diff --git a/README.md b/README.md
index a02a3e8..16a4836 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ The built image MUST:
 2. Bake function discovery output (manifest) at build time:
 
 ```dockerfile
-RUN mkdir -p /app/.tensorhub && python -m gen_worker.discover > /app/.tensorhub/endpoint.lock
+RUN mkdir -p /app/.cozy && python -m gen_worker.discover > /app/.tensorhub/endpoint.lock
 ```
 
 3. Use the Cozy worker runtime as the ENTRYPOINT:
@@ -50,7 +50,7 @@ uv add gen-worker[torch]
 
 ```python
 import msgspec
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 
 class Input(msgspec.Struct):
     prompt: str
@@ -59,7 +59,7 @@ class Output(msgspec.Struct):
     text: str
 
 @worker_function()
-def generate(ctx: RequestContext, payload: Input) -> Output:
+def generate(ctx: ActionContext, payload: Input) -> Output:
     return Output(text=f"Hello, {payload.prompt}!")
 ```
 
@@ -69,7 +69,7 @@ def generate(ctx: RequestContext, payload: Input) -> Output:
 - **Schema generation** - Input/output schemas extracted from msgspec types
 - **Model injection** - Dependency injection for ML models with caching
 - **Streaming output** - Support for incremental/streaming responses
-- **Progress reporting** - Built-in progress events via `RequestContext`
+- **Progress reporting** - Built-in progress events via `ActionContext`
 - **Perf metrics** - Best-effort per-run metrics emitted to gen-orchestrator (`metrics.*` worker events)
 - **Trainer runtime mode** - SDK-native trainer loop via `WORKER_MODE=trainer`
 - **File handling** - Upload/download assets via Cozy hub file API
@@ -81,7 +81,7 @@ def generate(ctx: RequestContext, payload: Input) -> Output:
 
 ```python
 import msgspec
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 
 class Input(msgspec.Struct):
     prompt: str
@@ -90,7 +90,7 @@ class Output(msgspec.Struct):
     result: str
 
 @worker_function()
-def my_function(ctx: RequestContext, payload: Input) -> Output:
+def my_function(ctx: ActionContext, payload: Input) -> Output:
     return Output(result=f"Processed: {payload.prompt}")
 ```
 
@@ -103,7 +103,7 @@ class Delta(msgspec.Struct):
     chunk: str
 
 @worker_function()
-def stream(ctx: RequestContext, payload: Input) -> Iterator[Delta]:
+def stream(ctx: ActionContext, payload: Input) -> Iterator[Delta]:
     for word in payload.prompt.split():
         if ctx.is_canceled():
             raise InterruptedError("canceled")
@@ -126,7 +126,7 @@ from gen_worker.injection import ModelRef, ModelRefSource as Src
 
 @worker_function()
 def generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipe: Annotated[DiffusionPipeline, ModelRef(Src.FIXED, "sd15")],
     payload: Input,
 ) -> Output:
@@ -149,7 +149,7 @@ flux = { ref = "black-forest-labs/flux.2-klein-4b", dtypes = ["bf16"] }
 from typing import Annotated
 import msgspec
 from diffusers import DiffusionPipeline
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 
 class Input(msgspec.Struct):
@@ -158,7 +158,7 @@ class Input(msgspec.Struct):
 
 @worker_function()
 def generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipe: Annotated[DiffusionPipeline, ModelRef(Src.PAYLOAD, "model")],
     payload: Input,
 ):
@@ -173,7 +173,7 @@ arbitrary repo refs in the payload.
 
 ```python
 @worker_function()
-def process(ctx: RequestContext, payload: Input) -> Output:
+def process(ctx: ActionContext, payload: Input) -> Output:
     # Save bytes and get asset reference
     asset = ctx.save_bytes(f"runs/{ctx.request_id}/outputs/output.png", image_bytes)
     return Output(result=asset.ref)
@@ -278,7 +278,7 @@ Orchestrator-injected (production contract):
 |----------|---------|-------------|
 | `WORKER_MODE` | `inference` | Runtime mode selector (`inference` or `trainer`) |
 | `SCHEDULER_PUBLIC_ADDR` | - | Scheduler address workers should dial |
-| `SCHEDULER_ADDRS` | - | Optional comma-separated LB seed addresses |
+| `SCHEDULER_ADDRS` | - | Optional comma-separated seed addresses for leader discovery |
 | `WORKER_JWT` | - | Worker-connect JWT (required; claims are authoritative) |
 
 Local dev / advanced (not injected by orchestrator):
@@ -289,11 +289,6 @@ Local dev / advanced (not injected by orchestrator):
 | `SCHEDULER_JWT_ISSUER` | - | Optional: expected `iss` when verifying WORKER_JWT locally |
 | `SCHEDULER_JWT_AUDIENCE` | - | Optional: expected `aud` when verifying WORKER_JWT locally |
 | `USE_TLS` | `false` | Local-dev knob for plaintext vs TLS gRPC; production typically terminates TLS upstream |
-| `LB_ONLY_RETRIES` | `true` | Retry via configured LB endpoint(s) only; ignore direct owner redirect hints |
-| `RECONNECT_DELAY` | `0.1` | Base reconnect backoff in seconds (exponential) |
-| `RECONNECT_MAX_DELAY` | `1.0` | Reconnect backoff cap in seconds |
-| `RECONNECT_JITTER_SECONDS` | `0.1` | Added jitter upper bound in seconds, capped by `RECONNECT_MAX_DELAY` |
-| `MAX_RECONNECT_ATTEMPTS` | `0` | Max reconnect attempts (`0` = infinite retries) |
 | `WORKER_MAX_CONCURRENCY` | - | Max concurrent task executions |
 | `WORKER_MAX_INPUT_BYTES` | - | Max input payload size |
 | `WORKER_MAX_OUTPUT_BYTES` | - | Max output payload size |
@@ -456,7 +451,7 @@ cache.get_vram_models()      # ["model-a"]
 from gen_worker.errors import RetryableError, ValidationError, FatalError
 
 @worker_function()
-def process(ctx: RequestContext, payload: Input) -> Output:
+def process(ctx: ActionContext, payload: Input) -> Output:
     if not payload.prompt:
         raise ValidationError("prompt is required")  # 400, no retry
 
diff --git a/docs/terminology.md b/docs/terminology.md
index b15d8ff..01d7158 100644
--- a/docs/terminology.md
+++ b/docs/terminology.md
@@ -7,8 +7,6 @@ This repo follows Cozy's canonical naming. There is no backward-compat layer for
 - `function`: invokable unit inside an endpoint release.
 - Invoke reference: `owner/endpoint/function[:tag]` (default tag is `prod`).
 - `release_id`: immutable identifier for a published endpoint release.
-- In control-plane release payloads, endpoint identity is endpoint-centric:
-  `endpoint.owner` / `endpoint.endpoint_name` are primary; `release.owner` is legacy compatibility.
 - `invoker` / `invoker_id`: the identity performing an invocation.
 - `artifacts`: uploaded source code and endpoint-owned build inputs/outputs.
   - Cozy Hub stores endpoint source tarballs in the endpoint artifacts bucket (`s3.endpoint_artifacts.*` / `S3_ENDPOINT_ARTIFACTS_*`).
diff --git a/examples/firered-image-edit/Dockerfile b/examples/firered-image-edit/Dockerfile
index 1351d5f..5c9919f 100644
--- a/examples/firered-image-edit/Dockerfile
+++ b/examples/firered-image-edit/Dockerfile
@@ -1,14 +1,13 @@
 # Tenant-supplied Dockerfile example (GPU).
 #
-# - Installs torch in a stable cacheable layer.
-# - Installs tenant deps from uv.lock without replacing torch.
+# - Installs torch + gen-worker in stable cacheable layers.
+# - Installs tenant deps from uv.lock without replacing torch/gen-worker.
 # - Bakes discovered functions into /app/.tensorhub/endpoint.lock at build time.
 #
 ARG PYTHON_VERSION=3.12
 FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS cozy_base
 
-ARG CUDA_VERSION=12.8
-ARG UV_TORCH_BACKEND=
+ARG UV_TORCH_BACKEND=cu126
 ARG TORCH_SPEC="~=2.10.0"
 
 WORKDIR /app
@@ -23,9 +22,10 @@ RUN --mount=type=cache,id=cozy-apt-cache,target=/var/cache/apt,sharing=locked \
     && apt-get clean
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
-    backend="${UV_TORCH_BACKEND:-cu$(printf '%s' "${CUDA_VERSION}" | tr -d '.')}" \
-    && uv pip install --system --break-system-packages --torch-backend "$backend" \
+    uv pip install --system --break-system-packages --torch-backend ${UV_TORCH_BACKEND} \
       "torch${TORCH_SPEC}"
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages gen-worker==0.3.0
 
 FROM cozy_base AS cozy_final
 
@@ -33,7 +33,7 @@ COPY pyproject.toml uv.lock /app/
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
     uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
-      --no-emit-package torch \
+      --no-emit-package torch --no-emit-package gen-worker \
       -o /tmp/requirements.all.txt \
     && grep -Ev '^(torch|triton|nvidia-|cuda-)' /tmp/requirements.all.txt > /tmp/requirements.txt \
     && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
diff --git a/examples/firered-image-edit/endpoint.toml b/examples/firered-image-edit/endpoint.toml
index 086e2a8..f39a8ec 100644
--- a/examples/firered-image-edit/endpoint.toml
+++ b/examples/firered-image-edit/endpoint.toml
@@ -7,7 +7,7 @@ main = "firered_image_edit.main"
 firered_image_edit = { ref = "fireredteam/firered-image-edit-1.0", dtypes = ["bf16"] }
 
 [host.requirements]
-cuda = "12.8"
+cuda = "12.6"
 
 [resources]
 vram_gb = 24
diff --git a/examples/firered-image-edit/pyproject.toml b/examples/firered-image-edit/pyproject.toml
index 6ff91ec..9374d51 100644
--- a/examples/firered-image-edit/pyproject.toml
+++ b/examples/firered-image-edit/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "FireRed Image Edit example (inference-only; Cozy manifest via endpoint.toml)"
 requires-python = ">=3.12"
 dependencies = [
-    "gen-worker[torch]",
+    "gen-worker[torch]==0.3.0",
     "diffusers @ git+https://github.com/huggingface/diffusers",
     "transformers>=4.51.3",
     "accelerate",
diff --git a/examples/firered-image-edit/src/firered_image_edit/main.py b/examples/firered-image-edit/src/firered_image_edit/main.py
index f1668c4..0b66024 100644
--- a/examples/firered-image-edit/src/firered_image_edit/main.py
+++ b/examples/firered-image-edit/src/firered_image_edit/main.py
@@ -10,7 +10,7 @@
 from diffusers import QwenImageEditPlusPipeline
 from PIL import Image
 
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 from gen_worker.types import Asset
 
@@ -47,7 +47,7 @@ class EditOutput(msgspec.Struct):
 
 @worker_function(_firered_resources)
 def edit(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         QwenImageEditPlusPipeline,
         ModelRef(Src.FIXED, "firered_image_edit"),
diff --git a/examples/firered-image-edit/uv.lock b/examples/firered-image-edit/uv.lock
index 320d631..077624f 100644
--- a/examples/firered-image-edit/uv.lock
+++ b/examples/firered-image-edit/uv.lock
@@ -503,7 +503,7 @@ dev = [
 requires-dist = [
     { name = "accelerate" },
     { name = "diffusers", git = "https://github.com/huggingface/diffusers" },
-    { name = "gen-worker", extras = ["torch"] },
+    { name = "gen-worker", extras = ["torch"], specifier = "==0.3.0" },
     { name = "pillow" },
     { name = "protobuf" },
     { name = "sentencepiece" },
diff --git a/examples/flux2-klein-4b/Dockerfile b/examples/flux2-klein-4b/Dockerfile
index cff5e4c..8326ef1 100644
--- a/examples/flux2-klein-4b/Dockerfile
+++ b/examples/flux2-klein-4b/Dockerfile
@@ -1,7 +1,7 @@
 # Tenant-supplied Dockerfile example.
 #
-# - Installs torch in a stable cacheable layer.
-# - Installs tenant deps from uv.lock without replacing torch.
+# - Installs torch + gen-worker in stable cacheable layers.
+# - Installs tenant deps from uv.lock without replacing torch/gen-worker.
 # - Bakes discovered functions into /app/.tensorhub/endpoint.lock at build time.
 #
 # Local build (GPU):
@@ -10,8 +10,7 @@
 ARG PYTHON_VERSION=3.12
 FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS cozy_base
 
-ARG CUDA_VERSION=12.8
-ARG UV_TORCH_BACKEND=
+ARG UV_TORCH_BACKEND=cu126
 ARG TORCH_SPEC="~=2.10.0"
 
 WORKDIR /app
@@ -27,19 +26,20 @@ RUN --mount=type=cache,id=cozy-apt-cache,target=/var/cache/apt,sharing=locked \
 
 # Stable runtime layers (avoid re-downloading torch/cu libs on every tenant build).
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
-    backend="${UV_TORCH_BACKEND:-cu$(printf '%s' "${CUDA_VERSION}" | tr -d '.')}" \
-    && uv pip install --system --break-system-packages --torch-backend "$backend" \
+    uv pip install --system --break-system-packages --torch-backend ${UV_TORCH_BACKEND} \
       "torch${TORCH_SPEC}"
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages gen-worker==0.3.0
 
 FROM cozy_base AS cozy_final
 
 # Copy lock metadata first so dependency layers are cacheable across source edits.
 COPY pyproject.toml uv.lock /app/
 
-# Install tenant dependencies into the global environment without replacing torch.
+# Install tenant dependencies into the global environment without replacing torch/gen-worker.
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
     uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
-      --no-emit-package torch \
+      --no-emit-package torch --no-emit-package gen-worker \
       -o /tmp/requirements.all.txt \
     && grep -Ev '^(torch|triton|nvidia-|cuda-)' /tmp/requirements.all.txt > /tmp/requirements.txt \
     && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
diff --git a/examples/flux2-klein-4b/README.md b/examples/flux2-klein-4b/README.md
index e222d4f..962b512 100644
--- a/examples/flux2-klein-4b/README.md
+++ b/examples/flux2-klein-4b/README.md
@@ -1,25 +1,40 @@
 # flux2-klein-4b
 
-FLUX.2-klein 4B endpoint with separate base/turbo functions and dtype-specific variants.
+FLUX.2-klein turbo example using Cozy’s injection pattern (4B + 9B variants).
 
-Naming convention in this repo:
+- The worker function only defines input/output + runs inference.
+- Fixed model selection is declared in code via `ModelRef(Src.FIXED, "<model-key>")`.
+- Model refs/dtypes are declared in `endpoint.toml [models]`.
+- This model is treated as a turbo model: the worker forces `num_inference_steps=8`.
+
+Steps:
+
+- `num_inference_steps` is accepted in the payload, but it is clamped to `[4, 8]` (rounded) for predictable cost/latency.
 
-- Base model ref: `black-forest-labs/flux.2-klein-4b-base`
-- Turbo model ref: `black-forest-labs/flux.2-klein-4b-turbo`
+Code uses:
 
-This avoids ambiguity with upstream naming where `flux.2-klein-4b` is commonly used for turbo variants.
+```py
+pipeline: Annotated[
+  Flux2KleinPipeline,
+  ModelRef(Src.FIXED, "flux2-klein-4b"),
+]
+```
 
 Functions:
 
-- `generate`: base bf16
-- `generate_turbo`: turbo bf16
-- `generate_fp8`: base fp8
-- `generate_turbo_fp8`: turbo fp8
-- `generate_nvfp4`: base nvfp4
-- `generate_turbo_nvfp4`: turbo nvfp4
+- `generate`: 4B bf16 (regular turbo baseline)
+- `generate_fp8`: 4B fp8
+- `generate_9b`: 9B bf16 (regular turbo baseline)
+- `generate_9b_fp8`: 9B fp8
+- `generate_int8`: int8-only
+- `generate_int4`: int4-only
 
-Notes:
+Notes on FP8:
 
-- Fixed model selection is declared in code via `ModelRef(Src.FIXED, "<model-key>")`.
-- Model refs/dtypes are declared in `endpoint.toml [models]`.
-- `num_inference_steps` is accepted in the payload, but clamped to `[4, 8]`.
+- FP8 support here is **weight-only** quantization via `torchao` (Diffusers TorchAoConfig).
+- GPUs vary: FP8 acceleration typically requires newer NVIDIA GPUs (e.g. Ada/Hopper class).
+
+Notes on INT8/INT4:
+
+- INT8/INT4 support here is **weight-only** quantization via `torchao` (Diffusers TorchAoConfig).
+- INT4 is experimental for diffusion; expect quality regressions or incompatibilities.
diff --git a/examples/flux2-klein-4b/endpoint.toml b/examples/flux2-klein-4b/endpoint.toml
index ada324a..fe645d6 100644
--- a/examples/flux2-klein-4b/endpoint.toml
+++ b/examples/flux2-klein-4b/endpoint.toml
@@ -1,20 +1,20 @@
 schema_version = 1
 
-name = "flux.2-klein-4b"
+name = "flux2-klein-4b"
 # Python import path used for function discovery (not a Docker ENTRYPOINT).
 main = "flux2_klein_4b.main"
 
 [models]
-flux2-klein-4b-base = { ref = "black-forest-labs/flux.2-klein-4b-base", dtypes = ["bf16"] }
-flux2-klein-4b-turbo = { ref = "black-forest-labs/flux.2-klein-4b-turbo", dtypes = ["bf16"] }
-flux2-klein-4b-base_fp8 = { ref = "black-forest-labs/flux.2-klein-4b-base", dtypes = ["fp8"] }
-flux2-klein-4b-turbo_fp8 = { ref = "black-forest-labs/flux.2-klein-4b-turbo", dtypes = ["fp8"] }
-flux2-klein-4b-base_nvfp4 = { ref = "black-forest-labs/flux.2-klein-4b-base", dtypes = ["nvfp4"] }
-flux2-klein-4b-turbo_nvfp4 = { ref = "black-forest-labs/flux.2-klein-4b-turbo", dtypes = ["nvfp4"] }
+flux2-klein-4b = { ref = "black-forest-labs/flux.2-klein-4b", dtypes = ["bf16"] }
+flux2-klein-4b_fp8 = { ref = "black-forest-labs/flux.2-klein-4b", dtypes = ["fp8"] }
+flux2-klein-9b = { ref = "black-forest-labs/flux.2-klein-9b", dtypes = ["bf16"] }
+flux2-klein-9b_fp8 = { ref = "black-forest-labs/flux.2-klein-9b", dtypes = ["fp8"] }
+flux2-klein-4b_int8 = { ref = "black-forest-labs/flux.2-klein-4b", dtypes = ["int8"] }
+flux2-klein-4b_int4 = { ref = "black-forest-labs/flux.2-klein-4b", dtypes = ["int4"] }
 
 [host.requirements]
 # If cuda is set, the platform treats this worker as requiring an NVIDIA GPU.
-cuda = "12.8"
+cuda = "12.6"
 
 [resources]
 vram_gb = 24
diff --git a/examples/flux2-klein-4b/pyproject.toml b/examples/flux2-klein-4b/pyproject.toml
index c1abd27..9d6cf0c 100644
--- a/examples/flux2-klein-4b/pyproject.toml
+++ b/examples/flux2-klein-4b/pyproject.toml
@@ -1,15 +1,15 @@
 [project]
 name = "flux2-klein-4b"
 version = "0.1.0"
-description = "Flux2-klein-4B endpoint (base+turbo; inference-only; Cozy manifest via endpoint.toml)"
+description = "Flux2-klein-4B example (inference-only; Cozy manifest via endpoint.toml)"
 requires-python = ">=3.12"
 dependencies = [
-    "gen-worker[torch]",
+    "gen-worker[torch]==0.3.0",
     "diffusers @ git+https://github.com/huggingface/diffusers.git@99e2cfff27dec514a43e260e885c5e6eca038b36",
     "transformers>=4.56,<5",
     "accelerate",
     "pillow",
-    # Needed for fp8/nvfp4 variants (diffusers TorchAoConfig and/or variant-aware loading).
+    # Needed for fp8 weight-only quantization variants (diffusers TorchAoConfig).
     "torchao",
 ]
 
diff --git a/examples/flux2-klein-4b/src/flux2_klein_4b/main.py b/examples/flux2-klein-4b/src/flux2_klein_4b/main.py
index 6ad278c..6f1823c 100644
--- a/examples/flux2-klein-4b/src/flux2_klein_4b/main.py
+++ b/examples/flux2-klein-4b/src/flux2_klein_4b/main.py
@@ -11,14 +11,13 @@
 from diffusers import Flux2KleinPipeline
 from PIL import Image
 
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 from gen_worker.types import Asset
 
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
 _flux_resources = ResourceRequirements()
-_nvfp4_resources = ResourceRequirements(compute_capability_min=10.0)
 _pipeline_locks_guard = threading.Lock()
 _pipeline_locks: dict[int, threading.Lock] = {}
 
@@ -53,7 +52,7 @@ def _should_enable_seq_offload() -> bool:
 
 
 def _generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline,
     payload: GenerateInput,
     model_key: str,
@@ -101,71 +100,88 @@ def _generate(
 
 @worker_function(_flux_resources)
 def generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
-        ModelRef(Src.FIXED, "flux2-klein-4b-base"),
+        ModelRef(Src.FIXED, "flux2-klein-4b"),
     ],
     payload: GenerateInput,
 ) -> GenerateOutput:
-    return _generate(ctx, pipeline, payload, "flux2-klein-4b-base")
+    return _generate(ctx, pipeline, payload, "flux2-klein-4b")
 
 
 @worker_function(_flux_resources)
-def generate_turbo(
-    ctx: RequestContext,
+def generate_fp8(
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
-        ModelRef(Src.FIXED, "flux2-klein-4b-turbo"),
+        ModelRef(Src.FIXED, "flux2-klein-4b_fp8"),
     ],
     payload: GenerateInput,
 ) -> GenerateOutput:
-    return _generate(ctx, pipeline, payload, "flux2-klein-4b-turbo")
+    """
+    FP8 function.
+
+    This endpoint is intended to run against an fp8-weight-only artifact (or an artifact
+    that the worker can load with torchao-backed fp8 quantization enabled).
+    """
+    return _generate(ctx, pipeline, payload, "flux2-klein-4b_fp8")
 
 
 @worker_function(_flux_resources)
-def generate_fp8(
-    ctx: RequestContext,
+def generate_9b(
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
-        ModelRef(Src.FIXED, "flux2-klein-4b-base_fp8"),
+        ModelRef(Src.FIXED, "flux2-klein-9b"),
     ],
     payload: GenerateInput,
 ) -> GenerateOutput:
-    return _generate(ctx, pipeline, payload, "flux2-klein-4b-base_fp8")
+    return _generate(ctx, pipeline, payload, "flux2-klein-9b")
 
 
 @worker_function(_flux_resources)
-def generate_turbo_fp8(
-    ctx: RequestContext,
+def generate_9b_fp8(
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
-        ModelRef(Src.FIXED, "flux2-klein-4b-turbo_fp8"),
+        ModelRef(Src.FIXED, "flux2-klein-9b_fp8"),
     ],
     payload: GenerateInput,
 ) -> GenerateOutput:
-    return _generate(ctx, pipeline, payload, "flux2-klein-4b-turbo_fp8")
+    return _generate(ctx, pipeline, payload, "flux2-klein-9b_fp8")
 
 
-@worker_function(_nvfp4_resources)
-def generate_nvfp4(
-    ctx: RequestContext,
+@worker_function(_flux_resources)
+def generate_int8(
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
-        ModelRef(Src.FIXED, "flux2-klein-4b-base_nvfp4"),
+        ModelRef(Src.FIXED, "flux2-klein-4b_int8"),
     ],
     payload: GenerateInput,
 ) -> GenerateOutput:
-    return _generate(ctx, pipeline, payload, "flux2-klein-4b-base_nvfp4")
+    """
+    INT8 function (weight-only).
 
+    This endpoint is intended to run against an int8-weight-only artifact (or an artifact
+    that the worker can load with torchao-backed int8 quantization enabled).
+    """
+    return _generate(ctx, pipeline, payload, "flux2-klein-4b_int8")
 
-@worker_function(_nvfp4_resources)
-def generate_turbo_nvfp4(
-    ctx: RequestContext,
+
+@worker_function(_flux_resources)
+def generate_int4(
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
-        ModelRef(Src.FIXED, "flux2-klein-4b-turbo_nvfp4"),
+        ModelRef(Src.FIXED, "flux2-klein-4b_int4"),
     ],
     payload: GenerateInput,
 ) -> GenerateOutput:
-    return _generate(ctx, pipeline, payload, "flux2-klein-4b-turbo_nvfp4")
+    """
+    INT4 function (weight-only).
+
+    This endpoint is experimental; expect quality regressions or incompatibilities.
+    """
+    return _generate(ctx, pipeline, payload, "flux2-klein-4b_int4")
diff --git a/examples/flux2-klein-4b/uv.lock b/examples/flux2-klein-4b/uv.lock
index 448d669..8353718 100644
--- a/examples/flux2-klein-4b/uv.lock
+++ b/examples/flux2-klein-4b/uv.lock
@@ -512,7 +512,7 @@ dev = [
 requires-dist = [
     { name = "accelerate" },
     { name = "diffusers", git = "https://github.com/huggingface/diffusers.git?rev=99e2cfff27dec514a43e260e885c5e6eca038b36" },
-    { name = "gen-worker", extras = ["torch"] },
+    { name = "gen-worker", extras = ["torch"], specifier = "==0.3.0" },
     { name = "pillow" },
     { name = "torchao" },
     { name = "transformers", specifier = ">=4.56,<5" },
diff --git a/examples/flux2-klein-9b/src/flux2_klein_9b/main.py b/examples/flux2-klein-9b/src/flux2_klein_9b/main.py
index 023ab93..1a14cf0 100644
--- a/examples/flux2-klein-9b/src/flux2_klein_9b/main.py
+++ b/examples/flux2-klein-9b/src/flux2_klein_9b/main.py
@@ -11,7 +11,7 @@
 from diffusers import Flux2KleinPipeline
 from PIL import Image
 
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 from gen_worker.types import Asset
 
@@ -53,7 +53,7 @@ def _should_enable_seq_offload() -> bool:
 
 
 def _generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline,
     payload: GenerateInput,
     model_key: str,
@@ -101,7 +101,7 @@ def _generate(
 
 @worker_function(_flux_resources)
 def generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
         ModelRef(Src.FIXED, "flux2-klein-9b-base"),
@@ -113,7 +113,7 @@ def generate(
 
 @worker_function(_flux_resources)
 def generate_turbo(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
         ModelRef(Src.FIXED, "flux2-klein-9b-turbo"),
@@ -125,7 +125,7 @@ def generate_turbo(
 
 @worker_function(_flux_resources)
 def generate_fp8(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
         ModelRef(Src.FIXED, "flux2-klein-9b-base_fp8"),
@@ -137,7 +137,7 @@ def generate_fp8(
 
 @worker_function(_flux_resources)
 def generate_turbo_fp8(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
         ModelRef(Src.FIXED, "flux2-klein-9b-turbo_fp8"),
@@ -149,7 +149,7 @@ def generate_turbo_fp8(
 
 @worker_function(_nvfp4_resources)
 def generate_nvfp4(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
         ModelRef(Src.FIXED, "flux2-klein-9b-base_nvfp4"),
@@ -161,7 +161,7 @@ def generate_nvfp4(
 
 @worker_function(_nvfp4_resources)
 def generate_turbo_nvfp4(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         Flux2KleinPipeline,
         ModelRef(Src.FIXED, "flux2-klein-9b-turbo_nvfp4"),
diff --git a/examples/image-gen/Dockerfile b/examples/image-gen/Dockerfile
index 1203d3c..cd4c800 100644
--- a/examples/image-gen/Dockerfile
+++ b/examples/image-gen/Dockerfile
@@ -1,14 +1,13 @@
 # Tenant-supplied Dockerfile example.
 #
-# - Installs torch in a stable cacheable layer.
