRATION.md
 5dd898347d3646cde4a74e10afa8ba1d12703e3d0c6df51e5113659a31808086	3606	docs/LATENCY_PROGRESS_JOURNAL.md
 f734981b1e98813719b5debe769dfeed0c63c127a35632cc54399f46e4b80e16	2760	docs/LEGACY_TEST_STABILITY.md
-8490b9076c14891cd93b5d91743f5ef58dd96d13d28d334cfa5d3372e1fd44b7	4208	docs/MIME_SET_ADAPTATION.md
+646f1fc5e4cb7ee423891ad6d212c83ce2eae604754b738ff8a78a3870d0bc7a	4607	docs/MIME_SET_ADAPTATION.md
 0fd1d1289229788be8bf8b4e6c81d807d8e32a223a90717f4a27d64b35c5d6ad	3005	docs/MORLET_PORTABILITY.md
 1cbb857ba962b29eae75d206aa49657f481e559085e062a2ef51ab4a6d87a220	3440	docs/PERIODIC_CACHE_EXECUTION.md
 a9e215279d354999c1f949f776a131b8cf4cec850abd81459c5c0d8cee0c8b03	4356	docs/PERIODIC_CAPTURE_TRAINING_CACHE.md
@@ -82,7 +82,7 @@ ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASE
 36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
 9ead6b89eacf034b52dcc15544e25be56e225c1b0432475b66c1a300adfa562d	3961	docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
 5a106355600b7223e516d5f270d81697a53e9c267930d34189860803b32c3fab	3116	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
-5136d783b69b378cd40908c0fbd404e4048f9933c505f086f46288024cf217f3	8309	docs/PHASESET_V2_PARENT_HOST.md
+667eba26ecbbe11016064e9068764a1a86320e47d68b8e1eb44b422936721e8e	9609	docs/PHASESET_V2_PARENT_HOST.md
 1eef1de33e2f1d193a6a02a0a982df0c9dea7b1f9406b4cbd155d22ef7378f51	3314	docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
 3d13f9b828c5deb62ff50e75dc6bd6c7663d3174ac636b4794c62412490a095b	2496	docs/PHASESET_V2_PARENT_TRAINING.md
@@ -96,8 +96,8 @@ a05236e99b1607555a4d45dc99b35a8b0a13b69257ef9ddb3a3f434cac576d4a	8323	docs/PREPA
 2a78ffbc8db0040747f95935d4bb77f40bdbf7755f3a4619a63e153fc4a6a7b1	6393	docs/RELEASE_VERIFICATION.md
 abf26d1ae851ea941df4045c9f97ae04cc1aee75d233c166116ed71fed0e1170	4012	docs/RESIDUAL_RESUME.md
 c2c47d122f77ff5976d4f148a1a39c3089e78c47213b071427fd203e638e676c	2807	docs/TMR_COMPONENTS.md
-a83d6c241bce7e0adb2add20b4d48247be03a2dda7b3d2cd8b9a40171e5bb23f	4424	docs/TMR_SET_ADAPTATION.md
-0ff9f1c8e8301ca6b13f1ee51e694a8dd05d452e21de9f6e28939263b7910067	4467	docs/WAMO_SET_ADAPTATION.md
+c310ce446533d9d0bb2ab1bebc30104c7ac17cb5cc84e34d1fa7bef2ad9eed19	5047	docs/TMR_SET_ADAPTATION.md
+b4723657c23ca109b8c4ee21862ba7826dd9c2e1229eec648d072bec48436c3a	4935	docs/WAMO_SET_ADAPTATION.md
 8d689f49b2704ff7c5988eff8a7e3e062f209b85d18425ccdba1e1099581079b	10658	docs/phasepair_research_contract_20260824_165840.md
 8d4b03326029b2a06478295b7328d61181d022604b1465bccd3a6cfcc8ce9704	2155	figures/EDITABLE_FIGURES.json
 efb48515925d5412bc840a5ae07415cda4645591deaa1ab6aca90e1e4f528591	4208	figures/README.md
@@ -178,7 +178,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 80e6d497ff9bb4354664cb1a054cdb550ab222e92c841813afc27af6d3759495	5603	src/phaseset_core/continuous_base_retrieval.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
-55813c17f038b9999c136160da54d3f307fe6d59fe75648cddc5ce608941808a	29782	src/phaseset_core/continuous_parent_host.py
+65b1288da19f30cbe92d877d2a3d27ca04cb804634d75c681806e830f0bc4db8	31178	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
 6af5ecfcadc139acfdb3fe2d2e00995a40e90522a821172bc44e7a3604028dbc	10571	src/phaseset_core/continuous_retrieval.py
@@ -195,7 +195,7 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 7c491ef7d65cddffa264d68d0a9fdd9216180bb741f85dd662cb570ca6e872f1	77027	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
-50a6240c62827c89e62939863ef5e12580e886b80e1997c15bf1423d37610779	8555	src/phaseset_core/literature_parent_host.py
+a037e15e8c70b8e37be634229f2fc799859d09c443150c79c47c2d6d2e598002	8708	src/phaseset_core/literature_parent_host.py
 02a11d20ac299c4886479b863f2f614512365a9f1fe800861b2f261dbdad1c7a	9101	src/phaseset_core/literature_parent_training.py
 f0d7c36ecda4326ca2b1f9461fdc812039cae33739595c60d40a436f0051edb7	6976	src/phaseset_core/mime_language.py
 f7cb1c0893b0b504359e34e39dd24651aaa86c1b65e98d2ed0469c6d5ce84d4d	6920	src/phaseset_core/mime_parent_curriculum.py
@@ -256,6 +256,7 @@ d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test
 3b23c2b08ed81029ca6239cbb63b98093077786163aaf5bb47ccfefb21c6ef24	7117	tests/test_parent_capture.py
 fc0c27fb68c7d119d1cc7a48afeeff5a707cab9ec206eb5ad85c606e31656490	9443	tests/test_parent_clip_rows.py
 487ee577859df867298041fe731166cd211506e5f211d3c508eeec5aa282640f	5825	tests/test_parent_disk_source.py
+a070e5ce27d454531125639eea5353a222fe01227c0fabee6d8be6dcdffc5e36	7126	tests/test_parent_optimizer_config.py
 32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
 cdf4d6dcea5c29e44ebb3f2f91945fd944a8026a7abe91eaa47beb59428f159c	12935	tests/test_parent_run_monitor.py
 d189e29ef4ef81b9604a352a7828ff97c789a79cafda18e8928056faf08a7825	13424	tests/test_parent_weak_cf.py
diff --git a/docs/MIME_SET_ADAPTATION.md b/docs/MIME_SET_ADAPTATION.md
index b73116a..446815e 100644
--- a/docs/MIME_SET_ADAPTATION.md
+++ b/docs/MIME_SET_ADAPTATION.md
@@ -48,6 +48,12 @@ two-layer GELU frame fusion, ratio epsilon 1e-8 and initial logit scale 1/0.07
 are disclosed implementation choices where author code is not available.
 The existing three MIME short-pilot slots govern eligible choices, not added HPO.
 
+`ParentHostConfig.for_literature("MIME-Set", ...)` declares AdamW, learning rate
+1e-4 and weight decay 1e-4 rather than inheriting the internal AdamW/0.01 decay.
+These settings are explicit in the run/checkpoint config and must match on
+resume. The common group host's 5% warmup/cosine schedule and gradient clipping
+at 1.0 are disclosed project adaptations; they are not asserted author defaults.
+
 Tests include original-input dimensions, independent full-time attention
 equations, all parameter families, endpoint swap, all K=3 permutations and
 input-gradient equivariance, dropout/checkpoint/RNG equality, missing root steps,
diff --git a/docs/PHASESET_V2_PARENT_HOST.md b/docs/PHASESET_V2_PARENT_HOST.md
index 6da4d58..3915ca4 100644
--- a/docs/PHASESET_V2_PARENT_HOST.md
+++ b/docs/PHASESET_V2_PARENT_HOST.md
@@ -1,7 +1,7 @@
 # Complete-parent base and residual training host
 
 `continuous_parent_host.py` connects the qualified full-gallery gradient replay
-to real FP32 AdamW updates, checkpoint selection and restart. It is not the old
+to real FP32 Adam(W) updates, checkpoint selection and restart. It is not the old
 window-unit training recipe, a formal-study authorization, a trained B2 anchor,
 or a literature-baseline implementation. A software qualification is not a
 native learning result. The formal study still requires the common bounded
@@ -54,7 +54,7 @@ never human `verified_false`. The original retrieval prefix and all validation
 rows stay human-only. Base stages reject CF rows, extra text columns and any CF weight. They optimize
 only the existing variable-positive symmetric retrieval InfoNCE.
 
-Optimizer: AdamW, weight decay 0.01, clipping 1.0, 5% warmup followed by cosine
+Internal default optimizer: AdamW, weight decay 0.01, clipping 1.0, 5% warmup followed by cosine
 decay, default residual learning rate 3e-4 and 20 complete epochs. Seeds remain
 1729, 2718 and 31415. Batch size is explicitly **parents**, not 128 windows.
 The short qualification schedules are not the formal/pilot freeze. There is
@@ -94,7 +94,7 @@ manifest, code including the source adapter, qualified runtime and frozen
 base identity. This caller-provided binding is not a substitute for real
 qualification or permission.
 
-Checkpoints contain model, AdamW, scheduler, Python/NumPy/Torch RNG, complete
+Checkpoints contain model, configured optimizer, scheduler, Python/NumPy/Torch RNG, complete
 epoch/batch cursor, parents seen, validation history and selected artifact.
 Resume verifies the exact predecessor digest, payload, same execution inputs,
 cursor/census, frozen anchor and optimizer/scheduler progress. A resumed host
@@ -113,6 +113,25 @@ manifests, checkpoints, caption text or licensed physical cache files.
 
 ## Qualification scope
 
+Optimizer family and weight decay are explicit `ParentHostConfig` settings,
+serialized in the run/checkpoint manifest and checked on resume. Internal
+base/residual defaults remain AdamW and 0.01 decay. `for_literature` supplies
+declared paper-optimizer starting points: TMR AdamW/0.01, WaMo Adam/0,
+MIME AdamW/1e-4, each at lr1e-4. Explicit bounded-pilot overrides remain
+visible; they are not hidden author defaults. The shared 5%-warmup/cosine,
+clipping, complete-parent schedule and validation endpoint are group-study
+adaptations, not claims of original single-/two-person training reproduction.
+The earlier hard-coded host could not express WaMo Adam or MIME decay; no
+native/formal learning used that host before this correction.
+
+The scoped server CPU qualification executed 17 cases with no skips. It
+includes actual analytic TMR/WaMo/MIME optimizer loops, independent coupled
+Adam versus decoupled AdamW first-update oracles, the unchanged legacy-default
+oracle, configuration-drift rejection before loading training inputs, and
+dropout interruption/resume equality for Adam/0 and AdamW/1e-4 across model,
+optimizer, scheduler and RNG. Production budget history was unchanged.
+These are software execution checks, not native training or retrieval results.
+
 Tests exercise actual registered 512D B0/B1/B2 forward and all-parameter
 checkpointed backward with active dropout, plus an independent per-window
 pooling oracle. Separate, clearly marked analytic fixtures exercise the real
diff --git a/docs/TMR_SET_ADAPTATION.md b/docs/TMR_SET_ADAPTATION.md
index c870aef..11c18b2 100644
--- a/docs/TMR_SET_ADAPTATION.md
+++ b/docs/TMR_SET_ADAPTATION.md
@@ -15,6 +15,15 @@ features and separate normalized MPNet sentence features, not sentence CLIP.
 Group, text and decoder defaults are 256D, FFN 1024, six layers, four heads,
 dropout 0.1. The capture hierarchy/PMA are additional disclosed capacity.
 
+The declared optimizer starting point is lr1e-4 from the
+[pinned training YAML](https://github.com/Mathux/TMR/blob/6d74688730d15d43b0a755ce2b0e1f2d76138fc1/configs/model/tmr.yaml),
+with AdamW from the
+[pinned optimizer implementation](https://github.com/Mathux/TMR/blob/6d74688730d15d43b0a755ce2b0e1f2d76138fc1/src/model/temos.py).
+That call leaves weight decay at the PyTorch AdamW default, made explicit as
+0.01 in this host. `ParentHostConfig.for_literature` exposes these values;
+bounded pilot overrides must be declared. The common group-study warmup/cosine
+schedule is an adaptation, not the upstream training loop.
+
 [The language loader](../src/phaseset_core/tmr_language.py) checks every
 tokenizer/config/checkpoint file against pinned public digests before loading
 from private directories, with local-only safetensors and no remote code.
diff --git a/docs/WAMO_SET_ADAPTATION.md b/docs/WAMO_SET_ADAPTATION.md
index c5e3219..a64d3ed 100644
--- a/docs/WAMO_SET_ADAPTATION.md
+++ b/docs/WAMO_SET_ADAPTATION.md
@@ -35,6 +35,13 @@ orthogonal or perfectly reconstructing. Initial reconstruction is tested.
 The existing three WaMo short-pilot slots, not an additional search, govern
 any permitted configuration selection before the common contract freezes.
 
+`ParentHostConfig.for_literature("WaMo-Set", ...)` declares Adam, learning rate
+1e-4 and zero weight decay (no decay is specified in the paper), instead of
+the internal host's AdamW/0.01 default. The settings are checkpoint/resume-bound;
+pilot overrides must be explicit. The common group host adds 5% warmup before
+cosine decay and clips gradients at 1.0. These are disclosed shared training
+adaptations, not extra purported author settings or convergence evidence.
+
 Multi-person/whole-parent extensions:
 
 - Reuse the admitted complete body22 capture contract; shared per-actor
diff --git a/src/phaseset_core/continuous_parent_host.py b/src/phaseset_core/continuous_parent_host.py
index 7b09b83..e25d1c1 100644
--- a/src/phaseset_core/continuous_parent_host.py
+++ b/src/phaseset_core/continuous_parent_host.py
@@ -81,6 +81,8 @@ class ParentHostConfig:
     cf_weight: float = 0.2
     cf_margin: float = 0.2
     stage: str = "residual"
+    optimizer: str = "adamw"
+    weight_decay: float = 0.01
 
     @classmethod
     def for_base(
@@ -105,6 +107,29 @@ def for_base(
             },
         )
 
+    @classmethod
+    def for_literature(cls, method, seed, train_components, validation_components, **settings):
+        """Declared paper optimizer defaults; shared group schedule is an adaptation.
+
+        TMR's pinned implementation uses AdamW with its .01 default decay.
+        WaMo specifies Adam; MIME specifies AdamW with 1e-4 decay. All specify
+        lr1e-4. Bounded pilot choices may override these explicitly; this
+        helper grants no pilot, native-training or original-reproduction claim.
+        """
+        defaults = {
+            "TMR-Set": {"optimizer": "adamw", "weight_decay": 0.01},
+            "WaMo-Set": {"optimizer": "adam", "weight_decay": 0.0},
+            "MIME-Set": {"optimizer": "adamw", "weight_decay": 1e-4},
+        }
+        if method not in defaults:
+            raise ValueError("need an explicit registered literature method")
+        return cls.for_base(
+            seed,
+            train_components,
+            validation_components,
+            **{"learning_rate": 1e-4, **defaults[method], **settings},
+        )
+
     def __post_init__(self):
         if type(self.seed) is not int or self.seed not in OFFICIAL_SEEDS:
             raise ValueError("host requires one of the three fixed seeds")
@@ -122,11 +147,13 @@ def __post_init__(self):
             raise ValueError("epoch count must be a positive integer")
         if type(self.parent_batch_size) is not int or self.parent_batch_size < 2:
             raise ValueError("parent batch size must be at least two")
-        for value in (self.learning_rate, self.cf_weight, self.cf_margin):
+        for value in (self.learning_rate, self.cf_weight, self.cf_margin, self.weight_decay):
             if type(value) not in (int, float) or not math.isfinite(value) or value < 0:
                 raise ValueError("loss/optimizer scalars must be finite nonnegative numbers")
         if self.learning_rate == 0:
             raise ValueError("learning rate must be positive")
+        if self.optimizer not in ("adam", "adamw"):
+            raise ValueError("optimizer must explicitly be adam or adamw")
         if self.stage not in ("base", "residual"):
             raise ValueError("host stage must explicitly be base or residual")
         if self.stage == "base" and (self.cf_weight != 0 or self.cf_margin != 0):
@@ -180,7 +207,7 @@ def _write_json_once(path: Path, payload: dict) -> None:
 
 
 class ContinuousParentTrainingHost:
-    """AdamW/5%-warmup/cosine, clip1, full-gallery validation, immutable resume.
+    """Explicit Adam(W)/5%-warmup/cosine, clip1, validation, immutable resume.
 
     FP32 only; no automatic BF16, shorter timeline, dropped tail, sampled
     negatives or toy-model fallback. One effective update is one complete
@@ -264,8 +291,9 @@ def _initialize(self, system, task, source, config, bindings):
             "steps_per_epoch": self._steps_per_epoch,
             "total_steps": self._total_steps,
         }
-        self._optimizer = torch.optim.AdamW(
-            self._parameters, lr=config.learning_rate, weight_decay=0.01
+        optimizer_type = torch.optim.Adam if config.optimizer == "adam" else torch.optim.AdamW
+        self._optimizer = optimizer_type(
+            self._parameters, lr=config.learning_rate, weight_decay=config.weight_decay
         )
         self._scheduler = torch.optim.lr_scheduler.LambdaLR(
             self._optimizer, lambda step: _learning_rate_multiplier(step, self._total_steps)
@@ -445,7 +473,7 @@ def _resume(self, path: Path, digest: str):
         if (
             self._scheduler.last_epoch != step
             or any(
-                group["lr"] != expected_lr or group["weight_decay"] != 0.01
+                group["lr"] != expected_lr or group["weight_decay"] != self.config.weight_decay
                 for group in self._optimizer.param_groups
             )
             or any(
diff --git a/src/phaseset_core/literature_parent_host.py b/src/phaseset_core/literature_parent_host.py
index 49ae378..733c014 100644
--- a/src/phaseset_core/literature_parent_host.py
+++ b/src/phaseset_core/literature_parent_host.py
@@ -60,6 +60,8 @@ class LiteratureParentTrainingHost(ContinuousParentTrainingHost):
     Each method keeps its own qualified full-negative objective/replay. Text
     is encoded once per full validation gallery; complete motion parents are
     streamed one at a time. Inter-X's original role-aware MIME is separate.
+    Adam versus AdamW and weight decay are explicit resume-bound configuration,
+    not silently forced to the internal base/residual AdamW .01 default.
     """
 
     def __init__(
diff --git a/tests/test_parent_optimizer_config.py b/tests/test_parent_optimizer_config.py
new file mode 100644
index 0000000..11ee66f
--- /dev/null
+++ b/tests/test_parent_optimizer_config.py
@@ -0,0 +1,163 @@
+"""Optimizer settings and resume identity, not native baseline convergence."""
+
+import copy
+from dataclasses import replace
+
+import pytest
+import torch
+
+from phaseset_core.continuous_parent_host import ContinuousParentTrainingHost, ParentHostConfig
+from phaseset_core.continuous_parent_training import ParentBackwardResult
+from phaseset_core.literature_parent_host import LiteratureParentTrainingHost
+from phaseset_core.training import TrainingCheckpointError, _load_torch_checkpoint, _stable_hash
+from test_continuous_parent_host import _setup
+from test_literature_parent_host import setup as literature_setup
+
+
+def test_internal_default_adamw_decay_and_manifest_are_unchanged(tmp_path):
+    model, task, source, cfg, bindings = _setup(tmp_path)
+    host = ContinuousParentTrainingHost(model, task, source, cfg, bindings)
+    assert type(host._optimizer) is torch.optim.AdamW
+    assert host._optimizer.param_groups[0]["weight_decay"] == 0.01
+    assert host._manifest["config"]["optimizer"] == "adamw"
+    assert host._manifest["config"]["weight_decay"] == 0.01
+    assert source.loaded == source.text_loaded == []
+
+
+@pytest.mark.parametrize(
+    "settings",
+    [
+        {"optimizer": "sgd"},
+        {"weight_decay": -1.0},
+        {"weight_decay": float("nan")},
+        {"weight_decay": float("inf")},
+        {"weight_decay": True},
+    ],
+)
+def test_invalid_optimizer_settings_are_rejected(settings):
+    with pytest.raises(ValueError):
+        ParentHostConfig.for_base(1729, ("C01",), ("C03",), **settings)
+
+
+@pytest.mark.parametrize("optimizer", ["adam", "adamw"])
+def test_clip_then_first_update_has_independent_coupled_decay_oracle(
+    optimizer, tmp_path, monkeypatch
+):
+    model, task, source, cfg, bindings = _setup(tmp_path)
+    cfg = replace(cfg, epochs=1, optimizer=optimizer, weight_decay=0.1)
+    old = float(model.calibration.log_scale.detach())
+
+    def forced_gradient(system, labels, text, **kwargs):
+        system.zero_grad(set_to_none=True)
+        system.calibration.log_scale.grad = torch.tensor(10.0)
+        return ParentBackwardResult(
+            1.0,
+            torch.zeros(len(labels.parents), len(labels.captions)),
+            len(labels.parents),
+            len(labels.captions),
+            0,
+            0,
+        )
+
+    monkeypatch.setattr(
+        "phaseset_core.continuous_parent_host.backward_loaded_parent_batch", forced_gradient
+    )
+    host = ContinuousParentTrainingHost(model, task, source, cfg, bindings)
+    host.fit(tmp_path / "run")
+    clipped = 10 / (10 + 1e-6)
+    if optimizer == "adam":
+        effective = clipped + cfg.weight_decay * old
+        expected = old - cfg.learning_rate * effective / (abs(effective) + 1e-8)
+    else:
+        expected = old * (1 - cfg.learning_rate * cfg.weight_decay)
+        expected -= cfg.learning_rate * clipped / (clipped + 1e-8)
+    assert float(model.calibration.log_scale.detach()) == pytest.approx(expected, abs=5e-7)
+
+
+@pytest.mark.parametrize(
+    "kind,optimizer,decay",
+    [("tmr", "adamw", 0.01), ("wamo", "adam", 0.0), ("mime", "adamw", 1e-4)],
+)
+def test_literature_host_uses_explicit_paper_optimizer_settings(
+    kind, optimizer, decay, tmp_path
+):
+    model, task, source, _, bindings = literature_setup(kind)
+    cfg = ParentHostConfig.for_literature(
+        {"tmr": "TMR-Set", "wamo": "WaMo-Set", "mime": "MIME-Set"}[kind],
+        1729,
+        ("C01",),
+        ("C03",),
+        epochs=2,
+        parent_batch_size=2,
+    )
+    host = LiteratureParentTrainingHost(model, task, source, cfg, bindings)
+    expected_type = torch.optim.Adam if optimizer == "adam" else torch.optim.AdamW
+    assert type(host._optimizer) is expected_type
+    assert host._optimizer.param_groups[0]["weight_decay"] == decay
+    assert host._optimizer.param_groups[0]["lr"] == 1e-4
+    assert host._manifest["config"]["optimizer"] == optimizer
+    assert host._manifest["config"]["weight_decay"] == decay
+    assert source.loaded == source.texts == []
+    initial = copy.deepcopy(model.state_dict())
+    result = host.fit(tmp_path / "run")
+    assert result.outcome == "COMPLETED" and result.global_step == 2
+    assert result.best_checkpoint is not None
+    assert len(result.validation_history) == 2
+    assert any(not torch.equal(initial[n], p) for n, p in model.state_dict().items())
+
+
+def test_paper_optimizer_helper_rejects_unknown_method_and_binds_explicit_pilot_override():
+    with pytest.raises(ValueError, match="registered literature"):
+        ParentHostConfig.for_literature("Other", 1729, ("C01",), ("C03",))
+    cfg = ParentHostConfig.for_literature(
+        "WaMo-Set", 1729, ("C01",), ("C03",), learning_rate=2e-4
+    )
+    assert cfg.optimizer == "adam" and cfg.weight_decay == 0.0
+    assert cfg.learning_rate == 2e-4
+
+
+@pytest.mark.parametrize("change", [{"optimizer": "adam"}, {"weight_decay": 1e-4}])
+def test_optimizer_drift_rejected_before_resume_loads_training_inputs(change, tmp_path):
+    model, task, source, cfg, bindings = _setup(tmp_path)
+    initial = copy.deepcopy(model)
+    previous = ContinuousParentTrainingHost(model, task, source, cfg, bindings).fit(
+        tmp_path / "previous", stop_after_steps=0
+    )
+    source.loaded.clear()
+    source.text_loaded.clear()
+    changed = ContinuousParentTrainingHost(initial, task, source, replace(cfg, **change), bindings)
+    with pytest.raises(TrainingCheckpointError, match="execution inputs drifted"):
+        changed.fit(
+            tmp_path / "changed",
+            resume_checkpoint=previous.latest_checkpoint.path,
+            resume_sha256=previous.latest_checkpoint.sha256,
+        )
+    assert source.loaded == source.text_loaded == []
+
+
+@pytest.mark.parametrize("optimizer,decay", [("adam", 0.0), ("adamw", 1e-4)])
+def test_literature_decay_dropout_interruption_resume_matches_uninterrupted(
+    optimizer, decay, tmp_path
+):
+    model, task, source, cfg, bindings = _setup(tmp_path, batch_size=2)
+    model.text_adapter[1] = torch.nn.Dropout(0.2)
+    cfg = replace(cfg, optimizer=optimizer, weight_decay=decay)
+    initial = copy.deepcopy(model)
+    complete = ContinuousParentTrainingHost(model, task, source, cfg, bindings).fit(tmp_path / "full")
+    interrupted = ContinuousParentTrainingHost(
+        copy.deepcopy(initial), task, source, cfg, bindings
+    ).fit(tmp_path / "interrupted", stop_after_steps=1)
+    resumed_model = copy.deepcopy(initial)
+    resumed = ContinuousParentTrainingHost(resumed_model, task, source, cfg, bindings).fit(
+        tmp_path / "resumed",
+        resume_checkpoint=interrupted.latest_checkpoint.path,
+        resume_sha256=interrupted.latest_checkpoint.sha256,
+    )
+    assert resumed.global_step == complete.global_step == 4
+    assert resumed.validation_history == complete.validation_history
+    for name, value in model.state_dict().items():
+        assert torch.equal(value, resumed_model.state_dict()[name])
+    expected, _ = _load_torch_checkpoint(complete.latest_checkpoint.path)
+    actual, _ = _load_torch_checkpoint(resumed.latest_checkpoint.path)
+    for key in ("model", "optimizer", "scheduler", "rng"):
+        assert _stable_hash(actual[key]) == _stable_hash(expected[key])

From 3c150b4a88148f382d861ecb609662b2718a1561 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 16:56:52 -0500
Subject: [PATCH 62/95] fix: wire MIME parent curriculum into actual optimizer
 host

---
 RELEASE_FILES.sha256                        |   9 +-
 docs/PHASESET_V2_LITERATURE_PARENT_HOST.md  |  11 +-
 docs/PHASESET_V2_MIME_CURRICULUM.md         |  24 ++++
 src/phaseset_core/literature_parent_host.py |  56 +++++++-
 src/phaseset_core/mime_parent_curriculum.py |   6 +-
 tests/test_mime_curriculum_host.py          | 146 ++++++++++++++++++++
 6 files changed, 245 insertions(+), 7 deletions(-)
 create mode 100644 tests/test_mime_curriculum_host.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 2d1f06e..a9a7f0d 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -76,10 +76,10 @@ cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHAS
 b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 1e0d2fb0b35a6900dfb03af4b9bfe1701eb2c843f566c0f842b47b03b11b645e	1276	docs/PHASESET_V2_ICMR_TARGET.md
-004dfc49150cc66ba71f7c303dda5dd5f9ce16ca4a220e7558b2da4914e94336	3978	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
+b5042e5fa002fa057ca4af61f4a06953662708d221016c460acd9cd0278c5863	4606	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
-36b69aec49e9f0c79ff8b9e505f00c1db0e20195ca5c6dfc5f8d34bef7aebb0f	1725	docs/PHASESET_V2_MIME_CURRICULUM.md
+b1bed8feb97cb7ca379c1952bb2201fc94a2810f48e59d0d8096fc31aac15be9	3302	docs/PHASESET_V2_MIME_CURRICULUM.md
 9ead6b89eacf034b52dcc15544e25be56e225c1b0432475b66c1a300adfa562d	3961	docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
 5a106355600b7223e516d5f270d81697a53e9c267930d34189860803b32c3fab	3116	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
 667eba26ecbbe11016064e9068764a1a86320e47d68b8e1eb44b422936721e8e	9609	docs/PHASESET_V2_PARENT_HOST.md
@@ -195,10 +195,10 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 7c491ef7d65cddffa264d68d0a9fdd9216180bb741f85dd662cb570ca6e872f1	77027	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
-a037e15e8c70b8e37be634229f2fc799859d09c443150c79c47c2d6d2e598002	8708	src/phaseset_core/literature_parent_host.py
+917784f4ea05ef48252118a95c024b516fde4230c3f08e2a274edf7b75064b19	11072	src/phaseset_core/literature_parent_host.py
 02a11d20ac299c4886479b863f2f614512365a9f1fe800861b2f261dbdad1c7a	9101	src/phaseset_core/literature_parent_training.py
 f0d7c36ecda4326ca2b1f9461fdc812039cae33739595c60d40a436f0051edb7	6976	src/phaseset_core/mime_language.py
-f7cb1c0893b0b504359e34e39dd24651aaa86c1b65e98d2ed0469c6d5ce84d4d	6920	src/phaseset_core/mime_parent_curriculum.py
+07092fa015f73af9da2b23dfa26c6e19e4c7a8b6a5cc53c316acb1d2d4b356e5	7245	src/phaseset_core/mime_parent_curriculum.py
 c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phaseset_core/mime_set.py
 9e7fa84554d5676f8ef75049b17b53580395c3c60be0f75fcb55996ccc8ef36d	37720	src/phaseset_core/models.py
 57bccd26419918de128839516955263a4823a2ead5c3465901c1c5278fbf5e43	14128	src/phaseset_core/morlet.py
@@ -250,6 +250,7 @@ e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/tes
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 ad4fb11844e43b26566803a0805dacf20e0a6b6cdf4e9741dcc98739141d6b2a	10614	tests/test_literature_parent_host.py
 4b4193cc100b1076e31fe8c9f3fd1801b0270368afd80eca93c6cce0e671482a	6224	tests/test_literature_parent_training.py
+7af87221b239a46d80e160341b4b4b3d7ccab85fe03b6e0368de463ee78c2b63	6575	tests/test_mime_curriculum_host.py
 b65a2178d7faf25372adeccdfcd19ee7804544665c6059f2d684801ccff83e11	6050	tests/test_mime_parent_curriculum.py
 773430e42927751a5b07ab20da7ddd716d13853f9496033476b2926198148ef8	14947	tests/test_mime_set.py
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
diff --git a/docs/PHASESET_V2_LITERATURE_PARENT_HOST.md b/docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
index 166f670..7194917 100644
--- a/docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
+++ b/docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
@@ -1,10 +1,19 @@
 # Complete-parent literature optimizer host
 
 `phaseset_core.literature_parent_host.LiteratureParentTrainingHost` uses the
-same AdamW, warmup/cosine, gradient clipping, validation checkpoint selector,
+same explicit Adam(W), warmup/cosine, gradient clipping, validation checkpoint selector,
 immutable attempt and checkpoint/resume loop as the complete-parent base and
 residual host. The original constructor still admits only its actual base/V2
 scorers; literature models enter through their own strict constructor.
+`ParentHostConfig.for_literature` supplies the disclosed method-specific
+optimizer/decay defaults; those settings are exact resume inputs.
+
+Actual MIME group training uses `MIMEParentTrainingHost`, not the generic
+uniform-batch literature host. Its `_batches` hook invokes the qualified
+training-only MIME curriculum at every epoch, including resume/census checks.
+The caller prepares anchors once from all learning-parent human CLIP rows;
+neighbor width and an owned snapshot of anchor values join the checkpoint manifest. The genuinely
+trainable MIME CLIP tower remains separate from these frozen sampling anchors.
 
 ## Objectives and inputs
 
diff --git a/docs/PHASESET_V2_MIME_CURRICULUM.md b/docs/PHASESET_V2_MIME_CURRICULUM.md
index 18bcda6..2451b00 100644
--- a/docs/PHASESET_V2_MIME_CURRICULUM.md
+++ b/docs/PHASESET_V2_MIME_CURRICULUM.md
@@ -21,6 +21,30 @@ is rebalanced without dropping/repeating parents; all human rows remain
 retrieval positives. Historical C00 can enter an outer fold's learning set,
 but final-test parents cannot. The host supplies exact learning components.
 
+The whole-parent anchor function now accepts the authenticated row selection
+returned by `ParentHumanClipRows`, instead of requiring the complete original
+development preparation batch to be re-encoded for every training subset.
+It still checks exactly all learning-human commitments and original caption
+digests: validation, held-out or appended weak columns cannot become anchors.
+This changes the group sampling seam only, not original dyadic MIME text
+admission or the legacy capture/storage receipt interfaces.
+
+`MIMEParentTrainingHost` connects this sampler to real method-specific updates,
+validation and exact resume. Anchor identity and explicit neighbor width are
+resume-bound. For a pilot population smaller than the batch ceiling, the
+curriculum cannot change the all-parent negative set; it can change order only.
+Report that limitation rather than claiming a curriculum benefit from pilot
+scores. The main population can form multiple no-replacement batches.
+
+The scoped registered-server CPU qualification executed ten cases with no
+skips: three actual host/row-selection cases plus seven existing sampler
+cases. It checks independent all-human anchor means, held-out/changed-text
+rejection, post-construction caller tensor mutation isolation, actual epoch
+hooks and optimizer updates, and bitwise model/optimizer/scheduler/RNG resume.
+Neighbor-width and anchor drift are refused before training data/text loads.
+Production budget history is unchanged. This is analytic software evidence,
+not native pilot learning, curriculum benefit or retrieval performance.
+
 Tests use analytic text/motion-free fixtures; no resulting number is baseline
 performance or human verification. Original dyadic MIME and the shared group
 adapter, original auxiliary/input fidelity checks, actual profile, convergence,
diff --git a/src/phaseset_core/literature_parent_host.py b/src/phaseset_core/literature_parent_host.py
index 733c014..9a1d64c 100644
--- a/src/phaseset_core/literature_parent_host.py
+++ b/src/phaseset_core/literature_parent_host.py
@@ -28,10 +28,11 @@
 )
 from .mime_language import TrainableMIMECLIP
 from .mime_set import MIMERetrieval, MIMESetMotion
+from .mime_parent_curriculum import MimeParentAnchors, mime_parent_epoch_batches
 from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
 from .tmr_parent_training import backward_loaded_tmr_parent_batch
 from .tmr_set import TMRSet, TMRTextBatch
-from .training import _capture_macro_bidirectional_r1, _capture_rng, _restore_rng
+from .training import _capture_macro_bidirectional_r1, _capture_rng, _restore_rng, _stable_hash
 from .wamo_set import WaMoSet
 
 
@@ -195,3 +196,56 @@ def _validate(self):
         finally:
             self.system.train(was_training)
             _restore_rng(previous_rng)
+
+
+class MIMEParentTrainingHost(LiteratureParentTrainingHost):
+    """Actual shared MIME optimizer with its training-only curriculum hook.
+
+    Anchors are prepared once from every official learning-parent human row,
+    independently of the trainable MIME language tower. Neighbor width and
+    anchor identity join the existing exact checkpoint/resume manifest.
+    Uniform warmup and cosine hardness follow the qualified public sampler;
+    this grants neither pilot slots nor original dyadic reproduction.
+    """
+
+    def __init__(
+        self,
+        system: MIMERetrieval,
+        task: ParentRetrievalTask,
+        source: LiteratureParentSource,
+        config: ParentHostConfig,
+        bindings: ParentHostBindings,
+        *,
+        anchors: MimeParentAnchors,
+        neighbor_window: int,
+    ):
+        if not isinstance(system, MIMERetrieval) or not isinstance(system.motion, MIMESetMotion):
+            raise TypeError("curriculum host requires the actual shared MIME group scorer")
+        # Own the sampling values: a frozen dataclass alone cannot stop the
+        # caller mutating its tensor after this host binds the manifest.
+        self._curriculum_anchors = MimeParentAnchors(
+            anchors.family_keys, anchors.embeddings.clone()
+        )
+        self._neighbor_window = neighbor_window
+        super().__init__(system, task, source, config, bindings)
+        self._manifest["mime_curriculum"] = {
+            "neighbor_window": neighbor_window,
+            "anchor_family_keys": self._curriculum_anchors.family_keys,
+            "anchor_embeddings_sha256": _stable_hash(self._curriculum_anchors.embeddings),
+            "sampler": "mime_parent_epoch_batches",
+            "uniform_epochs": 3,
+            "ramp_maximum_epoch_zero_based": 12,
+            "maximum_hardness": 0.25,
+            "source": "all_learning_parent_human_frozen_clip_mean",
+        }
+
+    def _batches(self, epoch: int):
+        return mime_parent_epoch_batches(
+            self.task,
+            self._curriculum_anchors,
+            train_components=self.config.train_components,
+            batch_size=self.config.parent_batch_size,
+            neighbor_window=self._neighbor_window,
+            seed=self.config.seed,
+            epoch=epoch,
+        )
diff --git a/src/phaseset_core/mime_parent_curriculum.py b/src/phaseset_core/mime_parent_curriculum.py
index 0bebce2..4403cb5 100644
--- a/src/phaseset_core/mime_parent_curriculum.py
+++ b/src/phaseset_core/mime_parent_curriculum.py
@@ -65,7 +65,11 @@ def mime_training_parent_anchors(
     labels = _learning_batch(
         task, train_components, batch_size=len(task.parents), seed=1729, epoch=0
     )
-    text, commitments, _ = _validate_text_batch(text_batch)
+    # A real parent host selects its exact training subset from the closed
+    # complete-development CLIP batch. Accept that authenticated selection,
+    # then require all and only learning-human commitments and original bytes.
+    # Extra weak/validation columns cannot pass this exact equality.
+    text, commitments, _ = _validate_text_batch(text_batch, allow_row_selection=True)
     if commitments != labels.caption_commitments:
         raise ValueError("MIME anchors need all and only learning-parent human rows in order")
     for caption, row in zip(labels.captions, text_batch.receipt.caption_rows, strict=True):
diff --git a/tests/test_mime_curriculum_host.py b/tests/test_mime_curriculum_host.py
new file mode 100644
index 0000000..aef85da
--- /dev/null
+++ b/tests/test_mime_curriculum_host.py
@@ -0,0 +1,146 @@
+"""Real sampler/optimizer wiring on analytic fixtures, not native pilot results."""
+
+import copy
+from dataclasses import replace
+
+import pytest
+import torch
+from torch.nn import functional as F
+
+from phaseset_core.continuous_parent_host import ParentHostConfig
+from phaseset_core.frozen_clip_text import select_frozen_clip_text_rows
+from phaseset_core.literature_parent_host import MIMEParentTrainingHost
+from phaseset_core.mime_parent_curriculum import (
+    mime_parent_epoch_batches,
+    mime_training_parent_anchors,
+)
+from phaseset_core.parent_clip_rows import ParentHumanClipRows
+from phaseset_core.parent_retrieval_task import ParentRetrievalTask
+from phaseset_core.training import TrainingCheckpointError, _load_torch_checkpoint, _stable_hash
+from test_literature_parent_host import setup
+from test_phaseset_frozen_clip_text import _fixture
+
+
+def human_rows(task, tmp_path):
+    tmp_path.mkdir(parents=True, exist_ok=True)
+    clip, _, _, _ = _fixture(tmp_path)
+    all_labels = task.batch(tuple(p.annotation_family_sha256 for p in task.parents))
+    original = clip.encode(all_labels.captions, all_labels.caption_commitments, batch_size=3)
+    provider = ParentHumanClipRows(task, original)
+    labels = task.batch(
+        tuple(p.annotation_family_sha256 for p in task.parents if p.component == "C01")
+    )
+    text, no_cf = provider.text(labels, training=True)
+    assert not no_cf.source_keys
+    return labels, text, provider
+
+
+def test_closed_human_row_selection_builds_train_only_anchors_with_independent_mean(tmp_path):
+    _, task, _, _, _ = setup("mime")
+    labels, text, provider = human_rows(task, tmp_path)
+    rng = _stable_hash(torch.get_rng_state())
+    anchors = mime_training_parent_anchors(task, text, train_components=("C01",))
+    assert _stable_hash(torch.get_rng_state()) == rng
+    assert anchors.family_keys == labels.motion_positive_keys
+    expected = []
+    for parent in labels.parents:
+        own = task.batch((parent.annotation_family_sha256,))
+        rows = [provider.original.embeddings[provider.indices[c]] for c in own.caption_commitments]
+        expected.append(F.normalize(torch.stack(rows).double().mean(0).float(), dim=0))
+    assert torch.equal(anchors.embeddings, torch.stack(expected))
+
+
+def test_cached_validation_or_changed_human_selection_is_not_a_training_anchor(tmp_path):
+    _, task, _, _, _ = setup("mime")
+    labels, text, provider = human_rows(task, tmp_path)
+    val = task.batch(
+        tuple(p.annotation_family_sha256 for p in task.parents if p.component == "C03")
+    )
+    # Equal-sized authentic cache subset, but from selection/held-out text.
+    wrong = select_frozen_clip_text_rows(
+        provider.original,
+        tuple(provider.indices[c] for c in val.caption_commitments[: len(labels.captions)]),
+    )
+    with pytest.raises(ValueError, match="all and only learning"):
+        mime_training_parent_anchors(task, wrong, train_components=("C01",))
+    changed = tuple(
+        replace(p, captions=("changed human", *p.captions[1:])) if p.component == "C01" else p
+        for p in task.parents
+    )
+    changed_task = ParentRetrievalTask(
+        changed, expected_family_keys=tuple(p.annotation_family_sha256 for p in changed)
+    )
+    with pytest.raises(ValueError, match="differs from"):
+        mime_training_parent_anchors(changed_task, text, train_components=("C01",))
+
+
+def test_actual_mime_host_uses_curriculum_epochs_and_resumes_all_state_bitwise(tmp_path):
+    model, task, source, _, bindings = setup("mime")
+    _, text, _ = human_rows(task, tmp_path / "text")
+    anchors = mime_training_parent_anchors(task, text, train_components=("C01",))
+    config = ParentHostConfig.for_literature(
+        "MIME-Set", 1729, ("C01",), ("C03",), epochs=4, parent_batch_size=2
+    )
+    initial = copy.deepcopy(model)
+
+    def host(system, *, width=2, anchors=anchors):
+        return MIMEParentTrainingHost(
+            system, task, source, config, bindings, anchors=anchors, neighbor_window=width
+        )
+
+    caller_anchors = replace(anchors, embeddings=anchors.embeddings.clone())
+    complete_host = host(model, anchors=caller_anchors)
+    caller_anchors.embeddings[0, 0] += 0.125
+    assert torch.equal(complete_host._curriculum_anchors.embeddings, anchors.embeddings)
+    assert complete_host._manifest["mime_curriculum"]["anchor_embeddings_sha256"] == _stable_hash(
+        anchors.embeddings
+    )
+    for epoch in (0, 3, 12):
+        assert complete_host._batches(epoch) == mime_parent_epoch_batches(
+            task,
+            anchors,
+            train_components=("C01",),
+            batch_size=2,
+            neighbor_window=2,
+            seed=1729,
+            epoch=epoch,
+        )
+    assert (
+        complete_host._manifest["mime_curriculum"]["source"]
+        == "all_learning_parent_human_frozen_clip_mean"
+    )
+    full = complete_host.fit(tmp_path / "full", checkpoint_keep_recent=2)
+    split = host(copy.deepcopy(initial)).fit(
+        tmp_path / "split", stop_after_steps=3, checkpoint_keep_recent=2
+    )
+    resumed = host(copy.deepcopy(initial)).fit(
+        tmp_path / "resumed",
+        resume_checkpoint=split.latest_checkpoint.path,
+        resume_sha256=split.latest_checkpoint.sha256,
+        checkpoint_keep_recent=2,
+    )
+    assert full.global_step == resumed.global_step == 4 and len(full.validation_history) == 4
+    expected, _ = _load_torch_checkpoint(full.latest_checkpoint.path)
+    actual, _ = _load_torch_checkpoint(resumed.latest_checkpoint.path)
+    for key in ("model", "optimizer", "scheduler", "rng"):
+        assert _stable_hash(actual[key]) == _stable_hash(expected[key])
+    assert resumed.validation_history == full.validation_history
+    source.loaded.clear()
+    source.texts.clear()
+    with pytest.raises(TrainingCheckpointError, match="execution inputs drifted"):
+        host(copy.deepcopy(initial), width=3).fit(
+            tmp_path / "width-drift",
+            resume_checkpoint=split.latest_checkpoint.path,
+            resume_sha256=split.latest_checkpoint.sha256,
+        )
+    assert source.loaded == source.texts == []
+    changed_anchors = anchors.embeddings.clone()
+    changed_anchors[0, 0] += 0.125
+    bad = replace(anchors, embeddings=changed_anchors)
+    with pytest.raises(TrainingCheckpointError, match="execution inputs drifted"):
+        host(copy.deepcopy(initial), anchors=bad).fit(
+            tmp_path / "anchor-drift",
+            resume_checkpoint=split.latest_checkpoint.path,
+            resume_sha256=split.latest_checkpoint.sha256,
+        )
+    assert source.loaded == source.texts == []

From 041be381a63223e8eb12eab817a74558a76ed82d Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 18:26:21 -0500
Subject: [PATCH 63/95] Harden parent optimizer cursor failure evidence

---
 src/phaseset_core/continuous_parent_host.py | 39 +++++++++++++++++----
 src/phaseset_core/parent_run_monitor.py     | 10 +++++-
 tests/test_continuous_parent_host.py        | 38 ++++++++++++++++++++
 3 files changed, 79 insertions(+), 8 deletions(-)

diff --git a/src/phaseset_core/continuous_parent_host.py b/src/phaseset_core/continuous_parent_host.py
index e25d1c1..6182b99 100644
--- a/src/phaseset_core/continuous_parent_host.py
+++ b/src/phaseset_core/continuous_parent_host.py
@@ -299,6 +299,9 @@ def _initialize(self, system, task, source, config, bindings):
             self._optimizer, lambda step: _learning_rate_multiplier(step, self._total_steps)
         )
         self._epoch = self._offset = self._step = self._parents_seen = self._sequence = 0
+        # A failure inside optimizer.step can occur after a partial update.
+        # In that window the completed-step cursor is genuinely unknown.
+        self._optimizer_step_inflight = False
         self._history: list[tuple[int, float]] = []
         self._best: CheckpointArtifact | None = None
         self._best_value: float | None = None
@@ -595,16 +598,18 @@ def fit(
                         self._parameters, 1.0, error_if_nonfinite=True
                     )
                     lr = self._optimizer.param_groups[0]["lr"]
+                    self._optimizer_step_inflight = True
                     self._optimizer.step()
+                    self._step += 1
+                    self._offset += 1
+                    self._parents_seen += len(labels.parents)
+                    self._optimizer_step_inflight = False
                     self._scheduler.step()
                     if (
                         not self._is_base
                         and _stable_hash(self.system.frozen_b2.state_dict()) != self._base_state
                     ):
                         raise RuntimeError("training mutated the shared frozen B2 anchor")
-                    self._step += 1
-                    self._offset += 1
-                    self._parents_seen += len(labels.parents)
                     self._checkpoint()
                     self._event(
                         "update",
@@ -636,8 +641,21 @@ def fit(
             raise
         finally:
             cleanup_error = None
+            terminal_cursor = None if self._optimizer_step_inflight else self._step
+            terminal_offset = None if self._optimizer_step_inflight else self._offset
+            terminal_parents_seen = None if self._optimizer_step_inflight else self._parents_seen
+            terminal_snapshot = {
+                "global_step": terminal_cursor,
+                "parent_batch_offset": terminal_offset,
+                "parents_seen": terminal_parents_seen,
+                "optimizer_step_cursor_ambiguous": self._optimizer_step_inflight,
+            }
             try:
-                self._monitor.finish(outcome=outcome, failure=primary_error)
+                self._monitor.finish(
+                    outcome=outcome,
+                    failure=primary_error,
+                    terminal_snapshot=terminal_snapshot,
+                )
             except BaseException as error:
                 cleanup_error = error
                 if primary_error is None:
@@ -645,16 +663,23 @@ def fit(
                 else:
                     primary_error.add_note(f"monitor close also failed: {error!r}")
             try:
-                self._event("terminal", outcome=outcome, failure_class=failure)
+                self._event(
+                    "terminal",
+                    outcome=outcome,
+                    failure_class=failure,
+                    **terminal_snapshot,
+                )
                 _write_json_once(
                     self._root / "terminal.json",
                     {
                         "outcome": outcome,
                         "failure_class": failure,
                         "failure_code": parent_failure_code(primary_error),
-                        "global_step": self._step,
+                        "global_step": terminal_cursor,
+                        "parent_batch_offset": terminal_offset,
+                        "optimizer_step_cursor_ambiguous": self._optimizer_step_inflight,
                         "completed_epochs": self._epoch,
-                        "parents_seen": self._parents_seen,
+                        "parents_seen": terminal_parents_seen,
                         "latest_checkpoint": None
                         if self._latest is None
                         else str(self._latest.path),
diff --git a/src/phaseset_core/parent_run_monitor.py b/src/phaseset_core/parent_run_monitor.py
index fedbf8b..3f94c31 100644
--- a/src/phaseset_core/parent_run_monitor.py
+++ b/src/phaseset_core/parent_run_monitor.py
@@ -186,7 +186,13 @@ def checkpoint(self, artifact: CheckpointArtifact, *, best: CheckpointArtifact |
             _record(self.root / "retention" / f"retired-{old_index:08d}.json", receipt)
             self._retired.add(old.path)
 
-    def finish(self, *, outcome: str, failure: BaseException | None):
+    def finish(
+        self,
+        *,
+        outcome: str,
+        failure: BaseException | None,
+        terminal_snapshot: dict | None = None,
+    ):
         """Stop heartbeat before writing host terminal; lease remains held."""
         self._stop.set()
         if self._thread is not None and self._thread.ident is not None:
@@ -196,6 +202,8 @@ def finish(self, *, outcome: str, failure: BaseException | None):
         self.check()
         if self._started is not None:
             with self._lock:
+                if terminal_snapshot is not None:
+                    self._snapshot.update(terminal_snapshot)
                 self._snapshot.update(
                     phase="TERMINAL", outcome=outcome, failure_code=parent_failure_code(failure)
                 )
diff --git a/tests/test_continuous_parent_host.py b/tests/test_continuous_parent_host.py
index e77e3af..5fead8d 100644
--- a/tests/test_continuous_parent_host.py
+++ b/tests/test_continuous_parent_host.py
@@ -128,6 +128,44 @@ def test_host_runs_optimizer_selects_validation_and_preserves_full_parent_census
         host.fit(tmp_path / "second")
 
 
+@pytest.mark.parametrize(
+    ("failure_phase", "expected_step", "ambiguous"),
+    [("optimizer", None, True), ("scheduler", 1, False)],
+)
+def test_failed_update_reports_honest_optimizer_cursor(
+    tmp_path, monkeypatch, failure_phase, expected_step, ambiguous
+):
+    system, task, source, config, bindings = _setup(tmp_path)
+    host = ContinuousParentTrainingHost(system, task, source, config, bindings)
+    target = host._optimizer if failure_phase == "optimizer" else host._scheduler
+    original_step = target.step
+
+    def update_then_fail(*args, **kwargs):
+        original_step(*args, **kwargs)
+        raise RuntimeError(f"injected {failure_phase} failure after update")
+
+    monkeypatch.setattr(target, "step", update_then_fail)
+    with pytest.raises(RuntimeError, match=f"injected {failure_phase} failure"):
+        host.fit(tmp_path / failure_phase)
+    terminal = json.loads((tmp_path / failure_phase / "terminal.json").read_text())
+    assert terminal["outcome"] == "FAILED"
+    assert terminal["global_step"] == expected_step
+    assert terminal["optimizer_step_cursor_ambiguous"] is ambiguous
+    event = _events(tmp_path / failure_phase)[-1]
+    heartbeat = json.loads(
+        sorted((tmp_path / failure_phase / "heartbeats").glob("heartbeat-*.json"))[-1].read_text()
+    )
+    for key in (
+        "global_step",
+        "parent_batch_offset",
+        "parents_seen",
+        "optimizer_step_cursor_ambiguous",
+    ):
+        assert event[key] == heartbeat[key] == terminal[key]
+    if failure_phase == "scheduler":
+        assert terminal["parents_seen"] == 4
+
+
 @pytest.mark.parametrize("stop", [0, 1, 2, 4])
 def test_host_interruption_resume_matches_uninterrupted_bitwise(tmp_path, stop):
     system, task, source, config, bindings = _setup(tmp_path, batch_size=2)

From 8739854607efb5ad580266891fafe4e4546ff283 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 18:28:56 -0500
Subject: [PATCH 64/95] Refresh release manifest for cursor integrity fix

---
 RELEASE_FILES.sha256 | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index a9a7f0d..0a09cae 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -178,7 +178,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 80e6d497ff9bb4354664cb1a054cdb550ab222e92c841813afc27af6d3759495	5603	src/phaseset_core/continuous_base_retrieval.py
 8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
-65b1288da19f30cbe92d877d2a3d27ca04cb804634d75c681806e830f0bc4db8	31178	src/phaseset_core/continuous_parent_host.py
+cf879bcbe1b52796d751deaf3870c99ec24a70f92bc7d129e3710a81143fe023	32443	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
 6af5ecfcadc139acfdb3fe2d2e00995a40e90522a821172bc44e7a3604028dbc	10571	src/phaseset_core/continuous_retrieval.py
@@ -207,7 +207,7 @@ c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phase
 be88afc2d543a9ceb227eda07046cc8e9221faf7af85888c1b303632da642dc8	3366	src/phaseset_core/parent_clip_rows.py
 0d91c16503f8704916eecd6c72e0b943dd06cf251ba6f7b847a32d5c009ab6dd	8826	src/phaseset_core/parent_disk_source.py
 be883315aa773e6c2d535ea981853ae92a791187338fd2b310b574f956174a82	7326	src/phaseset_core/parent_retrieval_task.py
-e0c9d77e9ccaf277785beb148a54afbdf542c607a6298e6b20db458828a32fe7	9331	src/phaseset_core/parent_run_monitor.py
+46baa360084180121c16e1ce4d0c0a21841c6ad91c462afd272d5b92b0a603ad	9528	src/phaseset_core/parent_run_monitor.py
 52b22af181cb52cf3c9c3584d15ea02a604e5c29d8abeaa0de217baf07d8af06	7027	src/phaseset_core/parent_weak_clip_rows.py
 7a616be629078df58b4289be72e389261c11e1fa53637eadcecb3967824dcb93	30197	src/phaseset_core/periodic.py
 e69b0d27d479b4499bae73c96f7854f9d07539043a0b66f0f75f6bc3e23c1f51	55415	src/phaseset_core/periodic_cache_execution.py
@@ -241,7 +241,7 @@ ae0b1cb7b79703de8cdd1406267a60101b05f191255da30fadaec6e6fbdde5d4	8598	tests/test
 c1115e9afed9fc3811c48439cc12a89e4b8bf9c7a6cb134d867aee0fc95ca6e6	6925	tests/test_checkpoint_observer_integration.py
 9a6d69be46d3df730acabdb7e2b7fd23a9f4a10ecb6895c013a6fc9d712f14e5	13066	tests/test_continuous_base_retrieval.py
 b0f9a8d920640d3c5455e9eacdeb87feecbaca87a8b1b06affe8ca297c78d075	3593	tests/test_continuous_capture_io.py
-1996c74bc514251d6213079416cd5d6dccc80914349d4bb61c25b83a8b789961	15517	tests/test_continuous_parent_host.py
+c0124a85e3154881a41c3d49706f81667663d5bfa996066e73ea299a72d586d0	17101	tests/test_continuous_parent_host.py
 8b91250de082536ab1d8b0118977e1d569e4f2698e5073ebce44c6ab4bd8324f	11609	tests/test_continuous_parent_training.py
 5ca49ce282fc8cff3654af8af56ade66cfa6b732afd7dcca3e92ffe06d4ce64a	6030	tests/test_continuous_phase_cache.py
 e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/test_continuous_retrieval.py

From 1cf95e6a1c38049e133e2a637dafe1f12702d989 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 19:13:08 -0500
Subject: [PATCH 65/95] Guard pilot reservations against concurrent formal
 freeze

---
 RELEASE_FILES.sha256              |  4 +--
 src/phaseset_core/study_budget.py | 11 ++++++
 tests/test_study_budget.py        | 59 +++++++++++++++++++++++++++++++
 3 files changed, 72 insertions(+), 2 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 0a09cae..ef49a7e 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -222,7 +222,7 @@ ad2ce56325f09d2efb17847ef3f947535f81f7099019275fdd074c12efeeb559	73588	src/phase
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
-b3c69e0c6b5b79adc0bc15472486e38df483ec4698ebe5b4fb064cd7a8f0968b	19796	src/phaseset_core/study_budget.py
+762197d482d78e92a5d13ef284c4a4c0fa9c4efcd5f0d7a5649fd2840992362d	20407	src/phaseset_core/study_budget.py
 a984350a2838101fde448eee44d32f22eec5dfa8487637fa0d7941f99de82aec	17205	src/phaseset_core/study_process.py
 febde5f65ea132a9921973be393a6fbea715c6f9b1b1d9f5f8df3654cf007696	25153	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
@@ -351,7 +351,7 @@ a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/tes
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
 8727e87f1a0d44db6a39b1e5cfd8e22e3bed05544ddaed07b66b759adee66d84	5587	tests/test_release_tree_batch.py
 eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
-1ede4189b31a502ced40838b9aa08b6ce0e726ecd34a8dc73adf6c56fd59c519	16744	tests/test_study_budget.py
+d1655ff80ed93c82013e5fb55f761cd169e1c6b7f906bc1dce61b38016275c6e	18477	tests/test_study_budget.py
 67b2e0ebb76d7def996bc3ce6242a7373fac4e1e7334dde5fb17bbfc995fce87	17565	tests/test_study_process.py
 96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
 18c5c10980d8e0d81eb1a6b351c0e0e770325c95bcca59e15763e5c86967ce78	4260	tests/test_tmr_feature_cache.py
diff --git a/src/phaseset_core/study_budget.py b/src/phaseset_core/study_budget.py
index b893932..9068cb8 100644
--- a/src/phaseset_core/study_budget.py
+++ b/src/phaseset_core/study_budget.py
@@ -259,6 +259,8 @@ def record_formal_step_limits(self, run_max_steps: dict[str, int], *, evidence:
             state = self._state()
             if state["step_limits"] is not None:
                 raise StudyBudgetError("formal step limits are immutable once recorded")
+            if any(row["purpose"] == "pilot" for row in state["pending"].values()):
+                raise StudyBudgetError("cannot freeze formal limits while a pilot is unsettled")
             if set(run_max_steps) != set(state["history"]["run_seeds"]):
                 raise StudyBudgetError("formal step limits must cover all 87 registered stages")
             for value in run_max_steps.values():
@@ -328,6 +330,15 @@ def reserve(
                     "profile/pilot IDs must not occupy registered formal run IDs"
                 )
             if purpose == "pilot":
+                if (
+                    state["step_limits"] is not None
+                    or usage["formal_reservations"]
+                    or state["pending"]
+                    or any(row["completed_steps"] is None for row in state["settled"].values())
+                ):
+                    raise StudyBudgetError(
+                        "pilot requires a settled known-cursor pre-formal journal"
+                    )
                 if same_run or family not in PILOT_ALLOCATION:
                     raise StudyBudgetError(
                         "each pilot stage is unique and uses a registered family"
diff --git a/tests/test_study_budget.py b/tests/test_study_budget.py
index c31a804..30c035c 100644
--- a/tests/test_study_budget.py
+++ b/tests/test_study_budget.py
@@ -268,6 +268,65 @@ def test_pilot_step_fraction_is_exact_and_unsupported_family_is_rejected(tmp_pat
     assert budget.summary()["usage"]["pilot_reservations"] == 0
 
 
+def test_pilot_admission_is_atomic_with_journal_cursor_and_formal_freeze(tmp_path):
+    (tmp_path / "unsettled").mkdir()
+    budget = create(tmp_path / "unsettled")
+    reserve(budget, attempt="analytic-profile")
+    with pytest.raises(StudyBudgetError, match="settled known-cursor"):
+        reserve(
+            budget,
+            attempt="analytic-pilot",
+            run_id="analytic-pilot",
+            purpose="pilot",
+            family="B2",
+            gpu_count=1,
+            planned_steps=12,
+        )
+    settle(budget, attempt="analytic-profile", completed_steps=None)
+    with pytest.raises(StudyBudgetError, match="settled known-cursor"):
+        reserve(
+            budget,
+            attempt="analytic-pilot",
+            run_id="analytic-pilot",
+            purpose="pilot",
+            family="B2",
+            gpu_count=1,
+            planned_steps=12,
+        )
+
+    (tmp_path / "freeze").mkdir()
+    budget = create(tmp_path / "freeze")
+    reserve(
+        budget,
+        attempt="analytic-pilot",
+        run_id="analytic-pilot",
+        purpose="pilot",
+        family="B2",
+        gpu_count=1,
+        planned_steps=12,
+    )
+    with pytest.raises(StudyBudgetError, match="pilot is unsettled"):
+        freeze(budget)
+    settle(
+        budget,
+        attempt="analytic-pilot",
+        actual_wall_seconds=0,
+        completed_steps=0,
+        outcome="NOT_STARTED",
+    )
+    freeze(budget)
+    with pytest.raises(StudyBudgetError, match="pre-formal"):
+        reserve(
+            budget,
+            attempt="analytic-late-pilot",
+            run_id="analytic-late-pilot",
+            purpose="pilot",
+            family="B2",
+            gpu_count=1,
+            planned_steps=12,
+        )
+
+
 def test_formal_needs_all_87_step_limits_once_and_exact_seed_schedule(tmp_path):
     budget = create(tmp_path)
     with pytest.raises(StudyBudgetError, match="freeze"):

From 86623f1155ffc5da0dea4be9637afa7a2b067ffa Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 19:30:23 -0500
Subject: [PATCH 66/95] Guard pilot cost floor inside budget reservation

---
 RELEASE_FILES.sha256              |  4 ++--
 src/phaseset_core/study_budget.py | 14 ++++++++++++++
 tests/test_study_budget.py        | 24 ++++++++++++++++++++++++
 3 files changed, 40 insertions(+), 2 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index ef49a7e..1ddf3ec 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -222,7 +222,7 @@ ad2ce56325f09d2efb17847ef3f947535f81f7099019275fdd074c12efeeb559	73588	src/phase
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
-762197d482d78e92a5d13ef284c4a4c0fa9c4efcd5f0d7a5649fd2840992362d	20407	src/phaseset_core/study_budget.py
+1c7fcc1e30aedf0da08b8dfac54e9580e1da1ead0d29b309c18cd5592642de16	21118	src/phaseset_core/study_budget.py
 a984350a2838101fde448eee44d32f22eec5dfa8487637fa0d7941f99de82aec	17205	src/phaseset_core/study_process.py
 febde5f65ea132a9921973be393a6fbea715c6f9b1b1d9f5f8df3654cf007696	25153	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
@@ -351,7 +351,7 @@ a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/tes
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
 8727e87f1a0d44db6a39b1e5cfd8e22e3bed05544ddaed07b66b759adee66d84	5587	tests/test_release_tree_batch.py
 eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
-d1655ff80ed93c82013e5fb55f761cd169e1c6b7f906bc1dce61b38016275c6e	18477	tests/test_study_budget.py
+188b166e9957df27437642dbce0091a3935d489135340c892dec8dfdabba8ef5	19504	tests/test_study_budget.py
 67b2e0ebb76d7def996bc3ce6242a7373fac4e1e7334dde5fb17bbfc995fce87	17565	tests/test_study_process.py
 96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
 18c5c10980d8e0d81eb1a6b351c0e0e770325c95bcca59e15763e5c86967ce78	4260	tests/test_tmr_feature_cache.py
diff --git a/src/phaseset_core/study_budget.py b/src/phaseset_core/study_budget.py
index 9068cb8..af1d703 100644
--- a/src/phaseset_core/study_budget.py
+++ b/src/phaseset_core/study_budget.py
@@ -289,6 +289,7 @@ def reserve(
         family: str | None = None,
         predecessor_attempt_id: str | None = None,
         root_cause: str | None = None,
+        future_budget_floor_gpu_seconds: float | None = None,
     ):
         """Reserve the full timeout+kill-grace envelope BEFORE child spawning."""
         for value, name in (
@@ -306,6 +307,10 @@ def reserve(
         _integer(gpu_count, "allocated GPU count")
         _integer(wall_time_limit_seconds, "wall envelope", minimum=1)
         _integer(planned_steps, "planned optimizer steps")
+        if future_budget_floor_gpu_seconds is not None:
+            if purpose != "pilot":
+                raise StudyBudgetError("future study floor applies only to pilot admission")
+            _seconds(future_budget_floor_gpu_seconds, "future study GPU floor")
         if purpose == "profile" and planned_steps != 0:
             raise StudyBudgetError("optimizer training cannot be hidden as an uncounted profile")
         if purpose != "profile" and (gpu_count < 1 or planned_steps < 1):
@@ -318,6 +323,15 @@ def reserve(
             usage = state["usage"]
             if usage["gpu_seconds"] + state["reserved_gpu_seconds"] + cost > LIMITS["gpu_seconds"]:
                 raise StudyBudgetError("study GPU budget exhausted or fully reserved")
+            if (
+                future_budget_floor_gpu_seconds is not None
+                and usage["gpu_seconds"]
+                + state["reserved_gpu_seconds"]
+                + cost
+                + future_budget_floor_gpu_seconds
+                > LIMITS["gpu_seconds"]
+            ):
+                raise StudyBudgetError("pilot plus future study floor exceeds GPU budget")
             if purpose != "formal" and (
                 predecessor_attempt_id is not None or root_cause is not None
             ):
diff --git a/tests/test_study_budget.py b/tests/test_study_budget.py
index 30c035c..77164e4 100644
--- a/tests/test_study_budget.py
+++ b/tests/test_study_budget.py
@@ -327,6 +327,30 @@ def test_pilot_admission_is_atomic_with_journal_cursor_and_formal_freeze(tmp_pat
         )
 
 
+def test_pilot_future_cost_floor_uses_current_locked_journal_not_old_summary(tmp_path):
+    budget = create(tmp_path)
+    old = budget.summary()
+    assert old["usage"]["gpu_seconds"] == 34
+    pilot_cost = 2 * 40
+    future_floor = 300 * 3600 - 34 - pilot_cost - 20
+    reserve(budget, attempt="profile-after-forecast")
+    settle(budget, attempt="profile-after-forecast", actual_wall_seconds=10.5)
+    assert budget.summary()["usage"]["gpu_seconds"] == 55
+    with pytest.raises(StudyBudgetError, match="future study floor"):
+        reserve(
+            budget,
+            attempt="pilot-after-profile",
+            run_id="pilot-after-profile",
+            purpose="pilot",
+            family="TMR-Set",
+            planned_steps=12,
+            future_budget_floor_gpu_seconds=future_floor,
+        )
+    assert budget.summary()["usage"]["pilot_reservations"] == 0
+    with pytest.raises(StudyBudgetError, match="only to pilot"):
+        reserve(budget, attempt="profile-with-floor", future_budget_floor_gpu_seconds=0)
+
+
 def test_formal_needs_all_87_step_limits_once_and_exact_seed_schedule(tmp_path):
     budget = create(tmp_path)
     with pytest.raises(StudyBudgetError, match="freeze"):

From 42f3c03fa3db30879aebe60413a8784a78df54db Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 21:24:25 -0500
Subject: [PATCH 67/95] Lock 40-parent 200-caption retrieval geometry

---
 tests/test_phaseset_evaluation.py | 36 +++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/tests/test_phaseset_evaluation.py b/tests/test_phaseset_evaluation.py
index d576dc4..925eee8 100644
--- a/tests/test_phaseset_evaluation.py
+++ b/tests/test_phaseset_evaluation.py
@@ -195,6 +195,42 @@ def test_primary_is_bidirectional_capture_macro_not_query_micro() -> None:
     assert contribution_primary == pytest.approx(report.full_gallery.primary)
 
 
+def test_sealed_parent_census_geometry_uses_40_motion_and_200_human_rows() -> None:
+    """Synthetic scores lock the actual parent/query geometry, not test outcomes."""
+
+    parent_count, rows_per_parent = 40, 5
+    scores = np.full((parent_count, parent_count * rows_per_parent), -10.0)
+    for parent in range(parent_count):
+        scores[parent, parent * rows_per_parent : (parent + 1) * rows_per_parent] = 10.0
+    # One C09 caption retrieves another parent; that parent also retrieves this
+    # nonpositive caption ahead of its five positive rows.
+    scores[1, 0] = 11.0
+    dataset = RetrievalDataset(
+        scores=np.ascontiguousarray(scores, dtype=np.float64),
+        motion_commitments=tuple(
+            _commitment("sealed-geometry-motion", parent) for parent in range(parent_count)
+        ),
+        caption_commitments=tuple(
+            _commitment("sealed-geometry-caption", row)
+            for row in range(parent_count * rows_per_parent)
+        ),
+        positive_motion_indices=tuple(
+            (row // rows_per_parent,) for row in range(parent_count * rows_per_parent)
+        ),
+        group_sizes=np.asarray([4] * 24 + [3] * 16, dtype=np.int64),
+        component_labels=("C09",) * 16 + ("C11",) * 8 + ("C15",) * 16,
+    )
+    report = evaluate_retrieval(dataset)
+    assert report.full_gallery.text_to_motion.query_count == 200
+    assert report.full_gallery.motion_to_text.query_count == 40
+    assert report.full_gallery.text_to_motion.aggregation_unit_count == 40
+    assert report.full_gallery.motion_to_text.aggregation_unit_count == 40
+    assert len(report.capture_contributions) == 40
+    assert report.full_gallery.text_to_motion.recall_at_1 == pytest.approx(39.8 / 40)
+    assert report.full_gallery.motion_to_text.recall_at_1 == pytest.approx(39 / 40)
+    assert report.full_gallery.primary == pytest.approx(0.985)
+
+
 def test_variable_positive_ties_use_commitments_in_both_directions() -> None:
     scores = np.zeros((4, 4), dtype=np.float64)
     positives = ((0, 1), (0,), (2, 3), (2,))

From 2dcf12d521e36feadac8b8e74fdd3ed7c60cd1c5 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 21:27:05 -0500
Subject: [PATCH 68/95] Refresh tracked release manifest for retrieval
 regression

---
 RELEASE_FILES.sha256 | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 1ddf3ec..d6d8e8c 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -315,7 +315,7 @@ a53fe5c6b53adb5c80421842f806fc45e8305360348d7065aa0ddfb0b9316dcb	6546	tests/test
 a74143036a41dd9d8779dc4a9b70fe20e5b38d013ef23778d13e73c11273dbd8	26066	tests/test_phaseset_controls.py
 a17a50e801c389b4e24b2840f2aafcb7b476cbef86e2e142ad0af6245a54718f	4515	tests/test_phaseset_data.py
 2f2fa0bf4d10771573896cf5d46eaaeffaaa6d9c52ca647a9ab9aa32a1766c87	14442	tests/test_phaseset_embody_adapter.py
-8bd6e95a35523d108153f668f8919e6f22858e8283e6308041751c6c92d0f904	18605	tests/test_phaseset_evaluation.py
+b9944ab5eb45854df49a003c867278afeac28bab10dea2c39aad1e519abbcdd3	20468	tests/test_phaseset_evaluation.py
 28ae2930b6e953909d8ff422284d8c2c580f94b65e022b8b2d9101b2c9f3a454	49018	tests/test_phaseset_execution.py
 e79f2cb54fc18bccef9b94adeaf8c1811b913d8ba2a22f77e4f35ad9669ad6db	1324	tests/test_phaseset_execution_chain_bound.py
 dfaca3506375cf25fef77f572b00709bd72c25d6c613340a95d66c6bb15aa57c	13749	tests/test_phaseset_experiments.py

From e9d76f80ae8f38c25a546d15b8fb81021c49c77c Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 22:40:46 -0500
Subject: [PATCH 69/95] Freeze V2 pretest decision rules and correct analysis
 units

