docs: state SMP DPT ViT-B16 conversion scope

README.md

@@ -43,6 +43,7 @@ shown alongside it uses a separately deployed model endpoint.
 | Fixed Swin-T/UPerNet, 224 × 224 | [Three-stage workflow](https://github.com/noelEOS/tensor-ee/blob/main/docs/swin.md) | Trained EE weights are public; this workflow also needs a local checkpoint, not yet public |
 | Prithvi Sen1Floods11, 224 × 224 crops | [Hand-composed crop API](https://github.com/noelEOS/tensor-ee/blob/main/docs/api.md#prithvi-sen1floods11) | The released Sen1Floods11 checkpoint runs live in staged exports, one crop or a region of whole crops, and matches PyTorch within 1.5e-05, on synthetic crops and on a real Sentinel-2 crop (water IoU 0.911 against its hand label, one crop). Accuracy is not established. |
 | SMP SegFormer-B0 or UPerNet with timm's Swin encoder | [Attention converter](https://github.com/noelEOS/tensor-ee/blob/main/docs/converter.md) | Conversion examples use random weights; the report identifies settings outside the validated records |
+| SMP DPT with a timm ViT-B/16 encoder, 224 × 224 | [Attention converter](https://github.com/noelEOS/tensor-ee/blob/main/docs/converter.md#smp-050-dpt-at-224--224) | Converts to graphs byte-identical to the hand-composed targets, full width and reduced. Full width is offline only and misses the 1e-4 absolute target; live agreement covers only the reduced encoder twin (rank-at-most-4 projection weights), the structured reduced DPT twin (7 requests, sampled head/logit readouts), and individual blocks' recorded settings. Random weights only; no accuracy claim. [Evidence and limits](https://github.com/noelEOS/tensor-ee/blob/main/docs/converter.md#smp-dpt-vit-b16-evidence-and-scope). |
 
 You can also compose a model from the [block catalogue](https://github.com/noelEOS/tensor-ee/blob/main/docs/building-blocks.md).
 It has 88 blocks: 23 in `tensor_ee/blocks/raster` and 65 in `tensor_ee/blocks/window`.

docs/README.md

@@ -10,15 +10,15 @@ documentation; the records after them hold the evidence the guides cite.
 | [Getting started](getting-started.md) | Install, the offline example, both demos and a first conversion, with expected output |
 | [Code Editor demo](../CODE_EDITOR.md) | The trained U-Net in the Earth Engine Code Editor: no install, no checkpoint |
 | [Browser demo](../DEMO.md) | A local web app that runs either trained model in Earth Engine, with an offline mode |
-| [ViT-B/16 encoder target](vit-b16-encoder224.md) | Hand-composed DPT encoder target, offline evidence and reduced live plan |
+| [ViT-B/16 encoder target](vit-b16-encoder224.md) | Hand-composed DPT encoder target, offline evidence and reduced live agreement on rank-at-most-4 projection weights ([record](evidence/live-fixtures/20261103T022505Z-vit_encoder_reduced.json)) |
 | [Models and their evidence](model.md) | Models built from the blocks, what each was checked against, and the trained checkpoints |
 | [U-Net single-chip walkthrough](water-chip.md) | The trained U-Net from Python: upload its weights, export one chip, check it against PyTorch |
 | [Release validation](validation.md) | The walkthrough's verified run, which the README figure was rendered from |
 | [Swin-T/UPerNet workflow](swin.md) | The fixed trained Swin-T/UPerNet as three saved-asset stages |
 | [API guide](api.md) | The CNN API: entry points, supported forms, numerical and validity rules |
 | [SMP CNNs](smp-models.md) | Compile standard segmentation-models-pytorch CNNs with `adapt_smp_model`: contract, evidence, live check |
 | [Your own CNN over a region](own-model.md) | A trained SMP Unet/resnet34 from checkpoint to a tiled, batch-exported region, with its halo cost |
-| [Converter guide](converter.md) | Convert, report, plan, build and run SegFormer-B0 and the Swin-T/UPerNet |
+| [Converter guide](converter.md) | Convert, report, plan and build SegFormer-B0, Swin-T/UPerNet and SMP DPT with timm ViT-B/16; runner evidence and limits |
 | [Block catalogue](building-blocks.md) | Every block's layout, locality, precision policy, status and evidence; the layers and the public API |
 | [Live fixtures](live-fixtures.md) | The harness that compares a block's Earth Engine result and its NumPy mirror with PyTorch |
 | [Library contract](library-contract.md) | The rules the library is built to: layers, tensor types, policies, metadata, gates |

docs/api.md

@@ -248,6 +248,30 @@ The historical
 research runners are archived at the tag `research-archive-2026-10-12`
 ([history](history/README.md)).
 