-# - Installs tenant deps from uv.lock without replacing torch.
+# - Installs torch + gen-worker in stable cacheable layers.
+# - Installs tenant deps from uv.lock without replacing torch/gen-worker.
 # - Bakes discovered functions into /app/.tensorhub/endpoint.lock at build time.
 #
 ARG PYTHON_VERSION=3.12
 FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS cozy_base
 
-ARG CUDA_VERSION=12.8
-ARG UV_TORCH_BACKEND=
+ARG UV_TORCH_BACKEND=cu126
 ARG TORCH_SPEC="~=2.10.0"
 
 WORKDIR /app
@@ -24,9 +23,10 @@ RUN --mount=type=cache,id=cozy-apt-cache,target=/var/cache/apt,sharing=locked \
     && apt-get clean
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
-    backend="${UV_TORCH_BACKEND:-cu$(printf '%s' "${CUDA_VERSION}" | tr -d '.')}" \
-    && uv pip install --system --break-system-packages --torch-backend "$backend" \
+    uv pip install --system --break-system-packages --torch-backend ${UV_TORCH_BACKEND} \
       "torch${TORCH_SPEC}"
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages gen-worker==0.3.0
 
 FROM cozy_base AS cozy_final
 
@@ -34,7 +34,7 @@ COPY pyproject.toml uv.lock /app/
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
     uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
-      --no-emit-package torch \
+      --no-emit-package torch --no-emit-package gen-worker \
       -o /tmp/requirements.all.txt \
     && grep -Ev '^(torch|triton|nvidia-|cuda-)' /tmp/requirements.all.txt > /tmp/requirements.txt \
     && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
diff --git a/examples/image-gen/endpoint.toml b/examples/image-gen/endpoint.toml
index de4a700..2daf35f 100644
--- a/examples/image-gen/endpoint.toml
+++ b/examples/image-gen/endpoint.toml
@@ -7,7 +7,7 @@ main = "image_gen.main"
 sdxl = { ref = "stabilityai/stable-diffusion-xl-base-1.0", dtypes = ["fp16", "bf16"] }
 
 [host.requirements]
-cuda = "12.8"
+cuda = "12.6"
 
 [resources]
 vram_gb = 12
diff --git a/examples/image-gen/pyproject.toml b/examples/image-gen/pyproject.toml
index 7902855..f6074d2 100644
--- a/examples/image-gen/pyproject.toml
+++ b/examples/image-gen/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "Image generation function"
 requires-python = ">=3.12"
 dependencies = [
-    "gen-worker[torch]",
+    "gen-worker[torch]==0.3.0",
     "diffusers",
     "transformers",
     "accelerate",
diff --git a/examples/image-gen/src/image_gen/main.py b/examples/image-gen/src/image_gen/main.py
index f9de15d..65999e4 100644
--- a/examples/image-gen/src/image_gen/main.py
+++ b/examples/image-gen/src/image_gen/main.py
@@ -9,7 +9,7 @@
 from diffusers import StableDiffusionXLPipeline
 from PIL import Image
 
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 from gen_worker.types import Asset
 
@@ -46,7 +46,7 @@ class GenerateOutput(msgspec.Struct):
 
 @worker_function(sdxl_resources)
 def generate_image(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         StableDiffusionXLPipeline,
         ModelRef(Src.FIXED, "sdxl"),
@@ -78,7 +78,7 @@ def generate_image(
 
 
 # @worker_function(resources=sdxl_resources)
-# def generate_image(ctx: RequestContext, pipeline: DiffusionPipeline = None, prompt_details: dict = None) -> bytes:
+# def generate_image(ctx: ActionContext, pipeline: DiffusionPipeline = None, prompt_details: dict = None) -> bytes:
 #     """
 #     Legacy function: Generates an image and returns raw bytes.
 #     Consider using generate_and_upload_image for complete workflows.
@@ -115,7 +115,7 @@ def generate_image(
 # s3_upload_resources = ResourceRequirements()
 
 # @worker_function(resources=s3_upload_resources)
-# def upload_image_to_s3(ctx: RequestContext, upload_details: dict) -> Dict[str, str]:
+# def upload_image_to_s3(ctx: ActionContext, upload_details: dict) -> Dict[str, str]:
 #     """
 #     Legacy function: Uploads image bytes to S3.
 #     Consider using generate_and_upload_image for complete workflows.
diff --git a/examples/image-gen/uv.lock b/examples/image-gen/uv.lock
index 74df27e..481720f 100644
--- a/examples/image-gen/uv.lock
+++ b/examples/image-gen/uv.lock
@@ -786,7 +786,7 @@ dev = [
 requires-dist = [
     { name = "accelerate" },
     { name = "diffusers" },
-    { name = "gen-worker", extras = ["torch"] },
+    { name = "gen-worker", extras = ["torch"], specifier = "==0.3.0" },
     { name = "pillow" },
     { name = "transformers" },
 ]
diff --git a/examples/marco-polo/Dockerfile b/examples/marco-polo/Dockerfile
deleted file mode 100644
index 8090d32..0000000
--- a/examples/marco-polo/Dockerfile
+++ /dev/null
@@ -1,25 +0,0 @@
-ARG PYTHON_VERSION=3.12
-FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim
-
-WORKDIR /app
-
-ENV UV_CACHE_DIR=/var/cache/uv \
-    UV_LINK_MODE=copy \
-    HOME=/root \
-    XDG_CACHE_HOME=/root/.cache
-
-COPY pyproject.toml uv.lock /app/
-
-RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
-    uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
-      -o /tmp/requirements.txt \
-    && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
-
-COPY . /app
-
-RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
-    uv pip install --system --break-system-packages --no-deps --no-sources /app \
-    && mkdir -p /app/.tensorhub \
-    && python -m gen_worker.discover > /app/.tensorhub/endpoint.lock
-
-ENTRYPOINT ["python", "-m", "gen_worker.entrypoint"]
diff --git a/examples/marco-polo/README.md b/examples/marco-polo/README.md
deleted file mode 100644
index ae730e7..0000000
--- a/examples/marco-polo/README.md
+++ /dev/null
@@ -1 +0,0 @@
-Minimal function for testing.
\ No newline at end of file
diff --git a/examples/marco-polo/endpoint.toml b/examples/marco-polo/endpoint.toml
deleted file mode 100644
index 9dc069d..0000000
--- a/examples/marco-polo/endpoint.toml
+++ /dev/null
@@ -1,7 +0,0 @@
-schema_version = 1
-
-name = "marco_polo"
-main = "marco_polo.main"
-
-[resources]
-max_inflight_requests = 10
diff --git a/examples/marco-polo/src/marco_polo/__init__.py b/examples/marco-polo/src/marco_polo/__init__.py
deleted file mode 100644
index 8b13789..0000000
--- a/examples/marco-polo/src/marco_polo/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/examples/marco-polo/src/marco_polo/main.py b/examples/marco-polo/src/marco_polo/main.py
deleted file mode 100644
index 2b4d48a..0000000
--- a/examples/marco-polo/src/marco_polo/main.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import msgspec
-from gen_worker import RequestContext, worker_function
-
-
-class MarcoPoloInput(msgspec.Struct):
-    text: str = ""
-
-
-class MarcoPoloOutput(msgspec.Struct):
-    response: str
-
-
-@worker_function()
-def marco_polo(ctx: RequestContext, data: MarcoPoloInput) -> MarcoPoloOutput:
-    """Returns 'polo' when input is 'marco'; otherwise a fallback response."""
-    # E2E keepalive: deterministic minimal handler used for latency tests.
-    if ctx.is_canceled():
-        raise InterruptedError("Request cancelled")
-
-    if str(data.text or "").strip().lower() == "marco":
-        return MarcoPoloOutput(response="polo")
-    
-    return MarcoPoloOutput(response="Bro you're supposed to say 'marco'!")
diff --git a/examples/medasr-transcribe/Dockerfile b/examples/medasr-transcribe/Dockerfile
index 8c87c3e..837f56e 100644
--- a/examples/medasr-transcribe/Dockerfile
+++ b/examples/medasr-transcribe/Dockerfile
@@ -1,15 +1,14 @@
 # Tenant-supplied Dockerfile example.
 #
-# - Installs torch in a stable cacheable layer.
-# - Installs tenant deps from uv.lock without replacing torch.
+# - Installs torch + gen-worker in stable cacheable layers.
+# - Installs tenant deps from uv.lock without replacing torch/gen-worker.
 # - Installs required system libs (libsndfile1) for soundfile.
 # - Bakes discovered functions into /app/.tensorhub/endpoint.lock at build time.
 #
 ARG PYTHON_VERSION=3.12
 FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS cozy_base
 
-ARG CUDA_VERSION=12.8
-ARG UV_TORCH_BACKEND=
+ARG UV_TORCH_BACKEND=cu126
 ARG TORCH_SPEC="~=2.10.0"
 
 WORKDIR /app
@@ -25,9 +24,10 @@ RUN --mount=type=cache,id=cozy-apt-cache,target=/var/cache/apt,sharing=locked \
     && apt-get clean
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
-    backend="${UV_TORCH_BACKEND:-cu$(printf '%s' "${CUDA_VERSION}" | tr -d '.')}" \
-    && uv pip install --system --break-system-packages --torch-backend "$backend" \
+    uv pip install --system --break-system-packages --torch-backend ${UV_TORCH_BACKEND} \
       "torch${TORCH_SPEC}"
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages gen-worker==0.3.0
 
 FROM cozy_base AS cozy_final
 
@@ -42,7 +42,7 @@ COPY pyproject.toml uv.lock /app/
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
     uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
-      --no-emit-package torch \
+      --no-emit-package torch --no-emit-package gen-worker \
       -o /tmp/requirements.all.txt \
     && grep -Ev '^(torch|triton|nvidia-|cuda-)' /tmp/requirements.all.txt > /tmp/requirements.txt \
     && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
diff --git a/examples/medasr-transcribe/endpoint.toml b/examples/medasr-transcribe/endpoint.toml
index 6362d69..dd7ebeb 100644
--- a/examples/medasr-transcribe/endpoint.toml
+++ b/examples/medasr-transcribe/endpoint.toml
@@ -7,7 +7,7 @@ main = "medasr_transcribe.main"
 medasr = { ref = "google/medasr", dtypes = ["fp16", "bf16"] }
 
 [host.requirements]
-cuda = "12.8"
+cuda = "12.6"
 
 [resources]
 vram_gb = 8
diff --git a/examples/medasr-transcribe/pyproject.toml b/examples/medasr-transcribe/pyproject.toml
index 44e0889..f2d7c0d 100644
--- a/examples/medasr-transcribe/pyproject.toml
+++ b/examples/medasr-transcribe/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "MedASR transcription worker function example"
 requires-python = ">=3.12"
 dependencies = [
-    "gen-worker[torch]",
+    "gen-worker[torch]==0.3.0",
     "numpy",
     "soundfile",
     "soxr",
diff --git a/examples/medasr-transcribe/src/medasr_transcribe/main.py b/examples/medasr-transcribe/src/medasr_transcribe/main.py
index 3956372..7b3b7f0 100644
--- a/examples/medasr-transcribe/src/medasr_transcribe/main.py
+++ b/examples/medasr-transcribe/src/medasr_transcribe/main.py
@@ -9,7 +9,7 @@
 import torch
 from transformers import AutoModelForCTC, AutoProcessor
 
-from gen_worker import RequestContext, Asset, ResourceRequirements, worker_function
+from gen_worker import ActionContext, Asset, ResourceRequirements, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 
 _MODEL_KEY = "medasr"
@@ -25,7 +25,7 @@ class MedASROutput(msgspec.Struct):
 
 @worker_function(ResourceRequirements())
 def medasr_transcribe(
-    ctx: RequestContext,
+    ctx: ActionContext,
     model: Annotated[AutoModelForCTC, ModelRef(Src.FIXED, _MODEL_KEY)],
     processor: Annotated[AutoProcessor, ModelRef(Src.FIXED, _MODEL_KEY)],
     payload: MedASRInput,
diff --git a/examples/medasr-transcribe/uv.lock b/examples/medasr-transcribe/uv.lock
index 75ae15d..2214c33 100644
--- a/examples/medasr-transcribe/uv.lock
+++ b/examples/medasr-transcribe/uv.lock
@@ -841,7 +841,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
-    { name = "gen-worker", extras = ["torch"] },
+    { name = "gen-worker", extras = ["torch"], specifier = "==0.3.0" },
     { name = "numpy" },
     { name = "soundfile" },
     { name = "soxr" },
diff --git a/examples/multi-sdxl-checkpoints/Dockerfile b/examples/multi-sdxl-checkpoints/Dockerfile
index f4c4f1b..4f87469 100644
--- a/examples/multi-sdxl-checkpoints/Dockerfile
+++ b/examples/multi-sdxl-checkpoints/Dockerfile
@@ -1,14 +1,13 @@
 # Tenant-supplied Dockerfile example.
 #
-# - Installs torch in a stable cacheable layer.
-# - Installs tenant deps from uv.lock without replacing torch.
+# - Installs torch + gen-worker in stable cacheable layers.
+# - Installs tenant deps from uv.lock without replacing torch/gen-worker.
 # - Bakes discovered functions into /app/.tensorhub/endpoint.lock at build time.
 #
 ARG PYTHON_VERSION=3.12
 FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS cozy_base
 
-ARG CUDA_VERSION=12.8
-ARG UV_TORCH_BACKEND=
+ARG UV_TORCH_BACKEND=cu126
 ARG TORCH_SPEC="~=2.10.0"
 
 WORKDIR /app
@@ -24,10 +23,11 @@ RUN --mount=type=cache,id=cozy-apt-cache,target=/var/cache/apt,sharing=locked \
     && apt-get clean
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
-    backend="${UV_TORCH_BACKEND:-cu$(printf '%s' "${CUDA_VERSION}" | tr -d '.')}" \
-    && uv pip install --system --break-system-packages --torch-backend "$backend" \
+    uv pip install --system --break-system-packages --torch-backend ${UV_TORCH_BACKEND} \
       "torch${TORCH_SPEC}"
 
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages gen-worker==0.3.0
 
 FROM cozy_base AS cozy_final
 
@@ -35,7 +35,7 @@ COPY pyproject.toml uv.lock /app/
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
     uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
-      --no-emit-package torch \
+      --no-emit-package torch --no-emit-package gen-worker \
       -o /tmp/requirements.all.txt \
     && grep -Ev '^(torch|triton|nvidia-|cuda-)' /tmp/requirements.all.txt > /tmp/requirements.txt \
     && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
diff --git a/examples/multi-sdxl-checkpoints/endpoint.toml b/examples/multi-sdxl-checkpoints/endpoint.toml
index 0a8f312..1fd8208 100644
--- a/examples/multi-sdxl-checkpoints/endpoint.toml
+++ b/examples/multi-sdxl-checkpoints/endpoint.toml
@@ -4,7 +4,7 @@ name = "multi-sdxl-checkpoints"
 main = "multi_sdxl_checkpoints.main"
 
 [host.requirements]
-cuda = "12.8"
+cuda = "12.6"
 
 [models.generate]
 sdxl-base = "stabilityai/stable-diffusion-xl-base-1.0"
diff --git a/examples/multi-sdxl-checkpoints/pyproject.toml b/examples/multi-sdxl-checkpoints/pyproject.toml
index a4a586e..f3ddf56 100644
--- a/examples/multi-sdxl-checkpoints/pyproject.toml
+++ b/examples/multi-sdxl-checkpoints/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "Multi SDXL checkpoints example with payload-based model selection"
 requires-python = ">=3.12"
 dependencies = [
-    "gen-worker[torch]",
+    "gen-worker[torch]==0.3.0",
     "diffusers",
     "transformers",
     "accelerate",
diff --git a/examples/multi-sdxl-checkpoints/src/multi_sdxl_checkpoints/main.py b/examples/multi-sdxl-checkpoints/src/multi_sdxl_checkpoints/main.py
index 9bae6c5..cd29ed7 100644
--- a/examples/multi-sdxl-checkpoints/src/multi_sdxl_checkpoints/main.py
+++ b/examples/multi-sdxl-checkpoints/src/multi_sdxl_checkpoints/main.py
@@ -22,7 +22,7 @@
 from diffusers import DiffusionPipeline
 from PIL import Image
 
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 from gen_worker.types import Asset
 
@@ -82,7 +82,7 @@ def _set_seed_and_perf(seed: Optional[int]) -> None:
 
 @worker_function(_sdxl_router_resources)
 def generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         DiffusionPipeline,
         ModelRef(Src.PAYLOAD, "model_key")  # Model key comes from payload.model_key
diff --git a/examples/multi-sdxl-checkpoints/uv.lock b/examples/multi-sdxl-checkpoints/uv.lock
index 872c7f7..07fa718 100644
--- a/examples/multi-sdxl-checkpoints/uv.lock
+++ b/examples/multi-sdxl-checkpoints/uv.lock
@@ -971,7 +971,7 @@ dev = [
 requires-dist = [
     { name = "accelerate" },
     { name = "diffusers" },
-    { name = "gen-worker", extras = ["torch"] },
+    { name = "gen-worker", extras = ["torch"], specifier = "==0.3.0" },
     { name = "pillow" },
     { name = "transformers" },
 ]
diff --git a/examples/openai-codex/Dockerfile b/examples/openai-codex/Dockerfile
index 73fb8fd..b0b3f67 100644
--- a/examples/openai-codex/Dockerfile
+++ b/examples/openai-codex/Dockerfile
@@ -1,7 +1,7 @@
 # Tenant-supplied Dockerfile example: OpenAI Codex CLI worker (Ubuntu 24.04).
 #
 # Contract:
-# - Installs dependencies from pyproject.toml/uv.lock.
+# - Installs gen-worker.
 # - Runs discovery at build time, baking /app/.tensorhub/endpoint.lock into the image.
 # - Uses gen-worker as the image ENTRYPOINT.
 #
@@ -53,12 +53,18 @@ RUN set -eu; \
   chmod +x /usr/local/bin/codex; \
   rm -rf /tmp/codex.tar.gz
 
+# Stable runtime layer.
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+  uv pip install --system --break-system-packages gen-worker==0.3.0
+
 # Copy lock metadata first (cache-friendly).
 COPY pyproject.toml uv.lock /app/
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
   uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
-    -o /tmp/requirements.txt \
+    --no-emit-package gen-worker \
+    -o /tmp/requirements.all.txt \
+  && grep -Ev '^(gen-worker)' /tmp/requirements.all.txt > /tmp/requirements.txt \
   && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
 
 # Copy app code late.
diff --git a/examples/openai-codex/pyproject.toml b/examples/openai-codex/pyproject.toml
index 8f7394f..52f20ed 100644
--- a/examples/openai-codex/pyproject.toml
+++ b/examples/openai-codex/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "Example Cozy worker that shells out to OpenAI Codex CLI and streams JSONL events."
 requires-python = ">=3.12"
 dependencies = [
-  "gen-worker",
+  "gen-worker==0.3.0",
   "msgspec",
 ]
 
diff --git a/examples/openai-codex/src/openai_codex_worker/main.py b/examples/openai-codex/src/openai_codex_worker/main.py
index 4f357df..a834f6c 100644
--- a/examples/openai-codex/src/openai_codex_worker/main.py
+++ b/examples/openai-codex/src/openai_codex_worker/main.py
@@ -5,7 +5,7 @@
 
 import msgspec
 
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 
 
 class CodexExecInput(msgspec.Struct):
@@ -20,7 +20,7 @@ class CodexEventDelta(msgspec.Struct):
 
 
 @worker_function(ResourceRequirements())
-def codex_exec(ctx: RequestContext, payload: CodexExecInput) -> Iterator[CodexEventDelta]:
+def codex_exec(ctx: ActionContext, payload: CodexExecInput) -> Iterator[CodexEventDelta]:
     """
     Run Codex in headless mode and stream JSONL events as incremental deltas.
 
diff --git a/examples/openai-codex/uv.lock b/examples/openai-codex/uv.lock
index 2d01c9e..a3aabb1 100644
--- a/examples/openai-codex/uv.lock
+++ b/examples/openai-codex/uv.lock
@@ -852,7 +852,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "gen-worker" },
+    { name = "gen-worker", specifier = "==0.3.0" },
     { name = "msgspec" },
 ]
 
diff --git a/examples/qwen-image-2512/Dockerfile b/examples/qwen-image-2512/Dockerfile
index 4fae936..6a531e4 100644
--- a/examples/qwen-image-2512/Dockerfile
+++ b/examples/qwen-image-2512/Dockerfile
@@ -1,14 +1,13 @@
 # Tenant-supplied Dockerfile example.
 #
 # - Installs torch in a stable cacheable layer.
-# - Installs tenant deps from uv.lock without replacing torch.
+# - Installs tenant deps from uv.lock without replacing torch/gen-worker.
 # - Bakes discovered functions into /app/.tensorhub/endpoint.lock at build time.
 #
 ARG PYTHON_VERSION=3.12
 FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS cozy_base
 
-ARG CUDA_VERSION=12.8
-ARG UV_TORCH_BACKEND=
+ARG UV_TORCH_BACKEND=cu126
 ARG TORCH_SPEC="~=2.10.0"
 
 WORKDIR /app
@@ -24,9 +23,10 @@ RUN --mount=type=cache,id=cozy-apt-cache,target=/var/cache/apt,sharing=locked \
     && apt-get clean
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
-    backend="${UV_TORCH_BACKEND:-cu$(printf '%s' "${CUDA_VERSION}" | tr -d '.')}" \
-    && uv pip install --system --break-system-packages --torch-backend "$backend" \
+    uv pip install --system --break-system-packages --torch-backend ${UV_TORCH_BACKEND} \
       "torch${TORCH_SPEC}"
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages gen-worker==0.3.0
 
 FROM cozy_base AS cozy_final
 
@@ -34,7 +34,7 @@ COPY pyproject.toml uv.lock /app/
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
     uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
-      --no-emit-package torch \
+      --no-emit-package torch --no-emit-package gen-worker \
       -o /tmp/requirements.all.txt \
     && grep -Ev '^(torch|triton|nvidia-|cuda-)' /tmp/requirements.all.txt > /tmp/requirements.txt \
     && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
diff --git a/examples/qwen-image-2512/endpoint.toml b/examples/qwen-image-2512/endpoint.toml
index e56bb6b..d0e3bc4 100644
--- a/examples/qwen-image-2512/endpoint.toml
+++ b/examples/qwen-image-2512/endpoint.toml
@@ -7,7 +7,7 @@ main = "qwen_image_2512.main"
 qwen_image = { ref = "qwen/qwen-image-2512", dtypes = ["bf16", "fp16"] }
 
 [host.requirements]
-cuda = "12.8"
+cuda = "12.6"
 
 [resources]
 vram_gb = 24
diff --git a/examples/qwen-image-2512/pyproject.toml b/examples/qwen-image-2512/pyproject.toml
index 342c9e7..14a3b92 100644
--- a/examples/qwen-image-2512/pyproject.toml
+++ b/examples/qwen-image-2512/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "Qwen Image 2512 generation function"
 requires-python = ">=3.12"
 dependencies = [
-    "gen-worker[torch]",
+    "gen-worker[torch]==0.3.0",
     "diffusers @ git+https://github.com/huggingface/diffusers",
     "transformers>=4.51.3",
     "accelerate",
diff --git a/examples/qwen-image-2512/src/qwen_image_2512/main.py b/examples/qwen-image-2512/src/qwen_image_2512/main.py
index e96b585..89d8c9b 100644
--- a/examples/qwen-image-2512/src/qwen_image_2512/main.py
+++ b/examples/qwen-image-2512/src/qwen_image_2512/main.py
@@ -11,7 +11,7 @@
 from diffusers import DiffusionPipeline
 from PIL import Image
 
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 from gen_worker.types import Asset
 
@@ -93,7 +93,7 @@ def _try_compile_transformer(pipeline: DiffusionPipeline) -> None:
 
 @worker_function(qwen_resources)
 def generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         DiffusionPipeline,
         ModelRef(Src.FIXED, "qwen_image"),
diff --git a/examples/qwen-image-2512/uv.lock b/examples/qwen-image-2512/uv.lock
index b96fbac..77af1b4 100644
--- a/examples/qwen-image-2512/uv.lock
+++ b/examples/qwen-image-2512/uv.lock
@@ -1598,7 +1598,7 @@ dev = [
 requires-dist = [
     { name = "accelerate" },
     { name = "diffusers", git = "https://github.com/huggingface/diffusers" },
-    { name = "gen-worker", extras = ["torch"] },
+    { name = "gen-worker", extras = ["torch"], specifier = "==0.3.0" },
     { name = "pillow" },
     { name = "transformers", specifier = ">=4.51.3" },
 ]
diff --git a/examples/sd15/Dockerfile b/examples/sd15/Dockerfile
index c52a5dc..99bb5a2 100644
--- a/examples/sd15/Dockerfile
+++ b/examples/sd15/Dockerfile
@@ -1,7 +1,7 @@
 # Tenant-supplied Dockerfile example.
 #
-# - Installs torch in a stable cacheable layer.
-# - Installs tenant deps from uv.lock without replacing torch.
+# - Installs torch + gen-worker in stable cacheable layers.
+# - Installs tenant deps from uv.lock without replacing torch/gen-worker.
 # - Bakes discovered functions into /app/.tensorhub/endpoint.lock at build time.
 #
 # Local build (GPU):
@@ -10,8 +10,7 @@
 ARG PYTHON_VERSION=3.12
 FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS cozy_base
 
-ARG CUDA_VERSION=12.8
-ARG UV_TORCH_BACKEND=
+ARG UV_TORCH_BACKEND=cu126
 ARG TORCH_SPEC="~=2.10.0"
 
 WORKDIR /app
@@ -28,19 +27,20 @@ RUN --mount=type=cache,id=cozy-apt-cache,target=/var/cache/apt,sharing=locked \
 
 # Stable runtime layers (avoid re-downloading torch/cu libs on every tenant build).
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
-    backend="${UV_TORCH_BACKEND:-cu$(printf '%s' "${CUDA_VERSION}" | tr -d '.')}" \
-    && uv pip install --system --break-system-packages --torch-backend "$backend" \
+    uv pip install --system --break-system-packages --torch-backend ${UV_TORCH_BACKEND} \
       "torch${TORCH_SPEC}"
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages gen-worker
 
 FROM cozy_base AS cozy_final
 
 # Copy lock metadata first so dependency layers are cacheable across source edits.
 COPY pyproject.toml uv.lock /app/
 
-# Install tenant dependencies into the global environment without replacing torch.