---
 RELEASE_FILES.sha256                       |   4 +-
 configs/phaseset_v2_analysis_rules.json    | 130 +++++++++++++++++++++
 configs/phaseset_v2_experiment_matrix.json |  17 ++-
 tests/test_phaseset_v2_analysis_rules.py   |  65 +++++++++++
 4 files changed, 209 insertions(+), 7 deletions(-)
 create mode 100644 configs/phaseset_v2_analysis_rules.json
 create mode 100644 tests/test_phaseset_v2_analysis_rules.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index d6d8e8c..2ab4e81 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -16,7 +16,8 @@ e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
 78d3b78d74ee222c1a34f157223cc56107d1dad0861f4a8d432c69ecd62a8ad7	7112	configs/phaseset/embody3d_split_v1.json
 5ed598e51799ac33bc4d9aef6e88aa4640cdfcd97a7f4ae4f589a27ece838144	2683	configs/phaseset/experiment_matrix.json
 195ae5151917647795224c1437faebd37e78b31ec62e92793919a6058f9bebfd	1476	configs/phaseset/training.json
-cd6fb3868996ab909d8a382cfc62a35fa5510e465ca697aae0a142afc50b9498	26169	configs/phaseset_v2_experiment_matrix.json
+7282eeba660484d1ae2df18e5e8d80772d755e4d3f7d9df41e35e0a59dcfeff3	7631	configs/phaseset_v2_analysis_rules.json
+482a4432ac95cfbd21d5b6363893377ab61f6c84373bcf07b7441c4bfa8891dd	26630	configs/phaseset_v2_experiment_matrix.json
 97a0296e84dfdf3255a52041e50e873664e3493edc2e47bb3f787accc3442911	2710	configs/phaseset_v2_relation_supervision.json
 59916f216450b4d79817f6c7f0574243e1092496e53a07a4f1a518047e427acd	994	configs/phaseset_v2_venue.json
 2970469ad0787e74ab4fabce2275486eb50c30c38f3cb7cf8f2800551d16991f	9540	docs/BASE_COHORT_QUALIFICATION.md
@@ -345,6 +346,7 @@ d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/tes
 11727c78e08d02dd433abf66ca75e7f3aafd77c10a98d94ea98f17560d6e86ba	13200	tests/test_phaseset_streaming_autograd.py
 ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test_phaseset_torch_periodic.py
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
+c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
 45b42266bc1c42c36af7eb7d07be2fc85d8b1d0a539ffb3674f98b47e1327254	4973	tests/test_release_artifact_audit.py
diff --git a/configs/phaseset_v2_analysis_rules.json b/configs/phaseset_v2_analysis_rules.json
new file mode 100644
index 0000000..23745cd
--- /dev/null
+++ b/configs/phaseset_v2_analysis_rules.json
@@ -0,0 +1,130 @@
+{
+  "schema": "phaseset-v2-pretest-analysis-rules-v1",
+  "status": "DRAFT_PRE_PILOT_NO_SCORES_REVIEW_REVISION",
+  "decision_source": "2026-09-26 PhaseSet-V2 amendment plus 2026-09-27 relation-supervision amendment",
+  "scope": "rules only; not the final data, checkpoint, comparator, gallery, or score manifest",
+  "task": {
+    "primary_text_source": "original_official_human_holistic_rows_only",
+    "primary_motion_unit": "complete_parent_capture",
+    "test_parent_count_expected": 40,
+    "test_human_caption_rows_expected": 200,
+    "test_parent_group_size_counts_expected": {"3": 16, "4": 24},
+    "test_parent_component_counts_expected": {"C09": 16, "C11": 8, "C15": 16},
+    "positive_rule": "each official caption row is positive only for its sealed parent family",
+    "primary_metric": "0.5 * (capture_macro_T2M_R1 + capture_macro_M2T_any_positive_R1)",
+    "query_scoring": "rank each trained seed separately; never average logits across seeds",
+    "statistical_unit": "parent_capture_not_released_episode_or_caption",
+    "sealed_test_uses_machine_weak_text": false
+  },
+  "pilot_comparator": {
+    "train_components": ["C01", "C02"],
+    "validation_components": ["C03"],
+    "pilot_training_seed": 1729,
+    "population": "all_34_C01_C02_complete_train_parents_and_all_15_C03_complete_validation_parents_official_human_rows_only",
+    "families": ["TMR-Set", "WaMo-Set", "MIME-Set"],
+    "candidate_budget_per_family": 3,
+    "candidate_metric": "C03_complete_parent_human_only_primary_metric",
+    "checkpoint_tie": "earliest_validation_checkpoint",
+    "within_family_exact_tie": "paper_learning_rate_then_half_then_double",
+    "between_family_exact_tie": "fewer_trainable_parameters_then_lower_measured_parent_latency_then_lexicographic_system_id",
+    "fidelity_qualification": [
+      "native_complete_parent_input_and_official_human_targets",
+      "source_to_paper_and_original_reference_input_output_loss_crosswalk_recorded_per_family",
+      "original_task_sanity_fixture_runs_and_expected_input_output_loss_semantics_match_original_reference_or_method_is_unqualified",
+      "all_paper_essential_objective_terms_present_with_nonzero_gradients_and_documented_multiperson_adaptation",
+      "tiny_original_task_overfit_or_equivalent_original_reference_sanity_passes_before_pilot_selection",
+      "finite_losses_and_validation_scores",
+      "successful_terminal_or_predeclared_early_stop",
+      "no_training_or_selection_on_sealed_test"
+    ],
+    "selection_rule": "best_C03_primary_metric_among_qualified_candidates_then_fixed_ties",
+    "all_unqualified_action": "no_B_star_and_no_main_superiority_claim",
+    "selected_B_star": null,
+    "selected_candidate": null,
+    "selection_status": "PENDING_PILOT_NOT_CHOSEN"
+  },
+  "v2_head_pilot_selection": {
+    "predecessor": "same_selected_B2_anchor_checkpoint_for_both_candidates",
+    "candidate_ids": ["v2-cf-half", "v2-cf-fixed"],
+    "candidate_metric": "C03_complete_parent_human_only_primary_metric",
+    "checkpoint_tie": "earliest_validation_checkpoint",
+    "candidate_exact_tie": "v2-cf-fixed_then_v2-cf-half",
+    "selection_rule": "best_C03_primary_metric_among_qualified_candidates_then_fixed_tie",
+    "qualification": "same_predecessor_population_steps_human_validation_and_finite_training_terminal",
+    "failed_both_action": "no_V2_configuration_and_no_formal_V2_launch",
+    "selected_candidate": null,
+    "status": "PENDING_PILOT_NOT_CHOSEN"
+  },
+  "formal_comparison": {
+    "training_seeds": [1729, 2718, 31415],
+    "one_comparator_identity_for_all_seeds": true,
+    "missing_or_invalid_seed": "keep_terminal_and_report_incomplete; three_seed_inference_and_corresponding_claim_UNASSESSABLE; do_not_drop_replace_or_recalculate_with_remaining_seeds",
+    "failed_comparator": "do_not_substitute_weaker_test_chosen_method; main_claim_unassessable",
+    "checkpoint_selection": "development_validation_only_before_any_formal_test_score",
+    "terminal_exclusions": "must_be_named_and_frozen_before_formal_test; no_post_score_exclusion",
+    "test_passes": 1
+  },
+  "inference": {
+    "resampling_unit": "parent_capture",
+    "seed_reduction": "mean_three_seed_paired_parent_effects_before_bootstrap",
+    "draws": 100000,
+    "rng": "numpy_PCG64",
+    "resampling_seed": 20260928,
+    "same_parent_index_stream_for_all_comparisons": true,
+    "confidence": "two_sided_95_percent_percentile",
+    "lower_sorted_zero_based_index": 2499,
+    "upper_sorted_zero_based_index": 97499,
+    "raw_p_h1": "one_sided_bootstrap_tail_(1+count(delta_draw<=0.02))/100001",
+    "h1_decision_calibration": "intentional_conjunctive_conservative_gate_one_sided_p_at_0.05_and_two_sided_95_percent_lower_above_0.02; interval_condition_is_stricter_than_the_p_threshold",
+    "raw_p_a1_a9": "two_sided_bootstrap_tail_min(1,2*min((1+count(delta_draw<=0))/100001,(1+count(delta_draw>=0))/100001))",
+    "holm_family": ["A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9"],
+    "holm_alpha": 0.05,
+    "scope_limit": "conditional_on_fixed_three_participant_components_models_gallery_and_seeds_not_population_generalization"
+  },
+  "claim_gates": {
+    "H1": {
+      "comparison": "PhaseSet-V2_minus_single_pilot_frozen_B_star",
+      "direction": "positive",
+      "null_margin_pp": 2,
+      "point_estimate_at_least_pp": 2,
+      "one_sided_raw_p_at_most": 0.05,
+      "confidence_lower_strictly_above_margin": true,
+      "rule": "all_conditions_required_for_superiority_language"
+    },
+    "A1_to_A9": {
+      "comparison": "PhaseSet-V2_minus_named_ablation_on_same_B2_and_human_task",
+      "null_margin_pp": 0,
+      "direction": "positive",
+      "raw_p_sidedness": "two_sided",
+      "holm_adjusted_p_at_most": 0.05,
+      "confidence_lower_strictly_positive": true,
+      "all_nine_reported_whether_or_not_any_pass": true
+    },
+    "topology_sensitive_package": "requires_A3_and_A4_gates; does_not_isolate_incidence_from_endpoint_binding",
+    "phase_family": "requires_A5_gate; no_claim_of_causal_delay_or_human_relation_truth",
+    "weak_counterfactual_training_utility": "A8_gate_may_support_retrieval_effect_only_not_verified_motion_falsehood",
+    "generic_local_relation": "A7_gate_required_before_claiming_more_than_generic_local_capacity",
+    "calibration_only": "A9_gate_required_before_claiming_more_than_scoring_calibration",
+    "temporal_order": "A2_does_not_isolate_local_from_capture_order",
+    "relation_challenge": "UNASSESSABLE_without_matching_independent_human_labels"
+  },
+  "secondary": {
+    "metrics": ["R@3", "R@5", "R@10", "Hard-32", "K_strata", "component_strata", "three_development_folds", "Inter-X"],
+    "three_fold_target": "positive_effect_in_each_prespecified_development_fold_descriptive_only",
+    "inter_x_scope": "dyadic_trained_applicability_not_Embody_zero_shot",
+    "nonperiodic_regression_target_pp": 1,
+    "nonperiodic_stratum_definition": "PENDING_PRETEST_FREEZE_OR_MARK_UNASSESSABLE",
+    "inference_cost_ratio_target": 2,
+    "cost_protocol": "PENDING_ACTUAL_GPU_PROFILE_THEN_FREEZE_HARDWARE_WORKLOAD_BATCH_REPETITIONS_P50_P95_PEAK_MEMORY_COLD_WARM",
+    "no_secondary_metric_replaces_primary": true
+  },
+  "negative_result_policy": {
+    "H1_fails": "no_superiority_claim; deliver_honest_negative_paper_or_technical_report",
+    "A3_or_A4_fails": "no_topology_sensitive_package_attribution",
+    "A5_fails": "no_phase_family_attribution",
+    "A7_or_A9_fails": "no_more_than_generic_local_or_calibration_claim_respectively",
+    "A8_fails": "no_weak_counterfactual_utility_claim",
+    "machine_relation_labels": "never_independent_human_truth",
+    "no_new_seeds_data_or_test_tuning": true
+  }
+}
diff --git a/configs/phaseset_v2_experiment_matrix.json b/configs/phaseset_v2_experiment_matrix.json
index d81f0ea..ffa82e4 100644
--- a/configs/phaseset_v2_experiment_matrix.json
+++ b/configs/phaseset_v2_experiment_matrix.json
@@ -23,9 +23,12 @@
     "epoch_expansion_must_precede_formal_freeze": true
   },
   "primary_split": {
-    "train_captures": 400,
-    "validation_captures": 96,
-    "test_captures": 76,
+    "train_released_episodes": 400,
+    "validation_released_episodes": 96,
+    "test_released_episodes": 76,
+    "effective_holistic_task_released_episodes": {"train": 398, "validation": 96, "test": 75},
+    "sealed_test_parent_captures": 40,
+    "sealed_test_official_human_rows": 200,
     "final_test_components": 3,
     "validation_component_labels": ["C00"],
     "sealed_test_component_labels": ["C09", "C11", "C15"]
@@ -56,7 +59,7 @@
     "coordination_text_adapter": "shared_512_GELU_512_on_frozen_CLIP",
     "human_holistic_caption_field": "all_scene_explained_rows_no_questionnaire_fusion",
     "retrieval_positive_identity": "admitted_parent_annotation_family_not_released_segment_or_actor_set",
-    "parent_capture_task_freeze": "PENDING_lineage_aggregation_gallery_and_statistics",
+    "parent_capture_task_freeze": "PRELEARNING_PARENT_SOURCE_AND_HUMAN_POSITIVE_FAMILY_SEALED_PHYSICAL_GALLERIES_AND_STATISTICS_PENDING",
     "no_periodic_support": "global_branch_fallback",
     "head_parameter_relative_tolerance": 0.05,
     "dummy_capacity_parameters": false,
@@ -67,12 +70,14 @@
   "frozen_strong_baseline": null,
   "success_thresholds": {
     "retrieval_mean_r1_gain_pp": 2,
-    "relation_challenge_gain_pp": 5,
     "positive_group_folds": 3,
+    "positive_group_folds_role": "descriptive_development_target_not_independent_inference",
     "nonperiodic_max_regression_pp": 1,
     "inference_cost_ratio": 2
   },
-  "relation_challenge": {
+  "historical_superseded_relation_challenge": {
+    "status": "UNASSESSABLE_WITHOUT_MATCHING_INDEPENDENT_HUMAN_LABELS_NOT_AN_ACTIVE_SUCCESS_GATE",
+    "historical_gain_pp": 5,
     "minimum_verified_text_pairs": 200,
     "minimum_real_captures": 100,
     "minimum_participant_components": 8,
diff --git a/tests/test_phaseset_v2_analysis_rules.py b/tests/test_phaseset_v2_analysis_rules.py
new file mode 100644
index 0000000..3f41382
--- /dev/null
+++ b/tests/test_phaseset_v2_analysis_rules.py
@@ -0,0 +1,65 @@
+"""Static pre-pilot V2 decision rules; this test never reads test motions or scores."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_v2_pretest_rules_match_frozen_matrix_and_supervision() -> None:
+    rules = json.loads((ROOT / "configs/phaseset_v2_analysis_rules.json").read_text())
+    matrix = json.loads((ROOT / "configs/phaseset_v2_experiment_matrix.json").read_text())
+    supervision = json.loads(
+        (ROOT / "configs/phaseset_v2_relation_supervision.json").read_text()
+    )
+
+    assert rules["schema"] == "phaseset-v2-pretest-analysis-rules-v1"
+    assert rules["status"] == "DRAFT_PRE_PILOT_NO_SCORES_REVIEW_REVISION"
+    assert rules["pilot_comparator"]["selected_B_star"] is None
+    assert rules["pilot_comparator"]["selected_candidate"] is None
+    assert rules["pilot_comparator"]["families"] == [
+        "TMR-Set",
+        "WaMo-Set",
+        "MIME-Set",
+    ]
+    assert rules["pilot_comparator"]["train_components"] == ["C01", "C02"]
+    assert rules["pilot_comparator"]["validation_components"] == ["C03"]
+    assert rules["v2_head_pilot_selection"]["candidate_ids"] == [
+        "v2-cf-half",
+        "v2-cf-fixed",
+    ]
+    assert rules["v2_head_pilot_selection"]["selected_candidate"] is None
+    assert "original_task_sanity" in " ".join(
+        rules["pilot_comparator"]["fidelity_qualification"]
+    )
+    assert matrix["primary_split"]["train_released_episodes"] == 400
+    assert matrix["primary_split"]["validation_released_episodes"] == 96
+    assert matrix["primary_split"]["test_released_episodes"] == 76
+    assert matrix["primary_split"]["sealed_test_parent_captures"] == 40
+    assert matrix["primary_split"]["sealed_test_official_human_rows"] == 200
+    assert "relation_challenge_gain_pp" not in matrix["success_thresholds"]
+    assert matrix["historical_superseded_relation_challenge"]["status"].startswith(
+        "UNASSESSABLE"
+    )
+    assert rules["formal_comparison"]["training_seeds"] == matrix["seeds"]
+    assert rules["task"]["sealed_test_uses_machine_weak_text"] is False
+    assert supervision["primary_retrieval"]["machine_text_in_primary_gallery"] is False
+    assert rules["claim_gates"]["H1"]["null_margin_pp"] == matrix[
+        "success_thresholds"
+    ]["retrieval_mean_r1_gain_pp"]
+    assert rules["claim_gates"]["H1"]["point_estimate_at_least_pp"] == 2
+    assert "intentional_conjunctive_conservative_gate" in rules["inference"][
+        "h1_decision_calibration"
+    ]
+    assert rules["secondary"]["inference_cost_ratio_target"] == matrix[
+        "success_thresholds"
+    ]["inference_cost_ratio"]
+    assert rules["inference"]["draws"] == 100_000
+    assert rules["inference"]["holm_family"] == [
+        f"A{number}" for number in range(1, 10)
+    ]
+    assert rules["claim_gates"]["relation_challenge"].startswith("UNASSESSABLE")
+    assert len(matrix["runs"]) == matrix["budget"]["formal_stages"] == 87

From a5b884f72df7c5326bb2bf1c16e506fa3a3978ab Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 23:14:31 -0500
Subject: [PATCH 70/95] Add V2 paired parent inference arithmetic witness

---
 RELEASE_FILES.sha256                 |   2 +
 src/phaseset_core/v2_statistics.py   | 283 +++++++++++++++++++++++++++
 tests/test_phaseset_v2_statistics.py | 241 +++++++++++++++++++++++
 3 files changed, 526 insertions(+)
 create mode 100644 src/phaseset_core/v2_statistics.py
 create mode 100644 tests/test_phaseset_v2_statistics.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 2ab4e81..bcb8eff 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -233,6 +233,7 @@ d1b92f7b65d750572a8447a48cd2a09cf12d7ec0fc3edd2f33c4088d0d77a14d	9442	src/phases
 b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
+e8e4fa5cf988c11bc7aecbff241f3c5c253533652b1a31ea2313ee78626c3371	11472	src/phaseset_core/v2_statistics.py
 c1b145691b73cb3fe56925de75b114dac6ba8a84fcae6ffeec282010fc36fa77	15762	src/phaseset_core/wamo_set.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
 ae334bee8e2762f022020942141cb52f3285d3d9cb07d53574b6c37f57b2a556	20274	tests/test_cached_residual_lifecycle.py
@@ -348,6 +349,7 @@ ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
+08d27f7109d678ca011ef63dd99429185eb7b8c16d5f22e266dc05fbd58ea383	9778	tests/test_phaseset_v2_statistics.py
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
 45b42266bc1c42c36af7eb7d07be2fc85d8b1d0a539ffb3674f98b47e1327254	4973	tests/test_release_artifact_audit.py
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
diff --git a/src/phaseset_core/v2_statistics.py b/src/phaseset_core/v2_statistics.py
new file mode 100644
index 0000000..ecc9e66
--- /dev/null
+++ b/src/phaseset_core/v2_statistics.py
@@ -0,0 +1,283 @@
+"""Data-free PhaseSet-V2 decision arithmetic, without result authority.
+
+The caller must supply independently ranked, paired parent-level hit rows from
+one frozen gallery. This module neither loads scores nor authenticates their
+origin. Its output is an algorithm witness, never a sealed-test report.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from fractions import Fraction
+import hashlib
+import json
+import math
+
+import numpy as np
+
+from phaseset_core.statistics import PairedCapture, summarize_variable_captions
+
+
+SEEDS = (1729, 2718, 31415)
+COMPARISONS = ("H1",) + tuple(f"A{index}" for index in range(1, 10))
+HOLM_FAMILY = COMPARISONS[1:]
+BOOTSTRAP_DRAWS = 100_000
+BOOTSTRAP_SEED = 20260928
+H1_MARGIN = Fraction(2, 100)
+STATUS = "ALGORITHM_ONLY_UNBOUND_NO_SEALED_TEST_AUTHORITY"
+
+
+class V2StatisticsError(ValueError):
+    """A predeclared comparison or paired query census was violated."""
+
+
+@dataclass(frozen=True, slots=True)
+class V2PairedComparison:
+    comparison_id: str
+    baseline_system_id: str
+    seed_blocks: tuple[tuple[int, tuple[PairedCapture, ...]], ...]
+
+
+@dataclass(frozen=True, slots=True)
+class V2DecisionRow:
+    comparison_id: str
+    baseline_system_id: str
+    seed_effects: tuple[float, float, float]
+    observed_effect: float
+    confidence_lower: float
+    confidence_upper: float
+    raw_p_value: float
+    holm_adjusted_p_value: float | None
+    claim_gate_passed: bool
+
+
+@dataclass(frozen=True, slots=True)
+class V2AlgorithmReport:
+    rows: tuple[V2DecisionRow, ...]
+    draws: int
+    resampling_seed: int
+    parent_count: int
+    caption_count: int
+    index_stream_sha256: str
+    input_rows_sha256: str
+    schema: str = "phaseset-v2-algorithm-witness-v1"
+    status: str = STATUS
+    scientific_or_test_authority: bool = False
+
+
+def _effect(row: PairedCapture) -> Fraction:
+    text = Fraction(
+        sum(row.treatment_text_to_motion_hits) - sum(row.control_text_to_motion_hits),
+        len(row.caption_ids),
+    )
+    motion = Fraction(
+        sum(row.treatment_motion_to_text_hits) - sum(row.control_motion_to_text_hits),
+        len(row.motion_ids),
+    )
+    return (text + motion) / 2
+
+
+def _census(row: PairedCapture) -> tuple[str, tuple[str, ...], tuple[str, ...]]:
+    return row.capture_id, row.caption_ids, row.motion_ids
+
+
+def _treatment_hits(row: PairedCapture) -> tuple[tuple[int, ...], tuple[int, ...]]:
+    return row.treatment_text_to_motion_hits, row.treatment_motion_to_text_hits
+
+
+def _normalise(
+    comparisons: object,
+    *,
+    selected_b_star: object,
+) -> tuple[tuple[V2PairedComparison, ...], np.ndarray, tuple[tuple[float, ...], ...], int, str]:
+    if type(selected_b_star) is not str or selected_b_star not in {
+        "TMR-Set", "WaMo-Set", "MIME-Set"
+    }:
+        raise V2StatisticsError("B* must be one pilot-qualified literature family")
+    if type(comparisons) is not tuple or len(comparisons) != len(COMPARISONS):
+        raise V2StatisticsError("exactly H1 and A1--A9 comparisons are required")
+    by_id: dict[str, V2PairedComparison] = {}
+    for comparison in comparisons:
+        if type(comparison) is not V2PairedComparison:
+            raise TypeError("comparison must be exact V2PairedComparison")
+        identifier = comparison.comparison_id
+        if identifier not in COMPARISONS or identifier in by_id:
+            raise V2StatisticsError("comparison IDs must be unique H1,A1--A9")
+        expected_baseline = selected_b_star if identifier == "H1" else identifier
+        if comparison.baseline_system_id != expected_baseline:
+            raise V2StatisticsError(f"{identifier} baseline differs from frozen registry")
+        by_id[identifier] = comparison
+    ordered = tuple(by_id[identifier] for identifier in COMPARISONS)
+
+    reference_census = None
+    reference_treatment = None
+    parent_count = None
+    caption_count = None
+    effects: list[list[float]] = []
+    per_seed: list[tuple[float, ...]] = []
+    digest_rows: list[dict[str, object]] = []
+    for comparison in ordered:
+        if (
+            type(comparison.seed_blocks) is not tuple
+            or len(comparison.seed_blocks) != len(SEEDS)
+        ):
+            raise V2StatisticsError("all three fixed seed blocks are required")
+        seed_effect_vectors: list[list[Fraction]] = []
+        digest_blocks: list[dict[str, object]] = []
+        for expected_seed, block in zip(SEEDS, comparison.seed_blocks, strict=True):
+            if type(block) is not tuple or len(block) != 2 or block[0] != expected_seed:
+                raise V2StatisticsError("seed blocks must be ordered 1729,2718,31415")
+            rows = block[1]
+            summary = summarize_variable_captions(rows)
+            if summary.capture_count < 2:
+                raise V2StatisticsError("at least two independent parents are required")
+            if tuple(row.capture_id for row in rows) != tuple(
+                sorted(row.capture_id for row in rows)
+            ):
+                raise V2StatisticsError("parent rows must be sorted by commitment")
+            census = tuple(_census(row) for row in rows)
+            treatment = tuple(_treatment_hits(row) for row in rows)
+            if reference_census is None:
+                reference_census = census
+                reference_treatment = {expected_seed: treatment}
+                parent_count = summary.capture_count
+                caption_count = summary.total_caption_count
+            else:
+                if census != reference_census or summary.total_caption_count != caption_count:
+                    raise V2StatisticsError("comparison/seed parent-query census differs")
+                assert reference_treatment is not None
+                prior_treatment = reference_treatment.setdefault(expected_seed, treatment)
+                if prior_treatment != treatment:
+                    raise V2StatisticsError("V2 treatment hits differ across comparisons")
+            fractions = [_effect(row) for row in rows]
+            seed_effect_vectors.append(fractions)
+            digest_blocks.append({
+                "seed": expected_seed,
+                "rows": [
+                    {
+                        "parent": row.capture_id,
+                        "caption_ids": row.caption_ids,
+                        "motion_ids": row.motion_ids,
+                        "treatment_t2m": row.treatment_text_to_motion_hits,
+                        "control_t2m": row.control_text_to_motion_hits,
+                        "treatment_m2t": row.treatment_motion_to_text_hits,
+                        "control_m2t": row.control_motion_to_text_hits,
+                    }
+                    for row in rows
+                ],
+            })
+        assert parent_count is not None
+        seed_means = tuple(float(sum(vector, start=Fraction()) / parent_count)
+                           for vector in seed_effect_vectors)
+        parent_effects = [
+            float(sum((vector[index] for vector in seed_effect_vectors), start=Fraction())
+                  / len(SEEDS))
+            for index in range(parent_count)
+        ]
+        effects.append(parent_effects)
+        per_seed.append(seed_means)
+        digest_rows.append({
+            "comparison_id": comparison.comparison_id,
+            "baseline_system_id": comparison.baseline_system_id,
+            "seed_blocks": digest_blocks,
+        })
+    payload = json.dumps(digest_rows, sort_keys=True, separators=(",", ":"),
+                         ensure_ascii=True).encode("ascii")
+    return (
+        ordered,
+        np.asarray(effects, dtype=np.float64),
+        tuple(per_seed),
+        parent_count,
+        hashlib.sha256(payload).hexdigest(),
+    )
+
+
+def _holm(raw_p: dict[str, float]) -> dict[str, float]:
+    ordered = sorted(raw_p.items(), key=lambda item: (item[1], item[0]))
+    running = 0.0
+    adjusted: dict[str, float] = {}
+    for rank, (identifier, probability) in enumerate(ordered):
+        running = max(running, min(1.0, (len(ordered) - rank) * probability))
+        adjusted[identifier] = running
+    return adjusted
+
+
+def v2_algorithm_report(
+    comparisons: object,
+    *,
+    selected_b_star: object,
+) -> V2AlgorithmReport:
+    """Apply the frozen arithmetic to paired hits, without granting result authority."""
+
+    ordered, effects, per_seed, parent_count, input_hash = _normalise(
+        comparisons, selected_b_star=selected_b_star
+    )
+    samples = np.empty((len(COMPARISONS), BOOTSTRAP_DRAWS), dtype=np.float64)
+    rng = np.random.Generator(np.random.PCG64(BOOTSTRAP_SEED))
+    index_hash = hashlib.sha256()
+    chunk = min(4096, max(1, 1_000_000 // parent_count))
+    for offset in range(0, BOOTSTRAP_DRAWS, chunk):
+        count = min(chunk, BOOTSTRAP_DRAWS - offset)
+        indices = rng.integers(0, parent_count, size=(count, parent_count), dtype=np.uint64)
+        index_hash.update(np.asarray(indices, dtype="<u8", order="C").tobytes())
+        samples[:, offset:offset + count] = effects[:, indices].mean(axis=2)
+
+    raw_p: dict[str, float] = {}
+    intervals: dict[str, tuple[float, float]] = {}
+    for index, identifier in enumerate(COMPARISONS):
+        draws = samples[index]
+        if not np.isfinite(draws).all():
+            raise V2StatisticsError("bootstrap contains non-finite effects")
+        if identifier == "H1":
+            raw_p[identifier] = (1 + int(np.count_nonzero(draws <= float(H1_MARGIN)))) / (
+                BOOTSTRAP_DRAWS + 1
+            )
+        else:
+            left = (1 + int(np.count_nonzero(draws <= 0.0))) / (BOOTSTRAP_DRAWS + 1)
+            right = (1 + int(np.count_nonzero(draws >= 0.0))) / (BOOTSTRAP_DRAWS + 1)
+            raw_p[identifier] = min(1.0, 2.0 * min(left, right))
+        draws.sort()
+        intervals[identifier] = (float(draws[2_499]), float(draws[97_499]))
+    adjusted = _holm({identifier: raw_p[identifier] for identifier in HOLM_FAMILY})
+    rows = []
+    for index, comparison in enumerate(ordered):
+        identifier = comparison.comparison_id
+        observed = float(effects[index].mean())
+        if not math.isfinite(observed):
+            raise V2StatisticsError("observed effect is not finite")
+        lower, upper = intervals[identifier]
+        if identifier == "H1":
+            gate = observed >= float(H1_MARGIN) and raw_p[identifier] <= 0.05 and (
+                lower > float(H1_MARGIN)
+            )
+            holm_p = None
+        else:
+            holm_p = adjusted[identifier]
+            gate = lower > 0.0 and holm_p <= 0.05
+        rows.append(V2DecisionRow(
+            comparison_id=identifier,
+            baseline_system_id=comparison.baseline_system_id,
+            seed_effects=per_seed[index],
+            observed_effect=observed,
+            confidence_lower=lower,
+            confidence_upper=upper,
+            raw_p_value=raw_p[identifier],
+            holm_adjusted_p_value=holm_p,
+            claim_gate_passed=gate,
+        ))
+    return V2AlgorithmReport(
+        rows=tuple(rows),
+        draws=BOOTSTRAP_DRAWS,
+        resampling_seed=BOOTSTRAP_SEED,
+        parent_count=parent_count,
+        caption_count=sum(len(row.caption_ids) for row in ordered[0].seed_blocks[0][1]),
+        index_stream_sha256=index_hash.hexdigest(),
+        input_rows_sha256=input_hash,
+    )
+
+
+__all__ = [
+    "BOOTSTRAP_DRAWS", "BOOTSTRAP_SEED", "COMPARISONS", "H1_MARGIN", "HOLM_FAMILY",
+    "SEEDS", "STATUS", "V2AlgorithmReport", "V2DecisionRow", "V2PairedComparison",
+    "V2StatisticsError", "v2_algorithm_report",
+]
diff --git a/tests/test_phaseset_v2_statistics.py b/tests/test_phaseset_v2_statistics.py
new file mode 100644
index 0000000..110fd9c
--- /dev/null
+++ b/tests/test_phaseset_v2_statistics.py
@@ -0,0 +1,241 @@
+"""Synthetic-only V2 inference arithmetic; no sealed scores or result authority."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+import hashlib
+
+import pytest
+
+from phaseset_core.statistics import PairedCapture
+from phaseset_core import statistics, v2_statistics
+
+
+def _key(label: str) -> str:
+    return hashlib.sha256(label.encode("ascii")).hexdigest()
+
+
+def _parent(index: int) -> PairedCapture:
+    captions = tuple(sorted(_key(f"caption-{index}-{ordinal}") for ordinal in range(index + 1)))
+    return PairedCapture(
+        capture_id=_key(f"parent-{index}"),
+        caption_ids=captions,
+        motion_ids=(_key(f"motion-{index}"),),
+        treatment_text_to_motion_hits=(1,) * len(captions),
+        control_text_to_motion_hits=(0,) * len(captions),
+        treatment_motion_to_text_hits=(1,),
+        control_motion_to_text_hits=(0,),
+    )
+
+
+def _comparisons() -> tuple[v2_statistics.V2PairedComparison, ...]:
+    parents = tuple(sorted((_parent(0), _parent(1), _parent(2)), key=lambda row: row.capture_id))
+    blocks = tuple((seed, parents) for seed in v2_statistics.SEEDS)
+    return tuple(
+        v2_statistics.V2PairedComparison(
+            comparison_id=identifier,
+            baseline_system_id="TMR-Set" if identifier == "H1" else identifier,
+            seed_blocks=blocks,
+        )
+        for identifier in v2_statistics.COMPARISONS
+    )
+
+
+@pytest.fixture(scope="module")
+def witness() -> v2_statistics.V2AlgorithmReport:
+    return v2_statistics.v2_algorithm_report(
+        _comparisons(), selected_b_star="TMR-Set"
+    )
+
+
+def test_v2_algorithm_shared_parent_stream_nine_way_holm_and_no_authority(
+    witness: v2_statistics.V2AlgorithmReport,
+) -> None:
+    assert witness.status == v2_statistics.STATUS
+    assert witness.scientific_or_test_authority is False
+    assert witness.draws == 100_000
+    assert witness.resampling_seed == 20260928
+    assert witness.parent_count == 3
+    assert witness.caption_count == 6
+    assert tuple(row.comparison_id for row in witness.rows) == v2_statistics.COMPARISONS
+    assert witness.index_stream_sha256 == (
+        "a1c6d56a77065d334654f04a3806dc9740fcd62d3e4383e1b51ca0e089af19cb"
+    )
+    assert len(witness.input_rows_sha256) == 64
+    assert witness.rows[0].baseline_system_id == "TMR-Set"
+    assert witness.rows[0].holm_adjusted_p_value is None
+    assert all(row.observed_effect == 1.0 for row in witness.rows)
+    assert all(row.confidence_lower == 1.0 for row in witness.rows)
+    assert all(row.claim_gate_passed for row in witness.rows)
+    assert all(row.holm_adjusted_p_value is not None for row in witness.rows[1:])
+    assert witness.rows[1].holm_adjusted_p_value == pytest.approx(18 / 100_001)
+
+
+def test_v2_comparison_order_is_canonical() -> None:
+    comparisons = _comparisons()
+    shuffled = comparisons[4:] + comparisons[:4]
+    ordered = v2_statistics.v2_algorithm_report(
+        shuffled, selected_b_star="TMR-Set"
+    )
+    assert tuple(row.comparison_id for row in ordered.rows) == v2_statistics.COMPARISONS
+
+
+def test_v2_parent_resampling_stream_matches_legacy_cpu_oracle(
+    witness: v2_statistics.V2AlgorithmReport,
+) -> None:
+    oracle = statistics.paired_seed_blocked_bootstrap(
+        _comparisons()[1].seed_blocks,
+        resampling_seed=v2_statistics.BOOTSTRAP_SEED,
+    )
+    assert witness.index_stream_sha256 == oracle.index_stream_sha256
+    assert witness.rows[1].observed_effect == oracle.observed_effect
+    assert witness.rows[1].confidence_lower == oracle.confidence_lower
+    assert witness.rows[1].confidence_upper == oracle.confidence_upper
+    assert witness.rows[1].raw_p_value == oracle.two_sided_p_value
+
+
+def test_v2_missing_seed_or_wrong_comparator_fails_before_bootstrap() -> None:
+    comparisons = _comparisons()
+    missing_seed = replace(comparisons[0], seed_blocks=comparisons[0].seed_blocks[:2])
+    with pytest.raises(v2_statistics.V2StatisticsError, match="three fixed seed"):
+        v2_statistics.v2_algorithm_report(
+            (missing_seed,) + comparisons[1:], selected_b_star="TMR-Set"
+        )
+    wrong_baseline = replace(comparisons[0], baseline_system_id="MIME-Set")
+    with pytest.raises(v2_statistics.V2StatisticsError, match="baseline differs"):
+        v2_statistics.v2_algorithm_report(
+            (wrong_baseline,) + comparisons[1:], selected_b_star="TMR-Set"
+        )
+
+
+def test_v2_treatment_and_query_census_are_identical_across_controls() -> None:
+    comparisons = list(_comparisons())
+    seed, rows = comparisons[1].seed_blocks[0]
+    changed = replace(rows[0], treatment_motion_to_text_hits=(0,))
+    comparisons[1] = replace(
+        comparisons[1],
+        seed_blocks=((seed, (changed,) + rows[1:]),) + comparisons[1].seed_blocks[1:],
+    )
+    with pytest.raises(v2_statistics.V2StatisticsError, match="treatment hits differ"):
+        v2_statistics.v2_algorithm_report(
+            tuple(comparisons), selected_b_star="TMR-Set"
+        )
+
+    comparisons = list(_comparisons())
+    seed, rows = comparisons[1].seed_blocks[2]
+    changed = replace(rows[0], treatment_motion_to_text_hits=(0,))
+    comparisons[1] = replace(
+        comparisons[1],
+        seed_blocks=comparisons[1].seed_blocks[:2] + ((seed, (changed,) + rows[1:]),),
+    )
+    with pytest.raises(v2_statistics.V2StatisticsError, match="treatment hits differ"):
+        v2_statistics.v2_algorithm_report(
+            tuple(comparisons), selected_b_star="TMR-Set"
+        )
+
+    comparisons = list(_comparisons())
+    seed, rows = comparisons[1].seed_blocks[0]
+    changed = replace(rows[0], caption_ids=(_key("wrong"),) + rows[0].caption_ids[1:])
+    comparisons[1] = replace(
+        comparisons[1],
+        seed_blocks=((seed, (changed,) + rows[1:]),) + comparisons[1].seed_blocks[1:],
+    )
+    with pytest.raises(ValueError, match="caption_ids|census"):
+        v2_statistics.v2_algorithm_report(
+            tuple(comparisons), selected_b_star="TMR-Set"
+        )
+
+
+def test_v2_parent_macro_and_seed_mean_precede_common_parent_resampling() -> None:
+    parents = tuple(sorted((_parent(0), _parent(2)), key=lambda row: row.capture_id))
+    patterns = {
+        1729: {1: ((1,), (1,)), 3: ((1, 0, 0), (0,))},
+        2718: {1: ((0,), (0,)), 3: ((1, 1, 0), (1,))},
+        31415: {1: ((1,), (0,)), 3: ((0, 0, 0), (0,))},
+    }
+    blocks = tuple(
+        (
+            seed,
+            tuple(
+                replace(
+                    row,
+                    treatment_text_to_motion_hits=patterns[seed][len(row.caption_ids)][0],
+                    treatment_motion_to_text_hits=patterns[seed][len(row.caption_ids)][1],
+                )
+                for row in parents
+            ),
+        )
+        for seed in v2_statistics.SEEDS
+    )
+    comparisons = tuple(
+        v2_statistics.V2PairedComparison(
+            comparison_id=identifier,
+            baseline_system_id="TMR-Set" if identifier == "H1" else identifier,
+            seed_blocks=blocks,
+        )
+        for identifier in v2_statistics.COMPARISONS
+    )
+    report = v2_statistics.v2_algorithm_report(
+        comparisons, selected_b_star="TMR-Set"
+    )
+    assert report.caption_count == 4
+    assert report.rows[0].seed_effects == pytest.approx((7 / 12, 5 / 12, 1 / 4))
+    assert report.rows[0].observed_effect == pytest.approx(5 / 12)
+    assert report.rows[0].confidence_lower == pytest.approx(1 / 3)
+    assert report.rows[0].confidence_upper == pytest.approx(1 / 2)
+    assert report.rows[0].raw_p_value == pytest.approx(1 / 100_001)
+
+
+def test_v2_h1_strict_margin_boundary_and_zero_effect_holm() -> None:
+    rows = tuple(
+        PairedCapture(
+            capture_id=_key(f"boundary-parent-{index}"),
+            caption_ids=tuple(sorted(_key(f"boundary-{index}-{ordinal}") for ordinal in range(25))),
+            motion_ids=(_key(f"boundary-motion-{index}"),),
+            treatment_text_to_motion_hits=(1,) + (0,) * 24,
+            control_text_to_motion_hits=(0,) * 25,
+            treatment_motion_to_text_hits=(0,),
+            control_motion_to_text_hits=(0,),
+        )
+        for index in range(2)
+    )
+    rows = tuple(sorted(rows, key=lambda row: row.capture_id))
+    blocks = tuple((seed, rows) for seed in v2_statistics.SEEDS)
+    comparisons = tuple(
+        v2_statistics.V2PairedComparison(
+            comparison_id=identifier,
+            baseline_system_id="TMR-Set" if identifier == "H1" else identifier,
+            seed_blocks=blocks,
+        )
+        for identifier in v2_statistics.COMPARISONS
+    )
+    report = v2_statistics.v2_algorithm_report(
+        comparisons, selected_b_star="TMR-Set"
+    )
+    assert report.rows[0].observed_effect == pytest.approx(0.02)
+    assert report.rows[0].confidence_lower == pytest.approx(0.02)
+    assert report.rows[0].claim_gate_passed is False
+
+    zero_blocks = tuple(
+        (
+            seed,
+            tuple(replace(row, treatment_text_to_motion_hits=(0,) * 25) for row in rows),
+        )
+        for seed in v2_statistics.SEEDS
+    )
+    zero_comparisons = tuple(replace(row, seed_blocks=zero_blocks) for row in comparisons)
+    zero_report = v2_statistics.v2_algorithm_report(
+        zero_comparisons, selected_b_star="TMR-Set"
+    )
+    assert all(row.claim_gate_passed is False for row in zero_report.rows)
+    assert all(row.raw_p_value == 1.0 for row in zero_report.rows[1:])
+    assert all(row.holm_adjusted_p_value == 1.0 for row in zero_report.rows[1:])
+
+
+def test_v2_holm_is_nine_way_step_down_with_fixed_tie_order() -> None:
+    raw = {f"A{index}": index / 1000 for index in range(1, 10)}
+    adjusted = v2_statistics._holm(raw)
+    assert adjusted["A1"] == pytest.approx(0.009)
+    assert adjusted["A2"] == pytest.approx(0.016)
+    assert adjusted["A3"] == pytest.approx(0.021)
+    assert adjusted["A9"] == pytest.approx(0.025)

From 41b3d81fbf300e444ab0432f5950232fea666042 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Sun, 27 Sep 2026 23:29:25 -0500
Subject: [PATCH 71/95] Assemble V2 paired statistics from frozen score census

---
 RELEASE_FILES.sha256                   |   2 +
 src/phaseset_core/v2_score_tables.py   | 146 +++++++++++++++++++++++
 tests/test_phaseset_v2_score_tables.py | 156 +++++++++++++++++++++++++
 3 files changed, 304 insertions(+)
 create mode 100644 src/phaseset_core/v2_score_tables.py
 create mode 100644 tests/test_phaseset_v2_score_tables.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index bcb8eff..5b44479 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -233,6 +233,7 @@ d1b92f7b65d750572a8447a48cd2a09cf12d7ec0fc3edd2f33c4088d0d77a14d	9442	src/phases
 b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
+cbeb8553a7b0b3bb68a092a98e394cb25917410dc4b13d3d7212cee91b456aae	5583	src/phaseset_core/v2_score_tables.py
 e8e4fa5cf988c11bc7aecbff241f3c5c253533652b1a31ea2313ee78626c3371	11472	src/phaseset_core/v2_statistics.py
 c1b145691b73cb3fe56925de75b114dac6ba8a84fcae6ffeec282010fc36fa77	15762	src/phaseset_core/wamo_set.py
 7d93120cff7411fd98ea0b15dc0102d98d8e47a9d013270f11af079fa6201e03	3217	tests/test_all_refs_audit.py
@@ -349,6 +350,7 @@ ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
+1df27542e87ae6d3d47ac0e6a865a6e9a5be49ea8a9bc5af61758b18e830b64a	6261	tests/test_phaseset_v2_score_tables.py
 08d27f7109d678ca011ef63dd99429185eb7b8c16d5f22e266dc05fbd58ea383	9778	tests/test_phaseset_v2_statistics.py
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
 45b42266bc1c42c36af7eb7d07be2fc85d8b1d0a539ffb3674f98b47e1327254	4973	tests/test_release_artifact_audit.py
diff --git a/src/phaseset_core/v2_score_tables.py b/src/phaseset_core/v2_score_tables.py
new file mode 100644
index 0000000..fd247bc
--- /dev/null
+++ b/src/phaseset_core/v2_score_tables.py
@@ -0,0 +1,146 @@
+"""Assemble PhaseSet-V2 paired outcomes from one frozen 40-by-200 score census.
+
+This module has no authority to decide whether scores came from official data,
+qualified checkpoints, or a single sealed test pass. The private execution host
+must verify those facts before passing the score tables and a pre-score census
+digest. The returned comparisons remain algorithm-only inputs.
+"""
+
+from __future__ import annotations
+
+from collections import Counter
+from dataclasses import dataclass
+
+import numpy as np
+
+from phaseset_core.evaluation import (
+    RetrievalDataset,
+    evaluate_retrieval,
+    retrieval_census_sha256,
+    validate_retrieval_dataset,
+)
+from phaseset_core.statistics import paired_captures_from_contributions
+from phaseset_core.v2_statistics import (
+    COMPARISONS,
+    SEEDS,
+    V2PairedComparison,
+    V2StatisticsError,
+)
+
+
+STATUS = "SCORE_TABLE_ASSEMBLY_ONLY_NO_SEALED_TEST_AUTHORITY"
+_B_STAR = frozenset({"TMR-Set", "WaMo-Set", "MIME-Set"})
+_K_COUNTS = {3: 16, 4: 24}
+_COMPONENT_COUNTS = {"C09": 16, "C11": 8, "C15": 16}
+
+
+@dataclass(frozen=True, slots=True)
+class V2ScoreTable:
+    system_id: str
+    seed: int
+    dataset: RetrievalDataset
+
+
+@dataclass(frozen=True, slots=True)
+class V2ScoreAssembly:
+    comparisons: tuple[V2PairedComparison, ...]
+    census_sha256: str
+    score_table_sha256s: tuple[tuple[str, int, str], ...]
+    parent_count: int = 40
+    caption_count: int = 200
+    status: str = STATUS
+    scientific_or_test_authority: bool = False
+
+
+def _sha256_hex(value: object) -> str:
+    if (
+        type(value) is not str
+        or len(value) != 64
+        or any(char not in "0123456789abcdef" for char in value)
+    ):
+        raise V2StatisticsError("pre-score census must be a lowercase SHA-256 digest")
+    return value
+
+
+def _check_task_geometry(dataset: RetrievalDataset) -> None:
+    if dataset.scores.shape != (40, 200):
+        raise V2StatisticsError("PhaseSet-V2 score geometry must be 40 parents by 200 rows")
+    if Counter(dataset.group_sizes.tolist()) != _K_COUNTS:
+        raise V2StatisticsError("parent K=3/K=4 census differs from frozen task")
+    if Counter(dataset.component_labels) != _COMPONENT_COUNTS:
+        raise V2StatisticsError("parent component census differs from frozen task")
+    if any(len(row) != 1 for row in dataset.positive_motion_indices):
+        raise V2StatisticsError("each caption row must have exactly one parent")
+    positive_counts = np.bincount(
+        [row[0] for row in dataset.positive_motion_indices], minlength=40
+    )
+    if not np.array_equal(positive_counts, np.full(40, 5, dtype=np.int64)):
+        raise V2StatisticsError("each parent must have five caption rows")
+
+
+def assemble_v2_score_tables(
+    tables: object,
+    *,
+    selected_b_star: object,
+    expected_census_sha256: object,
+) -> V2ScoreAssembly:
+    """Pair scored systems without giving caller-supplied scores result authority.
+
+    The host must compute ``expected_census_sha256`` from its independently
+    sealed, score-free full gallery before opening any model score. This function
+    checks all 33 tables against that same ordered census, then ranks each seed
+    separately using the public evaluator and pairs by parent commitments.
+    """
+
+    if type(selected_b_star) is not str or selected_b_star not in _B_STAR:
+        raise V2StatisticsError("B* must name an allowed literature family; host owns qualification")
+    expected_census = _sha256_hex(expected_census_sha256)
+    systems = ("PhaseSet-V2", selected_b_star, *COMPARISONS[1:])
+    expected_keys = {(system, seed) for system in systems for seed in SEEDS}
+    if type(tables) is not tuple or len(tables) != len(expected_keys):
+        raise V2StatisticsError("exactly 33 V2/B*/A1--A9 seed score tables are required")
+
+    checked: dict[tuple[str, int], RetrievalDataset] = {}
+    for row in tables:
+        if type(row) is not V2ScoreTable:
+            raise TypeError("every score table must be exact V2ScoreTable")
+        key = (row.system_id, row.seed)
+        if key not in expected_keys or key in checked:
+            raise V2StatisticsError("score system/seed is unexpected or duplicated")
+        dataset = validate_retrieval_dataset(row.dataset)
+        _check_task_geometry(dataset)
+        if retrieval_census_sha256(dataset) != expected_census:
+            raise V2StatisticsError("score table differs from pre-score frozen census")
+        checked[key] = dataset
+    if set(checked) != expected_keys:
+        raise V2StatisticsError("one or more fixed system/seed score tables are missing")
+
+    evaluated = {key: evaluate_retrieval(checked[key]) for key in sorted(checked)}
+    comparisons = []
+    for comparison_id in COMPARISONS:
+        baseline = selected_b_star if comparison_id == "H1" else comparison_id
+        seed_blocks = tuple(
+            (
+                seed,
+                paired_captures_from_contributions(
+                    evaluated[("PhaseSet-V2", seed)].capture_contributions,
+                    evaluated[(baseline, seed)].capture_contributions,
+                ),
+            )
+            for seed in SEEDS
+        )
+        comparisons.append(V2PairedComparison(comparison_id, baseline, seed_blocks))
+    return V2ScoreAssembly(
+        comparisons=tuple(comparisons),
+        census_sha256=expected_census,
+        score_table_sha256s=tuple(
+            (system, seed, evaluated[(system, seed)].dataset_sha256)
+            for system in systems
+            for seed in SEEDS
+        ),
+    )
+
+
+__all__ = [
+    "STATUS", "V2ScoreAssembly", "V2ScoreTable", "assemble_v2_score_tables",
+]
diff --git a/tests/test_phaseset_v2_score_tables.py b/tests/test_phaseset_v2_score_tables.py
new file mode 100644
index 0000000..3275bf5
--- /dev/null
+++ b/tests/test_phaseset_v2_score_tables.py
@@ -0,0 +1,156 @@
+"""Synthetic-only score-table geometry and pairing tests; no sealed scores."""
+
+from dataclasses import replace
+import hashlib
+
+import numpy as np
+import pytest
+
+from phaseset_core.evaluation import RetrievalDataset, retrieval_census_sha256
+from phaseset_core.v2_score_tables import (
+    STATUS,
+    V2ScoreTable,
+    assemble_v2_score_tables,
+)
+from phaseset_core.v2_statistics import SEEDS, V2StatisticsError, v2_algorithm_report
+
+
+def _commitment(prefix: str, index: int) -> bytes:
+    return hashlib.sha256(f"synthetic-{prefix}-{index}".encode()).digest()
+
+
+@pytest.fixture(scope="module")
+def score_fixture():
+    motions = tuple(_commitment("motion", index) for index in range(40))
+    captions = tuple(_commitment("caption", index) for index in range(200))
+    positives = tuple((index // 5,) for index in range(200))
+    sizes = np.asarray((3,) * 16 + (4,) * 24, dtype=np.int64)
+    components = ("C09",) * 16 + ("C11",) * 8 + ("C15",) * 16
+    systems = ("PhaseSet-V2", "TMR-Set", *(f"A{index}" for index in range(1, 10)))
+    rows = []
+    for system in systems:
+        for seed in SEEDS:
+            scores = np.full((40, 200), -1.0, dtype=np.float64)
+            for parent in range(40):
+                scores[parent, parent * 5:(parent + 1) * 5] = 1.0
+            if system == "A1" and seed == SEEDS[0]:
+                scores[1, 0] = 2.0
+            rows.append(V2ScoreTable(
+                system,
+                seed,
+                RetrievalDataset(scores, motions, captions, positives, sizes, components),
+            ))
+    return tuple(rows), retrieval_census_sha256(rows[0].dataset)
+
+
+def test_assembles_33_same_census_tables_and_keeps_algorithm_unbound(score_fixture):
+    tables, census = score_fixture
+    assembly = assemble_v2_score_tables(
+        tuple(reversed(tables)), selected_b_star="TMR-Set", expected_census_sha256=census
+    )
+    assert assembly.status == STATUS
+    assert not assembly.scientific_or_test_authority
+    assert assembly.parent_count == 40 and assembly.caption_count == 200
+    assert len(assembly.score_table_sha256s) == 33
+    assert tuple((system, seed) for system, seed, _ in assembly.score_table_sha256s) == tuple(
+        (system, seed)
+        for system in ("PhaseSet-V2", "TMR-Set", *(f"A{index}" for index in range(1, 10)))
+        for seed in SEEDS
+    )
+    assert tuple(row.comparison_id for row in assembly.comparisons) == (
+        "H1", *(f"A{index}" for index in range(1, 10))
+    )
+    assert all(
+        tuple(seed for seed, _ in comparison.seed_blocks) == SEEDS
+        for comparison in assembly.comparisons
+    )
+    assert assembly.comparisons[0].baseline_system_id == "TMR-Set"
+    first_seed_a1 = assembly.comparisons[1].seed_blocks[0][1]
+    assert len(first_seed_a1) == 40
+    assert sum(sum(row.control_text_to_motion_hits) for row in first_seed_a1) == 199
+    assert sum(sum(row.control_motion_to_text_hits) for row in first_seed_a1) == 39
+    report = v2_algorithm_report(assembly.comparisons, selected_b_star="TMR-Set")
+    assert report.parent_count == 40 and report.caption_count == 200
+    assert not report.scientific_or_test_authority
+
+
+@pytest.mark.parametrize("fault", ["missing", "duplicate", "wrong_system", "wrong_seed"])
+def test_rejects_incomplete_or_ambiguous_formal_matrix(score_fixture, fault):
+    tables, census = score_fixture
+    if fault == "missing":
+        bad = tables[:-1]
+    elif fault == "duplicate":
+        bad = (*tables[:-1], tables[0])
+    elif fault == "wrong_system":
+        bad = (*tables[:-1], replace(tables[-1], system_id="other"))
+    else:
+        bad = (*tables[:-1], replace(tables[-1], seed=999))
+    with pytest.raises(V2StatisticsError):
+        assemble_v2_score_tables(
+            bad, selected_b_star="TMR-Set", expected_census_sha256=census
+        )
+
+
+def test_rejects_score_table_that_changes_pre_score_census(score_fixture):
+    tables, census = score_fixture
+    changed = replace(
+        tables[-1].dataset,
+        caption_commitments=(
+            _commitment("different", 0), *tables[-1].dataset.caption_commitments[1:]
+        ),
+    )
+    with pytest.raises(V2StatisticsError, match="pre-score frozen census"):
+        assemble_v2_score_tables(
+            (*tables[:-1], replace(tables[-1], dataset=changed)),
+            selected_b_star="TMR-Set",
+            expected_census_sha256=census,
+        )
+
+
+@pytest.mark.parametrize(
+    "fault", ["shape", "multi_positive", "positive", "group_size", "component"]
+)
+def test_rejects_wrong_parent_task_geometry_before_scoring(score_fixture, fault):
+    tables, census = score_fixture
+    original = tables[-1].dataset
+    if fault == "shape":
+        changed = replace(
+            original,
+            scores=np.ascontiguousarray(original.scores[:, :-1]),
+            caption_commitments=original.caption_commitments[:-1],
+            positive_motion_indices=original.positive_motion_indices[:-1],
+        )
+    elif fault == "multi_positive":
+        changed = replace(
+            original,
+            positive_motion_indices=((0, 1), *original.positive_motion_indices[1:]),
+        )
+    elif fault == "positive":
+        changed = replace(
+            original,
+            positive_motion_indices=((1,), *original.positive_motion_indices[1:]),
+        )
+    elif fault == "group_size":
+        sizes = original.group_sizes.copy()
+        sizes[0] = 4
+        changed = replace(original, group_sizes=sizes)
+    else:
+        changed = replace(original, component_labels=("C11", *original.component_labels[1:]))
+    with pytest.raises(V2StatisticsError):
+        assemble_v2_score_tables(
+            (*tables[:-1], replace(tables[-1], dataset=changed)),
+            selected_b_star="TMR-Set",
+            expected_census_sha256=census,
+        )
+
+
+def test_requires_pilot_chosen_literature_identity_and_frozen_digest(score_fixture):
+    tables, census = score_fixture
+    with pytest.raises(V2StatisticsError, match="allowed literature family"):
+        assemble_v2_score_tables(
+            tables, selected_b_star="A1", expected_census_sha256=census
+        )
+    with pytest.raises(V2StatisticsError, match="lowercase SHA-256"):
+        assemble_v2_score_tables(
+            tables, selected_b_star="TMR-Set", expected_census_sha256="0" * 63
+        )

From f79e38087cabfb32395f733a8464bff6c2bf9167 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 00:33:18 -0500
Subject: [PATCH 72/95] Gate study attempts and formal freeze on bounded
 storage

---
 RELEASE_FILES.sha256               | 10 +--
 src/phaseset_core/study_budget.py  | 20 +++++-
 src/phaseset_core/study_process.py | 62 ++++++++++++++++++-
 src/phaseset_core/study_storage.py | 97 ++++++++++++++++++++++++++++++
 tests/test_study_budget.py         | 19 +++++-
 tests/test_study_process.py        | 73 ++++++++++++++++++++++
 tests/test_study_storage.py        | 74 +++++++++++++++++++++++
 7 files changed, 347 insertions(+), 8 deletions(-)
 create mode 100644 src/phaseset_core/study_storage.py
 create mode 100644 tests/test_study_storage.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 5b44479..4055974 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -223,8 +223,9 @@ ad2ce56325f09d2efb17847ef3f947535f81f7099019275fdd074c12efeeb559	73588	src/phase
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
-1c7fcc1e30aedf0da08b8dfac54e9580e1da1ead0d29b309c18cd5592642de16	21118	src/phaseset_core/study_budget.py
-a984350a2838101fde448eee44d32f22eec5dfa8487637fa0d7941f99de82aec	17205	src/phaseset_core/study_process.py
+47a2aa0806af373e5df40c611bdad425efd672588fe912d4470ee27d8985181a	21832	src/phaseset_core/study_budget.py
+78cbb47380af76db33acb04350f2307ffae290b6db06b9edd9f1866a2d876bfd	19877	src/phaseset_core/study_process.py
+61000792b33eb2f65512f88140bd2305d23e0d10fc21a64a372a698d701ea82a	4013	src/phaseset_core/study_storage.py
 febde5f65ea132a9921973be393a6fbea715c6f9b1b1d9f5f8df3654cf007696	25153	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
@@ -357,8 +358,9 @@ a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/tes
 801b38ae89732e692ce42a7928278cdc14b87d78e32d459e700ab60246606106	14458	tests/test_release_asset_inventory.py
 8727e87f1a0d44db6a39b1e5cfd8e22e3bed05544ddaed07b66b759adee66d84	5587	tests/test_release_tree_batch.py
 eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
-188b166e9957df27437642dbce0091a3935d489135340c892dec8dfdabba8ef5	19504	tests/test_study_budget.py
-67b2e0ebb76d7def996bc3ce6242a7373fac4e1e7334dde5fb17bbfc995fce87	17565	tests/test_study_process.py
+9a971a607eba6e35479fa599b7363f22a7b08f7d2e5b6a780467cf8f75d904ed	20049	tests/test_study_budget.py
+0dac3611a3c8641524dd43c216771f0f8f2f6e031dcdf586872a226bb28a67d0	20709	tests/test_study_process.py
+2bee29d6cde94c5ee34db5094cdc66ee752e3c6336196954f409718a54179889	2727	tests/test_study_storage.py
 96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
 18c5c10980d8e0d81eb1a6b351c0e0e770325c95bcca59e15763e5c86967ce78	4260	tests/test_tmr_feature_cache.py
 726ee82009ecb29ce87208749fb1780365640a52f2e2d6e55399e22119838b89	2988	tests/test_tmr_language.py
diff --git a/src/phaseset_core/study_budget.py b/src/phaseset_core/study_budget.py
index af1d703..fddd71d 100644
--- a/src/phaseset_core/study_budget.py
+++ b/src/phaseset_core/study_budget.py
@@ -14,6 +14,7 @@
 
 from .execution import _write_once
 from .host import _AttemptLease
+from .study_storage import StudyStorageHold, StudyStorageProjection, check_study_storage
 
 
 PILOT_ALLOCATION = {"TMR-Set": 3, "WaMo-Set": 3, "MIME-Set": 3, "B2": 1, "PhaseSet-V2-head": 2}
@@ -252,9 +253,22 @@ def summary(self):
                 "scientific_or_launch_authority": False,
             }
 
-    def record_formal_step_limits(self, run_max_steps: dict[str, int], *, evidence: str):
+    def record_formal_step_limits(
+        self,
+        run_max_steps: dict[str, int],
+        *,
+        evidence: str,
+        storage_projection: StudyStorageProjection,
+        storage_directory: str | Path,
+        temporary_directory: str | Path,
+    ):
         """Record the already frozen external schedule, not freeze the science."""
         _text(evidence, "dated external freeze evidence")
+        if (
+            not isinstance(storage_projection, StudyStorageProjection)
+            or storage_projection.cumulative_remaining_bytes is None
+        ):
+            raise StudyStorageHold("formal freeze needs a cumulative storage forecast")
         with _AttemptLease.acquire(self.root):
             state = self._state()
             if state["step_limits"] is not None:
@@ -265,6 +279,9 @@ def record_formal_step_limits(self, run_max_steps: dict[str, int], *, evidence:
                 raise StudyBudgetError("formal step limits must cover all 87 registered stages")
             for value in run_max_steps.values():
                 _integer(value, "formal max steps", minimum=1)
+            storage_admission = check_study_storage(
+                storage_directory, temporary_directory, storage_projection
+            )
             self._append(
                 self.root,
                 len(state["events"]),
@@ -272,6 +289,7 @@ def record_formal_step_limits(self, run_max_steps: dict[str, int], *, evidence:
                 {
                     "run_max_steps": run_max_steps,
                     "evidence": evidence,
+                    "storage_admission": storage_admission,
                 },
             )
 
diff --git a/src/phaseset_core/study_process.py b/src/phaseset_core/study_process.py
index 8abb8ca..a35947e 100644
--- a/src/phaseset_core/study_process.py
+++ b/src/phaseset_core/study_process.py
@@ -20,6 +20,12 @@
 
 from .host import _AttemptLease, _write_once_json
 from .study_budget import StudyBudget
+from .study_storage import (
+    StudyStorageHold,
+    StudyStorageProjection,
+    check_study_storage,
+    current_free_bytes,
+)
 
 
 class StudyProcessError(RuntimeError):
@@ -30,6 +36,24 @@ class StudyResourceHold(StudyProcessError):
     """A requested card is not actually idle; do not preempt others."""
 
 
+STORAGE_MONITOR_SECONDS = 30.0
+
+
+@contextmanager
+def _storage_lease(budget, enabled):
+    """Serialize storage-admitted training across this study's cards."""
+    if not enabled:
+        yield
+        return
+    root = budget.root / "storage-lease"
+    root.mkdir(parents=True, exist_ok=True, mode=0o700)
+    lease = _AttemptLease.acquire(root)
+    try:
+        yield
+    finally:
+        lease.release()
+
+
 @contextmanager
 def _termination_guard():
     received = []
@@ -193,6 +217,7 @@ def run_budgeted_process(
     cleanup_seconds: int,
     host_terminal_path: Path,
     bindings: dict,
+    storage_projection: StudyStorageProjection | None = None,
 ):
     """One immutable process attempt, never a retry or a scientific grant.
 
@@ -242,9 +267,29 @@ def run_budgeted_process(
     terminal = Path(host_terminal_path).resolve()
     if not cwd.is_dir() or terminal.exists():
         raise ValueError("existing working directory and fresh host terminal required")
+    purpose = reservation.get("purpose")
+    if purpose in ("pilot", "formal") and storage_projection is None:
+        raise StudyStorageHold("pilot/formal requires a family-specific storage projection")
+    if purpose == "formal" and (
+        storage_projection is None or storage_projection.cumulative_remaining_bytes is None
+    ):
+        raise StudyStorageHold("formal requires a reviewed cumulative remaining-byte forecast")
+    if storage_projection is not None and not environment.get("TMPDIR"):
+        raise StudyStorageHold("storage-admitted attempt needs an explicit TMPDIR")
+    storage = (
+        None
+        if storage_projection is None
+        else check_study_storage(root.parent, environment["TMPDIR"], storage_projection)
+    )
     check_gpu_free(gpu_uuids)
-    with _gpu_leases(budget, gpu_uuids), _termination_guard() as received_signals:
+    with (
+        _storage_lease(budget, storage_projection is not None),
+        _gpu_leases(budget, gpu_uuids),
+        _termination_guard() as received_signals,
+    ):
         check_gpu_free(gpu_uuids)
+        if storage_projection is not None:
+            storage = check_study_storage(root.parent, environment["TMPDIR"], storage_projection)
         root.mkdir(mode=0o700, exist_ok=False)
         lease = _AttemptLease.acquire(root)
         primary = None
@@ -258,6 +303,7 @@ def run_budgeted_process(
                     "gpu_uuids": gpu_uuids,
                     "bindings": bindings,
                     "host_terminal_path": str(terminal),
+                    "storage_admission": storage,
                     "scientific_or_launch_authority": False,
                 },
             )
@@ -267,11 +313,14 @@ def run_budgeted_process(
             ):
                 budget.reserve(**reservation)
                 process, started, timed_out, cleanup = None, None, False, None
+                storage_observations = 0
+                storage_min_available = None if storage is None else storage["available_bytes"]
                 try:
                     check_gpu_free(gpu_uuids)
                     if received_signals:
                         raise KeyboardInterrupt("controller terminated before child spawn")
                     started = time.monotonic()
+                    next_storage_sample = started + STORAGE_MONITOR_SECONDS
                     process = subprocess.Popen(
                         (
                             "/usr/bin/timeout",
@@ -293,6 +342,15 @@ def run_budgeted_process(
                     while not _leader_exited(process):
                         if received_signals:
                             raise KeyboardInterrupt("controller received termination signal")
+                        if storage_projection is not None and time.monotonic() >= next_storage_sample:
+                            available = current_free_bytes(root.parent)
+                            storage_observations += 1
+                            storage_min_available = min(storage_min_available, available)
+                            if available < storage_projection.monitor_floor_bytes:
+                                raise StudyStorageHold(
+                                    "RESOURCE_LIMIT: training fell below storage monitor floor"
+                                )
+                            next_storage_sample = time.monotonic() + STORAGE_MONITOR_SECONDS
                         if time.monotonic() - started >= timeout_seconds + kill_grace_seconds:
                             timed_out = True
                             break
@@ -385,6 +443,8 @@ def run_budgeted_process(
                     "cursor_error": cursor_error,
                     "actual_wall_seconds": wall,
                     "actual_gpu_seconds": wall * reservation["gpu_count"],
+                    "storage_monitor_observations": storage_observations,
+                    "storage_min_available_bytes": storage_min_available,
                     "cleanup": cleanup,
                     "primary_error": None if primary is None else repr(primary),
                     "scientific_or_launch_authority": False,
diff --git a/src/phaseset_core/study_storage.py b/src/phaseset_core/study_storage.py
new file mode 100644
index 0000000..890cd9e
--- /dev/null
+++ b/src/phaseset_core/study_storage.py
@@ -0,0 +1,97 @@
+"""Fail-closed disk admission for one whole-parent training attempt.
+
+This is a resource check, not scientific, dataset, or GPU launch authority.
+The caller supplies a family-specific checkpoint bound from an independently
+reviewed real-width serialization dry run; this module does not infer one from
+the current free space or a synthetic test fixture.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+import shutil
+
+
+class StudyStorageHold(RuntimeError):
+    """The attempt lacks a defensible byte bound or sufficient free space."""
+
+
+def _device(path: Path) -> int:
+    return path.stat().st_dev
+
+
+@dataclass(frozen=True)
+class StudyStorageProjection:
+    checkpoint_bound_bytes: int
+    additional_write_bytes: int
+    free_floor_bytes: int = 16 * 1024**3
+    cumulative_remaining_bytes: int | None = None
+
+    def __post_init__(self) -> None:
+        for name in (
+            "checkpoint_bound_bytes",
+            "additional_write_bytes",
+            "free_floor_bytes",
+        ):
+            value = getattr(self, name)
+            minimum = 1 if name == "checkpoint_bound_bytes" else 0
+            if type(value) is not int or value < minimum:
+                raise StudyStorageHold(f"{name} needs an exact nonnegative byte bound")
+        if self.cumulative_remaining_bytes is not None and (
+            type(self.cumulative_remaining_bytes) is not int
+            or self.cumulative_remaining_bytes < 1
+        ):
+            raise StudyStorageHold("cumulative remaining bytes must be a positive integer")
+
+    @property
+    def required_free_bytes(self) -> int:
+        # Two recent, up to two selected-best dependencies, the next payload,
+        # and the in-progress atomic-save temporary payload.
+        attempt = 6 * self.checkpoint_bound_bytes + self.additional_write_bytes
+        if self.cumulative_remaining_bytes is not None:
+            attempt = max(attempt, self.cumulative_remaining_bytes)
+        return attempt + self.free_floor_bytes
+
+    @property
+    def monitor_floor_bytes(self) -> int:
+        # Admission already reserves all six payloads.  During a valid write,
+        # free bytes may fall to the fixed floor without violating that bound.
+        return self.free_floor_bytes
+
+
+def current_free_bytes(path: Path) -> int:
+    return shutil.disk_usage(path).free
+
+
+def check_study_storage(
+    output_parent: str | Path,
+    temporary_directory: str | Path,
+    projection: StudyStorageProjection,
+) -> dict[str, int]:
+    """Read-only admission on one filesystem; recheck just before reservation.
+
+    ``output_parent`` is the already-existing parent of the fresh attempt.
+    Temporary files must remain on that same device, so a full system root
+    cannot be hidden by free data-volume space. Other users can change free
+    bytes after this snapshot; the host still handles ENOSPC as a failed run.
+    """
+    if not isinstance(projection, StudyStorageProjection):
+        raise StudyStorageHold("family-specific storage projection is required")
+    output = Path(output_parent).resolve(strict=True)
+    temporary = Path(temporary_directory).resolve(strict=True)
+    if not output.is_dir() or not temporary.is_dir():
+        raise StudyStorageHold("existing output parent and TMPDIR directories required")
+    if _device(output) != _device(temporary):
+        raise StudyStorageHold("attempt output and TMPDIR must share the same filesystem")
+    available = current_free_bytes(output)
+    if available < projection.required_free_bytes:
+        raise StudyStorageHold("RESOURCE_LIMIT: insufficient same-filesystem free bytes")
+    return {
+        "available_bytes": available,
+        "required_free_bytes": projection.required_free_bytes,
+        "checkpoint_bound_bytes": projection.checkpoint_bound_bytes,
+        "additional_write_bytes": projection.additional_write_bytes,
+        "free_floor_bytes": projection.free_floor_bytes,
+        "cumulative_remaining_bytes": projection.cumulative_remaining_bytes,
+    }
diff --git a/tests/test_study_budget.py b/tests/test_study_budget.py
index 77164e4..8e41858 100644
--- a/tests/test_study_budget.py
+++ b/tests/test_study_budget.py
@@ -11,6 +11,7 @@
 
 from phaseset_core.host import HostConfigurationError
 from phaseset_core.study_budget import PILOT_ALLOCATION, StudyBudget, StudyBudgetError
+from phaseset_core.study_storage import StudyStorageProjection
 
 
 def matrix():
@@ -82,7 +83,13 @@ def formal_steps():
 
 def freeze(budget):
     budget.record_formal_step_limits(
-        formal_steps(), evidence="synthetic schedule; NOT scientific freeze"
+        formal_steps(),
+        evidence="synthetic schedule; NOT scientific freeze",
+        storage_projection=StudyStorageProjection(
+            1, 0, free_floor_bytes=0, cumulative_remaining_bytes=1
+        ),
+        storage_directory=budget.root,
+        temporary_directory=budget.root,
     )
 
 
@@ -358,7 +365,15 @@ def test_formal_needs_all_87_step_limits_once_and_exact_seed_schedule(tmp_path):
     incomplete = formal_steps()
     incomplete.pop("V2-087")
     with pytest.raises(StudyBudgetError, match="all 87"):
-        budget.record_formal_step_limits(incomplete, evidence="analytic incomplete schedule")
+        budget.record_formal_step_limits(
+            incomplete,
+            evidence="analytic incomplete schedule",
+            storage_projection=StudyStorageProjection(
+                1, 0, free_floor_bytes=0, cumulative_remaining_bytes=1
+            ),
+            storage_directory=budget.root,
+            temporary_directory=budget.root,
+        )
     freeze(budget)
     with pytest.raises(StudyBudgetError, match="immutable"):
         freeze(budget)
diff --git a/tests/test_study_process.py b/tests/test_study_process.py
index a5ba33e..7b4d65a 100644
--- a/tests/test_study_process.py
+++ b/tests/test_study_process.py
@@ -15,6 +15,7 @@
 from phaseset_core.host import _AttemptLease, HostConfigurationError
 import phaseset_core.study_process as module
 from phaseset_core.study_process import StudyProcessError, StudyResourceHold, run_budgeted_process
+from phaseset_core.study_storage import StudyStorageHold, StudyStorageProjection
 from test_study_budget import create as create_budget
 
 
@@ -91,6 +92,78 @@ def test_non_linux_rejects_before_any_launch(tmp_path, monkeypatch):
     assert budget.summary()["unsettled_attempt_ids"] == []
 
 
+@linux
+def test_training_requires_storage_bound_before_attempt_or_budget(tmp_path):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    args["reservation"]["purpose"] = "pilot"
+    with pytest.raises(StudyStorageHold, match="family-specific"):
+        run_budgeted_process(budget, **args)
+    assert not args["process_directory"].exists()
+    assert budget.summary()["usage"]["pilot_reservations"] == 0
+
+
+@linux
+def test_formal_requires_cumulative_forecast_before_attempt(tmp_path):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    args["reservation"]["purpose"] = "formal"
+    args["environment"]["TMPDIR"] = str(tmp_path)
+    args["storage_projection"] = StudyStorageProjection(1, 0, free_floor_bytes=0)
+    with pytest.raises(StudyStorageHold, match="cumulative"):
+        run_budgeted_process(budget, **args)
+    assert not args["process_directory"].exists()
+
+
+@linux
+def test_profile_storage_snapshot_is_bound_into_launch(tmp_path):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    args["environment"]["TMPDIR"] = str(tmp_path)
+    args["storage_projection"] = StudyStorageProjection(1, 0, free_floor_bytes=0)
+    result = run_budgeted_process(budget, **args)
+    assert result["outcome"] == "COMPLETED"
+    launch = json.loads((args["process_directory"] / "launch.json").read_text())
+    assert launch["storage_admission"]["required_free_bytes"] == 6
+    assert launch["storage_admission"]["available_bytes"] >= 6
+
+
+@linux
+def test_storage_admitted_process_is_serialized_before_attempt(tmp_path):
+    budget = create_budget(tmp_path)
+    args = inputs(tmp_path)
+    args["environment"]["TMPDIR"] = str(tmp_path)
+    args["storage_projection"] = StudyStorageProjection(1, 0, free_floor_bytes=0)
+    lease_root = budget.root / "storage-lease"
+    lease_root.mkdir()
+    with _AttemptLease.acquire(lease_root):
+        with pytest.raises(HostConfigurationError):
+            run_budgeted_process(budget, **args)
+    assert not args["process_directory"].exists()
+
+
+@linux
+def test_storage_monitor_stops_only_owned_child_and_records_failure(tmp_path, monkeypatch):
+    budget = create_budget(tmp_path)
+    args = inputs(
+        tmp_path,
+        body="time.sleep(0.4); terminal.write_text(json.dumps({'global_step':0,'outcome':'COMPLETED'}))",
+    )
+    args["environment"]["TMPDIR"] = str(tmp_path)
+    args["storage_projection"] = StudyStorageProjection(1, 0, free_floor_bytes=100)
+    monkeypatch.setattr(module, "STORAGE_MONITOR_SECONDS", 0.02)
+    monkeypatch.setattr(module, "current_free_bytes", lambda _path: 0)
+    with pytest.raises(StudyStorageHold, match="storage monitor floor"):
+        run_budgeted_process(budget, **args)
+    result = report(args)
+    assert result["outcome"] == "FAILED"
+    assert "storage monitor floor" in result["primary_error"]
+    assert result["storage_monitor_observations"] >= 1
+    assert result["storage_min_available_bytes"] == 0
+    assert result["cleanup"]["owned_group_and_cuda_exit_verified"]
+    assert budget.summary()["unsettled_attempt_ids"] == []
+
+
 @linux
 def test_real_completed_child_logs_terminal_and_settlement_are_distinct(tmp_path):
     budget = create_budget(tmp_path)
diff --git a/tests/test_study_storage.py b/tests/test_study_storage.py
new file mode 100644
index 0000000..71daeea
--- /dev/null
+++ b/tests/test_study_storage.py
@@ -0,0 +1,74 @@
+"""Pure disk-admission arithmetic; no model, test gallery, or GPU is opened."""
+
+from types import SimpleNamespace
+
+import pytest
+
+import phaseset_core.study_storage as storage
+from phaseset_core.study_storage import (
+    StudyStorageHold,
+    StudyStorageProjection,
+    check_study_storage,
+)
+
+
+def test_projection_requires_explicit_real_family_bound():
+    for invalid in (0, -1, 1.0, True, None):
+        with pytest.raises(StudyStorageHold):
+            StudyStorageProjection(invalid, 0)
+    with pytest.raises(StudyStorageHold):
+        StudyStorageProjection(123, -1)
+    projection = StudyStorageProjection(200, 17, free_floor_bytes=1000)
+    assert projection.required_free_bytes == 6 * 200 + 17 + 1000
+    assert projection.monitor_floor_bytes == 1000
+    assert StudyStorageProjection(
+        200, 17, free_floor_bytes=1000, cumulative_remaining_bytes=3000
+    ).required_free_bytes == 4000
+    with pytest.raises(StudyStorageHold):
+        StudyStorageProjection(200, 0, cumulative_remaining_bytes=0)
+
+
+def test_exact_floor_admits_and_one_byte_less_holds(tmp_path, monkeypatch):
+    temporary = tmp_path / "tmp"
+    temporary.mkdir()
+    projection = StudyStorageProjection(200, 17, free_floor_bytes=1000)
+    needed = projection.required_free_bytes
+    monkeypatch.setattr(
+        storage.shutil,
+        "disk_usage",
+        lambda _path: SimpleNamespace(free=needed),
+    )
+    row = check_study_storage(tmp_path, temporary, projection)
+    assert row == {
+        "available_bytes": needed,
+        "required_free_bytes": needed,
+        "checkpoint_bound_bytes": 200,
+        "additional_write_bytes": 17,
+        "free_floor_bytes": 1000,
+        "cumulative_remaining_bytes": None,
+    }
+    monkeypatch.setattr(
+        storage.shutil,
+        "disk_usage",
+        lambda _path: SimpleNamespace(free=needed - 1),
+    )
+    with pytest.raises(StudyStorageHold, match="RESOURCE_LIMIT"):
+        check_study_storage(tmp_path, temporary, projection)
+
+
+def test_different_tmp_device_or_missing_bound_holds_without_output(tmp_path, monkeypatch):
+    temporary = tmp_path / "tmp"
+    temporary.mkdir()
+    with pytest.raises(StudyStorageHold, match="projection"):
+        check_study_storage(tmp_path, temporary, None)
+    with pytest.raises(FileNotFoundError):
+        check_study_storage(tmp_path / "not-created", temporary, StudyStorageProjection(1, 0))
+    original = storage._device
+    monkeypatch.setattr(
+        storage,
+        "_device",
+        lambda path: original(path) + (1 if path == temporary else 0),
+    )
+    with pytest.raises(StudyStorageHold, match="same"):
+        check_study_storage(tmp_path, temporary, StudyStorageProjection(1, 0))
+    assert not (tmp_path / "attempt").exists()

From 4515cc7be89bf75c7991080e2617dca24182577e Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 02:03:42 -0500
Subject: [PATCH 73/95] Add advisory V2 cumulative storage forecast

---
 src/phaseset_core/study_storage.py | 95 ++++++++++++++++++++++++++++++
 tests/test_study_storage.py        | 57 ++++++++++++++++++
 2 files changed, 152 insertions(+)

diff --git a/src/phaseset_core/study_storage.py b/src/phaseset_core/study_storage.py
index 890cd9e..4941912 100644
--- a/src/phaseset_core/study_storage.py
+++ b/src/phaseset_core/study_storage.py
@@ -9,14 +9,109 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+import hashlib
+import json
 from pathlib import Path
 import shutil
+from collections.abc import Mapping
 
 
 class StudyStorageHold(RuntimeError):
     """The attempt lacks a defensible byte bound or sufficient free space."""
 