+## SMP DPT with a timm ViT-B/16 encoder
+
+tensor-ee converts SMP DPT models with a timm ViT-B/16 encoder at 224 × 224.
+Use `tensor_ee.converter.convert.convert`, with SMP 0.5.0, timm 1.0.30,
+evaluation mode, unfused attention, one class token and cat readout; the
+[converter example and refusals](converter.md#smp-050-dpt-at-224--224)
+show the supported form. The result is a weight-free block graph and conversion
+report; `build` binds weights and produces dense logits on the abstract input grid.
+The caller prepares the crop and handles georeferenced relabeling.
+
+The [evidence summary](converter.md#smp-dpt-vit-b16-evidence-and-scope) keeps graph
+identity, offline mirror agreement, live agreement and model accuracy separate.
+Random-weight full-width and reduced conversions are byte-identical to their
+hand-composed targets. Full width is offline only: the DPT offline NumPy mirror
+misses the 1e-4 absolute target against both float32 and float64 PyTorch, with
+0 argmax flips. Float32 PyTorch itself differs from float64 PyTorch by more
+([numbers and dtype record](vit-b16-dpt224.md#numerical-scope)). Live agreement
+covers only the reduced encoder twin with rank-at-most-4 projection weights,
+the structured reduced DPT twin (`vit_dpt_reduced`, 7 requests, sampled head/logit
+readouts), and individual blocks' recorded settings. The full-width live run
+needs the staged runner and remains a follow-up. Random weights establish no
+model accuracy. ViT-S, ViT-L, DeiT-III, DINOv2 and patch 8 are later work;
+fused attention, register tokens and other readouts are outside the claim.
+
 ## Class rows beside spatial windows
 
 `tensor_ee.blocks.window.merge.concat_rows(ops, parts, *, rows, channels)`

docs/converter.md

@@ -21,7 +21,8 @@ PyTorch model ──convert──▶ block graph ──plan──▶ stages ─
 This guide begins with offline conversions of the two original models: SegFormer-B0,
 `Segformer("mit_b0")` of segmentation-models-pytorch (SMP) 0.5.0, and SMP's `UPerNet` with
 timm's Swin-T encoder, both at 224 × 224 with two input bands and two classes.
-The timm ViT-B/16 encoder and SMP DPT recognizers are described in section 6.
+tensor-ee converts SMP DPT models with a timm ViT-B/16 encoder at 224 × 224.
+The recognizers, refused settings and evidence limits are described in section 6.
 
 Two terms recur below. The **hand-built graphs** are the 19 SegFormer-B0 and Swin-T/UPerNet
 graphs composed by hand from the blocks and run live before the converter existed; the
@@ -42,14 +43,16 @@ Four kinds of evidence apply, and they are kept apart:
   conversion that succeeds says the library can express the model, not that it was validated.
 - **Offline mirror agreement**: NumPy mirrors against PyTorch, per block, in the
   [live fixture harness](live-fixtures.md). The converter adds none.
-- **Live agreement.** One run so far ([record of the run](evidence/x06-live.json)): the converted SegFormer-B0, under the planner's default four
+- **Live agreement.** For the original converted models, one runner run so far ([record of the run](evidence/x06-live.json)): the converted SegFormer-B0, under the planner's default four
   stages, run by the library runner on one chip with random weights, agreed with PyTorch within the
   1e-4 absolute target at every saved boundary (largest absolute error 3.58e-6; logits 2.98e-6, 0
   argmax disagreements). C1–C3 and the logits are bit-identical to the hand-staged run, and the
   logits to the single-graph run, which exported nothing else. The four stages took 92.2 batch
   EECU-seconds.
 - **Model accuracy**: not measured. The weights here are random.
 
+For ViT-B/16 and SMP DPT, see the separate [four-part evidence summary](#smp-dpt-vit-b16-evidence-and-scope) below; the SegFormer runner result does not validate them.
+
 ## Before you start
 
 Install the `swin` extra (SMP 0.5.0 and timm 1.0.30; [Getting started, Install](getting-started.md#1-install)).
@@ -600,11 +603,55 @@ no numerical execution, live run or model-accuracy claim. Individual blocks outs
 their validated settings remain outside in the report, including W14 convolutions;
 the hand model's reduced composition evidence has its own narrower scope.
 