+# Install tenant dependencies into the global environment without replacing torch/gen-worker.
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
     uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
-      --no-emit-package torch \
+      --no-emit-package torch --no-emit-package gen-worker \
       -o /tmp/requirements.all.txt \
     && grep -Ev '^(torch|triton|nvidia-|cuda-)' /tmp/requirements.all.txt > /tmp/requirements.txt \
     && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
diff --git a/examples/sd15/Dockerfile.local b/examples/sd15/Dockerfile.local
new file mode 100644
index 0000000..959a94c
--- /dev/null
+++ b/examples/sd15/Dockerfile.local
@@ -0,0 +1,72 @@
+# Local-dev Dockerfile for the sd15 example.
+#
+# Difference vs ./Dockerfile:
+# - Installs gen-worker from the local checkout (this repo) so you can test
+#   unreleased changes.
+#
+# Build from repo root:
+#   docker build -f examples/sd15/Dockerfile.local -t cozy-example-sd15-local:dev .
+#
+ARG PYTHON_VERSION=3.12
+FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS cozy_base
+
+ARG UV_TORCH_BACKEND=cu126
+ARG TORCH_SPEC="~=2.10.0"
+
+WORKDIR /app
+
+ENV UV_CACHE_DIR=/var/cache/uv
+ENV UV_LINK_MODE=copy
+
+RUN --mount=type=cache,id=cozy-apt-cache,target=/var/cache/apt,sharing=locked \
+    --mount=type=cache,id=cozy-apt-lists,target=/var/lib/apt/lists,sharing=locked \
+    apt-get update && apt-get install -y --no-install-recommends \
+    git \
+    && apt-get clean
+
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages --torch-backend ${UV_TORCH_BACKEND} \
+      "torch${TORCH_SPEC}"
+
+# Install gen-worker from the local checkout.
+COPY pyproject.toml uv.lock README.md /gen-worker/
+COPY src/ /gen-worker/src/
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages /gen-worker
+
+FROM cozy_base AS cozy_final
+
+# Copy lock metadata first so dependency layers are cacheable across source edits.
+COPY examples/sd15/pyproject.toml examples/sd15/uv.lock /app/
+
+# Install tenant dependencies into the global environment without replacing torch/gen-worker.
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
+      --no-emit-package torch --no-emit-package gen-worker \
+      -o /tmp/requirements.all.txt \
+    && grep -Ev '^(torch|triton|nvidia-|cuda-)' /tmp/requirements.all.txt > /tmp/requirements.txt \
+    && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
+
+# Copy app code late so app edits only invalidate the final layers.
+COPY examples/sd15/ /app/
+
+# Install the project itself without altering dependency layers.
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages --no-deps --no-sources /app
+
+# Bake discovered functions into the image so Cozy Hub can read them without executing tenant code.
+RUN mkdir -p /app/.tensorhub \
+    && python -m gen_worker.discover > /app/.tensorhub/endpoint.lock
+
+# Run as non-root at runtime.
+RUN groupadd --system --gid 10001 cozy \
+    && useradd --system --uid 10001 --gid cozy --create-home --home-dir /home/cozy --shell /usr/sbin/nologin cozy \
+    && chown -R cozy:cozy /app /home/cozy
+
+ENV HOME=/home/cozy
+ENV XDG_CACHE_HOME=/home/cozy/.cache
+ENV HF_HOME=/home/cozy/.cache/huggingface
+
+USER cozy:cozy
+
+ENTRYPOINT ["python", "-m", "gen_worker.entrypoint"]
diff --git a/examples/sd15/endpoint.toml b/examples/sd15/endpoint.toml
index 4248a9f..98d3a69 100644
--- a/examples/sd15/endpoint.toml
+++ b/examples/sd15/endpoint.toml
@@ -12,7 +12,7 @@ sd15_int4 = { ref = "stable-diffusion-v1-5/stable-diffusion-v1-5", dtypes = ["in
 
 [host.requirements]
 # If cuda is set, the platform treats this worker as requiring an NVIDIA GPU.
-cuda = "12.8"
+cuda = "12.6"
 
 [resources]
 vram_gb = 8
diff --git a/examples/sd15/pyproject.toml b/examples/sd15/pyproject.toml
index 9c83eac..c8fb76b 100644
--- a/examples/sd15/pyproject.toml
+++ b/examples/sd15/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.1"
 description = "Stable Diffusion 1.5 example (inference-only; Cozy manifest via endpoint.toml)"
 requires-python = ">=3.12"
 dependencies = [
-    "gen-worker[torch]",
+    "gen-worker[torch]==0.3.0",
     "diffusers",
     "transformers<5",
     "accelerate",
diff --git a/examples/sd15/src/sd15/main.py b/examples/sd15/src/sd15/main.py
index aa56eae..e5c5081 100644
--- a/examples/sd15/src/sd15/main.py
+++ b/examples/sd15/src/sd15/main.py
@@ -9,7 +9,7 @@
 from diffusers import StableDiffusionPipeline
 from PIL import Image
 
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 from gen_worker.types import Asset
 
@@ -46,7 +46,7 @@ class GenerateOutput(msgspec.Struct):
 
 @worker_function(_sd15_resources)
 def generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         StableDiffusionPipeline,
         ModelRef(Src.FIXED, "sd15"),
@@ -96,7 +96,7 @@ def generate(
 
 @worker_function(_sd15_resources)
 def generate_fp8(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         StableDiffusionPipeline,
         ModelRef(Src.FIXED, "sd15_fp8"),
@@ -114,7 +114,7 @@ def generate_fp8(
 
 @worker_function(_sd15_resources)
 def generate_int8(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         StableDiffusionPipeline,
         ModelRef(Src.FIXED, "sd15_int8"),
@@ -132,7 +132,7 @@ def generate_int8(
 
 @worker_function(_sd15_resources)
 def generate_int4(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         StableDiffusionPipeline,
         ModelRef(Src.FIXED, "sd15_int4"),
diff --git a/examples/sd15/uv.lock b/examples/sd15/uv.lock
index aded35c..8574804 100644
--- a/examples/sd15/uv.lock
+++ b/examples/sd15/uv.lock
@@ -1733,7 +1733,7 @@ requires-dist = [
     { name = "accelerate" },
     { name = "diffusers" },
     { name = "ftfy" },
-    { name = "gen-worker", extras = ["torch"] },
+    { name = "gen-worker", extras = ["torch"], specifier = "==0.3.0" },
     { name = "pillow" },
     { name = "torchao" },
     { name = "transformers", specifier = "<5" },
diff --git a/examples/marco-polo/.dockerignore b/examples/smoke-test/.dockerignore
similarity index 100%
rename from examples/marco-polo/.dockerignore
rename to examples/smoke-test/.dockerignore
diff --git a/examples/smoke-test/Dockerfile b/examples/smoke-test/Dockerfile
new file mode 100644
index 0000000..4fab034
--- /dev/null
+++ b/examples/smoke-test/Dockerfile
@@ -0,0 +1,53 @@
+# Tenant-supplied Dockerfile example (CPU).
+#
+# - Installs gen-worker in a stable cacheable layer.
+# - Installs torch (CPU) so Cozy Hub can record a stable backend version for tagging.
+# - Installs tenant deps from uv.lock without reinstalling gen-worker.
+# - Bakes discovered functions into /app/.tensorhub/endpoint.lock at build time.
+#
+ARG PYTHON_VERSION=3.12
+FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim
+
+WORKDIR /app
+
+ENV UV_CACHE_DIR=/var/cache/uv
+ENV UV_LINK_MODE=copy
+
+RUN --mount=type=cache,id=cozy-apt-cache,target=/var/cache/apt,sharing=locked \
+    --mount=type=cache,id=cozy-apt-lists,target=/var/lib/apt/lists,sharing=locked \
+    apt-get update && apt-get install -y --no-install-recommends \
+    git \
+    && apt-get clean
+
+ARG UV_TORCH_BACKEND=cpu
+ARG TORCH_SPEC="~=2.10.0"
+
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages --torch-backend ${UV_TORCH_BACKEND} \
+      "torch${TORCH_SPEC}"
+
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages gen-worker==0.3.0
+
+COPY pyproject.toml uv.lock /app/
+
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
+      --no-emit-package gen-worker --no-emit-package torch \
+      -o /tmp/requirements.txt \
+    && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
+
+COPY . /app
+
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages --no-deps --no-sources /app
+
+RUN mkdir -p /app/.tensorhub \
+    && python -m gen_worker.discover > /app/.tensorhub/endpoint.lock
+
+
+ENV HOME=/root
+ENV XDG_CACHE_HOME=/root/.cache
+
+
+ENTRYPOINT ["python", "-m", "gen_worker.entrypoint"]
diff --git a/examples/smoke-test/README.md b/examples/smoke-test/README.md
new file mode 100644
index 0000000..38bb06b
--- /dev/null
+++ b/examples/smoke-test/README.md
@@ -0,0 +1,14 @@
+Simple worker functions for smoke testing the orchestrator.
+
+Functions:
+- `add_numbers` - adds two integers
+- `multiply_numbers` - multiplies two integers
+- `image_gen_action` - smoke image generation (returns real `Asset` outputs; no ML)
+- `token_stream` - incremental output example (LLM-style token streaming)
+- `caption_prompts` - multi-item request example (`prompts[]`) for orchestrator split/combine + correlation tests
+
+Notes:
+- `image_gen_action` returns a list of output `Asset`s. Each asset is written via `ctx.save_bytes(...)` under
+  `runs/<request_id>/outputs/` so the orchestrator/file API can store it like a real request output.
+- `caption_prompts` keeps item order stable and emits request-scoped progress, making it a good test target for
+  orchestrator split/combine aggregation flows.
diff --git a/examples/smoke-test/endpoint.toml b/examples/smoke-test/endpoint.toml
new file mode 100644
index 0000000..a979f9d
--- /dev/null
+++ b/examples/smoke-test/endpoint.toml
@@ -0,0 +1,10 @@
+schema_version = 1
+
+name = "smoke-test"
+main = "smoke_test.main"
+
+[functions.caption_prompts]
+batch_dimension = "prompts"
+
+[resources]
+max_inflight_requests = 8
diff --git a/examples/marco-polo/pyproject.toml b/examples/smoke-test/pyproject.toml
similarity index 66%
rename from examples/marco-polo/pyproject.toml
rename to examples/smoke-test/pyproject.toml
index 4674920..858525b 100644
--- a/examples/marco-polo/pyproject.toml
+++ b/examples/smoke-test/pyproject.toml
@@ -1,10 +1,10 @@
 [project]
-name = "marco_polo"
-version = "1.0.0"
-description = "Simple function for testing"
+name = "smoke-test"
+version = "0.1.0"
+description = "Simple worker functions for smoke testing"
 requires-python = ">=3.12"
 dependencies = [
-    "gen-worker>=0.3.10",
+    "gen-worker==0.3.0",
 ]
 
 [dependency-groups]
@@ -15,7 +15,7 @@ requires = ["hatchling"]
 build-backend = "hatchling.build"
 
 [tool.hatch.build.targets.wheel]
-packages = ["src/marco_polo"]
+packages = ["src/smoke_test"]
 
 [tool.mypy]
 python_version = "3.12"
diff --git a/examples/smoke-test/src/smoke_test/__init__.py b/examples/smoke-test/src/smoke_test/__init__.py
new file mode 100644
index 0000000..c3616e0
--- /dev/null
+++ b/examples/smoke-test/src/smoke_test/__init__.py
@@ -0,0 +1 @@
+"""Package marker for example worker module."""
diff --git a/examples/smoke-test/src/smoke_test/example.py b/examples/smoke-test/src/smoke_test/example.py
new file mode 100644
index 0000000..fc32006
--- /dev/null
+++ b/examples/smoke-test/src/smoke_test/example.py
@@ -0,0 +1,148 @@
+import time
+import base64
+from typing import Iterator, List
+
+import msgspec
+
+from gen_worker import ActionContext, ResourceRequirements, worker_function
+from gen_worker.types import Asset
+
+
+class ImageGenInput(msgspec.Struct):
+    """Input parameters for image generation."""
+
+    prompt: str = "a tiny test image"
+    width: int = 1
+    height: int = 1
+    num_images: int = 1
+
+
+class ImageGenOutput(msgspec.Struct):
+    """Output from image generation (real file output)."""
+
+    images: List[Asset]
+
+
+# 1x1 PNG (valid image bytes). This is deterministic and avoids pulling in Pillow.
+_PNG_1X1 = base64.b64decode(
+    "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/x8AAwMBAAEnnX8AAAAASUVORK5CYII="
+)
+
+
+@worker_function(ResourceRequirements())
+def image_gen_action(ctx: ActionContext, data: ImageGenInput) -> ImageGenOutput:
+    """Example image generation function that returns real output Assets.
+
+    This is a smoke test: it does not run ML inference. It validates:
+    - request/response msgspec serialization
+    - output file creation + upload path wiring (ctx.save_bytes)
+    """
+    if ctx.is_canceled():
+        raise InterruptedError("Task cancelled")
+
+    out: List[Asset] = []
+    n = max(1, int(data.num_images))
+    for i in range(n):
+        ref = f"runs/{ctx.request_id}/outputs/image-{i+1}.png"
+        out.append(ctx.save_bytes(ref, _PNG_1X1))
+    return ImageGenOutput(images=out)
+
+
+class AddInput(msgspec.Struct):
+    a: int = 0
+    b: int = 0
+
+
+class AddOutput(msgspec.Struct):
+    result: int
+
+
+@worker_function(ResourceRequirements())
+def add_numbers(ctx: ActionContext, data: AddInput) -> AddOutput:
+    """Example function that adds two numbers."""
+    if ctx.is_canceled():
+        raise InterruptedError("Task cancelled")
+    time.sleep(0.1)
+    return AddOutput(result=data.a + data.b)
+
+
+class MultiplyInput(msgspec.Struct):
+    a: int = 0
+    b: int = 0
+
+
+class MultiplyOutput(msgspec.Struct):
+    result: int
+
+
+@worker_function(ResourceRequirements())
+def multiply_numbers(ctx: ActionContext, data: MultiplyInput) -> MultiplyOutput:
+    """Example function that multiplies two numbers."""
+    if ctx.is_canceled():
+        raise InterruptedError("Task cancelled")
+    time.sleep(0.1)
+    return MultiplyOutput(result=data.a * data.b)
+
+
+class TokenDelta(msgspec.Struct):
+    delta: str
+
+
+class StreamInput(msgspec.Struct):
+    text: str
+    delay_ms: int = 25
+
+
+@worker_function(ResourceRequirements())
+def token_stream(ctx: ActionContext, data: StreamInput) -> Iterator[TokenDelta]:
+    """Example incremental-output function (LLM-style token deltas)."""
+    for ch in data.text:
+        if ctx.is_canceled():
+            raise InterruptedError("Task cancelled")
+        yield TokenDelta(delta=ch)
+        if data.delay_ms > 0:
+            time.sleep(data.delay_ms / 1000.0)
+
+
+class PromptBatchInput(msgspec.Struct):
+    """Example multi-item request payload (partition axis: input.prompts[])."""
+
+    prompts: List[str]
+    style: str = "neutral"
+
+
+class PromptCaption(msgspec.Struct):
+    index: int
+    prompt: str
+    caption: str
+
+
+class PromptBatchOutput(msgspec.Struct):
+    items: List[PromptCaption]
+
+
+@worker_function(ResourceRequirements())
+def caption_prompts(ctx: ActionContext, data: PromptBatchInput) -> PromptBatchOutput:
+    """Example multi-item request handler for input.prompts[].
+
+    This function is intentionally simple and deterministic so it can be used in
+    e2e tests for partitioning and output correlation.
+    """
+    if ctx.is_canceled():
+        raise InterruptedError("Task cancelled")
+
+    out: List[PromptCaption] = []
+    total = max(1, len(data.prompts))
+    for i, prompt in enumerate(data.prompts):
+        if ctx.is_canceled():
+            raise InterruptedError("Task cancelled")
+        text = str(prompt or "").strip()
+        out.append(
+            PromptCaption(
+                index=i,
+                prompt=text,
+                caption=f"[{data.style}] {text}" if text else f"[{data.style}]",
+            )
+        )
+        ctx.progress((i + 1) / total, stage="multi_item_request")
+    return PromptBatchOutput(items=out)
diff --git a/examples/smoke-test/src/smoke_test/main.py b/examples/smoke-test/src/smoke_test/main.py
new file mode 100644
index 0000000..74d0489
--- /dev/null
+++ b/examples/smoke-test/src/smoke_test/main.py
@@ -0,0 +1,3 @@
+from .example import add_numbers, image_gen_action, multiply_numbers, token_stream
+
+__all__ = ["image_gen_action", "add_numbers", "multiply_numbers", "token_stream"]
diff --git a/examples/marco-polo/uv.lock b/examples/smoke-test/uv.lock
similarity index 53%
rename from examples/marco-polo/uv.lock
rename to examples/smoke-test/uv.lock
index 6c27a41..7ebd088 100644
--- a/examples/marco-polo/uv.lock
+++ b/examples/smoke-test/uv.lock
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+sdist = { url = "https://files.pythonhosted.org/packages/e0/2d/a891ca51311197f6ad14a7ef42e2399f36cf2f9bd44752b3dc4eab60fdc5/certifi-2026.1.4.tar.gz", hash = "sha256:ac726dd470482006e014ad384921ed6438c457018f4b3d204aea4281258b2120", size = 154268, upload-time = "2026-01-04T02:42:41.825Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/e6/ad/3cc14f097111b4de0040c83a525973216457bbeeb63739ef1ed275c1c021/certifi-2026.1.4-py3-none-any.whl", hash = "sha256:9943707519e4add1115f44c2bc244f782c0249876bf51b6599fee1ffbedd685c", size = 152900, upload-time = "2026-01-04T02:42:40.15Z" },
 ]
 
 [[package]]
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+version = "3.4.4"
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 [[package]]
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diff --git a/examples/z-image-lora/Dockerfile b/examples/z-image-lora/Dockerfile
index 1203d3c..cd4c800 100644
--- a/examples/z-image-lora/Dockerfile
+++ b/examples/z-image-lora/Dockerfile
@@ -1,14 +1,13 @@
 # Tenant-supplied Dockerfile example.
 #
-# - Installs torch in a stable cacheable layer.
-# - Installs tenant deps from uv.lock without replacing torch.
+# - Installs torch + gen-worker in stable cacheable layers.
+# - Installs tenant deps from uv.lock without replacing torch/gen-worker.
 # - Bakes discovered functions into /app/.tensorhub/endpoint.lock at build time.
 #
 ARG PYTHON_VERSION=3.12
 FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS cozy_base
 
-ARG CUDA_VERSION=12.8
-ARG UV_TORCH_BACKEND=
+ARG UV_TORCH_BACKEND=cu126
 ARG TORCH_SPEC="~=2.10.0"
 
 WORKDIR /app
@@ -24,9 +23,10 @@ RUN --mount=type=cache,id=cozy-apt-cache,target=/var/cache/apt,sharing=locked \
     && apt-get clean
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
-    backend="${UV_TORCH_BACKEND:-cu$(printf '%s' "${CUDA_VERSION}" | tr -d '.')}" \
-    && uv pip install --system --break-system-packages --torch-backend "$backend" \
+    uv pip install --system --break-system-packages --torch-backend ${UV_TORCH_BACKEND} \
       "torch${TORCH_SPEC}"
+RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
+    uv pip install --system --break-system-packages gen-worker==0.3.0
 
 FROM cozy_base AS cozy_final
 
@@ -34,7 +34,7 @@ COPY pyproject.toml uv.lock /app/
 
 RUN --mount=type=cache,id=cozy-uv-cache,target=/var/cache/uv,sharing=locked \
     uv export --no-dev --no-hashes --no-sources --no-emit-project --no-emit-local \
-      --no-emit-package torch \
+      --no-emit-package torch --no-emit-package gen-worker \
       -o /tmp/requirements.all.txt \
     && grep -Ev '^(torch|triton|nvidia-|cuda-)' /tmp/requirements.all.txt > /tmp/requirements.txt \
     && uv pip install --system --break-system-packages --no-deps -r /tmp/requirements.txt
diff --git a/examples/z-image-lora/endpoint.toml b/examples/z-image-lora/endpoint.toml
index 08f5fcf..080fb51 100644
--- a/examples/z-image-lora/endpoint.toml
+++ b/examples/z-image-lora/endpoint.toml
@@ -7,7 +7,7 @@ main = "z_image_lora.main"
 z-image-turbo = { ref = "tongyi-mai/z-image-turbo", dtypes = ["bf16", "fp16"] }
 
 [host.requirements]
-cuda = "12.8"
+cuda = "12.6"
 
 [resources]
 vram_gb = 16
diff --git a/examples/z-image-lora/pyproject.toml b/examples/z-image-lora/pyproject.toml
index 77dc2f0..e13f00f 100644
--- a/examples/z-image-lora/pyproject.toml
+++ b/examples/z-image-lora/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "Image generation with dynamic LoRA loading (z-image pattern)"
 requires-python = ">=3.12"
 dependencies = [
-    "gen-worker[torch]",
+    "gen-worker[torch]==0.3.0",
     "diffusers>=0.36.0",
     "transformers",
     "accelerate",
diff --git a/examples/z-image-lora/src/z_image_lora/main.py b/examples/z-image-lora/src/z_image_lora/main.py
index b24d060..16bb47a 100644
--- a/examples/z-image-lora/src/z_image_lora/main.py
+++ b/examples/z-image-lora/src/z_image_lora/main.py
@@ -19,7 +19,7 @@
 from diffusers import DiffusionPipeline
 from PIL import Image
 
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 from gen_worker.types import Asset
 
@@ -67,7 +67,7 @@ class GenerateOutput(msgspec.Struct):
 
 @worker_function(_z_image_resources)
 def generate_with_loras(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         DiffusionPipeline,
         ModelRef(Src.FIXED, "z-image-turbo"),
diff --git a/examples/z-image-lora/uv.lock b/examples/z-image-lora/uv.lock
index cf8fe8f..aed4a1d 100644
--- a/examples/z-image-lora/uv.lock
+++ b/examples/z-image-lora/uv.lock
@@ -2088,7 +2088,7 @@ dev = [
 requires-dist = [
     { name = "accelerate" },
     { name = "diffusers", specifier = ">=0.36.0" },
-    { name = "gen-worker", extras = ["torch"] },
+    { name = "gen-worker", extras = ["torch"], specifier = "==0.3.0" },
     { name = "peft" },
     { name = "pillow" },
     { name = "transformers" },
diff --git a/pyproject.toml b/pyproject.toml
index a1e0f40..cb741d9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "gen-worker"
-version = "0.3.10"
+version = "0.3.7"
 description = "A library used to build custom functions in Cozy Creator's serverless function platform."
 readme = "README.md"
 license = "MIT"
diff --git a/src/gen_worker/__init__.py b/src/gen_worker/__init__.py
index 84f37bc..6fc73af 100644
--- a/src/gen_worker/__init__.py
+++ b/src/gen_worker/__init__.py
@@ -1,7 +1,7 @@
 # Make src/gen_worker a Python package
 from .decorators import ResourceRequirements, worker_function, worker_websocket
 from .injection import ModelRef, ModelRefSource
-from .worker import RequestContext, RealtimeSocket
+from .worker import ActionContext, RealtimeSocket
 from .errors import AuthError, RetryableError, FatalError
 from .types import Asset
 from .model_interface import ModelManager
@@ -38,7 +38,7 @@
     "ResourceRequirements",
     "ModelRef",
     "ModelRefSource",
-    "RequestContext",
+    "ActionContext",
     "RealtimeSocket",
     "AuthError",
     "RetryableError",
diff --git a/src/gen_worker/cozy_cas.py b/src/gen_worker/cozy_cas.py
index d871f1d..0cdfb64 100644
--- a/src/gen_worker/cozy_cas.py
+++ b/src/gen_worker/cozy_cas.py
@@ -307,16 +307,26 @@ def _safe_symlink_dir(target: Path, link: Path) -> None:
     max_value=30,  # cap backoff at 30s between retries
 )
 async def _download_one_file(url: str, dst: Path, expected_size: int, expected_blake3: str) -> None:
-    import fcntl
+    """Download a single file with HTTP Range resume, size + blake3 validation.
+
+    Fully async — no blocking calls that would stall the event loop.
+    Caller is responsible for ensuring only one coroutine downloads a given
+    dst at a time (dedup by digest in _ensure_blobs).
+    """
     import logging
     log = logging.getLogger("gen_worker.download")
 
-    log.info("download_start path=%s expected_size=%s expected_blake3=%s", dst.name, expected_size, (expected_blake3 or "")[:16])
-    print(f"DEBUG download_start path={dst.name} expected_size={expected_size} expected_blake3={(expected_blake3 or '')[:16]}")
+    def _human_size(n: int) -> str:
+        if n >= 1 << 30:
+            return f"{n / (1 << 30):.1f}GB"
+        if n >= 1 << 20:
+            return f"{n / (1 << 20):.1f}MB"
+        if n >= 1 << 10:
+            return f"{n / (1 << 10):.1f}KB"
+        return f"{n}B"
 
+    # Already downloaded and valid?
     if dst.exists():
-        log.info("dst_exists path=%s size=%s", dst, dst.stat().st_size)
-        print(f"DEBUG dst_exists path={dst} size={dst.stat().st_size}")
         try:
             if expected_size and dst.stat().st_size != expected_size:
                 raise ValueError("size mismatch")
@@ -324,142 +334,109 @@ async def _download_one_file(url: str, dst: Path, expected_size: int, expected_b
                 got = _blake3_file(dst)
                 if got.lower() != expected_blake3.lower():
                     raise ValueError("blake3 mismatch")
+            log.info("download_cached path=%s size=%s", dst.name, _human_size(dst.stat().st_size))
             return
         except Exception:
-            # Fall through to re-download.
-            pass
-
-    # Use sock_read instead of total timeout so actively-streaming large files
-    # are not killed.  total=None lets multi-GB downloads run as long as data
-    # keeps flowing; sock_read=120 catches genuine stalls.
-    timeout = aiohttp.ClientTimeout(total=None, sock_connect=float(os.getenv("WORKER_MODEL_DOWNLOAD_SOCK_CONNECT_TIMEOUT_S", "60")),
-                                                                    sock_read=float(os.getenv("WORKER_MODEL_DOWNLOAD_SOCK_READ_TIMEOUT_S", "180")))
+            pass  # re-download
+
+    timeout = aiohttp.ClientTimeout(
+        total=None,
+        sock_connect=float(os.getenv("WORKER_MODEL_DOWNLOAD_SOCK_CONNECT_TIMEOUT_S", "60")),
+        sock_read=float(os.getenv("WORKER_MODEL_DOWNLOAD_SOCK_READ_TIMEOUT_S", "180")),
+    )
     tmp = dst.with_suffix(dst.suffix + ".part")
-    lock_path = dst.with_suffix(dst.suffix + ".lock")
 
-    # File-level exclusive lock: prevents concurrent writes to the same .part
-    # file even from different async tasks or downloader instances.