 
+V2_MATRIX_SHA256 = "482a4432ac95cfbd21d5b6363893377ab61f6c84373bcf07b7441c4bfa8891dd"
+
+
+def forecast_v2_cumulative_storage(
+    matrix_bytes: bytes,
+    *,
+    checkpoint_bounds_by_run: Mapping[str, int],
+    physical_test_cache_bytes: int,
+    sealed_output_bytes: int,
+    metadata_bytes_per_run: int,
+    uncertainty_bytes: int,
+) -> dict[str, int | str | bool]:
+    """Advisory full 87-stage storage arithmetic, never launch admission.
+
+    This does not authenticate per-run checkpoint specimens or prove cache,
+    output and TMPDIR mount identity. No caller may treat its numeric result
+    as a qualified ``StudyStorageProjection`` or GPU/formal-launch authority.
+    The production gate must separately verify specimen provenance, bound
+    every remaining family, check all target mounts and read free space under
+    the study lock. Missing run bounds fail closed even for this planning view.
+    """
+    if type(matrix_bytes) is not bytes or hashlib.sha256(matrix_bytes).hexdigest() != V2_MATRIX_SHA256:
+        raise StudyStorageHold("exact frozen V2 matrix bytes required")
+    try:
+        matrix = json.loads(matrix_bytes)
+    except (ValueError, UnicodeDecodeError) as exc:
+        raise StudyStorageHold("invalid V2 matrix JSON") from exc
+    runs = matrix.get("runs") if isinstance(matrix, dict) else None
+    expected_ids = [f"V2-{index:03d}" for index in range(1, 88)]
+    if (
+        not isinstance(matrix, dict)
+        or matrix.get("schema") != "phaseset-v2-training-matrix-v1"
+        or matrix.get("status") != "PLANNED_NOT_LAUNCHABLE"
+        or not isinstance(runs, list)
+        or len(runs) != 87
+        or [row.get("id") for row in runs if isinstance(row, dict)] != expected_ids
+        or matrix.get("budget", {}).get("formal_stages") != 87
+        or matrix.get("budget", {}).get("max_formal_starts") != 90
+        or matrix.get("budget", {}).get("max_root_cause_restarts") != 3
+    ):
+        raise StudyStorageHold("complete frozen 87-stage V2 matrix required")
+    if not isinstance(checkpoint_bounds_by_run, Mapping) or set(checkpoint_bounds_by_run) != set(expected_ids):
+        raise StudyStorageHold("one explicit checkpoint bound per formal run required")
+    for run_id, bound in checkpoint_bounds_by_run.items():
+        if type(bound) is not int or bound < 1:
+            raise StudyStorageHold(f"invalid checkpoint bound for {run_id}")
+    for name, value in (
+        ("physical_test_cache_bytes", physical_test_cache_bytes),
+        ("sealed_output_bytes", sealed_output_bytes),
+        ("metadata_bytes_per_run", metadata_bytes_per_run),
+        ("uncertainty_bytes", uncertainty_bytes),
+    ):
+        if type(value) is not int or value < 1:
+            raise StudyStorageHold(f"{name} needs a positive measured or reviewed byte bound")
+
+    retained_stage_bytes = 4 * sum(checkpoint_bounds_by_run.values())
+    maximum_payload_bytes = max(checkpoint_bounds_by_run.values())
+    restart_bytes = 3 * 4 * maximum_payload_bytes
+    atomic_transient_bytes = 2 * maximum_payload_bytes
+    metadata_bytes = 90 * metadata_bytes_per_run
+    free_floor_bytes = 16 * 1024**3
+    remaining = (
+        retained_stage_bytes
+        + restart_bytes
+        + atomic_transient_bytes
+        + physical_test_cache_bytes
+        + sealed_output_bytes
+        + metadata_bytes
+        + uncertainty_bytes
+    )
+    return {
+        "status": "ADVISORY_ARITHMETIC_ONLY_NOT_ADMISSION",
+        "admission_ready": False,
+        "scientific_or_launch_authority": False,
+        "bound_provenance_verified": False,
+        "target_mounts_verified": False,
+        "matrix_sha256": V2_MATRIX_SHA256,
+        "formal_stages": 87,
+        "formal_restart_slots": 3,
+        "retained_stage_bytes": retained_stage_bytes,
+        "restart_bytes": restart_bytes,
+        "atomic_transient_bytes": atomic_transient_bytes,
+        "physical_test_cache_bytes": physical_test_cache_bytes,
+        "sealed_output_bytes": sealed_output_bytes,
+        "metadata_bytes": metadata_bytes,
+        "uncertainty_bytes": uncertainty_bytes,
+        "remaining_bytes_excluding_floor": remaining,
+        "free_floor_bytes": free_floor_bytes,
+        "required_free_bytes": remaining + free_floor_bytes,
+    }
+
+
 def _device(path: Path) -> int:
     return path.stat().st_dev
 
diff --git a/tests/test_study_storage.py b/tests/test_study_storage.py
index 71daeea..f44d8e2 100644
--- a/tests/test_study_storage.py
+++ b/tests/test_study_storage.py
@@ -1,6 +1,7 @@
 """Pure disk-admission arithmetic; no model, test gallery, or GPU is opened."""
 
 from types import SimpleNamespace
+from pathlib import Path
 
 import pytest
 
@@ -9,9 +10,65 @@
     StudyStorageHold,
     StudyStorageProjection,
     check_study_storage,
+    forecast_v2_cumulative_storage,
 )
 
 
+_V2_MATRIX = (Path(__file__).parents[1] / "configs" / "phaseset_v2_experiment_matrix.json").read_bytes()
+
+
+def test_complete_v2_cumulative_forecast_keeps_all_stages_and_restart_envelope():
+    bounds = {f"V2-{index:03d}": 10 for index in range(1, 88)}
+    result = forecast_v2_cumulative_storage(
+        _V2_MATRIX,
+        checkpoint_bounds_by_run=bounds,
+        physical_test_cache_bytes=100,
+        sealed_output_bytes=200,
+        metadata_bytes_per_run=3,
+        uncertainty_bytes=400,
+    )
+    assert result["status"] == "ADVISORY_ARITHMETIC_ONLY_NOT_ADMISSION"
+    assert result["admission_ready"] is False
+    assert result["scientific_or_launch_authority"] is False
+    assert result["bound_provenance_verified"] is False
+    assert result["target_mounts_verified"] is False
+    assert result["formal_stages"] == 87
+    assert result["formal_restart_slots"] == 3
+    assert result["retained_stage_bytes"] == 3480
+    assert result["restart_bytes"] == 120
+    assert result["atomic_transient_bytes"] == 20
+    assert result["metadata_bytes"] == 270
+    assert result["remaining_bytes_excluding_floor"] == 4590
+    assert result["free_floor_bytes"] == 16 * 1024**3
+    assert result["required_free_bytes"] == 4590 + 16 * 1024**3
+
+
+def test_cumulative_forecast_fails_closed_on_drift_missing_bound_or_unmeasured_inputs():
+    bounds = {f"V2-{index:03d}": 10 for index in range(1, 88)}
+    inputs = dict(
+        checkpoint_bounds_by_run=bounds,
+        physical_test_cache_bytes=100,
+        sealed_output_bytes=200,
+        metadata_bytes_per_run=3,
+        uncertainty_bytes=400,
+    )
+    with pytest.raises(StudyStorageHold, match="exact frozen"):
+        forecast_v2_cumulative_storage(_V2_MATRIX + b"\n", **inputs)
+    with pytest.raises(StudyStorageHold, match="per formal run"):
+        forecast_v2_cumulative_storage(
+            _V2_MATRIX, **{**inputs, "checkpoint_bounds_by_run": {"V2-001": 10}}
+        )
+    with pytest.raises(StudyStorageHold, match="V2-087"):
+        forecast_v2_cumulative_storage(
+            _V2_MATRIX,
+            **{**inputs, "checkpoint_bounds_by_run": {**bounds, "V2-087": True}},
+        )
+    with pytest.raises(StudyStorageHold, match="physical_test_cache_bytes"):
+        forecast_v2_cumulative_storage(
+            _V2_MATRIX, **{**inputs, "physical_test_cache_bytes": 0}
+        )
+
+
 def test_projection_requires_explicit_real_family_bound():
     for invalid in (0, -1, 1.0, True, None):
         with pytest.raises(StudyStorageHold):

From 99b6d2975053afcad6a8250401bfed80a82948e6 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 02:06:12 -0500
Subject: [PATCH 74/95] Refresh release file manifest for storage forecast

---
 RELEASE_FILES.sha256 | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 4055974..38dcc12 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -225,7 +225,7 @@ eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phase
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
 47a2aa0806af373e5df40c611bdad425efd672588fe912d4470ee27d8985181a	21832	src/phaseset_core/study_budget.py
 78cbb47380af76db33acb04350f2307ffae290b6db06b9edd9f1866a2d876bfd	19877	src/phaseset_core/study_process.py
-61000792b33eb2f65512f88140bd2305d23e0d10fc21a64a372a698d701ea82a	4013	src/phaseset_core/study_storage.py
+934b4933795a2971d83811dc36865ddd43d0fd1670e8c2caf9ec53755d362c9b	8297	src/phaseset_core/study_storage.py
 febde5f65ea132a9921973be393a6fbea715c6f9b1b1d9f5f8df3654cf007696	25153	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
@@ -360,7 +360,7 @@ a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/tes
 eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test_render_editable_figures.py
 9a971a607eba6e35479fa599b7363f22a7b08f7d2e5b6a780467cf8f75d904ed	20049	tests/test_study_budget.py
 0dac3611a3c8641524dd43c216771f0f8f2f6e031dcdf586872a226bb28a67d0	20709	tests/test_study_process.py
-2bee29d6cde94c5ee34db5094cdc66ee752e3c6336196954f409718a54179889	2727	tests/test_study_storage.py
+5ae2b2baa3addc74ac4914fde10fab635f949c8f84b478ce764d4ffc71fbc32b	5128	tests/test_study_storage.py
 96fd7d990fb5446f8ae435dcc61d0339caa8f5503c547cbee6b74b4527ce7477	8462	tests/test_tmr_components.py
 18c5c10980d8e0d81eb1a6b351c0e0e770325c95bcca59e15763e5c86967ce78	4260	tests/test_tmr_feature_cache.py
 726ee82009ecb29ce87208749fb1780365640a52f2e2d6e55399e22119838b89	2988	tests/test_tmr_language.py

From f2b7256610f2baeb38ac73dd1bc64dfda51729af Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 02:34:36 -0500
Subject: [PATCH 75/95] Add true mean-difference DCT physical control primitive

---
 RELEASE_FILES.sha256                   |   3 +-
 src/phaseset_core/directional_phase.py | 110 +++++++++++++++++++-
 tests/test_true_mean_difference_dct.py | 134 +++++++++++++++++++++++++
 3 files changed, 245 insertions(+), 2 deletions(-)
 create mode 100644 tests/test_true_mean_difference_dct.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 38dcc12..45311dc 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -188,7 +188,7 @@ a46004b4c5a94dc84029a6cf5a54631e614e67f680c506feb0b92b435702160b	6873	src/phases
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
 9c9f86e262a7cf00809b16447dc5f5015a193e3af8eef703046c5e9468ababef	6825	src/phaseset_core/directional_calibration.py
-5fde45b3a8e25b095fa30bd8729ab7d2732a9909453462b87b04244aee5ef228	17265	src/phaseset_core/directional_phase.py
+9ebd4a9c79c8ad96178155cbd58e5db4aa3f620a1342937ebe51023b3b82c893	22180	src/phaseset_core/directional_phase.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
@@ -366,5 +366,6 @@ eb643d3d63d0833eef895fec42ca35095b7adb8874a6dcdf89ef7cb0bc36b198	7401	tests/test
 726ee82009ecb29ce87208749fb1780365640a52f2e2d6e55399e22119838b89	2988	tests/test_tmr_language.py
 9441ab431bc210814bbc5c94a353fc5178d133e729ffb75c67456cf07ed38d79	4559	tests/test_tmr_parent_training.py
 2ae860859c1f9282a02cea929e54fa950433ac8fba81c7510c2b900321d5ff29	9606	tests/test_tmr_set.py
+b3eaa9fc4160833e9bc8229af4980030ceabe8c7316885d2637de489e035f44e	5806	tests/test_true_mean_difference_dct.py
 342cea54b04c8b6fe0faae0cf3ae84a1abac9142792acf5fd042ae44937c62f4	11334	tests/test_wamo_set.py
 3aef8f056ef38ecbe5af51c283285b132db97f175c181018d8a4278d82131915	8150	tests/test_wheel_index_binding.py
diff --git a/src/phaseset_core/directional_phase.py b/src/phaseset_core/directional_phase.py
index fa2e2d9..7836a46 100644
--- a/src/phaseset_core/directional_phase.py
+++ b/src/phaseset_core/directional_phase.py
@@ -17,7 +17,7 @@
 
 from .contracts import PreparedGroupBatch, validate_prepared_group_batch
 from .continuous_capture import PreparedContinuousCapture
-from .morlet import SAMPLE_RATE_HZ, morlet_kernel_bank
+from .morlet import MORLET_FREQUENCIES_HZ, SAMPLE_RATE_HZ, morlet_kernel_bank
 from .periodic import ResourceLimitError, validate_energy_floors
 
 
@@ -399,3 +399,111 @@ def mean_difference_signal_dct(left: np.ndarray, right: np.ndarray) -> tuple[np.
     ) * math.sqrt(2.0 / length)
     basis[0] /= math.sqrt(2.0)
     return basis @ ((left + right) / 2), basis @ (left - right)
+
+
+def true_mean_difference_dct_features(
+    left: np.ndarray,
+    right: np.ndarray,
+    left_mask: np.ndarray,
+    right_mask: np.ndarray,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Six-band phase-free control from the *signals* (a+b)/2 and a-b.
+
+    This is not the legacy independent-self-power control. A shared
+    intersection mask is applied before either DCT so the sum/difference
+    identity and its cross term remain exact for the observed channels.
+    No Morlet phase, imaginary quadrature or directed lag enters the output.
+    The returned support means observed DCT samples, not calibrated phase
+    energy; a training host must not call it a fitted physical floor.
+
+    DCT-II bins are partitioned into six complete positive-frequency bands
+    at geometric midpoints between the frozen Morlet center frequencies.
+    Feature slots: log mean-signal power, log difference-signal power,
+    normalized cross-spectrum, log left power, log right power, common
+    observation coverage, and an observed-channel indicator.
+    """
+    if (
+        type(left) is not np.ndarray
+        or type(right) is not np.ndarray
+        or type(left_mask) is not np.ndarray
+        or type(right_mask) is not np.ndarray
+        or left.ndim != 2
+        or left.shape != right.shape
+        or left.shape != left_mask.shape
+        or left.shape != right_mask.shape
+        or not 2 <= left.shape[0] <= 40
+        or left.shape[1] < 1
+        or left_mask.dtype != np.bool_
+        or right_mask.dtype != np.bool_
+        or left.dtype.kind != "f"
+        or right.dtype.kind != "f"
+        or not np.isfinite(left).all()
+        or not np.isfinite(right).all()
+    ):
+        raise ValueError("DCT control requires finite [T,C] values and matching bool masks")
+    length = left.shape[0]
+    common = left_mask & right_mask
+    eligible = common.sum(axis=0) >= 2
+    features = np.zeros((len(MORLET_FREQUENCIES_HZ), PHASE_FEATURE_DIM), dtype=np.float64)
+    support = np.zeros((len(MORLET_FREQUENCIES_HZ),), dtype=np.bool_)
+    if not bool(eligible.any()):
+        return _readonly(features), _readonly(support)
+
+    # Construct the actual paired signals before any frequency transform.
+    a = np.where(common[:, eligible], left[:, eligible].astype(np.float64), 0.0)
+    b = np.where(common[:, eligible], right[:, eligible].astype(np.float64), 0.0)
+    if not np.isfinite(a).all() or not np.isfinite(b).all():
+        raise ValueError("DCT control endpoint conversion became nonfinite")
+    try:
+        with np.errstate(over="raise", invalid="raise"):
+            mean_signal = (a + b) / 2.0
+            difference_signal = a - b
+    except FloatingPointError as exc:
+        raise ValueError("DCT control paired signal became nonfinite") from exc
+    samples = np.arange(length, dtype=np.float64) + 0.5
+    bins = np.arange(1, length, dtype=np.int64)
+    frequencies = bins * SAMPLE_RATE_HZ / (2 * length)
+    centers = np.asarray(MORLET_FREQUENCIES_HZ, dtype=np.float64)
+    boundaries = np.sqrt(centers[:-1] * centers[1:])
+    band_for_bin = np.searchsorted(boundaries, frequencies, side="right")
+    basis = math.sqrt(2.0 / length) * np.cos(
+        np.pi * bins[:, None] * samples[None, :] / length
+    )
+    mean_dct = basis @ mean_signal
+    difference_dct = basis @ difference_signal
+    left_dct = basis @ a
+    right_dct = basis @ b
+    if not all(
+        np.isfinite(value).all()
+        for value in (mean_dct, difference_dct, left_dct, right_dct)
+    ):
+        raise ValueError("DCT control coefficients became nonfinite")
+    coverage = float(common.sum()) / common.size
+    for band in range(len(MORLET_FREQUENCIES_HZ)):
+        selected = band_for_bin == band
+        if not bool(selected.any()):
+            continue
+        try:
+            with np.errstate(over="raise", invalid="raise"):
+                mean_power = float(np.square(mean_dct[selected]).sum(axis=0).mean())
+                difference_power = float(np.square(difference_dct[selected]).sum(axis=0).mean())
+                left_power = float(np.square(left_dct[selected]).sum(axis=0).mean())
+                right_power = float(np.square(right_dct[selected]).sum(axis=0).mean())
+        except FloatingPointError as exc:
+            raise ValueError("DCT control band energy became nonfinite") from exc
+        total = 4 * mean_power + difference_power
+        cross = (4 * mean_power - difference_power) / total if total > 0 else 0.0
+        features[band] = (
+            math.log1p(mean_power),
+            math.log1p(difference_power),
+            cross,
+            math.log1p(left_power),
+            math.log1p(right_power),
+            coverage,
+            1.0,
+        )
+        support[band] = True
+    features[features == 0.0] = 0.0
+    if not np.isfinite(features).all():
+        raise ValueError("DCT control features became nonfinite")
+    return _readonly(features), _readonly(support)
diff --git a/tests/test_true_mean_difference_dct.py b/tests/test_true_mean_difference_dct.py
new file mode 100644
index 0000000..4cc8b10
--- /dev/null
+++ b/tests/test_true_mean_difference_dct.py
@@ -0,0 +1,134 @@
+"""A6 physical-control probes; no dataset, text, GPU, or fitted floor is used."""
+
+import numpy as np
+import pytest
+
+from phaseset_core.directional_phase import (
+    mean_difference_signal_dct,
+    true_mean_difference_dct_features,
+)
+
+
+def _band_signal() -> np.ndarray:
+    length = 40
+    samples = np.arange(length, dtype=np.float64) + 0.5
+    return np.cos(np.pi * 3 * samples / length)[:, None]
+
+
+def test_paired_signal_dct_preserves_cross_term_with_identical_endpoint_power():
+    signal = _band_signal()
+    mask = np.ones_like(signal, dtype=np.bool_)
+    same, same_support = true_mean_difference_dct_features(signal, signal, mask, mask)
+    opposite, opposite_support = true_mean_difference_dct_features(
+        signal, -signal, mask, mask
+    )
+    np.testing.assert_array_equal(same_support, opposite_support)
+    assert bool(same_support.all())
+    np.testing.assert_allclose(same[:, 3:5], opposite[:, 3:5], atol=1e-14)
+    assert same[0, 0] > 0 and same[0, 1] == 0.0
+    assert opposite[0, 0] == 0.0 and opposite[0, 1] > 0
+    assert same[0, 2] == pytest.approx(1.0, abs=1e-14)
+    assert opposite[0, 2] == pytest.approx(-1.0, abs=1e-14)
+
+
+def test_endpoint_swap_only_exchanges_endpoint_power_not_symmetric_dct_fields():
+    left = _band_signal()
+    right = 0.4 * left
+    mask = np.ones_like(left, dtype=np.bool_)
+    forward, support = true_mean_difference_dct_features(left, right, mask, mask)
+    reverse, reverse_support = true_mean_difference_dct_features(right, left, mask, mask)
+    np.testing.assert_array_equal(support, reverse_support)
+    np.testing.assert_allclose(forward[:, :3], reverse[:, :3], atol=1e-14)
+    np.testing.assert_allclose(forward[:, 3], reverse[:, 4], atol=1e-14)
+    np.testing.assert_allclose(forward[:, 4], reverse[:, 3], atol=1e-14)
+    np.testing.assert_array_equal(forward[:, 5:], reverse[:, 5:])
+
+
+def test_control_aggregates_complete_frequency_bands_not_only_center_bins():
+    length = 40
+    samples = np.arange(length, dtype=np.float64) + 0.5
+    # Bin 1 is below the first 0.75 Hz center but belongs to its low band.
+    signal = np.cos(np.pi * samples / length)[:, None]
+    mask = np.ones_like(signal, dtype=np.bool_)
+    features, support = true_mean_difference_dct_features(signal, signal, mask, mask)
+    assert bool(support[0])
+    assert features[0, 0] > 0
+    assert features[0, 2] == pytest.approx(1.0, abs=1e-14)
+    np.testing.assert_allclose(features[1:, 0], 0.0, atol=1e-27)
+
+
+def test_shared_mask_excludes_invalid_values_and_empty_support_is_positive_zero():
+    signal = _band_signal()
+    mask = np.ones_like(signal, dtype=np.bool_)
+    mask[:5] = False
+    corrupted = signal.copy()
+    corrupted[:5] = 10000.0
+    baseline, support = true_mean_difference_dct_features(signal, signal, mask, mask)
+    changed, changed_support = true_mean_difference_dct_features(
+        corrupted, signal, mask, mask
+    )
+    np.testing.assert_array_equal(changed, baseline)
+    np.testing.assert_array_equal(changed_support, support)
+    assert baseline[0, 5] == pytest.approx(35 / 40)
+
+    one_sample = np.zeros_like(mask)
+    one_sample[10] = True
+    empty, empty_support = true_mean_difference_dct_features(
+        signal, signal, one_sample, one_sample
+    )
+    assert not bool(empty_support.any())
+    assert not bool(np.count_nonzero(empty))
+    assert not bool(np.signbit(empty).any())
+    assert not empty.flags.writeable and not empty_support.flags.writeable
+
+
+def test_coverage_counts_missing_channels_not_only_spectrally_eligible_channels():
+    signal = np.concatenate((_band_signal(), _band_signal()), axis=1)
+    mask = np.ones_like(signal, dtype=np.bool_)
+    mask[:, 1] = False
+    features, support = true_mean_difference_dct_features(signal, signal, mask, mask)
+    assert bool(support[0])
+    assert features[0, 5] == pytest.approx(0.5)
+
+
+def test_multichannel_asymmetric_mask_band_sum_matches_full_orthonormal_dct():
+    rng = np.random.default_rng(1729)
+    left = rng.normal(size=(40, 4))
+    right = rng.normal(size=(40, 4))
+    left_mask = np.ones((40, 4), dtype=np.bool_)
+    right_mask = np.ones((40, 4), dtype=np.bool_)
+    left_mask[::4, 1] = False
+    right_mask[::5, 1] = False
+    right_mask[:, 3] = False
+    common = left_mask & right_mask
+    eligible = common.sum(axis=0) >= 2
+    masked_left = np.where(common, left, 0.0)
+    masked_right = np.where(common, right, 0.0)
+    full_mean, full_difference = mean_difference_signal_dct(masked_left, masked_right)
+    expected_mean = np.square(full_mean[1:, eligible]).sum(axis=0).mean()
+    expected_difference = np.square(full_difference[1:, eligible]).sum(axis=0).mean()
+    features, support = true_mean_difference_dct_features(
+        left, right, left_mask, right_mask
+    )
+    assert bool(support.all())
+    assert np.expm1(features[:, 0]).sum() == pytest.approx(expected_mean, rel=1e-12)
+    assert np.expm1(features[:, 1]).sum() == pytest.approx(expected_difference, rel=1e-12)
+    assert features[0, 5] == pytest.approx(common.sum() / common.size)
+
+
+def test_dct_control_rejects_bad_shapes_masks_and_nonfinite_values():
+    signal = _band_signal()
+    mask = np.ones_like(signal, dtype=np.bool_)
+    with pytest.raises(ValueError):
+        true_mean_difference_dct_features(signal, signal[:-1], mask, mask)
+    with pytest.raises(ValueError):
+        true_mean_difference_dct_features(signal, signal, mask.astype(np.int8), mask)
+    bad = signal.copy()
+    bad[0, 0] = np.nan
+    with pytest.raises(ValueError):
+        true_mean_difference_dct_features(bad, signal, mask, mask)
+    with pytest.raises(ValueError):
+        true_mean_difference_dct_features(1j * signal, signal, mask, mask)
+    huge = np.full_like(signal, 1e200)
+    with pytest.raises(ValueError, match="nonfinite"):
+        true_mean_difference_dct_features(huge, huge, mask, mask)

From 3b3cb10f305269006b8ce270667c9594ae739476 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 02:57:08 -0500
Subject: [PATCH 76/95] Wire true DCT relations into V2 retrieval seam

---
 README.md                                     |  10 +-
 RELEASE_FILES.sha256                          |  17 +-
 src/phaseset_core/continuous_capture.py       |  24 ++
 src/phaseset_core/continuous_phase_cache.py   |  12 +-
 src/phaseset_core/continuous_retrieval.py     |  60 ++++-
 .../continuous_training_input.py              |  11 +-
 src/phaseset_core/dct_calibration.py          |  96 +++++++
 src/phaseset_core/dct_relations.py            | 220 ++++++++++++++++
 src/phaseset_core/directional_phase.py        |  15 +-
 src/phaseset_core/temporal_coordination.py    |  35 ++-
 tests/test_dct_relations.py                   | 235 ++++++++++++++++++
 11 files changed, 707 insertions(+), 28 deletions(-)
 create mode 100644 src/phaseset_core/dct_calibration.py
 create mode 100644 src/phaseset_core/dct_relations.py
 create mode 100644 tests/test_dct_relations.py

diff --git a/README.md b/README.md
index 518c430..25be6bd 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,15 @@ verified counterfactual coordination supervision. The
 [87-stage matrix](configs/phaseset_v2_experiment_matrix.json) supersedes the
 affected legacy 33-run plan. New modules are independent research APIs; the
 production V2 training-host integration and converged literature reproductions
-are not yet qualified. [TMR-Set](docs/TMR_SET_ADAPTATION.md) now implements the
+are not yet qualified.
+The V2 A6 comparator now has a data-free, whole-capture relation seam that
+forms actual `(a+b)/2` and `a-b` velocity signals *before* six-band DCT,
+retaining their cross term. This is distinct from legacy system 03's separate
+endpoint self-power control and is not an InterEdit reproduction. A6 requires
+its own typed, training-population DCT-floor receipt, which is a declaration
+until the private host authenticates the real fit and manifest. Neither this
+seam nor its server CPU tests constitute an A6 training run.
+[TMR-Set](docs/TMR_SET_ADAPTATION.md) now implements the
 complete original VAE objectives with disclosed full-parent group adaptation;
 its pinned frozen language loader is not a substitute for native learning.
 [WaMo-Set](docs/WAMO_SET_ADAPTATION.md) implements learnable stationary analysis
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 45311dc..34af6de 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-ddb5009755af5a219159df8fafbc3bf5e5de5737c6dcc6c17cc72169a1df1513	15322	README.md
+024b4f56ec35c56b0b8bc0500f9d96a667bfc9090ffe88ab5dfef6b794cd639e	15841	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -177,18 +177,20 @@ d8125f420db0ec8d72faab80557f3a4c1ed983793d7ecdb042065829b5e73714	8686	src/phases
 213df61614f99afc5936dc42f897466945f1979339f7008fc23414e026b89324	45428	src/phaseset_core/capture_validation.py
 c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phaseset_core/cli.py
 80e6d497ff9bb4354664cb1a054cdb550ab222e92c841813afc27af6d3759495	5603	src/phaseset_core/continuous_base_retrieval.py
-8192db959852cc34a186e669008b00300e63f47c51dff98b6a24a797c4888a37	11379	src/phaseset_core/continuous_capture.py
+ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
 cf879bcbe1b52796d751deaf3870c99ec24a70f92bc7d129e3710a81143fe023	32443	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
-1dac4bb8eaafc4bdadb6330cc67fd6ac650967e1f19115cf1d4fb96d3d882694	6634	src/phaseset_core/continuous_phase_cache.py
-6af5ecfcadc139acfdb3fe2d2e00995a40e90522a821172bc44e7a3604028dbc	10571	src/phaseset_core/continuous_retrieval.py
-a46004b4c5a94dc84029a6cf5a54631e614e67f680c506feb0b92b435702160b	6873	src/phaseset_core/continuous_training_input.py
+e8b149fe97e960ca1800501ae6be20b471fb22946bfb3eca04c44d4e3ed1bc40	6896	src/phaseset_core/continuous_phase_cache.py
+3c79ff24f8259fead4b6f52abcedaf56eab269e9757f83f6d47a4531a762d433	13297	src/phaseset_core/continuous_retrieval.py
+99e639c12e7418fe6091e605345c5c1a93ffde1401fb88816d86e6f2a7c2ae89	7099	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
+963927ab242e3f323c8d7ec057f17a41d77ecdf2fa9984c825b17c5b67f44395	3892	src/phaseset_core/dct_calibration.py
+03cf5467341195a131cb0e97160c49befb0a409195a95d49be6a37d119278f54	10299	src/phaseset_core/dct_relations.py
 9c9f86e262a7cf00809b16447dc5f5015a193e3af8eef703046c5e9468ababef	6825	src/phaseset_core/directional_calibration.py
-9ebd4a9c79c8ad96178155cbd58e5db4aa3f620a1342937ebe51023b3b82c893	22180	src/phaseset_core/directional_phase.py
+d3489b44cfded0fd318ff5cde25c52cf373de9ff5c5f0d4745a3f5161520db61	22547	src/phaseset_core/directional_phase.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
@@ -226,7 +228,7 @@ a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phase
 47a2aa0806af373e5df40c611bdad425efd672588fe912d4470ee27d8985181a	21832	src/phaseset_core/study_budget.py
 78cbb47380af76db33acb04350f2307ffae290b6db06b9edd9f1866a2d876bfd	19877	src/phaseset_core/study_process.py
 934b4933795a2971d83811dc36865ddd43d0fd1670e8c2caf9ec53755d362c9b	8297	src/phaseset_core/study_storage.py
-febde5f65ea132a9921973be393a6fbea715c6f9b1b1d9f5f8df3654cf007696	25153	src/phaseset_core/temporal_coordination.py
+759ab33805009a5f7c9d28ccc6d68a6f9359af0585b81cf09de9c4d627bd4b1a	25646	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
 cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phaseset_core/tmr_language.py
@@ -250,6 +252,7 @@ c0124a85e3154881a41c3d49706f81667663d5bfa996066e73ea299a72d586d0	17101	tests/tes
 5ca49ce282fc8cff3654af8af56ade66cfa6b732afd7dcca3e92ffe06d4ce64a	6030	tests/test_continuous_phase_cache.py
 e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/test_continuous_retrieval.py
 2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
+88eb674d900415cd02a8fca4342d487cbddc943deec5b3e7b0b16a9ee4ca8531	10164	tests/test_dct_relations.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 ad4fb11844e43b26566803a0805dacf20e0a6b6cdf4e9741dcc98739141d6b2a	10614	tests/test_literature_parent_host.py
diff --git a/src/phaseset_core/continuous_capture.py b/src/phaseset_core/continuous_capture.py
index 973f666..5ef336f 100644
--- a/src/phaseset_core/continuous_capture.py
+++ b/src/phaseset_core/continuous_capture.py
@@ -9,7 +9,9 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
+import hashlib
 import math
+import struct
 
 import numpy as np
 
@@ -136,6 +138,28 @@ def windows(self) -> tuple[PreparedGroupSample, ...]:
         )
 
 
+def physical_view_sha256(capture: PreparedContinuousCapture) -> str:
+    """Commit to the exact prepared/augmented physical view, not only source ID.
+
+    This binds actor order, every body22 coordinate/mask byte, the common
+    transform and source identity. It distinguishes different yaw/materialized
+    arrays of the same released capture without exposing participant IDs.
+    """
+    if type(capture) is not PreparedContinuousCapture:
+        raise TypeError("physical view digest requires PreparedContinuousCapture")
+    digest = hashlib.sha256(b"phaseset-physical-view-v1\x00")
+    digest.update(bytes.fromhex(capture.source_sha256))
+    digest.update(struct.pack("<4Q", *capture.skeletons.shape))
+    digest.update(struct.pack("<q", capture.reference_frame))
+    digest.update(struct.pack("<d", float(capture.augmentation_yaw)))
+    digest.update(capture.group_center.astype("<f8", copy=False).tobytes())
+    for commitment in capture.actor_commitments:
+        digest.update(commitment)
+    digest.update(memoryview(capture.skeletons).cast("B"))
+    digest.update(memoryview(capture.track_mask).cast("B"))
+    return digest.hexdigest()
+
+
 def _capture_filter(values: np.ndarray, kernel: np.ndarray, chunk_frames: int) -> np.ndarray:
     """Use the full FIR halo at chunk seams; pad only true capture endpoints."""
     half = kernel.size // 2
diff --git a/src/phaseset_core/continuous_phase_cache.py b/src/phaseset_core/continuous_phase_cache.py
index 1b9b39c..2713dc2 100644
--- a/src/phaseset_core/continuous_phase_cache.py
+++ b/src/phaseset_core/continuous_phase_cache.py
@@ -8,14 +8,14 @@
 
 from __future__ import annotations
 
-from dataclasses import asdict
+from dataclasses import asdict, replace
 import json
 from pathlib import Path
 import shutil
 
 import numpy as np
 
-from .continuous_capture import PreparedContinuousCapture
+from .continuous_capture import PreparedContinuousCapture, physical_view_sha256
 from .directional_phase import (
     ACTOR_FEATURE_DIM,
     DirectionalPhaseField,
@@ -104,7 +104,12 @@ def write_continuous_phase_cache(
     with (destination / "metadata.json").open("x", encoding="utf-8") as stream:
         json.dump(metadata, stream, sort_keys=True)
         stream.write("\n")
-    return field
+    return replace(
+        field,
+        source_sha256=capture.source_sha256,
+        actor_commitments=capture.actor_commitments,
+        physical_view_sha256=physical_view_sha256(capture),
+    )
 
 
 def load_continuous_phase_cache(
@@ -159,4 +164,5 @@ def mapped(name, dtype, shape):
         mapped("root-patch-mask", np.bool_, (actors, patches)),
         validate_energy_floors(energy_floors),
         config,
+        source_sha256=metadata["source_sha256"],
     )
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
index 5bea986..cbb8830 100644
--- a/src/phaseset_core/continuous_retrieval.py
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -16,6 +16,8 @@
 
 from .capture_validation import _validate_text_batch
 from .continuous_training_input import ContinuousTrainingView
+from .dct_calibration import DctFloorReceipt
+from .dct_relations import DctRelationField
 from .frozen_clip_text import FrozenClipTextBatch
 from .pipeline import collate_group_samples, edge_budget_batches
 from .temporal_coordination import (
@@ -108,6 +110,8 @@ def __init__(
         *,
         use_topology: bool = True,
         strip_phase: bool = False,
+        relation_kind: str = "phase",
+        dct_floor_receipt: DctFloorReceipt | None = None,
         checkpoint_blocks: bool = True,
         base_window_batch_size: int = 1,
         base_edge_budget: int = 32768,
@@ -123,6 +127,25 @@ def __init__(
             raise ValueError("base_window_batch_size must be a positive integer")
         if type(base_edge_budget) is not int or base_edge_budget < 1:
             raise ValueError("base_edge_budget must be a positive integer")
+        if relation_kind not in ("phase", "true_mean_difference_dct"):
+            raise ValueError("relation_kind must be phase or true_mean_difference_dct")
+        if relation_kind == "phase":
+            if dct_floor_receipt is not None:
+                raise ValueError("DCT receipt must not enter the phase system")
+            self.register_buffer("dct_energy_floors", None)
+            self._dct_floor_receipt = None
+            self._dct_receipt_sha256 = None
+        else:
+            if strip_phase or type(dct_floor_receipt) is not DctFloorReceipt:
+                raise ValueError("A6 requires a typed independent DCT floor receipt")
+            # Persistent state binds the physical calibration to every model
+            # checkpoint; a changed external array cannot alter later scoring.
+            self.register_buffer(
+                "dct_energy_floors", torch.from_numpy(dct_floor_receipt.floors.copy())
+            )
+            self._dct_floor_receipt = dct_floor_receipt
+            self._dct_receipt_sha256 = dct_floor_receipt.sha256
+        self.relation_kind = relation_kind
         self.frozen_b2 = frozen_b2.requires_grad_(False).eval()
         self.coordination = TemporalIncidenceEncoder(
             width=512,
@@ -135,6 +158,31 @@ def __init__(
         self.base_window_batch_size = base_window_batch_size
         self.base_edge_budget = base_edge_budget
 
+    def get_extra_state(self) -> dict[str, str | None]:
+        """Bind A6 receipt identity to exact checkpoint resume, not only floor values."""
+        return {
+            "schema": "phaseset-v2-relation-state-v1",
+            "relation_kind": self.relation_kind,
+            "dct_floor_receipt_sha256": self._dct_receipt_sha256,
+        }
+
+    def set_extra_state(self, state: object) -> None:
+        if type(state) is not dict or state != self.get_extra_state():
+            raise ValueError("relation kind or DCT floor receipt changed across checkpoint resume")
+        self._check_dct_floor_binding()
+
+    def _check_dct_floor_binding(self) -> None:
+        if self.relation_kind == "phase":
+            return
+        if (
+            self._dct_floor_receipt.sha256 != self._dct_receipt_sha256
+            or not torch.equal(
+                self.dct_energy_floors.detach().cpu(),
+                torch.from_numpy(self._dct_floor_receipt.floors.copy()),
+            )
+        ):
+            raise ValueError("DCT calibration buffer or receipt changed after construction")
+
     def train(self, mode: bool = True):
         super().train(mode)
         self.frozen_b2.eval()
@@ -177,6 +225,7 @@ def score(
         ):
             raise ValueError("views must be a nonempty tuple of admitted continuous inputs")
         text, _, receipt_sha = _validate_text_batch(text_batch, allow_row_selection=True)
+        self._check_dct_floor_binding()
         device = next(self.coordination.parameters()).device
         keys = tuple(view.positive_capture_key for view in views)
         text = text.to(device)
@@ -188,7 +237,16 @@ def score(
             ):
                 raise ValueError("the coordination field must span the complete capture")
             base_rows.append(self.encode_global(view))
-            output = self.coordination.score_text(view.phase_field, adapted_text)
+            physical = (
+                view.phase_field
+                if self.relation_kind == "phase"
+                else DctRelationField.from_capture(
+                    view.capture,
+                    view.phase_field,
+                    dct_floor_receipt=self._dct_floor_receipt,
+                )
+            )
+            output = self.coordination.score_text(physical, adapted_text)
             relation_rows.append(output.cosine)
             supports.append(output.periodic_support)
         global_cosine = F.normalize(torch.stack(base_rows), dim=-1) @ F.normalize(text, dim=-1).T
diff --git a/src/phaseset_core/continuous_training_input.py b/src/phaseset_core/continuous_training_input.py
index dc962f5..4faa4a0 100644
--- a/src/phaseset_core/continuous_training_input.py
+++ b/src/phaseset_core/continuous_training_input.py
@@ -17,7 +17,7 @@
 
 import numpy as np
 
-from .continuous_capture import PreparedContinuousCapture
+from .continuous_capture import PreparedContinuousCapture, physical_view_sha256
 from .continuous_capture_io import _file_sha256, load_prepared_continuous_capture
 from .continuous_phase_cache import load_continuous_phase_cache
 from .directional_phase import (
@@ -131,7 +131,14 @@ def load(
         )
         if field.actor_count != capture.actor_count or field.patch_count != cache_record["patches"]:
             raise ValueError("physical cache actor/patch census differs")
-        return cls(capture, field)
+        return cls(
+            capture,
+            replace(
+                field,
+                actor_commitments=capture.actor_commitments,
+                physical_view_sha256=physical_view_sha256(capture),
+            ),
+        )
 
     def view(
         self,
diff --git a/src/phaseset_core/dct_calibration.py b/src/phaseset_core/dct_calibration.py
new file mode 100644
index 0000000..3e9c160
--- /dev/null
+++ b/src/phaseset_core/dct_calibration.py
@@ -0,0 +1,96 @@
+"""Typed, self-hashed declaration for an A6 training-only DCT floor fit.
+
+This schema makes population/config drift visible at the model seam. It does
+not independently prove that a fit ran or grant formal launch authority: the
+private controller must authenticate the source-manifest hash and exact
+registered training population before constructing a production model.
+"""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+import hashlib
+import json
+
+import numpy as np
+
+from .directional_phase import LocalPhaseConfig
+from .periodic import validate_energy_floors
+
+
+DCT_FLOOR_SCHEMA = "phaseset-v2-a6-dct-training-floor-v1"
+_POPULATIONS = frozenset({"main", "pilot", "fold_0", "fold_1", "fold_2"})
+_SEALED_COMPONENTS = frozenset({"C09", "C11", "C15"})
+
+
+def dct_config_sha256(config: LocalPhaseConfig) -> str:
+    if type(config) is not LocalPhaseConfig:
+        raise TypeError("DCT config digest requires LocalPhaseConfig")
+    encoded = json.dumps(asdict(config), sort_keys=True, separators=(",", ":")).encode()
+    return hashlib.sha256(b"phaseset-v2-dct-config-v1\x00" + encoded).hexdigest()
+
+
+def _sha256_hex(value: object) -> bool:
+    return (
+        type(value) is str
+        and len(value) == 64
+        and all(character in "0123456789abcdef" for character in value)
+    )
+
+
+@dataclass(frozen=True)
+class DctFloorReceipt:
+    """Declared fit lineage and immutable six-band floors, not a signed authority."""
+
+    schema: str
+    population_name: str
+    training_components: tuple[str, ...]
+    training_source_manifest_sha256: str
+    config_sha256: str
+    floors: np.ndarray
+
+    def __post_init__(self) -> None:
+        if self.schema != DCT_FLOOR_SCHEMA or self.population_name not in _POPULATIONS:
+            raise ValueError("DCT floor schema or population differs")
+        if (
+            type(self.training_components) is not tuple
+            or not self.training_components
+            or len(set(self.training_components)) != len(self.training_components)
+            or tuple(sorted(self.training_components)) != self.training_components
+            or any(
+                type(value) is not str
+                or len(value) != 3
+                or value[0] != "C"
+                or not value[1:].isdigit()
+                for value in self.training_components
+            )
+            or bool(set(self.training_components) & _SEALED_COMPONENTS)
+            or not _sha256_hex(self.training_source_manifest_sha256)
+            or not _sha256_hex(self.config_sha256)
+        ):
+            raise ValueError("DCT floor training population or manifest binding differs")
+        checked = validate_energy_floors(self.floors)
+        frozen = checked.copy()
+        frozen.setflags(write=False)
+        object.__setattr__(self, "floors", frozen)
+
+    @property
+    def sha256(self) -> str:
+        payload = {
+            "schema": self.schema,
+            "population_name": self.population_name,
+            "training_components": self.training_components,
+            "training_source_manifest_sha256": self.training_source_manifest_sha256,
+            "config_sha256": self.config_sha256,
+            "floors_float64_le_hex": self.floors.astype("<f8", copy=False).tobytes().hex(),
+        }
+        encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()
+        return hashlib.sha256(b"phaseset-v2-dct-floor-receipt-v1\x00" + encoded).hexdigest()
+
+    def require_config(self, config: LocalPhaseConfig) -> None:
+        if self.config_sha256 != dct_config_sha256(config):
+            raise ValueError("DCT floor receipt/config digest differs")
+
+    def require_population(self, name: str, training_components: tuple[str, ...]) -> None:
+        if self.population_name != name or self.training_components != training_components:
+            raise ValueError("DCT floor receipt/training population differs")
diff --git a/src/phaseset_core/dct_relations.py b/src/phaseset_core/dct_relations.py
new file mode 100644
index 0000000..205006a
--- /dev/null
+++ b/src/phaseset_core/dct_relations.py
@@ -0,0 +1,220 @@
+"""Signal-level mean/difference DCT edge field for the PhaseSet-V2 A6 control.
+
+The physical input is the shared-coordinate signed joint velocity, never a
+Morlet response or separate endpoint self-powers. DCT energy floors are an
+explicit, separately fitted training-population input; Morlet floors are not
+interchangeable. This module does not fit or admit those floors.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+
+from .continuous_capture import PreparedContinuousCapture, physical_view_sha256
+from .dct_calibration import DctFloorReceipt
+from .directional_phase import (
+    ACTOR_FEATURE_DIM,
+    PHASE_FEATURE_DIM,
+    DirectionalPhaseField,
+    LocalPhaseConfig,
+    LocalPairChunk,
+    _patch_intervals,
+    _readonly,
+    _signed_velocity_arrays,
+    canonical_edge_pairs,
+    true_mean_difference_dct_features,
+)
+from .periodic import ResourceLimitError
+
+
+@dataclass(frozen=True)
+class LocalDctPairChunk(LocalPairChunk):
+    """The legacy ``phase_mask`` slot means calibrated DCT evidence here."""
+
+    def reverse_features(self) -> np.ndarray:
+        """DCT mean/difference energies are symmetric; only endpoint powers swap."""
+        reversed_values = self.features.copy()
+        reversed_values[..., 3] = self.features[..., 4]
+        reversed_values[..., 4] = self.features[..., 3]
+        reversed_values[reversed_values == 0.0] = 0.0
+        return reversed_values
+
+
+@dataclass(frozen=True)
+class DctRelationField:
+    """Complete-capture A6 field with bounded, recomputed unordered edge chunks.
+
+    Every actor feature and root is recomputed from the same body22 capture as
+    the DCT velocities. The admitted phase field supplies only the frozen
+    patch configuration and a cross-check of source/view lineage; none of its
+    learned context or Morlet response is retained in this A6 field.
+    """
+
+    config: LocalPhaseConfig
+    intervals: tuple[tuple[int, int], ...]
+    velocities: np.ndarray  # float64 [K,T,66]
+    velocity_mask: np.ndarray  # bool [K,T,66]
+    actor_features: np.ndarray  # float32 [K,P,132]
+    actor_patch_mask: np.ndarray  # bool [K,P]
+    root_positions: np.ndarray  # float32 [K,P,3]
+    root_patch_mask: np.ndarray  # bool [K,P]
+    dct_floor_receipt: DctFloorReceipt  # declared, independently fitted population
+    source_sha256: str
+
+    @classmethod
+    def from_capture(
+        cls,
+        capture: PreparedContinuousCapture,
+        phase_field: DirectionalPhaseField,
+        *,
+        dct_floor_receipt: DctFloorReceipt,
+    ) -> DctRelationField:
+        if type(capture) is not PreparedContinuousCapture or type(phase_field) is not DirectionalPhaseField:
+            raise TypeError("A6 requires an admitted continuous capture and phase field")
+        if (
+            phase_field.actor_count != capture.actor_count
+            or phase_field.intervals[-1][1] != capture.frame_count
+            or not 2 <= phase_field.config.patch_frames <= 40
+            or phase_field.source_sha256 != capture.source_sha256
+            or phase_field.actor_commitments != capture.actor_commitments
+            or phase_field.physical_view_sha256 != physical_view_sha256(capture)
+        ):
+            raise ValueError("A6 source/actor/timeline/patch binding differs from admitted capture")
+        if type(dct_floor_receipt) is not DctFloorReceipt:
+            raise TypeError("A6 requires a typed DCT floor receipt")
+        dct_floor_receipt.require_config(phase_field.config)
+        values, masks = _signed_velocity_arrays(
+            capture.skeletons[None],
+            capture.track_mask[None],
+            np.ones((1, capture.frame_count), dtype=np.bool_),
+            np.ones((1, capture.actor_count), dtype=np.bool_),
+        )
+        shape = (capture.actor_count, capture.frame_count, 66)
+        velocities = _readonly(np.ascontiguousarray(values[0].reshape(shape)))
+        velocity_mask = _readonly(np.ascontiguousarray(masks[0].reshape(shape)))
+        intervals = _patch_intervals(capture.frame_count, phase_field.config)
+        if intervals != phase_field.intervals:
+            raise ValueError("A6 physical patch intervals differ from the admitted view")
+        actor_features = np.zeros(
+            (capture.actor_count, len(intervals), ACTOR_FEATURE_DIM), dtype=np.float32
+        )
+        actor_mask = np.zeros((capture.actor_count, len(intervals)), dtype=np.bool_)
+        roots = np.zeros((capture.actor_count, len(intervals), 3), dtype=np.float32)
+        root_mask = np.zeros_like(actor_mask)
+        for patch, (start, stop) in enumerate(intervals):
+            local_values = velocities[:, start:stop]
+            local_mask = velocity_mask[:, start:stop]
+            denominator = local_mask.sum(axis=1).clip(min=1)
+            mean = local_values.sum(axis=1) / denominator
+            rms = np.sqrt((local_values**2).sum(axis=1) / denominator)
+            actor_features[:, patch] = np.concatenate((mean, rms), axis=-1)
+            actor_mask[:, patch] = local_mask.any(axis=(1, 2))
+            pelvis_mask = capture.track_mask[:, start:stop, 0]
+            pelvis_values = capture.skeletons[:, start:stop, 0].astype(np.float64)
+            roots[:, patch] = (
+                np.where(pelvis_mask[..., None], pelvis_values, 0.0).sum(axis=1)
+                / pelvis_mask.sum(axis=1).clip(min=1)[:, None]
+            )
+            root_mask[:, patch] = pelvis_mask.any(axis=1)
+        return cls(
+            phase_field.config,
+            intervals,
+            velocities,
+            velocity_mask,
+            _readonly(actor_features),
+            _readonly(actor_mask),
+            _readonly(roots),
+            _readonly(root_mask),
+            dct_floor_receipt,
+            capture.source_sha256,
+        )
+
+    def __post_init__(self) -> None:
+        if (
+            type(self.config) is not LocalPhaseConfig
+            or type(self.intervals) is not tuple
+            or not self.intervals
+            or type(self.velocities) is not np.ndarray
+            or self.velocities.dtype != np.dtype(np.float64)
+            or self.velocities.ndim != 3
+            or self.velocities.shape[0] < 2
+            or self.velocities.shape[1:] != (self.intervals[-1][1], 66)
+            or type(self.velocity_mask) is not np.ndarray
+            or self.velocity_mask.dtype != np.dtype(np.bool_)
+            or self.velocity_mask.shape != self.velocities.shape
+            or type(self.actor_features) is not np.ndarray
+            or self.actor_features.shape != (self.actor_count, self.patch_count, ACTOR_FEATURE_DIM)
+            or self.actor_features.dtype != np.dtype(np.float32)
+            or type(self.actor_patch_mask) is not np.ndarray
+            or self.actor_patch_mask.shape != (self.actor_count, self.patch_count)
+            or self.actor_patch_mask.dtype != np.dtype(np.bool_)
+            or type(self.root_positions) is not np.ndarray
+            or self.root_positions.shape != (self.actor_count, self.patch_count, 3)
+            or self.root_positions.dtype != np.dtype(np.float32)
+            or type(self.root_patch_mask) is not np.ndarray
+            or self.root_patch_mask.shape != (self.actor_count, self.patch_count)
+            or self.root_patch_mask.dtype != np.dtype(np.bool_)
+            or not bool(np.isfinite(self.velocities).all())
+            or not bool(np.isfinite(self.actor_features).all())
+            or not bool(np.isfinite(self.root_positions).all())
+            or bool(np.any(self.velocities[~self.velocity_mask].view(np.uint64) != 0))
+            or type(self.source_sha256) is not str
+            or len(self.source_sha256) != 64
+            or any(c not in "0123456789abcdef" for c in self.source_sha256)
+        ):
+            raise ValueError("A6 signed velocity field or source identity is invalid")
+        if type(self.dct_floor_receipt) is not DctFloorReceipt:
+            raise TypeError("A6 requires a typed DCT floor receipt")
+        self.dct_floor_receipt.require_config(self.config)
+        self.velocities.setflags(write=False)
+        self.velocity_mask.setflags(write=False)
+        self.actor_features.setflags(write=False)
+        self.actor_patch_mask.setflags(write=False)
+        self.root_positions.setflags(write=False)
+        self.root_patch_mask.setflags(write=False)
+
+    @property
+    def actor_count(self) -> int:
+        return int(self.velocities.shape[0])
+
+    @property
+    def patch_count(self) -> int:
+        return len(self.intervals)
+
+    @property
+    def pair_count(self) -> int:
+        return self.actor_count * (self.actor_count - 1) // 2
+
+
+def local_dct_pair_chunk(field: DctRelationField, start: int, stop: int) -> LocalDctPairChunk:
+    """Recompute every edge/patch from the full signed timeline, without K×K storage."""
+    if type(field) is not DctRelationField:
+        raise TypeError("A6 pair chunk requires a DctRelationField")
+    if field.pair_count > field.config.edge_budget:
+        raise ResourceLimitError(
+            required_edges=field.pair_count, edge_budget=field.config.edge_budget
+        )
+    pairs = canonical_edge_pairs(field.actor_count, start, stop)
+    features = np.zeros((len(pairs), field.patch_count, 6, PHASE_FEATURE_DIM), dtype=np.float64)
+    evidence = np.zeros(features.shape[:-1], dtype=np.bool_)
+    support = np.zeros_like(evidence)
+    log_floors = np.log1p(field.dct_floor_receipt.floors)
+    for edge, (left, right) in enumerate(pairs):
+        for patch, (window_start, window_stop) in enumerate(field.intervals):
+            values, observed = true_mean_difference_dct_features(
+                field.velocities[left, window_start:window_stop],
+                field.velocities[right, window_start:window_stop],
+                field.velocity_mask[left, window_start:window_stop],
+                field.velocity_mask[right, window_start:window_stop],
+            )
+            valid = observed & (values[:, 3] > log_floors) & (values[:, 4] > log_floors)
+            features[edge, patch] = values
+            features[edge, patch, :, 6] = valid.astype(np.float64)
+            evidence[edge, patch] = valid
+            support[edge, patch] = observed
+    features[features == 0.0] = 0.0
+    return LocalDctPairChunk(
+        _readonly(pairs), _readonly(features), _readonly(evidence), _readonly(support)
+    )
diff --git a/src/phaseset_core/directional_phase.py b/src/phaseset_core/directional_phase.py
index 7836a46..d82ac70 100644
--- a/src/phaseset_core/directional_phase.py
+++ b/src/phaseset_core/directional_phase.py
@@ -8,7 +8,7 @@
 
 from __future__ import annotations
 
-from dataclasses import dataclass
+from dataclasses import dataclass, replace
 import math
 from pathlib import Path
 from typing import Iterator
@@ -16,7 +16,7 @@
 import numpy as np
 
 from .contracts import PreparedGroupBatch, validate_prepared_group_batch
-from .continuous_capture import PreparedContinuousCapture
+from .continuous_capture import PreparedContinuousCapture, physical_view_sha256
 from .morlet import MORLET_FREQUENCIES_HZ, SAMPLE_RATE_HZ, morlet_kernel_bank
 from .periodic import ResourceLimitError, validate_energy_floors
 
@@ -62,6 +62,9 @@ class DirectionalPhaseField:
     root_patch_mask: np.ndarray  # [K,P]
     energy_floors: np.ndarray
     config: LocalPhaseConfig
+    source_sha256: str | None = None
+    actor_commitments: tuple[bytes, ...] | None = None
+    physical_view_sha256: str | None = None
 
     @property
     def actor_count(self) -> int:
@@ -177,7 +180,7 @@ def continuous_directional_phase_field(
     if type(capture) is not PreparedContinuousCapture:
         raise TypeError("capture must be exactly PreparedContinuousCapture")
     floors = validate_energy_floors(energy_floors)
-    return _directional_fields_from_arrays(
+    field = _directional_fields_from_arrays(
         capture.skeletons[None],
         capture.track_mask[None],
         np.ones((1, capture.frame_count), dtype=np.bool_),
@@ -187,6 +190,12 @@ def continuous_directional_phase_field(
         floors,
         config,
     )[0]
+    return replace(
+        field,
+        source_sha256=capture.source_sha256,
+        actor_commitments=capture.actor_commitments,
+        physical_view_sha256=physical_view_sha256(capture),
+    )
 
 
 def _directional_fields_from_arrays(
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
index 8bd5227..902bdc6 100644
--- a/src/phaseset_core/temporal_coordination.py
+++ b/src/phaseset_core/temporal_coordination.py
@@ -22,9 +22,21 @@
     DirectionalPhaseField,
     local_pair_chunk,
 )
+from .dct_relations import DctRelationField, local_dct_pair_chunk
 from .objectives import variable_positive_symmetric_infonce
 
 
+RelationField = DirectionalPhaseField | DctRelationField
+
+
+def _relation_pair_chunk(field: RelationField, start: int, stop: int):
+    if type(field) is DctRelationField:
+        return local_dct_pair_chunk(field, start, stop)
+    if type(field) is DirectionalPhaseField:
+        return local_pair_chunk(field, start, stop)
+    raise TypeError("relation field must be a physical phase or A6 DCT field")
+
+
 class MaskedTemporalEncoder(nn.Module):
     """Two shared temporal layers; missing positions do not update either state.
 
@@ -124,9 +136,9 @@ def __init__(
         self.topology_scale = nn.Parameter(torch.tensor(0.1))
 
     def _half_edges(
-        self, field: DirectionalPhaseField, hidden: Tensor, start: int, stop: int
+        self, field: RelationField, hidden: Tensor, start: int, stop: int
     ) -> tuple[Tensor, Tensor, Tensor, Tensor]:
-        chunk = local_pair_chunk(field, start, stop)
+        chunk = _relation_pair_chunk(field, start, stop)
         device = hidden.device
         endpoints = torch.tensor(chunk.endpoints.copy(), dtype=torch.int64, device=device)
         forward = torch.tensor(chunk.features.copy(), dtype=hidden.dtype, device=device)
@@ -177,7 +189,7 @@ def _half_edges(
         )
 
     def _node_block(
-        self, field: DirectionalPhaseField, hidden: Tensor, start: int, stop: int
+        self, field: RelationField, hidden: Tensor, start: int, stop: int
     ) -> tuple[Tensor, Tensor, Tensor]:
         a, b, endpoints, mask = self._half_edges(field, hidden, start, stop)
         # K x 64 incidence block, not an all-pair K x K adjacency or score map.
@@ -192,7 +204,7 @@ def _node_block(
 
     def _edge_block(
         self,
-        field: DirectionalPhaseField,
+        field: RelationField,
         hidden: Tensor,
         nodes: Tensor,
         topology_mask: Tensor,
@@ -218,7 +230,7 @@ def _run_block(self, function, *inputs):
             return checkpoint(function, *inputs, use_reentrant=False)
         return function(*inputs)
 
-    def _encode_context(self, field: DirectionalPhaseField) -> tuple[Tensor, Tensor, Tensor]:
+    def _encode_context(self, field: RelationField) -> tuple[Tensor, Tensor, Tensor]:
         device = next(self.parameters()).device
         actor_values = torch.tensor(field.actor_features.copy(), dtype=torch.float32, device=device)
         actor_mask = torch.tensor(field.actor_patch_mask.copy(), device=device)
@@ -267,7 +279,7 @@ def node_block(value, block_start=start, block_stop=stop):
         return hidden, nodes, topology_mask
 
     def _readout(
-        self, field: DirectionalPhaseField, hidden: Tensor, nodes: Tensor, topology_mask: Tensor
+        self, field: RelationField, hidden: Tensor, nodes: Tensor, topology_mask: Tensor
     ) -> TemporalCoordinationOutput:
         device = hidden.device
         patch_sum = torch.zeros((field.patch_count, self.width), dtype=torch.float64, device=device)
@@ -294,12 +306,12 @@ def edge_block(value, node_value, block_start=start, block_stop=stop):
             pair_count,
         )
 
-    def forward(self, field: DirectionalPhaseField) -> TemporalCoordinationOutput:
+    def forward(self, field: RelationField) -> TemporalCoordinationOutput:
         return self._readout(field, *self._encode_context(field))
 
     def _directed_text_block(
         self,
-        field: DirectionalPhaseField,
+        field: RelationField,
         hidden: Tensor,
         nodes: Tensor,
         topology_mask: Tensor,
@@ -322,8 +334,9 @@ def _directed_text_block(
         )
         histories, _ = self.edge_temporal(packets, torch.cat((track_support, track_support)))
         # Zero-energy observations can carry track context but are not reliable
-        # periodic evidence. Coherence is a feature, never a threshold filter.
-        physical = local_pair_chunk(field, start, stop)
+        # physical evidence. Phase uses Morlet floors; A6 uses its independently
+        # fitted DCT floors. Neither treats mere track coverage as evidence.
+        physical = _relation_pair_chunk(field, start, stop)
         observable = torch.tensor(physical.phase_mask.any(axis=-1).copy(), device=hidden.device)
         observable = observable & track_support
         language_mask = torch.cat((observable, observable))
@@ -332,7 +345,7 @@ def _directed_text_block(
         return best, language_mask.any()
 
     def score_text(
-        self, field: DirectionalPhaseField, text_embeddings: Tensor
+        self, field: RelationField, text_embeddings: Tensor
     ) -> TemporalRelationScores:
         """Stream whole directed packets against whole captions on the full timeline.
 
diff --git a/tests/test_dct_relations.py b/tests/test_dct_relations.py
new file mode 100644
index 0000000..ebae384
--- /dev/null
+++ b/tests/test_dct_relations.py
@@ -0,0 +1,235 @@
+"""A6 data-free relation-field and model-seam checks, not trained retrieval."""
+
+from dataclasses import replace
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.continuous_retrieval import ContinuousRetrievalSystem
+from phaseset_core.dct_calibration import DCT_FLOOR_SCHEMA, DctFloorReceipt, dct_config_sha256
+from phaseset_core.dct_relations import DctRelationField, local_dct_pair_chunk
+from phaseset_core.directional_phase import (
+    LocalPhaseConfig,
+    continuous_directional_phase_field,
+    true_mean_difference_dct_features,
+)
+from phaseset_core.temporal_coordination import TemporalIncidenceEncoder
+from phaseset_core.training import BaseRetrievalSystem
+from test_continuous_retrieval import _MockB2
+from test_continuous_training_input import _input
+from test_phaseset_frozen_clip_text import _commitment, _fixture
+
+
+def _dct_view(tmp_path):
+    source, _, _, _ = _input(tmp_path)
+    view = source.view(allow_shared_yaw=False)
+    floors = _receipt(view.phase_field.config)
+    return view, DctRelationField.from_capture(
+        view.capture, view.phase_field, dct_floor_receipt=floors
+    ), floors
+
+
+def _receipt(config, values=None):
+    return DctFloorReceipt(
+        DCT_FLOOR_SCHEMA,
+        "main",
+        ("C01",),
+        "a" * 64,
+        dct_config_sha256(config),
+        np.zeros(6, dtype=np.float64) if values is None else values,
+    )
+
+
+def test_a6_receipt_rejects_morlet_or_test_population_and_config_drift():
+    config = LocalPhaseConfig()
+    receipt = _receipt(config)
+    receipt.require_population("main", ("C01",))
+    assert receipt.sha256 == _receipt(config).sha256
+    with pytest.raises(ValueError, match="population"):
+        receipt.require_population("fold_0", ("C01",))
+    with pytest.raises(ValueError, match="config"):
+        receipt.require_config(LocalPhaseConfig(hop_frames=10))
+    with pytest.raises(ValueError, match="population"):
+        DctFloorReceipt(
+            DCT_FLOOR_SCHEMA,
+            "main",
+            ("C09",),
+            "a" * 64,
+            dct_config_sha256(config),
+            np.zeros(6, np.float64),
+        )
+    with pytest.raises(ValueError, match="schema"):
+        DctFloorReceipt(
+            "phaseset-morlet-floor-v1",
+            "main",
+            ("C01",),
+            "a" * 64,
+            dct_config_sha256(config),
+            np.zeros(6, np.float64),
+        )
+
+
+def test_a6_edge_features_are_real_signal_dct_not_cached_morlet(tmp_path):
+    view, field, floors = _dct_view(tmp_path)
+    chunk = local_dct_pair_chunk(field, 0, field.pair_count)
+    first = chunk.endpoints[0]
+    start, stop = field.intervals[0]
+    direct, observed = true_mean_difference_dct_features(
+        field.velocities[first[0], start:stop],
+        field.velocities[first[1], start:stop],
+        field.velocity_mask[first[0], start:stop],
+        field.velocity_mask[first[1], start:stop],
+    )
+    np.testing.assert_array_equal(chunk.features[0, 0, :, :6], direct[:, :6])
+    np.testing.assert_array_equal(chunk.support_mask[0, 0], observed)
+    np.testing.assert_array_equal(chunk.phase_mask[0, 0], chunk.features[0, 0, :, 6] > 0)
+    assert bool(chunk.phase_mask.any())
+    for name in ("actor_features", "actor_patch_mask", "root_positions", "root_patch_mask"):
+        np.testing.assert_array_equal(getattr(field, name), getattr(view.phase_field, name))
+
+    # The phase response arrays can be absent and A6 still computes the same
+    # edge features from admitted signed velocities plus shared actor context.
+    without_morlet = replace(view.phase_field, responses=(), response_masks=(), response_centers=())
+    independent = DctRelationField.from_capture(
+        view.capture, without_morlet, dct_floor_receipt=floors
+    )
+    np.testing.assert_array_equal(local_dct_pair_chunk(independent, 0, 3).features, chunk.features)
+    forged_context = replace(
+        view.phase_field,
+        actor_features=np.zeros_like(view.phase_field.actor_features),
+        root_positions=np.zeros_like(view.phase_field.root_positions),
+    )
+    recomputed = DctRelationField.from_capture(
+        view.capture, forged_context, dct_floor_receipt=floors
+    )
+    np.testing.assert_array_equal(recomputed.actor_features, field.actor_features)
+    np.testing.assert_array_equal(recomputed.root_positions, field.root_positions)
+
+
+def test_a6_reverse_keeps_difference_power_and_swaps_only_endpoint_powers(tmp_path):
+    _, field, _ = _dct_view(tmp_path)
+    chunk = local_dct_pair_chunk(field, 0, 1)
+    reversed_values = chunk.reverse_features()
+    np.testing.assert_array_equal(reversed_values[..., :3], chunk.features[..., :3])
+    np.testing.assert_array_equal(reversed_values[..., 3], chunk.features[..., 4])
+    np.testing.assert_array_equal(reversed_values[..., 4], chunk.features[..., 3])
+    np.testing.assert_array_equal(reversed_values[..., 5:], chunk.features[..., 5:])
+
+
+def test_a6_zero_energy_has_coverage_but_no_calibrated_evidence(tmp_path):
+    view, _, floors = _dct_view(tmp_path)
+    stationary = replace(view.capture, skeletons=np.zeros_like(view.capture.skeletons))
+    matching = continuous_directional_phase_field(
+        stationary, energy_floors=view.phase_field.energy_floors
+    )
+    field = DctRelationField.from_capture(
+        stationary, matching, dct_floor_receipt=floors
+    )
+    chunk = local_dct_pair_chunk(field, 0, 1)
+    assert bool(chunk.support_mask.any())
+    assert not bool(chunk.phase_mask.any())
+    assert not bool(np.count_nonzero(chunk.features[..., 6]))
+    model = TemporalIncidenceEncoder(width=8)
+    result = model.score_text(field, torch.randn(2, 8))
+    assert not bool(result.periodic_support)
+    assert not bool(torch.count_nonzero(result.cosine))
+
+
+def test_a6_checkpointed_score_and_gradient_are_finite(tmp_path):
+    _, field, _ = _dct_view(tmp_path)
+    torch.manual_seed(1729)
+    model = TemporalIncidenceEncoder(width=8)
+    text = torch.randn(3, 8, requires_grad=True)
+    result = model.score_text(field, text)
+    assert bool(result.periodic_support)
+    assert result.cosine.shape == (3,)
+    result.cosine.sum().backward()
+    assert bool(torch.isfinite(text.grad).all())
+    assert any(
+        parameter.grad is not None and bool(parameter.grad.abs().sum() > 0)
+        for parameter in model.parameters()
+    )
+
+
+def test_a6_complete_retrieval_seam_uses_dct_field_and_frozen_text(tmp_path):
+    (tmp_path / "input").mkdir()
+    (tmp_path / "clip").mkdir()
+    view, _, receipt = _dct_view(tmp_path / "input")
+    clip, _, _, _ = _fixture(tmp_path / "clip")
+    text = clip.encode(
+        ("people move together", "people move apart"),
+        caption_commitments=(_commitment("one"), _commitment("two")),
+        batch_size=2,
+    )
+    model = ContinuousRetrievalSystem(
+        BaseRetrievalSystem(_MockB2(), embedding_dim=512),
+        relation_kind="true_mean_difference_dct",
+        dct_floor_receipt=receipt,
+    )
+    score = model.score((view,), text)
+    assert score.scores.shape == (1, 2)
+    assert bool(torch.isfinite(score.scores).all())
+    assert bool(score.periodic_support[0])
+    assert score.text_receipt_sha256 == text.receipt.sha256
+
+
+def test_a6_retrieval_seam_requires_independent_floors_and_keeps_head_capacity():
+    base = BaseRetrievalSystem(_MockB2(), embedding_dim=512)
+    with pytest.raises(ValueError, match="typed independent DCT floor receipt"):
+        ContinuousRetrievalSystem(base, relation_kind="true_mean_difference_dct")
+    with pytest.raises(ValueError, match="must not enter"):
+        ContinuousRetrievalSystem(base, dct_floor_receipt=_receipt(LocalPhaseConfig()))
+    phase = ContinuousRetrievalSystem(base)
+    floors = np.full(6, 1e-3, np.float64)
+    receipt = _receipt(LocalPhaseConfig(), floors)
+    dct = ContinuousRetrievalSystem(
+        base,
+        relation_kind="true_mean_difference_dct",
+        dct_floor_receipt=receipt,
+    )
+    floors[:] = 1e6
+    assert torch.equal(
+        dct.state_dict()["dct_energy_floors"], torch.full((6,), 1e-3, dtype=torch.float64)
+    )
+    assert "dct_energy_floors" not in phase.state_dict()
+    assert dct.state_dict()["_extra_state"]["dct_floor_receipt_sha256"] == receipt.sha256
+    changed = ContinuousRetrievalSystem(
+        base,
+        relation_kind="true_mean_difference_dct",
+        dct_floor_receipt=_receipt(LocalPhaseConfig(), np.full(6, 0.01, np.float64)),
+    )
+    with pytest.raises(ValueError, match="checkpoint resume"):
+        changed.load_state_dict(dct.state_dict())
+    phase_count = sum(p.numel() for p in phase.parameters() if p.requires_grad)
+    dct_count = sum(p.numel() for p in dct.parameters() if p.requires_grad)
+    assert phase_count == dct_count
+
+
+def test_a6_refuses_patch_longer_than_frozen_dct_contract(tmp_path):
+    view, _, floors = _dct_view(tmp_path)
+    for invalid in (1, 41):
+        mismatched = replace(view.phase_field, config=LocalPhaseConfig(patch_frames=invalid))
+        with pytest.raises(ValueError, match="binding"):
+            DctRelationField.from_capture(
+                view.capture, mismatched, dct_floor_receipt=floors
+            )
+
+
+def test_a6_refuses_same_shape_capture_field_lineage_mismatch(tmp_path):
+    view, _, floors = _dct_view(tmp_path)
+    assert view.phase_field.source_sha256 == view.capture.source_sha256
+    assert view.phase_field.actor_commitments == view.capture.actor_commitments
+    for wrong in (
+        replace(view.phase_field, source_sha256="b" * 64),
+        replace(view.phase_field, actor_commitments=view.phase_field.actor_commitments[::-1]),
+        replace(view.phase_field, physical_view_sha256="b" * 64),
+    ):
+        with pytest.raises(ValueError, match="binding"):
+            DctRelationField.from_capture(view.capture, wrong, dct_floor_receipt=floors)
+    rotated = replace(view.capture, augmentation_yaw=0.5)
+    with pytest.raises(ValueError, match="binding"):
+        DctRelationField.from_capture(rotated, view.phase_field, dct_floor_receipt=floors)
+    changed = replace(view.capture, skeletons=np.zeros_like(view.capture.skeletons))
+    with pytest.raises(ValueError, match="binding"):
+        DctRelationField.from_capture(changed, view.phase_field, dct_floor_receipt=floors)

From 894d2e22d93197b100ca7a53a8ac4b8c7858060a Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 03:11:19 -0500
Subject: [PATCH 77/95] Fit training-only A6 DCT energy floors with bounded
 census

---
 RELEASE_FILES.sha256                 |   3 +-
 src/phaseset_core/dct_calibration.py | 169 +++++++++++++++++++++++++++
 tests/test_dct_calibration.py        | 136 +++++++++++++++++++++
 3 files changed, 307 insertions(+), 1 deletion(-)
 create mode 100644 tests/test_dct_calibration.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 34af6de..b8a3c33 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -187,7 +187,7 @@ e8b149fe97e960ca1800501ae6be20b471fb22946bfb3eca04c44d4e3ed1bc40	6896	src/phases
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
-963927ab242e3f323c8d7ec057f17a41d77ecdf2fa9984c825b17c5b67f44395	3892	src/phaseset_core/dct_calibration.py
+a03bc907307f82452469e38d1b39c97c6c39febd4ad64f19d6ff320fd70f3171	11969	src/phaseset_core/dct_calibration.py
 03cf5467341195a131cb0e97160c49befb0a409195a95d49be6a37d119278f54	10299	src/phaseset_core/dct_relations.py
 9c9f86e262a7cf00809b16447dc5f5015a193e3af8eef703046c5e9468ababef	6825	src/phaseset_core/directional_calibration.py
 d3489b44cfded0fd318ff5cde25c52cf373de9ff5c5f0d4745a3f5161520db61	22547	src/phaseset_core/directional_phase.py
@@ -252,6 +252,7 @@ c0124a85e3154881a41c3d49706f81667663d5bfa996066e73ea299a72d586d0	17101	tests/tes
 5ca49ce282fc8cff3654af8af56ade66cfa6b732afd7dcca3e92ffe06d4ce64a	6030	tests/test_continuous_phase_cache.py
 e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/test_continuous_retrieval.py
 2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
+d27df99ab4a6d36bde033e4b5a3e41ba804d7304282fe5237fb1e4ed7cc3d603	5428	tests/test_dct_calibration.py
 88eb674d900415cd02a8fca4342d487cbddc943deec5b3e7b0b16a9ee4ca8531	10164	tests/test_dct_relations.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
diff --git a/src/phaseset_core/dct_calibration.py b/src/phaseset_core/dct_calibration.py
index 3e9c160..496d47d 100644
--- a/src/phaseset_core/dct_calibration.py
+++ b/src/phaseset_core/dct_calibration.py
@@ -9,16 +9,21 @@
 from __future__ import annotations
 
 from dataclasses import asdict, dataclass
+from collections.abc import Iterable
 import hashlib
 import json
 
 import numpy as np
 
 from .directional_phase import LocalPhaseConfig
+from .continuous_capture import PreparedContinuousCapture
+from .directional_calibration import _linear_fifth_percentile
+from .directional_phase import _patch_intervals, _signed_velocity_arrays, true_mean_difference_dct_features
 from .periodic import validate_energy_floors
 
 
 DCT_FLOOR_SCHEMA = "phaseset-v2-a6-dct-training-floor-v1"
+DCT_CALIBRATION_METHOD = "signed-vector-actor-patch-DCT-power/linear-fifth-percentile-v1"
 _POPULATIONS = frozenset({"main", "pilot", "fold_0", "fold_1", "fold_2"})
 _SEALED_COMPONENTS = frozenset({"C09", "C11", "C15"})
 