+### SMP DPT ViT-B/16: evidence and scope
+
+**tensor-ee converts SMP DPT models with a timm ViT-B/16 encoder at 224 × 224**.
+This claim uses SMP 0.5.0 and timm 1.0.30, evaluation mode, unfused attention,
+one class token and cat readout, as in the example above. Four claims stay separate:
+
+- **Graph identity.** The [timm encoder converter gate](../tests/test_timm_vit_converter.py)
+  and [SMP DPT converter gate](../tests/test_smp_dpt_converter.py) convert random-weight
+  models to graphs byte-identical to the hand-composed targets, at full width and
+  reduced width. Both builds bind the same symbolic table reads; reduced inline
+  gates also compare random-weight literals. These gates establish graph identity,
+  not numerical execution.
+- **Offline mirror agreement.** The [encoder evidence](vit-b16-encoder224.md)
+  and [DPT numerical evidence](vit-b16-dpt224.md#numerical-scope) compare NumPy
+  mirrors with PyTorch. The [reduced DPT fixture record](evidence/dpt-reduced-offline.json)
+  has largest absolute error 1.86e-9. Full width is **offline only** and does
+  **not meet the unchanged 1e-4 absolute target**: against float32 PyTorch,
+  fusion3 differs by 6.03e-4 and logits by 3.51e-4; against float64 PyTorch,
+  fusion3 differs by 1.18e-4 and logits by 1.50e-4. Activations reach about
+  190, where one float32 step is about 1.5e-5. Float32 PyTorch itself differs
+  from float64 PyTorch by more (fusion3 6.13e-4, logits 3.70e-4); there are
+  **0 argmax flips**. The [dtype record](evidence/dpt-dtype-offline.json)
+  retains these failures; neither rounding scale nor unchanged decisions relaxes
+  the absolute target.
+- **Live agreement.** It exists only for the
+  [reduced ViT encoder twin](evidence/live-fixtures/20261103T022505Z-vit_encoder_reduced.json)
+  with **rank-at-most-4 projection weights**, the
+  [reduced DPT twin](evidence/live-fixtures/20261103T041356Z-vit_dpt_reduced.json)
+  (`vit_dpt_reduced`, **7 requests**, structured rank-at-most-4 projection weights
+  and sampled head/logit readouts), plus the individual blocks' live records
+  linked in the [catalogue](building-blocks.md). Both twins matched their mirrors
+  bit for bit; the largest absolute EE-versus-PyTorch errors were 5.72e-6 for
+  the encoder and 1.86e-9 for DPT. This does not validate unrestricted weights
+  or promote settings outside individual block records. The full-width live run
+  has not been done; it needs the staged runner and remains a follow-up
+  ([proposal and limits](vit-b16-dpt224.md#offline-request-sizing-and-deferred-full-width-live-run)).
+- **Model accuracy.** None is claimed: random weights only, no accuracy evaluation.
+
+The claim does not cover ViT-S, ViT-L, DeiT-III, DINOv2 or patch 8; these are
+later work. Fused attention, register tokens and other readouts are also outside
+this claim and are refused by these recognizers. Parameterized settings that
+convert outside a block's validated record gain no validation from acceptance.
+
 ## Limits
 
 - Families: SMP SegFormer (keys and values one 7 × 7 window after the spatial reduction, so
   `mit_b0` at 224 × 224) and SMP UPerNet with timm's Swin. Only the settings in each block's record
-  are validated. The root timm ViT encoder returns one atomic pair; SMP DPT consumes four intermediate pairs and returns dense logits.
+  are validated. SMP DPT with a timm ViT-B/16 encoder at 224 × 224 also converts,
+  under the evidence limits above. The root timm ViT encoder returns one atomic pair; SMP DPT consumes four intermediate pairs and returns dense logits.
 - One input and one output; batch execution only. The planner cuts by rules and metadata: interactive
   memory and recomputation cost are not modeled, and its `extent` default was read off one hand plan
   (calibrating costs is deferred).

docs/model.md

@@ -182,18 +182,38 @@ or an accuracy benchmark.
 
 The [hand-composed ViT encoder target](vit-b16-encoder224.md) exposes every raw
 block's spatial/class pair and the final norm, with SMP DPT encoder parameter
-names. Reduced and full-width random-weight timm comparisons pass offline; the
-reduced interactive live attempt failed before encoder readback (client recursion).
-Only the input echo returned exactly. No encoder live agreement, converter
-equivalence, DPT decoder, or model accuracy is claimed.
+names. Reduced and full-width random-weight timm comparisons pass offline.
+The [first reduced live attempt](evidence/live-fixtures/20261102T191233Z-vit_encoder_reduced.json)
+failed before encoder readback because of client recursion; only its input echo
+returned exactly. The [passing rerun](evidence/live-fixtures/20261103T022505Z-vit_encoder_reduced.json)
+matched the mirror bit for bit at all encoder outputs, with largest absolute
+EE-versus-PyTorch error 5.72e-6. This live agreement covers only the reduced
+twin's recorded inputs and rank-at-most-4 projection weights. Full width remains
+offline only; no model accuracy is claimed.
+
+The [timm ViT converter gate](../tests/test_timm_vit_converter.py) converts
+random-weight full-width and reduced encoders to graphs byte-identical to this
+hand-composed target. That is graph identity, separate from offline mirror
+agreement and the reduced twin's live evidence. The [converter guide](converter.md#smp-dpt-vit-b16-evidence-and-scope)
+states the claim and its limits.
 