-    lock_path.parent.mkdir(parents=True, exist_ok=True)
-    lock_fd = open(lock_path, "w")
-    try:
-        fcntl.flock(lock_fd.fileno(), fcntl.LOCK_EX)
-        print(f"DEBUG file_lock_acquired path={dst.name}")
-
-        # Re-check dst after acquiring the lock — another holder might have
-        # already completed the download while we waited.
-        if dst.exists():
-            try:
-                if expected_size and dst.stat().st_size != expected_size:
-                    raise ValueError("size mismatch after lock")
-                if expected_blake3:
-                    got = _blake3_file(dst)
-                    if got.lower() != expected_blake3.lower():
-                        raise ValueError("blake3 mismatch after lock")
-                print(f"DEBUG file_lock_dst_completed path={dst.name} (another writer finished)")
-                return
-            except Exception:
-                pass
-
-        # If we have a partial file, try to resume via HTTP Range.
-        offset = 0
-        if tmp.exists():
-            try:
-                offset = tmp.stat().st_size
-            except OSError:
-                # Another coroutine may have renamed tmp→dst between the exists() check and stat().
-                offset = 0
-            if offset:
-                log.info("resume_attempt path=%s offset=%s expected_size=%s", dst.name, offset, expected_size)
-                print(f"DEBUG resume_attempt path={dst.name} offset={offset} expected_size={expected_size}")
-            if expected_size and offset > expected_size:
-                tmp.unlink(missing_ok=True)
-                offset = 0
-
-        # If the partial file is already complete, validate + finalize.
-        if offset and expected_size and offset == expected_size:
-            got = _blake3_file(tmp)
-            if expected_blake3 and got.lower() != expected_blake3.lower():
-                tmp.unlink(missing_ok=True)
-            else:
-                tmp.rename(dst)
-                return
-
-        headers: Dict[str, str] = {}
-        mode = "wb"
-        if offset and expected_size:
-            headers["Range"] = f"bytes={offset}-"
-            mode = "ab"
-            print(f"DEBUG range_header path={dst.name} Range=bytes={offset}- mode={mode}")
-
-        async def _stream_to_file(resp: aiohttp.ClientResponse, *, mode: str, start: int) -> None:
-            nonlocal expected_size
-            size = start
-            with open(tmp, mode) as f:
-                async for chunk in resp.content.iter_chunked(1 << 20):
-                    if not chunk:
-                        continue
-                    f.write(chunk)
-                    size += len(chunk)
-                    if expected_size and size > expected_size:
-                        raise ValueError("download exceeded expected size")
-
-        async with aiohttp.ClientSession(timeout=timeout) as session:
-            async with session.get(url, headers=headers) as resp:
-                content_range = str(resp.headers.get("Content-Range") or "").strip()
-                print(f"DEBUG http_response path={dst.name} status={resp.status} content_range={content_range!r} content_length={resp.headers.get('Content-Length', 'unknown')} offset={offset}")
-                # If the server ignored our Range request, restart from scratch to avoid
-                # duplicating bytes by appending a full response.
-                # Some gateways can return 206 with an unexpected range start.
-                # Treat that the same as a 200-on-resume and restart from byte 0.
-                if offset and (
-                    resp.status == 200
-                    or (
-                        resp.status == 206
-                        and not content_range.startswith(f"bytes {offset}-")
-                    )
-                ):
-                    print(f"DEBUG range_ignored path={dst.name} status={resp.status} content_range={content_range!r} restarting_from_zero=True")
-                    resp.release()
-                    async with session.get(url) as resp2:
-                        resp2.raise_for_status()
-                        print(f"DEBUG range_restart path={dst.name} status={resp2.status} content_length={resp2.headers.get('Content-Length', 'unknown')}")
-                        await _stream_to_file(resp2, mode="wb", start=0)
-                else:
-                    resp.raise_for_status()
-                    await _stream_to_file(resp, mode=mode, start=offset)
-
-        # Validate final file.
-        actual_size = tmp.stat().st_size
-        log.info("download_complete path=%s actual_size=%s expected_size=%s", dst.name, actual_size, expected_size)
-        print(f"DEBUG download_complete path={dst.name} actual_size={actual_size} expected_size={expected_size}")
-        if expected_size and actual_size != expected_size:
-            log.error("size_mismatch path=%s expected=%s got=%s url=%s", dst.name, expected_size, actual_size, url[:80])
-            print(f"DEBUG size_mismatch path={dst.name} expected={expected_size} got={actual_size} url={url[:80]}")
-            tmp.unlink(missing_ok=True)
-            raise ValueError(f"size mismatch (expected {expected_size}, got {actual_size})")
-        if expected_blake3:
-            got = _blake3_file(tmp)
-            log.info("blake3_check path=%s expected=%s got=%s", dst.name, (expected_blake3 or "")[:16], got[:16])
-            print(f"DEBUG blake3_check path={dst.name} expected={(expected_blake3 or '')[:16]} got={got[:16]}")
-            if got.lower() != expected_blake3.lower():
-                log.error("blake3_mismatch path=%s", dst.name)
-                print(f"DEBUG blake3_mismatch path={dst.name}")
-                tmp.unlink(missing_ok=True)
-                raise ValueError("blake3 mismatch")
-        # A concurrent coroutine may have already renamed tmp→dst (won the race).
-        # Use an atomic replace so we don't fail if dst now exists.
+    # Resume from partial download if available.
+    offset = 0
+    if tmp.exists():
         try:
-            tmp.replace(dst)
+            offset = tmp.stat().st_size
         except OSError:
-            # dst was created by another coroutine; .part is stale, just remove it.
+            offset = 0
+        if expected_size and offset > expected_size:
             tmp.unlink(missing_ok=True)
-    finally:
-        fcntl.flock(lock_fd.fileno(), fcntl.LOCK_UN)
-        lock_fd.close()
-        try:
-            lock_path.unlink(missing_ok=True)
-        except OSError:
-            pass
+            offset = 0
+
+    # Partial file already complete? Validate and finalize.
+    if offset and expected_size and offset == expected_size:
+        got = _blake3_file(tmp)
+        if expected_blake3 and got.lower() != expected_blake3.lower():
+            log.warning("partial_corrupt path=%s (blake3 mismatch, restarting)", dst.name)
+            tmp.unlink(missing_ok=True)
+            offset = 0
+        else:
+            tmp.rename(dst)
+            log.info("download_resumed_complete path=%s size=%s", dst.name, _human_size(expected_size))
+            return
+
+    headers: Dict[str, str] = {}
+    mode = "wb"
+    if offset and expected_size:
+        headers["Range"] = f"bytes={offset}-"
+        mode = "ab"
+        log.info("download_resume path=%s offset=%s/%s (%s/%s)",
+                 dst.name, offset, expected_size,
+                 _human_size(offset), _human_size(expected_size))
+    else:
+        log.info("download_start path=%s size=%s blake3=%s",
+                 dst.name, _human_size(expected_size) if expected_size else "unknown",
+                 (expected_blake3 or "n/a")[:16])
+
+    async def _stream(resp: aiohttp.ClientResponse, *, write_mode: str, start: int) -> None:
+        downloaded = start
+        last_log = start
+        log_every = max(expected_size // 10, 50 << 20) if expected_size else (100 << 20)
+        with open(tmp, write_mode) as f:
+            async for chunk in resp.content.iter_chunked(1 << 20):
+                if not chunk:
+                    continue
+                f.write(chunk)
+                downloaded += len(chunk)
+                if expected_size and downloaded > expected_size:
+                    raise ValueError(f"download exceeded expected size ({downloaded} > {expected_size})")
+                if downloaded - last_log >= log_every:
+                    pct = f" ({100 * downloaded // expected_size}%)" if expected_size else ""
+                    log.info("download_progress path=%s downloaded=%s%s",
+                             dst.name, _human_size(downloaded), pct)
+                    last_log = downloaded
+
+    async with aiohttp.ClientSession(timeout=timeout) as session:
+        async with session.get(url, headers=headers) as resp:
+            content_range = str(resp.headers.get("Content-Range") or "").strip()
+
+            # Server ignored Range or returned unexpected range start?
+            # Restart from byte 0 to avoid corrupted appends.
+            if offset and (
+                resp.status == 200
+                or (resp.status == 206 and not content_range.startswith(f"bytes {offset}-"))
+            ):
+                log.info("download_range_ignored path=%s status=%s (restarting from 0)", dst.name, resp.status)
+                resp.release()
+                async with session.get(url) as resp2:
+                    resp2.raise_for_status()
+                    await _stream(resp2, write_mode="wb", start=0)
+            else:
+                resp.raise_for_status()
+                await _stream(resp, write_mode=mode, start=offset)
+
+    # Validate.
+    actual_size = tmp.stat().st_size
+    if expected_size and actual_size != expected_size:
+        log.error("download_size_mismatch path=%s expected=%s got=%s", dst.name, expected_size, actual_size)
+        tmp.unlink(missing_ok=True)
+        raise ValueError(f"size mismatch (expected {expected_size}, got {actual_size})")
+
+    if expected_blake3:
+        got = _blake3_file(tmp)
+        if got.lower() != expected_blake3.lower():
+            log.error("download_blake3_mismatch path=%s expected=%s got=%s",
+                      dst.name, expected_blake3[:16], got[:16])
+            tmp.unlink(missing_ok=True)
+            raise ValueError("blake3 mismatch")
+
+    # Atomic finalize.
+    tmp.replace(dst)
+    log.info("download_done path=%s size=%s", dst.name, _human_size(actual_size))
 
 
 def _blake3_file(path: Path, chunk_size: int = 1 << 20) -> str:
diff --git a/src/gen_worker/cozy_pipeline_spec.py b/src/gen_worker/cozy_pipeline_spec.py
index c8c80f0..21fa5a7 100644
--- a/src/gen_worker/cozy_pipeline_spec.py
+++ b/src/gen_worker/cozy_pipeline_spec.py
@@ -1,21 +1,16 @@
 from __future__ import annotations
 
 import json
-import logging
 import os
-import tomllib
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, Dict, Optional, Tuple
 
 import yaml
 
-logger = logging.getLogger(__name__)
 
 COZY_PIPELINE_LOCK_FILENAME = "cozy.pipeline.lock.yaml"
 COZY_PIPELINE_FILENAME = "cozy.pipeline.yaml"
-PIPELINE_LOCK_TOML_FILENAME = "pipeline.lock"
-PIPELINE_TOML_FILENAME = "pipeline.toml"
 DIFFUSERS_MODEL_INDEX_FILENAME = "model_index.json"
 
 
@@ -38,13 +33,6 @@ def custom_pipeline_path(self) -> Optional[str]:
         s = str(v).strip()
         return s or None
 
-    @property
-    def variant(self) -> Optional[str]:
-        """Diffusers variant (e.g. 'fp16', 'fp8') from the pipeline spec."""
-        pipe = self.raw.get("pipe") or {}
-        v = str(pipe.get("variant") or "").strip()
-        return v or None
-
 
 def _safe_child_path(root: Path, rel: str) -> Path:
     # Ensure rel doesn't escape root (best-effort).
@@ -62,41 +50,24 @@ def load_cozy_pipeline_spec(model_root: Path) -> Optional[CozyPipelineSpec]:
     This is a worker-side helper used during pipeline loading to implement:
     - prefer `cozy.pipeline.lock.yaml` when present
     - fall back to `cozy.pipeline.yaml` otherwise
-    - fall back to `pipeline.lock` / `pipeline.toml` (TOML) if no YAML found
     """
     root = Path(model_root)
     lock_path = root / COZY_PIPELINE_LOCK_FILENAME
     spec_path = lock_path if lock_path.exists() else (root / COZY_PIPELINE_FILENAME)
-    if spec_path.exists():
-        raw = yaml.safe_load(spec_path.read_text(encoding="utf-8"))
-        if not isinstance(raw, dict):
-            raise ValueError("invalid cozy pipeline spec (expected mapping)")
-        api = str(raw.get("apiVersion") or "").strip()
-        kind = str(raw.get("kind") or "").strip()
-        if api and api != "v1":
-            raise ValueError(f"unsupported cozy pipeline apiVersion: {api!r}")
-        if kind and kind != "DiffusersPipeline":
-            raise ValueError(f"unsupported cozy pipeline kind: {kind!r}")
-        logger.info("DEBUG loaded cozy pipeline spec from %s", spec_path.name)
-        return CozyPipelineSpec(source_path=spec_path, raw=raw)
-
-    # Fallback: read pipeline.lock / pipeline.toml (TOML format, stored by tensorhub ingest).
-    toml_lock = root / PIPELINE_LOCK_TOML_FILENAME
-    toml_spec = toml_lock if toml_lock.exists() else (root / PIPELINE_TOML_FILENAME)
-    if toml_spec.exists():
-        raw = tomllib.loads(toml_spec.read_text(encoding="utf-8"))
-        if not isinstance(raw, dict):
-            raise ValueError("invalid pipeline toml (expected mapping)")
-        api = str(raw.get("apiVersion") or "").strip()
-        kind = str(raw.get("kind") or "").strip()
-        if api and api != "v1":
-            raise ValueError(f"unsupported pipeline toml apiVersion: {api!r}")
-        if kind and kind != "DiffusersPipeline":
-            raise ValueError(f"unsupported pipeline toml kind: {kind!r}")
-        logger.info("DEBUG loaded cozy pipeline spec from %s (toml fallback)", toml_spec.name)
-        return CozyPipelineSpec(source_path=toml_spec, raw=raw)
-
-    return None
+    if not spec_path.exists():
+        return None
+
+    raw = yaml.safe_load(spec_path.read_text(encoding="utf-8"))
+    if not isinstance(raw, dict):
+        raise ValueError("invalid cozy pipeline spec (expected mapping)")
+    api = str(raw.get("apiVersion") or "").strip()
+    kind = str(raw.get("kind") or "").strip()
+    if api and api != "v1":
+        raise ValueError(f"unsupported cozy pipeline apiVersion: {api!r}")
+    if kind and kind != "DiffusersPipeline":
+        raise ValueError(f"unsupported cozy pipeline kind: {kind!r}")
+
+    return CozyPipelineSpec(source_path=spec_path, raw=raw)
 
 
 def cozy_custom_pipeline_arg(model_root: Path, spec: CozyPipelineSpec) -> Optional[str]:
diff --git a/src/gen_worker/cozy_snapshot_v2_downloader.py b/src/gen_worker/cozy_snapshot_v2_downloader.py
index f15020c..645def1 100644
--- a/src/gen_worker/cozy_snapshot_v2_downloader.py
+++ b/src/gen_worker/cozy_snapshot_v2_downloader.py
@@ -2,58 +2,47 @@
 
 import asyncio
 import json
+import logging
 import os
 import re
 import shutil
 import threading
 from pathlib import Path
-from typing import Any, Coroutine, Dict, List, Optional
+from typing import Any, Coroutine, Dict, List, Optional, Set
 
-from .cozy_cas import _download_one_file as _download_one_file  # reuse verified Range-resume downloader
+from .cozy_cas import _download_one_file as _download_one_file
 from .cozy_cas import _norm_rel_path
 from .tensorhub_policy import default_resolve_preferences, detect_worker_capabilities
 from .tensorhub_v2 import CozyHubV2Client, CozyHubResolveArtifactResult, CozyHubSnapshotFile
 from .model_refs import CozyRef
 
-# Module-global blob download locks shared across ALL CozySnapshotV2Downloader
-# instances.  Without this, concurrent callers (startup prefetch, task handler,
-# LoadModelCommand) each create their own downloader instance with separate
-# _blob_locks dicts, allowing parallel writes to the same .part file and
-# causing file corruption (interleaved appends → oversized / invalid blobs).
+_log = logging.getLogger("gen_worker.download")
 
-# threading.Lock (not asyncio.Lock) is used so that the locks work correctly
-# even when callers run in different event loops (e.g. startup prefetch via
-# asyncio.run() in a thread vs. LoadModelCommand on the main loop).
-_GLOBAL_BLOB_LOCKS_LOCK = threading.Lock()
-_GLOBAL_BLOB_LOCKS: Dict[str, threading.Lock] = {}
 
+# ---------------------------------------------------------------------------
+# Snapshot build coordination (threading-based, works across event loops)
+# ---------------------------------------------------------------------------
 
 class _SnapshotEntry:
-    """Coordinates concurrent snapshot builds: one builder, zero-or-more waiters."""
+    """One builder, zero-or-more waiters."""
 
     def __init__(self) -> None:
-        self.event = threading.Event()  # set when snap_dir is ready (or build failed)
+        self.event = threading.Event()
         self.exception: Optional[BaseException] = None
 
 
-_GLOBAL_SNAPSHOT_LOCKS_LOCK = threading.Lock()
-_GLOBAL_SNAPSHOT_LOCKS: Dict[str, _SnapshotEntry] = {}
+_SNAP_LOCK = threading.Lock()
+_SNAP_ENTRIES: Dict[str, _SnapshotEntry] = {}
 
 
-def _get_blob_lock(digest: str) -> threading.Lock:
-    """Return (or create) a per-digest threading.Lock from the module-global map."""
-    with _GLOBAL_BLOB_LOCKS_LOCK:
-        lock = _GLOBAL_BLOB_LOCKS.get(digest)
-        if lock is None:
-            lock = threading.Lock()
-            _GLOBAL_BLOB_LOCKS[digest] = lock
-        return lock
-
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
 
 def _blob_path(blobs_root: Path, digest: str) -> Path:
     digest = (digest or "").strip().lower()
     if len(digest) < 4:
-        raise ValueError("invalid blake3 digest")
+        raise ValueError(f"invalid blake3 digest: {digest!r}")
     return blobs_root / "blake3" / digest[:2] / digest[2:4] / digest
 
 
@@ -61,25 +50,23 @@ def _blob_path(blobs_root: Path, digest: str) -> Path:
 
 
 def _strip_blake3_prefix(digest: str) -> str:
-    """Strip the 'blake3:' scheme prefix if present, returning the bare hex."""
+    """'blake3:abcd...' -> 'abcd...'"""
     d = (digest or "").strip().lower()
     if d.startswith("blake3:"):
-        d = d[len("blake3:"):]
+        return d[7:]
     return d
 
 
 def _is_part_file(path: str) -> bool:
-    """Return True if the path is a chunked part file (e.g. foo.part0001)."""
     return bool(_PART_FILE_RE.search(path))
 
 
 def _is_parts_manifest(path: str) -> bool:
-    """Return True if the path is a chunked-blob manifes (e.g. foo.parts.json)."""
     return path.endswith(".parts.json")
 
 
-def _resolve_field(obj: Any, *keys: str) -> Any:
-    """Get a field from either a dict or an attribute-bearing object, trying keys in order."""
+def _field(obj: Any, *keys: str) -> Any:
+    """Read a field from dict or object, trying keys in order."""
     for k in keys:
         if isinstance(obj, dict):
             v = obj.get(k)
@@ -106,38 +93,33 @@ def _try_hardlink_or_copy(src: Path, dst: Path) -> None:
     shutil.copy2(src, dst)
 
 
-def _coerce_resolved_model(ref: CozyRef, resolved: Any) -> CozyHubResolveArtifactResult:
-    """Coerce an orchestrator-provided resolved model object into CozyHubResolveArtifactResult.
-
-    Handles two wire shapes:
-      - Legacy:  .snapshot_digest (bare hex) + .files[]
-      - v2:      .snapshot_digest ('blake3:...' prefixed) + .entries[]  (new chunked-blob format)
+# ---------------------------------------------------------------------------
+# Coerce orchestrator wire format -> internal type
+# ---------------------------------------------------------------------------
 
-    Both protobuf attribute access and plain-dict access are supported.
-    """
-    snapshot_digest = str(_resolve_field(resolved, "snapshot_digest", "snapshotDigest") or "").strip()
+def _coerce_resolved_model(ref: CozyRef, resolved: Any) -> CozyHubResolveArtifactResult:
+    """Handle both legacy (.files[]) and v2 (.entries[], blake3:-prefixed digests)."""
+    snapshot_digest = str(_field(resolved, "snapshot_digest", "snapshotDigest") or "").strip()
     if not snapshot_digest:
         raise ValueError("resolved model missing snapshot_digest")
-    # Strip scheme prefix so the digest is a bare hex string suitable for path use.
     snapshot_digest = _strip_blake3_prefix(snapshot_digest) or snapshot_digest
 
-    # New format uses "entries"; legacy format uses "files".
-    files_raw = list(_resolve_field(resolved, "entries", "files") or [])
+    # v2 uses "entries", legacy uses "files"
+    files_raw = list(_field(resolved, "entries", "files") or [])
     files: List[CozyHubSnapshotFile] = []
     for ent in files_raw:
-        path = str(_resolve_field(ent, "path") or "").strip()
+        path = str(_field(ent, "path") or "").strip()
         if not path:
             continue
-        # Prefer bare "blake3" field; fall back to "digest" which may carry the prefix.
-        blake3_hex = str(_resolve_field(ent, "blake3", "BLAKE3") or "").strip().lower()
+        blake3_hex = str(_field(ent, "blake3", "BLAKE3") or "").strip().lower()
         if not blake3_hex:
-            digest_raw = str(_resolve_field(ent, "digest") or "").strip().lower()
-            blake3_hex = _strip_blake3_prefix(digest_raw)
-        url = str(_resolve_field(ent, "url") or "").strip() or None
-        size_bytes = int(_resolve_field(ent, "size_bytes") or 0)
+            blake3_hex = _strip_blake3_prefix(str(_field(ent, "digest") or ""))
+        url = str(_field(ent, "url") or "").strip() or None
+        size_bytes = int(_field(ent, "size_bytes") or 0)
         if not blake3_hex or not url:
             raise ValueError(f"resolved model file missing blake3/url: {path}")
         files.append(CozyHubSnapshotFile(path=path, size_bytes=size_bytes, blake3=blake3_hex, url=url))
+
     if not files:
         raise ValueError("resolved model has empty files list")
 
@@ -149,21 +131,16 @@ def _coerce_resolved_model(ref: CozyRef, resolved: Any) -> CozyHubResolveArtifac
     )
 
 
-class CozySnapshotV2Downloader:
-    """Cozy Hub v2 downloader.
-
-    Normal mode:
-      - resolve owner/repo:tag (or @digest) via resolve API
-      - download all referenced blobs to a local blob store
-      - materialize a snapshot checkout by hardlinking blobs into the snapshot tree
+# ---------------------------------------------------------------------------
+# Main downloader
+# ---------------------------------------------------------------------------
 
-    Issue #92 mode:
-      - if `resolved` is provided, skip Cozy Hub API calls and use the provided
-        presigned URLs directly.
+class CozySnapshotV2Downloader:
+    """Downloads blobs into a CAS layout, reassembles chunked files, materializes snapshot.
 
-    On-disk layout under <base_dir>:
-      - blobs/blake3/<aa>/<bb>/<digest>
-      - snapshots/<snapshot_digest>/...
+    Layout under <base_dir>:
+      blobs/blake3/<aa>/<bb>/<digest>
+      snapshots/<snapshot_digest>/...
     """
 
     def __init__(self, client: Optional[CozyHubV2Client]) -> None:
@@ -188,193 +165,194 @@ async def ensure_snapshot(
 
         snap_dir = snaps_root / res.snapshot_digest
         if snap_dir.exists():
+            _log.info("snapshot_cached digest=%s", res.snapshot_digest[:16])
             return snap_dir
 
-        # Claim building responsibility or wait for another caller already building
-        # this snapshot.  threading primitives (not asyncio.Lock) are used so that
-        # this works correctly across different event loops (startup prefetch thread
-        # vs. LoadModelCommand on the main loop).
+        # Coordinate concurrent builders via threading (works across event loops).
         loop = asyncio.get_running_loop()
-        with _GLOBAL_SNAPSHOT_LOCKS_LOCK:
-            if snap_dir.exists():  # double-check under the guard
+        with _SNAP_LOCK:
+            if snap_dir.exists():
                 return snap_dir
-            _snap_entry = _GLOBAL_SNAPSHOT_LOCKS.get(res.snapshot_digest)
-            if _snap_entry is None:
-                _snap_entry = _SnapshotEntry()
-                _GLOBAL_SNAPSHOT_LOCKS[res.snapshot_digest] = _snap_entry
-                _is_builder = True
+            entry = _SNAP_ENTRIES.get(res.snapshot_digest)
+            if entry is None:
+                entry = _SnapshotEntry()
+                _SNAP_ENTRIES[res.snapshot_digest] = entry
+                is_builder = True
             else:
-                _is_builder = False
-
-        if not _is_builder:
-            # Another caller is already building this snapshot; wait for it.
-            await loop.run_in_executor(None, _snap_entry.event.wait)
-            if _snap_entry.exception is not None:
-                raise RuntimeError(
-                    f"concurrent snapshot build failed for {res.snapshot_digest}"
-                ) from _snap_entry.exception
+                is_builder = False
+
+        if not is_builder:
+            _log.info("snapshot_waiting digest=%s (another builder active)", res.snapshot_digest[:16])
+            await loop.run_in_executor(None, entry.event.wait)
+            if entry.exception is not None:
+                raise RuntimeError(f"concurrent snapshot build failed") from entry.exception
             return snap_dir
 
         try:
-            if snap_dir.exists():
-                return snap_dir
-
+            _log.info("snapshot_build_start digest=%s files=%d", res.snapshot_digest[:16], len(res.files))
             await self._ensure_blobs(blobs_root, res.files)
 
             tmp = snaps_root / f"{res.snapshot_digest}.building"
+            if tmp.exists():
+                shutil.rmtree(tmp)
             tmp.mkdir(parents=True, exist_ok=True)
 
-            # Reassemble any chunked files (produced by chunkedblob on ingest).
-            # A ".parts.json" entry describes how to reassemble N part blobs into
-            # the original file.  Part blobs and the manifest itself are all already
-            # in the blob store at this point.
-            parts_manifest_entries = [f for f in res.files if _is_parts_manifest(f.path)]
-            part_file_paths = {f.path for f in res.files if _is_part_file(f.path)}
-
-            import logging as _logging
-            _log = _logging.getLogger("gen_worker.download")
+            self._reassemble_chunked(blobs_root, tmp, res.files)
+            self._materialize_regular(blobs_root, tmp, res.files)
 
-            for pm_entry in parts_manifest_entries:
-                _log.info("reassemble_start manifest=%s", pm_entry.path)
-                print(f"DEBUG reassemble_start manifest={pm_entry.path}")
-                parts_json_blob = _blob_path(blobs_root, pm_entry.blake3)
-                try:
-                    parts_manifest = json.loads(parts_json_blob.read_bytes())
-                except Exception as exc:
-                    raise ValueError(f"failed to parse parts manifest {pm_entry.path}: {exc}") from exc
-
-                original_path = str(parts_manifest.get("original_path") or "").strip()
-                if not original_path:
-                    raise ValueError(f"parts manifest {pm_entry.path} missing original_path")
-                parts = parts_manifest.get("parts") or []
-                if not parts:
-                    raise ValueError(f"parts manifest {pm_entry.path} has no parts")
-
-                rel = _norm_rel_path(original_path)
-                dst = tmp / rel
-                dst.parent.mkdir(parents=True, exist_ok=True)
-                if dst.exists():
-                    dst.unlink()
-
-                with open(dst, "wb") as out_f:
-                    for i, part in enumerate(parts):
-                        part_digest = _strip_blake3_prefix(
-                            str(part.get("digest") or "").strip().lower()
-                        )
-                        if not part_digest:
-                            raise ValueError(f"part entry in {pm_entry.path} missing digest")
-                        part_blob = _blob_path(blobs_root, part_digest)
-                        _log.info("  concat_part index=%d digest=%s exists=%s size=%s",
-                                  i, part_digest[:16], part_blob.exists(),
-                                  part_blob.stat().st_size if part_blob.exists() else -1)
-                        print(f"DEBUG   concat_part index={i} digest={part_digest[:16]} exists={part_blob.exists()} size={part_blob.stat().st_size if part_blob.exists() else -1}")
-                        with open(part_blob, "rb") as in_f:
-                            shutil.copyfileobj(in_f, out_f)
-
-            # Materialize regular files; skip part files and parts manifests since they
-            # have already been consumed above during reassembly.