@@ -94,3 +99,167 @@ def require_config(self, config: LocalPhaseConfig) -> None:
     def require_population(self, name: str, training_components: tuple[str, ...]) -> None:
         if self.population_name != name or self.training_components != training_components:
             raise ValueError("DCT floor receipt/training population differs")
+
+
+@dataclass(frozen=True)
+class DctCalibrationCapture:
+    capture_id: str  # admitted private source identity, never an actor-set positive ID
+    component: str
+    capture: PreparedContinuousCapture
+
+    def __post_init__(self) -> None:
+        if (
+            type(self.capture) is not PreparedContinuousCapture
+            or self.capture_id != self.capture.source_sha256
+            or type(self.component) is not str
+            or not self.component
+        ):
+            raise ValueError("A6 calibration record must bind exact prepared source identity")
+
+
+@dataclass(frozen=True)
+class DctEnergyCalibration:
+    floors: np.ndarray
+    capture_count: int
+    training_components: tuple[str, ...]
+    observed_counts: tuple[int, ...]
+    missing_counts: tuple[int, ...]
+    exact_zero_counts: tuple[int, ...]
+    method: str = DCT_CALIBRATION_METHOD
+
+
+def actor_patch_dct_power_observations(
+    capture: PreparedContinuousCapture,
+    config: LocalPhaseConfig,
+    *,
+    max_working_bytes: int = 1_073_741_824,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Unpaired marginal actor DCT power in the exact A6 endpoint power units.
+
+    A zero-valued second endpoint makes slot 3 of the already audited paired
+    descriptor the first actor's power. This shares the binning/mask/DCT
+    arithmetic with A6 while collecting no cross-actor or text information.
+    Observed zero power is retained for the fixed fifth percentile.
+    """
+    if type(capture) is not PreparedContinuousCapture or type(config) is not LocalPhaseConfig:
+        raise TypeError("DCT calibration requires an exact capture and config")
+    if not 2 <= config.patch_frames <= 40:
+        raise ValueError("DCT calibration patch length must be in [2,40]")
+    if type(max_working_bytes) is not int or max_working_bytes < 1:
+        raise ValueError("DCT calibration working-memory bound must be positive")
+    # _signed_velocity_arrays creates float64 coordinates/velocities and
+    # temporary derivative, relative-motion and validity arrays. The 128-byte
+    # per scalar upper envelope also covers small local DCT temporaries; this
+    # gate happens before any full-timeline float64 allocation.
+    projected = capture.actor_count * capture.frame_count * 66 * 128
+    if projected > max_working_bytes:
+        raise MemoryError("RESOURCE_LIMIT: A6 full-capture velocity working set exceeds budget")
+    values, masks = _signed_velocity_arrays(
+        capture.skeletons[None],
+        capture.track_mask[None],
+        np.ones((1, capture.frame_count), dtype=np.bool_),
+        np.ones((1, capture.actor_count), dtype=np.bool_),
+    )
+    shape = (capture.actor_count, capture.frame_count, 66)
+    signed, observed = values[0].reshape(shape), masks[0].reshape(shape)
+    intervals = _patch_intervals(capture.frame_count, config)
+    powers = np.zeros((capture.actor_count, len(intervals), 6), dtype=np.float64)
+    support = np.zeros(powers.shape, dtype=np.bool_)
+    for actor in range(capture.actor_count):
+        for patch, (start, stop) in enumerate(intervals):
+            actor_signal = signed[actor, start:stop]
+            actor_mask = observed[actor, start:stop]
+            features, band_support = true_mean_difference_dct_features(
+                actor_signal,
+                np.zeros_like(actor_signal),
+                actor_mask,
+                actor_mask,
+            )
+            power = np.expm1(features[:, 3])
+            if not bool(np.isfinite(power).all()) or bool(np.any(power < 0.0)):
+                raise ValueError("A6 marginal DCT power became invalid")
+            powers[actor, patch] = power
+            support[actor, patch] = band_support
+    powers[powers == 0.0] = 0.0
+    powers.setflags(write=False)
+    support.setflags(write=False)
+    return powers, support
+
+
+def fit_dct_energy_floors(
+    captures: Iterable[DctCalibrationCapture],
+    *,
+    training_components: tuple[str, ...],
+    expected_capture_ids: tuple[str, ...],
+    config: LocalPhaseConfig,
+    max_actor_patch_observations: int = 1_000_000,
+    max_working_bytes: int = 1_073_741_824,
+    max_total_velocity_scalars: int = 1_000_000_000,
+) -> DctEnergyCalibration:
+    """Fail closed unless exactly the declared complete training-parent census is read.
+
+    The private host must independently authenticate component assignments,
+    prepared capture files and the source-manifest digest before calling this
+    pure fitter. Neither validation nor final-test rows are sampled or read.
+    """
+    if (
+        type(training_components) is not tuple
+        or not training_components
+        or len(set(training_components)) != len(training_components)
+        or any(type(value) is not str or not value for value in training_components)
+        or bool(set(training_components) & _SEALED_COMPONENTS)
+        or type(expected_capture_ids) is not tuple
+        or not expected_capture_ids
+        or len(set(expected_capture_ids)) != len(expected_capture_ids)
+        or any(type(value) is not str or not value for value in expected_capture_ids)
+        or type(config) is not LocalPhaseConfig
+        or type(max_actor_patch_observations) is not int
+        or max_actor_patch_observations < 1
+        or type(max_working_bytes) is not int
+        or max_working_bytes < 1
+        or type(max_total_velocity_scalars) is not int
+        or max_total_velocity_scalars < 1
+    ):
+        raise ValueError("A6 complete train-only calibration contract is invalid")
+    allowed, expected = set(training_components), set(expected_capture_ids)
+    seen: set[str] = set()
+    observed_components: set[str] = set()
+    values: list[list[np.ndarray]] = [[] for _ in range(6)]
+    missing = np.zeros(6, dtype=np.int64)
+    total = 0
+    total_velocity_scalars = 0
+    for item in captures:
+        if type(item) is not DctCalibrationCapture:
+            raise TypeError("A6 calibration stream must contain exact capture records")
+        if item.component not in allowed or item.capture_id not in expected or item.capture_id in seen:
+            raise ValueError("A6 calibration stream contains held-out, repeated or foreign parent")
+        total += item.capture.actor_count * len(_patch_intervals(item.capture.frame_count, config))
+        if total > max_actor_patch_observations:
+            raise MemoryError("RESOURCE_LIMIT: A6 complete actor-patch population exceeds budget")
+        total_velocity_scalars += item.capture.actor_count * item.capture.frame_count * 66
+        if total_velocity_scalars > max_total_velocity_scalars:
+            raise MemoryError("RESOURCE_LIMIT: A6 full training timeline exceeds scalar budget")
+        powers, support = actor_patch_dct_power_observations(
+            item.capture, config, max_working_bytes=max_working_bytes
+        )
+        for band in range(6):
+            selected = support[..., band]
+            values[band].append(powers[..., band][selected])
+            missing[band] += int(selected.size - selected.sum())
+        seen.add(item.capture_id)
+        observed_components.add(item.component)
+    if seen != expected or observed_components != allowed:
+        raise ValueError("A6 calibration stream differs from the complete training census")
+    selected_values = tuple(np.concatenate(rows) for rows in values)
+    floors = validate_energy_floors(
+        np.array([_linear_fifth_percentile(row) for row in selected_values], dtype=np.float64)
+    ).copy()
+    floors.setflags(write=False)
+    return DctEnergyCalibration(
+        floors,
+        len(seen),
+        tuple(sorted(allowed)),
+        tuple(int(row.size) for row in selected_values),
+        tuple(int(count) for count in missing),
+        tuple(int(np.count_nonzero(row == 0.0)) for row in selected_values),
+    )
diff --git a/tests/test_dct_calibration.py b/tests/test_dct_calibration.py
new file mode 100644
index 0000000..d8e50f4
--- /dev/null
+++ b/tests/test_dct_calibration.py
@@ -0,0 +1,136 @@
+"""A6 train-only DCT floor arithmetic/census probes on synthetic body22."""
+
+from dataclasses import replace
+
+import numpy as np
+import pytest
+
+import phaseset_core.dct_calibration as calibration
+from phaseset_core.dct_calibration import (
+    DCT_CALIBRATION_METHOD,
+    DctCalibrationCapture,
+    actor_patch_dct_power_observations,
+    fit_dct_energy_floors,
+)
+from phaseset_core.directional_phase import LocalPhaseConfig
+from test_continuous_training_input import _input
+
+
+@pytest.fixture(scope="module")
+def synthetic_capture(tmp_path_factory):
+    source, _, _, _ = _input(tmp_path_factory.mktemp("a6-dct-fit"))
+    return source.capture
+
+
+def test_a6_actor_patch_power_is_finite_and_preserves_observed_zeros(synthetic_capture):
+    powers, support = actor_patch_dct_power_observations(
+        synthetic_capture, LocalPhaseConfig()
+    )
+    assert powers.shape == support.shape
+    assert powers.shape[0] == synthetic_capture.actor_count
+    assert powers.shape[-1] == 6
+    assert bool(np.isfinite(powers).all()) and bool((powers >= 0).all())
+    assert bool(support.any()) and bool((~support).any())
+    assert not powers.flags.writeable and not support.flags.writeable
+    assert not bool(np.count_nonzero(powers[~support]))
+
+    stationary = replace(
+        synthetic_capture, skeletons=np.zeros_like(synthetic_capture.skeletons)
+    )
+    zeros, stationary_support = actor_patch_dct_power_observations(
+        stationary, LocalPhaseConfig()
+    )
+    assert bool(stationary_support.any())
+    assert not bool(np.count_nonzero(zeros))
+    assert not bool(np.signbit(zeros).any())
+
+
+def test_a6_fifth_percentile_uses_exact_complete_parent_population(synthetic_capture):
+    other = replace(synthetic_capture, source_sha256="b" * 64)
+    records = (
+        DctCalibrationCapture("a" * 64, "C01", synthetic_capture),
+        DctCalibrationCapture("b" * 64, "C02", other),
+    )
+    powers, support = actor_patch_dct_power_observations(
+        synthetic_capture, LocalPhaseConfig()
+    )
+    fitted = fit_dct_energy_floors(
+        records,
+        training_components=("C01", "C02"),
+        expected_capture_ids=("a" * 64, "b" * 64),
+        config=LocalPhaseConfig(),
+    )
+    assert fitted.capture_count == 2
+    assert fitted.training_components == ("C01", "C02")
+    assert fitted.method == DCT_CALIBRATION_METHOD
+    assert not fitted.floors.flags.writeable
+    for band in range(6):
+        observed = powers[..., band][support[..., band]]
+        assert fitted.floors[band] == pytest.approx(
+            np.quantile(observed, 0.05, method="linear"), rel=1e-12, abs=1e-12
+        )
+        assert fitted.observed_counts[band] == 2 * len(observed)
+        assert fitted.missing_counts[band] == 2 * (support[..., band].size - len(observed))
+        assert fitted.exact_zero_counts[band] == 2 * int(np.count_nonzero(observed == 0))
+
+
+def test_a6_floor_fitter_rejects_test_missing_repeated_and_resource_shortcuts(
+    synthetic_capture,
+):
+    first = DctCalibrationCapture("a" * 64, "C01", synthetic_capture)
+    kwargs = dict(
+        training_components=("C01",),
+        expected_capture_ids=("a" * 64,),
+        config=LocalPhaseConfig(),
+    )
+    with pytest.raises(ValueError, match="contract"):
+        fit_dct_energy_floors((first,), **(kwargs | {"training_components": ("C09",)}))
+    with pytest.raises(ValueError, match="stream"):
+        fit_dct_energy_floors((), **kwargs)
+    with pytest.raises(ValueError, match="stream"):
+        fit_dct_energy_floors((first, first), **kwargs)
+    with pytest.raises(ValueError, match="stream"):
+        fit_dct_energy_floors(
+            (DctCalibrationCapture("a" * 64, "C00", synthetic_capture),), **kwargs
+        )
+    with pytest.raises(MemoryError, match="RESOURCE_LIMIT"):
+        fit_dct_energy_floors((first,), **(kwargs | {"max_actor_patch_observations": 1}))
+    with pytest.raises(MemoryError, match="working set"):
+        fit_dct_energy_floors(
+            (first,), **(kwargs | {"max_working_bytes": 1, "config": LocalPhaseConfig(hop_frames=10**9)})
+        )
+    with pytest.raises(MemoryError, match="scalar budget"):
+        fit_dct_energy_floors((first,), **(kwargs | {"max_total_velocity_scalars": 1}))
+    with pytest.raises(ValueError, match="patch length"):
+        actor_patch_dct_power_observations(
+            synthetic_capture, LocalPhaseConfig(patch_frames=1)
+        )
+
+
+def test_a6_cumulative_timeline_budget_rejects_second_parent_before_collection(
+    synthetic_capture, monkeypatch
+):
+    other = replace(synthetic_capture, source_sha256="b" * 64)
+    records = (
+        DctCalibrationCapture("a" * 64, "C01", synthetic_capture),
+        DctCalibrationCapture("b" * 64, "C02", other),
+    )
+    calls = 0
+    original = calibration.actor_patch_dct_power_observations
+
+    def counted(*args, **kwargs):
+        nonlocal calls
+        calls += 1
+        return original(*args, **kwargs)
+
+    monkeypatch.setattr(calibration, "actor_patch_dct_power_observations", counted)
+    one_parent_scalars = synthetic_capture.actor_count * synthetic_capture.frame_count * 66
+    with pytest.raises(MemoryError, match="scalar budget"):
+        fit_dct_energy_floors(
+            records,
+            training_components=("C01", "C02"),
+            expected_capture_ids=("a" * 64, "b" * 64),
+            config=LocalPhaseConfig(hop_frames=10**9),
+            max_total_velocity_scalars=one_parent_scalars,
+        )
+    assert calls == 1

From 15452a65181e948bd3175159102596ba8bba89f1 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 03:44:50 -0500
Subject: [PATCH 78/95] Encode relation checkpoint identity as tensor state

---
 RELEASE_FILES.sha256                      |  4 ++--
 src/phaseset_core/continuous_retrieval.py | 16 ++++++++++++----
 tests/test_dct_relations.py               | 18 +++++++++++++++++-
 3 files changed, 31 insertions(+), 7 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index b8a3c33..7231e61 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -182,7 +182,7 @@ ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phase
 cf879bcbe1b52796d751deaf3870c99ec24a70f92bc7d129e3710a81143fe023	32443	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 e8b149fe97e960ca1800501ae6be20b471fb22946bfb3eca04c44d4e3ed1bc40	6896	src/phaseset_core/continuous_phase_cache.py
-3c79ff24f8259fead4b6f52abcedaf56eab269e9757f83f6d47a4531a762d433	13297	src/phaseset_core/continuous_retrieval.py
+dea3811ba091df7ce4378bba755c59119e8e1df8515a2e9bf0755ac28e73ef33	13587	src/phaseset_core/continuous_retrieval.py
 99e639c12e7418fe6091e605345c5c1a93ffde1401fb88816d86e6f2a7c2ae89	7099	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
@@ -253,7 +253,7 @@ c0124a85e3154881a41c3d49706f81667663d5bfa996066e73ea299a72d586d0	17101	tests/tes
 e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/test_continuous_retrieval.py
 2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
 d27df99ab4a6d36bde033e4b5a3e41ba804d7304282fe5237fb1e4ed7cc3d603	5428	tests/test_dct_calibration.py
-88eb674d900415cd02a8fca4342d487cbddc943deec5b3e7b0b16a9ee4ca8531	10164	tests/test_dct_relations.py
+6b15a80fda8f707d0e32eaf72e311d24017d4bb062069dc41e0b4705bd72b080	11055	tests/test_dct_relations.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 ad4fb11844e43b26566803a0805dacf20e0a6b6cdf4e9741dcc98739141d6b2a	10614	tests/test_literature_parent_host.py
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
index cbb8830..f5d6ca4 100644
--- a/src/phaseset_core/continuous_retrieval.py
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -9,6 +9,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+import json
 
 import torch
 from torch import Tensor, nn
@@ -158,16 +159,23 @@ def __init__(
         self.base_window_batch_size = base_window_batch_size
         self.base_edge_budget = base_edge_budget
 
-    def get_extra_state(self) -> dict[str, str | None]:
-        """Bind A6 receipt identity to exact checkpoint resume, not only floor values."""
-        return {
+    def get_extra_state(self) -> Tensor:
+        """Tensor-encode the relation identity for tensor-only checkpoint hosts."""
+        payload = {
             "schema": "phaseset-v2-relation-state-v1",
             "relation_kind": self.relation_kind,
             "dct_floor_receipt_sha256": self._dct_receipt_sha256,
         }
+        encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8")
+        return torch.tensor(list(encoded), dtype=torch.uint8)
 
     def set_extra_state(self, state: object) -> None:
-        if type(state) is not dict or state != self.get_extra_state():
+        if (
+            type(state) is not Tensor
+            or state.dtype != torch.uint8
+            or state.ndim != 1
+            or not torch.equal(state.detach().cpu(), self.get_extra_state())
+        ):
             raise ValueError("relation kind or DCT floor receipt changed across checkpoint resume")
         self._check_dct_floor_binding()
 
diff --git a/tests/test_dct_relations.py b/tests/test_dct_relations.py
index ebae384..acb14c0 100644
--- a/tests/test_dct_relations.py
+++ b/tests/test_dct_relations.py
@@ -1,6 +1,7 @@
 """A6 data-free relation-field and model-seam checks, not trained retrieval."""
 
 from dataclasses import replace
+import json
 
 import numpy as np
 import pytest
@@ -193,7 +194,22 @@ def test_a6_retrieval_seam_requires_independent_floors_and_keeps_head_capacity()
         dct.state_dict()["dct_energy_floors"], torch.full((6,), 1e-3, dtype=torch.float64)
     )
     assert "dct_energy_floors" not in phase.state_dict()
-    assert dct.state_dict()["_extra_state"]["dct_floor_receipt_sha256"] == receipt.sha256
+    state_bytes = bytes(dct.state_dict()["_extra_state"].tolist())
+    assert json.loads(state_bytes)["dct_floor_receipt_sha256"] == receipt.sha256
+    assert all(type(value) is torch.Tensor for value in phase.state_dict().values())
+    assert all(type(value) is torch.Tensor for value in dct.state_dict().values())
+    ContinuousRetrievalSystem(base).load_state_dict(phase.state_dict())
+    ContinuousRetrievalSystem(
+        base, relation_kind="true_mean_difference_dct", dct_floor_receipt=receipt
+    ).load_state_dict(dct.state_dict())
+    with pytest.raises(ValueError, match="checkpoint resume"):
+        dct.set_extra_state(dct.get_extra_state().to(torch.int64))
+    mutated_state = dct.get_extra_state().clone()
+    mutated_state[0] ^= 1
+    with pytest.raises(ValueError, match="checkpoint resume"):
+        dct.set_extra_state(mutated_state)
+    with pytest.raises(ValueError, match="checkpoint resume"):
+        phase.set_extra_state(dct.get_extra_state())
     changed = ContinuousRetrievalSystem(
         base,
         relation_kind="true_mean_difference_dct",

From 5506bec26b283b3cadcdc0f4c16dce46ef09f28a Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 04:13:13 -0500
Subject: [PATCH 79/95] Add receipt-bound no-Morlet A6 disk views

---
 RELEASE_FILES.sha256                          | 12 +--
 src/phaseset_core/continuous_retrieval.py     | 22 +++--
 .../continuous_training_input.py              |  3 +-
 src/phaseset_core/dct_relations.py            | 79 ++++++++++++++--
 src/phaseset_core/parent_disk_source.py       | 34 ++++++-
 tests/test_dct_relations.py                   | 93 ++++++++++++++++++-
 tests/test_parent_disk_source.py              | 51 ++++++++++
 7 files changed, 272 insertions(+), 22 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 7231e61..6382591 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -182,13 +182,13 @@ ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phase
 cf879bcbe1b52796d751deaf3870c99ec24a70f92bc7d129e3710a81143fe023	32443	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 e8b149fe97e960ca1800501ae6be20b471fb22946bfb3eca04c44d4e3ed1bc40	6896	src/phaseset_core/continuous_phase_cache.py
-dea3811ba091df7ce4378bba755c59119e8e1df8515a2e9bf0755ac28e73ef33	13587	src/phaseset_core/continuous_retrieval.py
-99e639c12e7418fe6091e605345c5c1a93ffde1401fb88816d86e6f2a7c2ae89	7099	src/phaseset_core/continuous_training_input.py
+4d7844836578e697ecbd971d2cb0bc9cc74c1bb9c304d3b859f6a10dc62fd177	14371	src/phaseset_core/continuous_retrieval.py
+24c6f90d5ef8d6ccfaf0287433b5421e99544185f5197889cddb4f7874dd4f37	7158	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
 a03bc907307f82452469e38d1b39c97c6c39febd4ad64f19d6ff320fd70f3171	11969	src/phaseset_core/dct_calibration.py
-03cf5467341195a131cb0e97160c49befb0a409195a95d49be6a37d119278f54	10299	src/phaseset_core/dct_relations.py
+727d85b652932cb2b8a6e76b2c470ab009c0f30a723011c2ce581e8b2e548fe0	13070	src/phaseset_core/dct_relations.py
 9c9f86e262a7cf00809b16447dc5f5015a193e3af8eef703046c5e9468ababef	6825	src/phaseset_core/directional_calibration.py
 d3489b44cfded0fd318ff5cde25c52cf373de9ff5c5f0d4745a3f5161520db61	22547	src/phaseset_core/directional_phase.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
@@ -208,7 +208,7 @@ c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phase
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 0f0309c54a5b48a0493bb24f97eda39ff93be657295e1b0ebcc3dd6e9a8ecd76	6565	src/phaseset_core/parent_capture.py
 be88afc2d543a9ceb227eda07046cc8e9221faf7af85888c1b303632da642dc8	3366	src/phaseset_core/parent_clip_rows.py
-0d91c16503f8704916eecd6c72e0b943dd06cf251ba6f7b847a32d5c009ab6dd	8826	src/phaseset_core/parent_disk_source.py
+c0dd8d79012f8101cfc9790d046a171537684d87385285727578f2f42c143e87	10393	src/phaseset_core/parent_disk_source.py
 be883315aa773e6c2d535ea981853ae92a791187338fd2b310b574f956174a82	7326	src/phaseset_core/parent_retrieval_task.py
 46baa360084180121c16e1ce4d0c0a21841c6ad91c462afd272d5b92b0a603ad	9528	src/phaseset_core/parent_run_monitor.py
 52b22af181cb52cf3c9c3584d15ea02a604e5c29d8abeaa0de217baf07d8af06	7027	src/phaseset_core/parent_weak_clip_rows.py
@@ -253,7 +253,7 @@ c0124a85e3154881a41c3d49706f81667663d5bfa996066e73ea299a72d586d0	17101	tests/tes
 e48d9f9eb92180b18f9b9dcb159e8da451697835d54fec4d00c86e737a531dff	12747	tests/test_continuous_retrieval.py
 2128be288d7b7ed74d9c84acec4a22b5f6d5399a0b21d31b4c9aaa4061abe0e3	8878	tests/test_continuous_training_input.py
 d27df99ab4a6d36bde033e4b5a3e41ba804d7304282fe5237fb1e4ed7cc3d603	5428	tests/test_dct_calibration.py
-6b15a80fda8f707d0e32eaf72e311d24017d4bb062069dc41e0b4705bd72b080	11055	tests/test_dct_relations.py
+1564143a1f26f535805d0238ff035f036deb775bdf975e54ec4ef90a9b3da55e	14813	tests/test_dct_relations.py
 5ed2bd1ec476a41ab1601bf8aab96a4da0447e671c617e1289a048109cd670df	7454	tests/test_directional_calibration.py
 af12c2c1920f14cb9ea018f39178e91c47457f3008976dd8eab02fc1a164aa1d	4542	tests/test_editable_figures.py
 ad4fb11844e43b26566803a0805dacf20e0a6b6cdf4e9741dcc98739141d6b2a	10614	tests/test_literature_parent_host.py
@@ -264,7 +264,7 @@ b65a2178d7faf25372adeccdfcd19ee7804544665c6059f2d684801ccff83e11	6050	tests/test
 d75b28bf730f483b8980aea1aa1398c127e10e33ec901012db77d04ec2d3b79a	6775	tests/test_morlet_portability.py
 3b23c2b08ed81029ca6239cbb63b98093077786163aaf5bb47ccfefb21c6ef24	7117	tests/test_parent_capture.py
 fc0c27fb68c7d119d1cc7a48afeeff5a707cab9ec206eb5ad85c606e31656490	9443	tests/test_parent_clip_rows.py
-487ee577859df867298041fe731166cd211506e5f211d3c508eeec5aa282640f	5825	tests/test_parent_disk_source.py
+b3b3473c7925d3d9b4307be4539e809b06e3f7cca64da0a0db938ee1e2a6a152	8056	tests/test_parent_disk_source.py
 a070e5ce27d454531125639eea5353a222fe01227c0fabee6d8be6dcdffc5e36	7126	tests/test_parent_optimizer_config.py
 32b783f094f7b770623294f49a5c0ef960d1277daf29d9210a8858ec6d3d192a	4624	tests/test_parent_retrieval_task.py
 cdf4d6dcea5c29e44ebb3f2f91945fd944a8026a7abe91eaa47beb59428f159c	12935	tests/test_parent_run_monitor.py
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
index f5d6ca4..a76e554 100644
--- a/src/phaseset_core/continuous_retrieval.py
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -18,7 +18,8 @@
 from .capture_validation import _validate_text_batch
 from .continuous_training_input import ContinuousTrainingView
 from .dct_calibration import DctFloorReceipt
-from .dct_relations import DctRelationField
+from .dct_relations import DctRelationField, DctViewContext, require_dct_working_budget
+from .directional_phase import DirectionalPhaseField
 from .frozen_clip_text import FrozenClipTextBatch
 from .pipeline import collate_group_samples, edge_budget_batches
 from .temporal_coordination import (
@@ -244,16 +245,25 @@ def score(
                 view.phase_field.intervals[-1][1] != view.capture.frame_count
             ):
                 raise ValueError("the coordination field must span the complete capture")
+            if self.relation_kind != "phase":
+                if type(view.phase_field) is not DctViewContext:
+                    raise ValueError("A6 retrieval requires the DCT-only context")
+                if view.phase_field.dct_floor_receipt_sha256 != self._dct_receipt_sha256:
+                    raise ValueError("A6 view DCT floor receipt differs from the model")
+                require_dct_working_budget(
+                    view.capture.actor_count, view.capture.frame_count, view.phase_field.config
+                )
             base_rows.append(self.encode_global(view))
-            physical = (
-                view.phase_field
-                if self.relation_kind == "phase"
-                else DctRelationField.from_capture(
+            if self.relation_kind == "phase":
+                if type(view.phase_field) is not DirectionalPhaseField:
+                    raise ValueError("phase retrieval requires the complete Morlet field")
+                physical = view.phase_field
+            else:
+                physical = DctRelationField.from_capture(
                     view.capture,
                     view.phase_field,
                     dct_floor_receipt=self._dct_floor_receipt,
                 )
-            )
             output = self.coordination.score_text(physical, adapted_text)
             relation_rows.append(output.cosine)
             supports.append(output.periodic_support)
diff --git a/src/phaseset_core/continuous_training_input.py b/src/phaseset_core/continuous_training_input.py
index 4faa4a0..6e019c5 100644
--- a/src/phaseset_core/continuous_training_input.py
+++ b/src/phaseset_core/continuous_training_input.py
@@ -20,6 +20,7 @@
 from .continuous_capture import PreparedContinuousCapture, physical_view_sha256
 from .continuous_capture_io import _file_sha256, load_prepared_continuous_capture
 from .continuous_phase_cache import load_continuous_phase_cache
+from .dct_relations import DctViewContext
 from .directional_phase import (
     DirectionalPhaseField,
     LocalPhaseConfig,
@@ -67,7 +68,7 @@ def shared_yaw_capture(
 @dataclass(frozen=True)
 class ContinuousTrainingView:
     capture: PreparedContinuousCapture
-    phase_field: DirectionalPhaseField
+    phase_field: DirectionalPhaseField | DctViewContext
     reused_physical_cache: bool
 
     @property
diff --git a/src/phaseset_core/dct_relations.py b/src/phaseset_core/dct_relations.py
index 205006a..9c1e176 100644
--- a/src/phaseset_core/dct_relations.py
+++ b/src/phaseset_core/dct_relations.py
@@ -29,6 +29,20 @@
 from .periodic import ResourceLimitError
 
 
+def require_dct_working_budget(actor_count: int, frame_count: int, config: LocalPhaseConfig) -> None:
+    """Reject a complete A6 timeline before loading or encoding it."""
+    if (
+        type(actor_count) is not int
+        or actor_count < 2
+        or type(frame_count) is not int
+        or frame_count < 2
+        or type(config) is not LocalPhaseConfig
+    ):
+        raise ValueError("A6 working-set preflight requires valid capture dimensions and config")
+    if actor_count * frame_count * 66 * 128 > config.max_response_bytes:
+        raise MemoryError("RESOURCE_LIMIT: A6 full-timeline DCT working set exceeds budget")
+
+
 @dataclass(frozen=True)
 class LocalDctPairChunk(LocalPairChunk):
     """The legacy ``phase_mask`` slot means calibrated DCT evidence here."""
@@ -42,14 +56,55 @@ def reverse_features(self) -> np.ndarray:
         return reversed_values
 
 
+@dataclass(frozen=True)
+class DctViewContext:
+    """A6 lineage/patch context; it contains no Morlet response or cache."""
+
+    config: LocalPhaseConfig
+    intervals: tuple[tuple[int, int], ...]
+    source_sha256: str
+    actor_commitments: tuple[bytes, ...]
+    physical_view_sha256: str
+    dct_floor_receipt_sha256: str
+
+    @classmethod
+    def from_capture(
+        cls,
+        capture: PreparedContinuousCapture,
+        config: LocalPhaseConfig,
+        dct_floor_receipt: DctFloorReceipt,
+    ) -> DctViewContext:
+        if type(capture) is not PreparedContinuousCapture or type(config) is not LocalPhaseConfig:
+            raise TypeError("A6 context requires an exact prepared capture and config")
+        if type(dct_floor_receipt) is not DctFloorReceipt:
+            raise TypeError("A6 context requires a typed DCT floor receipt")
+        dct_floor_receipt.require_config(config)
+        if not 2 <= config.patch_frames <= 40:
+            raise ValueError("A6 context patch length must be in [2,40]")
+        require_dct_working_budget(capture.actor_count, capture.frame_count, config)
+        return cls(
+            config,
+            _patch_intervals(capture.frame_count, config),
+            capture.source_sha256,
+            capture.actor_commitments,
+            physical_view_sha256(capture),
+            dct_floor_receipt.sha256,
+        )
+
+    @property
+    def actor_count(self) -> int:
+        return len(self.actor_commitments)
+
+
 @dataclass(frozen=True)
 class DctRelationField:
     """Complete-capture A6 field with bounded, recomputed unordered edge chunks.
 
     Every actor feature and root is recomputed from the same body22 capture as
-    the DCT velocities. The admitted phase field supplies only the frozen
-    patch configuration and a cross-check of source/view lineage; none of its
-    learned context or Morlet response is retained in this A6 field.
+    the DCT velocities. A lightweight A6 context supplies frozen patch and
+    source/view lineage without loading Morlet cache. The legacy phase-field
+    context remains accepted for data-free compatibility tests only; neither
+    path retains a Morlet response in this A6 field.
     """
 
     config: LocalPhaseConfig
@@ -67,12 +122,15 @@ class DctRelationField:
     def from_capture(
         cls,
         capture: PreparedContinuousCapture,
-        phase_field: DirectionalPhaseField,
+        phase_field: DirectionalPhaseField | DctViewContext,
         *,
         dct_floor_receipt: DctFloorReceipt,
     ) -> DctRelationField:
-        if type(capture) is not PreparedContinuousCapture or type(phase_field) is not DirectionalPhaseField:
-            raise TypeError("A6 requires an admitted continuous capture and phase field")
+        if type(capture) is not PreparedContinuousCapture or type(phase_field) not in (
+            DirectionalPhaseField,
+            DctViewContext,
+        ):
+            raise TypeError("A6 requires an admitted capture and typed relation context")
         if (
             phase_field.actor_count != capture.actor_count
             or phase_field.intervals[-1][1] != capture.frame_count
@@ -85,6 +143,15 @@ def from_capture(
         if type(dct_floor_receipt) is not DctFloorReceipt:
             raise TypeError("A6 requires a typed DCT floor receipt")
         dct_floor_receipt.require_config(phase_field.config)
+        if (
+            type(phase_field) is DctViewContext
+            and phase_field.dct_floor_receipt_sha256 != dct_floor_receipt.sha256
+        ):
+            raise ValueError("A6 context DCT floor receipt differs from the model")
+        # The legacy config's response-byte gate is the shared physical
+        # working-set budget. Count complete signed-velocity intermediates
+        # before materializing any full-timeline float64 arrays.
+        require_dct_working_budget(capture.actor_count, capture.frame_count, phase_field.config)
         values, masks = _signed_velocity_arrays(
             capture.skeletons[None],
             capture.track_mask[None],
diff --git a/src/phaseset_core/parent_disk_source.py b/src/phaseset_core/parent_disk_source.py
index 04faa3a..7bb6138 100644
--- a/src/phaseset_core/parent_disk_source.py
+++ b/src/phaseset_core/parent_disk_source.py
@@ -16,7 +16,14 @@
 import numpy as np
 
 from .continuous_capture_io import load_prepared_continuous_capture
-from .continuous_training_input import ContinuousTrainingInput, shared_yaw_capture
+from .continuous_training_input import (
+    ContinuousTrainingInput,
+    ContinuousTrainingView,
+    shared_yaw_capture,
+)
+from .dct_calibration import DctFloorReceipt
+from .dct_relations import DctViewContext, require_dct_working_budget
+from .directional_phase import LocalPhaseConfig
 from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
 from .parent_clip_rows import ParentHumanClipRows
 from .parent_weak_clip_rows import ParentWeakClipRows
@@ -73,6 +80,7 @@ def __init__(
         yaw_eligibility: Mapping[str, bool],
         language_rows: FrozenTMRRowCache,
         energy_floors: np.ndarray | None = None,
+        dct_floor_receipt: DctFloorReceipt | None = None,
         human_clip_rows: ParentHumanClipRows | None = None,
         weak_clip_rows: ParentWeakClipRows | None = None,
     ):
@@ -91,6 +99,13 @@ def __init__(
             raise ValueError("every human-caption family needs explicit boolean yaw eligibility")
         if type(language_rows) is not FrozenTMRRowCache:
             raise TypeError("need the closed frozen TMR/WaMo human feature rows")
+        if energy_floors is not None and dct_floor_receipt is not None:
+            raise ValueError("phase and A6 floors must not be admitted together")
+        if dct_floor_receipt is not None:
+            if type(dct_floor_receipt) is not DctFloorReceipt:
+                raise TypeError("A6 disk source requires a typed DCT floor receipt")
+            dct_floor_receipt.require_config(LocalPhaseConfig())
+        self.dct_floor_receipt = dct_floor_receipt
         for parent in task.parents:
             record = self.records[parent.annotation_family_sha256]
             if parent.split == "test":
@@ -155,6 +170,23 @@ def capture(self, parent, *, seed, epoch, training):
         return shared_yaw_capture(capture, yaw_delta=angle)
 
     def view(self, parent, *, seed, epoch, training):
+        if self.dct_floor_receipt is not None:
+            record, _ = self._record_and_yaw(parent, seed=seed, epoch=epoch, training=training)
+            shape = record.prepared_record.get("shape")
+            if (
+                type(shape) is not list
+                or len(shape) != 4
+                or any(type(value) is not int for value in shape)
+                or shape[2:] != [22, 3]
+            ):
+                raise ValueError("A6 prepared shape is invalid")
+            require_dct_working_budget(shape[0], shape[1], LocalPhaseConfig())
+            capture = self.capture(parent, seed=seed, epoch=epoch, training=training)
+            return ContinuousTrainingView(
+                capture,
+                DctViewContext.from_capture(capture, LocalPhaseConfig(), self.dct_floor_receipt),
+                False,
+            )
         if self.energy_floors is None:
             return self.capture(parent, seed=seed, epoch=epoch, training=training)
         record, angle = self._record_and_yaw(parent, seed=seed, epoch=epoch, training=training)
diff --git a/tests/test_dct_relations.py b/tests/test_dct_relations.py
index acb14c0..438cd0c 100644
--- a/tests/test_dct_relations.py
+++ b/tests/test_dct_relations.py
@@ -9,7 +9,7 @@
 
 from phaseset_core.continuous_retrieval import ContinuousRetrievalSystem
 from phaseset_core.dct_calibration import DCT_FLOOR_SCHEMA, DctFloorReceipt, dct_config_sha256
-from phaseset_core.dct_relations import DctRelationField, local_dct_pair_chunk
+from phaseset_core.dct_relations import DctRelationField, DctViewContext, local_dct_pair_chunk
 from phaseset_core.directional_phase import (
     LocalPhaseConfig,
     continuous_directional_phase_field,
@@ -168,11 +168,100 @@ def test_a6_complete_retrieval_seam_uses_dct_field_and_frozen_text(tmp_path):
         relation_kind="true_mean_difference_dct",
         dct_floor_receipt=receipt,
     )
-    score = model.score((view,), text)
+    model.eval()
+    with pytest.raises(ValueError, match="DCT-only context"):
+        model.score((view,), text)
+    lightweight = replace(
+        view,
+        phase_field=DctViewContext.from_capture(
+            view.capture, view.phase_field.config, receipt
+        ),
+        reused_physical_cache=False,
+    )
+    assert not hasattr(lightweight.phase_field, "responses")
+    score = model.score((lightweight,), text)
     assert score.scores.shape == (1, 2)
     assert bool(torch.isfinite(score.scores).all())
     assert bool(score.periodic_support[0])
     assert score.text_receipt_sha256 == text.receipt.sha256
+    with pytest.raises(ValueError, match="complete Morlet field"):
+        ContinuousRetrievalSystem(
+            BaseRetrievalSystem(_MockB2(), embedding_dim=512)
+        ).score((lightweight,), text)
+
+
+def test_a6_lightweight_context_produces_identical_relation_features(tmp_path):
+    view, full_context, receipt = _dct_view(tmp_path)
+    context = DctViewContext.from_capture(view.capture, view.phase_field.config, receipt)
+    assert context.source_sha256 == view.capture.source_sha256
+    assert context.actor_count == view.capture.actor_count
+    assert not hasattr(context, "responses")
+    lightweight = DctRelationField.from_capture(
+        view.capture, context, dct_floor_receipt=receipt
+    )
+    for name in (
+        "velocities",
+        "velocity_mask",
+        "actor_features",
+        "actor_patch_mask",
+        "root_positions",
+        "root_patch_mask",
+    ):
+        np.testing.assert_array_equal(getattr(lightweight, name), getattr(full_context, name))
+    for name in ("features", "phase_mask", "support_mask"):
+        np.testing.assert_array_equal(
+            getattr(local_dct_pair_chunk(lightweight, 0, lightweight.pair_count), name),
+            getattr(local_dct_pair_chunk(full_context, 0, full_context.pair_count), name),
+        )
+    with pytest.raises(ValueError, match="binding"):
+        DctRelationField.from_capture(
+            view.capture,
+            replace(context, physical_view_sha256="0" * 64),
+            dct_floor_receipt=receipt,
+        )
+
+
+def test_a6_lightweight_context_refuses_full_timeline_resource_overflow(tmp_path):
+    view, _, _ = _dct_view(tmp_path)
+    config = LocalPhaseConfig(max_response_bytes=1)
+    with pytest.raises(MemoryError, match="RESOURCE_LIMIT"):
+        DctViewContext.from_capture(view.capture, config, _receipt(config))
+
+
+def test_a6_context_refuses_different_receipt_before_global_encoding(tmp_path, monkeypatch):
+    (tmp_path / "input").mkdir()
+    (tmp_path / "clip").mkdir()
+    view, _, receipt = _dct_view(tmp_path / "input")
+    clip, _, _, _ = _fixture(tmp_path / "clip")
+    text = clip.encode(
+        ("people move together",),
+        caption_commitments=(_commitment("one"),),
+        batch_size=1,
+    )
+    model = ContinuousRetrievalSystem(
+        BaseRetrievalSystem(_MockB2(), embedding_dim=512),
+        relation_kind="true_mean_difference_dct",
+        dct_floor_receipt=receipt,
+    )
+    context = DctViewContext.from_capture(view.capture, view.phase_field.config, receipt)
+    other = _receipt(view.phase_field.config, np.full(6, 0.01, np.float64))
+    assert other.sha256 != receipt.sha256
+    monkeypatch.setattr(model, "encode_global", lambda _: pytest.fail("global encoding started"))
+    with pytest.raises(ValueError, match="floor receipt"):
+        model.score(
+            (replace(view, phase_field=replace(context, dct_floor_receipt_sha256=other.sha256)),),
+            text,
+        )
+    with pytest.raises(ValueError, match="floor receipt"):
+        DctRelationField.from_capture(
+            view.capture, replace(context, dct_floor_receipt_sha256=other.sha256),
+            dct_floor_receipt=receipt,
+        )
+    with pytest.raises(MemoryError, match="RESOURCE_LIMIT"):
+        model.score(
+            (replace(view, phase_field=replace(context, config=LocalPhaseConfig(max_response_bytes=1))),),
+            text,
+        )
 
 
 def test_a6_retrieval_seam_requires_independent_floors_and_keeps_head_capacity():
diff --git a/tests/test_parent_disk_source.py b/tests/test_parent_disk_source.py
index 9e60f60..64565c0 100644
--- a/tests/test_parent_disk_source.py
+++ b/tests/test_parent_disk_source.py
@@ -11,6 +11,9 @@
     ParentDiskRecord,
     deterministic_parent_yaw,
 )
+from phaseset_core.dct_calibration import DCT_FLOOR_SCHEMA, DctFloorReceipt, dct_config_sha256
+from phaseset_core.dct_relations import DctViewContext
+from phaseset_core.directional_phase import LocalPhaseConfig
 from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
 from phaseset_core.tmr_feature_cache import FrozenTMRRowCache, save_frozen_tmr_rows
 from phaseset_core.tmr_set import TMRTextBatch
@@ -80,6 +83,54 @@ def test_phase_views_recompute_every_field_and_validation_reuses_cache(tmp_path)
     assert torch.equal(source.wamo_cls(parent.captions), text.tokens[:1, 0])
 
 
+def test_a6_disk_views_use_body22_without_morlet_cache(tmp_path, monkeypatch):
+    source, parent, original, task, row = _source(tmp_path, phase=False)
+    receipt = DctFloorReceipt(
+        DCT_FLOOR_SCHEMA,
+        "main",
+        ("C01",),
+        "a" * 64,
+        dct_config_sha256(LocalPhaseConfig()),
+        np.full(6, 1e-3, dtype=np.float64),
+    )
+    body_only = replace(row, physical_directory=None, physical_record=None)
+    dct_source = DevelopmentParentDiskSource(
+        task,
+        (body_only,),
+        yaw_eligibility={"b" * 64: True},
+        language_rows=source.language_rows,
+        dct_floor_receipt=receipt,
+    )
+    view = dct_source.view(parent, seed=1729, epoch=0, training=False)
+    assert type(view.phase_field) is DctViewContext
+    assert not view.reused_physical_cache and not hasattr(view.phase_field, "responses")
+    np.testing.assert_array_equal(view.capture.skeletons, original.capture.skeletons)
+    assert view.phase_field.physical_view_sha256
+    assert view.phase_field.dct_floor_receipt_sha256 == receipt.sha256
+    rotated = dct_source.view(parent, seed=1729, epoch=1, training=True)
+    assert type(rotated.phase_field) is DctViewContext
+    assert rotated.capture.augmentation_yaw != 0.0
+    enormous = replace(
+        body_only,
+        prepared_record={**body_only.prepared_record, "shape": [2, 1_000_000, 22, 3]},
+    )
+    dct_source.records[parent.annotation_family_sha256] = enormous
+    monkeypatch.setattr(
+        dct_source, "capture", lambda *args, **kwargs: pytest.fail("body load started")
+    )
+    with pytest.raises(MemoryError, match="RESOURCE_LIMIT"):
+        dct_source.view(parent, seed=1729, epoch=0, training=False)
+    with pytest.raises(ValueError, match="not be admitted together"):
+        DevelopmentParentDiskSource(
+            task,
+            (row,),
+            yaw_eligibility={"b" * 64: True},
+            language_rows=source.language_rows,
+            energy_floors=np.zeros(6),
+            dct_floor_receipt=receipt,
+        )
+
+
 def test_unapproved_caption_yaw_and_task_drift_do_not_get_silent_defaults(tmp_path):
     source, parent, original, task, row = _source(tmp_path, allowed=False)
     rotated = source.view(parent, seed=31415, epoch=0, training=True)

From ff52a5516453072edf52fe30125635a17281b29a Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 04:34:58 -0500
Subject: [PATCH 80/95] Document A6 native source qualification

---
 README.md                                   |  8 +++---
 RELEASE_FILES.sha256                        |  3 ++-
 docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md | 30 +++++++++++++++++++++
 3 files changed, 37 insertions(+), 4 deletions(-)
 create mode 100644 docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md

diff --git a/README.md b/README.md
index 25be6bd..2133df1 100644
--- a/README.md
+++ b/README.md
@@ -74,9 +74,11 @@ The V2 A6 comparator now has a data-free, whole-capture relation seam that
 forms actual `(a+b)/2` and `a-b` velocity signals *before* six-band DCT,
 retaining their cross term. This is distinct from legacy system 03's separate
 endpoint self-power control and is not an InterEdit reproduction. A6 requires
-its own typed, training-population DCT-floor receipt, which is a declaration
-until the private host authenticates the real fit and manifest. Neither this
-seam nor its server CPU tests constitute an A6 training run.
+its own typed, training-population DCT-floor receipt. Five real training-only
+floor populations and the [native source-binding qualification](docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md)
+have private server receipts; future run-bound GPU, cost, and checkpoint
+admission remains separate. Neither the source check nor its CPU tests
+constitute an A6 training run.
 [TMR-Set](docs/TMR_SET_ADAPTATION.md) now implements the
 complete original VAE objectives with disclosed full-parent group adaptation;
 its pinned frozen language loader is not a substitute for native learning.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 6382591..219ab1d 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-024b4f56ec35c56b0b8bc0500f9d96a667bfc9090ffe88ab5dfef6b794cd639e	15841	README.md
+fa146e311084988ce30bf90401da1d67adbaee0309387501eb3a37406c7c8aaf	15988	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -71,6 +71,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
+cb44019ac3459bb2e26f52e1be65d80d3ff065edd8d39aa0d84155772e0ee258	1893	docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md
 4f6373298ef558489e33a48ee7cf31090095cf19508c92b40ace682f461518bc	6458	docs/PHASESET_V2_AMENDMENT.md
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 6d50263826636377615082911b0de5afe871b8887f9419f95bbe4fb4db7f914d	3439	docs/PHASESET_V2_FROZEN_CLIP_ROWS.md
diff --git a/docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md b/docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md
new file mode 100644
index 0000000..791eb0c
--- /dev/null
+++ b/docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md
@@ -0,0 +1,30 @@
+# PhaseSet-V2 A6 source qualification (not training authority)
+
+The A6 comparator forms actual signed `(a+b)/2` and `a-b` joint-velocity
+signals before its six-band DCT. It does not use the Morlet response cache or
+Morlet energy floors. The code-level DCT-only disk path and receipt binding
+were qualified by 95 scoped CPU tests against source commit `5506bec`.
+
+A separate private, read-only qualification on the registered research server
+checked all 253 native development parent captures against their admitted
+body22 bytes and rebuilt the training-source manifest for each of the five
+registered DCT-floor populations. Their parent counts were main 202, pilot
+34, fold 0 134, and folds 1 and 2 188 each. The three sealed test components
+were absent. It checked the fitted DCT receipts against those exact population,
+configuration, calibration, and source-manifest identities, then repeated the
+body-byte audit after a native sanity computation.
+
+For that sanity computation, one deterministic four-person, 5,800-frame
+*training* capture ran the full-capture A6 relation encoder at width 512.
+Forward and backward completed with 49 finite gradient tensors. The run used
+one CPU, no CUDA, no captions, no test-gallery motion, and no optimizer step.
+The private immutable receipt bundle is identified by SHA-256
+`496bfa65844d252ac25dcb97094547cc627c30be3f48cf991ec8868eaf1f814e`.
+Neither the restricted body files, participant/source identifiers, floor
+values, nor the private receipt bundle are distributed here.
+
+The terminal status is `PASS_SOURCE_BINDING_ONLY`: it is not an A6 learning
+pilot, GPU resource profile, formal training run, retrieval score, or paper
+claim. Future attempts must bind their own run inputs and checkpoint to the
+verified receipt and satisfy the study-wide GPU, cost, and storage gates.
+Formal V2 training remains 0/87 and the sealed test has not been scored.

From deca131afea41fa026637f17f65ed5677cddc33e Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 05:14:01 -0500
Subject: [PATCH 81/95] Add PhaseSet V2 incidence-shuffle control

---
 README.md                                  |   5 ++
 RELEASE_FILES.sha256                       |   8 +-
 docs/PHASESET_V2_A4_INCIDENCE.md           |  25 ++++++
 src/phaseset_core/continuous_retrieval.py  |   6 ++
 src/phaseset_core/temporal_coordination.py |  76 ++++++++++++++++
 tests/test_phaseset_v2_a4_incidence.py     | 100 +++++++++++++++++++++
 6 files changed, 217 insertions(+), 3 deletions(-)
 create mode 100644 docs/PHASESET_V2_A4_INCIDENCE.md
 create mode 100644 tests/test_phaseset_v2_a4_incidence.py

diff --git a/README.md b/README.md
index 2133df1..cd0b6c9 100644
--- a/README.md
+++ b/README.md
@@ -79,6 +79,11 @@ floor populations and the [native source-binding qualification](docs/PHASESET_V2
 have private server receipts; future run-bound GPU, cost, and checkpoint
 admission remains separate. Neither the source check nor its CPU tests
 constitute an A6 training run.
+The [A4 incidence-shuffle control](docs/PHASESET_V2_A4_INCIDENCE.md) preserves
+physical pair packets, edge support and model capacity while deterministically
+reassigning supported half-edge values at the actor-incidence node input. Its
+shuffle is independent of private participant commitments. Software review
+and tests do not count as formal A4 training or retrieval results.
 [TMR-Set](docs/TMR_SET_ADAPTATION.md) now implements the
 complete original VAE objectives with disclosed full-parent group adaptation;
 its pinned frozen language loader is not a substitute for native learning.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 219ab1d..954d29a 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-fa146e311084988ce30bf90401da1d67adbaee0309387501eb3a37406c7c8aaf	15988	README.md
+92aaf6ec5e6297812119ec81e1bb3e77f8ce69e246e2e3bc94583c8357df9ffa	16366	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -71,6 +71,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
+09c14876d4e0055f4ea19c710523f31ec6aab505ee22ca6f21a2ada6414e03bc	1534	docs/PHASESET_V2_A4_INCIDENCE.md
 cb44019ac3459bb2e26f52e1be65d80d3ff065edd8d39aa0d84155772e0ee258	1893	docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md
 4f6373298ef558489e33a48ee7cf31090095cf19508c92b40ace682f461518bc	6458	docs/PHASESET_V2_AMENDMENT.md
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
@@ -183,7 +184,7 @@ ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phase
 cf879bcbe1b52796d751deaf3870c99ec24a70f92bc7d129e3710a81143fe023	32443	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 e8b149fe97e960ca1800501ae6be20b471fb22946bfb3eca04c44d4e3ed1bc40	6896	src/phaseset_core/continuous_phase_cache.py
-4d7844836578e697ecbd971d2cb0bc9cc74c1bb9c304d3b859f6a10dc62fd177	14371	src/phaseset_core/continuous_retrieval.py
+5d1cff169ced9aae0e4a6c8f92aab027a897bdbe155a82b51a70e0941ed5948f	14800	src/phaseset_core/continuous_retrieval.py
 24c6f90d5ef8d6ccfaf0287433b5421e99544185f5197889cddb4f7874dd4f37	7158	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
@@ -229,7 +230,7 @@ a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phase
 47a2aa0806af373e5df40c611bdad425efd672588fe912d4470ee27d8985181a	21832	src/phaseset_core/study_budget.py
 78cbb47380af76db33acb04350f2307ffae290b6db06b9edd9f1866a2d876bfd	19877	src/phaseset_core/study_process.py
 934b4933795a2971d83811dc36865ddd43d0fd1670e8c2caf9ec53755d362c9b	8297	src/phaseset_core/study_storage.py
-759ab33805009a5f7c9d28ccc6d68a6f9359af0585b81cf09de9c4d627bd4b1a	25646	src/phaseset_core/temporal_coordination.py
+3529bec3fb3902e94624f250b604d040f2c2809ec3c51a90dab2b0cc3695f907	28794	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
 cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phaseset_core/tmr_language.py
@@ -354,6 +355,7 @@ d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/tes
 11727c78e08d02dd433abf66ca75e7f3aafd77c10a98d94ea98f17560d6e86ba	13200	tests/test_phaseset_streaming_autograd.py
 ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test_phaseset_torch_periodic.py
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
+fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test_phaseset_v2_a4_incidence.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
 1df27542e87ae6d3d47ac0e6a865a6e9a5be49ea8a9bc5af61758b18e830b64a	6261	tests/test_phaseset_v2_score_tables.py
diff --git a/docs/PHASESET_V2_A4_INCIDENCE.md b/docs/PHASESET_V2_A4_INCIDENCE.md
new file mode 100644
index 0000000..a8fa12f
--- /dev/null
+++ b/docs/PHASESET_V2_A4_INCIDENCE.md
@@ -0,0 +1,25 @@
+# PhaseSet-V2 A4 incidence-shuffle control
+
+A4 is the registered `shuffle_explicit_incidence` control in the frozen V2
+matrix. It tests whether the assignment of physical pair relations to actors
+contributes beyond the multiset of pair packets. It is not a claim that every
+form of actor topology has been erased.
+
+The control uses the same trainable `ContinuousRetrievalSystem` and
+`TemporalIncidenceEncoder` as the full phase system. For each capture, local
+patch and canonical 64-edge block, it permutes only supported half-edge values
+entering `_node_block`. The endpoint slots, support mask, actor degree,
+coverage, physical pair-token stream, directed text packets and all parameters
+remain unchanged. A fixed nonnegative `incidence_shuffle_seed` determines the
+reassignment; private actor/group commitments do not enter its hash or neural
+features. This makes the ablation independent of participant identity and
+keeps the same data and capacity as the full arm. The seed is part of
+checkpoint extra state and mismatched checkpoint loads fail closed.
+
+The assignment is deterministic under canonical actor/edge order. Invalid
+half-edge positions are untouched; supported values are bijectively gathered,
+so their gradients remain connected. The control is phase-only and cannot be
+combined with the separately calibrated A6 DCT comparator. No training or
+paper result follows from implementation or software tests alone; formal
+A4 runs still require the frozen pilot, server GPU admission and their own
+immutable run receipts.
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
index a76e554..6296c97 100644
--- a/src/phaseset_core/continuous_retrieval.py
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -114,6 +114,7 @@ def __init__(
         strip_phase: bool = False,
         relation_kind: str = "phase",
         dct_floor_receipt: DctFloorReceipt | None = None,
+        incidence_shuffle_seed: int | None = None,
         checkpoint_blocks: bool = True,
         base_window_batch_size: int = 1,
         base_edge_budget: int = 32768,
@@ -131,6 +132,8 @@ def __init__(
             raise ValueError("base_edge_budget must be a positive integer")
         if relation_kind not in ("phase", "true_mean_difference_dct"):
             raise ValueError("relation_kind must be phase or true_mean_difference_dct")
+        if relation_kind != "phase" and incidence_shuffle_seed is not None:
+            raise ValueError("A4 incidence shuffle is a separate phase control, not A6")
         if relation_kind == "phase":
             if dct_floor_receipt is not None:
                 raise ValueError("DCT receipt must not enter the phase system")
@@ -153,6 +156,7 @@ def __init__(
             width=512,
             use_topology=use_topology,
             strip_phase=strip_phase,
+            incidence_shuffle_seed=incidence_shuffle_seed,
             checkpoint_blocks=checkpoint_blocks,
         )
         self.text_adapter = nn.Sequential(nn.Linear(512, 512), nn.GELU(), nn.Linear(512, 512))
@@ -167,6 +171,8 @@ def get_extra_state(self) -> Tensor:
             "relation_kind": self.relation_kind,
             "dct_floor_receipt_sha256": self._dct_receipt_sha256,
         }
+        if self.coordination.incidence_shuffle_seed is not None:
+            payload["incidence_shuffle_seed"] = self.coordination.incidence_shuffle_seed
         encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8")
         return torch.tensor(list(encoded), dtype=torch.uint8)
 
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
index 902bdc6..860f55f 100644
--- a/src/phaseset_core/temporal_coordination.py
+++ b/src/phaseset_core/temporal_coordination.py
@@ -9,6 +9,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+import hashlib
 import math
 
 import torch
@@ -37,6 +38,63 @@ def _relation_pair_chunk(field: RelationField, start: int, stop: int):
     raise TypeError("relation field must be a physical phase or A6 DCT field")
 
 
+def _shuffle_incidence_values(
+    a: Tensor,
+    b: Tensor,
+    mask: Tensor,
+    *,
+    seed: int,
+    block_start: int,
+) -> tuple[Tensor, Tensor]:
+    """A4: shuffle supported half-edge values, never their endpoint slots.
+
+    The operation is per capture, patch and canonical edge microblock. It
+    preserves every patch's half-edge multiset, edge support, degree and
+    coverage; only the values entering the actor-incidence node statistics are
+    reassigned. Pair tokens and their temporal/text path remain untouched.
+    """
+    if (
+        a.shape != b.shape
+        or a.ndim != 3
+        or mask.shape != a.shape[:2]
+        or mask.dtype != torch.bool
+        or type(seed) is not int
+        or not 0 <= seed < 2**63
+        or type(block_start) is not int
+        or block_start < 0
+    ):
+        raise ValueError("A4 requires valid support and a fixed seed")
+    edges, patches, width = a.shape
+    values = torch.stack((a, b), dim=1).permute(2, 0, 1, 3).reshape(patches, 2 * edges, width)
+    valid = mask.detach().cpu().T.repeat_interleave(2, dim=1).tolist()
+    rows: list[list[int]] = []
+    for patch, observed in enumerate(valid):
+        mapping = list(range(2 * edges))
+        slots = [index for index, present in enumerate(observed) if present]
+        length = len(slots)
+        if length > 1:
+            digest = hashlib.sha256(
+                b"phaseset-v2-A4-incidence-v1\0"
+                + seed.to_bytes(8, "big")
+                + block_start.to_bytes(8, "big")
+                + patch.to_bytes(8, "big")
+                + length.to_bytes(8, "big")
+            ).digest()
+            offset = int.from_bytes(digest[:8], "big") % length
+            step = int.from_bytes(digest[8:16], "big") % length or 1
+            while math.gcd(step, length) != 1:
+                step = (step + 1) % length or 1
+            if step == 1 and offset == 0:
+                offset = 1
+            for position, slot in enumerate(slots):
+                mapping[slot] = slots[(position * step + offset) % length]
+        rows.append(mapping)
+    indices = torch.tensor(rows, dtype=torch.int64, device=a.device)
+    shuffled = values.gather(1, indices[..., None].expand(-1, -1, width))
+    paired = shuffled.reshape(patches, edges, 2, width).permute(1, 2, 0, 3)
+    return paired[:, 0].contiguous(), paired[:, 1].contiguous()
+
+
 class MaskedTemporalEncoder(nn.Module):
     """Two shared temporal layers; missing positions do not update either state.
 
@@ -107,14 +165,22 @@ def __init__(
         *,
         use_topology: bool = True,
         strip_phase: bool = False,
+        incidence_shuffle_seed: int | None = None,
         checkpoint_blocks: bool = True,
     ) -> None:
         super().__init__()
         if type(width) is not int or width < 2:
             raise ValueError("width must be an integer >=2")
+        if incidence_shuffle_seed is not None and (
+            type(incidence_shuffle_seed) is not int
+            or not 0 <= incidence_shuffle_seed < 2**63
+            or not use_topology
+        ):
+            raise ValueError("A4 needs a fixed seed and active incidence topology")
         self.width = width
         self.use_topology = use_topology
         self.strip_phase = strip_phase
+        self.incidence_shuffle_seed = incidence_shuffle_seed
         self.checkpoint_blocks = checkpoint_blocks
         self.actor_projection = nn.Sequential(nn.Linear(ACTOR_FEATURE_DIM, width), nn.GELU())
         self.actor_temporal = MaskedTemporalEncoder(width)
@@ -192,6 +258,16 @@ def _node_block(
         self, field: RelationField, hidden: Tensor, start: int, stop: int
     ) -> tuple[Tensor, Tensor, Tensor]:
         a, b, endpoints, mask = self._half_edges(field, hidden, start, stop)
+        if self.incidence_shuffle_seed is not None:
+            if type(field) is not DirectionalPhaseField:
+                raise TypeError("A4 requires a physical phase field")
+            a, b = _shuffle_incidence_values(
+                a,
+                b,
+                mask,
+                seed=self.incidence_shuffle_seed,
+                block_start=start,
+            )
         # K x 64 incidence block, not an all-pair K x K adjacency or score map.
         left = F.one_hot(endpoints[:, 0], field.actor_count).T.to(torch.float64)
         right = F.one_hot(endpoints[:, 1], field.actor_count).T.to(torch.float64)
diff --git a/tests/test_phaseset_v2_a4_incidence.py b/tests/test_phaseset_v2_a4_incidence.py
new file mode 100644
index 0000000..754230e
--- /dev/null
+++ b/tests/test_phaseset_v2_a4_incidence.py
@@ -0,0 +1,100 @@
+"""A4 shuffles only explicit actor-incidence routing, not physical packets."""
+
+from __future__ import annotations
+
+import copy
+from dataclasses import replace
+
+import pytest
+import torch
+
+from phaseset_core.continuous_retrieval import ContinuousRetrievalSystem
+from phaseset_core.temporal_coordination import (
+    TemporalIncidenceEncoder,
+    _shuffle_incidence_values,
+)
+from phaseset_core.training import BaseRetrievalSystem
+from test_continuous_parent_training import _MockB2
+from test_phaseset_v2_coordination import field, motion
+
+
+def test_a4_reassigns_only_supported_half_edges_and_keeps_gradients() -> None:
+    a = torch.tensor([[[1.0], [7.0]], [[2.0], [8.0]], [[3.0], [9.0]]], requires_grad=True)
+    b = torch.tensor([[[4.0], [10.0]], [[5.0], [11.0]], [[6.0], [12.0]]], requires_grad=True)
+    mask = torch.tensor([[True, False], [True, True], [True, True]])
+    kwargs = {
+        "seed": 1729,
+        "block_start": 0,
+    }
+    left, right = _shuffle_incidence_values(a, b, mask, **kwargs)
+    repeated = _shuffle_incidence_values(a, b, mask, **kwargs)
+    assert torch.equal(left, repeated[0]) and torch.equal(right, repeated[1])
+    assert left[0, 1].item() == 7.0 and right[0, 1].item() == 10.0
+    for patch in range(2):
+        original = sorted(
+            value
+            for edge in range(3)
+            if bool(mask[edge, patch])
+            for value in (a[edge, patch, 0].item(), b[edge, patch, 0].item())
+        )
+        shuffled = sorted(
+            value
+            for edge in range(3)
+            if bool(mask[edge, patch])
+            for value in (left[edge, patch, 0].item(), right[edge, patch, 0].item())
+        )
+        assert shuffled == original
+    assert not torch.equal(left, a) or not torch.equal(right, b)
+    (left.sum() + right.sum()).backward()
+    assert torch.equal(a.grad, torch.ones_like(a))
+    assert torch.equal(b.grad, torch.ones_like(b))
+
+
+def test_a4_changes_incidence_nodes_but_not_packets_or_actor_order() -> None:
+    torch.manual_seed(1729)
+    full = TemporalIncidenceEncoder(16, checkpoint_blocks=False).eval()
+    shuffled = copy.deepcopy(full)
+    shuffled.incidence_shuffle_seed = 1729
+    physical = field(motion((0.0, 0.7, 1.8)))
+    original_hidden, original_nodes, original_mask = full._encode_context(physical)
+    shuffled_hidden, shuffled_nodes, shuffled_mask = shuffled._encode_context(physical)
+    assert torch.equal(original_hidden, shuffled_hidden)
+    assert torch.equal(original_mask, shuffled_mask)
+    assert bool(original_mask.any())
+    assert not torch.equal(original_nodes, shuffled_nodes)
+    before = full._half_edges(physical, original_hidden, 0, physical.pair_count)
+    after = shuffled._half_edges(physical, shuffled_hidden, 0, physical.pair_count)
+    assert all(torch.equal(x, y) for x, y in zip(before, after, strict=True))
+    first = shuffled(physical).embedding
+    renamed = replace(
+        physical,
+        actor_commitments=tuple(bytes([index]) * 32 for index in (4, 5, 6)),
+    )
+    assert torch.equal(first, shuffled(renamed).embedding)
+    reordered = field(motion((0.0, 0.7, 1.8), order=(2, 0, 1), padding=2))
+    permuted = shuffled(reordered).embedding
+    assert torch.equal(first, permuted)
+
+
+def test_a4_identity_is_checkpoint_bound_and_separate_from_a6() -> None:
+    phase = ContinuousRetrievalSystem(
+        BaseRetrievalSystem(_MockB2(), embedding_dim=512),
+        incidence_shuffle_seed=1729,
+    )
+    same = ContinuousRetrievalSystem(
+        BaseRetrievalSystem(_MockB2(), embedding_dim=512),
+        incidence_shuffle_seed=1729,
+    )
+    different = ContinuousRetrievalSystem(
+        BaseRetrievalSystem(_MockB2(), embedding_dim=512),
+        incidence_shuffle_seed=2718,
+    )
+    same.set_extra_state(phase.get_extra_state())
+    with pytest.raises(ValueError, match="relation kind"):
+        different.set_extra_state(phase.get_extra_state())
+    with pytest.raises(ValueError, match="separate phase control"):
+        ContinuousRetrievalSystem(
+            BaseRetrievalSystem(_MockB2(), embedding_dim=512),
+            relation_kind="true_mean_difference_dct",
+            incidence_shuffle_seed=1729,
+        )

From 0bf294db8282de1def3f4b983ce77a6c8ae83edc Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 05:41:13 -0500
Subject: [PATCH 82/95] Add PhaseSet V2 order-free temporal control

---
 README.md                                  |   4 +
 RELEASE_FILES.sha256                       |   8 +-
 docs/PHASESET_V2_A2_ORDER_FREE.md          |  28 +++++
 src/phaseset_core/continuous_retrieval.py  |  10 ++
 src/phaseset_core/temporal_coordination.py |  53 ++++++++-
 tests/test_phaseset_v2_a2_order_free.py    | 119 +++++++++++++++++++++
 6 files changed, 215 insertions(+), 7 deletions(-)
 create mode 100644 docs/PHASESET_V2_A2_ORDER_FREE.md
 create mode 100644 tests/test_phaseset_v2_a2_order_free.py

diff --git a/README.md b/README.md
index cd0b6c9..42a7f49 100644
--- a/README.md
+++ b/README.md
@@ -84,6 +84,10 @@ physical pair packets, edge support and model capacity while deterministically
 reassigning supported half-edge values at the actor-incidence node input. Its
 shuffle is independent of private participant commitments. Software review
 and tests do not count as formal A4 training or retrieval results.
+The [A2 order-free control](docs/PHASESET_V2_A2_ORDER_FREE.md) replaces all
+four learned temporal GRU paths with near-equal-capacity independent patch
+MLPs and masked means while retaining the same physical phase input. It tests
+the contribution of learned order, not the existence of physical phase.
 [TMR-Set](docs/TMR_SET_ADAPTATION.md) now implements the
 complete original VAE objectives with disclosed full-parent group adaptation;
 its pinned frozen language loader is not a substitute for native learning.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 954d29a..377ed80 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-92aaf6ec5e6297812119ec81e1bb3e77f8ce69e246e2e3bc94583c8357df9ffa	16366	README.md
+2e2bfa2efad615644961e25e177d46b5353211e363be450327ab1ee86fbbe2bb	16667	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -71,6 +71,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
+63437fbfa6d2415a81816bd72341e9755326a2e83403adccf968cfd2f4027c1a	1636	docs/PHASESET_V2_A2_ORDER_FREE.md
 09c14876d4e0055f4ea19c710523f31ec6aab505ee22ca6f21a2ada6414e03bc	1534	docs/PHASESET_V2_A4_INCIDENCE.md
 cb44019ac3459bb2e26f52e1be65d80d3ff065edd8d39aa0d84155772e0ee258	1893	docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md
 4f6373298ef558489e33a48ee7cf31090095cf19508c92b40ace682f461518bc	6458	docs/PHASESET_V2_AMENDMENT.md
@@ -184,7 +185,7 @@ ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phase
 cf879bcbe1b52796d751deaf3870c99ec24a70f92bc7d129e3710a81143fe023	32443	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 e8b149fe97e960ca1800501ae6be20b471fb22946bfb3eca04c44d4e3ed1bc40	6896	src/phaseset_core/continuous_phase_cache.py
-5d1cff169ced9aae0e4a6c8f92aab027a897bdbe155a82b51a70e0941ed5948f	14800	src/phaseset_core/continuous_retrieval.py
+7317ba9d625e398151630dc0e35c5668e6e67a34d022312f19691224e486d228	15269	src/phaseset_core/continuous_retrieval.py
 24c6f90d5ef8d6ccfaf0287433b5421e99544185f5197889cddb4f7874dd4f37	7158	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
@@ -230,7 +231,7 @@ a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phase
 47a2aa0806af373e5df40c611bdad425efd672588fe912d4470ee27d8985181a	21832	src/phaseset_core/study_budget.py
 78cbb47380af76db33acb04350f2307ffae290b6db06b9edd9f1866a2d876bfd	19877	src/phaseset_core/study_process.py
 934b4933795a2971d83811dc36865ddd43d0fd1670e8c2caf9ec53755d362c9b	8297	src/phaseset_core/study_storage.py
-3529bec3fb3902e94624f250b604d040f2c2809ec3c51a90dab2b0cc3695f907	28794	src/phaseset_core/temporal_coordination.py
+1f19ef76c68d8840c656389febd60a0765942371b5c81ccf81ba7203372c6418	30714	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
 cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phaseset_core/tmr_language.py
@@ -355,6 +356,7 @@ d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/tes
 11727c78e08d02dd433abf66ca75e7f3aafd77c10a98d94ea98f17560d6e86ba	13200	tests/test_phaseset_streaming_autograd.py
 ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test_phaseset_torch_periodic.py
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
+fdebacdcd59261eae511865425c5f3dfc33b00e29d7fc3941a17ebbb4eae7173	5036	tests/test_phaseset_v2_a2_order_free.py
 fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test_phaseset_v2_a4_incidence.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
diff --git a/docs/PHASESET_V2_A2_ORDER_FREE.md b/docs/PHASESET_V2_A2_ORDER_FREE.md
new file mode 100644
index 0000000..9cbd82c
--- /dev/null
+++ b/docs/PHASESET_V2_A2_ORDER_FREE.md
@@ -0,0 +1,28 @@
+# PhaseSet-V2 A2 order-free control
+
+A2 is the registered `remove_local_and_capture_order` control. It receives
+the same physical six-band phase field, frozen B2 anchor, text embeddings,
+losses, complete captures and actor–edge incidence as the full system. It
+removes the *learned temporal order* in the actor, incident-node, directed-edge
+and group-readout paths. It does not strip phase, subsample frames, shuffle
+actors, or change the physical Morlet input; A1 and A5 test other questions.
+
+Each ordered two-layer GRU stack is replaced by a shared pointwise
+`width → 6 × width → width` MLP with GELU and a masked mean. The MLP is
+independent at every patch and has no state, timestamp, position embedding or
+convolution. The final mean is over all supported capture patches. Its
+trainable parameter count is `12w² + 7w`, versus `12w² + 12w` for the two
+GRU cells; at the fixed `w=512`, this is approximately 0.081% smaller per
+replacement and introduces no inactive capacity padding.
+
+All invalid patch outputs are hard-gated to exact zero after the MLP. A2
+cannot be combined with the separate A3/A4/A5/A6 controls, and its model
+checkpoint records the order-free identity. The same tensor-only checkpoint
+identity also distinguishes the pre-existing A3 topology-off and A5
+phase-stripped switches from the full model. The full model's previous state
+payload remains unchanged.
+
+This is a mechanism control, not evidence that time order helps retrieval.
+That conclusion requires the frozen three-seed comparison on the official
+human-only task and the registered statistics; software tests alone are not
+the paper result.
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
index 6296c97..72519f2 100644
--- a/src/phaseset_core/continuous_retrieval.py
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -115,6 +115,7 @@ def __init__(
         relation_kind: str = "phase",
         dct_floor_receipt: DctFloorReceipt | None = None,
         incidence_shuffle_seed: int | None = None,
+        order_free: bool = False,
         checkpoint_blocks: bool = True,
         base_window_batch_size: int = 1,
         base_edge_budget: int = 32768,
@@ -134,6 +135,8 @@ def __init__(
             raise ValueError("relation_kind must be phase or true_mean_difference_dct")
         if relation_kind != "phase" and incidence_shuffle_seed is not None:
             raise ValueError("A4 incidence shuffle is a separate phase control, not A6")
+        if relation_kind != "phase" and order_free:
+            raise ValueError("A2 order-free matching is a separate phase control, not A6")
         if relation_kind == "phase":
             if dct_floor_receipt is not None:
                 raise ValueError("DCT receipt must not enter the phase system")
@@ -157,6 +160,7 @@ def __init__(
             use_topology=use_topology,
             strip_phase=strip_phase,
             incidence_shuffle_seed=incidence_shuffle_seed,
+            order_free=order_free,
             checkpoint_blocks=checkpoint_blocks,
         )
         self.text_adapter = nn.Sequential(nn.Linear(512, 512), nn.GELU(), nn.Linear(512, 512))
@@ -173,6 +177,12 @@ def get_extra_state(self) -> Tensor:
         }
         if self.coordination.incidence_shuffle_seed is not None:
             payload["incidence_shuffle_seed"] = self.coordination.incidence_shuffle_seed
+        if self.coordination.order_free:
+            payload["order_free"] = True
+        if not self.coordination.use_topology:
+            payload["use_topology"] = False
+        if self.coordination.strip_phase:
+            payload["strip_phase"] = True
         encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8")
         return torch.tensor(list(encoded), dtype=torch.uint8)
 
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
index 860f55f..24bb40d 100644
--- a/src/phaseset_core/temporal_coordination.py
+++ b/src/phaseset_core/temporal_coordination.py
@@ -139,6 +139,44 @@ def encode_chunk(
         return torch.stack(outputs, dim=1), torch.stack(layer_states)
 