 ## ViT-B/16 + DPT target, 224 × 224
 
 The [hand-composed DPT target](vit-b16-dpt224.md) reuses the merged ViT encoder
 and existing library blocks, with SMP 0.5.0 defaults, six bands and two classes.
-Its notes state the layout chain, offline comparisons, reduced composition
-fixture and measured export proposal. Full-width live agreement and model
-accuracy are not established. No DPT recognizer is added by this target.
-The reduced twin carries the 1e-4 claim. The native full-width absolute check is
-a documented strict expected failure, with a separate float64 ordering guard;
-full-width live validation is deferred to a follow-up and is not met here.
+The [SMP DPT recognizer](converter.md#smp-050-dpt-at-224--224) now converts this
+model; its [converter gate](../tests/test_smp_dpt_converter.py) compares
+random-weight full-width and reduced conversions byte for byte with the target.
+This establishes graph identity, not numerical execution.
+
+The [reduced DPT live record](evidence/live-fixtures/20261103T041356Z-vit_dpt_reduced.json)
+passed 7 requests, matching the mirror bit for bit at all ten readouts, with
+largest absolute EE-versus-PyTorch error 1.86e-9. This covers only the structured
+rank-at-most-4 reduced twin and its sampled head/logit readouts, not unrestricted
+weights. The reduced twin meets the unchanged 1e-4 absolute target. The offline
+NumPy mirror at full width misses that target against both float32 and float64
+PyTorch, with 0 argmax flips; float32 PyTorch itself differs from float64 by more
+([numerics and dtype record](vit-b16-dpt224.md#numerical-scope)). Its absolute
+check remains a strict expected failure, with a separate float64 ordering guard.
+Full-width live agreement has not been established: the staged runner is a
+follow-up. Random weights establish no model accuracy.

docs/vit-b16-dpt224.md

@@ -1,8 +1,11 @@
 # ViT-B/16 + DPT target at 224
 
-`tensor_ee.models.vit_b16_dpt224` is the hand-composed target for a later SMP
-DPT recognizer. It reuses `vit_b16_encoder224` and existing library blocks.
-It adds no block or converter support and is not loaded by `import tensor_ee`.
+`tensor_ee.models.vit_b16_dpt224` is the hand-composed target for the
+[SMP DPT recognizer](converter.md#smp-050-dpt-at-224--224). It reuses
+`vit_b16_encoder224` and existing library blocks and is not loaded by
+`import tensor_ee`. The [converter gate](../tests/test_smp_dpt_converter.py)
+checks byte-identical graphs at full and reduced width: graph identity only,
+separate from the offline and reduced live numerical evidence below.
 Weights are native NumPy float32 arrays or native/flattened `ServerArray`s,
 using SMP 0.5.0's `state_dict` names. Constructors build graphs only.
 

docs/vit-b16-encoder224.md

@@ -1,11 +1,14 @@
 # ViT-B/16 encoder target at 224
 
-`tensor_ee.models.vit_b16_encoder224` is a hand-composed target for a later
-converter recognizer. It is not imported by `import tensor_ee`. It uses existing
-blocks, with no converter or new block. It has offline composition and
-numerical evidence, plus live agreement for the reduced twin on the recorded
-rank-at-most-4 projection weights. Full width has offline evidence only. No
-converter equivalence, trained-model accuracy, or DPT decoder is claimed.
+`tensor_ee.models.vit_b16_encoder224` is the hand-composed target for the
+[timm ViT converter recognizer](converter.md#timm-vit-b16-encoder-at-224--224).
+It is not imported by `import tensor_ee` and uses existing library blocks.
+The [converter gate](../tests/test_timm_vit_converter.py) checks byte-identical
+graphs at full and reduced width; this is graph identity only. Separately, the
+target has offline composition and numerical evidence, plus live agreement for
+the reduced twin on the recorded rank-at-most-4 projection weights. Full width
+has offline evidence only. No trained-model accuracy is claimed. The DPT decoder
+has its [own target and evidence](vit-b16-dpt224.md).
 
 ## Contract and usage