-            for f in res.files:
-                if _is_parts_manifest(f.path) or f.path in part_file_paths:
-                    continue
-                rel = _norm_rel_path(f.path)
-                dst = tmp / rel
-                dst.parent.mkdir(parents=True, exist_ok=True)
-                src = _blob_path(blobs_root, f.blake3)
-                _try_hardlink_or_copy(src, dst)
-
-            # Another concurrent caller (e.g. a different downloader instance) may have
-            # already materialized and renamed the snapshot while we were assembling ours.
-            # In that case, discard our tmp dir and return the existing snapshot.
+            # Atomic rename; handle race with concurrent builder.
             if snap_dir.exists():
                 shutil.rmtree(tmp, ignore_errors=True)
-                return snap_dir
-            try:
-                tmp.rename(snap_dir)
-            except OSError:
-                # Lost the race — snap_dir was created between the exists() check and rename().
-                shutil.rmtree(tmp, ignore_errors=True)
-                if not snap_dir.exists():
-                    raise
+            else:
+                try:
+                    tmp.rename(snap_dir)
+                except OSError:
+                    shutil.rmtree(tmp, ignore_errors=True)
+                    if not snap_dir.exists():
+                        raise
+
+            _log.info("snapshot_build_done digest=%s", res.snapshot_digest[:16])
             return snap_dir
-        except BaseException as _exc:
-            _snap_entry.exception = _exc
+        except BaseException as exc:
+            entry.exception = exc
             raise
         finally:
-            _snap_entry.event.set()
+            entry.event.set()
 
-    async def _resolve(self, ref: CozyRef) -> CozyHubResolveArtifactResult:
-        if self._client is None:
-            raise RuntimeError("cozy hub api resolve is disabled")
-
-        prefs = default_resolve_preferences()
-        caps = detect_worker_capabilities()
-        return await self._client.resolve_artifact(
-            owner=ref.owner,
-            repo=ref.repo,
-            tag=ref.tag,
-            digest=ref.digest,
-            include_urls=True,
-            preferences=prefs,
-            capabilities=caps.to_dict(),
-        )
+    # ------------------------------------------------------------------
+    # Blob download (deduplicated, parallel)
+    # ------------------------------------------------------------------
 
     async def _ensure_blobs(self, blobs_root: Path, files: List[CozyHubSnapshotFile]) -> None:
-        import logging
-        log = logging.getLogger("gen_worker.download")
-
-        log.info("ensure_blobs total_files=%d", len(files))
-        print(f"DEBUG ensure_blobs total_files={len(files)}")
-        for f in files:
-            log.info("  entry path=%s size=%s digest=%s url_present=%s", f.path, f.size_bytes, (f.blake3 or "")[:16], bool(f.url))
-            print(f"DEBUG   entry path={f.path} size={f.size_bytes} digest={(f.blake3 or '')[:16]} url_present={bool(f.url)}")
-
-        all_blobs: List[tuple[CozyHubSnapshotFile, str, Path]] = []
+        # Deduplicate by digest — same blob referenced by multiple paths (e.g.
+        # fp16 and normal variants sharing the same part) is downloaded once.
+        seen: Set[str] = set()
+        unique: List[CozyHubSnapshotFile] = []
         for f in files:
             digest = (f.blake3 or "").strip().lower()
             if not digest:
                 raise ValueError(f"missing blake3 for {f.path}")
             if not f.url:
                 raise ValueError(f"missing url for {f.path}")
-            dst = _blob_path(blobs_root, digest)
-            dst.parent.mkdir(parents=True, exist_ok=True)
-            all_blobs.append((f, digest, dst))
+            if digest not in seen:
+                seen.add(digest)
+                unique.append(f)
+
+        _log.info("ensure_blobs total_entries=%d unique_blobs=%d", len(files), len(unique))
+
+        # Sort largest first for better overlap, then download in parallel.
+        unique.sort(key=lambda f: int(f.size_bytes or 0), reverse=True)
 
-        # Parallelize shard/blob downloads to reduce first-load latency for
-        # multi-file transformer checkpoints.
         max_conc = max(1, int(os.getenv("WORKER_MODEL_DOWNLOAD_CONCURRENCY", "4") or "4"))
         sem = asyncio.Semaphore(max_conc)
 
-        async def _ensure_one(f: CozyHubSnapshotFile, digest: str, dst: Path) -> None:
-            # Acquire the GLOBAL per-digest threading.Lock (loop-independent) so that
-            # concurrent callers across different downloader instances (startup prefetch,
-            # task request, LoadModelCommand) don't write to the same .part file in
-            # parallel.  run_in_executor is used so that waiting for the lock does not
-            # block the event loop.
-            _lock = _get_blob_lock(digest)
-            _lloop = asyncio.get_running_loop()
-            await _lloop.run_in_executor(None, _lock.acquire)
-            try:
+        async def _dl(f: CozyHubSnapshotFile) -> None:
+            digest = f.blake3.strip().lower()
+            dst = _blob_path(blobs_root, digest)
+            dst.parent.mkdir(parents=True, exist_ok=True)
+            if dst.exists():
+                _log.info("blob_cached path=%s digest=%s", f.path, digest[:16])
+                return
+            async with sem:
                 if dst.exists():
                     return
-                async with sem:
-                    if dst.exists():
-                        return
-                    assert f.url is not None
-                    await _download_one_file(
-                        f.url,
-                        dst,
-                        expected_size=int(f.size_bytes or 0),
-                        expected_blake3=digest,
-                    )
-            finally:
-                _lock.release()
+                _log.info("blob_download_start path=%s size=%s digest=%s",
+                          f.path, f.size_bytes, digest[:16])
+                assert f.url is not None  # validated above in _ensure_blobs loop
+                await _download_one_file(
+                    f.url,
+                    dst,
+                    expected_size=int(f.size_bytes or 0),
+                    expected_blake3=digest,
+                )
+                _log.info("blob_download_done path=%s digest=%s", f.path, digest[:16])
+
+        await asyncio.gather(*(_dl(f) for f in unique))
+
+    # ------------------------------------------------------------------
+    # Chunked file reassembly
+    # ------------------------------------------------------------------
+
+    def _reassemble_chunked(
+        self, blobs_root: Path, tmp: Path, files: List[CozyHubSnapshotFile]
+    ) -> None:
+        """Read .parts.json manifests and concatenate part blobs into original files."""
+        for f in files:
+            if not _is_parts_manifest(f.path):
+                continue
 
-        # Start larger blobs first for better overlap.
-        all_blobs.sort(key=lambda row: int(row[0].size_bytes or 0), reverse=True)
-        await asyncio.gather(*(_ensure_one(f, digest, dst) for f, digest, dst in all_blobs))
+            _log.info("reassemble_start manifest=%s", f.path)
+            manifest_blob = _blob_path(blobs_root, f.blake3)
+            manifest = json.loads(manifest_blob.read_bytes())
 
+            original_path = str(manifest.get("original_path") or "").strip()
+            if not original_path:
+                raise ValueError(f"parts manifest {f.path} missing original_path")
+            parts = manifest.get("parts") or []
+            if not parts:
+                raise ValueError(f"parts manifest {f.path} has no parts")
+
+            dst = tmp / _norm_rel_path(original_path)
+            dst.parent.mkdir(parents=True, exist_ok=True)
+            if dst.exists():
+                dst.unlink()
+
+            total_written = 0
+            with open(dst, "wb") as out_f:
+                for i, part in enumerate(parts):
+                    part_digest = _strip_blake3_prefix(str(part.get("digest") or ""))
+                    if not part_digest:
+                        raise ValueError(f"part[{i}] in {f.path} missing digest")
+                    part_blob = _blob_path(blobs_root, part_digest)
+                    part_size = part_blob.stat().st_size
+                    _log.info("  concat part=%d/%d digest=%s size=%s",
+                              i + 1, len(parts), part_digest[:16], part_size)
+                    with open(part_blob, "rb") as in_f:
+                        shutil.copyfileobj(in_f, out_f)
+                    total_written += part_size
+
+            _log.info("reassemble_done file=%s total_size=%s", original_path, total_written)
+
+    # ------------------------------------------------------------------
+    # Regular (non-chunked) file materialization
+    # ------------------------------------------------------------------
+
+    def _materialize_regular(
+        self, blobs_root: Path, tmp: Path, files: List[CozyHubSnapshotFile]
+    ) -> None:
+        """Hardlink/copy non-chunked blobs into the snapshot tree."""
+        part_paths = {f.path for f in files if _is_part_file(f.path)}
+        for f in files:
+            if _is_parts_manifest(f.path) or f.path in part_paths:
+                continue
+            dst = tmp / _norm_rel_path(f.path)
+            dst.parent.mkdir(parents=True, exist_ok=True)
+            src = _blob_path(blobs_root, f.blake3)
+            _try_hardlink_or_copy(src, dst)
+
+    # ------------------------------------------------------------------
+    # Hub resolution
+    # ------------------------------------------------------------------
+
+    async def _resolve(self, ref: CozyRef) -> CozyHubResolveArtifactResult:
+        if self._client is None:
+            raise RuntimeError("cozy hub api resolve is disabled")
+        prefs = default_resolve_preferences()
+        caps = detect_worker_capabilities()
+        return await self._client.resolve_artifact(
+            owner=ref.owner,
+            repo=ref.repo,
+            tag=ref.tag,
+            digest=ref.digest,
+            include_urls=True,
+            preferences=prefs,
+            capabilities=caps.to_dict(),
+        )
 
 
+# ---------------------------------------------------------------------------
+# Convenience wrappers
+# ---------------------------------------------------------------------------
 
 async def ensure_snapshot_async(
     *,
@@ -384,7 +362,6 @@ async def ensure_snapshot_async(
     token: Optional[str],
     resolved: Optional[Any] = None,
 ) -> Path:
-    """Async version of ensure_snapshot_sync for use in async contexts."""
     client: Optional[CozyHubV2Client] = None
     if resolved is None:
         if not (base_url or "").strip():
diff --git a/src/gen_worker/decorators.py b/src/gen_worker/decorators.py
index d73012f..c7447fe 100644
--- a/src/gen_worker/decorators.py
+++ b/src/gen_worker/decorators.py
@@ -1,33 +1,7 @@
-import math
 from typing import Any, Callable, Dict, Optional, TypeVar
 
 F = TypeVar("F", bound=Callable[..., Any])
 
-
-def canonicalize_compute_capability_min(raw: Any) -> str:
-    """
-    Normalize function-level compute capability minima into canonical v1 form.
-
-    Canonical form is a decimal string with one fractional digit (for example
-    "10.0", "8.9").
-    """
-    if isinstance(raw, str):
-        v = raw.strip()
-        if v == "":
-            raise ValueError("compute_capability_min must not be empty")
-        try:
-            n = float(v)
-        except Exception as e:  # pragma: no cover - defensive
-            raise ValueError("compute_capability_min must be numeric") from e
-    elif isinstance(raw, (int, float)):
-        n = float(raw)
-    else:
-        raise ValueError("compute_capability_min must be numeric")
-
-    if not math.isfinite(n) or n <= 0:
-        raise ValueError("compute_capability_min must be > 0")
-    return f"{n:.1f}"
-
 class ResourceRequirements:
     """
     Specifies the resource requirements for a worker function.
@@ -45,7 +19,7 @@ def __init__(
         memory_hint_mb: Optional[int] = None,
         stage_profile: Optional[str] = None,
         stage_traits: Optional[list[str]] = None,
-        compute_capability_min: Optional[float | str] = None,
+        compute_capability_min: Optional[float] = None,
     ) -> None:
         self.batch_size_min = batch_size_min
         self.batch_size_target = batch_size_target
@@ -55,6 +29,7 @@ def __init__(
         self.memory_hint_mb = memory_hint_mb
         self.stage_profile = stage_profile
         self.stage_traits = list(stage_traits or [])
+        self.compute_capability_min = compute_capability_min
         self._requirements: Dict[str, Any] = {}
         if batch_size_min is not None:
             self._requirements["batch_size_min"] = int(batch_size_min)
@@ -73,9 +48,10 @@ def __init__(
         if self.stage_traits:
             self._requirements["stage_traits"] = [str(x).strip() for x in self.stage_traits if str(x).strip()]
         if compute_capability_min is not None:
-            self._requirements["compute_capability"] = {
-                "min": canonicalize_compute_capability_min(compute_capability_min)
-            }
+            val = float(compute_capability_min)
+            if val <= 0:
+                raise ValueError(f"compute_capability_min must be positive, got {val}")
+            self._requirements["compute_capability"] = {"min": f"{val:.1f}"}
 
     def to_dict(self) -> Dict[str, Any]:
         """Returns a dictionary representation of the defined requirements."""
diff --git a/src/gen_worker/diffusers_model_manager.py b/src/gen_worker/diffusers_model_manager.py
index b4d720d..3fe4d9a 100644
--- a/src/gen_worker/diffusers_model_manager.py
+++ b/src/gen_worker/diffusers_model_manager.py
@@ -55,7 +55,6 @@ async def load_model_into_vram(self, model_id: str) -> bool:
                         local_path = self._downloader.download(model_id, cache_dir)
                 except Exception as e:
                     logger.warning("DiffusersModelManager: download failed for %s: %s", model_id, e)
-                    return False
 
             loaded = await self._loader.load(model_id, model_path=local_path)
             logger.info(
diff --git a/src/gen_worker/discover.py b/src/gen_worker/discover.py
index 522fa15..2779f94 100644
--- a/src/gen_worker/discover.py
+++ b/src/gen_worker/discover.py
@@ -25,7 +25,7 @@
 
 import msgspec
 
-from gen_worker import RequestContext
+from gen_worker import ActionContext
 from gen_worker.injection import ModelRef
 
 from gen_worker.tensorhub_toml import (
@@ -34,7 +34,6 @@
     load_tensorhub_toml,
 )
 from gen_worker.names import slugify_endpoint_name, slugify_function_name
-from gen_worker.decorators import canonicalize_compute_capability_min
 
 
 def _type_id(t: type) -> Dict[str, str]:
@@ -189,7 +188,6 @@ def _extract_function_metadata(func: Any, module_name: str) -> Dict[str, Any]:
                 res_dict.update(raw)
         except Exception:
             pass
-    _normalize_compute_capability_resource(res_dict, func.__name__)
 
     hints = typing.get_type_hints(func, globalns=func.__globals__, include_extras=True)
     sig = inspect.signature(func)
@@ -197,12 +195,12 @@ def _extract_function_metadata(func: Any, module_name: str) -> Dict[str, Any]:
 
     if len(params) < 2:
         raise ValueError(
-            f"{func.__name__}: must accept (ctx: RequestContext, payload: msgspec.Struct, ...)"
+            f"{func.__name__}: must accept (ctx: ActionContext, payload: msgspec.Struct, ...)"
         )
 
     ctx_name = params[0].name
-    if hints.get(ctx_name) is not RequestContext:
-        raise ValueError(f"{func.__name__}: first param must be ctx: RequestContext")
+    if hints.get(ctx_name) is not ActionContext:
+        raise ValueError(f"{func.__name__}: first param must be ctx: ActionContext")
 
     payload_type = None
     payload_param = None
@@ -447,11 +445,6 @@ def _module_is_in_project(mod: Any) -> bool:
                 seen_functions.add(key)
                 fn_meta = _extract_function_metadata(obj, module_name)
                 functions.append(fn_meta)
-        functions.sort(key=lambda fn: (
-            str(fn.get("name") or ""),
-            str(fn.get("python_name") or ""),
-            str(fn.get("module") or ""),
-        ))
         return functions
 
     # Fallback: scan the filesystem for decorated functions.
@@ -484,41 +477,9 @@ def _module_is_in_project(mod: Any) -> bool:
                 print(f"warning: failed to extract metadata from {name}: {e}", file=sys.stderr)
                 raise
 
-    functions.sort(key=lambda fn: (
-        str(fn.get("name") or ""),
-        str(fn.get("python_name") or ""),
-        str(fn.get("module") or ""),
-    ))
     return functions
 
 
-def _normalize_compute_capability_resource(resources: Dict[str, Any], fn_name: str) -> None:
-    if not isinstance(resources, dict):
-        return
-
-    # Convenience alias for decorators/configs that use a flat key.
-    if "compute_capability" not in resources and "compute_capability_min" in resources:
-        resources["compute_capability"] = {"min": resources.get("compute_capability_min")}
-    resources.pop("compute_capability_min", None)
-
-    if "compute_capability" not in resources:
-        return
-
-    raw = resources.get("compute_capability")
-    min_raw: Any = None
-    if isinstance(raw, dict):
-        min_raw = raw.get("min")
-    else:
-        min_raw = raw
-    if min_raw is None:
-        raise ValueError(f"{fn_name}: resources.compute_capability.min is required when compute_capability is set")
-    try:
-        min_norm = canonicalize_compute_capability_min(min_raw)
-    except ValueError as e:
-        raise ValueError(f"{fn_name}: invalid resources.compute_capability.min: {e}") from e
-    resources["compute_capability"] = {"min": min_norm}
-
-
 def discover_manifest(root: Optional[Path] = None) -> Dict[str, Any]:
     """
     Discover functions and load tensorhub manifest config to build complete manifest.
@@ -546,7 +507,6 @@ def discover_manifest(root: Optional[Path] = None) -> Dict[str, Any]:
             if isinstance(base, dict):
                 merged.update(base)
             merged.update(hints)
-            _normalize_compute_capability_resource(merged, fn_name)
             fn["resources"] = merged
 
         batch_path = (tensorhub_manifest.function_batch_dimensions.get(fn_name) or "").strip()
diff --git a/src/gen_worker/entrypoint.py b/src/gen_worker/entrypoint.py
index 7b3b63d..b3c6ca0 100644
--- a/src/gen_worker/entrypoint.py
+++ b/src/gen_worker/entrypoint.py
@@ -244,18 +244,8 @@ def _run_main() -> int:
     worker_id = os.getenv("WORKER_ID", "").strip()
     worker_jwt = os.getenv("WORKER_JWT", "").strip()
     use_tls_env = os.getenv("USE_TLS")
-    def _parse_float_env(name: str, default: float) -> float:
-        raw = (os.getenv(name) or "").strip()
-        if not raw:
-            return default
-        try:
-            return float(raw)
-        except Exception:
-            return default
-
-    reconnect_delay = _parse_float_env("RECONNECT_DELAY", 0.1)
+    reconnect_delay = int(os.getenv("RECONNECT_DELAY", "5") or "5")
     max_reconnect_attempts = int(os.getenv("MAX_RECONNECT_ATTEMPTS", "0") or "0")
-    lb_only_retries = (os.getenv("LB_ONLY_RETRIES", "true") or "true").strip().lower() in ("1", "true", "t", "yes", "y", "on")
 
     if not scheduler_addr_raw:
         logger.error("SCHEDULER_PUBLIC_ADDR is required (scheduler dial address). Refusing to start worker.")
@@ -276,9 +266,8 @@ def _parse_float_env(name: str, default: float) -> float:
     logger.info("  User Function Modules: %s", user_modules)
     logger.info("  Worker ID: %s", worker_id or "(from JWT)")
     logger.info("  Use TLS: %s", use_tls)
-    logger.info("  Reconnect Delay (base): %.3fs", reconnect_delay)
+    logger.info("  Reconnect Delay: %ss", reconnect_delay)
     logger.info("  Max Reconnect Attempts: %s", max_reconnect_attempts or "Infinite")
-    logger.info("  LB-only retries: %s", lb_only_retries)
     logger.info("  Model Cache Dir: %s", cache_cfg["model_cache_dir"])
     if cache_cfg["local_model_cache_dir"]:
         logger.info("  Local Model Cache Dir: %s", cache_cfg["local_model_cache_dir"])
@@ -287,7 +276,7 @@ def _parse_float_env(name: str, default: float) -> float:
         logger.error(
             "No user function modules found. A baked manifest at /app/.tensorhub/endpoint.lock is required.\n"
             "Your Dockerfile should run discovery at build time:\n"
-            "  RUN mkdir -p /app/.tensorhub && python -m gen_worker.discover > /app/.tensorhub/endpoint.lock"
+            "  RUN mkdir -p /app/.cozy && python -m gen_worker.discover > /app/.tensorhub/endpoint.lock"
         )
         return 1
 
@@ -305,7 +294,6 @@ def _parse_float_env(name: str, default: float) -> float:
             use_tls=use_tls,
             reconnect_delay=reconnect_delay,
             max_reconnect_attempts=max_reconnect_attempts,
-            lb_only_retries=lb_only_retries,
             manifest=manifest,
         )
         worker.run()
diff --git a/src/gen_worker/model_ref_downloader.py b/src/gen_worker/model_ref_downloader.py
index 4bbe8c3..f6aa338 100644
--- a/src/gen_worker/model_ref_downloader.py
+++ b/src/gen_worker/model_ref_downloader.py
@@ -19,7 +19,6 @@
 )
 from .hf_downloader import HuggingFaceHubDownloader
 from .model_refs import CozyRef, ParsedModelRef, parse_model_ref
-import threading
 
 # Per-task resolved manifests provided by gen-orchestrator (issue #92).
 # Shape: {canonical_model_id: ResolvedCozyModel-like object}
@@ -60,26 +59,6 @@ def _get_prefs_for_ref(canonical_ref: str) -> Mapping[str, Any]:
     return v if isinstance(v, Mapping) else {}
 
 
-def _lookup_resolved_cozy_entry(resolved_mapping: Optional[Mapping[str, Any]], canonical_ref: str) -> Any:
-    if resolved_mapping is None:
-        return None
-    key = str(canonical_ref or "").strip()
-    if not key:
-        return None
-    candidates = [key]
-    if key.startswith("cozy:"):
-        candidates.append(key.split(":", 1)[1].strip())
-    else:
-        candidates.append(f"cozy:{key}")
-    for cand in candidates:
-        if not cand:
-            continue
-        ent = resolved_mapping.get(cand)
-        if ent is not None:
-            return ent
-    return None
-
-
 class ModelRefDownloader(ModelDownloader):
     """Composite downloader for phase-1 model refs.