 
+class OrderFreeTemporalEncoder(nn.Module):
+    """A2: equal-capacity independent patch MLP followed by masked mean.
+
+    No state, position embedding, convolution or ordered reduction is used.
+    The 6x expansion matches two width-preserving GRUCells within 0.1% in
+    trainable parameters, without inert capacity-padding tensors.
+    """
+
+    def __init__(self, width: int) -> None:
+        super().__init__()
+        if type(width) is not int or width < 2:
+            raise ValueError("width must be an integer >=2")
+        self.width = width
+        self.pointwise = nn.Sequential(
+            nn.Linear(width, 6 * width),
+            nn.GELU(),
+            nn.Linear(6 * width, width),
+        )
+
+    def forward(self, sequence: Tensor, mask: Tensor) -> tuple[Tensor, Tensor]:
+        if (
+            sequence.ndim != 3
+            or sequence.shape[2] != self.width
+            or mask.shape != sequence.shape[:2]
+            or mask.dtype != torch.bool
+            or sequence.shape[1] == 0
+        ):
+            raise ValueError("A2 requires nonempty [N,P,width] and bool[N,P]")
+        outputs = self.pointwise(sequence)
+        outputs = torch.where(mask[..., None], outputs, torch.zeros_like(outputs))
+        count = mask.sum(dim=1).clamp_min(1)
+        embedding = (
+            outputs.to(torch.float64).sum(dim=1) / count[:, None].to(torch.float64)
+        ).to(outputs.dtype)
+        embedding = torch.where(mask.any(dim=1)[:, None], embedding, torch.zeros_like(embedding))
+        return outputs, embedding
+
+
 @dataclass(frozen=True)
 class TemporalCoordinationOutput:
     patch_tokens: Tensor
@@ -166,6 +204,7 @@ def __init__(
         use_topology: bool = True,
         strip_phase: bool = False,
         incidence_shuffle_seed: int | None = None,
+        order_free: bool = False,
         checkpoint_blocks: bool = True,
     ) -> None:
         super().__init__()
@@ -177,13 +216,19 @@ def __init__(
             or not use_topology
         ):
             raise ValueError("A4 needs a fixed seed and active incidence topology")
+        if type(order_free) is not bool or (
+            order_free and (not use_topology or strip_phase or incidence_shuffle_seed is not None)
+        ):
+            raise ValueError("A2 must remove order from the otherwise full phase system")
         self.width = width
         self.use_topology = use_topology
         self.strip_phase = strip_phase
         self.incidence_shuffle_seed = incidence_shuffle_seed
+        self.order_free = order_free
         self.checkpoint_blocks = checkpoint_blocks
+        temporal_encoder = OrderFreeTemporalEncoder if order_free else MaskedTemporalEncoder
         self.actor_projection = nn.Sequential(nn.Linear(ACTOR_FEATURE_DIM, width), nn.GELU())
-        self.actor_temporal = MaskedTemporalEncoder(width)
+        self.actor_temporal = temporal_encoder(width)
         self.half_edge = nn.Sequential(
             nn.Linear(2 * width + 6 * PHASE_FEATURE_DIM + 3, width),
             nn.GELU(),
@@ -193,12 +238,12 @@ def __init__(
         self.node_mlp = nn.Sequential(
             nn.Linear(3 * width + 2, width), nn.GELU(), nn.Linear(width, width)
         )
-        self.node_temporal = MaskedTemporalEncoder(width)
+        self.node_temporal = temporal_encoder(width)
         self.edge_delta = nn.Sequential(
             nn.Linear(3 * width, width), nn.GELU(), nn.Linear(width, width)
         )
-        self.edge_temporal = MaskedTemporalEncoder(width)
-        self.group_temporal = MaskedTemporalEncoder(width)
+        self.edge_temporal = temporal_encoder(width)
+        self.group_temporal = temporal_encoder(width)
         self.topology_scale = nn.Parameter(torch.tensor(0.1))
 
     def _half_edges(
diff --git a/tests/test_phaseset_v2_a2_order_free.py b/tests/test_phaseset_v2_a2_order_free.py
new file mode 100644
index 0000000..dd760a7
--- /dev/null
+++ b/tests/test_phaseset_v2_a2_order_free.py
@@ -0,0 +1,119 @@
+"""A2 removes neural local/capture order without removing the physical input."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.continuous_retrieval import ContinuousRetrievalSystem
+from phaseset_core.temporal_coordination import (
+    MaskedTemporalEncoder,
+    OrderFreeTemporalEncoder,
+    TemporalIncidenceEncoder,
+)
+from phaseset_core.training import BaseRetrievalSystem
+from test_continuous_parent_training import _MockB2
+from test_phaseset_v2_coordination import field, motion
+
+
+def _count(module: torch.nn.Module) -> int:
+    return sum(parameter.numel() for parameter in module.parameters() if parameter.requires_grad)
+
+
+def test_a2_independent_temporal_mlp_matches_capacity_and_ignores_patch_order() -> None:
+    ratio = _count(OrderFreeTemporalEncoder(512)) / _count(MaskedTemporalEncoder(512))
+    assert abs(ratio - 1) < 0.01
+    torch.manual_seed(1729)
+    model = OrderFreeTemporalEncoder(8)
+    sequence = torch.randn(2, 5, 8, requires_grad=True)
+    mask = torch.tensor([[True, False, True, True, False], [True, True, False, True, True]])
+    permutation = torch.tensor([4, 2, 0, 3, 1])
+    original, embedding = model(sequence, mask)
+    reordered, permuted_embedding = model(sequence[:, permutation], mask[:, permutation])
+    torch.testing.assert_close(reordered, original[:, permutation], rtol=0, atol=0)
+    torch.testing.assert_close(permuted_embedding, embedding, rtol=0, atol=2e-7)
+    assert not bool(original[~mask].view(torch.int32).any())
+    embedding.sum().backward()
+    assert bool(torch.isfinite(sequence.grad).all())
+    assert not bool(sequence.grad[~mask].view(torch.int32).any())
+    assert bool(sequence.grad[mask].abs().sum() > 0)
+
+
+def test_a2_complete_phase_field_is_order_free_but_full_temporal_is_not() -> None:
+    torch.manual_seed(2718)
+    physical = field(motion((0.0, 0.7, 1.8)))
+    order = np.arange(physical.patch_count - 1, -1, -1)
+    reordered = replace(
+        physical,
+        intervals=tuple(physical.intervals[index] for index in order),
+        actor_features=physical.actor_features[:, order].copy(),
+        actor_patch_mask=physical.actor_patch_mask[:, order].copy(),
+        root_positions=physical.root_positions[:, order].copy(),
+        root_patch_mask=physical.root_patch_mask[:, order].copy(),
+    )
+    a2 = TemporalIncidenceEncoder(8, order_free=True, checkpoint_blocks=False).eval()
+    full = TemporalIncidenceEncoder(8, checkpoint_blocks=False).eval()
+    assert abs(_count(a2) / _count(full) - 1) < 0.05
+    text = torch.randn(3, 8)
+    first = a2.score_text(physical, text)
+    second = a2.score_text(reordered, text)
+    torch.testing.assert_close(
+        first.coordination.embedding, second.coordination.embedding, rtol=0, atol=2e-7
+    )
+    torch.testing.assert_close(first.cosine, second.cosine, rtol=0, atol=2e-7)
+    torch.testing.assert_close(
+        first.coordination.patch_tokens[order],
+        second.coordination.patch_tokens,
+        rtol=0,
+        atol=2e-7,
+    )
+    assert not torch.equal(full(physical).embedding, full(reordered).embedding)
+    actor_reordered = field(motion((0.0, 0.7, 1.8), order=(2, 0, 1), padding=2))
+    torch.testing.assert_close(
+        first.coordination.embedding,
+        a2(actor_reordered).embedding,
+        rtol=0,
+        atol=2e-7,
+    )
+    torch.testing.assert_close(
+        first.cosine,
+        a2.score_text(actor_reordered, text).cosine,
+        rtol=0,
+        atol=2e-7,
+    )
+    first.cosine.sum().backward()
+    for module in (a2.actor_temporal, a2.node_temporal, a2.edge_temporal, a2.group_temporal):
+        assert any(
+            parameter.grad is not None and bool(parameter.grad.abs().sum() > 0)
+            for parameter in module.parameters()
+        )
+
+
+def test_a2_a3_a5_checkpoint_control_identity_and_a6_exclusion() -> None:
+    def system(**kwargs):
+        return ContinuousRetrievalSystem(
+            BaseRetrievalSystem(_MockB2(), embedding_dim=512), **kwargs
+        )
+
+    full = system()
+    a2 = system(order_free=True)
+    a3 = system(use_topology=False)
+    a5 = system(strip_phase=True)
+    for variant in (a2, a3, a5):
+        with pytest.raises(ValueError, match="relation kind"):
+            variant.set_extra_state(full.get_extra_state())
+        with pytest.raises(ValueError, match="relation kind"):
+            full.set_extra_state(variant.get_extra_state())
+    with pytest.raises(ValueError, match="separate phase control"):
+        system(relation_kind="true_mean_difference_dct", order_free=True)
+    with pytest.raises(ValueError, match="otherwise full phase"):
+        system(order_free=True, use_topology=False)
+    with pytest.raises(ValueError, match="otherwise full phase"):
+        system(order_free=True, strip_phase=True)
+    with pytest.raises(ValueError, match="otherwise full phase"):
+        system(order_free=True, incidence_shuffle_seed=1729)
+    with pytest.raises(ValueError, match="relation kind"):
+        a2.load_state_dict(full.state_dict())

From ec3ac562429d3350fc0bbd05b03aae959a362acd Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 06:15:53 -0500
Subject: [PATCH 83/95] Add capacity-matched PhaseSet V2 pair-bag control

---
 README.md                                  |   4 +
 RELEASE_FILES.sha256                       |  12 +-
 docs/PHASESET_V2_A2_ORDER_FREE.md          |   2 +-
 docs/PHASESET_V2_A3_PAIR_BAG.md            |  31 +++++
 src/phaseset_core/continuous_retrieval.py  |   6 +
 src/phaseset_core/temporal_coordination.py |  58 ++++++++
 tests/test_phaseset_v2_a2_order_free.py    |   5 +-
 tests/test_phaseset_v2_a3_pair_bag.py      | 154 +++++++++++++++++++++
 8 files changed, 264 insertions(+), 8 deletions(-)
 create mode 100644 docs/PHASESET_V2_A3_PAIR_BAG.md
 create mode 100644 tests/test_phaseset_v2_a3_pair_bag.py

diff --git a/README.md b/README.md
index 42a7f49..e028a25 100644
--- a/README.md
+++ b/README.md
@@ -88,6 +88,10 @@ The [A2 order-free control](docs/PHASESET_V2_A2_ORDER_FREE.md) replaces all
 four learned temporal GRU paths with near-equal-capacity independent patch
 MLPs and masked means while retaining the same physical phase input. It tests
 the contribution of learned order, not the existence of physical phase.
+The [A3 capacity-matched pair-bag control](docs/PHASESET_V2_A3_PAIR_BAG.md)
+keeps the full head's active node and edge modules but pools relation packets
+without assigning them to actor nodes. It is distinct from the legacy
+topology-off pair-only diagnostic and has no reported retrieval result yet.
 [TMR-Set](docs/TMR_SET_ADAPTATION.md) now implements the
 complete original VAE objectives with disclosed full-parent group adaptation;
 its pinned frozen language loader is not a substitute for native learning.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 377ed80..74b6580 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-2e2bfa2efad615644961e25e177d46b5353211e363be450327ab1ee86fbbe2bb	16667	README.md
+8e8bf53ee324061cf73aa6cb43884a56bd0ac3d5724fa8af0bf4045c927f8068	16967	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -71,7 +71,8 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
-63437fbfa6d2415a81816bd72341e9755326a2e83403adccf968cfd2f4027c1a	1636	docs/PHASESET_V2_A2_ORDER_FREE.md
+f98b8d0c2a4e9f75e65d46556951472cd4838f32f2561cc00934c2fc9f810bb8	1647	docs/PHASESET_V2_A2_ORDER_FREE.md
+794bcdc4fe9d2c7cf6014157a7958851c3e86256006b74844fb97013117a4bbf	1920	docs/PHASESET_V2_A3_PAIR_BAG.md
 09c14876d4e0055f4ea19c710523f31ec6aab505ee22ca6f21a2ada6414e03bc	1534	docs/PHASESET_V2_A4_INCIDENCE.md
 cb44019ac3459bb2e26f52e1be65d80d3ff065edd8d39aa0d84155772e0ee258	1893	docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md
 4f6373298ef558489e33a48ee7cf31090095cf19508c92b40ace682f461518bc	6458	docs/PHASESET_V2_AMENDMENT.md
@@ -185,7 +186,7 @@ ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phase
 cf879bcbe1b52796d751deaf3870c99ec24a70f92bc7d129e3710a81143fe023	32443	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 e8b149fe97e960ca1800501ae6be20b471fb22946bfb3eca04c44d4e3ed1bc40	6896	src/phaseset_core/continuous_phase_cache.py
-7317ba9d625e398151630dc0e35c5668e6e67a34d022312f19691224e486d228	15269	src/phaseset_core/continuous_retrieval.py
+cf10237c331dabfabdcd8242de09ddab68da31438c4d3b681827f6f605726d2a	15549	src/phaseset_core/continuous_retrieval.py
 24c6f90d5ef8d6ccfaf0287433b5421e99544185f5197889cddb4f7874dd4f37	7158	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
@@ -231,7 +232,7 @@ a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phase
 47a2aa0806af373e5df40c611bdad425efd672588fe912d4470ee27d8985181a	21832	src/phaseset_core/study_budget.py
 78cbb47380af76db33acb04350f2307ffae290b6db06b9edd9f1866a2d876bfd	19877	src/phaseset_core/study_process.py
 934b4933795a2971d83811dc36865ddd43d0fd1670e8c2caf9ec53755d362c9b	8297	src/phaseset_core/study_storage.py
-1f19ef76c68d8840c656389febd60a0765942371b5c81ccf81ba7203372c6418	30714	src/phaseset_core/temporal_coordination.py
+a526798da508ac79dabca562d23af51e2095bb48ec73319781784a4b1b7be23e	33620	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
 cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phaseset_core/tmr_language.py
@@ -356,7 +357,8 @@ d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/tes
 11727c78e08d02dd433abf66ca75e7f3aafd77c10a98d94ea98f17560d6e86ba	13200	tests/test_phaseset_streaming_autograd.py
 ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test_phaseset_torch_periodic.py
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
-fdebacdcd59261eae511865425c5f3dfc33b00e29d7fc3941a17ebbb4eae7173	5036	tests/test_phaseset_v2_a2_order_free.py
+4ee992cf41d66cf7e2cae98f4ec00e69b080bf05a9d9f005e29099b92ee6ab4d	5099	tests/test_phaseset_v2_a2_order_free.py
+4679d7fb1fc5e19f88ade47039c413adb51dbe5473a4206947338b2b7a70ab60	7217	tests/test_phaseset_v2_a3_pair_bag.py
 fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test_phaseset_v2_a4_incidence.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
diff --git a/docs/PHASESET_V2_A2_ORDER_FREE.md b/docs/PHASESET_V2_A2_ORDER_FREE.md
index 9cbd82c..4bebb46 100644
--- a/docs/PHASESET_V2_A2_ORDER_FREE.md
+++ b/docs/PHASESET_V2_A2_ORDER_FREE.md
@@ -18,7 +18,7 @@ replacement and introduces no inactive capacity padding.
 All invalid patch outputs are hard-gated to exact zero after the MLP. A2
 cannot be combined with the separate A3/A4/A5/A6 controls, and its model
 checkpoint records the order-free identity. The same tensor-only checkpoint
-identity also distinguishes the pre-existing A3 topology-off and A5
+identity also distinguishes A3 pair-bag, the legacy topology-off switch and A5
 phase-stripped switches from the full model. The full model's previous state
 payload remains unchanged.
 
diff --git a/docs/PHASESET_V2_A3_PAIR_BAG.md b/docs/PHASESET_V2_A3_PAIR_BAG.md
new file mode 100644
index 0000000..57d9bb0
--- /dev/null
+++ b/docs/PHASESET_V2_A3_PAIR_BAG.md
@@ -0,0 +1,31 @@
+# PhaseSet-V2 A3 capacity-matched pair-bag control
+
+A3 is the registered `pair_bag` mechanism control. It uses the same physical
+phase field, frozen B2 anchor, text adapter, training captions, objective,
+complete-capture timeline and parameterized head as V2 full. It removes the
+explicit assignment of relation half-edges to actor nodes. It does **not**
+discard relations, phase, temporal order or the shared incident-node and
+edge-update modules.
+
+The pair-bag streams all valid unordered pairs in 64-edge blocks. Each block
+contributes the sum, square sum and count of both directed half-edge packets
+to global per-patch moments. A symmetric masked mean of actor histories and
+those global packet moments form one context vector per patch, broadcast to
+identical node streams. The existing node MLP, two-layer node GRU,
+node-conditioned edge update, two-layer edge GRU and ordered group GRU remain
+active. The same pair packets then enter the normal unordered readout and
+directed text score; only the explicit actor–edge incidence map is absent.
+No dense `K × K` adjacency is built.
+
+Consequently, rerouting an unchanged multiset of pair packets between actor
+endpoint slots leaves A3 unchanged while it can change V2 full. A3 and full
+have exactly the same trainable parameter count. `use_topology=False` remains
+the distinct legacy pair-only/K=2 diagnostic; it is **not** the registered A3
+because it leaves node and edge-update capacity inactive. A3 is mutually
+exclusive with A2/A4/A5/A6. Its identity is bound in tensor-only checkpoints.
+
+With exactly two valid actors, the context gate remains off, the topology
+values and gradients are positive zero, and A3 is bitwise equal to V2 full on
+the same weights. This implementation and its tests are software qualification,
+not a claim of retrieval superiority. The registered three-seed, human-only
+comparison and parent-cluster statistics are still required.
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
index 72519f2..4da103c 100644
--- a/src/phaseset_core/continuous_retrieval.py
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -116,6 +116,7 @@ def __init__(
         dct_floor_receipt: DctFloorReceipt | None = None,
         incidence_shuffle_seed: int | None = None,
         order_free: bool = False,
+        pair_bag: bool = False,
         checkpoint_blocks: bool = True,
         base_window_batch_size: int = 1,
         base_edge_budget: int = 32768,
@@ -137,6 +138,8 @@ def __init__(
             raise ValueError("A4 incidence shuffle is a separate phase control, not A6")
         if relation_kind != "phase" and order_free:
             raise ValueError("A2 order-free matching is a separate phase control, not A6")
+        if relation_kind != "phase" and pair_bag:
+            raise ValueError("A3 pair-bag matching is a separate phase control, not A6")
         if relation_kind == "phase":
             if dct_floor_receipt is not None:
                 raise ValueError("DCT receipt must not enter the phase system")
@@ -161,6 +164,7 @@ def __init__(
             strip_phase=strip_phase,
             incidence_shuffle_seed=incidence_shuffle_seed,
             order_free=order_free,
+            pair_bag=pair_bag,
             checkpoint_blocks=checkpoint_blocks,
         )
         self.text_adapter = nn.Sequential(nn.Linear(512, 512), nn.GELU(), nn.Linear(512, 512))
@@ -179,6 +183,8 @@ def get_extra_state(self) -> Tensor:
             payload["incidence_shuffle_seed"] = self.coordination.incidence_shuffle_seed
         if self.coordination.order_free:
             payload["order_free"] = True
+        if self.coordination.pair_bag:
+            payload["pair_bag"] = True
         if not self.coordination.use_topology:
             payload["use_topology"] = False
         if self.coordination.strip_phase:
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
index 24bb40d..65e5950 100644
--- a/src/phaseset_core/temporal_coordination.py
+++ b/src/phaseset_core/temporal_coordination.py
@@ -205,6 +205,7 @@ def __init__(
         strip_phase: bool = False,
         incidence_shuffle_seed: int | None = None,
         order_free: bool = False,
+        pair_bag: bool = False,
         checkpoint_blocks: bool = True,
     ) -> None:
         super().__init__()
@@ -220,11 +221,17 @@ def __init__(
             order_free and (not use_topology or strip_phase or incidence_shuffle_seed is not None)
         ):
             raise ValueError("A2 must remove order from the otherwise full phase system")
+        if type(pair_bag) is not bool or (
+            pair_bag
+            and (not use_topology or strip_phase or incidence_shuffle_seed is not None or order_free)
+        ):
+            raise ValueError("A3 must pool pair packets in the otherwise full phase system")
         self.width = width
         self.use_topology = use_topology
         self.strip_phase = strip_phase
         self.incidence_shuffle_seed = incidence_shuffle_seed
         self.order_free = order_free
+        self.pair_bag = pair_bag
         self.checkpoint_blocks = checkpoint_blocks
         temporal_encoder = OrderFreeTemporalEncoder if order_free else MaskedTemporalEncoder
         self.actor_projection = nn.Sequential(nn.Linear(ACTOR_FEATURE_DIM, width), nn.GELU())
@@ -323,6 +330,18 @@ def _node_block(
         degree = (left + right) @ mask.to(torch.float64)
         return total, second, degree
 
+    def _bag_block(
+        self, field: RelationField, hidden: Tensor, start: int, stop: int
+    ) -> tuple[Tensor, Tensor, Tensor]:
+        """A3 global packet moments without assigning values to actor nodes."""
+        a, b, _endpoints, mask = self._half_edges(field, hidden, start, stop)
+        a64, b64 = a.to(torch.float64), b.to(torch.float64)
+        return (
+            (a64 + b64).sum(dim=0),
+            (a64.square() + b64.square()).sum(dim=0),
+            2 * mask.to(torch.float64).sum(dim=0),
+        )
+
     def _edge_block(
         self,
         field: RelationField,
@@ -361,6 +380,45 @@ def _encode_context(self, field: RelationField) -> tuple[Tensor, Tensor, Tensor]
         # relations. A field spans one capture; no set pooling or window reset
         # is inserted between local patches along a consistent actor track.
         hidden, _ = self.actor_temporal(hidden, actor_mask)
+        if self.pair_bag:
+            total = torch.zeros((field.patch_count, self.width), dtype=torch.float64, device=device)
+            second = torch.zeros_like(total)
+            count = torch.zeros(field.patch_count, dtype=torch.float64, device=device)
+            for start in range(0, field.pair_count, 64):
+                stop = min(field.pair_count, start + 64)
+
+                def bag_block(value, block_start=start, block_stop=stop):
+                    return self._bag_block(field, value, block_start, block_stop)
+
+                local_total, local_second, local_count = self._run_block(bag_block, hidden)
+                total, second, count = (
+                    total + local_total,
+                    second + local_second,
+                    count + local_count,
+                )
+            mean = total / count.clamp_min(1)[:, None]
+            m2 = (second / count.clamp_min(1)[:, None] - mean.square()).clamp_min(0)
+            actor_count = actor_mask.sum(dim=0).clamp_min(1).to(torch.float64)
+            actor_mean = (
+                hidden.to(torch.float64) * actor_mask[..., None]
+            ).sum(dim=0) / actor_count[:, None]
+            coverage = count / (field.actor_count * (field.actor_count - 1))
+            stats = torch.cat(
+                (
+                    actor_mean.to(hidden.dtype),
+                    mean.to(hidden.dtype),
+                    m2.to(hidden.dtype),
+                    coverage[:, None].to(hidden.dtype),
+                    (1 - coverage)[:, None].to(hidden.dtype),
+                ),
+                dim=-1,
+            )
+            topology_mask = (count >= 4)[None].expand(field.actor_count, -1)
+            nodes = self.node_mlp(stats)[None].expand(field.actor_count, -1, -1)
+            nodes = torch.where(topology_mask[..., None], nodes, torch.zeros_like(nodes))
+            nodes, _ = self.node_temporal(nodes, topology_mask)
+            nodes = torch.where(topology_mask[..., None], nodes, torch.zeros_like(nodes))
+            return hidden, nodes, topology_mask
         shape = (field.actor_count, field.patch_count, self.width)
         total = torch.zeros(shape, dtype=torch.float64, device=device)
         second = torch.zeros_like(total)
diff --git a/tests/test_phaseset_v2_a2_order_free.py b/tests/test_phaseset_v2_a2_order_free.py
index dd760a7..963e496 100644
--- a/tests/test_phaseset_v2_a2_order_free.py
+++ b/tests/test_phaseset_v2_a2_order_free.py
@@ -100,9 +100,10 @@ def system(**kwargs):
 
     full = system()
     a2 = system(order_free=True)
-    a3 = system(use_topology=False)
+    a3 = system(pair_bag=True)
+    legacy_pair_only = system(use_topology=False)
     a5 = system(strip_phase=True)
-    for variant in (a2, a3, a5):
+    for variant in (a2, a3, legacy_pair_only, a5):
         with pytest.raises(ValueError, match="relation kind"):
             variant.set_extra_state(full.get_extra_state())
         with pytest.raises(ValueError, match="relation kind"):
diff --git a/tests/test_phaseset_v2_a3_pair_bag.py b/tests/test_phaseset_v2_a3_pair_bag.py
new file mode 100644
index 0000000..1b09e2e
--- /dev/null
+++ b/tests/test_phaseset_v2_a3_pair_bag.py
@@ -0,0 +1,154 @@
+"""A3 keeps the full head's capacity while removing actor--edge incidence."""
+
+from __future__ import annotations
+
+import copy
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.continuous_retrieval import ContinuousRetrievalSystem
+from phaseset_core.directional_phase import canonical_edge_pairs
+from phaseset_core.temporal_coordination import TemporalIncidenceEncoder
+from phaseset_core.training import BaseRetrievalSystem
+from test_continuous_parent_training import _MockB2
+from test_phaseset_v2_coordination import field, motion
+
+
+def _parameters(module: torch.nn.Module) -> int:
+    return sum(parameter.numel() for parameter in module.parameters() if parameter.requires_grad)
+
+
+def test_a3_full_capacity_active_topology_and_actor_permutation() -> None:
+    torch.manual_seed(1729)
+    full = TemporalIncidenceEncoder(32, checkpoint_blocks=False)
+    bag = copy.deepcopy(full)
+    bag.pair_bag = True
+    assert _parameters(bag) == _parameters(full)
+    source = field(motion((0.0, 0.3, 1.1, 2.0)))
+    output = bag(source)
+    assert bool(output.topology_mask.any())
+    assert bool(output.topology_nodes.abs().sum() > 0)
+    permuted = bag(field(motion((0.0, 0.3, 1.1, 2.0), order=(3, 1, 0, 2), padding=5)))
+    torch.testing.assert_close(output.embedding, permuted.embedding, rtol=0, atol=2e-7)
+    output.embedding.sum().backward()
+    for module in (bag.node_mlp, bag.node_temporal, bag.edge_delta):
+        assert any(
+            parameter.grad is not None and bool(parameter.grad.abs().sum() > 0)
+            for parameter in module.parameters()
+        )
+    assert bag.topology_scale.grad is not None
+    assert bool(bag.topology_scale.grad.abs().sum() > 0)
+
+
+def test_a3_identical_pair_packet_multiset_removes_incidence_only() -> None:
+    torch.manual_seed(2718)
+    source = field(motion((0.0, 0.3, 1.1, 2.0)))
+    endpoints = torch.from_numpy(canonical_edge_pairs(4, 0, 6).copy())
+    patches, width = source.patch_count, 16
+    values = torch.arange(6 * patches * width, dtype=torch.float32).reshape(6, patches, width)
+    a = (values.remainder(31) - 15) / 7
+    b = (values.remainder(19) - 9) / 5
+    mask = torch.ones((6, patches), dtype=torch.bool)
+    routing = (5, 1, 4, 3, 2, 0)
+    text = torch.randn(3, width)
+
+    def patched(model: TemporalIncidenceEncoder, order: tuple[int, ...]):
+        def half_edges(_field, hidden, start, stop):
+            index = torch.tensor(order[start:stop], device=hidden.device)
+            return (
+                a.to(hidden.device)[index],
+                b.to(hidden.device)[index],
+                endpoints.to(hidden.device)[start:stop],
+                mask.to(hidden.device)[start:stop],
+            )
+
+        model._half_edges = half_edges
+        return model(source), model.score_text(source, text)
+
+    bag = TemporalIncidenceEncoder(width, pair_bag=True, checkpoint_blocks=False).eval()
+    bag_rerouted = copy.deepcopy(bag)
+    baseline, baseline_score = patched(bag, tuple(range(6)))
+    rerouted, rerouted_score = patched(bag_rerouted, routing)
+    torch.testing.assert_close(baseline.topology_nodes, rerouted.topology_nodes, rtol=0, atol=2e-7)
+    torch.testing.assert_close(baseline.embedding, rerouted.embedding, rtol=0, atol=2e-7)
+    torch.testing.assert_close(baseline_score.cosine, rerouted_score.cosine, rtol=0, atol=2e-7)
+    assert torch.equal(baseline_score.periodic_support, rerouted_score.periodic_support)
+
+    full = TemporalIncidenceEncoder(width, checkpoint_blocks=False).eval()
+    full_rerouted = copy.deepcopy(full)
+    (attached, _), (changed, _) = patched(full, tuple(range(6))), patched(full_rerouted, routing)
+    assert not torch.equal(attached.topology_nodes, changed.topology_nodes)
+
+
+def test_a3_k2_topology_and_gradients_are_positive_zero_and_full_equal() -> None:
+    torch.manual_seed(31415)
+    full = TemporalIncidenceEncoder(16, checkpoint_blocks=False)
+    bag = copy.deepcopy(full)
+    bag.pair_bag = True
+    source = field(motion((0.0, np.pi / 2)))
+    expected, result = full(source), bag(source)
+    assert torch.equal(expected.embedding, result.embedding)
+    assert torch.equal(expected.patch_tokens, result.patch_tokens)
+    assert torch.count_nonzero(result.topology_nodes) == 0
+    assert not bool(torch.signbit(result.topology_nodes).any())
+    result.embedding.sum().backward()
+    topology = list(bag.node_mlp.parameters()) + list(bag.node_temporal.parameters())
+    topology += list(bag.edge_delta.parameters()) + [bag.topology_scale]
+    for parameter in topology:
+        assert parameter.grad is not None and torch.count_nonzero(parameter.grad) == 0
+        assert not bool(torch.signbit(parameter.grad).any())
+
+
+def test_a3_crosses_64_edge_boundary_without_actor_order_shortcut() -> None:
+    torch.manual_seed(1729)
+    phases = tuple(index / 11 for index in range(13))
+    model = TemporalIncidenceEncoder(8, pair_bag=True, checkpoint_blocks=False).eval()
+    original = model(field(motion(phases)))
+    reordered = model(field(motion(phases, order=tuple(reversed(range(13))), padding=2)))
+    assert torch.equal(original.embedding, reordered.embedding)
+    assert torch.equal(original.patch_tokens, reordered.patch_tokens)
+    assert bool(original.topology_mask.any())
+    checkpointed = copy.deepcopy(model)
+    checkpointed.checkpoint_blocks = True
+    replay = checkpointed(field(motion(phases)))
+    assert torch.equal(original.embedding, replay.embedding)
+    source = field(motion(phases))
+    text = torch.randn(2, 8)
+    eager_scores = model.score_text(source, text)
+    replay_scores = checkpointed.score_text(source, text)
+    assert torch.equal(eager_scores.cosine, replay_scores.cosine)
+    assert torch.equal(eager_scores.periodic_support, replay_scores.periodic_support)
+    eager_scores.cosine.sum().backward()
+    replay_scores.cosine.sum().backward()
+    for eager_parameter, replay_parameter in zip(model.parameters(), checkpointed.parameters(), strict=True):
+        assert eager_parameter.grad is not None and replay_parameter.grad is not None
+        torch.testing.assert_close(eager_parameter.grad, replay_parameter.grad, rtol=1e-6, atol=1e-7)
+
+
+def test_a3_control_identity_exclusivity_and_exact_512d_capacity() -> None:
+    def system(**kwargs):
+        return ContinuousRetrievalSystem(
+            BaseRetrievalSystem(_MockB2(), embedding_dim=512), **kwargs
+        )
+
+    full = system()
+    a3 = system(pair_bag=True)
+    assert _parameters(a3.coordination) == _parameters(full.coordination)
+    with pytest.raises(ValueError, match="relation kind"):
+        a3.set_extra_state(full.get_extra_state())
+    with pytest.raises(ValueError, match="relation kind"):
+        full.set_extra_state(a3.get_extra_state())
+    with pytest.raises(ValueError, match="relation kind"):
+        a3.load_state_dict(full.state_dict())
+    with pytest.raises(ValueError, match="separate phase control"):
+        system(relation_kind="true_mean_difference_dct", pair_bag=True)
+    for conflict in (
+        {"use_topology": False},
+        {"strip_phase": True},
+        {"incidence_shuffle_seed": 1729},
+        {"order_free": True},
+    ):
+        with pytest.raises(ValueError, match="otherwise full phase"):
+            system(pair_bag=True, **conflict)

From 2e8273dd2e2803571ef146ac9350e7a77d570771 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 06:58:22 -0500
Subject: [PATCH 84/95] Bind PhaseSet V2 mechanisms to registered parent runs

---
 README.md                                   |   5 +
 RELEASE_FILES.sha256                        |   7 +-
 docs/PHASESET_V2_MECHANISM_FACTORY.md       |  56 +++
 src/phaseset_core/continuous_parent_host.py |  44 ++-
 src/phaseset_core/v2_mechanism_factory.py   | 386 ++++++++++++++++++++
 tests/test_phaseset_v2_mechanism_factory.py | 247 +++++++++++++
 6 files changed, 740 insertions(+), 5 deletions(-)
 create mode 100644 docs/PHASESET_V2_MECHANISM_FACTORY.md
 create mode 100644 src/phaseset_core/v2_mechanism_factory.py
 create mode 100644 tests/test_phaseset_v2_mechanism_factory.py

diff --git a/README.md b/README.md
index e028a25..c527696 100644
--- a/README.md
+++ b/README.md
@@ -92,6 +92,11 @@ The [A3 capacity-matched pair-bag control](docs/PHASESET_V2_A3_PAIR_BAG.md)
 keeps the full head's active node and edge modules but pools relation packets
 without assigning them to actor nodes. It is distinct from the legacy
 topology-off pair-only diagnostic and has no reported retrieval result yet.
+The [matrix-bound V2 mechanism factory](docs/PHASESET_V2_MECHANISM_FACTORY.md)
+constructs the implemented main B2 and full/A2/A3/A4/A5/A6/A8 host paths
+with registered seeds, predecessor/terminal and source bindings. Its 32
+focused CPU checks passed on the registered server; it grants no pilot or
+formal launch, and A1/A7/A9, fold hosts and Inter-X remain held.
 [TMR-Set](docs/TMR_SET_ADAPTATION.md) now implements the
 complete original VAE objectives with disclosed full-parent group adaptation;
 its pinned frozen language loader is not a substitute for native learning.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 74b6580..d9e81a5 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-8e8bf53ee324061cf73aa6cb43884a56bd0ac3d5724fa8af0bf4045c927f8068	16967	README.md
+5ff930915463dbfb0d309cd35fcb95b384747370aeef4a8f0d5230314e91ba37	17330	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -83,6 +83,7 @@ b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASE
 1e0d2fb0b35a6900dfb03af4b9bfe1701eb2c843f566c0f842b47b03b11b645e	1276	docs/PHASESET_V2_ICMR_TARGET.md
 b5042e5fa002fa057ca4af61f4a06953662708d221016c460acd9cd0278c5863	4606	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
+58cd55fd5d741f2e6616e4e11e3633762e6b7ec4941cdd08c3858375ca45fa32	3522	docs/PHASESET_V2_MECHANISM_FACTORY.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 b1bed8feb97cb7ca379c1952bb2201fc94a2810f48e59d0d8096fc31aac15be9	3302	docs/PHASESET_V2_MIME_CURRICULUM.md
 9ead6b89eacf034b52dcc15544e25be56e225c1b0432475b66c1a300adfa562d	3961	docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
@@ -183,7 +184,7 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 80e6d497ff9bb4354664cb1a054cdb550ab222e92c841813afc27af6d3759495	5603	src/phaseset_core/continuous_base_retrieval.py
 ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
-cf879bcbe1b52796d751deaf3870c99ec24a70f92bc7d129e3710a81143fe023	32443	src/phaseset_core/continuous_parent_host.py
+3a75c6185167ee3dd952169fa0bae43e1e879a6253d681e35e0b4cf424bbbed3	34377	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 e8b149fe97e960ca1800501ae6be20b471fb22946bfb3eca04c44d4e3ed1bc40	6896	src/phaseset_core/continuous_phase_cache.py
 cf10237c331dabfabdcd8242de09ddab68da31438c4d3b681827f6f605726d2a	15549	src/phaseset_core/continuous_retrieval.py
@@ -240,6 +241,7 @@ d1b92f7b65d750572a8447a48cd2a09cf12d7ec0fc3edd2f33c4088d0d77a14d	9442	src/phases
 b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
+85d5066a4cb940e195187903cebbfd28bf9e8e9dfad696d307778b191bfa0dad	17226	src/phaseset_core/v2_mechanism_factory.py
 cbeb8553a7b0b3bb68a092a98e394cb25917410dc4b13d3d7212cee91b456aae	5583	src/phaseset_core/v2_score_tables.py
 e8e4fa5cf988c11bc7aecbff241f3c5c253533652b1a31ea2313ee78626c3371	11472	src/phaseset_core/v2_statistics.py
 c1b145691b73cb3fe56925de75b114dac6ba8a84fcae6ffeec282010fc36fa77	15762	src/phaseset_core/wamo_set.py
@@ -362,6 +364,7 @@ ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test
 fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test_phaseset_v2_a4_incidence.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
+757b6181df4ee1cb906d26f5ed8ce19bcbc31ce6be152a24ce8449b7a7c6d030	11180	tests/test_phaseset_v2_mechanism_factory.py
 1df27542e87ae6d3d47ac0e6a865a6e9a5be49ea8a9bc5af61758b18e830b64a	6261	tests/test_phaseset_v2_score_tables.py
 08d27f7109d678ca011ef63dd99429185eb7b8c16d5f22e266dc05fbd58ea383	9778	tests/test_phaseset_v2_statistics.py
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
diff --git a/docs/PHASESET_V2_MECHANISM_FACTORY.md b/docs/PHASESET_V2_MECHANISM_FACTORY.md
new file mode 100644
index 0000000..60899b5
--- /dev/null
+++ b/docs/PHASESET_V2_MECHANISM_FACTORY.md
@@ -0,0 +1,56 @@
+# PhaseSet-V2 matrix-bound mechanism construction
+
+`build_v2_mechanism` reads the exact registered 87-row matrix bytes and one
+run ID, an admitted training-source digest, and a validation-selected complete-parent
+B2 checkpoint with its exact file digest. It constructs the currently implemented complete-parent residual
+systems only: PhaseSet-V2 full and A2/A3/A4/A5/A6/A8. It verifies the row's
+fixed seed, split and same-seed B2 predecessor, and refuses A1/A7/A9, the old
+head, base and literature rows until their distinct implementations exist.
+There is no fallback to the full model for an unsupported system.
+
+| Row | Constructor difference |
+|---|---|
+| PhaseSet-V2 | full signed-vector phase and incidence model |
+| A2 | order-free temporal MLP/mean paths |
+| A3 | capacity-matched pair-bag context |
+| A4 | explicit incidence shuffle seeded by the registered training seed |
+| A5 | explicit phase fields stripped; energy/support retained |
+| A6 | true mean/difference-signal DCT with a typed, main-population floor receipt |
+| A8 | full model; `bind_v2_parent_host_config` sets only the CF loss weight to zero |
+
+For main rows, host config binding also checks the exact twelve development
+training components, C00 validation, fixed seed and residual stage. A8 is
+derived from the same nonzero-CF candidate, changing only its loss weight;
+the caller must still present the same admitted weak training text pool as
+full. Residual initialization uses a forked CPU RNG seeded by the registered
+row, so same-seed full/A8 begin from the same trainable weights without
+mutating the caller's RNG. The A6 receipt must match the admitted
+training-source manifest digest in addition to its physical config and
+main population.
+
+`make_v2_b2_base_host` constructs a main B2 host that writes the registered
+base row identity into its checkpoint manifest. It creates the registered
+SocialTemporal B2 architecture under the row's forked CPU seed and requires
+the 30-epoch registered schedule; a short pilot cannot carry a formal row ID.
+The residual builder verifies that row identity, stage, seed, population,
+checkpoint file/state digests, a completed 30-epoch terminal and its final
+validation-selected checkpoint digest, then loads strict B2 weights into the
+registered architecture. `make_v2_parent_host`
+is the single row-bound residual host path: it compares checkpoint/source
+bindings, applies A8's loss transformation and persists the residual row,
+system and predecessor in every checkpoint manifest. It also requires the
+registered 20-epoch residual schedule. A direct host carrying
+registered A8 identity rejects a positive CF weight.
+
+This factory does not independently prove that the private nine-run B2
+qualification selected the supplied checkpoint, authenticate private assets,
+freeze pilot hyperparameters or max steps, claim storage/GPU capacity, or
+launch training. That selection and the admitted source digest remain private
+admission responsibilities. Fold model identity can be constructed only with
+its registered fold-specific B2 training/validation population; its residual
+host binding is still held. Inter-X remains unimplemented and fails closed.
+The returned `launch_authority` is always false.
+The public CLI likewise remains fail-closed until a qualified private
+production adapter is attached. The host factories are executable row-bound
+construction, but no formal run is admitted by calling them. This is executable mechanism selection,
+not a completed 87-stage runner or a retrieval result.
diff --git a/src/phaseset_core/continuous_parent_host.py b/src/phaseset_core/continuous_parent_host.py
index 6182b99..e477abd 100644
--- a/src/phaseset_core/continuous_parent_host.py
+++ b/src/phaseset_core/continuous_parent_host.py
@@ -187,6 +187,21 @@ def __post_init__(self):
                 raise ValueError("execution bindings must be lowercase SHA-256 keys")
 
 
+@dataclass(frozen=True)
+class ParentHostRunIdentity:
+    """Registered row identity persisted in every V2 parent checkpoint."""
+
+    run_id: str
+    system_id: str
+    predecessor_run_id: str | None
+
+    def __post_init__(self):
+        if not self.run_id.startswith("V2-") or not self.system_id:
+            raise ValueError("registered V2 run identity is malformed")
+        if self.predecessor_run_id is not None and not self.predecessor_run_id.startswith("V2-"):
+            raise ValueError("registered V2 predecessor identity is malformed")
+
+
 @dataclass(frozen=True)
 class ParentHostReport:
     outcome: str
@@ -224,6 +239,8 @@ def __init__(
         source: ParentTrainingSource,
         config: ParentHostConfig,
         bindings: ParentHostBindings,
+        *,
+        run_identity: ParentHostRunIdentity | None = None,
     ):
         if not isinstance(system, (ContinuousRetrievalSystem, ContinuousBaseRetrievalSystem)):
             raise TypeError("this host requires an actual complete-parent base or V2 scorer")
@@ -232,9 +249,19 @@ def __init__(
             raise ValueError("host stage does not match the actual scorer")
         if (bindings.frozen_base_checkpoint_sha256 is None) != self._is_base:
             raise ValueError("only a residual stage binds a frozen B2 checkpoint")
-        self._initialize(system, task, source, config, bindings)
-
-    def _initialize(self, system, task, source, config, bindings):
+        if run_identity is not None:
+            if type(run_identity) is not ParentHostRunIdentity:
+                raise TypeError("registered run identity must be exact")
+            if self._is_base != (run_identity.predecessor_run_id is None):
+                raise ValueError("registered predecessor does not match host stage")
+            if run_identity.system_id == "A8" and config.cf_weight != 0:
+                raise ValueError("registered A8 host must disable only CF loss")
+            if run_identity.system_id != "A8" and not self._is_base and config.cf_weight <= 0:
+                raise ValueError("registered residual host requires the CF objective")
+        self._run_identity = run_identity
+        self._initialize(system, task, source, config, bindings, run_identity)
+
+    def _initialize(self, system, task, source, config, bindings, run_identity):
         """Shared optimizer/checkpoint loop; public constructors admit scorers."""
         if any(parameter.dtype != torch.float32 for parameter in system.parameters()):
             raise ValueError("this host has only been qualified for FP32")
@@ -291,6 +318,8 @@ def _initialize(self, system, task, source, config, bindings):
             "steps_per_epoch": self._steps_per_epoch,
             "total_steps": self._total_steps,
         }
+        if run_identity is not None:
+            self._manifest["registered_run_identity"] = asdict(run_identity)
         optimizer_type = torch.optim.Adam if config.optimizer == "adam" else torch.optim.AdamW
         self._optimizer = optimizer_type(
             self._parameters, lr=config.learning_rate, weight_decay=config.weight_decay
@@ -686,7 +715,16 @@ def fit(
                         "latest_checkpoint_sha256": None
                         if self._latest is None
                         else self._latest.sha256,
+                        "best_checkpoint": None
+                        if self._best is None
+                        else str(self._best.path),
+                        "best_checkpoint_sha256": None
+                        if self._best is None
+                        else self._best.sha256,
                         "best_validation_r1": self._best_value,
+                        "registered_run_identity": None
+                        if self._run_identity is None
+                        else asdict(self._run_identity),
                         "monitor": self._monitor.summary(),
                         "formal_authority": False,
                     },
diff --git a/src/phaseset_core/v2_mechanism_factory.py b/src/phaseset_core/v2_mechanism_factory.py
new file mode 100644
index 0000000..7d071e5
--- /dev/null
+++ b/src/phaseset_core/v2_mechanism_factory.py
@@ -0,0 +1,386 @@
+"""Bind implemented V2 residual mechanisms to exact registered run rows.
+
+This constructs models and host configs; it cannot authorize an optimizer
+step, GPU reservation, data read, pilot or formal run. Unsupported rows fail
+closed instead of silently becoming the full model.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, replace
+from collections.abc import Mapping
+import hashlib
+import json
+from pathlib import Path
+
+import torch
+
+from .continuous_parent_host import (
+    ContinuousParentTrainingHost,
+    ParentHostBindings,
+    ParentHostConfig,
+    ParentHostRunIdentity,
+    ParentTrainingSource,
+)
+from .continuous_base_retrieval import ContinuousBaseRetrievalSystem
+from .continuous_retrieval import ContinuousRetrievalSystem
+from .dct_calibration import DctFloorReceipt
+from .directional_phase import LocalPhaseConfig
+from .study_storage import V2_MATRIX_SHA256
+from .parent_retrieval_task import ParentRetrievalTask
+from .training import (
+    OFFICIAL_SEEDS,
+    _load_torch_checkpoint,
+    _stable_hash,
+    build_registered_base_training_system,
+)
+
+
+class V2MechanismHold(ValueError):
+    """A row cannot yet be constructed as its registered scientific system."""
+
+
+_SUPPORTED = frozenset({"PhaseSet-V2", "A2", "A3", "A4", "A5", "A6", "A8"})
+
+
+def _sha256_key(value: str) -> bool:
+    return type(value) is str and len(value) == 64 and all(c in "0123456789abcdef" for c in value)
+
+
+@dataclass(frozen=True)
+class V2SelectedB2Checkpoint:
+    """Private admission's validation-selected complete-parent B2 artifact."""
+
+    path: Path
+    sha256: str
+    terminal_path: Path
+    terminal_sha256: str
+
+    def __post_init__(self):
+        if (
+            not isinstance(self.path, Path)
+            or not _sha256_key(self.sha256)
+            or not isinstance(self.terminal_path, Path)
+            or not _sha256_key(self.terminal_sha256)
+        ):
+            raise V2MechanismHold("selected B2 checkpoint and terminal digests are required")
+
+
+@dataclass(frozen=True)
+class V2MechanismBuild:
+    run_id: str
+    system_id: str
+    seed: int
+    split: str
+    max_epochs: int
+    predecessor_run_id: str
+    predecessor_checkpoint_sha256: str
+    training_source_manifest_sha256: str
+    training_components: tuple[str, ...] | None
+    validation_components: tuple[str, ...] | None
+    model: ContinuousRetrievalSystem
+    counterfactual_objective_enabled: bool
+    launch_authority: bool = False
+
+
+def _registered_matrix(matrix_bytes: bytes) -> dict:
+    if (
+        type(matrix_bytes) is not bytes
+        or hashlib.sha256(matrix_bytes).hexdigest() != V2_MATRIX_SHA256
+    ):
+        raise V2MechanismHold("exact frozen V2 matrix bytes required")
+    matrix = json.loads(matrix_bytes)
+    rows = matrix.get("runs") if type(matrix) is dict else None
+    if (
+        type(matrix) is not dict
+        or matrix.get("schema") != "phaseset-v2-training-matrix-v1"
+        or matrix.get("status") != "PLANNED_NOT_LAUNCHABLE"
+        or type(rows) is not list
+        or len(rows) != 87
+        or any(type(row) is not dict or row.get("id") != f"V2-{i:03d}" for i, row in enumerate(rows, 1))
+    ):
+        raise V2MechanismHold("the complete registered 87-row matrix is required")
+    return matrix
+
+
+def _registered_row(matrix_bytes: bytes, run_id: str) -> tuple[dict, dict]:
+    matrix = _registered_matrix(matrix_bytes)
+    rows = matrix["runs"]
+    if type(run_id) is not str or not run_id.startswith("V2-"):
+        raise V2MechanismHold("an exact V2 run ID is required")
+    candidates = [row for row in rows if row["id"] == run_id]
+    if len(candidates) != 1:
+        raise V2MechanismHold("V2 run ID is absent from the fixed matrix")
+    row = candidates[0]
+    if row.get("system") not in _SUPPORTED:
+        raise V2MechanismHold(f"{row.get('system')} is not an implemented V2 mechanism")
+    if row.get("seed") not in OFFICIAL_SEEDS or row.get("status") != "PLANNED":
+        raise V2MechanismHold("V2 seed or row status differs from the plan")
+    dependencies = row.get("depends_on")
+    if type(dependencies) is not list or len(dependencies) != 1:
+        raise V2MechanismHold("a residual must bind exactly one B2 predecessor")
+    predecessor = next((item for item in rows if item["id"] == dependencies[0]), None)
+    if (
+        predecessor is None
+        or predecessor.get("system") != "B2"
+        or predecessor.get("seed") != row["seed"]
+        or predecessor.get("split") != row.get("split")
+    ):
+        raise V2MechanismHold("residual B2 predecessor does not match seed and split")
+    if row["system"] == "PhaseSet-V2":
+        if row.get("group") not in {"main_residual", "group_held_out", "external_dyadic"}:
+            raise V2MechanismHold("full V2 row belongs to an unexpected stage")
+        if row.get("group") == "external_dyadic":
+            raise V2MechanismHold("external dyadic population binding is not implemented")
+    elif row.get("group") != "mechanism_ablation" or row.get("split") != "main":
+        raise V2MechanismHold("mechanism control must belong to the main ablation group")
+    return matrix, row
+
+
+def make_v2_b2_base_host(
+    matrix_bytes: bytes,
+    run_id: str,
+    task: ParentRetrievalTask,
+    source: ParentTrainingSource,
+    config: ParentHostConfig,
+    bindings: ParentHostBindings,
+) -> ContinuousParentTrainingHost:
+    """Bind a main B2 base to the registered row before checkpoint creation."""
+    matrix = _registered_matrix(matrix_bytes)
+    row = next((item for item in matrix["runs"] if item["id"] == run_id), None)
+    if (
+        row is None
+        or row.get("system") != "B2"
+        or row.get("group") != "internal_base"
+        or row.get("split") != "main"
+        or row.get("status") != "PLANNED"
+        or type(config) is not ParentHostConfig
+        or type(bindings) is not ParentHostBindings
+    ):
+        raise V2MechanismHold("registered main B2 base row and model required")
+    development = matrix["pilot_and_group_fold_components"]["development"]
+    validation = tuple(matrix["primary_split"]["validation_component_labels"])
+    train = tuple(component for component in development if component not in validation)
+    if (
+        row["seed"] != config.seed
+        or config.stage != "base"
+        or config.epochs != row["max_epochs"]
+        or config.train_components != train
+        or config.validation_components != validation
+        or bindings.frozen_base_checkpoint_sha256 is not None
+    ):
+        raise V2MechanismHold("B2 host population, seed or stage differs from matrix")
+    with torch.random.fork_rng(devices=[]):
+        torch.random.default_generator.manual_seed(row["seed"])
+        base_model = build_registered_base_training_system("B2")
+    return ContinuousParentTrainingHost(
+        ContinuousBaseRetrievalSystem(base_model),
+        task,
+        source,
+        config,
+        bindings,
+        run_identity=ParentHostRunIdentity(run_id, "B2", None),
+    )
+
+
+def build_v2_mechanism(
+    matrix_bytes: bytes,
+    run_id: str,
+    *,
+    selected_b2: V2SelectedB2Checkpoint,
+    training_source_manifest_sha256: str,
+    dct_floor_receipt: DctFloorReceipt | None = None,
+    checkpoint_blocks: bool = True,
+) -> V2MechanismBuild:
+    """Construct the exact implemented mechanism, never a launch decision."""
+    matrix, row = _registered_row(matrix_bytes, run_id)
+    if type(selected_b2) is not V2SelectedB2Checkpoint:
+        raise V2MechanismHold("validation-selected B2 artifact is required")
+    if not _sha256_key(training_source_manifest_sha256):
+        raise V2MechanismHold("admitted training-source manifest digest is required")
+    payload, artifact = _load_torch_checkpoint(
+        selected_b2.path, expected_sha256=selected_b2.sha256
+    )
+    if (
+        selected_b2.terminal_path.is_symlink()
+        or not selected_b2.terminal_path.is_file()
+        or selected_b2.terminal_path.parent.resolve() != artifact.path.parent
+    ):
+        raise V2MechanismHold("selected B2 terminal must be an owned regular file")
+    terminal_bytes = selected_b2.terminal_path.read_bytes()
+    if hashlib.sha256(terminal_bytes).hexdigest() != selected_b2.terminal_sha256:
+        raise V2MechanismHold("selected B2 terminal digest mismatch")
+    try:
+        terminal = json.loads(terminal_bytes)
+    except (ValueError, UnicodeDecodeError) as error:
+        raise V2MechanismHold("selected B2 terminal is unreadable") from error
+    saved_state_digest = payload.get("state_sha256")
+    without_digest = dict(payload)
+    without_digest.pop("state_sha256", None)
+    if saved_state_digest != _stable_hash(without_digest):
+        raise V2MechanismHold("selected B2 checkpoint state digest is invalid")
+    manifest = payload.get("manifest")
+    expected_identity = {
+        "run_id": row["depends_on"][0],
+        "system_id": "B2",
+        "predecessor_run_id": None,
+    }
+    if (
+        type(manifest) is not dict
+        or manifest.get("schema") != "phaseset-complete-parent-host-v1"
+        or manifest.get("registered_run_identity") != expected_identity
+        or type(manifest.get("config")) is not dict
+        or manifest["config"].get("seed") != row["seed"]
+        or manifest["config"].get("stage") != "base"
+        or manifest["config"].get("epochs") != 30
+    ):
+        raise V2MechanismHold("selected B2 checkpoint does not bind the predecessor row")
+    predecessor = next(item for item in matrix["runs"] if item["id"] == expected_identity["run_id"])
+    if (
+        type(terminal) is not dict
+        or terminal.get("outcome") != "COMPLETED"
+        or terminal.get("failure_class") is not None
+        or terminal.get("registered_run_identity") != expected_identity
+        or terminal.get("completed_epochs") != predecessor["max_epochs"]
+        or type(manifest.get("total_steps")) is not int
+        or terminal.get("global_step") != manifest["total_steps"]
+        or terminal.get("best_checkpoint") != str(artifact.path)
+        or terminal.get("best_checkpoint_sha256") != artifact.sha256
+        or terminal.get("best_validation_r1") != payload.get("best_validation_r1")
+    ):
+        raise V2MechanismHold("B2 terminal does not attest the completed selected predecessor")
+    best = payload.get("best")
+    if (
+        type(best) is not tuple
+        or best != (str(artifact.path), None)
+        or payload.get("best_validation_r1") is None
+        or not payload.get("validation_history")
+    ):
+        raise V2MechanismHold("B2 artifact is not its validation-selected checkpoint")
+    saved_model = payload.get("model")
+    if not isinstance(saved_model, Mapping) or not saved_model or any(
+        type(key) is not str or not key.startswith("base.") for key in saved_model
+    ):
+        raise V2MechanismHold("selected B2 model state is malformed")
+    base_state = {key.removeprefix("base."): value for key, value in saved_model.items()}
+    with torch.random.fork_rng(devices=[]):
+        torch.random.default_generator.manual_seed(row["seed"])
+        frozen_b2 = build_registered_base_training_system("B2")
+    try:
+        frozen_b2.load_state_dict(base_state, strict=True)
+    except (RuntimeError, ValueError) as error:
+        raise V2MechanismHold("selected B2 weights do not match the B2 architecture") from error
+    system_id = row["system"]
+    if system_id != "A6" and dct_floor_receipt is not None:
+        raise V2MechanismHold("a DCT floor receipt may only enter A6")
+    if system_id == "A6" and type(dct_floor_receipt) is not DctFloorReceipt:
+        raise V2MechanismHold("A6 requires its typed training-only DCT floor receipt")
+    if type(checkpoint_blocks) is not bool:
+        raise V2MechanismHold("checkpointing choice must be explicit boolean")
+    training_components = validation_components = None
+    if row["split"] == "main":
+        development = matrix["pilot_and_group_fold_components"]["development"]
+        validation = matrix["primary_split"]["validation_component_labels"]
+        training_components = tuple(component for component in development if component not in validation)
+        validation_components = tuple(validation)
+        if len(training_components) != 12 or validation_components != ("C00",):
+            raise V2MechanismHold("main training/validation components differ from the plan")
+    elif row["split"].startswith("group_fold_"):
+        folds = matrix["pilot_and_group_fold_components"]["folds"]
+        fold = next((item for item in folds if item["run_split"] == row["split"]), None)
+        if fold is None or matrix["pilot_and_group_fold_components"]["reuse_main_checkpoints"] is not False:
+            raise V2MechanismHold("registered group fold population is unavailable")
+        training_components = tuple(fold["train"])
+        validation_components = tuple(matrix["pilot_and_group_fold_components"]["fold_validation"])
+    if (
+        tuple(manifest["config"].get("train_components", ())) != training_components
+        or tuple(manifest["config"].get("validation_components", ())) != validation_components
+    ):
+        raise V2MechanismHold("selected B2 population differs from residual population")
+    if system_id == "A6":
+        dct_floor_receipt.require_config(LocalPhaseConfig())
+        dct_floor_receipt.require_population("main", training_components)
+        if dct_floor_receipt.training_source_manifest_sha256 != training_source_manifest_sha256:
+            raise V2MechanismHold("A6 floor source differs from admitted training source")
+    kwargs = {
+        "order_free": system_id == "A2",
+        "pair_bag": system_id == "A3",
+        "incidence_shuffle_seed": row["seed"] if system_id == "A4" else None,
+        "strip_phase": system_id == "A5",
+        "relation_kind": "true_mean_difference_dct" if system_id == "A6" else "phase",
+        "dct_floor_receipt": dct_floor_receipt,
+        "checkpoint_blocks": checkpoint_blocks,
+    }
+    # The host seeds inside fit, which is too late for model initialization.
+    # Forking CPU RNG makes same-seed ablations start from identical residual
+    # parameters without changing the caller's ambient random stream.
+    with torch.random.fork_rng(devices=[]):
+        torch.random.default_generator.manual_seed(row["seed"])
+        model = ContinuousRetrievalSystem(frozen_b2, **kwargs)
+    return V2MechanismBuild(
+        run_id=run_id,
+        system_id=system_id,
+        seed=row["seed"],
+        split=row["split"],
+        max_epochs=row["max_epochs"],
+        predecessor_run_id=row["depends_on"][0],
+        predecessor_checkpoint_sha256=artifact.sha256,
+        training_source_manifest_sha256=training_source_manifest_sha256,
+        training_components=training_components,
+        validation_components=validation_components,
+        model=model,
+        counterfactual_objective_enabled=system_id != "A8",
+    )
+
+
+def bind_v2_parent_host_config(
+    build: V2MechanismBuild,
+    config: ParentHostConfig,
+) -> ParentHostConfig:
+    """A8 changes only CF loss weight, not model or weak training pool."""
+    if type(build) is not V2MechanismBuild or type(config) is not ParentHostConfig:
+        raise TypeError("V2 build and complete-parent host config required")
+    if config.seed != build.seed or config.stage != "residual":
+        raise V2MechanismHold("host seed/stage differs from the registered V2 row")
+    if config.epochs != build.max_epochs:
+        raise V2MechanismHold("registered residual schedule differs from matrix")
+    if build.split != "main":
+        raise V2MechanismHold("fold and Inter-X host population binding is not yet implemented")
+    if (
+        config.train_components != build.training_components
+        or config.validation_components != build.validation_components
+    ):
+        raise V2MechanismHold("host training/validation components differ from the matrix")
+    if build.system_id == "A8":
+        if config.cf_weight <= 0:
+            raise V2MechanismHold("A8 must be derived from the same nonzero CF candidate")
+        return replace(config, cf_weight=0.0)
+    if config.cf_weight <= 0:
+        raise V2MechanismHold("full and other controls require the frozen CF objective")
+    return config
+
+
+def make_v2_parent_host(
+    build: V2MechanismBuild,
+    task: ParentRetrievalTask,
+    source: ParentTrainingSource,
+    config: ParentHostConfig,
+    bindings: ParentHostBindings,
+    *,
+    admitted_training_source_manifest_sha256: str,
+) -> ContinuousParentTrainingHost:
+    """Single row-bound V2 host path; still not a formal-run admission."""
+    if type(build) is not V2MechanismBuild or type(bindings) is not ParentHostBindings:
+        raise TypeError("exact V2 build and execution bindings required")
+    if (
+        bindings.frozen_base_checkpoint_sha256 != build.predecessor_checkpoint_sha256
+        or admitted_training_source_manifest_sha256 != build.training_source_manifest_sha256
+    ):
+        raise V2MechanismHold("host checkpoint or admitted training source differs from build")
+    bound_config = bind_v2_parent_host_config(build, config)
+    identity = ParentHostRunIdentity(
+        build.run_id, build.system_id, build.predecessor_run_id
+    )
+    return ContinuousParentTrainingHost(
+        build.model, task, source, bound_config, bindings, run_identity=identity
+    )
diff --git a/tests/test_phaseset_v2_mechanism_factory.py b/tests/test_phaseset_v2_mechanism_factory.py
new file mode 100644
index 0000000..bc85ace
--- /dev/null
+++ b/tests/test_phaseset_v2_mechanism_factory.py
@@ -0,0 +1,247 @@
+"""Exact V2 matrix rows construct the intended residual, never launch it."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+import hashlib
+import json
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.continuous_parent_host import ParentHostBindings, ParentHostConfig
+from phaseset_core.dct_calibration import DCT_FLOOR_SCHEMA, DctFloorReceipt, dct_config_sha256
+from phaseset_core.directional_phase import LocalPhaseConfig
+from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from phaseset_core.training import (
+    _atomic_torch_save,
+    _stable_hash,
+    build_registered_base_training_system,
+)
+from phaseset_core.v2_mechanism_factory import (
+    V2MechanismHold,
+    V2SelectedB2Checkpoint,
+    bind_v2_parent_host_config,
+    build_v2_mechanism,
+    make_v2_b2_base_host,
+    make_v2_parent_host,
+)
+MATRIX = (Path(__file__).parents[1] / "configs/phaseset_v2_experiment_matrix.json").read_bytes()
+MAIN_TRAIN = ("C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08", "C10", "C12", "C13", "C14")
+FOLD_0_TRAIN = ("C01", "C02", "C04", "C05", "C07", "C08", "C12", "C13")
+SOURCE_SHA = "a" * 64
+
+
+def _selected(
+    tmp_path, *, run_id="V2-007", seed=1729,
+    train_components=MAIN_TRAIN, validation_components=("C00",),
+):
+    with torch.random.fork_rng(devices=[]):
+        torch.random.default_generator.manual_seed(seed)
+        base = build_registered_base_training_system("B2")
+    population_key = hashlib.sha256("|".join(train_components).encode()).hexdigest()[:8]
+    path = tmp_path / f"{run_id}-{population_key}-best-validation.pt"
+    payload = {
+        "manifest": {
+            "schema": "phaseset-complete-parent-host-v1",
+            "config": {
+                "stage": "base", "seed": seed, "epochs": 30,
+                "train_components": train_components,
+                "validation_components": validation_components,
+            },
+            "registered_run_identity": {
+                "run_id": run_id, "system_id": "B2", "predecessor_run_id": None,
+            },
+        },
+        "global_step": 1,
+        "best": (str(path.resolve()), None),
+        "best_validation_r1": 0.5,
+        "validation_history": [(1, 0.5)],
+        "model": {f"base.{key}": value for key, value in base.state_dict().items()},
+    }
+    payload["manifest"]["total_steps"] = 30
+    payload["state_sha256"] = _stable_hash(payload)
+    artifact = _atomic_torch_save(path, payload)
+    terminal = {
+        "outcome": "COMPLETED", "failure_class": None,
+        "registered_run_identity": payload["manifest"]["registered_run_identity"],
+        "completed_epochs": 30, "global_step": 30,
+        "best_checkpoint": str(artifact.path),
+        "best_checkpoint_sha256": artifact.sha256,
+        "best_validation_r1": 0.5,
+    }
+    terminal_path = tmp_path / f"{run_id}-{population_key}-terminal.json"
+    terminal_bytes = (json.dumps(terminal, sort_keys=True) + "\n").encode()
+    terminal_path.write_bytes(terminal_bytes)
+    return V2SelectedB2Checkpoint(
+        artifact.path, artifact.sha256, terminal_path,
+        hashlib.sha256(terminal_bytes).hexdigest(),
+    )
+
+
+def _build(tmp_path, run_id, *, selected=None, source_sha=SOURCE_SHA, **kwargs):
+    if selected is None:
+        selected = _selected(tmp_path)
+    return build_v2_mechanism(
+        MATRIX, run_id, selected_b2=selected,
+        training_source_manifest_sha256=source_sha, **kwargs
+    )
+
+
+def _dct_receipt(*, population: str = "main") -> DctFloorReceipt:
+    return DctFloorReceipt(
+        DCT_FLOOR_SCHEMA,
+        population,
+        MAIN_TRAIN,
+        "a" * 64,
+        dct_config_sha256(LocalPhaseConfig()),
+        np.ones(6, dtype=np.float64),
+    )
+
+
+@pytest.mark.parametrize(
+    ("run_id", "system_id", "attribute", "expected"),
+    (
+        ("V2-022", "PhaseSet-V2", "pair_bag", False),
+        ("V2-028", "A2", "order_free", True),
+        ("V2-031", "A3", "pair_bag", True),
+        ("V2-034", "A4", "incidence_shuffle_seed", 1729),
+        ("V2-037", "A5", "strip_phase", True),
+        ("V2-046", "A8", "pair_bag", False),
+    ),
+)
+def test_registered_main_mechanisms_bind_exact_run_and_live_model(
+    tmp_path, run_id: str, system_id: str, attribute: str, expected: object
+) -> None:
+    build = _build(tmp_path, run_id)
+    assert (build.run_id, build.system_id, build.seed, build.split) == (
+        run_id, system_id, 1729, "main"
+    )
+    assert build.predecessor_run_id == "V2-007"
+    assert len(build.predecessor_checkpoint_sha256) == 64
+    assert build.training_components == MAIN_TRAIN
+    assert build.validation_components == ("C00",)
+    assert getattr(build.model.coordination, attribute) == expected
+    assert build.counterfactual_objective_enabled is (system_id != "A8")
+    assert build.launch_authority is False
+
+
+def test_a6_requires_exact_population_and_uses_only_dct_relations(tmp_path) -> None:
+    selected = _selected(tmp_path)
+    with pytest.raises(V2MechanismHold, match="typed"):
+        _build(tmp_path, "V2-040", selected=selected)
+    with pytest.raises(ValueError, match="population"):
+        _build(tmp_path, "V2-040", selected=selected, dct_floor_receipt=_dct_receipt(population="pilot"))
+    with pytest.raises(V2MechanismHold, match="floor source"):
+        _build(tmp_path, "V2-040", selected=selected, source_sha="b" * 64, dct_floor_receipt=_dct_receipt())
+    build = _build(tmp_path, "V2-040", selected=selected, dct_floor_receipt=_dct_receipt())
+    assert build.model.relation_kind == "true_mean_difference_dct"
+    assert build.model.coordination.strip_phase is False
+    assert build.model.coordination.pair_bag is False
+    with pytest.raises(V2MechanismHold, match="only enter A6"):
+        _build(tmp_path, "V2-031", selected=selected, dct_floor_receipt=_dct_receipt())
+
+
+def test_a8_disables_only_cf_objective_and_preserves_weak_pool_host_contract(tmp_path) -> None:
+    selected = _selected(tmp_path)
+    full = _build(tmp_path, "V2-022", selected=selected)
+    a8 = _build(tmp_path, "V2-046", selected=selected)
+    config = ParentHostConfig(1729, MAIN_TRAIN, ("C00",), cf_weight=0.2, cf_margin=0.2)
+    assert bind_v2_parent_host_config(full, config) is config
+    bound = bind_v2_parent_host_config(a8, config)
+    assert bound == replace(config, cf_weight=0.0)
+    assert bound.cf_margin == config.cf_margin
+    assert a8.model.coordination.pair_bag is False
+    full_trainable = {name: value for name, value in full.model.state_dict().items() if not name.startswith("frozen_b2.")}
+    a8_trainable = {name: value for name, value in a8.model.state_dict().items() if not name.startswith("frozen_b2.")}
+    assert full_trainable.keys() == a8_trainable.keys()
+    assert all(torch.equal(value, a8_trainable[name]) for name, value in full_trainable.items())
+    with pytest.raises(V2MechanismHold, match="nonzero CF candidate"):
+        bind_v2_parent_host_config(a8, replace(config, cf_weight=0.0))
+    with pytest.raises(V2MechanismHold, match="components"):
+        bind_v2_parent_host_config(a8, replace(config, train_components=("C01", "C02")))
+
+
+def test_unsupported_rows_and_matrix_drift_fail_without_full_model_fallback(tmp_path) -> None:
+    selected = _selected(tmp_path)
+    for run_id in ("V2-025", "V2-043", "V2-049", "V2-007"):
+        with pytest.raises(V2MechanismHold, match="not an implemented V2 mechanism"):
+            _build(tmp_path, run_id, selected=selected)
+    with pytest.raises(V2MechanismHold, match="exact frozen"):
+        build_v2_mechanism(MATRIX + b"\n", "V2-031", selected_b2=selected, training_source_manifest_sha256=SOURCE_SHA)
+    with pytest.raises(V2MechanismHold, match="absent"):
+        _build(tmp_path, "V2-999", selected=selected)
+
+
+def test_fold_model_identity_is_available_but_fold_host_population_is_held(tmp_path) -> None:
+    selected = _selected(
+        tmp_path, run_id="V2-052",
+        train_components=FOLD_0_TRAIN, validation_components=("C03",),
+    )
+    build = _build(tmp_path, "V2-053", selected=selected)
+    assert build.system_id == "PhaseSet-V2"
+    assert build.split == "group_fold_1"
+    assert build.predecessor_run_id == "V2-052"
+    assert build.training_components == FOLD_0_TRAIN
+    assert build.validation_components == ("C03",)
+    config = ParentHostConfig(1729, MAIN_TRAIN, ("C00",))
+    with pytest.raises(V2MechanismHold, match="not yet implemented"):
+        bind_v2_parent_host_config(build, config)
+    selected_main_population = _selected(tmp_path, run_id="V2-052")
+    with pytest.raises(V2MechanismHold, match="population"):
+        _build(tmp_path, "V2-053", selected=selected_main_population)
+
+
+def test_predecessor_row_and_artifact_drift_fail_closed(tmp_path) -> None:
+    selected = _selected(tmp_path)
+    with pytest.raises(V2MechanismHold, match="predecessor row"):
+        _build(tmp_path, "V2-023", selected=selected)
+    wrong_digest = replace(selected, sha256="0" * 64)
+    with pytest.raises(Exception, match="digest mismatch"):
+        _build(tmp_path, "V2-022", selected=wrong_digest)
+    other = _selected(tmp_path, run_id="V2-008", seed=2718)
+    a = _build(tmp_path, "V2-022", selected=selected)
+    b = _build(tmp_path, "V2-023", selected=other)
+    a_state = dict(a.model.named_parameters())
+    b_state = dict(b.model.named_parameters())
+    assert any(not torch.equal(a_state[key], b_state[key]) for key in a_state if not key.startswith("frozen_b2."))
+
+
+def test_single_host_entrypoint_binds_a8_and_persists_row_identity(tmp_path) -> None:
+    build = _build(tmp_path, "V2-046")
+    rows = tuple(
+        ParentCaptionRecord(
+            hashlib.sha256(f"source-{i}".encode()).hexdigest(),
+            hashlib.sha256(f"family-{i}".encode()).hexdigest(),
+            component,
+            "validation" if component == "C00" else "train",
+            (f"human row {i}",),
+        )
+        for i, component in enumerate((*MAIN_TRAIN, "C00"))
+    )
+    task = ParentRetrievalTask(rows, expected_family_keys=tuple(row.annotation_family_sha256 for row in rows))
+    config = ParentHostConfig(1729, MAIN_TRAIN, ("C00",), cf_weight=0.2)
+    bindings = ParentHostBindings("b" * 64, "c" * 64, "d" * 64, build.predecessor_checkpoint_sha256)
+    host = make_v2_parent_host(
+        build, task, object(), config, bindings,
+        admitted_training_source_manifest_sha256=SOURCE_SHA,
+    )
+    assert host.config.cf_weight == 0.0
+    assert host._manifest["registered_run_identity"] == {
+        "run_id": "V2-046", "system_id": "A8", "predecessor_run_id": "V2-007",
+    }
+    with pytest.raises(V2MechanismHold, match="checkpoint or admitted training source"):
+        make_v2_parent_host(
+            build, task, object(), config, replace(bindings, frozen_base_checkpoint_sha256="e" * 64),
+            admitted_training_source_manifest_sha256=SOURCE_SHA,
+        )
+    base_host = make_v2_b2_base_host(
+        MATRIX, "V2-007", task, object(),
+        ParentHostConfig.for_base(1729, MAIN_TRAIN, ("C00",)),
+        ParentHostBindings("b" * 64, "c" * 64, "d" * 64, None),
+    )
+    assert base_host._manifest["registered_run_identity"] == {
+        "run_id": "V2-007", "system_id": "B2", "predecessor_run_id": None,
+    }

From 3dcc8fd47dc12caa28879898bde7e0a2fde524b4 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 07:39:43 -0500
Subject: [PATCH 85/95] Restore literature host compatibility with registered
 identities

---
 RELEASE_FILES.sha256                        | 2 +-
 src/phaseset_core/literature_parent_host.py | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index d9e81a5..1c8d935 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -203,7 +203,7 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 7c491ef7d65cddffa264d68d0a9fdd9216180bb741f85dd662cb570ca6e872f1	77027	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
-917784f4ea05ef48252118a95c024b516fde4230c3f08e2a274edf7b75064b19	11072	src/phaseset_core/literature_parent_host.py
+1fb8e81b89b58e995a08a016aa3efad5074a269907e5d51da040fb0918d64335	11164	src/phaseset_core/literature_parent_host.py
 02a11d20ac299c4886479b863f2f614512365a9f1fe800861b2f261dbdad1c7a	9101	src/phaseset_core/literature_parent_training.py
 f0d7c36ecda4326ca2b1f9461fdc812039cae33739595c60d40a436f0051edb7	6976	src/phaseset_core/mime_language.py
 07092fa015f73af9da2b23dfa26c6e19e4c7a8b6a5cc53c316acb1d2d4b356e5	7245	src/phaseset_core/mime_parent_curriculum.py
diff --git a/src/phaseset_core/literature_parent_host.py b/src/phaseset_core/literature_parent_host.py
index 9a1d64c..7fd3c1f 100644
--- a/src/phaseset_core/literature_parent_host.py
+++ b/src/phaseset_core/literature_parent_host.py
@@ -93,7 +93,8 @@ def __init__(
             raise ValueError("literature models train independently, not on a frozen B2 checkpoint")
         self._is_base = True  # independent trainable model, shared loop has no anchor
         self._method = method
-        self._initialize(system, task, source, config, bindings)
+        self._run_identity = None  # literature rows have no V2 mechanism predecessor
+        self._initialize(system, task, source, config, bindings, None)
         self._manifest.update(
             schema="phaseset-literature-parent-host-v1",
             literature_method=method,

From 81e5772ccc6c9efb224253ecc7c420ba27c42f85 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 08:18:54 -0500
Subject: [PATCH 86/95] Implement speed-only periodic A1 control with bound
 floors

---
 RELEASE_FILES.sha256                          |  23 +-
 docs/PHASESET_V2_A1_SPEED_CONTROL.md          |  30 +++
 docs/PHASESET_V2_MECHANISM_FACTORY.md         |  14 +-
 docs/PHASESET_V2_PARENT_DISK_SOURCE.md        |   6 +
 src/phaseset_core/continuous_phase_cache.py   |  20 +-
 src/phaseset_core/continuous_retrieval.py     |  51 ++++-
 .../continuous_training_input.py              |  11 +-
 src/phaseset_core/directional_calibration.py  |  29 ++-
 src/phaseset_core/directional_phase.py        |  32 ++-
 src/phaseset_core/parent_disk_source.py       |  22 +-
 src/phaseset_core/speed_calibration.py        | 104 +++++++++
 src/phaseset_core/v2_mechanism_factory.py     |  21 +-
 tests/test_phaseset_v2_a1_speed.py            | 204 ++++++++++++++++++
 tests/test_phaseset_v2_mechanism_factory.py   |  35 ++-
 14 files changed, 570 insertions(+), 32 deletions(-)
 create mode 100644 docs/PHASESET_V2_A1_SPEED_CONTROL.md
 create mode 100644 src/phaseset_core/speed_calibration.py
 create mode 100644 tests/test_phaseset_v2_a1_speed.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 1c8d935..89fc34a 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -71,6 +71,7 @@ c7a113407b0cf6c60ddaa6e8aa786cc3652fda0dcb6330fa870abfecc71c4f8d	3316	docs/PHASE
 81c52ad4a5d6391a57988b5cab5d29f869c5069b0962bca5577ed9c5bedfea39	1568	docs/PHASESET_GITHUB_MIGRATION_RECEIPT.json
 f98ebe2bfed932c63725f88522f3b98f25ce092daeeb61e92578b7931376e3a0	27672	docs/PHASESET_LITERATURE_REVIEW.md
 cffee3955d5134b007d8ea29b52f92b13a284d8347950698a082cad2659e5045	14130	docs/PHASESET_RESEARCH_CONTRACT.md
+1a3dbb04f7750f6ca6107a4ddfa2c30b073bfcf1e74e01418c9e5cb879f54f72	1935	docs/PHASESET_V2_A1_SPEED_CONTROL.md
 f98b8d0c2a4e9f75e65d46556951472cd4838f32f2561cc00934c2fc9f810bb8	1647	docs/PHASESET_V2_A2_ORDER_FREE.md
 794bcdc4fe9d2c7cf6014157a7958851c3e86256006b74844fb97013117a4bbf	1920	docs/PHASESET_V2_A3_PAIR_BAG.md
 09c14876d4e0055f4ea19c710523f31ec6aab505ee22ca6f21a2ada6414e03bc	1534	docs/PHASESET_V2_A4_INCIDENCE.md
@@ -83,11 +84,11 @@ b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASE
 1e0d2fb0b35a6900dfb03af4b9bfe1701eb2c843f566c0f842b47b03b11b645e	1276	docs/PHASESET_V2_ICMR_TARGET.md
 b5042e5fa002fa057ca4af61f4a06953662708d221016c460acd9cd0278c5863	4606	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
-58cd55fd5d741f2e6616e4e11e3633762e6b7ec4941cdd08c3858375ca45fa32	3522	docs/PHASESET_V2_MECHANISM_FACTORY.md
+a13629700b95d956d08ea3de0b14b6098dc435b490d0a8f1fe43281db8af99b0	3934	docs/PHASESET_V2_MECHANISM_FACTORY.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 b1bed8feb97cb7ca379c1952bb2201fc94a2810f48e59d0d8096fc31aac15be9	3302	docs/PHASESET_V2_MIME_CURRICULUM.md
 9ead6b89eacf034b52dcc15544e25be56e225c1b0432475b66c1a300adfa562d	3961	docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
-5a106355600b7223e516d5f270d81697a53e9c267930d34189860803b32c3fab	3116	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
+1c077c87ee2de81e6afd9f0a6541f7940a64d1496d8db3eddd10e0d0028acd7d	3475	docs/PHASESET_V2_PARENT_DISK_SOURCE.md
 667eba26ecbbe11016064e9068764a1a86320e47d68b8e1eb44b422936721e8e	9609	docs/PHASESET_V2_PARENT_HOST.md
 1eef1de33e2f1d193a6a02a0a982df0c9dea7b1f9406b4cbd155d22ef7378f51	3314	docs/PHASESET_V2_PARENT_OPERATION_MONITOR.md
 a4ff32606557f35ade891fbf0786b96cb544bea9ceca8c1d4e58a47836415d4f	3325	docs/PHASESET_V2_PARENT_TASK.md
@@ -186,16 +187,16 @@ ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phase
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
 3a75c6185167ee3dd952169fa0bae43e1e879a6253d681e35e0b4cf424bbbed3	34377	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
-e8b149fe97e960ca1800501ae6be20b471fb22946bfb3eca04c44d4e3ed1bc40	6896	src/phaseset_core/continuous_phase_cache.py
-cf10237c331dabfabdcd8242de09ddab68da31438c4d3b681827f6f605726d2a	15549	src/phaseset_core/continuous_retrieval.py
-24c6f90d5ef8d6ccfaf0287433b5421e99544185f5197889cddb4f7874dd4f37	7158	src/phaseset_core/continuous_training_input.py
+564bfe9b1717ac6e539e62726a245b27fc84103e235a1ed1ab1bf04569b36f7a	7483	src/phaseset_core/continuous_phase_cache.py
+09480e48941efb42709224b52f066ee78cb126a20a96e7aa173481f8a9e6116e	18310	src/phaseset_core/continuous_retrieval.py
+d66eb2fd534a31701c7dcb8b4fdb634b8db6186b423e83fcc3af5c60152aa8c8	7381	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
 f0f50300b6d209af8211872b17de86bd445f9f239f8d369220bc3236e51e03e2	17054	src/phaseset_core/data.py
 a03bc907307f82452469e38d1b39c97c6c39febd4ad64f19d6ff320fd70f3171	11969	src/phaseset_core/dct_calibration.py
 727d85b652932cb2b8a6e76b2c470ab009c0f30a723011c2ce581e8b2e548fe0	13070	src/phaseset_core/dct_relations.py
-9c9f86e262a7cf00809b16447dc5f5015a193e3af8eef703046c5e9468ababef	6825	src/phaseset_core/directional_calibration.py
-d3489b44cfded0fd318ff5cde25c52cf373de9ff5c5f0d4745a3f5161520db61	22547	src/phaseset_core/directional_phase.py
+78ee596a0a7ae34e686acf6e0c1da46d1e15d70466d54f5df4e18ffdefdf753f	7776	src/phaseset_core/directional_calibration.py
+4c7fa6c88bba905035365a236b32e7203679cf1d9f8eba321b9d72c7d1210f93	23779	src/phaseset_core/directional_phase.py
 e1617f38d4ff751cf5d736da3eac99af678286ee934eb0ad4defa0f28da3e81a	27430	src/phaseset_core/embody_adapter.py
 72f47efa82da668a950e79a2ece53ece60736361b55390dd7c89f48fd24208fd	57367	src/phaseset_core/evaluation.py
 eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phaseset_core/execution.py
@@ -213,7 +214,7 @@ c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phase
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 0f0309c54a5b48a0493bb24f97eda39ff93be657295e1b0ebcc3dd6e9a8ecd76	6565	src/phaseset_core/parent_capture.py
 be88afc2d543a9ceb227eda07046cc8e9221faf7af85888c1b303632da642dc8	3366	src/phaseset_core/parent_clip_rows.py
-c0dd8d79012f8101cfc9790d046a171537684d87385285727578f2f42c143e87	10393	src/phaseset_core/parent_disk_source.py
+c2c9822208d738e29f36c79a4bdf48c81dea271d570df23bcb971fa649e33dac	11642	src/phaseset_core/parent_disk_source.py
 be883315aa773e6c2d535ea981853ae92a791187338fd2b310b574f956174a82	7326	src/phaseset_core/parent_retrieval_task.py
 46baa360084180121c16e1ce4d0c0a21841c6ad91c462afd272d5b92b0a603ad	9528	src/phaseset_core/parent_run_monitor.py
 52b22af181cb52cf3c9c3584d15ea02a604e5c29d8abeaa0de217baf07d8af06	7027	src/phaseset_core/parent_weak_clip_rows.py
@@ -227,6 +228,7 @@ a4dddc01470e2c2f6596733c338527da74eb5b4254dd0599c39a3deb0ee1a860	25500	src/phase
 273225e6696351ccb16a3a9e7a653b55af18c64b316a4e5d80c7b265625cc5c1	17929	src/phaseset_core/preprocessing.py
 ad2ce56325f09d2efb17847ef3f947535f81f7099019275fdd074c12efeeb559	73588	src/phaseset_core/production.py
 6f60feada83ebf1a55635bb657a5a3998e9fc77fa85012aad46e4f8d90d344db	37785	src/phaseset_core/publication.py
+ff3b8022698bd2c1357c3dd11df2babf10e4d4ccbc00f135dab227ae34b5e1cd	4237	src/phaseset_core/speed_calibration.py
 2292bc9f142c3b84804845d2682d52ba211609d5a54f976555db1ba4b886aae0	23917	src/phaseset_core/split_audit.py
 eba60052653bec6631a9e5d28828858802315b0d242e2eb610db2b8a4f45b2ce	54164	src/phaseset_core/statistics.py
 a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phaseset_core/streaming_autograd.py
@@ -241,7 +243,7 @@ d1b92f7b65d750572a8447a48cd2a09cf12d7ec0fc3edd2f33c4088d0d77a14d	9442	src/phases
 b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
-85d5066a4cb940e195187903cebbfd28bf9e8e9dfad696d307778b191bfa0dad	17226	src/phaseset_core/v2_mechanism_factory.py
+6992c9843cd9380622e5d98b076006f6dd4844f4e718488fff0ae9ce25412b7d	18475	src/phaseset_core/v2_mechanism_factory.py
 cbeb8553a7b0b3bb68a092a98e394cb25917410dc4b13d3d7212cee91b456aae	5583	src/phaseset_core/v2_score_tables.py
 e8e4fa5cf988c11bc7aecbff241f3c5c253533652b1a31ea2313ee78626c3371	11472	src/phaseset_core/v2_statistics.py
 c1b145691b73cb3fe56925de75b114dac6ba8a84fcae6ffeec282010fc36fa77	15762	src/phaseset_core/wamo_set.py
@@ -359,12 +361,13 @@ d632ae495d5e8cc7863bebc4b32f1a582eb65ea4a40ce87fb6551373ab0e40c5	16496	tests/tes
 11727c78e08d02dd433abf66ca75e7f3aafd77c10a98d94ea98f17560d6e86ba	13200	tests/test_phaseset_streaming_autograd.py
 ae349d5c5f678a0bf3ff30ac868aec70d14bf5113b97e55ffe56aeb25de305f4	7350	tests/test_phaseset_torch_periodic.py
 280daf77e254abd08f9f06d7bc71f1e3fbb9dcdd17b15d4569f7ad0934504e1e	52526	tests/test_phaseset_training.py
+d7b1d991814eed83c888e1e62a88b5bbf877b1c92401f616d10bc1574c7275d0	8593	tests/test_phaseset_v2_a1_speed.py
 4ee992cf41d66cf7e2cae98f4ec00e69b080bf05a9d9f005e29099b92ee6ab4d	5099	tests/test_phaseset_v2_a2_order_free.py
 4679d7fb1fc5e19f88ade47039c413adb51dbe5473a4206947338b2b7a70ab60	7217	tests/test_phaseset_v2_a3_pair_bag.py
 fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test_phaseset_v2_a4_incidence.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
-757b6181df4ee1cb906d26f5ed8ce19bcbc31ce6be152a24ce8449b7a7c6d030	11180	tests/test_phaseset_v2_mechanism_factory.py
+fed691f1bceb341c418e5f505081a83fcba64469f16f8a2b378c8dedc3edc3b5	12899	tests/test_phaseset_v2_mechanism_factory.py
 1df27542e87ae6d3d47ac0e6a865a6e9a5be49ea8a9bc5af61758b18e830b64a	6261	tests/test_phaseset_v2_score_tables.py
 08d27f7109d678ca011ef63dd99429185eb7b8c16d5f22e266dc05fbd58ea383	9778	tests/test_phaseset_v2_statistics.py
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
diff --git a/docs/PHASESET_V2_A1_SPEED_CONTROL.md b/docs/PHASESET_V2_A1_SPEED_CONTROL.md
new file mode 100644
index 0000000..4886867
--- /dev/null
+++ b/docs/PHASESET_V2_A1_SPEED_CONTROL.md
@@ -0,0 +1,30 @@
+# A1: speed-only periodic-input control
+
+A1 is the matrix-registered speed-only ablation, not a replacement for the
+full PhaseSet-V2 model or a retrieval result. The common B2 checkpoint,
+captions, weak counterfactual branch, text adapter, six Morlet filters,
+relation encoder, scoring module, run seed and trainable capacity are retained.
+Only the periodic frontend changes. For each observed pelvis or pelvis-relative
+joint velocity vector `v`, A1 broadcasts `||v||₂ / sqrt(3)` into its three XYZ
+channels *before* convolution. This removes sign and direction while
+preserving each vector's squared energy. Taking the magnitude of a complex
+Morlet response would be a different control; removing its phase fields is A5.
+
+The full model's geometric root branch and frozen B2 global branch still see
+the original group geometry. Therefore full-versus-A1 can only support a claim
+about signed direction in the **periodic input**, not an assertion that the
+entire A1 system is spatially direction-blind.
+
+Speed responses have their own cache schema, and cache loading declares the
+expected frontend. Main, pilot and each external fold need separate floors
+fitted from their complete training actor/patch/band population. The fit
+rejects mixed frontends or physical configurations. A typed speed-floor
+receipt binds population, training-source manifest, actual fit config and six
+floor values; source loading, model scoring and checkpoint extra-state reject
+identity drift. Signed-vector floors or caches are not interchangeable.
+
+The software constructor does not authorize a formal run. It does not fit
+real-data floors, prove storage/GPU capacity, run a pilot, touch sealed test,
+or establish an ablation effect. Those steps require private admission and
+immutable server receipts. The official human-only evaluation gallery is
+unchanged by the user's [relation-supervision amendment](PHASESET_V2_RELATION_SUPERVISION_AMENDMENT.md).
diff --git a/docs/PHASESET_V2_MECHANISM_FACTORY.md b/docs/PHASESET_V2_MECHANISM_FACTORY.md
index 60899b5..51d1848 100644
--- a/docs/PHASESET_V2_MECHANISM_FACTORY.md
+++ b/docs/PHASESET_V2_MECHANISM_FACTORY.md
@@ -3,14 +3,15 @@
 `build_v2_mechanism` reads the exact registered 87-row matrix bytes and one
 run ID, an admitted training-source digest, and a validation-selected complete-parent
 B2 checkpoint with its exact file digest. It constructs the currently implemented complete-parent residual
-systems only: PhaseSet-V2 full and A2/A3/A4/A5/A6/A8. It verifies the row's
-fixed seed, split and same-seed B2 predecessor, and refuses A1/A7/A9, the old
+systems only: PhaseSet-V2 full and A1/A2/A3/A4/A5/A6/A8. It verifies the row's
+fixed seed, split and same-seed B2 predecessor, and refuses A7/A9, the old
 head, base and literature rows until their distinct implementations exist.
 There is no fallback to the full model for an unsupported system.
 
 | Row | Constructor difference |
 |---|---|
 | PhaseSet-V2 | full signed-vector phase and incidence model |
+| A1 | speed-magnitude input before the same six Morlet convolutions, with an independent typed training-floor receipt and physical cache |
 | A2 | order-free temporal MLP/mean paths |
 | A3 | capacity-matched pair-bag context |
 | A4 | explicit incidence shuffle seeded by the registered training seed |
@@ -24,9 +25,12 @@ derived from the same nonzero-CF candidate, changing only its loss weight;
 the caller must still present the same admitted weak training text pool as
 full. Residual initialization uses a forked CPU RNG seeded by the registered
 row, so same-seed full/A8 begin from the same trainable weights without
-mutating the caller's RNG. The A6 receipt must match the admitted
-training-source manifest digest in addition to its physical config and
-main population.
+mutating the caller's RNG. The A1/A6 receipts must match the admitted
+training-source manifest digest in addition to their physical config and
+main population. A1 also requires a speed-only source and checks its receipt
+against the model at host binding; it cannot silently use signed-vector cached
+responses. The periodic transform and its claim boundary are specified in
+[A1 speed control](PHASESET_V2_A1_SPEED_CONTROL.md).
 
 `make_v2_b2_base_host` constructs a main B2 host that writes the registered
 base row identity into its checkpoint manifest. It creates the registered
diff --git a/docs/PHASESET_V2_PARENT_DISK_SOURCE.md b/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
index 73b9c60..26a8d7c 100644
--- a/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
+++ b/docs/PHASESET_V2_PARENT_DISK_SOURCE.md
@@ -28,6 +28,12 @@ This source does not fit them or authorize reusing main-experiment floors in
 a held-out fold. Source, annotation, floor, feature, augmentation-rule and
 runtime identities belong in the private execution inputs.
 
+For A1, the source requires a separate speed-only cache and a typed floor
+receipt. It checks the loaded physical config and floor values against that
+receipt before yielding a view. Shared-yaw recomputation preserves the
+selected speed-only frontend rather than falling back to signed vectors.
+See the [A1 control contract](PHASESET_V2_A1_SPEED_CONTROL.md).
+
 The optional [frozen human CLIP rows](PHASESET_V2_FROZEN_CLIP_ROWS.md) connect
 the base/V2 human-only text interface without loading a tower at training.
 The legacy verified-CF branch still needs real human provenance. Under the
diff --git a/src/phaseset_core/continuous_phase_cache.py b/src/phaseset_core/continuous_phase_cache.py
index 2713dc2..2f9cf6b 100644
--- a/src/phaseset_core/continuous_phase_cache.py
+++ b/src/phaseset_core/continuous_phase_cache.py
@@ -20,6 +20,7 @@
     ACTOR_FEATURE_DIM,
     DirectionalPhaseField,
     LocalPhaseConfig,
+    VELOCITY_MODES,
     _directional_fields_from_arrays,
 )
 from .morlet import morlet_kernel_bank
@@ -27,6 +28,13 @@
 
 
 CACHE_SCHEMA = "phaseset-continuous-signed-response-cache-v1"
+SPEED_CACHE_SCHEMA = "phaseset-continuous-speed-response-cache-v1"
+
+
+def _cache_schema(velocity_mode: str) -> str:
+    if velocity_mode not in VELOCITY_MODES:
+        raise ValueError("unknown periodic velocity frontend")
+    return CACHE_SCHEMA if velocity_mode == "signed_vector" else SPEED_CACHE_SCHEMA
 
 
 def write_continuous_phase_cache(
@@ -35,6 +43,7 @@ def write_continuous_phase_cache(
     *,
     config: LocalPhaseConfig = LocalPhaseConfig(),
     max_disk_bytes: int = 16 * 2**30,
+    velocity_mode: str = "signed_vector",
 ) -> DirectionalPhaseField:
     """Write a fresh private physical cache; zero floors are collection-only.
 
@@ -46,6 +55,7 @@ def write_continuous_phase_cache(
         raise TypeError("capture must be exactly PreparedContinuousCapture")
     if type(max_disk_bytes) is not int or max_disk_bytes < 1:
         raise ValueError("disk byte budget must be a positive integer")
+    schema = _cache_schema(velocity_mode)
     destination = Path(directory)
     if destination.exists():
         raise FileExistsError("a physical cache must not overwrite an existing attempt")
@@ -74,6 +84,7 @@ def write_continuous_phase_cache(
         validate_energy_floors(np.zeros(6, dtype=np.float64)),
         config,
         response_directory=destination,
+        velocity_mode=velocity_mode,
     )[0]
     small_arrays = {
         "actor-features": field.actor_features,
@@ -93,7 +104,7 @@ def write_continuous_phase_cache(
         with (destination / f"{name}.npy").open("xb") as stream:
             np.save(stream, value, allow_pickle=False)
     metadata = {
-        "schema": CACHE_SCHEMA,
+        "schema": schema,
         "source_sha256": capture.source_sha256,
         "actor_count": capture.actor_count,
         "frame_count": capture.frame_count,
@@ -117,6 +128,7 @@ def load_continuous_phase_cache(
     *,
     expected_source_sha256: str,
     energy_floors: np.ndarray,
+    expected_velocity_mode: str = "signed_vector",
 ) -> DirectionalPhaseField:
     """Read-only maps with declared capture lineage and explicit fitted floors.
 
@@ -126,7 +138,10 @@ def load_continuous_phase_cache(
     """
     root = Path(directory)
     metadata = json.loads((root / "metadata.json").read_text(encoding="utf-8"))
-    if metadata["schema"] != CACHE_SCHEMA or metadata["source_sha256"] != expected_source_sha256:
+    if (
+        metadata["schema"] != _cache_schema(expected_velocity_mode)
+        or metadata["source_sha256"] != expected_source_sha256
+    ):
         raise ValueError("physical cache schema or capture source lineage differs")
     config = LocalPhaseConfig(**metadata["config"])
     intervals = tuple(tuple(row) for row in metadata["intervals"])
@@ -165,4 +180,5 @@ def mapped(name, dtype, shape):
         validate_energy_floors(energy_floors),
         config,
         source_sha256=metadata["source_sha256"],
+        velocity_mode=expected_velocity_mode,
     )
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
index 4da103c..4270ae7 100644
--- a/src/phaseset_core/continuous_retrieval.py
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -11,6 +11,7 @@
 from dataclasses import dataclass
 import json
 
+import numpy as np
 import torch
 from torch import Tensor, nn
 from torch.nn import functional as F
@@ -19,7 +20,8 @@
 from .continuous_training_input import ContinuousTrainingView
 from .dct_calibration import DctFloorReceipt
 from .dct_relations import DctRelationField, DctViewContext, require_dct_working_budget
-from .directional_phase import DirectionalPhaseField
+from .directional_phase import DirectionalPhaseField, LocalPhaseConfig, VELOCITY_MODES
+from .speed_calibration import SpeedFloorReceipt
 from .frozen_clip_text import FrozenClipTextBatch
 from .pipeline import collate_group_samples, edge_budget_batches
 from .temporal_coordination import (
@@ -117,6 +119,8 @@ def __init__(
         incidence_shuffle_seed: int | None = None,
         order_free: bool = False,
         pair_bag: bool = False,
+        periodic_velocity_mode: str = "signed_vector",
+        speed_floor_receipt: SpeedFloorReceipt | None = None,
         checkpoint_blocks: bool = True,
         base_window_batch_size: int = 1,
         base_edge_budget: int = 32768,
@@ -134,6 +138,23 @@ def __init__(
             raise ValueError("base_edge_budget must be a positive integer")
         if relation_kind not in ("phase", "true_mean_difference_dct"):
             raise ValueError("relation_kind must be phase or true_mean_difference_dct")
+        if periodic_velocity_mode not in VELOCITY_MODES or (
+            relation_kind != "phase" and periodic_velocity_mode != "signed_vector"
+        ):
+            raise ValueError("speed-only frontend is a distinct Morlet phase control")
+        if periodic_velocity_mode == "speed_only":
+            if type(speed_floor_receipt) is not SpeedFloorReceipt:
+                raise ValueError("A1 requires its independent training-only speed floor receipt")
+            speed_floor_receipt.require_config(LocalPhaseConfig())
+            self.register_buffer("speed_energy_floors", torch.from_numpy(speed_floor_receipt.floors.copy()))
+            self._speed_floor_receipt = speed_floor_receipt
+            self._speed_receipt_sha256 = speed_floor_receipt.sha256
+        else:
+            if speed_floor_receipt is not None:
+                raise ValueError("speed floor receipt must not enter another periodic system")
+            self.register_buffer("speed_energy_floors", None)
+            self._speed_floor_receipt = None
+            self._speed_receipt_sha256 = None
         if relation_kind != "phase" and incidence_shuffle_seed is not None:
             raise ValueError("A4 incidence shuffle is a separate phase control, not A6")
         if relation_kind != "phase" and order_free:
@@ -157,6 +178,7 @@ def __init__(
             self._dct_floor_receipt = dct_floor_receipt
             self._dct_receipt_sha256 = dct_floor_receipt.sha256
         self.relation_kind = relation_kind
+        self.periodic_velocity_mode = periodic_velocity_mode
         self.frozen_b2 = frozen_b2.requires_grad_(False).eval()
         self.coordination = TemporalIncidenceEncoder(
             width=512,
@@ -179,6 +201,9 @@ def get_extra_state(self) -> Tensor:
             "relation_kind": self.relation_kind,
             "dct_floor_receipt_sha256": self._dct_receipt_sha256,
         }
+        if self.periodic_velocity_mode != "signed_vector":
+            payload["periodic_velocity_mode"] = self.periodic_velocity_mode
+            payload["speed_floor_receipt_sha256"] = self._speed_receipt_sha256
         if self.coordination.incidence_shuffle_seed is not None:
             payload["incidence_shuffle_seed"] = self.coordination.incidence_shuffle_seed
         if self.coordination.order_free:
@@ -199,8 +224,9 @@ def set_extra_state(self, state: object) -> None:
             or state.ndim != 1
             or not torch.equal(state.detach().cpu(), self.get_extra_state())
         ):
-            raise ValueError("relation kind or DCT floor receipt changed across checkpoint resume")
+            raise ValueError("relation identity or floor receipt changed across checkpoint resume")
         self._check_dct_floor_binding()
+        self._check_speed_floor_binding()
 
     def _check_dct_floor_binding(self) -> None:
         if self.relation_kind == "phase":
@@ -214,6 +240,22 @@ def _check_dct_floor_binding(self) -> None:
         ):
             raise ValueError("DCT calibration buffer or receipt changed after construction")
 
+    def _check_speed_floor_binding(self, field: DirectionalPhaseField | None = None) -> None:
+        if self.periodic_velocity_mode != "speed_only":
+            return
+        if (
+            self._speed_floor_receipt.sha256 != self._speed_receipt_sha256
+            or not torch.equal(
+                self.speed_energy_floors.detach().cpu(),
+                torch.from_numpy(self._speed_floor_receipt.floors.copy()),
+            )
+        ):
+            raise ValueError("A1 speed calibration buffer or receipt changed")
+        if field is not None:
+            self._speed_floor_receipt.require_config(field.config)
+            if not np.array_equal(field.energy_floors, self._speed_floor_receipt.floors):
+                raise ValueError("A1 view floors differ from the model receipt")
+
     def train(self, mode: bool = True):
         super().train(mode)
         self.frozen_b2.eval()
@@ -257,6 +299,7 @@ def score(
             raise ValueError("views must be a nonempty tuple of admitted continuous inputs")
         text, _, receipt_sha = _validate_text_batch(text_batch, allow_row_selection=True)
         self._check_dct_floor_binding()
+        self._check_speed_floor_binding()
         device = next(self.coordination.parameters()).device
         keys = tuple(view.positive_capture_key for view in views)
         text = text.to(device)
@@ -279,6 +322,10 @@ def score(
             if self.relation_kind == "phase":
                 if type(view.phase_field) is not DirectionalPhaseField:
                     raise ValueError("phase retrieval requires the complete Morlet field")
+                if view.phase_field.velocity_mode != self.periodic_velocity_mode:
+                    raise ValueError("periodic velocity frontend differs from the registered model")
+                if self.periodic_velocity_mode == "speed_only":
+                    self._check_speed_floor_binding(view.phase_field)
                 physical = view.phase_field
             else:
                 physical = DctRelationField.from_capture(
diff --git a/src/phaseset_core/continuous_training_input.py b/src/phaseset_core/continuous_training_input.py
index 6e019c5..5b06d94 100644
--- a/src/phaseset_core/continuous_training_input.py
+++ b/src/phaseset_core/continuous_training_input.py
@@ -97,6 +97,7 @@ def load(
         *,
         cache_record: dict[str, Any],
         energy_floors: np.ndarray,
+        expected_velocity_mode: str = "signed_vector",
     ) -> ContinuousTrainingInput:
         prepared_record = cache_record["prepared_record"]
         if prepared_record["augmentation_yaw"] != 0.0:
@@ -128,7 +129,10 @@ def load(
         if metadata["frame_count"] != capture.frame_count:
             raise ValueError("physical cache timeline differs from prepared body22")
         field = load_continuous_phase_cache(
-            root, expected_source_sha256=capture.source_sha256, energy_floors=energy_floors
+            root,
+            expected_source_sha256=capture.source_sha256,
+            energy_floors=energy_floors,
+            expected_velocity_mode=expected_velocity_mode,
         )
         if field.actor_count != capture.actor_count or field.patch_count != cache_record["patches"]:
             raise ValueError("physical cache actor/patch census differs")
@@ -166,6 +170,9 @@ def view(
         # No zero-yaw features or approximate RMS/complex rotations are reused.
         # The existing RAM/edge gates explicitly reject oversized full captures.
         field = continuous_directional_phase_field(
-            rotated, energy_floors=self.cached_field.energy_floors, config=selected_config
+            rotated,
+            energy_floors=self.cached_field.energy_floors,
+            config=selected_config,
+            velocity_mode=self.cached_field.velocity_mode,
         )
         return ContinuousTrainingView(rotated, field, False)
diff --git a/src/phaseset_core/directional_calibration.py b/src/phaseset_core/directional_calibration.py
index c398f14..19dabfa 100644
--- a/src/phaseset_core/directional_calibration.py
+++ b/src/phaseset_core/directional_calibration.py
@@ -13,12 +13,13 @@
 
 import numpy as np
 
-from .directional_phase import DirectionalPhaseField
+from .directional_phase import DirectionalPhaseField, LocalPhaseConfig
 from .periodic import validate_energy_floors
 
 
 SEALED_EMBODY_COMPONENTS = frozenset(("C09", "C11", "C15"))
 CALIBRATION_METHOD = "signed-vector-actor-patch-power/linear-fifth-percentile-v1"
+SPEED_CALIBRATION_METHOD = "speed-only-actor-patch-power/linear-fifth-percentile-v1"
 
 
 @dataclass(frozen=True)
@@ -37,6 +38,7 @@ class DirectionalEnergyCalibration:
     missing_counts: tuple[int, ...]
     exact_zero_counts: tuple[int, ...]
     method: str = CALIBRATION_METHOD
+    config: LocalPhaseConfig = LocalPhaseConfig()
 
 
 def actor_patch_power_observations(
@@ -120,6 +122,8 @@ def fit_directional_energy_floors(
     missing = np.zeros(6, dtype=np.int64)
     total = 0
     observed_components: set[str] = set()
+    velocity_mode: str | None = None
+    physical_config: LocalPhaseConfig | None = None
     for capture in captures:
         # Admission precedes physical collection, including for validation rows.
         if capture.component not in allowed:
@@ -127,6 +131,14 @@ def fit_directional_energy_floors(
         if capture.capture_id not in expected or capture.capture_id in seen:
             raise ValueError("calibration capture is unexpected or repeated")
         field = capture.field
+        if velocity_mode is None:
+            velocity_mode = field.velocity_mode
+        elif field.velocity_mode != velocity_mode:
+            raise ValueError("training calibration may not mix periodic velocity frontends")
+        if physical_config is None:
+            physical_config = field.config
+        elif field.config != physical_config:
+            raise ValueError("training calibration may not mix physical configurations")
         total += field.actor_count * field.patch_count
         if total > max_actor_patch_observations:
             raise MemoryError("RESOURCE_LIMIT: complete actor-patch population exceeds budget")
@@ -144,11 +156,14 @@ def fit_directional_energy_floors(
         np.array([_linear_fifth_percentile(row) for row in values], dtype=np.float64)
     ).copy()
     floors.setflags(write=False)
+    assert physical_config is not None
     return DirectionalEnergyCalibration(
-        floors,
-        len(seen),
-        tuple(sorted(allowed)),
-        tuple(int(row.size) for row in values),
-        tuple(int(count) for count in missing),
-        tuple(int(np.count_nonzero(row == 0.0)) for row in values),
+        energy_floors=floors,
+        capture_count=len(seen),
+        training_components=tuple(sorted(allowed)),
+        observed_counts=tuple(int(row.size) for row in values),
+        missing_counts=tuple(int(count) for count in missing),
+        exact_zero_counts=tuple(int(np.count_nonzero(row == 0.0)) for row in values),
+        method=CALIBRATION_METHOD if velocity_mode == "signed_vector" else SPEED_CALIBRATION_METHOD,
+        config=physical_config,
     )
diff --git a/src/phaseset_core/directional_phase.py b/src/phaseset_core/directional_phase.py
index d82ac70..eeb8d9a 100644
--- a/src/phaseset_core/directional_phase.py
+++ b/src/phaseset_core/directional_phase.py
@@ -23,6 +23,7 @@
 
 PHASE_FEATURE_DIM = 7
 ACTOR_FEATURE_DIM = 132
+VELOCITY_MODES = frozenset({"signed_vector", "speed_only"})
 
 
 @dataclass(frozen=True)
@@ -56,7 +57,7 @@ class DirectionalPhaseField:
     response_masks: tuple[np.ndarray, ...]  # six [K,N_b,22,3] masks
     response_centers: tuple[np.ndarray, ...]
     intervals: tuple[tuple[int, int], ...]
-    actor_features: np.ndarray  # [K,P,132], signed mean and per-channel RMS
+    actor_features: np.ndarray  # [K,P,132], selected-frontend mean and per-channel RMS
     actor_patch_mask: np.ndarray  # [K,P]
     root_positions: np.ndarray  # [K,P,3]
     root_patch_mask: np.ndarray  # [K,P]
@@ -65,6 +66,11 @@ class DirectionalPhaseField:
     source_sha256: str | None = None
     actor_commitments: tuple[bytes, ...] | None = None
     physical_view_sha256: str | None = None
+    velocity_mode: str = "signed_vector"
+
+    def __post_init__(self) -> None:
+        if self.velocity_mode not in VELOCITY_MODES:
+            raise ValueError("unknown periodic velocity frontend")
 
     @property
     def actor_count(self) -> int:
@@ -109,6 +115,19 @@ def signed_joint_velocity(batch: PreparedGroupBatch) -> tuple[np.ndarray, np.nda
     )
 
 
+def speed_only_joint_velocity(batch: PreparedGroupBatch) -> tuple[np.ndarray, np.ndarray]:
+    """Remove vector direction before Morlet while preserving per-frame L2 energy."""
+    signed, mask = signed_joint_velocity(batch)
+    return _speed_only_arrays(signed), mask
+
+
+def _speed_only_arrays(signed: np.ndarray) -> np.ndarray:
+    speed = np.linalg.norm(signed, axis=-1) / math.sqrt(3.0)
+    values = np.broadcast_to(speed[..., None], signed.shape).copy()
+    values[values == 0.0] = 0.0
+    return _readonly(values)
+
+
 def _signed_velocity_arrays(
     skeletons: np.ndarray,
     track_mask: np.ndarray,
@@ -145,6 +164,7 @@ def directional_phase_fields(
     *,
     energy_floors: np.ndarray,
     config: LocalPhaseConfig = LocalPhaseConfig(),
+    velocity_mode: str = "signed_vector",
 ) -> tuple[DirectionalPhaseField, ...]:
     """Compute each actor/band once, preserving the unchanged physical kernels.
 
@@ -162,6 +182,7 @@ def directional_phase_fields(
         checked.valid_lengths,
         floors,
         config,
+        velocity_mode=velocity_mode,
     )
 
 
@@ -170,6 +191,7 @@ def continuous_directional_phase_field(
     *,
     energy_floors: np.ndarray,
     config: LocalPhaseConfig = LocalPhaseConfig(),
+    velocity_mode: str = "signed_vector",
 ) -> DirectionalPhaseField:
     """Analyze a whole capture without the legacy short-window time limit.
 
@@ -189,6 +211,7 @@ def continuous_directional_phase_field(
         (capture.frame_count,),
         floors,
         config,
+        velocity_mode=velocity_mode,
     )[0]
     return replace(
         field,
@@ -208,7 +231,11 @@ def _directional_fields_from_arrays(
     floors: np.ndarray,
     config: LocalPhaseConfig,
     response_directory: Path | None = None,
+    *,
+    velocity_mode: str = "signed_vector",
 ) -> tuple[DirectionalPhaseField, ...]:
+    if velocity_mode not in VELOCITY_MODES:
+        raise ValueError("unknown periodic velocity frontend")
     required_edges = sum(count * (count - 1) // 2 for count in actor_counts)
     if required_edges > config.edge_budget:
         raise ResourceLimitError(required_edges=required_edges, edge_budget=config.edge_budget)
@@ -222,6 +249,8 @@ def _directional_fields_from_arrays(
     if response_directory is None and projected > config.max_response_bytes:
         raise MemoryError("RESOURCE_LIMIT: actor Morlet responses exceed the byte budget")
     signed, signed_mask = _signed_velocity_arrays(skeletons, track_mask, frame_mask, actor_mask)
+    if velocity_mode == "speed_only":
+        signed = _speed_only_arrays(signed)
     results = []
     for row, (count, length) in enumerate(zip(actor_counts, valid_lengths, strict=True)):
         values = signed[row, :count, :length].reshape(count, length, 66)
@@ -306,6 +335,7 @@ def _directional_fields_from_arrays(
                 _readonly(root_mask),
                 floors,
                 config,
+                velocity_mode=velocity_mode,
             )
         )
     return tuple(results)
diff --git a/src/phaseset_core/parent_disk_source.py b/src/phaseset_core/parent_disk_source.py
index 7bb6138..6df5321 100644
--- a/src/phaseset_core/parent_disk_source.py
+++ b/src/phaseset_core/parent_disk_source.py
@@ -23,7 +23,8 @@
 )
 from .dct_calibration import DctFloorReceipt
 from .dct_relations import DctViewContext, require_dct_working_budget
-from .directional_phase import LocalPhaseConfig
+from .directional_phase import LocalPhaseConfig, VELOCITY_MODES
+from .speed_calibration import SpeedFloorReceipt
 from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
 from .parent_clip_rows import ParentHumanClipRows
 from .parent_weak_clip_rows import ParentWeakClipRows
@@ -83,6 +84,8 @@ def __init__(
         dct_floor_receipt: DctFloorReceipt | None = None,
         human_clip_rows: ParentHumanClipRows | None = None,
         weak_clip_rows: ParentWeakClipRows | None = None,
+        velocity_mode: str = "signed_vector",
+        speed_floor_receipt: SpeedFloorReceipt | None = None,
     ):
         if type(task) is not ParentRetrievalTask or any(
             type(record) is not ParentDiskRecord for record in records
@@ -101,6 +104,20 @@ def __init__(
             raise TypeError("need the closed frozen TMR/WaMo human feature rows")
         if energy_floors is not None and dct_floor_receipt is not None:
             raise ValueError("phase and A6 floors must not be admitted together")
+        if velocity_mode not in VELOCITY_MODES or (
+            velocity_mode == "speed_only" and energy_floors is None
+        ):
+            raise ValueError("speed-only source requires an independent phase cache and floors")
+        if velocity_mode == "speed_only":
+            if type(speed_floor_receipt) is not SpeedFloorReceipt:
+                raise ValueError("A1 source requires an independent speed floor receipt")
+            speed_floor_receipt.require_config(LocalPhaseConfig())
+            if not np.array_equal(energy_floors, speed_floor_receipt.floors):
+                raise ValueError("A1 source floors differ from its training receipt")
+        elif speed_floor_receipt is not None:
+            raise ValueError("speed floor receipt cannot enter a signed-vector source")
+        self.velocity_mode = velocity_mode
+        self.speed_floor_receipt = speed_floor_receipt
         if dct_floor_receipt is not None:
             if type(dct_floor_receipt) is not DctFloorReceipt:
                 raise TypeError("A6 disk source requires a typed DCT floor receipt")
@@ -195,7 +212,10 @@ def view(self, parent, *, seed, epoch, training):
             record.physical_directory,
             cache_record=record.physical_record,
             energy_floors=self.energy_floors,
+            expected_velocity_mode=self.velocity_mode,
         )
+        if self.speed_floor_receipt is not None:
+            self.speed_floor_receipt.require_config(complete.cached_field.config)
         return complete.view(
             yaw_delta=angle,
             allow_shared_yaw=self.yaw_eligibility[parent.annotation_family_sha256],
diff --git a/src/phaseset_core/speed_calibration.py b/src/phaseset_core/speed_calibration.py
new file mode 100644
index 0000000..25872ea
--- /dev/null
+++ b/src/phaseset_core/speed_calibration.py
@@ -0,0 +1,104 @@
+"""Typed train-only floor declaration for the A1 speed-magnitude Morlet frontend.
+
+The receipt binds the declared population and input/configuration but is not
+itself proof that the fit ran; private admission verifies the source census.
+"""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+import hashlib
+import json
+
+import numpy as np
+
+from .directional_calibration import DirectionalEnergyCalibration, SPEED_CALIBRATION_METHOD
+from .directional_phase import LocalPhaseConfig
+from .periodic import validate_energy_floors
+
+
+SPEED_FLOOR_SCHEMA = "phaseset-v2-a1-speed-training-floor-v1"
+_POPULATIONS = frozenset({"main", "pilot", "fold_0", "fold_1", "fold_2"})
+_SEALED = frozenset({"C09", "C11", "C15"})
+
+
+def speed_config_sha256(config: LocalPhaseConfig) -> str:
+    if type(config) is not LocalPhaseConfig:
+        raise TypeError("A1 floor configuration requires LocalPhaseConfig")
+    encoded = json.dumps(asdict(config), sort_keys=True, separators=(",", ":")).encode()
+    return hashlib.sha256(b"phaseset-v2-speed-config-v1\0" + encoded).hexdigest()
+
+
+def _sha256_key(value: object) -> bool:
+    return type(value) is str and len(value) == 64 and all(c in "0123456789abcdef" for c in value)
+
+
+@dataclass(frozen=True)
+class SpeedFloorReceipt:
+    schema: str
+    population_name: str
+    training_components: tuple[str, ...]
+    training_source_manifest_sha256: str
+    config_sha256: str
+    floors: np.ndarray
+
+    def __post_init__(self) -> None:
+        if self.schema != SPEED_FLOOR_SCHEMA or self.population_name not in _POPULATIONS:
+            raise ValueError("A1 floor schema or population differs")
+        if (
+            type(self.training_components) is not tuple
+            or not self.training_components
+            or len(set(self.training_components)) != len(self.training_components)
+            or tuple(sorted(self.training_components)) != self.training_components
+            or bool(set(self.training_components) & _SEALED)
+            or not _sha256_key(self.training_source_manifest_sha256)
+            or not _sha256_key(self.config_sha256)
+        ):
+            raise ValueError("A1 floor training population or source binding differs")
+        checked = validate_energy_floors(self.floors)
+        frozen = checked.copy()
+        frozen.setflags(write=False)
+        object.__setattr__(self, "floors", frozen)
+
+    @classmethod
+    def from_fit(
+        cls,
+        fit: DirectionalEnergyCalibration,
+        *,
+        population_name: str,
+        training_source_manifest_sha256: str,
+        config: LocalPhaseConfig,
+    ) -> SpeedFloorReceipt:
+        if type(fit) is not DirectionalEnergyCalibration or fit.method != SPEED_CALIBRATION_METHOD:
+            raise ValueError("A1 floor receipt requires the complete speed-only fit")
+        if fit.config != config:
+            raise ValueError("A1 floor receipt/config differs from the actual training fit")
+        return cls(
+            SPEED_FLOOR_SCHEMA,
+            population_name,
+            fit.training_components,
+            training_source_manifest_sha256,
+            speed_config_sha256(config),
+            fit.energy_floors,
+        )
+
+    @property
+    def sha256(self) -> str:
+        payload = {
+            "schema": self.schema,
+            "population_name": self.population_name,
+            "training_components": self.training_components,
+            "training_source_manifest_sha256": self.training_source_manifest_sha256,
+            "config_sha256": self.config_sha256,
+            "floors_float64_le_hex": self.floors.astype("<f8", copy=False).tobytes().hex(),
+        }
+        encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()
+        return hashlib.sha256(b"phaseset-v2-speed-floor-receipt-v1\0" + encoded).hexdigest()
+
+    def require_config(self, config: LocalPhaseConfig) -> None:
+        if self.config_sha256 != speed_config_sha256(config):
+            raise ValueError("A1 floor receipt/config differs")
+
+    def require_population(self, name: str, components: tuple[str, ...]) -> None:
+        if self.population_name != name or self.training_components != components:
+            raise ValueError("A1 floor receipt/training population differs")
diff --git a/src/phaseset_core/v2_mechanism_factory.py b/src/phaseset_core/v2_mechanism_factory.py
index 7d071e5..878d3e9 100644
--- a/src/phaseset_core/v2_mechanism_factory.py
+++ b/src/phaseset_core/v2_mechanism_factory.py
@@ -25,6 +25,7 @@
 from .continuous_base_retrieval import ContinuousBaseRetrievalSystem
 from .continuous_retrieval import ContinuousRetrievalSystem
 from .dct_calibration import DctFloorReceipt
+from .speed_calibration import SpeedFloorReceipt
 from .directional_phase import LocalPhaseConfig
 from .study_storage import V2_MATRIX_SHA256
 from .parent_retrieval_task import ParentRetrievalTask
@@ -40,7 +41,7 @@ class V2MechanismHold(ValueError):
     """A row cannot yet be constructed as its registered scientific system."""
 
 
-_SUPPORTED = frozenset({"PhaseSet-V2", "A2", "A3", "A4", "A5", "A6", "A8"})
+_SUPPORTED = frozenset({"PhaseSet-V2", "A1", "A2", "A3", "A4", "A5", "A6", "A8"})
 
 
 def _sha256_key(value: str) -> bool:
@@ -190,6 +191,7 @@ def build_v2_mechanism(
     selected_b2: V2SelectedB2Checkpoint,
     training_source_manifest_sha256: str,
     dct_floor_receipt: DctFloorReceipt | None = None,
+    speed_floor_receipt: SpeedFloorReceipt | None = None,
     checkpoint_blocks: bool = True,
 ) -> V2MechanismBuild:
     """Construct the exact implemented mechanism, never a launch decision."""
@@ -275,6 +277,10 @@ def build_v2_mechanism(
         raise V2MechanismHold("a DCT floor receipt may only enter A6")
     if system_id == "A6" and type(dct_floor_receipt) is not DctFloorReceipt:
         raise V2MechanismHold("A6 requires its typed training-only DCT floor receipt")
+    if system_id != "A1" and speed_floor_receipt is not None:
+        raise V2MechanismHold("a speed floor receipt may only enter A1")
+    if system_id == "A1" and type(speed_floor_receipt) is not SpeedFloorReceipt:
+        raise V2MechanismHold("A1 requires its typed training-only speed floor receipt")
     if type(checkpoint_blocks) is not bool:
         raise V2MechanismHold("checkpointing choice must be explicit boolean")
     training_components = validation_components = None
@@ -302,6 +308,11 @@ def build_v2_mechanism(
         dct_floor_receipt.require_population("main", training_components)
         if dct_floor_receipt.training_source_manifest_sha256 != training_source_manifest_sha256:
             raise V2MechanismHold("A6 floor source differs from admitted training source")
+    if system_id == "A1":
+        speed_floor_receipt.require_config(LocalPhaseConfig())
+        speed_floor_receipt.require_population("main", training_components)
+        if speed_floor_receipt.training_source_manifest_sha256 != training_source_manifest_sha256:
+            raise V2MechanismHold("A1 floor source differs from admitted training source")
     kwargs = {
         "order_free": system_id == "A2",
         "pair_bag": system_id == "A3",
@@ -309,6 +320,8 @@ def build_v2_mechanism(
         "strip_phase": system_id == "A5",
         "relation_kind": "true_mean_difference_dct" if system_id == "A6" else "phase",
         "dct_floor_receipt": dct_floor_receipt,
+        "periodic_velocity_mode": "speed_only" if system_id == "A1" else "signed_vector",
+        "speed_floor_receipt": speed_floor_receipt,
         "checkpoint_blocks": checkpoint_blocks,
     }
     # The host seeds inside fit, which is too late for model initialization.
@@ -378,6 +391,12 @@ def make_v2_parent_host(
     ):
         raise V2MechanismHold("host checkpoint or admitted training source differs from build")
     bound_config = bind_v2_parent_host_config(build, config)
+    if build.system_id == "A1" and (
+        getattr(source, "velocity_mode", None) != "speed_only"
+        or getattr(getattr(source, "speed_floor_receipt", None), "sha256", None)
+        != build.model._speed_receipt_sha256
+    ):
+        raise V2MechanismHold("A1 host requires the registered speed-only source and floor receipt")
     identity = ParentHostRunIdentity(
         build.run_id, build.system_id, build.predecessor_run_id
     )
diff --git a/tests/test_phaseset_v2_a1_speed.py b/tests/test_phaseset_v2_a1_speed.py
new file mode 100644
index 0000000..eb4f779
--- /dev/null
+++ b/tests/test_phaseset_v2_a1_speed.py
@@ -0,0 +1,204 @@
+"""A1 speed-only physical input and cache separation, not research scores."""
+
+from __future__ import annotations
+
+import copy
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.continuous_capture import prepare_continuous_capture
+from phaseset_core.continuous_phase_cache import (
+    load_continuous_phase_cache,
+    write_continuous_phase_cache,
+)
+from phaseset_core.contracts import PreparedGroupBatch, group_commitment
+from phaseset_core.directional_calibration import (
+    DirectionalCalibrationCapture,
+    SPEED_CALIBRATION_METHOD,
+    fit_directional_energy_floors,
+)
+from phaseset_core.directional_phase import (
+    LocalPhaseConfig,
+    continuous_directional_phase_field,
+    directional_phase_fields,
+    local_pair_chunk,
+    signed_joint_velocity,
+    speed_only_joint_velocity,
+)
+from phaseset_core.temporal_coordination import TemporalIncidenceEncoder
+from phaseset_core.pipeline import PrivateCaptureArrays
+from phaseset_core.speed_calibration import SpeedFloorReceipt
+
+
+def _opposite_batch() -> PreparedGroupBatch:
+    frames = 200
+    skeletons = np.zeros((1, 2, frames, 22, 3), dtype=np.float32)
+    signal = np.sin(2 * np.pi * 1.125 * np.arange(frames) / 20).astype(np.float32)
+    skeletons[0, 0, :, 1:, 0] = signal[:, None]
+    skeletons[0, 1, :, 1:, 0] = -signal[:, None]
+    keys = (b"a" * 32, b"b" * 32)
+    return PreparedGroupBatch(
+        skeletons,
+        np.ones((1, 2), dtype=np.bool_),
+        np.ones((1, frames), dtype=np.bool_),
+        np.ones((1, 2, frames, 22), dtype=np.bool_),
+        (keys,),
+        (group_commitment(keys),),
+    )
+
+
+def test_a1_speed_removes_direction_before_morlet_but_preserves_vector_energy() -> None:
+    batch = _opposite_batch()
+    signed, signed_mask = signed_joint_velocity(batch)
+    speed, speed_mask = speed_only_joint_velocity(batch)
+    np.testing.assert_array_equal(signed_mask, speed_mask)
+    np.testing.assert_array_equal(speed[0, 0], speed[0, 1])
+    np.testing.assert_allclose(
+        np.sum(speed**2, axis=-1), np.sum(signed**2, axis=-1), rtol=1e-14, atol=1e-14
+    )
+    assert np.any(signed[0, 0] != signed[0, 1])
+    floors = np.zeros(6, dtype=np.float64)
+    full = directional_phase_fields(batch, energy_floors=floors)[0]
+    a1 = directional_phase_fields(batch, energy_floors=floors, velocity_mode="speed_only")[0]
+    assert (full.velocity_mode, a1.velocity_mode) == ("signed_vector", "speed_only")
+    assert any(np.any(row[0] != row[1]) for row in full.responses)
+    for row in a1.responses:
+        np.testing.assert_array_equal(row[0], row[1])
+    np.testing.assert_array_equal(full.root_positions, a1.root_positions)
+
+
+def test_a1_actor_permutation_holey_padding_and_pair_reversal_are_exact() -> None:
+    reference = _opposite_batch()
+    skeletons = np.zeros((1, 5, 200, 22, 3), dtype=np.float32)
+    track = np.zeros((1, 5, 200, 22), dtype=np.bool_)
+    skeletons[0, 1] = reference.skeletons[0, 1]
+    skeletons[0, 3] = reference.skeletons[0, 0]
+    track[0, 1] = reference.track_mask[0, 1]
+    track[0, 3] = reference.track_mask[0, 0]
+    candidate = PreparedGroupBatch(
+        skeletons,
+        np.array([[False, True, False, True, False]], dtype=np.bool_),
+        np.ones((1, 200), dtype=np.bool_),
+        track,
+        ((None, b"b" * 32, None, b"a" * 32, None),),
+        reference.group_commitments,
+    )
+    floors = np.zeros(6, dtype=np.float64)
+    left = directional_phase_fields(reference, energy_floors=floors, velocity_mode="speed_only")[0]
+    right = directional_phase_fields(candidate, energy_floors=floors, velocity_mode="speed_only")[0]
+    for a, b in zip(left.responses, right.responses, strict=True):
+        np.testing.assert_array_equal(a, b)
+    pair = local_pair_chunk(left, 0, 1)
+    np.testing.assert_array_equal(pair.features, local_pair_chunk(right, 0, 1).features)
+    reverse = pair.reverse_features()
+    np.testing.assert_array_equal(reverse[..., 0], pair.features[..., 0])
+    np.testing.assert_array_equal(reverse[..., 1], -pair.features[..., 1])
+    np.testing.assert_array_equal(reverse[..., 3], pair.features[..., 4])
+    np.testing.assert_array_equal(reverse[..., 4], pair.features[..., 3])
+
+
+def test_a1_k2_topology_gradient_is_positive_zero_and_pair_only_equal() -> None:
+    torch.manual_seed(1729)
+    full = TemporalIncidenceEncoder(32)
+    pair = copy.deepcopy(full)
+    pair.use_topology = False
+    source = directional_phase_fields(
+        _opposite_batch(), energy_floors=np.zeros(6, np.float64), velocity_mode="speed_only"
+    )[0]
+    result, control = full(source), pair(source)
+    assert torch.equal(result.embedding, control.embedding)
+    assert torch.count_nonzero(result.topology_nodes) == 0
+    assert not bool(torch.signbit(result.topology_nodes).any())
+    result.embedding.sum().backward()
+    topology = list(full.node_mlp.parameters()) + list(full.node_temporal.parameters())
+    topology += list(full.edge_delta.parameters()) + [full.topology_scale]
+    for parameter in topology:
+        assert parameter.grad is not None and torch.count_nonzero(parameter.grad) == 0
+        assert not bool(torch.signbit(parameter.grad).any())
+
+
+def _continuous_capture():
+    frames = 600
+    joints = np.zeros((frames, 2, 22, 3), dtype=np.float32)
+    signal = np.sin(2 * np.pi * 1.125 * np.arange(frames) / 30).astype(np.float32)
+    joints[:, 0, 1:, 0] = signal[:, None]
+    joints[:, 1, 1:, 0] = -signal[:, None]
+    return prepare_continuous_capture(
+        PrivateCaptureArrays(
+            joints,
+            np.ones(joints.shape[:-1], dtype=np.bool_),
+            (b"a" * 32, b"b" * 32),
+            "a" * 64,
+        )
+    )
+
+
+def test_a1_has_separate_complete_cache_and_training_only_floor_method(tmp_path) -> None:
+    capture = _continuous_capture()
+    floors = np.zeros(6, dtype=np.float64)
+    ram = continuous_directional_phase_field(
+        capture, energy_floors=floors, velocity_mode="speed_only"
+    )
+    cache = tmp_path / "a1-physical"
+    raw = write_continuous_phase_cache(capture, cache, velocity_mode="speed_only")
+    mapped = load_continuous_phase_cache(
+        cache,
+        expected_source_sha256=capture.source_sha256,
+        energy_floors=floors,
+        expected_velocity_mode="speed_only",
+    )
+    assert raw.velocity_mode == mapped.velocity_mode == "speed_only"
+    for expected, actual in zip(ram.responses, mapped.responses, strict=True):
+        np.testing.assert_array_equal(expected.view(np.uint64), actual.view(np.uint64))
+    with pytest.raises(ValueError, match="schema"):
+        load_continuous_phase_cache(
+            cache, expected_source_sha256=capture.source_sha256, energy_floors=floors
+        )
+    fit = fit_directional_energy_floors(
+        (DirectionalCalibrationCapture(capture.source_sha256, "C01", raw),),
+        training_components=("C01",),
+        expected_capture_ids=(capture.source_sha256,),
+    )
+    assert fit.method == SPEED_CALIBRATION_METHOD
+    receipt = SpeedFloorReceipt.from_fit(
+        fit,
+        population_name="pilot",
+        training_source_manifest_sha256="b" * 64,
+        config=raw.config,
+    )
+    assert receipt.population_name == "pilot" and len(receipt.sha256) == 64
+    with pytest.raises(ValueError, match="actual training fit"):
+        SpeedFloorReceipt.from_fit(
+            fit,
+            population_name="pilot",
+            training_source_manifest_sha256="b" * 64,
+            config=LocalPhaseConfig(patch_frames=20),
+        )
+    with pytest.raises(ValueError, match="frontends"):
+        fit_directional_energy_floors(
+            (
+                DirectionalCalibrationCapture(capture.source_sha256, "C01", raw),
+                DirectionalCalibrationCapture("other", "C01", continuous_directional_phase_field(capture, energy_floors=floors)),
+            ),
+            training_components=("C01",),
+            expected_capture_ids=(capture.source_sha256, "other"),
+        )
+    with pytest.raises(ValueError, match="physical configurations"):
+        fit_directional_energy_floors(
+            (
+                DirectionalCalibrationCapture(capture.source_sha256, "C01", raw),
+                DirectionalCalibrationCapture(
+                    "other", "C01",
+                    continuous_directional_phase_field(
+                        capture,
+                        energy_floors=floors,
+                        velocity_mode="speed_only",
+                        config=LocalPhaseConfig(patch_frames=20),
+                    ),
+                ),
+            ),
+            training_components=("C01",),
+            expected_capture_ids=(capture.source_sha256, "other"),
+        )
diff --git a/tests/test_phaseset_v2_mechanism_factory.py b/tests/test_phaseset_v2_mechanism_factory.py
index bc85ace..738f88c 100644
--- a/tests/test_phaseset_v2_mechanism_factory.py
+++ b/tests/test_phaseset_v2_mechanism_factory.py
@@ -14,6 +14,7 @@
 from phaseset_core.continuous_parent_host import ParentHostBindings, ParentHostConfig
 from phaseset_core.dct_calibration import DCT_FLOOR_SCHEMA, DctFloorReceipt, dct_config_sha256
 from phaseset_core.directional_phase import LocalPhaseConfig
+from phaseset_core.speed_calibration import SPEED_FLOOR_SCHEMA, SpeedFloorReceipt, speed_config_sha256
 from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
 from phaseset_core.training import (
     _atomic_torch_save,
@@ -101,6 +102,17 @@ def _dct_receipt(*, population: str = "main") -> DctFloorReceipt:
     )
 
 
+def _speed_receipt(*, population: str = "main") -> SpeedFloorReceipt:
+    return SpeedFloorReceipt(
+        SPEED_FLOOR_SCHEMA,
+        population,
+        MAIN_TRAIN,
+        SOURCE_SHA,
+        speed_config_sha256(LocalPhaseConfig()),
+        np.ones(6, dtype=np.float64),
+    )
+
+
 @pytest.mark.parametrize(
     ("run_id", "system_id", "attribute", "expected"),
     (
@@ -144,6 +156,27 @@ def test_a6_requires_exact_population_and_uses_only_dct_relations(tmp_path) -> N
         _build(tmp_path, "V2-031", selected=selected, dct_floor_receipt=_dct_receipt())
 
 
+def test_a1_binds_independent_speed_floor_and_preserves_residual_capacity(tmp_path) -> None:
+    selected = _selected(tmp_path)
+    with pytest.raises(V2MechanismHold, match="typed"):
+        _build(tmp_path, "V2-025", selected=selected)
+    with pytest.raises(ValueError, match="population"):
+        _build(tmp_path, "V2-025", selected=selected, speed_floor_receipt=_speed_receipt(population="pilot"))
+    with pytest.raises(V2MechanismHold, match="floor source"):
+        _build(tmp_path, "V2-025", selected=selected, source_sha="b" * 64, speed_floor_receipt=_speed_receipt())
+    full = _build(tmp_path, "V2-022", selected=selected)
+    a1 = _build(tmp_path, "V2-025", selected=selected, speed_floor_receipt=_speed_receipt())
+    assert a1.system_id == "A1" and a1.model.periodic_velocity_mode == "speed_only"
+    assert a1.model.relation_kind == "phase" and a1.counterfactual_objective_enabled
+    assert sum(p.numel() for p in full.model.parameters() if p.requires_grad) == sum(
+        p.numel() for p in a1.model.parameters() if p.requires_grad
+    )
+    with pytest.raises(ValueError, match="checkpoint resume"):
+        a1.model.set_extra_state(full.model.get_extra_state())
+    with pytest.raises(V2MechanismHold, match="only enter A1"):
+        _build(tmp_path, "V2-022", selected=selected, speed_floor_receipt=_speed_receipt())
+
+
 def test_a8_disables_only_cf_objective_and_preserves_weak_pool_host_contract(tmp_path) -> None:
     selected = _selected(tmp_path)
     full = _build(tmp_path, "V2-022", selected=selected)
@@ -166,7 +199,7 @@ def test_a8_disables_only_cf_objective_and_preserves_weak_pool_host_contract(tmp
 
 def test_unsupported_rows_and_matrix_drift_fail_without_full_model_fallback(tmp_path) -> None:
     selected = _selected(tmp_path)
-    for run_id in ("V2-025", "V2-043", "V2-049", "V2-007"):
+    for run_id in ("V2-043", "V2-049", "V2-007"):
         with pytest.raises(V2MechanismHold, match="not an implemented V2 mechanism"):
             _build(tmp_path, run_id, selected=selected)
     with pytest.raises(V2MechanismHold, match="exact frozen"):

From 20998304f97de04faa8ce46e8f396fe9a9463df4 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 08:46:12 -0500
Subject: [PATCH 87/95] Preserve checkpoint identity error compatibility

---
 RELEASE_FILES.sha256                      | 2 +-
 src/phaseset_core/continuous_retrieval.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 89fc34a..9d128bf 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -188,7 +188,7 @@ ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phase
 3a75c6185167ee3dd952169fa0bae43e1e879a6253d681e35e0b4cf424bbbed3	34377	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 564bfe9b1717ac6e539e62726a245b27fc84103e235a1ed1ab1bf04569b36f7a	7483	src/phaseset_core/continuous_phase_cache.py
-09480e48941efb42709224b52f066ee78cb126a20a96e7aa173481f8a9e6116e	18310	src/phaseset_core/continuous_retrieval.py
+e0f3674225163ce5495d1a58a9f64037638fcfbb8f57444701851c93899125c3	18317	src/phaseset_core/continuous_retrieval.py
 d66eb2fd534a31701c7dcb8b4fdb634b8db6186b423e83fcc3af5c60152aa8c8	7381	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
index 4270ae7..1a97d11 100644
--- a/src/phaseset_core/continuous_retrieval.py
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -224,7 +224,7 @@ def set_extra_state(self, state: object) -> None:
             or state.ndim != 1
             or not torch.equal(state.detach().cpu(), self.get_extra_state())
         ):
-            raise ValueError("relation identity or floor receipt changed across checkpoint resume")
+            raise ValueError("relation kind, frontend, or floor receipt changed across checkpoint resume")
         self._check_dct_floor_binding()
         self._check_speed_floor_binding()
 

From de80dc862292eb9fdbc0255e9b56de05ff5b1e5a Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 11:02:34 -0500
Subject: [PATCH 88/95] Implement phase-independent A7 generic local control

---
 README.md                                   | 12 ++-
 RELEASE_FILES.sha256                        | 15 ++--
 docs/PHASESET_V2_A7_GENERIC_LOCAL.md        | 35 +++++++++
 docs/PHASESET_V2_MECHANISM_FACTORY.md       |  7 +-
 src/phaseset_core/continuous_retrieval.py   |  9 +++
 src/phaseset_core/temporal_coordination.py  | 83 ++++++++++++---------
 src/phaseset_core/v2_mechanism_factory.py   |  3 +-
 tests/test_phaseset_v2_coordination.py      | 67 +++++++++++++++++
 tests/test_phaseset_v2_mechanism_factory.py | 19 ++++-
 9 files changed, 201 insertions(+), 49 deletions(-)
 create mode 100644 docs/PHASESET_V2_A7_GENERIC_LOCAL.md

diff --git a/README.md b/README.md
index c527696..096f1c8 100644
--- a/README.md
+++ b/README.md
@@ -93,10 +93,14 @@ keeps the full head's active node and edge modules but pools relation packets
 without assigning them to actor nodes. It is distinct from the legacy
 topology-off pair-only diagnostic and has no reported retrieval result yet.
 The [matrix-bound V2 mechanism factory](docs/PHASESET_V2_MECHANISM_FACTORY.md)
-constructs the implemented main B2 and full/A2/A3/A4/A5/A6/A8 host paths
-with registered seeds, predecessor/terminal and source bindings. Its 32
-focused CPU checks passed on the registered server; it grants no pilot or
-formal launch, and A1/A7/A9, fold hosts and Inter-X remain held.
+constructs the implemented main B2 and full/A1/A2/A3/A4/A5/A6/A7/A8 host paths
+with registered seeds, predecessor/terminal and source bindings. The A7
+focused registered-server CPU qualification passed 42 tests with one
+CUDA-only skip; it grants no pilot or
+formal launch, and A9, fold hosts and Inter-X remain held. A7 is a
+[generic local relation control](docs/PHASESET_V2_A7_GENERIC_LOCAL.md): its
+scores ignore phase features, while its shared input transport still incurs
+phase-cache preparation cost.
 [TMR-Set](docs/TMR_SET_ADAPTATION.md) now implements the
 complete original VAE objectives with disclosed full-parent group adaptation;
 its pinned frozen language loader is not a substitute for native learning.
diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 9d128bf..f198854 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -9,7 +9,7 @@ a9cb2d366c1a36a48dc840c697b844f173f1947eb6c022c399d2d4725ce6ed93	791	CONTRIBUTIN
 cac1d22ba72c8bec57d01dab993e001f5d002eb73346e2a666dedd996186e3bb	1096	LICENSE
 761307ccabbb869559bc7b9a22c0df72a5521ff1d1278eed77b564cfe5870e31	1367	LICENSE_TMR.md
 b5613fee419a712d8909e243c73547a7d0a5915900365e801cc3840abdbc50ab	2199	MIGRATION.md
-5ff930915463dbfb0d309cd35fcb95b384747370aeef4a8f0d5230314e91ba37	17330	README.md
+b0a88c6f90e097550acc7dc2053642eb488b958b58711a9d62ac97b0272c5619	17553	README.md
 adaa9b29f0b73606055b727affb459418f5e986c932b330c1b93ef1007a4aedb	7828	RELEASE_MANIFEST.md
 876add20b16390ac8329efd2f94039f0304508d75995af1b556b70203027d8c2	579	SECURITY.md
 e126fea9f0f73e87bab2a596c08192271336a95b065218a5003180a029c52498	31798	STATUS.md
@@ -76,6 +76,7 @@ f98b8d0c2a4e9f75e65d46556951472cd4838f32f2561cc00934c2fc9f810bb8	1647	docs/PHASE
 794bcdc4fe9d2c7cf6014157a7958851c3e86256006b74844fb97013117a4bbf	1920	docs/PHASESET_V2_A3_PAIR_BAG.md
 09c14876d4e0055f4ea19c710523f31ec6aab505ee22ca6f21a2ada6414e03bc	1534	docs/PHASESET_V2_A4_INCIDENCE.md
 cb44019ac3459bb2e26f52e1be65d80d3ff065edd8d39aa0d84155772e0ee258	1893	docs/PHASESET_V2_A6_SOURCE_QUALIFICATION.md
+fdcf40e6f0f30729808c66c55d9a0817b1c6b71032751e0baa5e1a287771794e	2252	docs/PHASESET_V2_A7_GENERIC_LOCAL.md
 4f6373298ef558489e33a48ee7cf31090095cf19508c92b40ace682f461518bc	6458	docs/PHASESET_V2_AMENDMENT.md
 9a2067d2412fb619408af75cf0d7644c24a4086b7bb3a15dfca9f13ad77a3b44	21120	docs/PHASESET_V2_CALIBRATION_AND_CACHE.md
 6d50263826636377615082911b0de5afe871b8887f9419f95bbe4fb4db7f914d	3439	docs/PHASESET_V2_FROZEN_CLIP_ROWS.md
@@ -84,7 +85,7 @@ b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASE
 1e0d2fb0b35a6900dfb03af4b9bfe1701eb2c843f566c0f842b47b03b11b645e	1276	docs/PHASESET_V2_ICMR_TARGET.md
 b5042e5fa002fa057ca4af61f4a06953662708d221016c460acd9cd0278c5863	4606	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
-a13629700b95d956d08ea3de0b14b6098dc435b490d0a8f1fe43281db8af99b0	3934	docs/PHASESET_V2_MECHANISM_FACTORY.md
+7b809de35a65e52d51b11bb3def76979a2f557342d1ba5d55af9fb01469d58d5	4209	docs/PHASESET_V2_MECHANISM_FACTORY.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 b1bed8feb97cb7ca379c1952bb2201fc94a2810f48e59d0d8096fc31aac15be9	3302	docs/PHASESET_V2_MIME_CURRICULUM.md
 9ead6b89eacf034b52dcc15544e25be56e225c1b0432475b66c1a300adfa562d	3961	docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
@@ -188,7 +189,7 @@ ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phase
 3a75c6185167ee3dd952169fa0bae43e1e879a6253d681e35e0b4cf424bbbed3	34377	src/phaseset_core/continuous_parent_host.py
 296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
 564bfe9b1717ac6e539e62726a245b27fc84103e235a1ed1ab1bf04569b36f7a	7483	src/phaseset_core/continuous_phase_cache.py
-e0f3674225163ce5495d1a58a9f64037638fcfbb8f57444701851c93899125c3	18317	src/phaseset_core/continuous_retrieval.py
+a0bfc58b51d6ac65432f0d4a45ba7df78836fe0fc9b5613b1a23a2974bea264d	18760	src/phaseset_core/continuous_retrieval.py
 d66eb2fd534a31701c7dcb8b4fdb634b8db6186b423e83fcc3af5c60152aa8c8	7381	src/phaseset_core/continuous_training_input.py
 2e5531954e3c589bec79d8c3246182001782ddfef7b0409f060e2440b5ad49e6	21701	src/phaseset_core/contracts.py
 bfdfba8797aff6412e5b2c2d46d5cc0473a39980e1fb3c7b68f6299dbbc2c92a	27143	src/phaseset_core/controls.py
@@ -235,7 +236,7 @@ a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phase
 47a2aa0806af373e5df40c611bdad425efd672588fe912d4470ee27d8985181a	21832	src/phaseset_core/study_budget.py
 78cbb47380af76db33acb04350f2307ffae290b6db06b9edd9f1866a2d876bfd	19877	src/phaseset_core/study_process.py
 934b4933795a2971d83811dc36865ddd43d0fd1670e8c2caf9ec53755d362c9b	8297	src/phaseset_core/study_storage.py
-a526798da508ac79dabca562d23af51e2095bb48ec73319781784a4b1b7be23e	33620	src/phaseset_core/temporal_coordination.py
+c0c12458fcd8b3829165a4ddedf301721e727f04401d5765b1bcf3b8cffa3485	34648	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
 cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phaseset_core/tmr_language.py
@@ -243,7 +244,7 @@ d1b92f7b65d750572a8447a48cd2a09cf12d7ec0fc3edd2f33c4088d0d77a14d	9442	src/phases
 b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
-6992c9843cd9380622e5d98b076006f6dd4844f4e718488fff0ae9ce25412b7d	18475	src/phaseset_core/v2_mechanism_factory.py
+23b91815662ca6a53be4241c6e3718ab71a29205ff67b71ef47eeacb745cbd63	18525	src/phaseset_core/v2_mechanism_factory.py
 cbeb8553a7b0b3bb68a092a98e394cb25917410dc4b13d3d7212cee91b456aae	5583	src/phaseset_core/v2_score_tables.py
 e8e4fa5cf988c11bc7aecbff241f3c5c253533652b1a31ea2313ee78626c3371	11472	src/phaseset_core/v2_statistics.py
 c1b145691b73cb3fe56925de75b114dac6ba8a84fcae6ffeec282010fc36fa77	15762	src/phaseset_core/wamo_set.py
@@ -366,8 +367,8 @@ d7b1d991814eed83c888e1e62a88b5bbf877b1c92401f616d10bc1574c7275d0	8593	tests/test
 4679d7fb1fc5e19f88ade47039c413adb51dbe5473a4206947338b2b7a70ab60	7217	tests/test_phaseset_v2_a3_pair_bag.py
 fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test_phaseset_v2_a4_incidence.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
-a72dfbdefe8c8b972c343ae2f471fdf8cbe07330f411dc8b38f1351334be7ede	18263	tests/test_phaseset_v2_coordination.py
-fed691f1bceb341c418e5f505081a83fcba64469f16f8a2b378c8dedc3edc3b5	12899	tests/test_phaseset_v2_mechanism_factory.py
+1ff21b1e8133cf14b0672d984a061cfbe71c58c155e1e53f4e40c5280f3f74fe	21461	tests/test_phaseset_v2_coordination.py
+35dee9fec1423c278312b8abfe42d4e4beda75e280a8d617d8204eb578f09b4e	13761	tests/test_phaseset_v2_mechanism_factory.py
 1df27542e87ae6d3d47ac0e6a865a6e9a5be49ea8a9bc5af61758b18e830b64a	6261	tests/test_phaseset_v2_score_tables.py
 08d27f7109d678ca011ef63dd99429185eb7b8c16d5f22e266dc05fbd58ea383	9778	tests/test_phaseset_v2_statistics.py
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
diff --git a/docs/PHASESET_V2_A7_GENERIC_LOCAL.md b/docs/PHASESET_V2_A7_GENERIC_LOCAL.md
new file mode 100644
index 0000000..bc724b9
--- /dev/null
+++ b/docs/PHASESET_V2_A7_GENERIC_LOCAL.md
@@ -0,0 +1,35 @@
+# A7: generic local relation control
+
+A7 is the registered `V2-043`–`V2-045` residual control. It keeps the same
+frozen B2 global anchor, adapted whole-sentence text vectors, complete-capture
+timeline, actor-incidence topology, directed local-packet matching, calibrated
+score and weak counterfactual training objective as PhaseSet-V2. The only
+mechanism change is the input to each directed half-edge: shared per-actor
+signed-velocity patch mean/RMS, the endpoint actor histories and their relative
+pelvis position. There is no six-band relation descriptor, Morlet response,
+phase mask or energy-floor input to the A7 neural path. A valid local packet
+requires both actor tracks, not physical-band support.
+
+Each edge is still generated in canonical 64-edge blocks; the half-edge input
+width is `2 * width + 3`. At width 512, the A7 encoder has 21,504 fewer
+active parameters than the full physical encoder, approximately 0.13% of the
+full encoder. No dummy trainable parameters are added. A7 cannot be combined
+with topology-off, A2/A3/A4/A5, DCT or speed-only controls. Checkpoint extra
+state records `generic_local=true`, so a full-model checkpoint cannot resume
+as A7 or vice versa. The matrix-bound host retains the same nonzero CF weight
+and training-text eligibility; unlike A8, it does not suppress CF loss.
+
+This is a *phase-independent score* control, not yet a phase-free end-to-end
+data pipeline. The current common `ContinuousTrainingInput` transport loads
+and verifies a shared physical cache for all residual rows and recomputes it
+after eligible yaw augmentation. A7 ignores its physical response arrays when
+scoring, but cache generation, storage and yaw recomputation still incur that
+cost. Any later runtime/FLOPs table must report (i) active A7 neural work and
+(ii) the shared phase-cache frontend separately, or qualify a genuinely
+phase-free input route before making an end-to-end cost claim. A7 must not be
+credited with a lower data-preparation cost merely because its score is
+independent of phase.
+
+Construction and passing software tests are not training results. The public
+factory has `launch_authority=false`; pilot, formal runs and sealed-test
+evaluation require independent private admissions and terminal receipts.
diff --git a/docs/PHASESET_V2_MECHANISM_FACTORY.md b/docs/PHASESET_V2_MECHANISM_FACTORY.md
index 51d1848..197815a 100644
--- a/docs/PHASESET_V2_MECHANISM_FACTORY.md
+++ b/docs/PHASESET_V2_MECHANISM_FACTORY.md
@@ -3,8 +3,8 @@
 `build_v2_mechanism` reads the exact registered 87-row matrix bytes and one
 run ID, an admitted training-source digest, and a validation-selected complete-parent
 B2 checkpoint with its exact file digest. It constructs the currently implemented complete-parent residual
-systems only: PhaseSet-V2 full and A1/A2/A3/A4/A5/A6/A8. It verifies the row's
-fixed seed, split and same-seed B2 predecessor, and refuses A7/A9, the old
+systems only: PhaseSet-V2 full and A1/A2/A3/A4/A5/A6/A7/A8. It verifies the row's
+fixed seed, split and same-seed B2 predecessor, and refuses A9, the old
 head, base and literature rows until their distinct implementations exist.
 There is no fallback to the full model for an unsupported system.
 
@@ -17,6 +17,7 @@ There is no fallback to the full model for an unsupported system.
 | A4 | explicit incidence shuffle seeded by the registered training seed |
 | A5 | explicit phase fields stripped; energy/support retained |
 | A6 | true mean/difference-signal DCT with a typed, main-population floor receipt |
+| A7 | signed local actor kinematics and relative pelvis relation, without explicit phase; same CF pool/loss |
 | A8 | full model; `bind_v2_parent_host_config` sets only the CF loss weight to zero |
 
 For main rows, host config binding also checks the exact twelve development
@@ -31,6 +32,8 @@ main population. A1 also requires a speed-only source and checks its receipt
 against the model at host binding; it cannot silently use signed-vector cached
 responses. The periodic transform and its claim boundary are specified in
 [A1 speed control](PHASESET_V2_A1_SPEED_CONTROL.md).
+A7's score does not consume phase responses, but the current common transport
+still loads the phase cache; see [A7 cost boundary](PHASESET_V2_A7_GENERIC_LOCAL.md).
 