 
@@ -149,7 +128,7 @@ async def _download_async(self, parsed: ParsedModelRef, dest_dir: Path) -> Path:
         if parsed.scheme == "cozy" and parsed.cozy is not None:
             canonical = parsed.cozy.canonical()
             resolved_mapping = _resolved_cozy_models_by_id.get()
-            resolved_entry = _lookup_resolved_cozy_entry(resolved_mapping, canonical)
+            resolved_entry = resolved_mapping.get(canonical) if resolved_mapping is not None else None
 
             if resolved_entry is not None:
                 return await ensure_snapshot_async(
@@ -292,14 +271,14 @@ def _run_in_thread(coro: Coroutine[Any, Any, Path]) -> str:
     out: dict[str, str] = {}
     err: dict[str, BaseException] = {}
 
-    ctx = contextvars.copy_context()
-
     def runner() -> None:
         try:
-            out["v"] = ctx.run(asyncio.run, coro).as_posix()
+            out["v"] = asyncio.run(coro).as_posix()
         except BaseException as e:
             err["e"] = e
 
+    import threading
+
     t = threading.Thread(target=runner, daemon=True)
     t.start()
     t.join()
diff --git a/src/gen_worker/model_refs.py b/src/gen_worker/model_refs.py
index 3bc784b..7f9ee3a 100644
--- a/src/gen_worker/model_refs.py
+++ b/src/gen_worker/model_refs.py
@@ -16,8 +16,8 @@ def repo_id(self) -> str:
 
     def canonical(self) -> str:
         if self.digest:
-            return f"{self.repo_id()}@{self.digest}"
-        return f"{self.repo_id()}:{self.tag}"
+            return f"cozy:{self.repo_id()}@{self.digest}"
+        return f"cozy:{self.repo_id()}:{self.tag}"
 
 
 @dataclass(frozen=True)
@@ -37,13 +37,6 @@ class ParsedModelRef:
     cozy: Optional[CozyRef] = None
     hf: Optional[HuggingFaceRef] = None
 
-    def canonical(self) -> str:
-        if self.scheme == "cozy" and self.cozy is not None:
-            return self.cozy.canonical()
-        if self.scheme == "hf" and self.hf is not None:
-            return self.hf.canonical()
-        raise ValueError("invalid parsed model ref")
-
 
 def _strip_scheme(raw: str) -> tuple[Optional[str], str]:
     s = (raw or "").strip()
diff --git a/src/gen_worker/names.py b/src/gen_worker/names.py
index 15dbf02..05454c0 100644
--- a/src/gen_worker/names.py
+++ b/src/gen_worker/names.py
@@ -29,10 +29,6 @@ def slugify_name(raw: str) -> str:
     return raw
 
 
-def slugify_owner_name(raw: str) -> str:
-    return slugify_name(raw)
-
-
 def slugify_endpoint_name(raw: str) -> str:
     return slugify_name(raw)
 
diff --git a/src/gen_worker/pb/frontend_pb2.py b/src/gen_worker/pb/frontend_pb2.py
index e3b1e83..1f4d0af 100644
--- a/src/gen_worker/pb/frontend_pb2.py
+++ b/src/gen_worker/pb/frontend_pb2.py
@@ -24,7 +24,7 @@
 
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0e\x66rontend.proto\x12\x0b\x66rontend.v1\"C\n\rActionOptions\x12\n\n\x02id\x18\x01 \x01(\t\x12\x12\n\ntimeout_ms\x18\x02 \x01(\x03J\x04\x08\x03\x10\x04R\x0cretry_policy\"\x84\x03\n\x12VariantPreferences\x12;\n\x05quant\x18\x01 \x01(\x0e\x32,.frontend.v1.VariantPreferences.Quantization\x12<\n\tpackaging\x18\x02 \x01(\x0e\x32).frontend.v1.VariantPreferences.Packaging\x12\x36\n\x06layout\x18\x03 \x01(\x0e\x32&.frontend.v1.VariantPreferences.Layout\"B\n\x0cQuantization\x12\x15\n\x11QUANT_UNSPECIFIED\x10\x00\x12\x08\n\x04\x46P16\x10\x01\x12\x08\n\x04\x42\x46\x31\x36\x10\x02\x12\x07\n\x03\x46P8\x10\x03\"F\n\tPackaging\x12\x19\n\x15PACKAGING_UNSPECIFIED\x10\x00\x12\x0f\n\x0bSAFETENSORS\x10\x01\x12\r\n\tFLASHPACK\x10\x02\"/\n\x06Layout\x12\x16\n\x12LAYOUT_UNSPECIFIED\x10\x00\x12\r\n\tDIFFUSERS\x10\x01\"\x95\x02\n\x14\x45xecuteActionRequest\x12\x15\n\rfunction_name\x18\x01 \x01(\t\x12\x15\n\rinput_payload\x18\x02 \x01(\x0c\x12+\n\x07options\x18\x03 \x01(\x0b\x32\x1a.frontend.v1.ActionOptions\x12\x12\n\nrelease_id\x18\x04 \x01(\t\x12\x12\n\ninvoker_id\x18\x06 \x01(\t\x12\x1a\n\x12required_repo_refs\x18\x07 \x03(\t\x12\r\n\x05owner\x18\x08 \x01(\t\x12\x36\n\rvariant_prefs\x18\t \x01(\x0b\x32\x1f.frontend.v1.VariantPreferencesJ\x04\x08\x05\x10\x06R\x11required_model_id\"\'\n\x15\x45xecuteActionResponse\x12\x0e\n\x06run_id\x18\x01 \x01(\t\"\"\n\x10\x43\x61ncelRunRequest\x12\x0e\n\x06run_id\x18\x01 \x01(\t\"\x1f\n\x11\x43\x61ncelRunResponse\x12\n\n\x02ok\x18\x01 \x01(\x08\"\x84\x01\n\x0cRealtimeOpen\x12\x12\n\nsession_id\x18\x01 \x01(\t\x12\x12\n\nrelease_id\x18\x02 \x01(\t\x12\x15\n\rfunction_name\x18\x03 \x01(\t\x12\r\n\x05owner\x18\x04 \x01(\t\x12\x12\n\ninvoker_id\x18\x05 \x01(\t\x12\x12\n\ntimeout_ms\x18\x06 \x01(\x03\"B\n\rRealtimeFrame\x12\x12\n\nsession_id\x18\x01 \x01(\t\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\x12\x0f\n\x07is_text\x18\x03 \x01(\x08\"3\n\rRealtimeClose\x12\x12\n\nsession_id\x18\x01 \x01(\t\x12\x0e\n\x06reason\x18\x02 \x01(\t\"\xa2\x01\n\x14RealtimeProxyMessage\x12)\n\x04open\x18\x01 \x01(\x0b\x32\x19.frontend.v1.RealtimeOpenH\x00\x12+\n\x05\x66rame\x18\x02 \x01(\x0b\x32\x1a.frontend.v1.RealtimeFrameH\x00\x12+\n\x05\x63lose\x18\x03 \x01(\x0b\x32\x1a.frontend.v1.RealtimeCloseH\x00\x42\x05\n\x03msg2\x92\x02\n\x0f\x46rontendService\x12V\n\rExecuteAction\x12!.frontend.v1.ExecuteActionRequest\x1a\".frontend.v1.ExecuteActionResponse\x12J\n\tCancelRun\x12\x1d.frontend.v1.CancelRunRequest\x1a\x1e.frontend.v1.CancelRunResponse\x12[\n\x0fRealtimeSession\x12!.frontend.v1.RealtimeProxyMessage\x1a!.frontend.v1.RealtimeProxyMessage(\x01\x30\x01\x42GZEgithub.com/cozy-creator/gen-orchestrator/pkg/pb/frontendv1;frontendv1b\x06proto3')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0e\x66rontend.proto\x12\x0b\x66rontend.v1\"C\n\rActionOptions\x12\n\n\x02id\x18\x01 \x01(\t\x12\x12\n\ntimeout_ms\x18\x02 \x01(\x03J\x04\x08\x03\x10\x04R\x0cretry_policy\"\x84\x03\n\x12VariantPreferences\x12;\n\x05quant\x18\x01 \x01(\x0e\x32,.frontend.v1.VariantPreferences.Quantization\x12<\n\tpackaging\x18\x02 \x01(\x0e\x32).frontend.v1.VariantPreferences.Packaging\x12\x36\n\x06layout\x18\x03 \x01(\x0e\x32&.frontend.v1.VariantPreferences.Layout\"B\n\x0cQuantization\x12\x15\n\x11QUANT_UNSPECIFIED\x10\x00\x12\x08\n\x04\x46P16\x10\x01\x12\x08\n\x04\x42\x46\x31\x36\x10\x02\x12\x07\n\x03\x46P8\x10\x03\"F\n\tPackaging\x12\x19\n\x15PACKAGING_UNSPECIFIED\x10\x00\x12\x0f\n\x0bSAFETENSORS\x10\x01\x12\r\n\tFLASHPACK\x10\x02\"/\n\x06Layout\x12\x16\n\x12LAYOUT_UNSPECIFIED\x10\x00\x12\r\n\tDIFFUSERS\x10\x01\"\x95\x02\n\x14\x45xecuteActionRequest\x12\x15\n\rfunction_name\x18\x01 \x01(\t\x12\x15\n\rinput_payload\x18\x02 \x01(\x0c\x12+\n\x07options\x18\x03 \x01(\x0b\x32\x1a.frontend.v1.ActionOptions\x12\x12\n\nrelease_id\x18\x04 \x01(\t\x12\x12\n\ninvoker_id\x18\x06 \x01(\t\x12\x1a\n\x12required_repo_refs\x18\x07 \x03(\t\x12\r\n\x05owner\x18\x08 \x01(\t\x12\x36\n\rvariant_prefs\x18\t \x01(\x0b\x32\x1f.frontend.v1.VariantPreferencesJ\x04\x08\x05\x10\x06R\x11required_model_id\"+\n\x15\x45xecuteActionResponse\x12\x12\n\nrequest_id\x18\x01 \x01(\t\"*\n\x14\x43\x61ncelRequestRequest\x12\x12\n\nrequest_id\x18\x01 \x01(\t\"#\n\x15\x43\x61ncelRequestResponse\x12\n\n\x02ok\x18\x01 \x01(\x08\"\x84\x01\n\x0cRealtimeOpen\x12\x12\n\nsession_id\x18\x01 \x01(\t\x12\x12\n\nrelease_id\x18\x02 \x01(\t\x12\x15\n\rfunction_name\x18\x03 \x01(\t\x12\r\n\x05owner\x18\x04 \x01(\t\x12\x12\n\ninvoker_id\x18\x05 \x01(\t\x12\x12\n\ntimeout_ms\x18\x06 \x01(\x03\"B\n\rRealtimeFrame\x12\x12\n\nsession_id\x18\x01 \x01(\t\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\x12\x0f\n\x07is_text\x18\x03 \x01(\x08\"3\n\rRealtimeClose\x12\x12\n\nsession_id\x18\x01 \x01(\t\x12\x0e\n\x06reason\x18\x02 \x01(\t\"\xa2\x01\n\x14RealtimeProxyMessage\x12)\n\x04open\x18\x01 \x01(\x0b\x32\x19.frontend.v1.RealtimeOpenH\x00\x12+\n\x05\x66rame\x18\x02 \x01(\x0b\x32\x1a.frontend.v1.RealtimeFrameH\x00\x12+\n\x05\x63lose\x18\x03 \x01(\x0b\x32\x1a.frontend.v1.RealtimeCloseH\x00\x42\x05\n\x03msg2\x9e\x02\n\x0f\x46rontendService\x12V\n\rExecuteAction\x12!.frontend.v1.ExecuteActionRequest\x1a\".frontend.v1.ExecuteActionResponse\x12V\n\rCancelRequest\x12!.frontend.v1.CancelRequestRequest\x1a\".frontend.v1.CancelRequestResponse\x12[\n\x0fRealtimeSession\x12!.frontend.v1.RealtimeProxyMessage\x1a!.frontend.v1.RealtimeProxyMessage(\x01\x30\x01\x42GZEgithub.com/cozy-creator/gen-orchestrator/pkg/pb/frontendv1;frontendv1b\x06proto3')
 
 _globals = globals()
 _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
@@ -45,19 +45,19 @@
   _globals['_EXECUTEACTIONREQUEST']._serialized_start=492
   _globals['_EXECUTEACTIONREQUEST']._serialized_end=769
   _globals['_EXECUTEACTIONRESPONSE']._serialized_start=771
-  _globals['_EXECUTEACTIONRESPONSE']._serialized_end=810
-  _globals['_CANCELRUNREQUEST']._serialized_start=812
-  _globals['_CANCELRUNREQUEST']._serialized_end=846
-  _globals['_CANCELRUNRESPONSE']._serialized_start=848
-  _globals['_CANCELRUNRESPONSE']._serialized_end=879
-  _globals['_REALTIMEOPEN']._serialized_start=882
-  _globals['_REALTIMEOPEN']._serialized_end=1014
-  _globals['_REALTIMEFRAME']._serialized_start=1016
-  _globals['_REALTIMEFRAME']._serialized_end=1082
-  _globals['_REALTIMECLOSE']._serialized_start=1084
-  _globals['_REALTIMECLOSE']._serialized_end=1135
-  _globals['_REALTIMEPROXYMESSAGE']._serialized_start=1138
-  _globals['_REALTIMEPROXYMESSAGE']._serialized_end=1300
-  _globals['_FRONTENDSERVICE']._serialized_start=1303
-  _globals['_FRONTENDSERVICE']._serialized_end=1577
+  _globals['_EXECUTEACTIONRESPONSE']._serialized_end=814
+  _globals['_CANCELREQUESTREQUEST']._serialized_start=816
+  _globals['_CANCELREQUESTREQUEST']._serialized_end=858
+  _globals['_CANCELREQUESTRESPONSE']._serialized_start=860
+  _globals['_CANCELREQUESTRESPONSE']._serialized_end=895
+  _globals['_REALTIMEOPEN']._serialized_start=898
+  _globals['_REALTIMEOPEN']._serialized_end=1030
+  _globals['_REALTIMEFRAME']._serialized_start=1032
+  _globals['_REALTIMEFRAME']._serialized_end=1098
+  _globals['_REALTIMECLOSE']._serialized_start=1100
+  _globals['_REALTIMECLOSE']._serialized_end=1151
+  _globals['_REALTIMEPROXYMESSAGE']._serialized_start=1154
+  _globals['_REALTIMEPROXYMESSAGE']._serialized_end=1316
+  _globals['_FRONTENDSERVICE']._serialized_start=1319
+  _globals['_FRONTENDSERVICE']._serialized_end=1605
 # @@protoc_insertion_point(module_scope)
diff --git a/src/gen_worker/pb/frontend_pb2_grpc.py b/src/gen_worker/pb/frontend_pb2_grpc.py
index dcd9c3b..a3d4fb3 100644
--- a/src/gen_worker/pb/frontend_pb2_grpc.py
+++ b/src/gen_worker/pb/frontend_pb2_grpc.py
@@ -40,10 +40,10 @@ def __init__(self, channel):
                 request_serializer=frontend__pb2.ExecuteActionRequest.SerializeToString,
                 response_deserializer=frontend__pb2.ExecuteActionResponse.FromString,
                 _registered_method=True)
-        self.CancelRun = channel.unary_unary(
-                '/frontend.v1.FrontendService/CancelRun',
-                request_serializer=frontend__pb2.CancelRunRequest.SerializeToString,
-                response_deserializer=frontend__pb2.CancelRunResponse.FromString,
+        self.CancelRequest = channel.unary_unary(
+                '/frontend.v1.FrontendService/CancelRequest',
+                request_serializer=frontend__pb2.CancelRequestRequest.SerializeToString,
+                response_deserializer=frontend__pb2.CancelRequestResponse.FromString,
                 _registered_method=True)
         self.RealtimeSession = channel.stream_stream(
                 '/frontend.v1.FrontendService/RealtimeSession',
@@ -63,8 +63,8 @@ def ExecuteAction(self, request, context):
         context.set_details('Method not implemented!')
         raise NotImplementedError('Method not implemented!')
 
-    def CancelRun(self, request, context):
-        """2) Cancel an in-flight action/job.
+    def CancelRequest(self, request, context):
+        """2) Cancel an in-flight request.
         """
         context.set_code(grpc.StatusCode.UNIMPLEMENTED)
         context.set_details('Method not implemented!')
@@ -85,10 +85,10 @@ def add_FrontendServiceServicer_to_server(servicer, server):
                     request_deserializer=frontend__pb2.ExecuteActionRequest.FromString,
                     response_serializer=frontend__pb2.ExecuteActionResponse.SerializeToString,
             ),
-            'CancelRun': grpc.unary_unary_rpc_method_handler(
-                    servicer.CancelRun,
-                    request_deserializer=frontend__pb2.CancelRunRequest.FromString,
-                    response_serializer=frontend__pb2.CancelRunResponse.SerializeToString,
+            'CancelRequest': grpc.unary_unary_rpc_method_handler(
+                    servicer.CancelRequest,
+                    request_deserializer=frontend__pb2.CancelRequestRequest.FromString,
+                    response_serializer=frontend__pb2.CancelRequestResponse.SerializeToString,
             ),
             'RealtimeSession': grpc.stream_stream_rpc_method_handler(
                     servicer.RealtimeSession,
@@ -135,7 +135,7 @@ def ExecuteAction(request,
             _registered_method=True)
 
     @staticmethod
-    def CancelRun(request,
+    def CancelRequest(request,
             target,
             options=(),
             channel_credentials=None,
@@ -148,9 +148,9 @@ def CancelRun(request,
         return grpc.experimental.unary_unary(
             request,
             target,
-            '/frontend.v1.FrontendService/CancelRun',
-            frontend__pb2.CancelRunRequest.SerializeToString,
-            frontend__pb2.CancelRunResponse.FromString,
+            '/frontend.v1.FrontendService/CancelRequest',
+            frontend__pb2.CancelRequestRequest.SerializeToString,
+            frontend__pb2.CancelRequestResponse.FromString,
             options,
             channel_credentials,
             insecure,
diff --git a/src/gen_worker/pb/worker_scheduler_pb2.py b/src/gen_worker/pb/worker_scheduler_pb2.py
index 1c8b3bf..d82c370 100644
--- a/src/gen_worker/pb/worker_scheduler_pb2.py
+++ b/src/gen_worker/pb/worker_scheduler_pb2.py
@@ -24,7 +24,7 @@
 
 
 
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 _globals = globals()
 _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
@@ -52,80 +52,78 @@
   _globals['_WORKERRESOURCES_FUNCTIONCONCURRENCYENTRY']._serialized_end=864
   _globals['_FUNCTIONSCHEMA']._serialized_start=867
   _globals['_FUNCTIONSCHEMA']._serialized_end=1031
-  _globals['_ACTIVEASSIGNMENTRESUME']._serialized_start=1034
-  _globals['_ACTIVEASSIGNMENTRESUME']._serialized_end=1219
-  _globals['_WORKERREGISTRATION']._serialized_start=1222
-  _globals['_WORKERREGISTRATION']._serialized_end=1428
-  _globals['_LOADMODELCOMMAND']._serialized_start=1430
-  _globals['_LOADMODELCOMMAND']._serialized_end=1466
-  _globals['_UNLOADMODELCOMMAND']._serialized_start=1468
-  _globals['_UNLOADMODELCOMMAND']._serialized_end=1506
-  _globals['_INTERRUPTTASKCOMMAND']._serialized_start=1508
-  _globals['_INTERRUPTTASKCOMMAND']._serialized_end=1630
-  _globals['_RUNTIMEBATCHINGCONFIG']._serialized_start=1633
-  _globals['_RUNTIMEBATCHINGCONFIG']._serialized_end=1816
-  _globals['_RUNTIMEBATCHINGCONFIGCOMMAND']._serialized_start=1818
-  _globals['_RUNTIMEBATCHINGCONFIGCOMMAND']._serialized_end=1901
-  _globals['_RUNTIMEBATCHINGCONFIGRESULT']._serialized_start=1903
-  _globals['_RUNTIMEBATCHINGCONFIGRESULT']._serialized_end=2012
-  _globals['_RESOLVEDCOZYMODELFILE']._serialized_start=2014
-  _globals['_RESOLVEDCOZYMODELFILE']._serialized_end=2100
-  _globals['_RESOLVEDCOZYMODEL']._serialized_start=2102
-  _globals['_RESOLVEDCOZYMODEL']._serialized_end=2198
-  _globals['_COZYMODELURLREFRESHREQUEST']._serialized_start=2200
-  _globals['_COZYMODELURLREFRESHREQUEST']._serialized_end=2246
-  _globals['_COZYMODELURLREFRESHRESPONSE']._serialized_start=2248
-  _globals['_COZYMODELURLREFRESHRESPONSE']._serialized_end=2346
-  _globals['_TASKEXECUTIONREQUEST']._serialized_start=2349
-  _globals['_TASKEXECUTIONREQUEST']._serialized_end=2886
-  _globals['_TASKEXECUTIONREQUEST_RESOLVEDCOZYMODELSBYIDENTRY']._serialized_start=2792
-  _globals['_TASKEXECUTIONREQUEST_RESOLVEDCOZYMODELSBYIDENTRY']._serialized_end=2886
-  _globals['_TASKEXECUTIONRESULT']._serialized_start=2889
-  _globals['_TASKEXECUTIONRESULT']._serialized_end=3199
-  _globals['_BATCHEXECUTIONITEM']._serialized_start=3202
-  _globals['_BATCHEXECUTIONITEM']._serialized_end=3692
-  _globals['_BATCHEXECUTIONITEM_RESOLVEDCOZYMODELSBYIDENTRY']._serialized_start=2792
-  _globals['_BATCHEXECUTIONITEM_RESOLVEDCOZYMODELSBYIDENTRY']._serialized_end=2886
-  _globals['_BATCHEXECUTIONREQUEST']._serialized_start=3695
-  _globals['_BATCHEXECUTIONREQUEST']._serialized_end=3883
-  _globals['_BATCHEXECUTIONITEMRESULT']._serialized_start=3886
-  _globals['_BATCHEXECUTIONITEMRESULT']._serialized_end=4201
-  _globals['_BATCHEXECUTIONRESULT']._serialized_start=4203
-  _globals['_BATCHEXECUTIONRESULT']._serialized_end=4298
-  _globals['_WORKEREVENT']._serialized_start=4301
-  _globals['_WORKEREVENT']._serialized_end=4520
-  _globals['_INCREMENTALTOKENDELTA']._serialized_start=4523
-  _globals['_INCREMENTALTOKENDELTA']._serialized_end=4800
-  _globals['_INCREMENTALTOKENSTREAMDONE']._serialized_start=4803
-  _globals['_INCREMENTALTOKENSTREAMDONE']._serialized_end=5043
-  _globals['_INCREMENTALTOKENSTREAMERROR']._serialized_start=5046
-  _globals['_INCREMENTALTOKENSTREAMERROR']._serialized_end=5310
-  _globals['_REALTIMEOPENCOMMAND']._serialized_start=5313
-  _globals['_REALTIMEOPENCOMMAND']._serialized_end=5506
-  _globals['_REALTIMEFRAME']._serialized_start=5508
-  _globals['_REALTIMEFRAME']._serialized_end=5574
-  _globals['_REALTIMECLOSECOMMAND']._serialized_start=5576
-  _globals['_REALTIMECLOSECOMMAND']._serialized_end=5634
-  _globals['_LOADMODELRESULT']._serialized_start=5636
-  _globals['_LOADMODELRESULT']._serialized_end=5711
-  _globals['_UNLOADMODELRESULT']._serialized_start=5713
-  _globals['_UNLOADMODELRESULT']._serialized_end=5790
-  _globals['_MODELSPEC']._serialized_start=5792
-  _globals['_MODELSPEC']._serialized_end=5832
-  _globals['_MODELSBYKEY']._serialized_start=5835
-  _globals['_MODELSBYKEY']._serialized_end=5975
-  _globals['_MODELSBYKEY_MODELSENTRY']._serialized_start=5905
-  _globals['_MODELSBYKEY_MODELSENTRY']._serialized_end=5975
-  _globals['_ENDPOINTCONFIG']._serialized_start=5978
-  _globals['_ENDPOINTCONFIG']._serialized_end=6559
-  _globals['_ENDPOINTCONFIG_REPOREFBYKEYENTRY']._serialized_start=6320
-  _globals['_ENDPOINTCONFIG_REPOREFBYKEYENTRY']._serialized_end=6371
-  _globals['_ENDPOINTCONFIG_RESOLVEDCOZYMODELSBYVARIANTREFENTRY']._serialized_start=6373
-  _globals['_ENDPOINTCONFIG_RESOLVEDCOZYMODELSBYVARIANTREFENTRY']._serialized_end=6475
-  _globals['_ENDPOINTCONFIG_MODELSBYFUNCTIONENTRY']._serialized_start=6477
-  _globals['_ENDPOINTCONFIG_MODELSBYFUNCTIONENTRY']._serialized_end=6559
-  _globals['_WORKERSCHEDULERMESSAGE']._serialized_start=6562
-  _globals['_WORKERSCHEDULERMESSAGE']._serialized_end=8103
-  _globals['_SCHEDULERWORKERSERVICE']._serialized_start=8105
-  _globals['_SCHEDULERWORKERSERVICE']._serialized_end=8226
+  _globals['_WORKERREGISTRATION']._serialized_start=1034
+  _globals['_WORKERREGISTRATION']._serialized_end=1174
+  _globals['_LOADMODELCOMMAND']._serialized_start=1176
+  _globals['_LOADMODELCOMMAND']._serialized_end=1212
+  _globals['_UNLOADMODELCOMMAND']._serialized_start=1214
+  _globals['_UNLOADMODELCOMMAND']._serialized_end=1252
+  _globals['_INTERRUPTTASKCOMMAND']._serialized_start=1254
+  _globals['_INTERRUPTTASKCOMMAND']._serialized_end=1342
+  _globals['_RUNTIMEBATCHINGCONFIG']._serialized_start=1345
+  _globals['_RUNTIMEBATCHINGCONFIG']._serialized_end=1528
+  _globals['_RUNTIMEBATCHINGCONFIGCOMMAND']._serialized_start=1530
+  _globals['_RUNTIMEBATCHINGCONFIGCOMMAND']._serialized_end=1613
+  _globals['_RUNTIMEBATCHINGCONFIGRESULT']._serialized_start=1615
+  _globals['_RUNTIMEBATCHINGCONFIGRESULT']._serialized_end=1724
+  _globals['_RESOLVEDCOZYMODELFILE']._serialized_start=1726
+  _globals['_RESOLVEDCOZYMODELFILE']._serialized_end=1812
+  _globals['_RESOLVEDCOZYMODEL']._serialized_start=1814
+  _globals['_RESOLVEDCOZYMODEL']._serialized_end=1910
+  _globals['_COZYMODELURLREFRESHREQUEST']._serialized_start=1912
+  _globals['_COZYMODELURLREFRESHREQUEST']._serialized_end=1958
+  _globals['_COZYMODELURLREFRESHRESPONSE']._serialized_start=1960
+  _globals['_COZYMODELURLREFRESHRESPONSE']._serialized_end=2058
+  _globals['_TASKEXECUTIONREQUEST']._serialized_start=2061
+  _globals['_TASKEXECUTIONREQUEST']._serialized_end=2564
+  _globals['_TASKEXECUTIONREQUEST_RESOLVEDCOZYMODELSBYIDENTRY']._serialized_start=2470
+  _globals['_TASKEXECUTIONREQUEST_RESOLVEDCOZYMODELSBYIDENTRY']._serialized_end=2564
+  _globals['_TASKEXECUTIONRESULT']._serialized_start=2567
+  _globals['_TASKEXECUTIONRESULT']._serialized_end=2823
+  _globals['_BATCHEXECUTIONITEM']._serialized_start=2826
+  _globals['_BATCHEXECUTIONITEM']._serialized_end=3282
+  _globals['_BATCHEXECUTIONITEM_RESOLVEDCOZYMODELSBYIDENTRY']._serialized_start=2470
+  _globals['_BATCHEXECUTIONITEM_RESOLVEDCOZYMODELSBYIDENTRY']._serialized_end=2564
+  _globals['_BATCHEXECUTIONREQUEST']._serialized_start=3285
+  _globals['_BATCHEXECUTIONREQUEST']._serialized_end=3473
+  _globals['_BATCHEXECUTIONITEMRESULT']._serialized_start=3476
+  _globals['_BATCHEXECUTIONITEMRESULT']._serialized_end=3737
+  _globals['_BATCHEXECUTIONRESULT']._serialized_start=3739
+  _globals['_BATCHEXECUTIONRESULT']._serialized_end=3834
+  _globals['_WORKEREVENT']._serialized_start=3837
+  _globals['_WORKEREVENT']._serialized_end=4002
+  _globals['_INCREMENTALTOKENDELTA']._serialized_start=4005
+  _globals['_INCREMENTALTOKENDELTA']._serialized_end=4248
+  _globals['_INCREMENTALTOKENSTREAMDONE']._serialized_start=4251
+  _globals['_INCREMENTALTOKENSTREAMDONE']._serialized_end=4457
+  _globals['_INCREMENTALTOKENSTREAMERROR']._serialized_start=4460
+  _globals['_INCREMENTALTOKENSTREAMERROR']._serialized_end=4690
+  _globals['_REALTIMEOPENCOMMAND']._serialized_start=4693
+  _globals['_REALTIMEOPENCOMMAND']._serialized_end=4886
+  _globals['_REALTIMEFRAME']._serialized_start=4888
+  _globals['_REALTIMEFRAME']._serialized_end=4954
+  _globals['_REALTIMECLOSECOMMAND']._serialized_start=4956
+  _globals['_REALTIMECLOSECOMMAND']._serialized_end=5014
+  _globals['_LOADMODELRESULT']._serialized_start=5016
+  _globals['_LOADMODELRESULT']._serialized_end=5091
+  _globals['_UNLOADMODELRESULT']._serialized_start=5093
+  _globals['_UNLOADMODELRESULT']._serialized_end=5170
+  _globals['_MODELSPEC']._serialized_start=5172
+  _globals['_MODELSPEC']._serialized_end=5212
+  _globals['_MODELSBYKEY']._serialized_start=5215
+  _globals['_MODELSBYKEY']._serialized_end=5355
+  _globals['_MODELSBYKEY_MODELSENTRY']._serialized_start=5285
+  _globals['_MODELSBYKEY_MODELSENTRY']._serialized_end=5355
+  _globals['_ENDPOINTCONFIG']._serialized_start=5358
+  _globals['_ENDPOINTCONFIG']._serialized_end=5939
+  _globals['_ENDPOINTCONFIG_REPOREFBYKEYENTRY']._serialized_start=5700
+  _globals['_ENDPOINTCONFIG_REPOREFBYKEYENTRY']._serialized_end=5751
+  _globals['_ENDPOINTCONFIG_RESOLVEDCOZYMODELSBYVARIANTREFENTRY']._serialized_start=5753
+  _globals['_ENDPOINTCONFIG_RESOLVEDCOZYMODELSBYVARIANTREFENTRY']._serialized_end=5855
+  _globals['_ENDPOINTCONFIG_MODELSBYFUNCTIONENTRY']._serialized_start=5857
+  _globals['_ENDPOINTCONFIG_MODELSBYFUNCTIONENTRY']._serialized_end=5939
+  _globals['_WORKERSCHEDULERMESSAGE']._serialized_start=5942
+  _globals['_WORKERSCHEDULERMESSAGE']._serialized_end=7483
+  _globals['_SCHEDULERWORKERSERVICE']._serialized_start=7485
+  _globals['_SCHEDULERWORKERSERVICE']._serialized_end=7606
 # @@protoc_insertion_point(module_scope)
diff --git a/src/gen_worker/pipeline_loader.py b/src/gen_worker/pipeline_loader.py
index 72be53f..ea20b2f 100644
--- a/src/gen_worker/pipeline_loader.py
+++ b/src/gen_worker/pipeline_loader.py
@@ -537,24 +537,21 @@ def get_torch_dtype(dtype_str: Optional[str], model_id: str) -> Any:
 
 def detect_diffusers_variant(model_path: Path) -> Optional[str]:
     """
-    Detect a diffusers `variant=` value ("bf16", "fp8", "fp16", "int8", "int4", "nvfp4")
-    from files on disk.