 `make_v2_b2_base_host` constructs a main B2 host that writes the registered
 base row identity into its checkpoint manifest. It creates the registered
diff --git a/src/phaseset_core/continuous_retrieval.py b/src/phaseset_core/continuous_retrieval.py
index 1a97d11..87a037f 100644
--- a/src/phaseset_core/continuous_retrieval.py
+++ b/src/phaseset_core/continuous_retrieval.py
@@ -119,6 +119,7 @@ def __init__(
         incidence_shuffle_seed: int | None = None,
         order_free: bool = False,
         pair_bag: bool = False,
+        generic_local: bool = False,
         periodic_velocity_mode: str = "signed_vector",
         speed_floor_receipt: SpeedFloorReceipt | None = None,
         checkpoint_blocks: bool = True,
@@ -138,6 +139,11 @@ def __init__(
             raise ValueError("base_edge_budget must be a positive integer")
         if relation_kind not in ("phase", "true_mean_difference_dct"):
             raise ValueError("relation_kind must be phase or true_mean_difference_dct")
+        if type(generic_local) is not bool or (
+            generic_local
+            and (relation_kind != "phase" or periodic_velocity_mode != "signed_vector")
+        ):
+            raise ValueError("A7 requires signed local actor kinematics without DCT or speed controls")
         if periodic_velocity_mode not in VELOCITY_MODES or (
             relation_kind != "phase" and periodic_velocity_mode != "signed_vector"
         ):
@@ -187,6 +193,7 @@ def __init__(
             incidence_shuffle_seed=incidence_shuffle_seed,
             order_free=order_free,
             pair_bag=pair_bag,
+            generic_local=generic_local,
             checkpoint_blocks=checkpoint_blocks,
         )
         self.text_adapter = nn.Sequential(nn.Linear(512, 512), nn.GELU(), nn.Linear(512, 512))
@@ -210,6 +217,8 @@ def get_extra_state(self) -> Tensor:
             payload["order_free"] = True
         if self.coordination.pair_bag:
             payload["pair_bag"] = True
+        if self.coordination.generic_local:
+            payload["generic_local"] = True
         if not self.coordination.use_topology:
             payload["use_topology"] = False
         if self.coordination.strip_phase:
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
index 65e5950..4f96453 100644
--- a/src/phaseset_core/temporal_coordination.py
+++ b/src/phaseset_core/temporal_coordination.py
@@ -21,6 +21,7 @@
     ACTOR_FEATURE_DIM,
     PHASE_FEATURE_DIM,
     DirectionalPhaseField,
+    canonical_edge_pairs,
     local_pair_chunk,
 )
 from .dct_relations import DctRelationField, local_dct_pair_chunk
@@ -206,6 +207,7 @@ def __init__(
         incidence_shuffle_seed: int | None = None,
         order_free: bool = False,
         pair_bag: bool = False,
+        generic_local: bool = False,
         checkpoint_blocks: bool = True,
     ) -> None:
         super().__init__()
@@ -226,18 +228,30 @@ def __init__(
             and (not use_topology or strip_phase or incidence_shuffle_seed is not None or order_free)
         ):
             raise ValueError("A3 must pool pair packets in the otherwise full phase system")
+        if type(generic_local) is not bool or (
+            generic_local
+            and (
+                not use_topology
+                or strip_phase
+                or incidence_shuffle_seed is not None
+                or order_free
+                or pair_bag
+            )
+        ):
+            raise ValueError("A7 must use the full temporal-incidence path without physical phase")
         self.width = width
         self.use_topology = use_topology
         self.strip_phase = strip_phase
         self.incidence_shuffle_seed = incidence_shuffle_seed
         self.order_free = order_free
         self.pair_bag = pair_bag
+        self.generic_local = generic_local
         self.checkpoint_blocks = checkpoint_blocks
         temporal_encoder = OrderFreeTemporalEncoder if order_free else MaskedTemporalEncoder
         self.actor_projection = nn.Sequential(nn.Linear(ACTOR_FEATURE_DIM, width), nn.GELU())
         self.actor_temporal = temporal_encoder(width)
         self.half_edge = nn.Sequential(
-            nn.Linear(2 * width + 6 * PHASE_FEATURE_DIM + 3, width),
+            nn.Linear(2 * width + (0 if generic_local else 6 * PHASE_FEATURE_DIM) + 3, width),
             nn.GELU(),
             nn.Linear(width, width),
         )
@@ -256,14 +270,21 @@ def __init__(
     def _half_edges(
         self, field: RelationField, hidden: Tensor, start: int, stop: int
     ) -> tuple[Tensor, Tensor, Tensor, Tensor]:
-        chunk = _relation_pair_chunk(field, start, stop)
+        if self.generic_local:
+            if type(field) is not DirectionalPhaseField or field.velocity_mode != "signed_vector":
+                raise TypeError("A7 requires signed local actor kinematics, not a physical relation")
+            pairs = canonical_edge_pairs(field.actor_count, start, stop)
+        else:
+            chunk = _relation_pair_chunk(field, start, stop)
+            pairs = chunk.endpoints
         device = hidden.device
-        endpoints = torch.tensor(chunk.endpoints.copy(), dtype=torch.int64, device=device)
-        forward = torch.tensor(chunk.features.copy(), dtype=hidden.dtype, device=device)
-        reverse = torch.tensor(chunk.reverse_features(), dtype=hidden.dtype, device=device)
-        if self.strip_phase:
-            forward[..., :2] = 0.0
-            reverse[..., :2] = 0.0
+        endpoints = torch.tensor(pairs.copy(), dtype=torch.int64, device=device)
+        if not self.generic_local:
+            forward = torch.tensor(chunk.features.copy(), dtype=hidden.dtype, device=device)
+            reverse = torch.tensor(chunk.reverse_features(), dtype=hidden.dtype, device=device)
+            if self.strip_phase:
+                forward[..., :2] = 0.0
+                reverse[..., :2] = 0.0
         left, right = endpoints[:, 0], endpoints[:, 1]
         roots = torch.tensor(field.root_positions.copy(), dtype=hidden.dtype, device=device)
         root_mask = torch.tensor(field.root_patch_mask.copy(), device=device)
@@ -274,31 +295,22 @@ def _half_edges(
             torch.zeros_like(relative_root),
         )
         left_hidden, right_hidden = hidden[left], hidden[right]
+        forward_values = (left_hidden, right_hidden, relative_root) if self.generic_local else (
+            left_hidden, right_hidden, forward.flatten(start_dim=2), relative_root
+        )
+        reverse_values = (right_hidden, left_hidden, -relative_root) if self.generic_local else (
+            right_hidden, left_hidden, reverse.flatten(start_dim=2), -relative_root
+        )
         a = self.half_edge(
-            torch.cat(
-                (
-                    left_hidden,
-                    right_hidden,
-                    forward.flatten(start_dim=2),
-                    relative_root,
-                ),
-                dim=-1,
-            )
+            torch.cat(forward_values, dim=-1)
         )
         b = self.half_edge(
-            torch.cat(
-                (
-                    right_hidden,
-                    left_hidden,
-                    reverse.flatten(start_dim=2),
-                    -relative_root,
-                ),
-                dim=-1,
-            )
+            torch.cat(reverse_values, dim=-1)
         )
-        mask = torch.tensor(chunk.support_mask.any(axis=-1).copy(), device=device)
         actor_mask = torch.tensor(field.actor_patch_mask.copy(), device=device)
-        mask = mask & actor_mask[left] & actor_mask[right]
+        mask = actor_mask[left] & actor_mask[right]
+        if not self.generic_local:
+            mask = mask & torch.tensor(chunk.support_mask.any(axis=-1).copy(), device=device)
         return (
             torch.where(mask[..., None], a, torch.zeros_like(a)),
             torch.where(mask[..., None], b, torch.zeros_like(b)),
@@ -512,12 +524,15 @@ def _directed_text_block(
             (a + self.topology_scale.tanh() * delta_a, b + self.topology_scale.tanh() * delta_b)
         )
         histories, _ = self.edge_temporal(packets, torch.cat((track_support, track_support)))
-        # Zero-energy observations can carry track context but are not reliable
-        # physical evidence. Phase uses Morlet floors; A6 uses its independently
-        # fitted DCT floors. Neither treats mere track coverage as evidence.
-        physical = _relation_pair_chunk(field, start, stop)
-        observable = torch.tensor(physical.phase_mask.any(axis=-1).copy(), device=hidden.device)
-        observable = observable & track_support
+        # A7 is a generic local relation control: track support is evidence,
+        # and neither Morlet response nor energy floor is consulted. Physical
+        # systems retain their independent phase/DCT floor qualification.
+        if self.generic_local:
+            observable = track_support
+        else:
+            physical = _relation_pair_chunk(field, start, stop)
+            observable = torch.tensor(physical.phase_mask.any(axis=-1).copy(), device=hidden.device)
+            observable = observable & track_support
         language_mask = torch.cat((observable, observable))
         similarities = F.normalize(histories, dim=-1) @ text.T
         best = similarities.masked_fill(~language_mask[..., None], -torch.inf).amax(dim=(0, 1))
diff --git a/src/phaseset_core/v2_mechanism_factory.py b/src/phaseset_core/v2_mechanism_factory.py
index 878d3e9..23ecacc 100644
--- a/src/phaseset_core/v2_mechanism_factory.py
+++ b/src/phaseset_core/v2_mechanism_factory.py
@@ -41,7 +41,7 @@ class V2MechanismHold(ValueError):
     """A row cannot yet be constructed as its registered scientific system."""
 
 
-_SUPPORTED = frozenset({"PhaseSet-V2", "A1", "A2", "A3", "A4", "A5", "A6", "A8"})
+_SUPPORTED = frozenset({"PhaseSet-V2", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8"})
 
 
 def _sha256_key(value: str) -> bool:
@@ -318,6 +318,7 @@ def build_v2_mechanism(
         "pair_bag": system_id == "A3",
         "incidence_shuffle_seed": row["seed"] if system_id == "A4" else None,
         "strip_phase": system_id == "A5",
+        "generic_local": system_id == "A7",
         "relation_kind": "true_mean_difference_dct" if system_id == "A6" else "phase",
         "dct_floor_receipt": dct_floor_receipt,
         "periodic_velocity_mode": "speed_only" if system_id == "A1" else "signed_vector",
diff --git a/tests/test_phaseset_v2_coordination.py b/tests/test_phaseset_v2_coordination.py
index da46574..92194c4 100644
--- a/tests/test_phaseset_v2_coordination.py
+++ b/tests/test_phaseset_v2_coordination.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import copy
+from dataclasses import replace
 import itertools
 import json
 import os
@@ -312,6 +313,72 @@ def test_group_with_no_derived_support_has_exact_zero_neural_output():
     assert not torch.signbit(result.embedding).any()
 
 
+def test_a7_generic_local_uses_actor_kinematics_but_never_physical_relations(monkeypatch):
+    torch.manual_seed(1729)
+    source = field()
+    generic = TemporalIncidenceEncoder(32, generic_local=True, checkpoint_blocks=False)
+    text = torch.randn(2, 32)
+    original = generic.score_text(source, text)
+    # A7 must not depend on Morlet arrays, masks, centers or fitted floors.
+    phase_free = replace(
+        source,
+        responses=(),
+        response_masks=(),
+        response_centers=(),
+        energy_floors=np.full(6, np.inf),
+    )
+
+    def reject_physical(*_args):
+        raise AssertionError("A7 read a physical pair relation")
+
+    monkeypatch.setattr("phaseset_core.temporal_coordination.local_pair_chunk", reject_physical)
+    changed = generic.score_text(phase_free, text)
+    assert torch.equal(original.cosine, changed.cosine)
+    assert torch.equal(original.coordination.embedding, changed.coordination.embedding)
+    assert bool(changed.periodic_support)
+    with pytest.raises(AssertionError, match="physical pair"):
+        TemporalIncidenceEncoder(32, checkpoint_blocks=False)(source)
+
+
+def test_a7_permutation_padding_gradient_and_capacity_contract():
+    torch.manual_seed(2718)
+    generic = TemporalIncidenceEncoder(32, generic_local=True, checkpoint_blocks=False)
+    full = TemporalIncidenceEncoder(32, checkpoint_blocks=False)
+    phase = (0.0, 0.25, 0.75, 1.5)
+    original = generic(field(motion(phase)))
+    permuted = generic(field(motion(phase, order=(3, 1, 0, 2), padding=5)))
+    assert torch.equal(original.embedding, permuted.embedding)
+    assert torch.equal(original.patch_mask, permuted.patch_mask)
+    assert generic.half_edge[0].in_features == 2 * 32 + 3
+    assert full.half_edge[0].in_features == 2 * 32 + 6 * 7 + 3
+    full_count = sum(parameter.numel() for parameter in full.parameters())
+    generic_count = sum(parameter.numel() for parameter in generic.parameters())
+    assert abs(full_count - generic_count) / full_count < 0.05
+    original.embedding.sum().backward()
+    assert generic.half_edge[0].weight.grad is not None
+    assert torch.count_nonzero(generic.half_edge[0].weight.grad) > 0
+
+
+def test_a7_missing_tracks_are_exact_zero_and_k2_topology_stays_gated():
+    torch.manual_seed(31415)
+    generic = TemporalIncidenceEncoder(32, generic_local=True, checkpoint_blocks=False)
+    missing = generic(field(motion(missing=True)))
+    assert not missing.patch_mask.any()
+    assert torch.count_nonzero(missing.embedding) == 0
+    assert not torch.signbit(missing.embedding).any()
+    dyad = field(motion((0.0, 0.3)))
+    paired = copy.deepcopy(generic)
+    paired.use_topology = False
+    group_output, pair_output = generic(dyad), paired(dyad)
+    assert torch.equal(group_output.embedding, pair_output.embedding)
+    assert torch.count_nonzero(group_output.topology_nodes) == 0
+    group_output.embedding.sum().backward()
+    for parameter in generic.node_mlp.parameters():
+        assert parameter.grad is not None and torch.count_nonzero(parameter.grad) == 0
+    with pytest.raises(ValueError, match="A7 must use"):
+        TemporalIncidenceEncoder(32, generic_local=True, use_topology=False)
+
+
 def test_capture_readout_distinguishes_AB_from_BA_and_sorts_storage_order():
     torch.manual_seed(1729)
     reader = OrderedCaptureReadout(8)
diff --git a/tests/test_phaseset_v2_mechanism_factory.py b/tests/test_phaseset_v2_mechanism_factory.py
index 738f88c..9659003 100644
--- a/tests/test_phaseset_v2_mechanism_factory.py
+++ b/tests/test_phaseset_v2_mechanism_factory.py
@@ -121,6 +121,7 @@ def _speed_receipt(*, population: str = "main") -> SpeedFloorReceipt:
         ("V2-031", "A3", "pair_bag", True),
         ("V2-034", "A4", "incidence_shuffle_seed", 1729),
         ("V2-037", "A5", "strip_phase", True),
+        ("V2-043", "A7", "generic_local", True),
         ("V2-046", "A8", "pair_bag", False),
     ),
 )
@@ -177,6 +178,22 @@ def test_a1_binds_independent_speed_floor_and_preserves_residual_capacity(tmp_pa
         _build(tmp_path, "V2-022", selected=selected, speed_floor_receipt=_speed_receipt())
 
 
+def test_a7_preserves_cf_host_and_checkpoint_identity(tmp_path) -> None:
+    selected = _selected(tmp_path)
+    full = _build(tmp_path, "V2-022", selected=selected)
+    a7 = _build(tmp_path, "V2-043", selected=selected)
+    assert a7.counterfactual_objective_enabled
+    assert a7.model.coordination.generic_local
+    assert a7.model.relation_kind == "phase"
+    assert a7.model.periodic_velocity_mode == "signed_vector"
+    config = ParentHostConfig(1729, MAIN_TRAIN, ("C00",), cf_weight=0.2, cf_margin=0.2)
+    assert bind_v2_parent_host_config(a7, config) is config
+    with pytest.raises(ValueError, match="checkpoint resume"):
+        a7.model.set_extra_state(full.model.get_extra_state())
+    with pytest.raises(ValueError, match="checkpoint resume"):
+        full.model.set_extra_state(a7.model.get_extra_state())
+
+
 def test_a8_disables_only_cf_objective_and_preserves_weak_pool_host_contract(tmp_path) -> None:
     selected = _selected(tmp_path)
     full = _build(tmp_path, "V2-022", selected=selected)
@@ -199,7 +216,7 @@ def test_a8_disables_only_cf_objective_and_preserves_weak_pool_host_contract(tmp
 
 def test_unsupported_rows_and_matrix_drift_fail_without_full_model_fallback(tmp_path) -> None:
     selected = _selected(tmp_path)
-    for run_id in ("V2-043", "V2-049", "V2-007"):
+    for run_id in ("V2-049", "V2-007"):
         with pytest.raises(V2MechanismHold, match="not an implemented V2 mechanism"):
             _build(tmp_path, run_id, selected=selected)
     with pytest.raises(V2MechanismHold, match="exact frozen"):

From d185321849a2e4e3e302a70f0911721087399171 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 11:41:35 -0500
Subject: [PATCH 89/95] Bind all nine V2 base qualification rows to parent
 hosts

---
 RELEASE_FILES.sha256                        |  6 +--
 docs/PHASESET_V2_MECHANISM_FACTORY.md       | 11 +++--
 src/phaseset_core/v2_mechanism_factory.py   | 31 ++++++++++----
 tests/test_phaseset_v2_mechanism_factory.py | 45 +++++++++++++++++++++
 4 files changed, 79 insertions(+), 14 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index f198854..dd00157 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -85,7 +85,7 @@ b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASE
 1e0d2fb0b35a6900dfb03af4b9bfe1701eb2c843f566c0f842b47b03b11b645e	1276	docs/PHASESET_V2_ICMR_TARGET.md
 b5042e5fa002fa057ca4af61f4a06953662708d221016c460acd9cd0278c5863	4606	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
-7b809de35a65e52d51b11bb3def76979a2f557342d1ba5d55af9fb01469d58d5	4209	docs/PHASESET_V2_MECHANISM_FACTORY.md
+932cc27f1e4c53f3ee09042514949cf9b2132758cee2d1dd71d709dda4a08884	4435	docs/PHASESET_V2_MECHANISM_FACTORY.md
 4d6b023e98c96116fa338db02005200716e0f85a1e5417228b5f375492abddd9	4269	docs/PHASESET_V2_MECHANISM_VERIFICATION.md
 b1bed8feb97cb7ca379c1952bb2201fc94a2810f48e59d0d8096fc31aac15be9	3302	docs/PHASESET_V2_MIME_CURRICULUM.md
 9ead6b89eacf034b52dcc15544e25be56e225c1b0432475b66c1a300adfa562d	3961	docs/PHASESET_V2_NATIVE_PROCESS_PROFILE.md
@@ -244,7 +244,7 @@ d1b92f7b65d750572a8447a48cd2a09cf12d7ec0fc3edd2f33c4088d0d77a14d	9442	src/phases
 b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
-23b91815662ca6a53be4241c6e3718ab71a29205ff67b71ef47eeacb745cbd63	18525	src/phaseset_core/v2_mechanism_factory.py
+f75c1399bc1717196b814fcf33ee8383cee694f7d7d7e4001b1e935af361ee94	19267	src/phaseset_core/v2_mechanism_factory.py
 cbeb8553a7b0b3bb68a092a98e394cb25917410dc4b13d3d7212cee91b456aae	5583	src/phaseset_core/v2_score_tables.py
 e8e4fa5cf988c11bc7aecbff241f3c5c253533652b1a31ea2313ee78626c3371	11472	src/phaseset_core/v2_statistics.py
 c1b145691b73cb3fe56925de75b114dac6ba8a84fcae6ffeec282010fc36fa77	15762	src/phaseset_core/wamo_set.py
@@ -368,7 +368,7 @@ d7b1d991814eed83c888e1e62a88b5bbf877b1c92401f616d10bc1574c7275d0	8593	tests/test
 fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test_phaseset_v2_a4_incidence.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 1ff21b1e8133cf14b0672d984a061cfbe71c58c155e1e53f4e40c5280f3f74fe	21461	tests/test_phaseset_v2_coordination.py
-35dee9fec1423c278312b8abfe42d4e4beda75e280a8d617d8204eb578f09b4e	13761	tests/test_phaseset_v2_mechanism_factory.py
+90ede69f8695413a5cbc38c45b67602e5b3ed26212ac09e7f33c568b0129f719	15608	tests/test_phaseset_v2_mechanism_factory.py
 1df27542e87ae6d3d47ac0e6a865a6e9a5be49ea8a9bc5af61758b18e830b64a	6261	tests/test_phaseset_v2_score_tables.py
 08d27f7109d678ca011ef63dd99429185eb7b8c16d5f22e266dc05fbd58ea383	9778	tests/test_phaseset_v2_statistics.py
 8f5f02d335d1aca0a260f4748f5a0d33c6770f09a0f5d12435956597832c6a3f	15874	tests/test_public_release_tooling.py
diff --git a/docs/PHASESET_V2_MECHANISM_FACTORY.md b/docs/PHASESET_V2_MECHANISM_FACTORY.md
index 197815a..728a26a 100644
--- a/docs/PHASESET_V2_MECHANISM_FACTORY.md
+++ b/docs/PHASESET_V2_MECHANISM_FACTORY.md
@@ -35,10 +35,13 @@ responses. The periodic transform and its claim boundary are specified in
 A7's score does not consume phase responses, but the current common transport
 still loads the phase cache; see [A7 cost boundary](PHASESET_V2_A7_GENERIC_LOCAL.md).
 
-`make_v2_b2_base_host` constructs a main B2 host that writes the registered
-base row identity into its checkpoint manifest. It creates the registered
-SocialTemporal B2 architecture under the row's forked CPU seed and requires
-the 30-epoch registered schedule; a short pilot cannot carry a formal row ID.
+`make_v2_registered_base_host` constructs each of the nine main B0/B1/B2
+qualification hosts from its exact row, including the registered architecture,
+official seed, twelve training components, C00 validation and 30-epoch
+schedule. It writes the system/run identity into the checkpoint manifest and
+does not assign formal launch authority. The prior `make_v2_b2_base_host`
+entry point remains B2-only and delegates to this same checked path; a short
+pilot cannot carry a formal row ID. All nine row/seed combinations are tested.
 The residual builder verifies that row identity, stage, seed, population,
 checkpoint file/state digests, a completed 30-epoch terminal and its final
 validation-selected checkpoint digest, then loads strict B2 weights into the
diff --git a/src/phaseset_core/v2_mechanism_factory.py b/src/phaseset_core/v2_mechanism_factory.py
index 23ecacc..1fb0d1d 100644
--- a/src/phaseset_core/v2_mechanism_factory.py
+++ b/src/phaseset_core/v2_mechanism_factory.py
@@ -138,7 +138,7 @@ def _registered_row(matrix_bytes: bytes, run_id: str) -> tuple[dict, dict]:
     return matrix, row
 
 
-def make_v2_b2_base_host(
+def make_v2_registered_base_host(
     matrix_bytes: bytes,
     run_id: str,
     task: ParentRetrievalTask,
@@ -146,19 +146,20 @@ def make_v2_b2_base_host(
     config: ParentHostConfig,
     bindings: ParentHostBindings,
 ) -> ContinuousParentTrainingHost:
-    """Bind a main B2 base to the registered row before checkpoint creation."""
+    """Bind a main B0/B1/B2 qualification run to its exact matrix row."""
     matrix = _registered_matrix(matrix_bytes)
     row = next((item for item in matrix["runs"] if item["id"] == run_id), None)
     if (
         row is None
-        or row.get("system") != "B2"
+        or row.get("system") not in {"B0", "B1", "B2"}
         or row.get("group") != "internal_base"
         or row.get("split") != "main"
         or row.get("status") != "PLANNED"
+        or row.get("seed") not in OFFICIAL_SEEDS
         or type(config) is not ParentHostConfig
         or type(bindings) is not ParentHostBindings
     ):
-        raise V2MechanismHold("registered main B2 base row and model required")
+        raise V2MechanismHold("registered main B0/B1/B2 base row and model required")
     development = matrix["pilot_and_group_fold_components"]["development"]
     validation = tuple(matrix["primary_split"]["validation_component_labels"])
     train = tuple(component for component in development if component not in validation)
@@ -170,20 +171,36 @@ def make_v2_b2_base_host(
         or config.validation_components != validation
         or bindings.frozen_base_checkpoint_sha256 is not None
     ):
-        raise V2MechanismHold("B2 host population, seed or stage differs from matrix")
+        raise V2MechanismHold("base host population, seed or stage differs from matrix")
     with torch.random.fork_rng(devices=[]):
         torch.random.default_generator.manual_seed(row["seed"])
-        base_model = build_registered_base_training_system("B2")
+        base_model = build_registered_base_training_system(row["system"])
     return ContinuousParentTrainingHost(
         ContinuousBaseRetrievalSystem(base_model),
         task,
         source,
         config,
         bindings,
-        run_identity=ParentHostRunIdentity(run_id, "B2", None),
+        run_identity=ParentHostRunIdentity(run_id, row["system"], None),
     )
 
 
+def make_v2_b2_base_host(
+    matrix_bytes: bytes,
+    run_id: str,
+    task: ParentRetrievalTask,
+    source: ParentTrainingSource,
+    config: ParentHostConfig,
+    bindings: ParentHostBindings,
+) -> ContinuousParentTrainingHost:
+    """Compatibility entry point that continues to admit B2 rows only."""
+    matrix = _registered_matrix(matrix_bytes)
+    row = next((item for item in matrix["runs"] if item["id"] == run_id), None)
+    if row is None or row.get("system") != "B2":
+        raise V2MechanismHold("registered main B2 base row required")
+    return make_v2_registered_base_host(matrix_bytes, run_id, task, source, config, bindings)
+
+
 def build_v2_mechanism(
     matrix_bytes: bytes,
     run_id: str,
diff --git a/tests/test_phaseset_v2_mechanism_factory.py b/tests/test_phaseset_v2_mechanism_factory.py
index 9659003..5d318b2 100644
--- a/tests/test_phaseset_v2_mechanism_factory.py
+++ b/tests/test_phaseset_v2_mechanism_factory.py
@@ -27,6 +27,7 @@
     bind_v2_parent_host_config,
     build_v2_mechanism,
     make_v2_b2_base_host,
+    make_v2_registered_base_host,
     make_v2_parent_host,
 )
 MATRIX = (Path(__file__).parents[1] / "configs/phaseset_v2_experiment_matrix.json").read_bytes()
@@ -295,3 +296,47 @@ def test_single_host_entrypoint_binds_a8_and_persists_row_identity(tmp_path) ->
     assert base_host._manifest["registered_run_identity"] == {
         "run_id": "V2-007", "system_id": "B2", "predecessor_run_id": None,
     }
+
+
+@pytest.mark.parametrize(
+    ("run_id", "system_id", "seed"),
+    tuple(
+        (f"V2-{3 * architecture + offset:03d}", system_id, seed)
+        for architecture, system_id in enumerate(("B0", "B1", "B2"))
+        for offset, seed in enumerate((1729, 2718, 31415), 1)
+    ),
+)
+def test_registered_base_host_covers_all_three_qualification_architectures(
+    run_id: str, system_id: str, seed: int,
+) -> None:
+    rows = tuple(
+        ParentCaptionRecord(
+            hashlib.sha256(f"base-source-{i}".encode()).hexdigest(),
+            hashlib.sha256(f"base-family-{i}".encode()).hexdigest(),
+            component,
+            "validation" if component == "C00" else "train",
+            (f"human row {i}",),
+        )
+        for i, component in enumerate((*MAIN_TRAIN, "C00"))
+    )
+    task = ParentRetrievalTask(
+        rows, expected_family_keys=tuple(row.annotation_family_sha256 for row in rows)
+    )
+    config = ParentHostConfig.for_base(seed, MAIN_TRAIN, ("C00",))
+    bindings = ParentHostBindings("b" * 64, "c" * 64, "d" * 64, None)
+    host = make_v2_registered_base_host(MATRIX, run_id, task, object(), config, bindings)
+    assert host.system.system_id == system_id
+    assert host._manifest["registered_run_identity"] == {
+        "run_id": run_id, "system_id": system_id, "predecessor_run_id": None,
+    }
+    assert host._steps_per_epoch == 1
+    with pytest.raises(V2MechanismHold, match="B2 base row"):
+        make_v2_b2_base_host(MATRIX, "V2-001", task, object(), config, bindings)
+    with pytest.raises(V2MechanismHold, match="seed or stage"):
+        make_v2_registered_base_host(
+            MATRIX, run_id, task, object(),
+            ParentHostConfig.for_base(
+                2718 if seed != 2718 else 31415, MAIN_TRAIN, ("C00",)
+            ),
+            bindings,
+        )

From 7e5138d83bd2b4b1f8eead32dfbc7ecfd0bbcc6d Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 12:53:44 -0500
Subject: [PATCH 90/95] Add calibrated legacy PhaseSet score control

---
 RELEASE_FILES.sha256                       |  3 +-
 src/phaseset_core/temporal_coordination.py | 61 ++++++++++++++++-
 tests/test_phaseset_v2_a9_legacy_score.py  | 80 ++++++++++++++++++++++
 3 files changed, 142 insertions(+), 2 deletions(-)
 create mode 100644 tests/test_phaseset_v2_a9_legacy_score.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index dd00157..cb21998 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -236,7 +236,7 @@ a0d56f1ddc63782c43b909125227c5f0be34bd16ed0a651a4a403e0a53c3de8b	36845	src/phase
 47a2aa0806af373e5df40c611bdad425efd672588fe912d4470ee27d8985181a	21832	src/phaseset_core/study_budget.py
 78cbb47380af76db33acb04350f2307ffae290b6db06b9edd9f1866a2d876bfd	19877	src/phaseset_core/study_process.py
 934b4933795a2971d83811dc36865ddd43d0fd1670e8c2caf9ec53755d362c9b	8297	src/phaseset_core/study_storage.py
-c0c12458fcd8b3829165a4ddedf301721e727f04401d5765b1bcf3b8cffa3485	34648	src/phaseset_core/temporal_coordination.py
+bddd2124cac00d4d6b193cdcec1c2f801b958720434b98a8600bb187e45ed471	36851	src/phaseset_core/temporal_coordination.py
 47458d07e0c2132b0c4451195d99bf15ff754766453e840284ffc06617b88ae0	14095	src/phaseset_core/tmr_components.py
 974fa0a326fb7e3b6b4897551d5aacdac9220a935025689437b2d7b7b84072d4	5566	src/phaseset_core/tmr_feature_cache.py
 cf8f38b22fc2f5b2cff3ea39a8512892b5e1d6e739e199b5e4b7b5e49c4e6c9a	6550	src/phaseset_core/tmr_language.py
@@ -366,6 +366,7 @@ d7b1d991814eed83c888e1e62a88b5bbf877b1c92401f616d10bc1574c7275d0	8593	tests/test
 4ee992cf41d66cf7e2cae98f4ec00e69b080bf05a9d9f005e29099b92ee6ab4d	5099	tests/test_phaseset_v2_a2_order_free.py
 4679d7fb1fc5e19f88ade47039c413adb51dbe5473a4206947338b2b7a70ab60	7217	tests/test_phaseset_v2_a3_pair_bag.py
 fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test_phaseset_v2_a4_incidence.py
+6747a55d9c743766491ce62db6562b3e077bca67da1b8104cfdf894f94aff326	3559	tests/test_phaseset_v2_a9_legacy_score.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 1ff21b1e8133cf14b0672d984a061cfbe71c58c155e1e53f4e40c5280f3f74fe	21461	tests/test_phaseset_v2_coordination.py
 90ede69f8695413a5cbc38c45b67602e5b3ed26212ac09e7f33c568b0129f719	15608	tests/test_phaseset_v2_mechanism_factory.py
diff --git a/src/phaseset_core/temporal_coordination.py b/src/phaseset_core/temporal_coordination.py
index 4f96453..af63bd7 100644
--- a/src/phaseset_core/temporal_coordination.py
+++ b/src/phaseset_core/temporal_coordination.py
@@ -25,7 +25,7 @@
     local_pair_chunk,
 )
 from .dct_relations import DctRelationField, local_dct_pair_chunk
-from .objectives import variable_positive_symmetric_infonce
+from .objectives import TextBandMLP, band_text_scores, variable_positive_symmetric_infonce
 
 
 RelationField = DirectionalPhaseField | DctRelationField
@@ -653,6 +653,65 @@ def forward(
         return scale * ((1 - alpha) * global_cosine + alpha * coordination_cosine)
 
 
+@dataclass(frozen=True)
+class LegacyCalibratedScoreOutput:
+    """A9 score-only output; it does not certify a complete-capture old host."""
+
+    scores: Tensor
+    global_cosine: Tensor
+    legacy_relation_cosine: Tensor
+    text_band_embeddings: Tensor
+    periodic_support: Tensor
+
+
+class LegacyPhaseSetCalibratedHead(nn.Module):
+    """Keep the v0.2 six-token/text representation, change only score scale.
+
+    The old head adds a learned periodic residual to temperature-scaled base
+    logits. A9 instead feeds the *same* band cosine and the frozen B2 cosine
+    into the V2 common-scale calibration. No phase, topology, text projector,
+    or token representation is changed here. The complete-capture host and
+    registered A9 run identity remain separate execution gates.
+    """
+
+    def __init__(self, embedding_dim: int = 512) -> None:
+        super().__init__()
+        self.text_band_mlp = TextBandMLP(embedding_dim)
+        self.calibration = CalibratedCoordinationScore()
+
+    def forward(
+        self,
+        group_tokens: Tensor,
+        band_mask: Tensor,
+        text_embeddings: Tensor,
+        frozen_base_cosine: Tensor,
+    ) -> LegacyCalibratedScoreOutput:
+        text_bands = self.text_band_mlp(text_embeddings)
+        relation_cosine = band_text_scores(group_tokens, band_mask, text_bands)
+        if (
+            type(frozen_base_cosine) is not Tensor
+            or frozen_base_cosine.dtype != torch.float32
+            or frozen_base_cosine.shape != relation_cosine.shape
+            or frozen_base_cosine.device != relation_cosine.device
+            or not frozen_base_cosine.is_contiguous()
+            or frozen_base_cosine.requires_grad
+        ):
+            raise ValueError("A9 needs frozen contiguous base cosines [B,Q], not logits")
+        support = band_mask.any(dim=1).contiguous()
+        scores = self.calibration(
+            frozen_base_cosine,
+            relation_cosine,
+            coordination_support=support,
+        )
+        return LegacyCalibratedScoreOutput(
+            scores.contiguous(),
+            frozen_base_cosine,
+            relation_cosine,
+            text_bands,
+            support,
+        )
+
+
 def complete_relation_text_scores(
     relation_tokens: Tensor, relation_mask: Tensor, text_embeddings: Tensor
 ) -> Tensor:
diff --git a/tests/test_phaseset_v2_a9_legacy_score.py b/tests/test_phaseset_v2_a9_legacy_score.py
new file mode 100644
index 0000000..c48211b
--- /dev/null
+++ b/tests/test_phaseset_v2_a9_legacy_score.py
@@ -0,0 +1,80 @@
+"""A9 changes the old PhaseSet score calibration, not its six-token features."""
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from phaseset_core.objectives import PhaseSetRetrievalHead
+from phaseset_core.temporal_coordination import LegacyPhaseSetCalibratedHead
+
+
+def test_a9_preserves_old_band_text_representation_bitwise() -> None:
+    torch.manual_seed(1729)
+    old = PhaseSetRetrievalHead(8)
+    calibrated = LegacyPhaseSetCalibratedHead(8)
+    calibrated.text_band_mlp.load_state_dict(old.text_band_mlp.state_dict())
+    tokens = torch.randn((3, 6, 8), dtype=torch.float32).contiguous()
+    mask = torch.tensor(
+        [[True] * 6, [True, False, True, False, False, False], [False] * 6],
+        dtype=torch.bool,
+    ).contiguous()
+    text = torch.randn((4, 8), dtype=torch.float32).contiguous()
+    base_cosine = torch.zeros((3, 4), dtype=torch.float32).contiguous()
+
+    old_output = old(tokens, mask, text, base_cosine)
+    new_output = calibrated(tokens, mask, text, base_cosine)
+    assert torch.equal(new_output.text_band_embeddings, old_output.text_band_embeddings)
+    assert torch.equal(new_output.legacy_relation_cosine, old_output.periodic_scores)
+    assert torch.equal(new_output.periodic_support, mask.any(dim=1))
+
+
+def test_a9_uses_common_cosine_scale_and_exact_global_fallback() -> None:
+    torch.manual_seed(2718)
+    head = LegacyPhaseSetCalibratedHead(8)
+    tokens = torch.randn((2, 6, 8), dtype=torch.float32, requires_grad=True)
+    mask = torch.tensor(
+        [[False] * 6, [True] * 6], dtype=torch.bool
+    ).contiguous()
+    text = torch.randn((3, 8), dtype=torch.float32).contiguous()
+    base_cosine = torch.tensor(
+        [[0.2, -0.3, 0.4], [-0.1, 0.5, 0.7]], dtype=torch.float32
+    ).contiguous()
+
+    output = head(tokens, mask, text, base_cosine)
+    alpha = head.calibration.mixture_logit.sigmoid()
+    scale = head.calibration.log_scale.exp()
+    torch.testing.assert_close(alpha, torch.tensor(0.1))
+    torch.testing.assert_close(output.scores[0], scale * base_cosine[0])
+    torch.testing.assert_close(
+        output.scores[1],
+        scale * ((1 - alpha) * base_cosine[1] + alpha * output.legacy_relation_cosine[1]),
+    )
+    output.scores.sum().backward()
+    assert torch.count_nonzero(tokens.grad[0]) == 0
+    assert torch.count_nonzero(tokens.grad[1]) > 0
+    assert head.calibration.mixture_logit.grad is not None
+    assert torch.isfinite(head.calibration.mixture_logit.grad).all()
+    assert torch.count_nonzero(head.calibration.mixture_logit.grad) > 0
+    assert head.calibration.log_scale.grad is not None
+    assert torch.isfinite(head.calibration.log_scale.grad).all()
+    assert torch.count_nonzero(head.calibration.log_scale.grad) > 0
+    assert head.text_band_mlp.band_embedding.grad is not None
+
+
+def test_a9_refuses_base_logits_or_trainable_base_scores() -> None:
+    head = LegacyPhaseSetCalibratedHead(8)
+    tokens = torch.zeros((1, 6, 8), dtype=torch.float32).contiguous()
+    mask = torch.ones((1, 6), dtype=torch.bool).contiguous()
+    text = torch.ones((2, 8), dtype=torch.float32).contiguous()
+    with pytest.raises(ValueError, match="cosines"):
+        head(tokens, mask, text, torch.tensor([[5.0, -2.0]], dtype=torch.float32))
+    with pytest.raises(ValueError, match="frozen"):
+        head(
+            tokens,
+            mask,
+            text,
+            torch.zeros((1, 2), dtype=torch.float32, requires_grad=True),
+        )
+    with pytest.raises(ValueError, match="frozen"):
+        head(tokens, mask, text, torch.zeros((2, 1), dtype=torch.float32))

From 914fcb6f3cce04c437fafbee7f272468ec398e4c Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 13:22:35 -0500
Subject: [PATCH 91/95] Pool legacy PhaseSet tokens over complete captures

---
 RELEASE_FILES.sha256                     |   2 +
 src/phaseset_core/legacy_capture.py      |  94 +++++++++++
 tests/test_phaseset_v2_legacy_capture.py | 189 +++++++++++++++++++++++
 3 files changed, 285 insertions(+)
 create mode 100644 src/phaseset_core/legacy_capture.py
 create mode 100644 tests/test_phaseset_v2_legacy_capture.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index cb21998..34887e9 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -205,6 +205,7 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 7c491ef7d65cddffa264d68d0a9fdd9216180bb741f85dd662cb570ca6e872f1	77027	src/phaseset_core/frozen_clip_text.py
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
+84c7052b74cb8be4cddcfb11fea633b9bb46d3e3f2690761db3134ddb1c3f6b7	3982	src/phaseset_core/legacy_capture.py
 1fb8e81b89b58e995a08a016aa3efad5074a269907e5d51da040fb0918d64335	11164	src/phaseset_core/literature_parent_host.py
 02a11d20ac299c4886479b863f2f614512365a9f1fe800861b2f261dbdad1c7a	9101	src/phaseset_core/literature_parent_training.py
 f0d7c36ecda4326ca2b1f9461fdc812039cae33739595c60d40a436f0051edb7	6976	src/phaseset_core/mime_language.py
@@ -369,6 +370,7 @@ fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test
 6747a55d9c743766491ce62db6562b3e077bca67da1b8104cfdf894f94aff326	3559	tests/test_phaseset_v2_a9_legacy_score.py
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 1ff21b1e8133cf14b0672d984a061cfbe71c58c155e1e53f4e40c5280f3f74fe	21461	tests/test_phaseset_v2_coordination.py
+2aae2397f6c40d929b0eb9b61d340d3ece6d48a17da01d6b3bd2767c2b6945b3	7513	tests/test_phaseset_v2_legacy_capture.py
 90ede69f8695413a5cbc38c45b67602e5b3ed26212ac09e7f33c568b0129f719	15608	tests/test_phaseset_v2_mechanism_factory.py
 1df27542e87ae6d3d47ac0e6a865a6e9a5be49ea8a9bc5af61758b18e830b64a	6261	tests/test_phaseset_v2_score_tables.py
 08d27f7109d678ca011ef63dd99429185eb7b8c16d5f22e266dc05fbd58ea383	9778	tests/test_phaseset_v2_statistics.py
diff --git a/src/phaseset_core/legacy_capture.py b/src/phaseset_core/legacy_capture.py
new file mode 100644
index 0000000..20e7166
--- /dev/null
+++ b/src/phaseset_core/legacy_capture.py
@@ -0,0 +1,94 @@
+"""Use every accepted complete-capture window with the old six-token model.
+
+This is the motion-representation seam shared by the registered old-head and
+A9 score-calibrated controls. It is not a training host or data admission.
+The legacy PhaseSet encoder still sees each original 200-frame window; only
+the capture-level pooling is new. No rejected interval is silently scored.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor
+from torch.utils.checkpoint import checkpoint
+
+from .continuous_capture import PreparedContinuousCapture
+from .controls import PhaseSetSystem
+from .models import _fixed_binary_tree_sum
+from .periodic import BAND_COUNT, DEFAULT_EDGE_CHUNK_SIZE
+from .pipeline import collate_group_samples
+
+
+@dataclass(frozen=True)
+class LegacyCaptureTokenOutput:
+    tokens: Tensor  # float32 [1,6,D]
+    band_mask: Tensor  # bool [1,6]
+    supporting_window_count: Tensor  # int64 [6]
+    accepted_window_count: int
+    timeline_window_count: int
+
+
+def encode_legacy_capture_tokens(
+    encoder: PhaseSetSystem,
+    capture: PreparedContinuousCapture,
+    *,
+    edge_chunk_size: int = DEFAULT_EDGE_CHUNK_SIZE,
+    checkpoint_windows: bool = True,
+) -> LegacyCaptureTokenOutput:
+    """Masked-mean the old token of every accepted 10-second window.
+
+    The encoder is the exact old full system 08, not a V2 temporal packet
+    encoder. It computes all unordered actor pairs for each accepted window.
+    A band with no observed support has an exact positive-zero output, and
+    the returned support count remains separate from the neural features.
+    """
+    if type(encoder) is not PhaseSetSystem or encoder.system_id != "08":
+        raise TypeError("complete legacy capture requires exact PhaseSet system 08")
+    if type(capture) is not PreparedContinuousCapture:
+        raise TypeError("complete legacy capture requires prepared physical input")
+    if type(edge_chunk_size) is not int or edge_chunk_size < 1:
+        raise ValueError("edge_chunk_size must be a positive exact integer")
+    if type(checkpoint_windows) is not bool:
+        raise TypeError("checkpoint_windows must be an exact bool")
+    windows = capture.windows()
+    values: list[Tensor] = []
+    counts = torch.zeros((BAND_COUNT,), dtype=torch.int64, device=encoder._device_anchor.device)
+    anchor = torch.zeros((), dtype=torch.float32, device=encoder._device_anchor.device)
+    for window in windows:
+        batch = collate_group_samples((window,))
+        if checkpoint_windows and torch.is_grad_enabled():
+            # Bind this exact window in the closure: backward may recompute it
+            # after the Python loop has advanced to a later capture interval.
+            def encode(_anchor: Tensor, fixed_batch=batch) -> tuple[Tensor, Tensor]:
+                result = encoder(fixed_batch, edge_chunk_size=edge_chunk_size)
+                return result.tokens, result.band_mask
+
+            tokens, band_mask = checkpoint(encode, anchor, use_reentrant=False)
+        else:
+            output = encoder(batch, edge_chunk_size=edge_chunk_size)
+            tokens, band_mask = output.tokens, output.band_mask
+        mask = band_mask[0]
+        values.append(torch.where(
+            mask[:, None], tokens[0].to(torch.float64),
+            torch.zeros_like(tokens[0], dtype=torch.float64),
+        ))
+        counts = counts + mask.to(torch.int64)
+    if not values:
+        raise ValueError("capture has no accepted window")
+    sums = _fixed_binary_tree_sum(values)
+    support = counts > 0
+    pooled = torch.where(
+        support[:, None],
+        sums / counts.clamp_min(1)[:, None],
+        torch.zeros_like(sums),
+    ).to(torch.float32)
+    pooled = torch.where(support[:, None], pooled, torch.zeros_like(pooled))
+    return LegacyCaptureTokenOutput(
+        pooled[None].contiguous(),
+        support[None].contiguous(),
+        counts.contiguous(),
+        len(windows),
+        len(capture.decisions),
+    )
diff --git a/tests/test_phaseset_v2_legacy_capture.py b/tests/test_phaseset_v2_legacy_capture.py
new file mode 100644
index 0000000..1bc7a93
--- /dev/null
+++ b/tests/test_phaseset_v2_legacy_capture.py
@@ -0,0 +1,189 @@
+"""Data-free full-capture pooling of the unchanged old six-token encoder."""
+
+from __future__ import annotations
+
+import copy
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.capture_pooling import CaptureWindowPlan, pool_capture_windows
+from phaseset_core.continuous_capture import prepare_continuous_capture
+from phaseset_core.controls import PhaseSetSystem
+from phaseset_core.legacy_capture import encode_legacy_capture_tokens
+from phaseset_core.models import GroupTokenOutput
+from phaseset_core.pipeline import PrivateCaptureArrays, collate_group_samples
+
+
+def _capture(
+    order: tuple[int, ...] = (0, 1, 2), *, windows: int = 3,
+    reject_middle: bool = True,
+):
+    frames, actors = 300 * windows, 3
+    time = np.arange(frames, dtype=np.float64) / 30.0
+    motion = np.zeros((frames, actors, 22, 3), dtype=np.float32)
+    for actor in range(actors):
+        motion[:, actor, :, 0] = (
+            actor + np.sin(2 * np.pi * 1.125 * time + actor)
+        )[:, None]
+        motion[:, actor, :, 2] = (
+            0.25 * actor + np.cos(2 * np.pi * 0.75 * time + actor)
+        )[:, None]
+        motion[:, actor, 1:, 0] += 0.3 * np.sin(2 * np.pi * 2.5 * time)[:, None]
+    tracked = np.ones(motion.shape[:-1], dtype=np.bool_)
+    if reject_middle:
+        tracked[300:360] = False
+    commitments = tuple(bytes([actor + 1]) * 32 for actor in range(actors))
+    return prepare_continuous_capture(
+        PrivateCaptureArrays(
+            motion[:, order].copy(),
+            tracked[:, order].copy(),
+            tuple(commitments[actor] for actor in order),
+            "a" * 64,
+        )
+    )
+
+
+def _encoder(width: int = 8) -> PhaseSetSystem:
+    return PhaseSetSystem(
+        "08",
+        embedding_dim=width,
+        hidden_dim=8,
+        energy_floors=np.full((6,), 1e-10, dtype=np.float64),
+        edge_budget=64,
+    ).eval()
+
+
+def test_legacy_complete_capture_consumes_every_accepted_window_and_backpropagates() -> None:
+    torch.manual_seed(1729)
+    capture = _capture()
+    encoder = _encoder()
+    output = encode_legacy_capture_tokens(encoder, capture, edge_chunk_size=64)
+    assert output.timeline_window_count == 3
+    assert output.accepted_window_count == 2
+    windows = capture.windows()
+    assert len(windows) == 2
+    direct = [
+        encoder(collate_group_samples((window,)), edge_chunk_size=64)
+        for window in windows
+    ]
+    tokens = torch.stack([row.tokens[0].to(torch.float64) for row in direct])
+    masks = torch.stack([row.band_mask[0] for row in direct])
+    counts = masks.sum(0)
+    expected = torch.where(
+        counts[:, None] > 0,
+        torch.where(masks[:, :, None], tokens, 0.0).sum(0) / counts.clamp_min(1)[:, None],
+        0.0,
+    ).to(torch.float32)
+    assert torch.equal(output.band_mask[0], counts > 0)
+    assert torch.equal(output.supporting_window_count, counts)
+    assert torch.equal(output.tokens[0], expected)
+    weights = torch.arange(1, 49, dtype=torch.float32).reshape(1, 6, 8) / 48
+    (output.tokens * weights).sum().backward()
+    assert encoder.encoder is not None
+    for module in (
+        encoder.encoder.half_edge_encoder,
+        encoder.encoder.pair_encoder,
+        encoder.encoder.topology_encoder,
+        encoder.encoder.common_postprocess,
+    ):
+        assert any(
+            parameter.grad is not None
+            and bool(torch.isfinite(parameter.grad).all())
+            and bool(torch.count_nonzero(parameter.grad) > 0)
+            for parameter in module.parameters()
+        )
+
+
+def test_legacy_complete_capture_is_actor_permutation_invariant() -> None:
+    torch.manual_seed(2718)
+    encoder = _encoder()
+    original = encode_legacy_capture_tokens(encoder, _capture())
+    permuted = encode_legacy_capture_tokens(encoder, _capture((2, 0, 1)))
+    assert torch.equal(original.tokens, permuted.tokens)
+    assert torch.equal(original.band_mask, permuted.band_mask)
+    assert torch.equal(original.supporting_window_count, permuted.supporting_window_count)
+
+
+def test_legacy_capture_partial_support_matches_frozen_tree_pooling() -> None:
+    capture = _capture(windows=5, reject_middle=False)
+    encoder = _encoder(width=512)
+    mask = np.ascontiguousarray(np.array([
+        [1, 1, 1, 0, 0, 0],
+        [1, 0, 1, 0, 0, 1],
+        [1, 1, 0, 0, 0, 1],
+        [1, 1, 1, 0, 0, 0],
+        [1, 1, 0, 0, 1, 0],
+    ], dtype=np.bool_))
+    tokens = np.zeros((5, 6, 512), dtype=np.float32)
+    tokens[:, 0, 0] = np.array([1e20, 1, -1e20, 1, 1], dtype=np.float32)
+    for index in range(5):
+        for band in range(1, 6):
+            if mask[index, band]:
+                tokens[index, band, 0] = np.float32((index + 1) * (band + 1))
+    outputs = [
+        GroupTokenOutput(
+            torch.from_numpy(tokens[index : index + 1]),
+            torch.from_numpy(mask[index : index + 1]),
+            torch.zeros((1, 6, 512)),
+            torch.zeros((1, 6, 512)),
+            torch.zeros((1, 6), dtype=torch.int64),
+            torch.zeros((1, 6), dtype=torch.int64),
+        )
+        for index in range(5)
+    ]
+    with patch.object(encoder, "forward", side_effect=outputs) as mocked:
+        actual = encode_legacy_capture_tokens(
+            encoder, capture, checkpoint_windows=False,
+        )
+    assert mocked.call_count == 5
+    windows = capture.windows()
+    commitments = tuple(bytes.fromhex(window.window_sha256) for window in windows)
+    plan = CaptureWindowPlan(
+        capture.group_commitment,
+        commitments,
+        tuple(window.source_start_frame for window in windows),
+    )
+    expected = pool_capture_windows(
+        plan,
+        window_commitments=commitments,
+        base_embeddings=np.zeros((5, 512), dtype=np.float32),
+        tokens=np.ascontiguousarray(tokens),
+        band_mask=mask,
+    )
+    np.testing.assert_array_equal(actual.tokens[0].detach().numpy(), expected.tokens)
+    np.testing.assert_array_equal(actual.band_mask[0].numpy(), expected.band_mask)
+    np.testing.assert_array_equal(
+        actual.supporting_window_count.numpy(), expected.valid_window_count
+    )
+    assert actual.supporting_window_count.tolist() == [5, 4, 3, 0, 1, 2]
+    assert not bool(actual.tokens[0, 3].view(torch.int32).any())
+
+
+def test_legacy_window_checkpoint_matches_direct_forward_and_backward() -> None:
+    torch.manual_seed(31415)
+    capture = _capture()
+    checkpointed = _encoder()
+    direct = copy.deepcopy(checkpointed)
+    a = encode_legacy_capture_tokens(checkpointed, capture, checkpoint_windows=True)
+    b = encode_legacy_capture_tokens(direct, capture, checkpoint_windows=False)
+    assert torch.equal(a.tokens, b.tokens)
+    assert torch.equal(a.band_mask, b.band_mask)
+    weights = torch.arange(1, 49, dtype=torch.float32).reshape(1, 6, 8) / 48
+    (a.tokens * weights).sum().backward()
+    (b.tokens * weights).sum().backward()
+    for left, right in zip(checkpointed.parameters(), direct.parameters(), strict=True):
+        if left.grad is None or right.grad is None:
+            assert left.grad is None and right.grad is None
+        else:
+            torch.testing.assert_close(left.grad, right.grad, rtol=0, atol=0)
+
+
+def test_legacy_capture_rejects_nonlegacy_system_and_invalid_chunk() -> None:
+    capture = _capture()
+    with pytest.raises(TypeError, match="system 08"):
+        encode_legacy_capture_tokens(PhaseSetSystem("00", embedding_dim=8), capture)
+    with pytest.raises(ValueError, match="chunk_size"):
+        encode_legacy_capture_tokens(_encoder(), capture, edge_chunk_size=0)

From 07e783bec58903670eaf4b659b4671e8a99dc504 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 13:47:09 -0500
Subject: [PATCH 92/95] Add provisional whole-parent legacy and A9 scoring seam

---
 RELEASE_FILES.sha256                          |   3 +
 docs/PHASESET_V2_LEGACY_PARENT_SCORE.md       |  27 +++
 .../legacy_continuous_retrieval.py            | 193 ++++++++++++++++++
 tests/test_phaseset_v2_legacy_parent_score.py |  95 +++++++++
 4 files changed, 318 insertions(+)
 create mode 100644 docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
 create mode 100644 src/phaseset_core/legacy_continuous_retrieval.py
 create mode 100644 tests/test_phaseset_v2_legacy_parent_score.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 34887e9..ca1ae84 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -83,6 +83,7 @@ fdcf40e6f0f30729808c66c55d9a0817b1c6b71032751e0baa5e1a287771794e	2252	docs/PHASE
 b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 1e0d2fb0b35a6900dfb03af4b9bfe1701eb2c843f566c0f842b47b03b11b645e	1276	docs/PHASESET_V2_ICMR_TARGET.md
+1169a5a485c5f4f9644641c1f37c24c5a9af6cfb91d31029a916d058c2ea47a4	1549	docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
 b5042e5fa002fa057ca4af61f4a06953662708d221016c460acd9cd0278c5863	4606	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 932cc27f1e4c53f3ee09042514949cf9b2132758cee2d1dd71d709dda4a08884	4435	docs/PHASESET_V2_MECHANISM_FACTORY.md
@@ -206,6 +207,7 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
 84c7052b74cb8be4cddcfb11fea633b9bb46d3e3f2690761db3134ddb1c3f6b7	3982	src/phaseset_core/legacy_capture.py
+99e0d24f8484e555d63e0fd972e9a454f95c3013e458fdfb59e485794ddb017c	8279	src/phaseset_core/legacy_continuous_retrieval.py
 1fb8e81b89b58e995a08a016aa3efad5074a269907e5d51da040fb0918d64335	11164	src/phaseset_core/literature_parent_host.py
 02a11d20ac299c4886479b863f2f614512365a9f1fe800861b2f261dbdad1c7a	9101	src/phaseset_core/literature_parent_training.py
 f0d7c36ecda4326ca2b1f9461fdc812039cae33739595c60d40a436f0051edb7	6976	src/phaseset_core/mime_language.py
@@ -371,6 +373,7 @@ fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 1ff21b1e8133cf14b0672d984a061cfbe71c58c155e1e53f4e40c5280f3f74fe	21461	tests/test_phaseset_v2_coordination.py
 2aae2397f6c40d929b0eb9b61d340d3ece6d48a17da01d6b3bd2767c2b6945b3	7513	tests/test_phaseset_v2_legacy_capture.py
+5c457c17c74ac395898288947b4a48e3c559ba35286669cd67cd0d8a27d40789	4158	tests/test_phaseset_v2_legacy_parent_score.py
 90ede69f8695413a5cbc38c45b67602e5b3ed26212ac09e7f33c568b0129f719	15608	tests/test_phaseset_v2_mechanism_factory.py
 1df27542e87ae6d3d47ac0e6a865a6e9a5be49ea8a9bc5af61758b18e830b64a	6261	tests/test_phaseset_v2_score_tables.py
 08d27f7109d678ca011ef63dd99429185eb7b8c16d5f22e266dc05fbd58ea383	9778	tests/test_phaseset_v2_statistics.py
diff --git a/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md b/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
new file mode 100644
index 0000000..79fab48
--- /dev/null
+++ b/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
@@ -0,0 +1,27 @@
+# Old PhaseSet and A9 complete-parent score seam
+
+`LegacyWholeCaptureRetrievalSystem` scores every accepted ten-second window of
+an already prepared capture, with no actor or edge sampling. Its legacy motion
+branch remains the old system-08 Morlet/13D/six-band encoder; each band token is
+the masked mean over all supporting windows. The frozen B2 branch uses the same
+complete-parent mean and normalization as the V2 base readout.
+
+Two score modes share this representation:
+
+- `old`: frozen B2 temperature-scaled logits plus the original bounded
+  `PhaseSetRetrievalHead` relation residual.
+- `A9`: frozen B2 cosine and the same old relation cosine under the common
+  learned cosine calibration. This changes score scale, not relation features
+  or the old text-band projection architecture.
+
+The mode, window batching, edge chunk and scalar-floor value hash are bound to
+the scorer state. The generic complete-parent training host deliberately does
+**not** accept this scorer yet. A floor value hash is not proof that the floor
+was fitted from the admitted training population. A typed training-only scalar
+floor receipt, registered old/A9 run identities and their predecessor binding
+are separate prerequisites before formal training or sealed evaluation.
+
+The data-free CPU qualification checks both modes' score decomposition, frozen
+B2 gradients, floor mutation rejection, host rejection, and the existing old
+capture/A9 regression suites. Passing those checks is software qualification,
+not a retrieval result or evidence for a paper claim.
diff --git a/src/phaseset_core/legacy_continuous_retrieval.py b/src/phaseset_core/legacy_continuous_retrieval.py
new file mode 100644
index 0000000..b5fd99e
--- /dev/null
+++ b/src/phaseset_core/legacy_continuous_retrieval.py
@@ -0,0 +1,193 @@
+"""Whole-parent scorer for the unchanged old PhaseSet representation.
+
+This is a score seam, not a qualified training host or a floor estimator. The
+caller must bind an independently fitted old scalar floor to its private input
+manifest before using this with native captures. No final-test data are opened.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+import json
+
+import numpy as np
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+from .capture_validation import _validate_text_batch
+from .continuous_capture import PreparedContinuousCapture
+from .controls import PhaseSetSystem
+from .frozen_clip_text import FrozenClipTextBatch
+from .legacy_capture import encode_legacy_capture_tokens
+from .objectives import PhaseSetRetrievalHead
+from .periodic import DEFAULT_EDGE_CHUNK_SIZE, validate_edge_chunk_size
+from .pipeline import collate_group_samples, edge_budget_batches
+from .temporal_coordination import LegacyPhaseSetCalibratedHead
+from .training import BaseRetrievalSystem
+
+
+@dataclass(frozen=True)
+class LegacyWholeCaptureScoreOutput:
+    scores: Tensor
+    global_cosine: Tensor
+    legacy_relation_cosine: Tensor
+    periodic_support: Tensor
+    text_receipt_sha256: str
+    capture_keys: tuple[str, ...]
+
+
+class LegacyWholeCaptureRetrievalSystem(nn.Module):
+    """Frozen B2 plus old six-token head, with either old or A9 scoring.
+
+    Both modes use the same old 13D descriptor, six group tokens, old text-band
+    projector and complete-capture masked mean. A9 alone replaces the old
+    logit-plus-residual score with the common-scale cosine calibration.
+    """
+
+    def __init__(
+        self,
+        frozen_b2: BaseRetrievalSystem,
+        legacy_encoder: PhaseSetSystem,
+        *,
+        score_mode: str,
+        edge_chunk_size: int = DEFAULT_EDGE_CHUNK_SIZE,
+        checkpoint_windows: bool = True,
+        base_window_batch_size: int = 1,
+        base_edge_budget: int = 32768,
+    ) -> None:
+        super().__init__()
+        if (
+            not isinstance(frozen_b2, BaseRetrievalSystem)
+            or frozen_b2.system_id != "B2"
+            or frozen_b2.embedding_dim != 512
+        ):
+            raise ValueError("old whole-capture controls require the frozen 512D B2")
+        if (
+            type(legacy_encoder) is not PhaseSetSystem
+            or legacy_encoder.system_id != "08"
+            or legacy_encoder.embedding_dim != 512
+            or legacy_encoder.encoder is None
+        ):
+            raise ValueError("old whole-capture controls require exact system 08 at 512D")
+        if score_mode not in ("old", "A9"):
+            raise ValueError("score_mode must be old or A9")
+        if type(checkpoint_windows) is not bool:
+            raise TypeError("checkpoint_windows must be an exact bool")
+        if type(base_window_batch_size) is not int or base_window_batch_size < 1:
+            raise ValueError("base_window_batch_size must be positive")
+        if type(base_edge_budget) is not int or base_edge_budget < 1:
+            raise ValueError("base_edge_budget must be positive")
+        self.edge_chunk_size = validate_edge_chunk_size(edge_chunk_size)
+        self.checkpoint_windows = checkpoint_windows
+        self.base_window_batch_size = base_window_batch_size
+        self.base_edge_budget = base_edge_budget
+        self.score_mode = score_mode
+        self.frozen_b2 = frozen_b2.requires_grad_(False).eval()
+        self.legacy_encoder = legacy_encoder
+        self.head = (
+            PhaseSetRetrievalHead(512)
+            if score_mode == "old"
+            else LegacyPhaseSetCalibratedHead(512)
+        )
+        self._floor_sha256 = self._current_floor_sha256()
+
+    def _current_floor_sha256(self) -> str:
+        floors = self.legacy_encoder.encoder._energy_floors
+        if type(floors) is not np.ndarray or floors.dtype != np.float64 or floors.shape != (6,):
+            raise ValueError("old scalar floor vector changed shape or type")
+        return hashlib.sha256(floors.tobytes(order="C")).hexdigest()
+
+    def get_extra_state(self) -> Tensor:
+        payload = {
+            "schema": "phaseset-v2-legacy-whole-capture-v1",
+            "score_mode": self.score_mode,
+            "old_scalar_floor_sha256": self._floor_sha256,
+            "edge_chunk_size": self.edge_chunk_size,
+            "checkpoint_windows": self.checkpoint_windows,
+            "base_window_batch_size": self.base_window_batch_size,
+            "base_edge_budget": self.base_edge_budget,
+        }
+        encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("ascii")
+        return torch.tensor(list(encoded), dtype=torch.uint8)
+
+    def set_extra_state(self, state: object) -> None:
+        if (
+            type(state) is not Tensor
+            or state.dtype != torch.uint8
+            or state.ndim != 1
+            or not torch.equal(state.detach().cpu(), self.get_extra_state())
+            or self._current_floor_sha256() != self._floor_sha256
+        ):
+            raise ValueError("old mode, scalar floor or capture readout changed on resume")
+
+    def train(self, mode: bool = True):
+        super().train(mode)
+        self.frozen_b2.eval()
+        return self
+
+    @torch.no_grad()
+    def encode_global(self, capture: PreparedContinuousCapture) -> Tensor:
+        self.frozen_b2.eval()
+        windows = capture.windows()
+        batches = edge_budget_batches(
+            windows,
+            max_total_edges=self.base_edge_budget,
+            max_batch_size=self.base_window_batch_size,
+        )
+        rows = torch.cat(
+            [self.frozen_b2.encode_trainable(collate_group_samples(batch)) for batch in batches]
+        )
+        if rows.shape != (len(windows), 512):
+            raise RuntimeError("frozen B2 omitted a complete-capture window")
+        return F.normalize(rows.to(torch.float64).mean(dim=0).to(torch.float32), dim=0)
+
+    def score(
+        self,
+        captures: tuple[PreparedContinuousCapture, ...],
+        text_batch: FrozenClipTextBatch,
+    ) -> LegacyWholeCaptureScoreOutput:
+        if (
+            type(captures) is not tuple
+            or not captures
+            or any(type(capture) is not PreparedContinuousCapture for capture in captures)
+        ):
+            raise ValueError("old scorer requires complete prepared capture rows")
+        if self._current_floor_sha256() != self._floor_sha256:
+            raise ValueError("old scalar floor vector changed after construction")
+        text, _, receipt_sha = _validate_text_batch(text_batch, allow_row_selection=True)
+        device = next(self.legacy_encoder.parameters()).device
+        if next(self.frozen_b2.parameters()).device != device:
+            raise ValueError("frozen B2 and old encoder must share a device")
+        text = text.to(device).contiguous()
+        global_rows, tokens, masks = [], [], []
+        for capture in captures:
+            global_rows.append(self.encode_global(capture))
+            old = encode_legacy_capture_tokens(
+                self.legacy_encoder,
+                capture,
+                edge_chunk_size=self.edge_chunk_size,
+                checkpoint_windows=self.checkpoint_windows,
+            )
+            tokens.append(old.tokens[0])
+            masks.append(old.band_mask[0])
+        global_embeddings = torch.stack(global_rows).contiguous()
+        group_tokens = torch.stack(tokens).contiguous()
+        band_mask = torch.stack(masks).contiguous()
+        cosine = F.normalize(global_embeddings, dim=-1) @ F.normalize(text, dim=-1).T
+        if self.score_mode == "old":
+            base_logits = self.frozen_b2.scores(global_embeddings, text).detach().contiguous()
+            output = self.head(group_tokens, band_mask, text, base_logits)
+            relation_cosine = output.periodic_scores
+        else:
+            output = self.head(group_tokens, band_mask, text, cosine.detach().contiguous())
+            relation_cosine = output.legacy_relation_cosine
+        return LegacyWholeCaptureScoreOutput(
+            output.scores.contiguous(),
+            cosine.detach().contiguous(),
+            relation_cosine,
+            band_mask.any(dim=1).contiguous(),
+            receipt_sha,
+            tuple(capture.source_sha256 for capture in captures),
+        )
diff --git a/tests/test_phaseset_v2_legacy_parent_score.py b/tests/test_phaseset_v2_legacy_parent_score.py
new file mode 100644
index 0000000..edbaea0
--- /dev/null
+++ b/tests/test_phaseset_v2_legacy_parent_score.py
@@ -0,0 +1,95 @@
+"""Data-free old/A9 complete-parent score and host-boundary qualifications."""
+
+from __future__ import annotations
+
+import copy
+import math
+
+import numpy as np
+import pytest
+import torch
+from torch.nn import functional as F
+
+from phaseset_core.capture_validation import _validate_text_batch
+from phaseset_core.continuous_parent_host import ContinuousParentTrainingHost
+from phaseset_core.controls import PhaseSetSystem
+from phaseset_core.legacy_continuous_retrieval import LegacyWholeCaptureRetrievalSystem
+from phaseset_core.periodic import BAND_COUNT
+from test_continuous_parent_host import _setup
+from test_continuous_parent_training import _case
+
+
+def _legacy(base, mode: str) -> LegacyWholeCaptureRetrievalSystem:
+    encoder = PhaseSetSystem(
+        "08",
+        embedding_dim=512,
+        hidden_dim=8,
+        energy_floors=np.full((BAND_COUNT,), 1e-10, dtype=np.float64),
+        edge_budget=64,
+    )
+    return LegacyWholeCaptureRetrievalSystem(
+        copy.deepcopy(base), encoder, score_mode=mode, edge_chunk_size=64
+    )
+
+
+@pytest.mark.parametrize("mode", ["old", "A9"])
+def test_complete_legacy_score_uses_frozen_b2_and_registered_head_math(tmp_path, mode):
+    v2, views, _labels, text, _clip = _case(tmp_path)
+    system = _legacy(v2.frozen_b2, mode)
+    capture = views[0].capture
+    expected_count = len(capture.windows())
+    assert expected_count > 0
+    output = system.score((capture,), text)
+    assert output.scores.shape == (1, len(text.receipt.caption_rows))
+    assert output.global_cosine.shape == output.scores.shape
+    assert output.legacy_relation_cosine.shape == output.scores.shape
+    assert output.capture_keys == (capture.source_sha256,)
+    assert output.periodic_support.shape == (1,)
+    assert bool(torch.isfinite(output.scores).all())
+    assert bool((output.global_cosine.abs() <= 1.00001).all())
+    text_embeddings, _, _ = _validate_text_batch(text, allow_row_selection=True)
+    global_embedding = system.encode_global(capture)[None]
+    if mode == "old":
+        base_logits = system.frozen_b2.scores(global_embedding, text_embeddings)
+        expected_scores = (
+            base_logits
+            + system.head.residual_lambda.tanh()[0] * output.legacy_relation_cosine
+        )
+    else:
+        alpha = system.head.calibration.mixture_logit.sigmoid()
+        alpha = torch.where(output.periodic_support[:, None], alpha, torch.zeros_like(alpha))
+        scale = system.head.calibration.log_scale.clamp(math.log(1e-3), math.log(100)).exp()
+        expected_scores = scale * (
+            (1 - alpha) * output.global_cosine
+            + alpha * output.legacy_relation_cosine
+        )
+    torch.testing.assert_close(output.scores, expected_scores, rtol=0, atol=0)
+    torch.testing.assert_close(
+        output.global_cosine,
+        F.normalize(global_embedding, dim=-1) @ F.normalize(text_embeddings, dim=-1).T,
+        rtol=0,
+        atol=0,
+    )
+    output.scores.sum().backward()
+    assert all(parameter.grad is None for parameter in system.frozen_b2.parameters())
+    assert any(
+        parameter.grad is not None and bool(torch.isfinite(parameter.grad).all())
+        for parameter in system.legacy_encoder.parameters()
+    )
+    assert any(parameter.grad is not None for parameter in system.head.parameters())
+
+
+@pytest.mark.parametrize("mode", ["old", "A9"])
+def test_legacy_parent_score_is_not_yet_a_registered_training_host(tmp_path, mode):
+    v2, task, source, config, bindings = _setup(tmp_path)
+    system = _legacy(v2.frozen_b2, mode)
+    with pytest.raises(TypeError, match="base or V2 scorer"):
+        ContinuousParentTrainingHost(system, task, source, config, bindings)
+    assert system.get_extra_state().dtype == torch.uint8
+    saved = system.get_extra_state().clone()
+    system.set_extra_state(saved)
+    with pytest.raises(ValueError, match="changed on resume"):
+        system.set_extra_state(torch.empty(0, dtype=torch.uint8))
+    system.legacy_encoder.encoder._energy_floors = np.full((6,), 0.1, dtype=np.float64)
+    with pytest.raises(ValueError, match="changed after construction"):
+        system.score((source.views[task.parents[0].source_sha256].capture,), None)

From 49e0f238096e57772d5eb02204cd7b2816a42542 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 14:15:14 -0500
Subject: [PATCH 93/95] Bind legacy scalar floor receipts to whole-parent
 scorer

---
 RELEASE_FILES.sha256                          |   8 +-
 docs/PHASESET_V2_LEGACY_PARENT_SCORE.md       |  21 +-
 .../legacy_continuous_retrieval.py            |  39 ++-
 .../legacy_scalar_calibration.py              | 279 ++++++++++++++++++
 tests/test_phaseset_v2_legacy_parent_score.py |  30 +-
 ...t_phaseset_v2_legacy_scalar_calibration.py |  98 ++++++
 6 files changed, 460 insertions(+), 15 deletions(-)
 create mode 100644 src/phaseset_core/legacy_scalar_calibration.py
 create mode 100644 tests/test_phaseset_v2_legacy_scalar_calibration.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index ca1ae84..7068c4d 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -83,7 +83,7 @@ fdcf40e6f0f30729808c66c55d9a0817b1c6b71032751e0baa5e1a287771794e	2252	docs/PHASE
 b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 1e0d2fb0b35a6900dfb03af4b9bfe1701eb2c843f566c0f842b47b03b11b645e	1276	docs/PHASESET_V2_ICMR_TARGET.md
-1169a5a485c5f4f9644641c1f37c24c5a9af6cfb91d31029a916d058c2ea47a4	1549	docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
+b5f782397000487dc9e3a37a8cb345e830b102631b7f869b8e54a37c208e8859	2075	docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
 b5042e5fa002fa057ca4af61f4a06953662708d221016c460acd9cd0278c5863	4606	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 932cc27f1e4c53f3ee09042514949cf9b2132758cee2d1dd71d709dda4a08884	4435	docs/PHASESET_V2_MECHANISM_FACTORY.md
@@ -207,7 +207,8 @@ eb3149dbb20156168051442acae158e6009b92ce41f705f974f4a6f232129857	83444	src/phase
 6d600f0affbea8e69cb2e1df8589ff53acf785eae34cc16f742b0d2176554644	107360	src/phaseset_core/host.py
 3d4bfeebd0940ef11d0b2253c187fa6e716de369d4f273c03bf7f55a598dc400	44734	src/phaseset_core/latency_progress_journal.py
 84c7052b74cb8be4cddcfb11fea633b9bb46d3e3f2690761db3134ddb1c3f6b7	3982	src/phaseset_core/legacy_capture.py
-99e0d24f8484e555d63e0fd972e9a454f95c3013e458fdfb59e485794ddb017c	8279	src/phaseset_core/legacy_continuous_retrieval.py
+f9edf6694574b9b40ba920d44a34fb7de5ba06f3e18dd1634c43b908448e9d19	9679	src/phaseset_core/legacy_continuous_retrieval.py
+4c64fe3390f5921a1fe3f06c70abd8228e30ee1c6a64b7e53ac2c2bf2c829579	11981	src/phaseset_core/legacy_scalar_calibration.py
 1fb8e81b89b58e995a08a016aa3efad5074a269907e5d51da040fb0918d64335	11164	src/phaseset_core/literature_parent_host.py
 02a11d20ac299c4886479b863f2f614512365a9f1fe800861b2f261dbdad1c7a	9101	src/phaseset_core/literature_parent_training.py
 f0d7c36ecda4326ca2b1f9461fdc812039cae33739595c60d40a436f0051edb7	6976	src/phaseset_core/mime_language.py
@@ -373,7 +374,8 @@ fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 1ff21b1e8133cf14b0672d984a061cfbe71c58c155e1e53f4e40c5280f3f74fe	21461	tests/test_phaseset_v2_coordination.py
 2aae2397f6c40d929b0eb9b61d340d3ece6d48a17da01d6b3bd2767c2b6945b3	7513	tests/test_phaseset_v2_legacy_capture.py
-5c457c17c74ac395898288947b4a48e3c559ba35286669cd67cd0d8a27d40789	4158	tests/test_phaseset_v2_legacy_parent_score.py
+b9d44f8c94b0e110141c66b00358cc11d799078417122a48c61924c819968b6f	4986	tests/test_phaseset_v2_legacy_parent_score.py
+278279ce18dd0f74c397bc69f99828afcacf9c1c6b5466fe34d1071df3bf99e8	4075	tests/test_phaseset_v2_legacy_scalar_calibration.py
 90ede69f8695413a5cbc38c45b67602e5b3ed26212ac09e7f33c568b0129f719	15608	tests/test_phaseset_v2_mechanism_factory.py
 1df27542e87ae6d3d47ac0e6a865a6e9a5be49ea8a9bc5af61758b18e830b64a	6261	tests/test_phaseset_v2_score_tables.py
 08d27f7109d678ca011ef63dd99429185eb7b8c16d5f22e266dc05fbd58ea383	9778	tests/test_phaseset_v2_statistics.py
diff --git a/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md b/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
index 79fab48..4b9b927 100644
--- a/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
+++ b/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
@@ -14,12 +14,21 @@ Two score modes share this representation:
   learned cosine calibration. This changes score scale, not relation features
   or the old text-band projection architecture.
 
-The mode, window batching, edge chunk and scalar-floor value hash are bound to
-the scorer state. The generic complete-parent training host deliberately does
-**not** accept this scorer yet. A floor value hash is not proof that the floor
-was fitted from the admitted training population. A typed training-only scalar
-floor receipt, registered old/A9 run identities and their predecessor binding
-are separate prerequisites before formal training or sealed evaluation.
+The mode, window batching, edge chunk, scalar-floor values and typed floor
+receipt are bound to the scorer state. The receipt's method is the unchanged
+old 20 Hz five-speed-channel Morlet actor-local power, with a linear fifth
+percentile per band over every accepted actor-window in the declared training
+parent census. Observed exact-zero powers count; absent Morlet support does
+not. Each band's observed and missing counts must total the actor-window
+census. The receipt binds the private source-manifest digest, population,
+physical kernel identity, floor bytes and diagnostics. The data-free test uses
+a synthetic receipt and does not imply that native floors were fitted.
+
+The generic complete-parent training host deliberately does **not** accept
+this scorer yet. The registered real training-only scalar-floor receipt,
+old/A9 run identities, predecessor checkpoint binding and score-plus-VJP host
+qualification are separate prerequisites before formal training or sealed
+evaluation.
 