+    Detect a diffusers `variant=` value ("bf16", "fp8", "fp16", "int8", "int4") from files on disk.
 
     Many diffusers repos store weights as:
       - `unet/diffusion_pytorch_model.fp16.safetensors`
       - `text_encoder/model.fp16.safetensors`
       - sharded: `*.fp16.safetensors.index.json` + `*.fp16-00001-of-0000N.safetensors`
     """
-    candidates = ["bf16", "fp8", "fp16", "int8", "int4", "nvfp4"]
+    candidates = ["bf16", "fp8", "fp16", "int8", "int4"]
     for p in model_path.rglob("*"):
         if not p.is_file():
             continue
         name = p.name.lower()
         for v in candidates:
             if f".{v}." in name and name.endswith((".safetensors", ".json")):
-                print(f"DEBUG detect_diffusers_variant matched variant={v} file={p.name}")
                 return v
-    print(f"DEBUG detect_diffusers_variant no variant found in {model_path}")
     return None
 
 
@@ -1458,17 +1455,6 @@ async def load(
             raise ModelNotFoundError(model_id, path)
 
         config = config or PipelineConfig(model_path=str(path))
-        if config.variant is None:
-            # Prefer variant from cozy/pipeline spec, fall back to file-name scan.
-            try:
-                from .cozy_pipeline_spec import load_cozy_pipeline_spec
-
-                spec = load_cozy_pipeline_spec(path)
-                if spec is not None and spec.variant:
-                    config.variant = spec.variant
-                    print(f"DEBUG pipeline_loader variant_from_spec={config.variant}")
-            except Exception:
-                pass
         if config.variant is None:
             config.variant = detect_diffusers_variant(path)
 
diff --git a/src/gen_worker/run_metrics_v1.py b/src/gen_worker/run_metrics_v1.py
index 0db2b0e..b23dee0 100644
--- a/src/gen_worker/run_metrics_v1.py
+++ b/src/gen_worker/run_metrics_v1.py
@@ -41,18 +41,6 @@ def _extract_snapshot_digest(resolved_entry: Any) -> Optional[str]:
     return snap or None
 
 
-def _resolved_entry_for_model_id(resolved: Mapping[str, Any], canonical_model_id: str) -> Any:
-    key = str(canonical_model_id or "").strip()
-    if not key:
-        return None
-    ent = resolved.get(key)
-    if ent is not None:
-        return ent
-    if key.startswith("cozy:"):
-        return resolved.get(key.split(":", 1)[1].strip())
-    return resolved.get(f"cozy:{key}")
-
-
 def _extract_resolved_files(resolved_entry: Any) -> List[Any]:
     if resolved_entry is None:
         return []
@@ -102,17 +90,6 @@ def _cache_dir() -> Path:
     return worker_model_cache_dir()
 
 
-def _canonicalize_model_id(raw: str) -> str:
-    s = str(raw or "").strip()
-    if not s:
-        return s
-    try:
-        parsed = parse_model_ref(s)
-        return parsed.canonical()
-    except Exception:
-        return s
-
-
 @dataclass
 class ModelMetricsV1:
     model_id: str
@@ -380,8 +357,8 @@ def best_effort_init_model_metrics(
     - vram_models/disk_models are the worker-reported canonical lists.
     - For Cozy refs with resolved snapshot digests, we can also check snapshot dirs.
     """
-    vram_set = set(_canonicalize_model_id(str(x)) for x in (vram_models or []))
-    disk_set = set(_canonicalize_model_id(str(x)) for x in (disk_models or []))
+    vram_set = set(str(x) for x in (vram_models or []))
+    disk_set = set(str(x) for x in (disk_models or []))
     base = cache_dir or _cache_dir()
 
     resolved = rm.resolved_cozy_models_by_id or {}
@@ -389,11 +366,17 @@ def best_effort_init_model_metrics(
         mid = str(raw or "").strip()
         if not mid:
             continue
-        canon = _canonicalize_model_id(mid)
+        canon = mid
         snap: Optional[str] = None
         cache_state: Optional[str] = None
+        try:
+            parsed = parse_model_ref(mid)
+            if parsed.scheme == "cozy" and parsed.cozy is not None:
+                canon = parsed.cozy.canonical()
+        except Exception:
+            pass
 
-        resolved_entry = _resolved_entry_for_model_id(resolved, canon) if isinstance(resolved, Mapping) else None
+        resolved_entry = resolved.get(canon) if isinstance(resolved, Mapping) else None
         snap = _extract_snapshot_digest(resolved_entry)
 
         if canon in vram_set:
diff --git a/src/gen_worker/tensorhub_toml.py b/src/gen_worker/tensorhub_toml.py
index b6074dc..5a6e186 100644
--- a/src/gen_worker/tensorhub_toml.py
+++ b/src/gen_worker/tensorhub_toml.py
@@ -10,7 +10,7 @@
 from .names import slugify_function_name
 
 _DEFAULT_DTYPES: tuple[str, ...] = ("fp16", "bf16")
-_ALLOWED_DTYPES: frozenset[str] = frozenset({"fp16", "bf16", "fp8", "fp32", "int8", "int4", "nvfp4"})
+_ALLOWED_DTYPES: frozenset[str] = frozenset({"fp16", "bf16", "fp8", "fp32", "int8", "int4"})
 
 _RE_CLAUSE = re.compile(r"^\s*(>=|<=|==|~=|>|<)?\s*([0-9]+(?:\.[0-9]+)*)\s*$")
 _RE_VERSION_PREFIX = re.compile(r"^\s*([0-9]+(?:\.[0-9]+)*)")
diff --git a/src/gen_worker/testing/http_runner.py b/src/gen_worker/testing/http_runner.py
index c92253f..b6d905a 100644
--- a/src/gen_worker/testing/http_runner.py
+++ b/src/gen_worker/testing/http_runner.py
@@ -17,7 +17,7 @@
 
 from gen_worker.cache_paths import worker_model_cache_dir
 from gen_worker.pb import worker_scheduler_pb2 as pb
-from gen_worker.worker import RequestContext, Worker
+from gen_worker.worker import ActionContext, Worker
 
 
 def _load_manifest(path: Path) -> dict[str, Any]:
@@ -123,7 +123,7 @@ def run_task_sync(
         )
 
         # Mirror _handle_run_request's ctx construction, but use local output backend.
-        ctx = RequestContext(
+        ctx = ActionContext(
             rid,
             emitter=self._emit_progress_event,
             owner=str(owner or "") or None,
diff --git a/src/gen_worker/testing/test_output_save_contract.py b/src/gen_worker/testing/test_output_save_contract.py
index 3d7944f..94d7dec 100644
--- a/src/gen_worker/testing/test_output_save_contract.py
+++ b/src/gen_worker/testing/test_output_save_contract.py
@@ -7,7 +7,7 @@
 import unittest
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 
-from gen_worker.worker import RequestContext
+from gen_worker.worker import ActionContext
 
 
 class _UploadHandler(BaseHTTPRequestHandler):
@@ -43,7 +43,7 @@ def do_PUT(self) -> None:  # noqa: N802
 class OutputSaveContractTest(unittest.TestCase):
     def test_save_bytes_local_output_accepts_path_agnostic_refs(self) -> None:
         with tempfile.TemporaryDirectory() as td:
-            ctx = RequestContext(
+            ctx = ActionContext(
                 request_id="run-1",
                 owner="alice",
                 local_output_dir=td,
@@ -56,7 +56,7 @@ def test_save_bytes_local_output_accepts_path_agnostic_refs(self) -> None:
 
     def test_save_bytes_rejects_url_refs(self) -> None:
         with tempfile.TemporaryDirectory() as td:
-            ctx = RequestContext(request_id="run-2", local_output_dir=td)
+            ctx = ActionContext(request_id="run-2", local_output_dir=td)
             with self.assertRaises(ValueError):
                 ctx.save_bytes("https://example.test/out.bin", b"x")
 
@@ -66,7 +66,7 @@ def test_save_bytes_uploads_to_tensorhub_file_api(self) -> None:
         t.start()
         try:
             base = f"http://127.0.0.1:{srv.server_address[1]}"
-            ctx = RequestContext(
+            ctx = ActionContext(
                 request_id="run-3",
                 owner="alice",
                 file_api_base_url=base,
diff --git a/src/gen_worker/wire_protocol.py b/src/gen_worker/wire_protocol.py
index 49a8bd4..6d8be9f 100644
--- a/src/gen_worker/wire_protocol.py
+++ b/src/gen_worker/wire_protocol.py
@@ -3,7 +3,7 @@
 # Worker<->scheduler gRPC wire protocol version.
 # MAJOR: breaking changes, MINOR: additive/backward-compatible changes.
 WIRE_PROTOCOL_MAJOR = 1
-WIRE_PROTOCOL_MINOR = 2
+WIRE_PROTOCOL_MINOR = 0
 
 
 def wire_protocol_version_string() -> str:
diff --git a/src/gen_worker/worker.py b/src/gen_worker/worker.py
index 9ff4b10..80f12a7 100644
--- a/src/gen_worker/worker.py
+++ b/src/gen_worker/worker.py
@@ -28,7 +28,6 @@
 from types import ModuleType
 import hashlib
 import msgspec
-from collections import deque
 try:
     import torch
 except Exception:  # pragma: no cover - optional dependency
@@ -65,7 +64,7 @@
 from .model_interface import ModelManagementInterface
 from .downloader import CozyHubDownloader, ModelDownloader
 from .model_ref_downloader import ModelRefDownloader
-from .model_refs import ParsedModelRef, parse_model_ref
+from .model_refs import parse_model_ref
 from .types import Asset
 from .model_cache import ModelCache, ModelCacheStats, ModelLocation
 from .run_metrics_v1 import RunMetricsV1, best_effort_bytes_downloaded, best_effort_init_model_metrics, safe_json_bytes
@@ -144,7 +143,7 @@ async def close(self) -> None:  # pragma: no cover - interface
 class _RealtimeSessionState:
     session_id: str
     spec: _WebsocketSpec
-    ctx: RequestContext
+    ctx: ActionContext
     loop: asyncio.AbstractEventLoop
     in_q: "asyncio.Queue[Optional[bytes]]"
     closed: threading.Event
@@ -378,7 +377,7 @@ def get_key(self, kid: Optional[str]) -> Optional[Any]:
                 return self._keys[kid]
             return None
 
-class RequestContext:
+class ActionContext:
     """Context object passed to action functions, allowing cancellation."""
     def __init__(
         self,
@@ -479,7 +478,7 @@ def _resolve_local_output_path(self, ref: str) -> Optional[str]:
         """
         Dev-only local output backend.
 
-        When local_output_dir is set, RequestContext.save_* will write outputs to disk
+        When local_output_dir is set, ActionContext.save_* will write outputs to disk
         instead of using Cozy Hub's file API.
         """
         base = (self._local_output_dir or "").strip()
@@ -808,9 +807,8 @@ def __init__(
         worker_id: Optional[str] = None,
         worker_jwt: str = "",
         use_tls: bool = False,
-        reconnect_delay: float = 0.1,
+        reconnect_delay: int = 5,
         max_reconnect_attempts: int = 0,  # 0 means infinite retries
-        lb_only_retries: bool = True,
         model_manager: Optional[ModelManagementInterface] = None, # Optional model manager
         downloader: Optional[ModelDownloader] = None,  # Optional model downloader
         manifest: Optional[Dict[str, Any]] = None,  # Optional manifest from build
@@ -824,9 +822,8 @@ def __init__(
             worker_id: Unique ID for this worker (generated if not provided).
             worker_jwt: Worker-connect JWT (required).
             use_tls: Whether to use TLS for the connection.
-            reconnect_delay: Base seconds for exponential reconnect attempts.
+            reconnect_delay: Seconds to wait between reconnection attempts.
             max_reconnect_attempts: Max reconnect attempts (0 = infinite).
-            lb_only_retries: Retry through LB endpoint/seed list only; ignore leader redirects.
             model_manager: Optional model manager.
             downloader: Optional model downloader.
             manifest: Optional manifest dict (baked in at build time) containing models, resources, etc.
@@ -841,9 +838,6 @@ def __init__(
         self._warn_model_resolve_s = float(os.getenv("WORKER_WARN_MODEL_RESOLVE_S", "30") or "30")
         self._warn_model_load_s = float(os.getenv("WORKER_WARN_MODEL_LOAD_S", "60") or "60")
         self._warn_inference_s = float(os.getenv("WORKER_WARN_INFERENCE_S", "60") or "60")
-        self._emit_detailed_task_events = str(os.getenv("WORKER_EMIT_DETAILED_TASK_EVENTS", "0") or "0").strip().lower() in (
-            "1", "true", "t", "yes", "y", "on",
-        )
         self.user_module_names = user_module_names # Store module names
         self.worker_id = worker_id or f"py-worker-{os.getpid()}"
         self.worker_jwt = (worker_jwt or "").strip()
@@ -852,7 +846,6 @@ def __init__(
         self.use_tls = use_tls
         self.reconnect_delay = reconnect_delay
         self.max_reconnect_attempts = max_reconnect_attempts
-        self.lb_only_retries = lb_only_retries
         self.max_input_bytes = int(os.getenv("WORKER_MAX_INPUT_BYTES", "0"))
         self.max_output_bytes = int(os.getenv("WORKER_MAX_OUTPUT_BYTES", "0"))
 
@@ -874,14 +867,8 @@ def __init__(
 
         self._task_specs: Dict[str, _TaskSpec] = {}
         self._ws_specs: Dict[str, _WebsocketSpec] = {}
-        self._active_tasks: Dict[str, RequestContext] = {}
+        self._active_tasks: Dict[str, ActionContext] = {}
         self._active_tasks_lock = threading.Lock()
-        self._assignment_epoch_by_request: Dict[str, int] = {}
-        self._stream_seq_by_key: Dict[Tuple[str, str], int] = {}
-        self._stream_replay_buffers_by_request: Dict[str, deque[Tuple[float, bytes]]] = {}
-        self._stream_tracking_lock = threading.Lock()
-        self._stream_replay_max_frames = int(os.getenv("WORKER_STREAM_REPLAY_MAX_FRAMES", "1024") or "1024")
-        self._stream_replay_window_seconds = int(os.getenv("WORKER_STREAM_REPLAY_WINDOW_SECONDS", "900") or "900")
         self._request_batch_context: Dict[str, Tuple[str, str]] = {}  # request_id -> (batch_id, item_id)
         self._request_batch_context_lock = threading.Lock()
         self.max_concurrency = int(os.getenv("WORKER_MAX_CONCURRENCY", "0"))
@@ -940,11 +927,9 @@ def __init__(
         self._realtime_sessions: Dict[str, _RealtimeSessionState] = {}
         self._realtime_lock = threading.Lock()
 
-        self._reconnect_delay_base = max(0.0, float(reconnect_delay))
-        self._reconnect_delay_max = float(os.getenv("RECONNECT_MAX_DELAY", "1.0") or "1.0")
-        if self._reconnect_delay_max <= 0:
-            self._reconnect_delay_max = 1.0
-        self._reconnect_jitter_seconds = float(os.getenv("RECONNECT_JITTER_SECONDS", "0.1") or "0.1")
+        self._reconnect_delay_base = max(0, reconnect_delay)
+        self._reconnect_delay_max = int(os.getenv("RECONNECT_MAX_DELAY", "60"))
+        self._reconnect_jitter = float(os.getenv("RECONNECT_JITTER_SECONDS", "1.0"))
 
         resolved_model_manager = model_manager
         if resolved_model_manager is None:
@@ -999,7 +984,7 @@ def __init__(
         self._fixed_model_spec_by_key: Dict[str, Dict[str, Any]] = {}
         self._payload_model_spec_by_key_by_function: Dict[str, Dict[str, Dict[str, Any]]] = {}
         # Orchestrator-resolved manifests received in EndpointConfig (startup prefetch baseline).
-        # Keys should be canonical model ref strings (e.g. "owner/repo@sha256:<digest>").
+        # Keys should be canonical model ref strings (e.g. "cozy:owner/repo@sha256:<digest>").
         self._resolved_cozy_models_by_id_baseline: Dict[str, Any] = {}
         self._prefetch_lock = threading.Lock()
         self._prefetch_thread: Optional[threading.Thread] = None
@@ -1146,22 +1131,6 @@ def _extract_leader_addr(details: Optional[str]) -> Optional[str]:
     def _is_protocol_incompatibility(details: Optional[str]) -> bool:
         return bool(details and details.startswith("unsupported_worker_protocol:"))
 
-    @staticmethod
-    def _is_non_transient_auth_error(code: Any, details: Optional[str]) -> bool:
-        # Authentication/authorization failures are non-transient for the
-        # current worker identity/JWT. Reconnecting in a tight loop only creates
-        # avoidable LB/orchestrator churn.
-        if code in (grpc.StatusCode.UNAUTHENTICATED, grpc.StatusCode.PERMISSION_DENIED):
-            return True
-        d = (details or "").strip()
-        return d in (
-            "worker_not_expected",
-            "invalid worker token claims",
-            "invalid worker token",
-            "missing worker token",
-            "missing auth metadata",
-        )
-
     def _handle_protocol_incompatibility(self, details: str) -> None:
         logger.error(
             "Scheduler rejected worker wire protocol %s (%s). "
@@ -1181,32 +1150,11 @@ def _handle_protocol_incompatibility(self, details: str) -> None:
         self._close_connection()
         self._stop_event.set()
 
-    def _handle_non_transient_connect_error(self, code: Any, details: str) -> None:
-        logger.error(
-            "Scheduler rejected worker connect as non-transient (%s: %s). "
-            "Stopping worker to avoid reconnect churn.",
-            code,
-            details,
-        )
-        self._emit_startup_phase(
-            "scheduler_connect_non_transient_rejected",
-            status="error",
-            level=logging.ERROR,
-            emit_worker_event=False,
-            grpc_code=str(code),
-            grpc_details=str(details),
-        )
-        self._running = False
-        self._close_connection()
-        self._stop_event.set()
-
     def _set_scheduler_addr(self, addr: str) -> None:
         addr = addr.strip()
         if not addr:
             return
         self.scheduler_addr = addr
-        if self.lb_only_retries:
-            return
         if addr not in self.scheduler_addrs:
             self.scheduler_addrs.insert(0, addr)
 
@@ -1294,16 +1242,11 @@ def _emit_progress_event(self, event: Dict[str, Any]) -> None:
 
     def _emit_worker_event_bytes(self, request_id: str, event_type: str, payload_json: bytes) -> None:
         """Best-effort worker->scheduler WorkerEvent emitter (must never fail a run)."""
-        et = str(event_type or "").strip()
-        if not et:
-            return
-        if not self._should_emit_worker_event(et):
-            return
         try:
             msg = pb.WorkerSchedulerMessage(
                 worker_event=pb.WorkerEvent(
                     request_id=str(request_id or ""),
-                    event_type=et,
+                    event_type=str(event_type or ""),
                     payload_json=bytes(payload_json or b"{}"),
                 )
             )
@@ -1311,21 +1254,6 @@ def _emit_worker_event_bytes(self, request_id: str, event_type: str, payload_jso
         except Exception:
             return
 
-    def _should_emit_worker_event(self, event_type: str) -> bool:
-        et = str(event_type or "").strip().lower()
-        if et == "":
-            return False
-        if self._emit_detailed_task_events:
-            return True
-
-        # Keep high-signal diagnostics by default and suppress high-volume
-        # lifecycle/metrics chatter on the hot path.
-        if et.startswith("task."):
-            return et.endswith(".failed") or et.endswith(".rejected") or et.endswith(".stuck")
-        if et.startswith("metrics."):
-            return False
-        return True
-
     def _emit_incremental_delta_typed(
         self,
         *,
@@ -1684,12 +1612,12 @@ def _inspect_task_spec(self, func: Callable[..., Any]) -> _TaskSpec:
         sig = inspect.signature(func)
         params = list(sig.parameters.values())
         if not params:
-            raise ValueError("must accept ctx: RequestContext as first arg")
+            raise ValueError("must accept ctx: ActionContext as first arg")
 
         ctx_name = params[0].name
         ctx_type = hints.get(ctx_name)
-        if ctx_type is not RequestContext:
-            raise ValueError("first argument must be ctx: RequestContext")
+        if ctx_type is not ActionContext:
+            raise ValueError("first argument must be ctx: ActionContext")
 
         injections: list[InjectionSpec] = []
         payload_type: Optional[type[msgspec.Struct]] = None
@@ -1805,11 +1733,11 @@ def _inspect_websocket_spec(self, func: Callable[..., Any]) -> _WebsocketSpec:
         sig = inspect.signature(func)
         params = list(sig.parameters.values())
         if len(params) < 2:
-            raise ValueError("websocket handler must accept (ctx: RequestContext, sock: RealtimeSocket, ...)")
+            raise ValueError("websocket handler must accept (ctx: ActionContext, sock: RealtimeSocket, ...)")
 
         ctx_name = params[0].name
-        if hints.get(ctx_name) is not RequestContext:
-            raise ValueError("first argument must be ctx: RequestContext")
+        if hints.get(ctx_name) is not ActionContext:
+            raise ValueError("first argument must be ctx: ActionContext")
 
         # We do not enforce a concrete socket type here; it is worker-owned and may
         # be provided by the runtime. We only validate that the param exists.
@@ -1897,166 +1825,17 @@ def _infer_return_type(self, func: Callable[..., Any]) -> Optional[type[msgspec.
 
         return return_type
 
-    def _next_stream_seq(self, request_id: str, stream_kind: str) -> int:
-        rid = str(request_id or "").strip()
-        kind = str(stream_kind or "").strip() or "worker_event"
-        if not rid:
-            return 0
-        with self._stream_tracking_lock:
-            key = (rid, kind)
-            cur = int(self._stream_seq_by_key.get(key, 0) or 0)
-            nxt = cur + 1
-            self._stream_seq_by_key[key] = nxt
-            return nxt
-
-    def _assignment_epoch_for_request(self, request_id: str) -> int:
-        rid = str(request_id or "").strip()
-        if not rid:
-            return 0
-        with self._stream_tracking_lock:
-            return int(self._assignment_epoch_by_request.get(rid, 0) or 0)
-
-    def _record_replay_message(self, request_id: str, message: WorkerSchedulerMessage) -> None:
-        rid = str(request_id or "").strip()
-        if not rid:
-            return
-        try:
-            raw = bytes(message.SerializeToString())
-        except Exception:
-            return
-        now = time.time()
-        with self._stream_tracking_lock:
-            buf = self._stream_replay_buffers_by_request.get(rid)
-            if buf is None:
-                maxlen = self._stream_replay_max_frames if self._stream_replay_max_frames > 0 else 1024
-                buf = deque(maxlen=maxlen)
-                self._stream_replay_buffers_by_request[rid] = buf
-            buf.append((now, raw))
-            if self._stream_replay_window_seconds > 0:
-                cutoff = now - float(self._stream_replay_window_seconds)
-                while buf and buf[0][0] < cutoff:
-                    buf.popleft()
-
-    def _prepare_stream_contract_fields(self, message: WorkerSchedulerMessage) -> Optional[str]:
-        msg_type = None
-        try:
-            msg_type = message.WhichOneof("msg")
-        except Exception:
-            msg_type = None
-        if msg_type == "run_result":
-            rr = getattr(message, "run_result", None)
-            if rr is None:
-                return None
-            rid = str(getattr(rr, "request_id", "") or "").strip()
-            if not rid:
-                return None
-            epoch = int(getattr(rr, "assignment_attempt_epoch", 0) or 0)
-            if epoch <= 0:
-                epoch = self._assignment_epoch_for_request(rid)
-                if epoch > 0:
-                    rr.assignment_attempt_epoch = int(epoch)
-            if int(getattr(rr, "stream_seq", 0) or 0) <= 0:
-                rr.stream_seq = int(self._next_stream_seq(rid, "run_result"))
-            return rid
-        if msg_type == "worker_event":
-            ev = getattr(message, "worker_event", None)
-            if ev is None:
-                return None
-            rid = str(getattr(ev, "request_id", "") or "").strip()
-            if not rid:
-                return None
-            epoch = int(getattr(ev, "assignment_attempt_epoch", 0) or 0)
-            if epoch <= 0:
-                epoch = self._assignment_epoch_for_request(rid)
-                if epoch > 0:
-                    ev.assignment_attempt_epoch = int(epoch)
-            if int(getattr(ev, "stream_seq", 0) or 0) <= 0:
-                ev.stream_seq = int(self._next_stream_seq(rid, "worker_event"))
-            return rid
-        if msg_type == "incremental_token_delta":
-            ev = getattr(message, "incremental_token_delta", None)
-            if ev is None:
-                return None
-            rid = str(getattr(ev, "request_id", "") or "").strip()
-            if not rid:
-                return None
-            epoch = int(getattr(ev, "assignment_attempt_epoch", 0) or 0)
-            if epoch <= 0:
-                epoch = self._assignment_epoch_for_request(rid)
-                if epoch > 0:
-                    ev.assignment_attempt_epoch = int(epoch)
-            if int(getattr(ev, "sequence", 0) or 0) <= 0:
-                item_id = str(getattr(ev, "item_id", "") or "").strip() or "item-0"
-                ev.sequence = int(self._next_stream_seq(rid, f"incremental:{item_id}"))
-            return rid
-        if msg_type == "incremental_token_stream_done":
-            ev = getattr(message, "incremental_token_stream_done", None)
-            if ev is None:
-                return None
-            rid = str(getattr(ev, "request_id", "") or "").strip()
-            if not rid:
-                return None
-            epoch = int(getattr(ev, "assignment_attempt_epoch", 0) or 0)
-            if epoch <= 0:
-                epoch = self._assignment_epoch_for_request(rid)
-                if epoch > 0:
-                    ev.assignment_attempt_epoch = int(epoch)
-            if int(getattr(ev, "sequence", 0) or 0) <= 0:
-                item_id = str(getattr(ev, "item_id", "") or "").strip() or "item-0"
-                ev.sequence = int(self._next_stream_seq(rid, f"incremental:{item_id}"))
-            return rid
-        if msg_type == "incremental_token_stream_error":
-            ev = getattr(message, "incremental_token_stream_error", None)
-            if ev is None:
-                return None
-            rid = str(getattr(ev, "request_id", "") or "").strip()
-            if not rid:
-                return None
-            epoch = int(getattr(ev, "assignment_attempt_epoch", 0) or 0)
-            if epoch <= 0:
-                epoch = self._assignment_epoch_for_request(rid)
-                if epoch > 0:
-                    ev.assignment_attempt_epoch = int(epoch)
-            if int(getattr(ev, "sequence", 0) or 0) <= 0:
-                item_id = str(getattr(ev, "item_id", "") or "").strip() or "item-0"
-                ev.sequence = int(self._next_stream_seq(rid, f"incremental:{item_id}"))
-            return rid
-        if msg_type == "batch_run_result":
-            br = getattr(message, "batch_run_result", None)
-            if br is None:
-                return None
-            first_request_id: Optional[str] = None
-            for item in list(getattr(br, "items", []) or []):
-                rid = str(getattr(item, "request_id", "") or "").strip()
-                if not rid:
-                    continue
-                if first_request_id is None:
-                    first_request_id = rid
-                epoch = int(getattr(item, "assignment_attempt_epoch", 0) or 0)
-                if epoch <= 0:
-                    epoch = self._assignment_epoch_for_request(rid)
-                    if epoch > 0:
-                        item.assignment_attempt_epoch = int(epoch)
-                if int(getattr(item, "stream_seq", 0) or 0) <= 0:
-                    item_id = str(getattr(item, "item_id", "") or "").strip() or "item-0"
-                    item.stream_seq = int(self._next_stream_seq(rid, f"batch_result:{item_id}"))
-            return first_request_id
-        return None
-
-    def _send_message(self, message: WorkerSchedulerMessage, *, track_replay: bool = True) -> None:
+    def _send_message(self, message: WorkerSchedulerMessage) -> None:
         """Add a message to the outgoing queue."""