 The data-free CPU qualification checks both modes' score decomposition, frozen
 B2 gradients, floor mutation rejection, host rejection, and the existing old
diff --git a/src/phaseset_core/legacy_continuous_retrieval.py b/src/phaseset_core/legacy_continuous_retrieval.py
index b5fd99e..36e3fdd 100644
--- a/src/phaseset_core/legacy_continuous_retrieval.py
+++ b/src/phaseset_core/legacy_continuous_retrieval.py
@@ -21,6 +21,7 @@
 from .controls import PhaseSetSystem
 from .frozen_clip_text import FrozenClipTextBatch
 from .legacy_capture import encode_legacy_capture_tokens
+from .legacy_scalar_calibration import LegacyScalarFloorReceipt
 from .objectives import PhaseSetRetrievalHead
 from .periodic import DEFAULT_EDGE_CHUNK_SIZE, validate_edge_chunk_size
 from .pipeline import collate_group_samples, edge_budget_batches
@@ -52,6 +53,7 @@ def __init__(
         legacy_encoder: PhaseSetSystem,
         *,
         score_mode: str,
+        floor_receipt: LegacyScalarFloorReceipt,
         edge_chunk_size: int = DEFAULT_EDGE_CHUNK_SIZE,
         checkpoint_windows: bool = True,
         base_window_batch_size: int = 1,
@@ -73,6 +75,8 @@ def __init__(
             raise ValueError("old whole-capture controls require exact system 08 at 512D")
         if score_mode not in ("old", "A9"):
             raise ValueError("score_mode must be old or A9")
+        if type(floor_receipt) is not LegacyScalarFloorReceipt:
+            raise TypeError("old/A9 complete-parent scorer requires a typed training-only floor receipt")
         if type(checkpoint_windows) is not bool:
             raise TypeError("checkpoint_windows must be an exact bool")
         if type(base_window_batch_size) is not int or base_window_batch_size < 1:
@@ -84,26 +88,54 @@ def __init__(
         self.base_window_batch_size = base_window_batch_size
         self.base_edge_budget = base_edge_budget
         self.score_mode = score_mode
+        self.floor_receipt = floor_receipt
         self.frozen_b2 = frozen_b2.requires_grad_(False).eval()
         self.legacy_encoder = legacy_encoder
+        self.register_buffer(
+            "_bound_old_scalar_floors",
+            torch.from_numpy(floor_receipt.floors.copy()),
+            persistent=True,
+        )
         self.head = (
             PhaseSetRetrievalHead(512)
             if score_mode == "old"
             else LegacyPhaseSetCalibratedHead(512)
         )
         self._floor_sha256 = self._current_floor_sha256()
+        self._verify_floor_binding()
 
     def _current_floor_sha256(self) -> str:
         floors = self.legacy_encoder.encoder._energy_floors
-        if type(floors) is not np.ndarray or floors.dtype != np.float64 or floors.shape != (6,):
+        if (
+            type(floors) is not np.ndarray
+            or floors.dtype != np.float64
+            or floors.shape != (6,)
+            or not floors.flags.c_contiguous
+        ):
             raise ValueError("old scalar floor vector changed shape or type")
         return hashlib.sha256(floors.tobytes(order="C")).hexdigest()
 
+    def _verify_floor_binding(self) -> None:
+        floors = self.legacy_encoder.encoder._energy_floors
+        bound = self._bound_old_scalar_floors.detach().cpu()
+        if (
+            self._current_floor_sha256() != self._floor_sha256
+            or bound.dtype != torch.float64
+            or bound.shape != (6,)
+            or not bound.isfinite().all()
+            or floors.tobytes(order="C") != self.floor_receipt.floors.tobytes(order="C")
+            or bound.numpy().tobytes(order="C") != self.floor_receipt.floors.tobytes(order="C")
+        ):
+            raise ValueError("old scalar floor vector changed after construction or differs from receipt")
+
     def get_extra_state(self) -> Tensor:
         payload = {
             "schema": "phaseset-v2-legacy-whole-capture-v1",
             "score_mode": self.score_mode,
             "old_scalar_floor_sha256": self._floor_sha256,
+            "old_scalar_floor_receipt_sha256": self.floor_receipt.sha256,
+            "training_source_manifest_sha256": self.floor_receipt.training_source_manifest_sha256,
+            "old_scalar_config_sha256": self.floor_receipt.config_sha256,
             "edge_chunk_size": self.edge_chunk_size,
             "checkpoint_windows": self.checkpoint_windows,
             "base_window_batch_size": self.base_window_batch_size,
@@ -118,9 +150,9 @@ def set_extra_state(self, state: object) -> None:
             or state.dtype != torch.uint8
             or state.ndim != 1
             or not torch.equal(state.detach().cpu(), self.get_extra_state())
-            or self._current_floor_sha256() != self._floor_sha256
         ):
             raise ValueError("old mode, scalar floor or capture readout changed on resume")
+        self._verify_floor_binding()
 
     def train(self, mode: bool = True):
         super().train(mode)
@@ -154,8 +186,7 @@ def score(
             or any(type(capture) is not PreparedContinuousCapture for capture in captures)
         ):
             raise ValueError("old scorer requires complete prepared capture rows")
-        if self._current_floor_sha256() != self._floor_sha256:
-            raise ValueError("old scalar floor vector changed after construction")
+        self._verify_floor_binding()
         text, _, receipt_sha = _validate_text_batch(text_batch, allow_row_selection=True)
         device = next(self.legacy_encoder.parameters()).device
         if next(self.frozen_b2.parameters()).device != device:
diff --git a/src/phaseset_core/legacy_scalar_calibration.py b/src/phaseset_core/legacy_scalar_calibration.py
new file mode 100644
index 0000000..8a64198
--- /dev/null
+++ b/src/phaseset_core/legacy_scalar_calibration.py
@@ -0,0 +1,279 @@
+"""Train-only scalar Morlet floors for the unchanged old PhaseSet frontend.
+
+One observation is one actor, accepted 200-frame window and physical band.
+The complete admitted parent census is required; no pair, caption, score or
+model output participates in fitting. A private source manifest must separately
+prove that the supplied captures are the registered training population.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+from dataclasses import dataclass
+import hashlib
+import json
+
+import numpy as np
+
+from .continuous_capture import PreparedContinuousCapture
+from .contracts import skeleton_to_activity
+from .directional_calibration import _linear_fifth_percentile
+from .morlet import MORLET_ORACLE_SHA256, SAMPLE_RATE_HZ
+from .periodic import (
+    BAND_COUNT,
+    _actor_marginal_powers,
+    _precompute_actor_responses,
+    validate_energy_floors,
+)
+from .pipeline import collate_group_samples
+
+
+LEGACY_SCALAR_CALIBRATION_METHOD = (
+    "old-five-speed-channel-actor-window-Morlet-power/linear-fifth-percentile-v1"
+)
+LEGACY_SCALAR_FLOOR_SCHEMA = "phaseset-v2-legacy-scalar-training-floor-v1"
+_SEALED = frozenset(("C09", "C11", "C15"))
+
+
+def legacy_scalar_config_sha256() -> str:
+    """Bind the old five-channel, 20-Hz physical kernel and estimator identity."""
+    payload = {
+        "activity": "five-masked-body-group-speed-channels/20Hz/v1",
+        "morlet_oracle_sha256": MORLET_ORACLE_SHA256,
+        "sample_rate_hz": SAMPLE_RATE_HZ,
+        "method": LEGACY_SCALAR_CALIBRATION_METHOD,
+    }
+    raw = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("ascii")
+    return hashlib.sha256(b"phaseset-v2-legacy-scalar-config-v1\0" + raw).hexdigest()
+
+
+def _sha256_key(value: object) -> bool:
+    return type(value) is str and len(value) == 64 and all(c in "0123456789abcdef" for c in value)
+
+
+@dataclass(frozen=True)
+class LegacyScalarCalibrationCapture:
+    source_sha256: str
+    component: str
+    capture: PreparedContinuousCapture
+
+
+@dataclass(frozen=True)
+class LegacyScalarEnergyCalibration:
+    floors: np.ndarray
+    capture_count: int
+    accepted_window_count: int
+    actor_window_count: int
+    training_components: tuple[str, ...]
+    observed_counts: tuple[int, ...]
+    missing_counts: tuple[int, ...]
+    exact_zero_counts: tuple[int, ...]
+    method: str = LEGACY_SCALAR_CALIBRATION_METHOD
+    config_sha256: str = legacy_scalar_config_sha256()
+
+
+def fit_legacy_scalar_energy_floors(
+    captures: Iterable[LegacyScalarCalibrationCapture],
+    *,
+    training_components: tuple[str, ...],
+    expected_capture_ids: tuple[str, ...],
+    max_actor_window_observations: int = 1_000_000,
+) -> LegacyScalarEnergyCalibration:
+    """Fit six fifth-percentile floors from every admitted training parent.
+
+    The old pair descriptor uses Morlet power over the shared endpoint mask.
+    Calibration uses each actor's marginal response mask, as an independent
+    actor-local observation, and includes observed exact-zero powers. Absence
+    of physical support is counted but excluded from the percentile. A runtime
+    limit raises RESOURCE_LIMIT; it never samples or truncates the population.
+    """
+    if (
+        type(training_components) is not tuple
+        or not training_components
+        or tuple(sorted(set(training_components))) != training_components
+        or set(training_components) & _SEALED
+    ):
+        raise ValueError("old scalar floors require sorted, unique training components")
+    if (
+        type(expected_capture_ids) is not tuple
+        or not expected_capture_ids
+        or len(set(expected_capture_ids)) != len(expected_capture_ids)
+        or any(not _sha256_key(value) for value in expected_capture_ids)
+    ):
+        raise ValueError("old scalar floor parent census must be explicit and unique")
+    if type(max_actor_window_observations) is not int or max_actor_window_observations < 1:
+        raise ValueError("old scalar floor observation budget must be positive")
+    allowed = set(training_components)
+    expected = set(expected_capture_ids)
+    seen: set[str] = set()
+    seen_components: set[str] = set()
+    samples: list[list[float]] = [[] for _ in range(BAND_COUNT)]
+    missing = np.zeros((BAND_COUNT,), dtype=np.int64)
+    accepted_windows = actor_windows = 0
+    for row in captures:
+        if type(row) is not LegacyScalarCalibrationCapture:
+            raise TypeError("old scalar floor stream needs exact capture records")
+        if (
+            row.component not in allowed
+            or row.source_sha256 not in expected
+            or row.source_sha256 in seen
+        ):
+            raise ValueError("old scalar floor stream has nontraining, repeated or unknown parent")
+        capture = row.capture
+        if (
+            type(capture) is not PreparedContinuousCapture
+            or capture.source_sha256 != row.source_sha256
+            or capture.augmentation_yaw != 0.0
+        ):
+            raise ValueError("old scalar floors require the admitted unaugmented physical parent")
+        windows = capture.windows()
+        actor_windows += len(windows) * capture.actor_count
+        if actor_windows > max_actor_window_observations:
+            raise MemoryError("RESOURCE_LIMIT: old scalar floor population exceeds budget")
+        for window in windows:
+            activity = skeleton_to_activity(collate_group_samples((window,)))
+            actor_count = activity.actor_counts[0]
+            length = activity.valid_lengths[0]
+            responses, _bands, length_mask = _precompute_actor_responses(
+                activity.activities[0, :actor_count],
+                activity.activity_mask[0, :actor_count],
+                length,
+            )
+            for response in responses:
+                powers = _actor_marginal_powers(response, length_mask)
+                for band in range(BAND_COUNT):
+                    mask = response.response_mask[band]
+                    if not bool(length_mask[band]) or mask is None or not bool(mask.any()):
+                        missing[band] += 1
+                    else:
+                        power = float(powers[band])
+                        if not np.isfinite(power) or power < 0.0:
+                            raise ValueError("old scalar physical power is nonfinite or negative")
+                        samples[band].append(power if power else 0.0)
+            accepted_windows += 1
+        seen.add(row.source_sha256)
+        seen_components.add(row.component)
+    if seen != expected or seen_components != allowed:
+        raise ValueError("old scalar fit did not consume the complete training census")
+    values = tuple(np.asarray(row, dtype=np.float64) for row in samples)
+    if any(int(row.size) + int(missing[band]) != actor_windows for band, row in enumerate(values)):
+        raise AssertionError("old scalar calibration lost an actor-window observation")
+    floors = validate_energy_floors(
+        np.asarray([_linear_fifth_percentile(row) for row in values], dtype=np.float64)
+    ).copy()
+    floors.setflags(write=False)
+    return LegacyScalarEnergyCalibration(
+        floors=floors,
+        capture_count=len(seen),
+        accepted_window_count=accepted_windows,
+        actor_window_count=actor_windows,
+        training_components=training_components,
+        observed_counts=tuple(int(row.size) for row in values),
+        missing_counts=tuple(int(value) for value in missing),
+        exact_zero_counts=tuple(int(np.count_nonzero(row == 0.0)) for row in values),
+    )
+
+
+@dataclass(frozen=True)
+class LegacyScalarFloorReceipt:
+    schema: str
+    population_name: str
+    training_components: tuple[str, ...]
+    training_source_manifest_sha256: str
+    config_sha256: str
+    floors: np.ndarray
+    capture_count: int
+    accepted_window_count: int
+    actor_window_count: int
+    observed_counts: tuple[int, ...]
+    missing_counts: tuple[int, ...]
+    exact_zero_counts: tuple[int, ...]
+
+    def __post_init__(self) -> None:
+        if self.schema != LEGACY_SCALAR_FLOOR_SCHEMA or self.population_name != "main":
+            raise ValueError("old scalar floor schema or registered population differs")
+        if (
+            type(self.training_components) is not tuple
+            or not self.training_components
+            or tuple(sorted(set(self.training_components))) != self.training_components
+            or bool(set(self.training_components) & _SEALED)
+            or not _sha256_key(self.training_source_manifest_sha256)
+            or self.config_sha256 != legacy_scalar_config_sha256()
+        ):
+            raise ValueError("old scalar floor training source or physical config differs")
+        if (
+            type(self.capture_count) is not int
+            or self.capture_count < 1
+            or type(self.accepted_window_count) is not int
+            or self.accepted_window_count < self.capture_count
+            or type(self.actor_window_count) is not int
+            or self.actor_window_count < 2 * self.accepted_window_count
+            or any(
+                type(counts) is not tuple
+                or len(counts) != BAND_COUNT
+                or any(type(value) is not int or value < 0 for value in counts)
+                for counts in (self.observed_counts, self.missing_counts, self.exact_zero_counts)
+            )
+            or any(
+                self.observed_counts[band] + self.missing_counts[band] != self.actor_window_count
+                or self.exact_zero_counts[band] > self.observed_counts[band]
+                or self.observed_counts[band] == 0
+                for band in range(BAND_COUNT)
+            )
+        ):
+            raise ValueError("old scalar floor diagnostics do not cover every actor-window")
+        checked = validate_energy_floors(self.floors)
+        frozen = checked.copy()
+        frozen.setflags(write=False)
+        object.__setattr__(self, "floors", frozen)
+
+    @classmethod
+    def from_fit(
+        cls,
+        fit: LegacyScalarEnergyCalibration,
+        *,
+        training_source_manifest_sha256: str,
+    ) -> LegacyScalarFloorReceipt:
+        if (
+            type(fit) is not LegacyScalarEnergyCalibration
+            or fit.method != LEGACY_SCALAR_CALIBRATION_METHOD
+            or fit.config_sha256 != legacy_scalar_config_sha256()
+        ):
+            raise ValueError("old scalar floor receipt requires the complete physical fit")
+        return cls(
+            LEGACY_SCALAR_FLOOR_SCHEMA,
+            "main",
+            fit.training_components,
+            training_source_manifest_sha256,
+            fit.config_sha256,
+            fit.floors,
+            fit.capture_count,
+            fit.accepted_window_count,
+            fit.actor_window_count,
+            fit.observed_counts,
+            fit.missing_counts,
+            fit.exact_zero_counts,
+        )
+
+    @property
+    def sha256(self) -> str:
+        payload = {
+            "schema": self.schema,
+            "population_name": self.population_name,
+            "training_components": self.training_components,
+            "training_source_manifest_sha256": self.training_source_manifest_sha256,
+            "config_sha256": self.config_sha256,
+            "floors_float64_le_hex": self.floors.astype("<f8", copy=False).tobytes().hex(),
+            "capture_count": self.capture_count,
+            "accepted_window_count": self.accepted_window_count,
+            "actor_window_count": self.actor_window_count,
+            "observed_counts": self.observed_counts,
+            "missing_counts": self.missing_counts,
+            "exact_zero_counts": self.exact_zero_counts,
+        }
+        encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("ascii")
+        return hashlib.sha256(b"phaseset-v2-legacy-scalar-floor-receipt-v1\0" + encoded).hexdigest()
+
+    def require_population(self, components: tuple[str, ...]) -> None:
+        if self.training_components != components:
+            raise ValueError("old scalar floor training population differs")
diff --git a/tests/test_phaseset_v2_legacy_parent_score.py b/tests/test_phaseset_v2_legacy_parent_score.py
index edbaea0..1d566af 100644
--- a/tests/test_phaseset_v2_legacy_parent_score.py
+++ b/tests/test_phaseset_v2_legacy_parent_score.py
@@ -14,21 +14,43 @@
 from phaseset_core.continuous_parent_host import ContinuousParentTrainingHost
 from phaseset_core.controls import PhaseSetSystem
 from phaseset_core.legacy_continuous_retrieval import LegacyWholeCaptureRetrievalSystem
+from phaseset_core.legacy_scalar_calibration import (
+    LEGACY_SCALAR_FLOOR_SCHEMA,
+    LegacyScalarFloorReceipt,
+    legacy_scalar_config_sha256,
+)
 from phaseset_core.periodic import BAND_COUNT
 from test_continuous_parent_host import _setup
 from test_continuous_parent_training import _case
 
 
 def _legacy(base, mode: str) -> LegacyWholeCaptureRetrievalSystem:
+    floors = np.full((BAND_COUNT,), 1e-10, dtype=np.float64)
     encoder = PhaseSetSystem(
         "08",
         embedding_dim=512,
         hidden_dim=8,
-        energy_floors=np.full((BAND_COUNT,), 1e-10, dtype=np.float64),
+        energy_floors=floors,
         edge_budget=64,
     )
+    # Synthetic binding fixture only: this is not an admitted native training receipt.
+    receipt = LegacyScalarFloorReceipt(
+        LEGACY_SCALAR_FLOOR_SCHEMA,
+        "main",
+        ("C01",),
+        "f" * 64,
+        legacy_scalar_config_sha256(),
+        floors,
+        1,
+        1,
+        3,
+        (3,) * BAND_COUNT,
+        (0,) * BAND_COUNT,
+        (0,) * BAND_COUNT,
+    )
     return LegacyWholeCaptureRetrievalSystem(
-        copy.deepcopy(base), encoder, score_mode=mode, edge_chunk_size=64
+        copy.deepcopy(base), encoder, score_mode=mode,
+        floor_receipt=receipt, edge_chunk_size=64,
     )
 
 
@@ -93,3 +115,7 @@ def test_legacy_parent_score_is_not_yet_a_registered_training_host(tmp_path, mod
     system.legacy_encoder.encoder._energy_floors = np.full((6,), 0.1, dtype=np.float64)
     with pytest.raises(ValueError, match="changed after construction"):
         system.score((source.views[task.parents[0].source_sha256].capture,), None)
+    system.legacy_encoder.encoder._energy_floors = system.floor_receipt.floors.copy()
+    system._bound_old_scalar_floors[0] = 0.1
+    with pytest.raises(ValueError, match="differs from receipt"):
+        system.set_extra_state(saved)
diff --git a/tests/test_phaseset_v2_legacy_scalar_calibration.py b/tests/test_phaseset_v2_legacy_scalar_calibration.py
new file mode 100644
index 0000000..51d5779
--- /dev/null
+++ b/tests/test_phaseset_v2_legacy_scalar_calibration.py
@@ -0,0 +1,98 @@
+"""Data-free contract tests for old five-channel scalar Morlet floors."""
+
+from dataclasses import replace
+
+import numpy as np
+import pytest
+
+from phaseset_core.contracts import skeleton_to_activity
+from phaseset_core.legacy_scalar_calibration import (
+    LEGACY_SCALAR_CALIBRATION_METHOD,
+    LegacyScalarCalibrationCapture,
+    LegacyScalarFloorReceipt,
+    fit_legacy_scalar_energy_floors,
+    legacy_scalar_config_sha256,
+)
+from phaseset_core.periodic import _actor_marginal_powers, _precompute_actor_responses
+from phaseset_core.pipeline import collate_group_samples
+from test_phaseset_v2_legacy_capture import _capture
+
+
+def _row():
+    capture = _capture(windows=1, reject_middle=False)
+    return LegacyScalarCalibrationCapture(capture.source_sha256, "C01", capture)
+
+
+def _fit(rows, *, expected=("a" * 64,), components=("C01",)):
+    return fit_legacy_scalar_energy_floors(
+        rows, training_components=components, expected_capture_ids=expected,
+    )
+
+
+def test_old_scalar_floor_is_fifth_percentile_of_every_actor_window() -> None:
+    row = _row()
+    fit = _fit((row,))
+    window = row.capture.windows()[0]
+    activity = skeleton_to_activity(collate_group_samples((window,)))
+    responses, _, length_mask = _precompute_actor_responses(
+        activity.activities[0, :3], activity.activity_mask[0, :3], 200,
+    )
+    direct = np.stack([_actor_marginal_powers(item, length_mask) for item in responses])
+    ordered = np.sort(direct, axis=0)
+    expected = ordered[0] + (ordered[1] - ordered[0]) * 0.1
+    np.testing.assert_array_equal(fit.floors, expected)
+    assert fit.method == LEGACY_SCALAR_CALIBRATION_METHOD
+    assert fit.config_sha256 == legacy_scalar_config_sha256()
+    assert fit.capture_count == fit.accepted_window_count == 1
+    assert fit.actor_window_count == 3
+    assert fit.observed_counts == (3,) * 6
+    assert fit.missing_counts == (0,) * 6
+    assert not fit.floors.flags.writeable
+    receipt = LegacyScalarFloorReceipt.from_fit(
+        fit, training_source_manifest_sha256="f" * 64,
+    )
+    assert receipt.capture_count == 1
+    assert receipt.actor_window_count == 3
+    np.testing.assert_array_equal(receipt.floors, fit.floors)
+    assert len(receipt.sha256) == 64
+    receipt.require_population(("C01",))
+    with pytest.raises(ValueError, match="population"):
+        receipt.require_population(("C02",))
+
+
+def test_old_scalar_floor_refuses_incomplete_duplicate_sealed_or_augmented_sources() -> None:
+    row = _row()
+    with pytest.raises(ValueError, match="complete training census"):
+        _fit((row,), expected=("a" * 64, "b" * 64))
+    with pytest.raises(ValueError, match="repeated"):
+        _fit((row, row))
+    with pytest.raises(ValueError, match="training components"):
+        _fit((row,), components=("C15",))
+    with pytest.raises(ValueError, match="unaugmented"):
+        _fit((replace(row, capture=replace(row.capture, augmentation_yaw=0.1)),))
+    with pytest.raises(MemoryError, match="RESOURCE_LIMIT"):
+        fit_legacy_scalar_energy_floors(
+            (row,), training_components=("C01",), expected_capture_ids=("a" * 64,),
+            max_actor_window_observations=2,
+        )
+
+
+def test_old_scalar_observed_zero_power_is_not_treated_as_missing() -> None:
+    row = _row()
+    stationary = replace(row.capture, skeletons=np.zeros_like(row.capture.skeletons))
+    fit = _fit((replace(row, capture=stationary),))
+    assert fit.observed_counts == (3,) * 6
+    assert fit.missing_counts == (0,) * 6
+    assert fit.exact_zero_counts == (3,) * 6
+    assert fit.floors.tobytes() == np.zeros((6,), dtype=np.float64).tobytes()
+
+
+def test_old_scalar_receipt_rejects_fake_coverage_and_wrong_manifest() -> None:
+    fit = _fit((_row(),))
+    with pytest.raises(ValueError, match="training source"):
+        LegacyScalarFloorReceipt.from_fit(fit, training_source_manifest_sha256="bad")
+    receipt = LegacyScalarFloorReceipt.from_fit(
+        fit, training_source_manifest_sha256="f" * 64,
+    )
+    with pytest.raises(ValueError, match="diagnostics"):
+        replace(receipt, observed_counts=(2,) * 6)

From b4c7584a5f16671bfcd865a55c48c7f10bb50c5a Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 14:53:34 -0500
Subject: [PATCH 94/95] Connect legacy full-parent scorers to typed host
 bindings

---
 RELEASE_FILES.sha256                          |  10 +-
 docs/PHASESET_V2_LEGACY_PARENT_SCORE.md       |  24 ++--
 src/phaseset_core/continuous_parent_host.py   |  72 ++++++++++-
 .../continuous_parent_training.py             |  18 ++-
 src/phaseset_core/parent_disk_source.py       |  19 ++-
 tests/test_phaseset_v2_legacy_parent_score.py | 120 +++++++++++++++++-
 6 files changed, 232 insertions(+), 31 deletions(-)

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 7068c4d..90e0ed8 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -83,7 +83,7 @@ fdcf40e6f0f30729808c66c55d9a0817b1c6b71032751e0baa5e1a287771794e	2252	docs/PHASE
 b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 1e0d2fb0b35a6900dfb03af4b9bfe1701eb2c843f566c0f842b47b03b11b645e	1276	docs/PHASESET_V2_ICMR_TARGET.md
-b5f782397000487dc9e3a37a8cb345e830b102631b7f869b8e54a37c208e8859	2075	docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
+a8e122c5bdac18280f4c186d6d2aaff8feccc415bc88aa7ecf72b4033610bc43	2553	docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
 b5042e5fa002fa057ca4af61f4a06953662708d221016c460acd9cd0278c5863	4606	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 932cc27f1e4c53f3ee09042514949cf9b2132758cee2d1dd71d709dda4a08884	4435	docs/PHASESET_V2_MECHANISM_FACTORY.md
@@ -187,8 +187,8 @@ c0bcf4fa676310a82564b7edd8a2b9ba78edfd01aceb751ee6f2d5b99120228f	16007	src/phase
 80e6d497ff9bb4354664cb1a054cdb550ab222e92c841813afc27af6d3759495	5603	src/phaseset_core/continuous_base_retrieval.py
 ad2f6cbeddafa5531adeb949e419ee948744a8e97bf793a62ad0660a77de36e4	12556	src/phaseset_core/continuous_capture.py
 50335890a5f78ffd320ad5533a5c589817bf4401f1dede769d7146563d97b4d9	3062	src/phaseset_core/continuous_capture_io.py
-3a75c6185167ee3dd952169fa0bae43e1e879a6253d681e35e0b4cf424bbbed3	34377	src/phaseset_core/continuous_parent_host.py
-296230b0fda66a551ddbe026d02d6d7d47cd30df786a99c1cdfb9d54b43b4ef9	19383	src/phaseset_core/continuous_parent_training.py
+22fcbb5182e88c06eca1052e398c1ce67bb17ddb350f423f52bc13dec408453d	37857	src/phaseset_core/continuous_parent_host.py
+0330c2050dfcfebe8da08eb5e848bd74f063fc9b9df7710bd20a1972fb602846	19635	src/phaseset_core/continuous_parent_training.py
 564bfe9b1717ac6e539e62726a245b27fc84103e235a1ed1ab1bf04569b36f7a	7483	src/phaseset_core/continuous_phase_cache.py
 a0bfc58b51d6ac65432f0d4a45ba7df78836fe0fc9b5613b1a23a2974bea264d	18760	src/phaseset_core/continuous_retrieval.py
 d66eb2fd534a31701c7dcb8b4fdb634b8db6186b423e83fcc3af5c60152aa8c8	7381	src/phaseset_core/continuous_training_input.py
@@ -219,7 +219,7 @@ c86e8e54de9f1e5f592d41b59bb5342b7fb68400188d8c8af61417f459943a42	17290	src/phase
 95091a9d8cff83917832578a36478e8219d2f4b245c207cc2d432ef24cf461dc	10503	src/phaseset_core/objectives.py
 0f0309c54a5b48a0493bb24f97eda39ff93be657295e1b0ebcc3dd6e9a8ecd76	6565	src/phaseset_core/parent_capture.py
 be88afc2d543a9ceb227eda07046cc8e9221faf7af85888c1b303632da642dc8	3366	src/phaseset_core/parent_clip_rows.py
-c2c9822208d738e29f36c79a4bdf48c81dea271d570df23bcb971fa649e33dac	11642	src/phaseset_core/parent_disk_source.py
+c15e1436000b3fc6eba22ba900e27372f30f30dd02534b8713729255504c11a8	12685	src/phaseset_core/parent_disk_source.py
 be883315aa773e6c2d535ea981853ae92a791187338fd2b310b574f956174a82	7326	src/phaseset_core/parent_retrieval_task.py
 46baa360084180121c16e1ce4d0c0a21841c6ad91c462afd272d5b92b0a603ad	9528	src/phaseset_core/parent_run_monitor.py
 52b22af181cb52cf3c9c3584d15ea02a604e5c29d8abeaa0de217baf07d8af06	7027	src/phaseset_core/parent_weak_clip_rows.py
@@ -374,7 +374,7 @@ fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 1ff21b1e8133cf14b0672d984a061cfbe71c58c155e1e53f4e40c5280f3f74fe	21461	tests/test_phaseset_v2_coordination.py
 2aae2397f6c40d929b0eb9b61d340d3ece6d48a17da01d6b3bd2767c2b6945b3	7513	tests/test_phaseset_v2_legacy_capture.py
-b9d44f8c94b0e110141c66b00358cc11d799078417122a48c61924c819968b6f	4986	tests/test_phaseset_v2_legacy_parent_score.py
+4dbd3fa91ebda8402edb81af281ffdbb1be8d730119e9e461b350471e464dd04	10100	tests/test_phaseset_v2_legacy_parent_score.py
 278279ce18dd0f74c397bc69f99828afcacf9c1c6b5466fe34d1071df3bf99e8	4075	tests/test_phaseset_v2_legacy_scalar_calibration.py
 90ede69f8695413a5cbc38c45b67602e5b3ed26212ac09e7f33c568b0129f719	15608	tests/test_phaseset_v2_mechanism_factory.py
 1df27542e87ae6d3d47ac0e6a865a6e9a5be49ea8a9bc5af61758b18e830b64a	6261	tests/test_phaseset_v2_score_tables.py
diff --git a/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md b/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
index 4b9b927..eaac67d 100644
--- a/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
+++ b/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
@@ -24,13 +24,19 @@ census. The receipt binds the private source-manifest digest, population,
 physical kernel identity, floor bytes and diagnostics. The data-free test uses
 a synthetic receipt and does not imply that native floors were fitted.
 
-The generic complete-parent training host deliberately does **not** accept
-this scorer yet. The registered real training-only scalar-floor receipt,
-old/A9 run identities, predecessor checkpoint binding and score-plus-VJP host
-qualification are separate prerequisites before formal training or sealed
-evaluation.
+The complete-parent training host admits this scorer only when the source,
+scorer and host bindings name the same typed scalar-floor receipt, its training
+population matches the host, and the run ID/seed/system/B2 predecessor is one
+of the six old/A9 rows in the V2 matrix. The current host candidate also pins
+main training/validation components, 20 epochs and its optimizer/CF defaults;
+these defaults are not a completed pilot freeze. A short synthetic run cannot
+be labeled as a formal row. The base and V2 host paths reject a
+legacy receipt. The host still does not establish data rights,
+provenance or run budget by itself; the private operator must verify those
+inputs and the real server qualification before formal training. No final-test
+interface is added.
 
-The data-free CPU qualification checks both modes' score decomposition, frozen
-B2 gradients, floor mutation rejection, host rejection, and the existing old
-capture/A9 regression suites. Passing those checks is software qualification,
-not a retrieval result or evidence for a paper claim.
+Data-free tests check both modes' score decomposition, frozen B2 gradients,
+floor mutation rejection, unbound-host rejection, guarded score/VJP replay and
+short-run mislabeling rejection on synthetic inputs. Passing those checks is
+software qualification, not a retrieval result or evidence for a paper claim.
diff --git a/src/phaseset_core/continuous_parent_host.py b/src/phaseset_core/continuous_parent_host.py
index e477abd..1b9115c 100644
--- a/src/phaseset_core/continuous_parent_host.py
+++ b/src/phaseset_core/continuous_parent_host.py
@@ -31,6 +31,8 @@
     validate_parent_text_batch,
 )
 from .continuous_retrieval import ContinuousRetrievalSystem
+from .legacy_continuous_retrieval import LegacyWholeCaptureRetrievalSystem
+from .legacy_scalar_calibration import LegacyScalarFloorReceipt
 from .continuous_training_input import ContinuousTrainingView
 from .frozen_clip_text import FrozenClipTextBatch
 from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalBatch, ParentRetrievalTask
@@ -174,10 +176,18 @@ class ParentHostBindings:
     code_manifest_sha256: str
     runtime_sha256: str
     frozen_base_checkpoint_sha256: str | None
+    legacy_floor_receipt_sha256: str | None = None
+    legacy_training_source_manifest_sha256: str | None = None
+    legacy_frozen_b2_state_sha256: str | None = None
 
     def __post_init__(self):
         for name, value in asdict(self).items():
-            if name == "frozen_base_checkpoint_sha256" and value is None:
+            if name in (
+                "frozen_base_checkpoint_sha256",
+                "legacy_floor_receipt_sha256",
+                "legacy_training_source_manifest_sha256",
+                "legacy_frozen_b2_state_sha256",
+            ) and value is None:
                 continue
             if (
                 type(value) is not str
@@ -234,7 +244,7 @@ class ContinuousParentTrainingHost:
 
     def __init__(
         self,
-        system: ContinuousRetrievalSystem | ContinuousBaseRetrievalSystem,
+        system: ContinuousRetrievalSystem | ContinuousBaseRetrievalSystem | LegacyWholeCaptureRetrievalSystem,
         task: ParentRetrievalTask,
         source: ParentTrainingSource,
         config: ParentHostConfig,
@@ -242,13 +252,67 @@ def __init__(
         *,
         run_identity: ParentHostRunIdentity | None = None,
     ):
-        if not isinstance(system, (ContinuousRetrievalSystem, ContinuousBaseRetrievalSystem)):
-            raise TypeError("this host requires an actual complete-parent base or V2 scorer")
+        if not isinstance(
+            system,
+            (ContinuousRetrievalSystem, ContinuousBaseRetrievalSystem, LegacyWholeCaptureRetrievalSystem),
+        ):
+            raise TypeError("this host requires an actual complete-parent base, V2 or legacy scorer")
         self._is_base = isinstance(system, ContinuousBaseRetrievalSystem)
+        self._is_legacy = isinstance(system, LegacyWholeCaptureRetrievalSystem)
         if config.stage != ("base" if self._is_base else "residual"):
             raise ValueError("host stage does not match the actual scorer")
         if (bindings.frozen_base_checkpoint_sha256 is None) != self._is_base:
             raise ValueError("only a residual stage binds a frozen B2 checkpoint")
+        if self._is_legacy:
+            receipt = getattr(source, "legacy_floor_receipt", None)
+            if (
+                type(receipt) is not LegacyScalarFloorReceipt
+                or receipt.sha256 != system.floor_receipt.sha256
+                or bindings.legacy_floor_receipt_sha256 != receipt.sha256
+                or getattr(source, "legacy_training_source_manifest_sha256", None)
+                != receipt.training_source_manifest_sha256
+                or bindings.legacy_training_source_manifest_sha256
+                != receipt.training_source_manifest_sha256
+                or bindings.legacy_frozen_b2_state_sha256
+                != _stable_hash(system.frozen_b2.state_dict())
+            ):
+                raise ValueError("legacy source, scorer, B2 state and host must bind one typed floor receipt")
+            receipt.require_population(tuple(sorted(config.train_components)))
+            if (
+                tuple(sorted(config.train_components))
+                != ("C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08", "C10", "C12", "C13", "C14")
+                or tuple(sorted(config.validation_components)) != ("C00",)
+                or config.epochs != 20
+                or config.parent_batch_size != 128
+                or config.learning_rate != 3e-4
+                or config.cf_weight != 0.2
+                or config.cf_margin != 0.2
+                or config.optimizer != "adamw"
+                or config.weight_decay != 0.01
+            ):
+                raise ValueError("legacy registered main schedule/population differs from current host candidate")
+            registered = {
+                ("old", 1729): ("V2-019", "PhaseSet-v0.2-on-B2", "V2-007"),
+                ("old", 2718): ("V2-020", "PhaseSet-v0.2-on-B2", "V2-008"),
+                ("old", 31415): ("V2-021", "PhaseSet-v0.2-on-B2", "V2-009"),
+                ("A9", 1729): ("V2-049", "A9", "V2-007"),
+                ("A9", 2718): ("V2-050", "A9", "V2-008"),
+                ("A9", 31415): ("V2-051", "A9", "V2-009"),
+            }
+            expected = registered.get((system.score_mode, config.seed))
+            if type(run_identity) is not ParentHostRunIdentity or expected is None or (
+                run_identity.run_id, run_identity.system_id, run_identity.predecessor_run_id
+            ) != expected:
+                raise ValueError("legacy host requires its exact frozen-matrix run and B2 predecessor")
+        elif any(
+            value is not None
+            for value in (
+                bindings.legacy_floor_receipt_sha256,
+                bindings.legacy_training_source_manifest_sha256,
+                bindings.legacy_frozen_b2_state_sha256,
+            )
+        ):
+            raise ValueError("legacy floor binding cannot enter a nonlegacy host")
         if run_identity is not None:
             if type(run_identity) is not ParentHostRunIdentity:
                 raise TypeError("registered run identity must be exact")
diff --git a/src/phaseset_core/continuous_parent_training.py b/src/phaseset_core/continuous_parent_training.py
index 8ba3e7a..261a1e2 100644
--- a/src/phaseset_core/continuous_parent_training.py
+++ b/src/phaseset_core/continuous_parent_training.py
@@ -22,6 +22,7 @@
 from .continuous_base_retrieval import ContinuousBaseRetrievalSystem
 from .continuous_capture import PreparedContinuousCapture
 from .continuous_retrieval import ContinuousRetrievalSystem
+from .legacy_continuous_retrieval import LegacyWholeCaptureRetrievalSystem
 from .continuous_training_input import ContinuousTrainingView
 from .frozen_clip_text import FrozenClipTextBatch
 from .objectives import variable_positive_symmetric_infonce
@@ -271,7 +272,7 @@ def validate_parent_text_batch(
 
 
 def backward_loaded_parent_batch(
-    system: ContinuousRetrievalSystem | ContinuousBaseRetrievalSystem,
+    system: ContinuousRetrievalSystem | ContinuousBaseRetrievalSystem | LegacyWholeCaptureRetrievalSystem,
     labels: ParentRetrievalBatch,
     text_batch: FrozenClipTextBatch,
     *,
@@ -288,8 +289,11 @@ def backward_loaded_parent_batch(
     replay; a deterministic private source may instead derive yaw from epoch
     and source. No physical batch tuple or all-capture graph is retained here.
     """
-    if not isinstance(system, (ContinuousRetrievalSystem, ContinuousBaseRetrievalSystem)):
-        raise TypeError("complete parent training requires a base or V2 retrieval system")
+    if not isinstance(
+        system,
+        (ContinuousRetrievalSystem, ContinuousBaseRetrievalSystem, LegacyWholeCaptureRetrievalSystem),
+    ):
+        raise TypeError("complete parent training requires a base, V2 or legacy retrieval system")
     if (
         not labels.parents
         or len(set(labels.motion_source_keys)) != len(labels.parents)
@@ -408,13 +412,13 @@ def backward_loaded_parent_batch(
 
 
 def parent_input_capture(
-    system: ContinuousRetrievalSystem | ContinuousBaseRetrievalSystem,
+    system: ContinuousRetrievalSystem | ContinuousBaseRetrievalSystem | LegacyWholeCaptureRetrievalSystem,
     view: ContinuousTrainingView | PreparedContinuousCapture,
 ) -> PreparedContinuousCapture:
-    """Keep the actual base and residual physical-input contracts distinct."""
-    if isinstance(system, ContinuousBaseRetrievalSystem):
+    """Keep base/legacy body captures separate from the V2 phase view."""
+    if isinstance(system, (ContinuousBaseRetrievalSystem, LegacyWholeCaptureRetrievalSystem)):
         if type(view) is not PreparedContinuousCapture:
-            raise ValueError("base loader must return a prepared capture, not a phase view")
+            raise ValueError("base/legacy loader must return a prepared capture, not a phase view")
         return view
     if type(view) is not ContinuousTrainingView:
         raise ValueError("residual loader must return the complete phase training view")
diff --git a/src/phaseset_core/parent_disk_source.py b/src/phaseset_core/parent_disk_source.py
index 6df5321..351710a 100644
--- a/src/phaseset_core/parent_disk_source.py
+++ b/src/phaseset_core/parent_disk_source.py
@@ -23,6 +23,7 @@
 )
 from .dct_calibration import DctFloorReceipt
 from .dct_relations import DctViewContext, require_dct_working_budget
+from .legacy_scalar_calibration import LegacyScalarFloorReceipt
 from .directional_phase import LocalPhaseConfig, VELOCITY_MODES
 from .speed_calibration import SpeedFloorReceipt
 from .parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
@@ -61,7 +62,7 @@ def deterministic_parent_yaw(source_sha256: str, *, seed: int, epoch: int) -> fl
 class DevelopmentParentDiskSource:
     """Stream exact full timelines; never keep a dataset-sized RAM cache.
 
-    TMR/WaMo and base ``capture`` views load body arrays only. Residual ``view``
+    TMR/WaMo, base and typed legacy ``capture`` views load body arrays only. V2 ``view``
     additionally loads the complete physical cache and the operator-admitted
     training-only floors, recomputing all physical features for nonzero yaw.
     No stale zero-yaw approximation, timeline truncation or sampled window
@@ -86,6 +87,8 @@ def __init__(
         weak_clip_rows: ParentWeakClipRows | None = None,
         velocity_mode: str = "signed_vector",
         speed_floor_receipt: SpeedFloorReceipt | None = None,
+        legacy_floor_receipt: LegacyScalarFloorReceipt | None = None,
+        legacy_training_source_manifest_sha256: str | None = None,
     ):
         if type(task) is not ParentRetrievalTask or any(
             type(record) is not ParentDiskRecord for record in records
@@ -104,6 +107,20 @@ def __init__(
             raise TypeError("need the closed frozen TMR/WaMo human feature rows")
         if energy_floors is not None and dct_floor_receipt is not None:
             raise ValueError("phase and A6 floors must not be admitted together")
+        if legacy_floor_receipt is not None:
+            if (
+                type(legacy_floor_receipt) is not LegacyScalarFloorReceipt
+                or energy_floors is not None
+                or dct_floor_receipt is not None
+                or speed_floor_receipt is not None
+                or legacy_training_source_manifest_sha256
+                != legacy_floor_receipt.training_source_manifest_sha256
+            ):
+                raise ValueError("legacy capture-only source requires its typed scalar receipt alone")
+        elif legacy_training_source_manifest_sha256 is not None:
+            raise ValueError("legacy training manifest cannot enter a nonlegacy source")
+        self.legacy_floor_receipt = legacy_floor_receipt
+        self.legacy_training_source_manifest_sha256 = legacy_training_source_manifest_sha256
         if velocity_mode not in VELOCITY_MODES or (
             velocity_mode == "speed_only" and energy_floors is None
         ):
diff --git a/tests/test_phaseset_v2_legacy_parent_score.py b/tests/test_phaseset_v2_legacy_parent_score.py
index 1d566af..a61b472 100644
--- a/tests/test_phaseset_v2_legacy_parent_score.py
+++ b/tests/test_phaseset_v2_legacy_parent_score.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import copy
+from dataclasses import replace
 import math
 
 import numpy as np
@@ -12,6 +13,8 @@
 
 from phaseset_core.capture_validation import _validate_text_batch
 from phaseset_core.continuous_parent_host import ContinuousParentTrainingHost
+from phaseset_core.continuous_parent_host import ParentHostRunIdentity
+from phaseset_core.continuous_parent_training import backward_loaded_parent_batch
 from phaseset_core.controls import PhaseSetSystem
 from phaseset_core.legacy_continuous_retrieval import LegacyWholeCaptureRetrievalSystem
 from phaseset_core.legacy_scalar_calibration import (
@@ -20,11 +23,15 @@
     legacy_scalar_config_sha256,
 )
 from phaseset_core.periodic import BAND_COUNT
+from phaseset_core.temporal_coordination import coordination_objective
+from phaseset_core.training import _capture_rng, _restore_rng, _stable_hash
 from test_continuous_parent_host import _setup
-from test_continuous_parent_training import _case
+from test_continuous_parent_training import _case, _empty_cf
 
 
-def _legacy(base, mode: str) -> LegacyWholeCaptureRetrievalSystem:
+def _legacy(
+    base, mode: str, components: tuple[str, ...] = ("C01",),
+) -> LegacyWholeCaptureRetrievalSystem:
     floors = np.full((BAND_COUNT,), 1e-10, dtype=np.float64)
     encoder = PhaseSetSystem(
         "08",
@@ -37,7 +44,7 @@ def _legacy(base, mode: str) -> LegacyWholeCaptureRetrievalSystem:
     receipt = LegacyScalarFloorReceipt(
         LEGACY_SCALAR_FLOOR_SCHEMA,
         "main",
-        ("C01",),
+        components,
         "f" * 64,
         legacy_scalar_config_sha256(),
         floors,
@@ -102,10 +109,10 @@ def test_complete_legacy_score_uses_frozen_b2_and_registered_head_math(tmp_path,
 
 
 @pytest.mark.parametrize("mode", ["old", "A9"])
-def test_legacy_parent_score_is_not_yet_a_registered_training_host(tmp_path, mode):
+def test_legacy_parent_host_rejects_unbound_floor_source(tmp_path, mode):
     v2, task, source, config, bindings = _setup(tmp_path)
     system = _legacy(v2.frozen_b2, mode)
-    with pytest.raises(TypeError, match="base or V2 scorer"):
+    with pytest.raises(ValueError, match="one typed floor receipt"):
         ContinuousParentTrainingHost(system, task, source, config, bindings)
     assert system.get_extra_state().dtype == torch.uint8
     saved = system.get_extra_state().clone()
@@ -119,3 +126,106 @@ def test_legacy_parent_score_is_not_yet_a_registered_training_host(tmp_path, mod
     system._bound_old_scalar_floors[0] = 0.1
     with pytest.raises(ValueError, match="differs from receipt"):
         system.set_extra_state(saved)
+
+
+@pytest.mark.parametrize("mode", ["old", "A9"])
+def test_legacy_complete_parent_score_vjp_matches_dense_synthetic_population(
+    tmp_path, mode,
+):
+    v2, views, labels, text, _ = _case(tmp_path)
+    system = _legacy(v2.frozen_b2, mode, components=("C01", "C02"))
+    dense = copy.deepcopy(system)
+    captures = tuple(view.capture for view in views)
+    by_source = {capture.source_sha256: capture for capture in captures}
+    starting_rng = _capture_rng()
+    dense_scores = torch.cat([dense.score((capture,), text).scores for capture in captures])
+    empty = dense_scores.new_empty((0,))
+    dense_loss = coordination_objective(
+        dense_scores,
+        labels.positive_mask(device=dense_scores.device),
+        cf_positive_scores=empty,
+        cf_negative_scores=empty,
+        verified_negative_mask=torch.empty((0,), dtype=torch.bool),
+        cf_weight=0.2,
+        margin=0.2,
+    )
+    dense_loss.backward()
+    _restore_rng(starting_rng)
+    result = backward_loaded_parent_batch(
+        system, labels, text,
+        load_view=lambda source: by_source[source],
+        counterfactuals=_empty_cf(), cf_weight=0.2, margin=0.2,
+    )
+    torch.testing.assert_close(result.scores, dense_scores.detach(), rtol=2e-6, atol=2e-7)
+    torch.testing.assert_close(
+        torch.tensor(result.loss), dense_loss.detach(), rtol=2e-6, atol=2e-7,
+    )
+    assert result.replayed_captures == 2
+    assert all(parameter.grad is None for parameter in system.frozen_b2.parameters())
+    assert any(parameter.grad is not None for parameter in system.head.parameters())
+    for (name, actual), (expected_name, expected) in zip(
+        system.named_parameters(), dense.named_parameters(), strict=True,
+    ):
+        assert name == expected_name
+        if name.startswith("frozen_b2."):
+            assert actual.grad is expected.grad is None
+        else:
+            assert actual.grad is not None and expected.grad is not None
+            torch.testing.assert_close(actual.grad, expected.grad, rtol=3e-5, atol=3e-6)
+
+
+def test_legacy_host_cannot_label_short_synthetic_run_as_registered_matrix_row(tmp_path):
+    v2, task, source, config, bindings = _setup(tmp_path)
+    system = _legacy(v2.frozen_b2, "old", components=("C01", "C02"))
+    source.views = {key: view.capture for key, view in source.views.items()}
+    source.legacy_floor_receipt = system.floor_receipt
+    source.legacy_training_source_manifest_sha256 = (
+        system.floor_receipt.training_source_manifest_sha256
+    )
+    bindings = replace(
+        bindings,
+        legacy_floor_receipt_sha256=system.floor_receipt.sha256,
+        legacy_training_source_manifest_sha256=system.floor_receipt.training_source_manifest_sha256,
+        legacy_frozen_b2_state_sha256=_stable_hash(system.frozen_b2.state_dict()),
+    )
+    identity = ParentHostRunIdentity("V2-019", "PhaseSet-v0.2-on-B2", "V2-007")
+    with pytest.raises(ValueError, match="schedule/population"):
+        ContinuousParentTrainingHost(
+            system, task, source, config, bindings, run_identity=identity,
+        )
+    with pytest.raises(ValueError, match="one typed floor receipt"):
+        ContinuousParentTrainingHost(
+            system, task, source, config,
+            replace(bindings, legacy_training_source_manifest_sha256="e" * 64),
+            run_identity=identity,
+        )
+    with pytest.raises(ValueError, match="nonlegacy host"):
+        ContinuousParentTrainingHost(v2, task, source, config, bindings)
+    main_components = (
+        "C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08",
+        "C10", "C12", "C13", "C14",
+    )
+    registered = _legacy(v2.frozen_b2, "old", components=main_components)
+    source.legacy_floor_receipt = registered.floor_receipt
+    source.legacy_training_source_manifest_sha256 = (
+        registered.floor_receipt.training_source_manifest_sha256
+    )
+    registered_bindings = replace(
+        bindings,
+        legacy_floor_receipt_sha256=registered.floor_receipt.sha256,
+        legacy_frozen_b2_state_sha256=_stable_hash(registered.frozen_b2.state_dict()),
+    )
+    full_config = replace(
+        config, train_components=main_components, validation_components=("C00",),
+        epochs=20, parent_batch_size=128,
+    )
+    with pytest.raises(ValueError, match="exact frozen-matrix"):
+        ContinuousParentTrainingHost(
+            registered, task, source, full_config, registered_bindings,
+            run_identity=ParentHostRunIdentity("V2-049", "A9", "V2-007"),
+        )
+    with pytest.raises(ValueError, match="missing a requested learning component"):
+        ContinuousParentTrainingHost(
+            registered, task, source, full_config, registered_bindings,
+            run_identity=identity,
+        )

From 5cacc7466941c25155e2801611f5f07be97edc47 Mon Sep 17 00:00:00 2001
From: appleweiping <vipinapple986@gmail.com>
Date: Mon, 28 Sep 2026 15:30:23 -0500
Subject: [PATCH 95/95] Bind registered legacy controls to selected B2 parent
 host

---
 RELEASE_FILES.sha256                      |   6 +-
 docs/PHASESET_V2_LEGACY_PARENT_SCORE.md   |  14 +-
 src/phaseset_core/v2_legacy_factory.py    | 171 +++++++++++++++
 src/phaseset_core/v2_mechanism_factory.py |  40 ++--
 tests/test_phaseset_v2_legacy_factory.py  | 256 ++++++++++++++++++++++
 5 files changed, 471 insertions(+), 16 deletions(-)
 create mode 100644 src/phaseset_core/v2_legacy_factory.py
 create mode 100644 tests/test_phaseset_v2_legacy_factory.py

diff --git a/RELEASE_FILES.sha256 b/RELEASE_FILES.sha256
index 90e0ed8..a04da97 100644
--- a/RELEASE_FILES.sha256
+++ b/RELEASE_FILES.sha256
@@ -83,7 +83,7 @@ fdcf40e6f0f30729808c66c55d9a0817b1c6b71032751e0baa5e1a287771794e	2252	docs/PHASE
 b5313996cc1e80e4b9fabc58b2d306efd0a51d05288b4cc856b77d7f438529b3	2526	docs/PHASESET_V2_FROZEN_LANGUAGE_ROWS.md
 0cf908e9dfff51ed307f9f09e9e96144c0445bfff8c35ec258f8e3f3f7d094be	6867	docs/PHASESET_V2_HANDOFF_INTEGRATION.md
 1e0d2fb0b35a6900dfb03af4b9bfe1701eb2c843f566c0f842b47b03b11b645e	1276	docs/PHASESET_V2_ICMR_TARGET.md
-a8e122c5bdac18280f4c186d6d2aaff8feccc415bc88aa7ecf72b4033610bc43	2553	docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
+5e180412adf601b10d5f9f3c05ca0140b1d93bbd31439af95745f26b48294b2e	3282	docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
 b5042e5fa002fa057ca4af61f4a06953662708d221016c460acd9cd0278c5863	4606	docs/PHASESET_V2_LITERATURE_PARENT_HOST.md
 ed094b856390f21490b60c9aa84a4d866112b2c5001f459fae07b2c7d6befcea	2668	docs/PHASESET_V2_LITERATURE_PARENT_TRAINING.md
 932cc27f1e4c53f3ee09042514949cf9b2132758cee2d1dd71d709dda4a08884	4435	docs/PHASESET_V2_MECHANISM_FACTORY.md
@@ -248,7 +248,8 @@ d1b92f7b65d750572a8447a48cd2a09cf12d7ec0fc3edd2f33c4088d0d77a14d	9442	src/phases
 b4bc9462c0b9a05ff3c26a4395c6be1f9df0c07bb64103a3ba8805dfe3187d56	16713	src/phaseset_core/tmr_set.py
 2c43fa274a5b10040b8df2283585424204f3380030213b0f85a9e2c18cbffc3c	20463	src/phaseset_core/torch_periodic.py
 168d94a87ba328ddccbb86595e62fdbdbe7910d59cd4f1c10b6089ccab54827c	175962	src/phaseset_core/training.py
-f75c1399bc1717196b814fcf33ee8383cee694f7d7d7e4001b1e935af361ee94	19267	src/phaseset_core/v2_mechanism_factory.py
+c2501a1753f2b0ce0d87e0a695f9a16c6fbe9eb0d6440e59051c69728361a68b	6693	src/phaseset_core/v2_legacy_factory.py
+7ed3ca9cc8e534230b63d46395c63bd7c8f552f8004470b97bc1e9878c5004f1	19714	src/phaseset_core/v2_mechanism_factory.py
 cbeb8553a7b0b3bb68a092a98e394cb25917410dc4b13d3d7212cee91b456aae	5583	src/phaseset_core/v2_score_tables.py
 e8e4fa5cf988c11bc7aecbff241f3c5c253533652b1a31ea2313ee78626c3371	11472	src/phaseset_core/v2_statistics.py
 c1b145691b73cb3fe56925de75b114dac6ba8a84fcae6ffeec282010fc36fa77	15762	src/phaseset_core/wamo_set.py
@@ -374,6 +375,7 @@ fa1c43ea09bea11d4e43aac6fc70f789679083852fc23d9be527076802952c15	4154	tests/test
 c855095323e7c6c68cdd071508b83829ec5b71878f5a0aeedd13686d7f7751c1	2964	tests/test_phaseset_v2_analysis_rules.py
 1ff21b1e8133cf14b0672d984a061cfbe71c58c155e1e53f4e40c5280f3f74fe	21461	tests/test_phaseset_v2_coordination.py
 2aae2397f6c40d929b0eb9b61d340d3ece6d48a17da01d6b3bd2767c2b6945b3	7513	tests/test_phaseset_v2_legacy_capture.py
+09ddbdb38d2c48c08ea09be0618a462b399c1cffe971cae9ea019cef003d8425	10128	tests/test_phaseset_v2_legacy_factory.py
 4dbd3fa91ebda8402edb81af281ffdbb1be8d730119e9e461b350471e464dd04	10100	tests/test_phaseset_v2_legacy_parent_score.py
 278279ce18dd0f74c397bc69f99828afcacf9c1c6b5466fe34d1071df3bf99e8	4075	tests/test_phaseset_v2_legacy_scalar_calibration.py
 90ede69f8695413a5cbc38c45b67602e5b3ed26212ac09e7f33c568b0129f719	15608	tests/test_phaseset_v2_mechanism_factory.py
diff --git a/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md b/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
index eaac67d..e48623d 100644
--- a/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
+++ b/docs/PHASESET_V2_LEGACY_PARENT_SCORE.md
@@ -36,7 +36,19 @@ provenance or run budget by itself; the private operator must verify those
 inputs and the real server qualification before formal training. No final-test
 interface is added.
 
+`build_v2_legacy_control` constructs only registered V2-019/020/021 (old)
+and V2-049/050/051 (A9) rows from the exact frozen matrix. It reuses the
+same validation-selected B2 checkpoint/terminal verifier as the V2 residual
+factory, requires the same-seed B2 predecessor and main population, and
+checks that the typed old scalar floor names the admitted training source.
+`make_v2_legacy_parent_host` then binds that build, floor, predecessor and
+source to the complete-parent host. The build explicitly carries
+`launch_authority=False`: construction and synthetic tests cannot admit a
+native optimizer step, decide the pilot schedule, or turn an unverified floor
+receipt into real-data evidence.
+
 Data-free tests check both modes' score decomposition, frozen B2 gradients,
 floor mutation rejection, unbound-host rejection, guarded score/VJP replay and
-short-run mislabeling rejection on synthetic inputs. Passing those checks is
+short-run mislabeling rejection, and six registered factory rows on synthetic
+inputs. Passing those checks is
 software qualification, not a retrieval result or evidence for a paper claim.
diff --git a/src/phaseset_core/v2_legacy_factory.py b/src/phaseset_core/v2_legacy_factory.py
new file mode 100644
index 0000000..afb6903
--- /dev/null
+++ b/src/phaseset_core/v2_legacy_factory.py
@@ -0,0 +1,171 @@
+"""Construct registered old PhaseSet and A9 controls on the selected B2.
+
+This binds scientific row identity but never grants GPU, pilot, data-rights or
+formal-training authority. The private operator still verifies the complete
+input manifest, resource budget and frozen runtime before any optimizer step.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import torch
+
+from .continuous_parent_host import (
+    ContinuousParentTrainingHost,
+    ParentHostBindings,
+    ParentHostConfig,
+    ParentHostRunIdentity,
+    ParentTrainingSource,
+)
+from .controls import PhaseSetSystem
+from .legacy_continuous_retrieval import LegacyWholeCaptureRetrievalSystem
+from .legacy_scalar_calibration import LegacyScalarFloorReceipt
+from .parent_retrieval_task import ParentRetrievalTask
+from .training import OFFICIAL_SEEDS
+from .v2_mechanism_factory import (
+    V2MechanismHold,
+    V2SelectedB2Checkpoint,
+    _load_selected_b2_predecessor,
+    _registered_matrix,
+    _sha256_key,
+)
+
+
+@dataclass(frozen=True)
+class V2LegacyBuild:
+    run_id: str
+    system_id: str
+    seed: int
+    max_epochs: int
+    predecessor_run_id: str
+    predecessor_checkpoint_sha256: str
+    training_source_manifest_sha256: str
+    floor_receipt_sha256: str
+    training_components: tuple[str, ...]
+    validation_components: tuple[str, ...]
+    model: LegacyWholeCaptureRetrievalSystem
+    launch_authority: bool = False
+
+
+def build_v2_legacy_control(
+    matrix_bytes: bytes,
+    run_id: str,
+    *,
+    selected_b2: V2SelectedB2Checkpoint,
+    floor_receipt: LegacyScalarFloorReceipt,
+    training_source_manifest_sha256: str,
+    checkpoint_windows: bool = True,
+) -> V2LegacyBuild:
+    """Load one exact B2 predecessor and old/A9 row with a typed floor."""
+    matrix = _registered_matrix(matrix_bytes)
+    row = next((item for item in matrix["runs"] if item["id"] == run_id), None)
+    if (
+        type(run_id) is not str
+        or row is None
+        or row.get("system") not in ("PhaseSet-v0.2-on-B2", "A9")
+        or row.get("group")
+        != ("main_residual" if row["system"] == "PhaseSet-v0.2-on-B2" else "mechanism_ablation")
+        or row.get("split") != "main"
+        or row.get("seed") not in OFFICIAL_SEEDS
+        or row.get("max_epochs") != 20
+        or row.get("status") != "PLANNED"
+        or type(row.get("depends_on")) is not list
+        or len(row["depends_on"]) != 1
+    ):
+        raise V2MechanismHold("registered main old/A9 row is required")
+    predecessor = next(
+        (item for item in matrix["runs"] if item["id"] == row["depends_on"][0]), None
+    )
+    if (
+        predecessor is None
+        or predecessor.get("system") != "B2"
+        or predecessor.get("seed") != row["seed"]
+        or predecessor.get("split") != "main"
+    ):
+        raise V2MechanismHold("old/A9 row needs its same-seed B2 predecessor")
+    if type(floor_receipt) is not LegacyScalarFloorReceipt:
+        raise V2MechanismHold("old/A9 needs the typed training-only scalar floor")
+    if not _sha256_key(training_source_manifest_sha256):
+        raise V2MechanismHold("admitted training-source manifest digest is required")
+    development = tuple(matrix["pilot_and_group_fold_components"]["development"])
+    validation = tuple(matrix["primary_split"]["validation_component_labels"])
+    train = tuple(component for component in development if component not in validation)
+    if len(train) != 12 or validation != ("C00",):
+        raise V2MechanismHold("old/A9 main development components differ from the matrix")
+    floor_receipt.require_population(train)
+    if floor_receipt.training_source_manifest_sha256 != training_source_manifest_sha256:
+        raise V2MechanismHold("old/A9 floor source differs from admitted training source")
+    frozen_b2, artifact, manifest = _load_selected_b2_predecessor(
+        matrix, row, selected_b2
+    )
+    if (
+        tuple(manifest["config"].get("train_components", ())) != train
+        or tuple(manifest["config"].get("validation_components", ())) != validation
+    ):
+        raise V2MechanismHold("selected B2 population differs from old/A9 population")
+    mode = "old" if row["system"] == "PhaseSet-v0.2-on-B2" else "A9"
+    with torch.random.fork_rng(devices=[]):
+        torch.random.default_generator.manual_seed(row["seed"])
+        encoder = PhaseSetSystem("08", embedding_dim=512, energy_floors=floor_receipt.floors)
+        model = LegacyWholeCaptureRetrievalSystem(
+            frozen_b2,
+            encoder,
+            score_mode=mode,
+            floor_receipt=floor_receipt,
+            checkpoint_windows=checkpoint_windows,
+        )
+    return V2LegacyBuild(
+        run_id=run_id,
+        system_id=row["system"],
+        seed=row["seed"],
+        max_epochs=row["max_epochs"],
+        predecessor_run_id=row["depends_on"][0],
+        predecessor_checkpoint_sha256=artifact.sha256,
+        training_source_manifest_sha256=training_source_manifest_sha256,
+        floor_receipt_sha256=floor_receipt.sha256,
+        training_components=train,
+        validation_components=validation,
+        model=model,
+    )
+
+
+def make_v2_legacy_parent_host(
+    build: V2LegacyBuild,
+    task: ParentRetrievalTask,
+    source: ParentTrainingSource,
+    config: ParentHostConfig,
+    bindings: ParentHostBindings,
+    *,
+    admitted_training_source_manifest_sha256: str,
+) -> ContinuousParentTrainingHost:
+    """Connect the registered build to the complete-parent training host."""
+    if (
+        type(build) is not V2LegacyBuild
+        or type(config) is not ParentHostConfig
+        or type(bindings) is not ParentHostBindings
+    ):
+        raise TypeError("exact old/A9 build, host config and bindings are required")
+    if (
+        config.seed != build.seed
+        or config.stage != "residual"
+        or config.epochs != build.max_epochs
+        or config.train_components != build.training_components
+        or config.validation_components != build.validation_components
+        or admitted_training_source_manifest_sha256 != build.training_source_manifest_sha256
+        or bindings.frozen_base_checkpoint_sha256 != build.predecessor_checkpoint_sha256
+        or bindings.legacy_floor_receipt_sha256 != build.floor_receipt_sha256
+        or bindings.legacy_training_source_manifest_sha256
+        != build.training_source_manifest_sha256
+    ):
+        raise V2MechanismHold("old/A9 host schedule, predecessor, floor or source differs from build")
+    return ContinuousParentTrainingHost(
+        build.model,
+        task,
+        source,
+        config,
+        bindings,
+        run_identity=ParentHostRunIdentity(
+            build.run_id, build.system_id, build.predecessor_run_id
+        ),
+    )
diff --git a/src/phaseset_core/v2_mechanism_factory.py b/src/phaseset_core/v2_mechanism_factory.py
index 1fb0d1d..c0ccdb6 100644
--- a/src/phaseset_core/v2_mechanism_factory.py
+++ b/src/phaseset_core/v2_mechanism_factory.py
@@ -31,6 +31,8 @@
 from .parent_retrieval_task import ParentRetrievalTask
 from .training import (
     OFFICIAL_SEEDS,
+    BaseRetrievalSystem,
+    CheckpointArtifact,
     _load_torch_checkpoint,
     _stable_hash,
     build_registered_base_training_system,
@@ -201,22 +203,14 @@ def make_v2_b2_base_host(
     return make_v2_registered_base_host(matrix_bytes, run_id, task, source, config, bindings)
 
 
-def build_v2_mechanism(
-    matrix_bytes: bytes,
-    run_id: str,
-    *,
+def _load_selected_b2_predecessor(
+    matrix: dict,
+    row: dict,
     selected_b2: V2SelectedB2Checkpoint,
-    training_source_manifest_sha256: str,
-    dct_floor_receipt: DctFloorReceipt | None = None,
-    speed_floor_receipt: SpeedFloorReceipt | None = None,
-    checkpoint_blocks: bool = True,
-) -> V2MechanismBuild:
-    """Construct the exact implemented mechanism, never a launch decision."""
-    matrix, row = _registered_row(matrix_bytes, run_id)
+) -> tuple[BaseRetrievalSystem, CheckpointArtifact, dict]:
+    """One terminal/checkpoint authority path shared by registered residuals."""
     if type(selected_b2) is not V2SelectedB2Checkpoint:
         raise V2MechanismHold("validation-selected B2 artifact is required")
-    if not _sha256_key(training_source_manifest_sha256):
-        raise V2MechanismHold("admitted training-source manifest digest is required")
     payload, artifact = _load_torch_checkpoint(
         selected_b2.path, expected_sha256=selected_b2.sha256
     )
@@ -289,6 +283,26 @@ def build_v2_mechanism(
         frozen_b2.load_state_dict(base_state, strict=True)
     except (RuntimeError, ValueError) as error:
         raise V2MechanismHold("selected B2 weights do not match the B2 architecture") from error
+    return frozen_b2, artifact, manifest
+
+
+def build_v2_mechanism(
+    matrix_bytes: bytes,
+    run_id: str,
+    *,
+    selected_b2: V2SelectedB2Checkpoint,
+    training_source_manifest_sha256: str,
+    dct_floor_receipt: DctFloorReceipt | None = None,
+    speed_floor_receipt: SpeedFloorReceipt | None = None,
+    checkpoint_blocks: bool = True,
+) -> V2MechanismBuild:
+    """Construct the exact implemented mechanism, never a launch decision."""
+    matrix, row = _registered_row(matrix_bytes, run_id)
+    if not _sha256_key(training_source_manifest_sha256):
+        raise V2MechanismHold("admitted training-source manifest digest is required")
+    frozen_b2, artifact, manifest = _load_selected_b2_predecessor(
+        matrix, row, selected_b2
+    )
     system_id = row["system"]
     if system_id != "A6" and dct_floor_receipt is not None:
         raise V2MechanismHold("a DCT floor receipt may only enter A6")
diff --git a/tests/test_phaseset_v2_legacy_factory.py b/tests/test_phaseset_v2_legacy_factory.py
new file mode 100644
index 0000000..aa7af40
--- /dev/null
+++ b/tests/test_phaseset_v2_legacy_factory.py
@@ -0,0 +1,256 @@
+"""Frozen-matrix old/A9 construction; synthetic receipts confer no authority."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+import hashlib
+import json
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+
+from phaseset_core.continuous_parent_host import ParentHostBindings, ParentHostConfig
+from phaseset_core.legacy_scalar_calibration import (
+    LEGACY_SCALAR_FLOOR_SCHEMA,
+    LegacyScalarFloorReceipt,
+    legacy_scalar_config_sha256,
+)
+from phaseset_core.parent_retrieval_task import ParentCaptionRecord, ParentRetrievalTask
+from phaseset_core.training import _stable_hash
+from phaseset_core.v2_legacy_factory import (
+    V2LegacyBuild,
+    build_v2_legacy_control,
+    make_v2_legacy_parent_host,
+)
+from phaseset_core.v2_mechanism_factory import V2MechanismHold
+from test_phaseset_v2_mechanism_factory import MATRIX, MAIN_TRAIN, _selected
+
+SOURCE_SHA = "a" * 64
+
+
+def _receipt(*, components=MAIN_TRAIN, source=SOURCE_SHA):
+    # The fixture tests row binding only; it is not a real calibrated receipt.
+    return LegacyScalarFloorReceipt(
+        LEGACY_SCALAR_FLOOR_SCHEMA,
+        "main",
+        components,
+        source,
+        legacy_scalar_config_sha256(),
+        np.full((6,), 1e-10, dtype=np.float64),
+        202,
+        4820,
+        19280,
+        (19280,) * 6,
+        (0,) * 6,
+        (0,) * 6,
+    )
+
+
+@pytest.mark.parametrize(
+    ("run_id", "seed", "predecessor", "mode"),
+    (
+        ("V2-019", 1729, "V2-007", "old"),
+        ("V2-020", 2718, "V2-008", "old"),
+        ("V2-021", 31415, "V2-009", "old"),
+        ("V2-049", 1729, "V2-007", "A9"),
+        ("V2-050", 2718, "V2-008", "A9"),
+        ("V2-051", 31415, "V2-009", "A9"),
+    ),
+)
+def test_all_six_registered_rows_construct_only_their_selected_b2(
+    tmp_path, run_id, seed, predecessor, mode,
+):
+    selected = _selected(tmp_path, run_id=predecessor, seed=seed)
+    build = build_v2_legacy_control(
+        MATRIX, run_id, selected_b2=selected,
+        floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+    )
+    assert type(build) is V2LegacyBuild
+    assert (build.run_id, build.seed, build.predecessor_run_id) == (
+        run_id, seed, predecessor,
+    )
+    assert build.model.score_mode == mode
+    assert build.model.legacy_encoder.system_id == "08"
+    assert build.training_components == MAIN_TRAIN
+    assert build.validation_components == ("C00",)
+    assert build.predecessor_checkpoint_sha256 == selected.sha256
+    assert build.floor_receipt_sha256 == build.model.floor_receipt.sha256
+    assert build.launch_authority is False
+    assert all(not p.requires_grad for p in build.model.frozen_b2.parameters())
+    assert build.model.head is not None
+
+
+def test_old_and_a9_have_identical_same_seed_encoder_initialization(tmp_path):
+    selected = _selected(tmp_path)
+    old = build_v2_legacy_control(
+        MATRIX, "V2-019", selected_b2=selected,
+        floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+    )
+    a9 = build_v2_legacy_control(
+        MATRIX, "V2-049", selected_b2=selected,
+        floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+    )
+    for key, value in old.model.legacy_encoder.state_dict().items():
+        assert torch.equal(value, a9.model.legacy_encoder.state_dict()[key])
+    assert _stable_hash(old.model.frozen_b2.state_dict()) == _stable_hash(
+        a9.model.frozen_b2.state_dict()
+    )
+    assert type(old.model.head) is not type(a9.model.head)
+
+
+def test_nonlegacy_rows_or_wrong_b2_and_floor_fail_closed(tmp_path):
+    selected = _selected(tmp_path)
+    with pytest.raises(V2MechanismHold, match="registered main old/A9"):
+        build_v2_legacy_control(
+            MATRIX, "V2-022", selected_b2=selected,
+            floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+        )
+    with pytest.raises(V2MechanismHold, match="exact frozen"):
+        build_v2_legacy_control(
+            MATRIX + b"\n", "V2-019", selected_b2=selected,
+            floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+        )
+    with pytest.raises(V2MechanismHold, match="typed"):
+        build_v2_legacy_control(
+            MATRIX, "V2-019", selected_b2=selected,
+            floor_receipt=None, training_source_manifest_sha256=SOURCE_SHA,
+        )
+    with pytest.raises(V2MechanismHold, match="floor source"):
+        build_v2_legacy_control(
+            MATRIX, "V2-019", selected_b2=selected,
+            floor_receipt=_receipt(), training_source_manifest_sha256="b" * 64,
+        )
+    with pytest.raises(ValueError, match="population"):
+        build_v2_legacy_control(
+            MATRIX, "V2-019", selected_b2=selected,
+            floor_receipt=_receipt(components=("C01",)),
+            training_source_manifest_sha256=SOURCE_SHA,
+        )
+    with pytest.raises(V2MechanismHold, match="predecessor row"):
+        build_v2_legacy_control(
+            MATRIX, "V2-020", selected_b2=selected,
+            floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+        )
+    with pytest.raises(Exception, match="digest mismatch"):
+        build_v2_legacy_control(
+            MATRIX, "V2-019", selected_b2=replace(selected, sha256="0" * 64),
+            floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+        )
+
+
+def test_host_factory_rejects_unbound_config_and_predecessor_before_data_read(tmp_path):
+    selected = _selected(tmp_path)
+    build = build_v2_legacy_control(
+        MATRIX, "V2-019", selected_b2=selected,
+        floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+    )
+    config = ParentHostConfig(1729, MAIN_TRAIN, ("C00",))
+    bindings = ParentHostBindings(
+        "b" * 64, "c" * 64, "d" * 64, selected.sha256,
+        build.floor_receipt_sha256, SOURCE_SHA,
+        _stable_hash(build.model.frozen_b2.state_dict()),
+    )
+    with pytest.raises(V2MechanismHold, match="host schedule"):
+        make_v2_legacy_parent_host(
+            build, None, None, replace(config, epochs=19), bindings,
+            admitted_training_source_manifest_sha256=SOURCE_SHA,
+        )
+    with pytest.raises(V2MechanismHold, match="host schedule"):
+        make_v2_legacy_parent_host(
+            build, None, None, config,
+            replace(bindings, frozen_base_checkpoint_sha256="e" * 64),
+            admitted_training_source_manifest_sha256=SOURCE_SHA,
+        )
+    with pytest.raises(V2MechanismHold, match="host schedule"):
+        make_v2_legacy_parent_host(
+            build, None, None, config, bindings,
+            admitted_training_source_manifest_sha256="e" * 64,
+        )
+
+
+@pytest.mark.parametrize(
+    ("run_id", "seed", "predecessor", "system_id"),
+    (
+        ("V2-019", 1729, "V2-007", "PhaseSet-v0.2-on-B2"),
+        ("V2-020", 2718, "V2-008", "PhaseSet-v0.2-on-B2"),
+        ("V2-021", 31415, "V2-009", "PhaseSet-v0.2-on-B2"),
+        ("V2-049", 1729, "V2-007", "A9"),
+        ("V2-050", 2718, "V2-008", "A9"),
+        ("V2-051", 31415, "V2-009", "A9"),
+    ),
+)
+def test_registered_host_persists_each_row_and_floor_without_loading_data(
+    tmp_path, run_id, seed, predecessor, system_id,
+):
+    selected = _selected(tmp_path, run_id=predecessor, seed=seed)
+    build = build_v2_legacy_control(
+        MATRIX, run_id, selected_b2=selected,
+        floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+    )
+    rows = tuple(
+        ParentCaptionRecord(
+            hashlib.sha256(f"old-source-{i}".encode()).hexdigest(),
+            hashlib.sha256(f"old-family-{i}".encode()).hexdigest(),
+            component,
+            "validation" if component == "C00" else "train",
+            (f"synthetic human row {i}",),
+        )
+        for i, component in enumerate((*MAIN_TRAIN, "C00"))
+    )
+    task = ParentRetrievalTask(
+        rows, expected_family_keys=tuple(row.annotation_family_sha256 for row in rows)
+    )
+    source = SimpleNamespace(
+        legacy_floor_receipt=build.model.floor_receipt,
+        legacy_training_source_manifest_sha256=SOURCE_SHA,
+    )
+    config = ParentHostConfig(seed, MAIN_TRAIN, ("C00",))
+    bindings = ParentHostBindings(
+        "b" * 64, "c" * 64, "d" * 64, selected.sha256,
+        build.floor_receipt_sha256, SOURCE_SHA,
+        _stable_hash(build.model.frozen_b2.state_dict()),
+    )
+    host = make_v2_legacy_parent_host(
+        build, task, source, config, bindings,
+        admitted_training_source_manifest_sha256=SOURCE_SHA,
+    )
+    assert host._manifest["registered_run_identity"] == {
+        "run_id": run_id,
+        "system_id": system_id,
+        "predecessor_run_id": predecessor,
+    }
+    assert host.bindings.legacy_floor_receipt_sha256 == build.floor_receipt_sha256
+    assert host._steps_per_epoch == 1
+
+
+def test_shared_b2_helper_rejects_terminal_digest_and_semantic_drift(tmp_path):
+    selected = _selected(tmp_path)
+    original = json.loads(selected.terminal_path.read_bytes())
+    selected.terminal_path.write_bytes(b"corrupt terminal")
+    with pytest.raises(V2MechanismHold, match="terminal digest mismatch"):
+        build_v2_legacy_control(
+            MATRIX, "V2-019", selected_b2=selected,
+            floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+        )
+    mutations = (
+        ("outcome", "FAILED"),
+        ("registered_run_identity", {
+            "run_id": "V2-008", "system_id": "B2", "predecessor_run_id": None,
+        }),
+        ("completed_epochs", 29),
+        ("best_checkpoint", "not-the-selected-checkpoint.pt"),
+    )
+    for field, value in mutations:
+        terminal = {**original, field: value}
+        raw = (json.dumps(terminal, sort_keys=True) + "\n").encode()
+        selected.terminal_path.write_bytes(raw)
+        changed = replace(
+            selected, terminal_sha256=hashlib.sha256(raw).hexdigest()
+        )
+        with pytest.raises(V2MechanismHold, match="B2 terminal does not attest"):
+            build_v2_legacy_control(
+                MATRIX, "V2-019", selected_b2=changed,
+                floor_receipt=_receipt(), training_source_manifest_sha256=SOURCE_SHA,
+            )