-        replay_request_id = self._prepare_stream_contract_fields(message)
         if self._running and not self._stop_event.is_set():
             try:
                 self._outgoing_queue.put_nowait(message)
-                if track_replay and replay_request_id:
-                    self._record_replay_message(replay_request_id, message)
             except queue.Full:
                  logger.error("Outgoing message queue is full. Message dropped!")
         else:
             logger.warning("Attempted to send message while worker is stopping or stopped.")
 
-    def _materialize_assets(self, ctx: RequestContext, obj: Any) -> None:
+    def _materialize_assets(self, ctx: ActionContext, obj: Any) -> None:
         if isinstance(obj, Asset):
             self._materialize_asset(ctx, obj)
             return
@@ -2076,7 +1855,7 @@ def _materialize_assets(self, ctx: RequestContext, obj: Any) -> None:
                 except Exception:
                     continue
 
-    def _materialize_asset(self, ctx: RequestContext, asset: Asset) -> None:
+    def _materialize_asset(self, ctx: ActionContext, asset: Asset) -> None:
         if asset.local_path:
             return
         ref = (asset.ref or "").strip()
@@ -2309,7 +2088,7 @@ def connect(self) -> bool:
         attempted: set[str] = set()
         while True:
             addr = None
-            if (not self.lb_only_retries) and self._leader_hint and self._leader_hint not in attempted:
+            if self._leader_hint and self._leader_hint not in attempted:
                 addr = self._leader_hint
                 self._leader_hint = None
             else:
@@ -2374,18 +2153,10 @@ def _connect_once(self) -> bool:
             leader = self._extract_leader_addr(details)
             if code == grpc.StatusCode.FAILED_PRECONDITION and self._is_protocol_incompatibility(details):
                 self._handle_protocol_incompatibility(str(details))
-            elif self._is_non_transient_auth_error(code, details):
-                self._handle_non_transient_connect_error(code, str(details))
             elif code == grpc.StatusCode.FAILED_PRECONDITION and leader:
-                if self.lb_only_retries:
-                    logger.warning(
-                        "Scheduler returned ownership hint for %s, but LB-only retries are enabled; reconnecting via configured LB seed(s).",
-                        self.scheduler_addr,
-                    )
-                else:
-                    logger.warning(f"Scheduler returned not_leader for {self.scheduler_addr}; redirecting to {leader}")
-                    self._leader_hint = leader
-                    self._set_scheduler_addr(leader)
+                logger.warning(f"Scheduler returned not_leader for {self.scheduler_addr}; redirecting to {leader}")
+                self._leader_hint = leader
+                self._set_scheduler_addr(leader)
             else:
                 logger.error(f"Failed to connect to scheduler: {code} - {details}")
             self._emit_startup_phase(
@@ -2439,70 +2210,6 @@ def _heartbeat_loop(self) -> None:
                     self._handle_connection_error()
                     break # Stop heartbeating on error
 
-    def _next_reconnect_delay(self, reconnect_count: int) -> float:
-        backoff = self._reconnect_delay_base * (2 ** max(reconnect_count - 1, 0))
-        backoff = min(backoff, self._reconnect_delay_max)
-        jitter = random.uniform(0, self._reconnect_jitter_seconds) if self._reconnect_jitter_seconds > 0 else 0.0
-        return min(self._reconnect_delay_max, max(0.0, backoff + jitter))
-
-    def _build_active_assignment_resume_hints(self) -> List[Any]:
-        hints: List[Any] = []
-        with self._active_tasks_lock:
-            active_ids = list(self._active_tasks.keys())
-        with self._stream_tracking_lock:
-            for request_id in active_ids:
-                rid = str(request_id or "").strip()
-                if not rid:
-                    continue
-                epoch = int(self._assignment_epoch_by_request.get(rid, 0) or 0)
-                if epoch <= 0:
-                    continue
-                worker_event_seq = int(self._stream_seq_by_key.get((rid, "worker_event"), 0) or 0)
-                run_result_seq = int(self._stream_seq_by_key.get((rid, "run_result"), 0) or 0)
-                incr_max = 0
-                for key, seq in self._stream_seq_by_key.items():
-                    if not key or len(key) != 2:
-                        continue
-                    if key[0] != rid:
-                        continue
-                    if str(key[1]).startswith("incremental:"):
-                        incr_max = max(incr_max, int(seq or 0))
-                hints.append(
-                    pb.ActiveAssignmentResume(
-                        request_id=rid,
-                        assignment_attempt_epoch=epoch,
-                        last_run_result_seq=run_result_seq,
-                        last_worker_event_seq=worker_event_seq,
-                        last_incremental_seq=incr_max,
-                    )
-                )
-        return hints
-
-    def _replay_active_assignment_buffers(self) -> None:
-        now = time.time()
-        replayed = 0
-        with self._stream_tracking_lock:
-            request_ids = list(self._stream_replay_buffers_by_request.keys())
-            for rid in request_ids:
-                buf = self._stream_replay_buffers_by_request.get(rid)
-                if buf is None or len(buf) == 0:
-                    continue
-                if self._stream_replay_window_seconds > 0:
-                    cutoff = now - float(self._stream_replay_window_seconds)
-                    while buf and buf[0][0] < cutoff:
-                        buf.popleft()
-                frames = list(buf)
-                for _, raw in frames:
-                    try:
-                        msg = pb.WorkerSchedulerMessage()
-                        msg.ParseFromString(raw)
-                        self._send_message(msg, track_replay=False)
-                        replayed += 1
-                    except Exception:
-                        continue
-        if replayed > 0:
-            logger.info("Replayed %d buffered worker stream message(s) after reconnect.", replayed)
-
     def _register_worker(self, is_heartbeat: bool = False) -> None:
         """Create and send a registration/heartbeat message."""
         try:
@@ -2559,9 +2266,9 @@ def _register_worker(self, is_heartbeat: bool = False) -> None:
                 vram_models = self._model_manager.get_vram_loaded_models()
                 supports_model_loading_flag = True
             elif self._model_cache:
-                # Cache-only workers can still report VRAM/disk inventory, but
-                # they do not support explicit Load/UnloadModelCommand handling.
+                # Use model cache for VRAM-loaded models if no legacy model_manager
                 vram_models = self._model_cache.get_vram_models()
+                supports_model_loading_flag = True
 
             # Get disk-cached and downloading models from model cache
             if self._model_cache:
@@ -2648,14 +2355,11 @@ def _register_worker(self, is_heartbeat: bool = False) -> None:
                 is_heartbeat=is_heartbeat,
                 protocol_major=WIRE_PROTOCOL_MAJOR,
                 protocol_minor=WIRE_PROTOCOL_MINOR,
-                active_assignments=self._build_active_assignment_resume_hints(),
             )
             message = pb.WorkerSchedulerMessage(worker_registration=registration)
             # logger.info(f"DEBUG: Preparing to send registration. Resource object: {resources}")
             # logger.info(f"DEBUG: Value being sent for runpod_pod_id: '{resources.runpod_pod_id}'")
             self._send_message(message)
-            if not is_heartbeat and self._reconnect_count > 1:
-                self._replay_active_assignment_buffers()
 
             # Best-effort: report disk inventory + volume identity for NFS-aware scheduling/debug.
             # This is intentionally redundant with WorkerResources.disk_models but adds the missing
@@ -2842,7 +2546,11 @@ def run(self) -> None:
                         break
 
                     if self._running and not self._stop_event.is_set():
-                        delay = self._next_reconnect_delay(self._reconnect_count)
+                        backoff = self._reconnect_delay_base * (2 ** max(self._reconnect_count - 1, 0))
+                        if self._reconnect_delay_max > 0:
+                            backoff = min(backoff, self._reconnect_delay_max)
+                        jitter = random.uniform(0, self._reconnect_jitter) if self._reconnect_jitter > 0 else 0
+                        delay = backoff + jitter
                         logger.info(f"Connection attempt {self._reconnect_count} failed. Retrying in {delay:.2f} seconds...")
                         # Wait for delay, but break if stop event is set during wait
                         if self._stop_event.wait(delay):
@@ -2996,8 +2704,6 @@ def _receive_loop(self) -> None:
                     self._leader_hint = leader
                     self._set_scheduler_addr(leader)
                 self._handle_connection_error()
-            elif self._is_non_transient_auth_error(code, details):
-                self._handle_non_transient_connect_error(code, str(details))
             elif code == grpc.StatusCode.CANCELLED:
                 logger.warning("gRPC stream unexpectedly cancelled by server or network.")
                 self._handle_connection_error()
@@ -3049,13 +2755,7 @@ def _process_message(self, message: WorkerSchedulerMessage) -> None:
             request_id = cmd.request_id
             item_ids = [str(x).strip() for x in list(getattr(cmd, "item_ids", []) or []) if str(x).strip()]
             cancel_queued_only = bool(getattr(cmd, "cancel_queued_only", False))
-            assignment_attempt_epoch = int(getattr(cmd, "assignment_attempt_epoch", 0) or 0)
-            self._handle_interrupt_request(
-                request_id,
-                item_ids=item_ids,
-                cancel_queued_only=cancel_queued_only,
-                assignment_attempt_epoch=assignment_attempt_epoch,
-            )
+            self._handle_interrupt_request(request_id, item_ids=item_ids, cancel_queued_only=cancel_queued_only)
         elif msg_type == "runtime_batching_config_cmd":
             self._handle_runtime_batching_config_cmd(message.runtime_batching_config_cmd)
         elif msg_type == "realtime_open_cmd":
@@ -3071,7 +2771,7 @@ def _process_message(self, message: WorkerSchedulerMessage) -> None:
             cfg = message.endpoint_config
             resolved_by_variant = dict(getattr(cfg, "resolved_cozy_models_by_variant_ref", {}) or {})
             logger.info(
-                "Received EndpointConfig (endpoint-artifacts) (supported=%d required=%d resolved=%d)",
+                "Received EndpointConfig (supported=%d required=%d resolved=%d)",
                 len(cfg.supported_repo_refs),
                 len(cfg.required_variant_refs),
                 len(resolved_by_variant),
@@ -3529,7 +3229,7 @@ def _handle_load_model_cmd(self, cmd: LoadModelCommand) -> None:
                 # Set resolved cozy models context so downloads can use orchestrator-resolved URLs.
                 from .model_ref_downloader import reset_resolved_cozy_models_by_id, set_resolved_cozy_models_by_id
                 per_cmd = dict(getattr(cmd, "resolved_cozy_models_by_id", {}) or {})
-                baseline = self._resolved_cozy_models_by_id_baseline or {}
+                baseline = getattr(self, "_resolved_cozy_models_by_id_baseline", None) or {}
                 merged = {**baseline, **per_cmd} if per_cmd else dict(baseline)
                 tok = set_resolved_cozy_models_by_id(merged or None)
                 try:
@@ -3665,16 +3365,13 @@ def _handle_run_request(self, request: TaskExecutionRequest) -> None:
         request_id = request.request_id
         function_name = request.function_name
         input_payload = request.input_payload
-        assignment_attempt_epoch = int(getattr(request, "assignment_attempt_epoch", 0) or 0)
         required_model_id_for_exec = ""
         timeout_ms = int(getattr(request, "timeout_ms", 0) or 0)
         owner = str(getattr(request, "owner", "") or "") or (self.owner or "")
         invoker_id = str(getattr(request, "invoker_id", "") or "")
         file_base_url = str(getattr(request, "file_base_url", "") or "")
         file_token = str(getattr(request, "file_token", "") or "")
-        resolved_cozy_models_by_id = self._canonicalize_resolved_models_map(
-            dict(getattr(request, "resolved_cozy_models_by_id", {}) or {})
-        )
+        resolved_cozy_models_by_id = dict(getattr(request, "resolved_cozy_models_by_id", {}) or {})
         parent_request_id = str(getattr(request, "parent_request_id", "") or "").strip() or None
         child_request_id = str(getattr(request, "child_request_id", "") or "").strip() or None
         item_id = str(getattr(request, "item_id", "") or "").strip() or None
@@ -3773,7 +3470,7 @@ def _handle_run_request(self, request: TaskExecutionRequest) -> None:
             except Exception:
                 pass
 
-        ctx = RequestContext(
+        ctx = ActionContext(
             request_id,
             emitter=self._emit_progress_event,
             owner=owner or None,
@@ -3807,9 +3504,6 @@ def _handle_run_request(self, request: TaskExecutionRequest) -> None:
                   self._send_task_result(request_id, False, None, "retryable", True, "worker busy", error_msg)
                   return
              self._active_tasks[request_id] = ctx
-        if assignment_attempt_epoch > 0:
-            with self._stream_tracking_lock:
-                self._assignment_epoch_by_request[request_id] = assignment_attempt_epoch
 
         # Execute function in a separate thread to avoid blocking the receive loop
         thread = threading.Thread(
@@ -3847,43 +3541,21 @@ def _handle_batch_run_request(self, request: Any) -> None:
                 timeout_ms=int(getattr(item, "timeout_ms", 0) or 0),
                 owner=str(getattr(item, "owner", "") or ""),
                 invoker_id=str(getattr(item, "invoker_id", "") or ""),
-                resolved_cozy_models_by_id=self._canonicalize_resolved_models_map(
-                    dict(getattr(item, "resolved_cozy_models_by_id", {}) or {})
-                ),
+                resolved_cozy_models_by_id=dict(getattr(item, "resolved_cozy_models_by_id", {}) or {}),
                 parent_request_id=str(getattr(item, "parent_request_id", "") or ""),
                 child_request_id=str(getattr(item, "child_request_id", "") or ""),
                 item_id=item_id,
                 item_index=int(getattr(item, "item_index", 0) or 0),
-                assignment_attempt_epoch=int(getattr(item, "assignment_attempt_epoch", 0) or 0),
             )
             self._handle_run_request(req)
 
-    def _handle_interrupt_request(
-        self,
-        request_id: str,
-        *,
-        item_ids: Optional[List[str]] = None,
-        cancel_queued_only: bool = False,
-        assignment_attempt_epoch: int = 0,
-    ) -> None:
+    def _handle_interrupt_request(self, request_id: str, *, item_ids: Optional[List[str]] = None, cancel_queued_only: bool = False) -> None:
         """Handle a request to interrupt/cancel a running task."""
-        request_id = str(request_id or "").strip()
-        if assignment_attempt_epoch > 0:
-            expected = self._assignment_epoch_for_request(request_id)
-            if expected > 0 and int(assignment_attempt_epoch) != int(expected):
-                logger.info(
-                    "Ignoring stale interrupt for request_id=%s got_epoch=%d expected_epoch=%d",
-                    request_id,
-                    assignment_attempt_epoch,
-                    expected,
-                )
-                return
         logger.info(
-            "Received interrupt request for request_id=%s item_ids=%s cancel_queued_only=%s epoch=%d",
+            "Received interrupt request for request_id=%s item_ids=%s cancel_queued_only=%s",
             request_id,
             item_ids or [],
             cancel_queued_only,
-            assignment_attempt_epoch,
         )
         with self._active_tasks_lock:
             ctx = self._active_tasks.get(request_id)
@@ -3931,7 +3603,7 @@ def _handle_realtime_open_cmd(self, cmd: Any) -> None:
                             materialized_input_urls[ks] = vs
             except Exception:
                 pass
-        ctx = RequestContext(
+        ctx = ActionContext(
             session_id,
             emitter=self._emit_progress_event,
             owner=owner or None,
@@ -4053,7 +3725,7 @@ def _handle_realtime_close_cmd(self, cmd: Any) -> None:
 
     def _execute_task(
         self,
-        ctx: RequestContext,
+        ctx: ActionContext,
         spec: _TaskSpec,
         input_payload: bytes,
     ) -> None:
@@ -4074,7 +3746,7 @@ def _execute_task(
             required_models=list(getattr(ctx, "required_models", []) or []),
             resolved_cozy_models_by_id=resolved_map,
         )
-        # Attach to ctx so RequestContext.save_* and injection paths can accumulate.
+        # Attach to ctx so ActionContext.save_* and injection paths can accumulate.
         try:
             setattr(ctx, "_run_metrics", rm)
         except Exception:
@@ -4133,17 +3805,12 @@ def _execute_task(
 
         models_in_use: set[str] = set()
         inference_watchdog: Optional[threading.Timer] = None
-        print(f"DEBUG [execute_task] entered request_id={request_id} function={spec.name} payload_bytes={len(input_payload or b'')} timeout_ms={ctx.timeout_ms or 'none'}", flush=True)
         try:
-            _is_canceled = ctx.is_canceled()
-            print(f"DEBUG [execute_task] cancellation_check request_id={request_id} is_canceled={_is_canceled} deadline={getattr(ctx, '_deadline', None)}", flush=True)
-            if _is_canceled:
+            if ctx.is_canceled():
                 raise CanceledError("canceled")
 
             # Decode payload strictly.
-            print(f"DEBUG [execute_task] decoding_payload request_id={request_id} payload_type={spec.payload_type}", flush=True)
             input_obj = msgspec.msgpack.decode(input_payload, type=spec.payload_type)
-            print(f"DEBUG [execute_task] payload_decoded request_id={request_id} input={input_obj!r}", flush=True)
             # Optional post-decode constraints (e.g. clamping) declared on the payload type.
             try:
                 from .payload_constraints import apply_payload_constraints
@@ -4151,9 +3818,7 @@ def _execute_task(
                 _ = apply_payload_constraints(input_obj)
             except Exception:
                 pass
-            print(f"DEBUG [execute_task] materializing_assets request_id={request_id}", flush=True)
             self._materialize_assets(ctx, input_obj)
-            print(f"DEBUG [execute_task] assets_materialized request_id={request_id}", flush=True)
             # Best-effort extract diffusion-ish numeric fields for metrics.run.
             try:
                 def _get_num(name: str) -> Optional[float]:
@@ -4187,10 +3852,8 @@ def _get_num(name: str) -> Optional[float]:
             call_kwargs[spec.ctx_param] = ctx
             call_kwargs[spec.payload_param] = input_obj
 
-            print(f"DEBUG [execute_task] injection_loop request_id={request_id} injections={len(spec.injections)}", flush=True)
             for inj in spec.injections:
                 resolve_t0 = time.monotonic()
-                print(f"DEBUG [execute_task] resolving_model request_id={request_id} param={inj.param_name}", flush=True)
                 resolve_watchdog = self._start_task_phase_watchdog(
                     request_id=request_id,
                     phase="model_resolve",
@@ -4273,9 +3936,12 @@ def _get_num(name: str) -> Optional[float]:
                     },
                 )
                 try:
-                    print(f"DEBUG [execute_task] loading_model request_id={request_id} param={inj.param_name} model_id={canon_model_id}", flush=True)
                     call_kwargs[inj.param_name] = self._resolve_injected_value(ctx, inj.param_type, model_id, inj)
-                    print(f"DEBUG [execute_task] model_loaded request_id={request_id} param={inj.param_name} model_id={canon_model_id} pipeline_type={type(call_kwargs[inj.param_name]).__name__}", flush=True)
+                    logger.info(
+                        "[request_id=%s] model load resolved: param=%s model=%s duration_ms=%d",
+                        request_id, inj.param_name, canon_model_id,
+                        int((time.monotonic() - load_t0) * 1000),
+                    )
                     self._emit_task_event(
                         request_id,
                         "task.model_load.completed",
@@ -4304,19 +3970,15 @@ def _get_num(name: str) -> Optional[float]:
                         load_watchdog.cancel()
 
             # Invoke.
-            t_infer0 = time.monotonic()
-            _infer_warn_s = float(getattr(self, "_warn_inference_s", 60.0))
             logger.info(
-                "inference.start request_id=%s function=%s timeout_ms=%s warn_after_s=%.1f",
-                request_id,
-                spec.name,
-                ctx.timeout_ms if ctx.timeout_ms else "none",
-                _infer_warn_s,
+                "[request_id=%s] all injections resolved, entering inference for function=%s canceled=%s",
+                request_id, spec.name, ctx.is_canceled(),
             )
+            t_infer0 = time.monotonic()
             inference_watchdog = self._start_task_phase_watchdog(
                 request_id=request_id,
                 phase="inference",
-                warn_after_s=_infer_warn_s,
+                warn_after_s=float(getattr(self, "_warn_inference_s", 60.0)),
                 payload={"function_name": spec.name, "output_mode": spec.output_mode},
             )
             self._emit_task_event(
@@ -4324,14 +3986,14 @@ def _get_num(name: str) -> Optional[float]:
                 "task.inference.started",
                 {"function_name": spec.name, "output_mode": spec.output_mode},
             )
-            print(f"DEBUG [execute_task] calling_func request_id={request_id} function={spec.name} is_canceled={ctx.is_canceled()} kwargs_keys={list(call_kwargs.keys())}", flush=True)
+            logger.info("[request_id=%s] calling %s", request_id, spec.name)
             if inspect.iscoroutinefunction(spec.func):
                 result = asyncio.run(spec.func(**call_kwargs))
             elif inspect.isasyncgenfunction(spec.func):
                 result = spec.func(**call_kwargs)
             else:
                 result = spec.func(**call_kwargs)
-            print(f"DEBUG [execute_task] func_returned request_id={request_id} function={spec.name} result_type={type(result).__name__}", flush=True)
+            logger.info("[request_id=%s] %s returned, output_mode=%s", request_id, spec.name, spec.output_mode)
 
             if ctx.is_canceled():
                 raise CanceledError("canceled")
@@ -4454,10 +4116,6 @@ async def consume_async() -> None:
 
         except Exception as e:
             error_type, retryable, safe_message, error_message = self._map_exception(e)
-            if error_type == "internal":
-                logger.exception("task %s FAILED [%s] %s", request_id, spec.name, error_message)
-            else:
-                logger.error("task %s failed [%s] %s: %s", request_id, spec.name, error_type, error_message)
             if inference_watchdog is not None:
                 inference_watchdog.cancel()
             self._emit_task_event(
@@ -4564,11 +4222,6 @@ async def consume_async() -> None:
 
             with self._active_tasks_lock:
                 self._active_tasks.pop(request_id, None)
-            with self._stream_tracking_lock:
-                self._assignment_epoch_by_request.pop(request_id, None)
-                self._stream_replay_buffers_by_request.pop(request_id, None)
-                for key in [k for k in self._stream_seq_by_key.keys() if k and k[0] == request_id]:
-                    self._stream_seq_by_key.pop(key, None)
 
     def _get_local_model_cache(self) -> Optional[Any]:
         """
@@ -4632,7 +4285,7 @@ def _shared_disk_volume_info(self, path: Optional[Path] = None) -> Dict[str, Any
         except Exception:
             return {}
 
-    def _resolve_injected_value(self, ctx: RequestContext, requested_type: Any, model_id: str, inj: InjectionSpec) -> Any:
+    def _resolve_injected_value(self, ctx: ActionContext, requested_type: Any, model_id: str, inj: InjectionSpec) -> Any:
         qn = type_qualname(requested_type)
         rm: Optional[RunMetricsV1] = getattr(ctx, "_run_metrics", None)
 
@@ -4762,13 +4415,7 @@ def _resolve_injected_value(self, ctx: RequestContext, requested_type: Any, mode
                         # If we have an orchestrator-resolved manifest, estimate missing bytes.
                         resolved_entry = None
                         try:
-                            resolved_map = getattr(ctx, "resolved_cozy_models_by_id", None) or {}
-                            resolved_entry = resolved_map.get(canon)
-                            if resolved_entry is None:
-                                if canon.startswith("cozy:"):
-                                    resolved_entry = resolved_map.get(canon.split(":", 1)[1].strip())
-                                else:
-                                    resolved_entry = resolved_map.get(f"cozy:{canon}")
+                            resolved_entry = (getattr(ctx, "resolved_cozy_models_by_id", None) or {}).get(canon)
                         except Exception:
                             resolved_entry = None
                         bytes_dl = None
@@ -4883,27 +4530,17 @@ def _resolve_injected_value(self, ctx: RequestContext, requested_type: Any, mode
                                     kwargs["custom_pipeline"] = custom_pipeline
                             except Exception:
                                 pass
-                            # Read variant from cozy/pipeline spec (authoritative).
-                            if spec.variant:
-                                kwargs["variant"] = spec.variant
-                                print(f"DEBUG cozy_spec_variant={spec.variant} source={spec.source_path.name}")
-                    except Exception as _spec_exc:
-                        print(f"DEBUG cozy_spec_load_error={_spec_exc}")
-
-                    # Fallback: scan files on disk for diffusers variant naming.
-                    if "variant" not in kwargs:
-                        try:
-                            from gen_worker.pipeline_loader import detect_diffusers_variant
+                    except Exception:
+                        pass
 
-                            variant = detect_diffusers_variant(local_path)
-                            if variant is not None:
-                                kwargs["variant"] = variant
-                                print(f"DEBUG detect_diffusers_variant={variant} path={local_path}")
-                            else:
-                                print(f"DEBUG detect_diffusers_variant=None path={local_path}")
-                        except Exception as _detect_exc:
-                            print(f"DEBUG detect_variant_error={_detect_exc}")
-                    print(f"DEBUG from_pretrained variant={kwargs.get('variant')} path={local_path}")
+                    try:
+                        from gen_worker.pipeline_loader import detect_diffusers_variant
+
+                        variant = detect_diffusers_variant(local_path)
+                        if variant is not None:
+                            kwargs["variant"] = variant
+                    except Exception:
+                        pass
 
                     # Quantized weight-only inference requires explicit loader hints.
                     #
@@ -4952,7 +4589,7 @@ def _resolve_injected_value(self, ctx: RequestContext, requested_type: Any, mode
                             device_is_cuda = str(ctx.device).startswith("cuda") and torch.cuda.is_available()
                             variant = str(kwargs.get("variant") or "").strip().lower()
                             if device_is_cuda:
-                                if variant in ("fp8", "int8", "int4", "nvfp4") and hasattr(torch.cuda, "is_bf16_supported") and torch.cuda.is_bf16_supported():
+                                if variant in ("fp8", "int8", "int4") and hasattr(torch.cuda, "is_bf16_supported") and torch.cuda.is_bf16_supported():
                                     kwargs["torch_dtype"] = torch.bfloat16
                                 else:
                                     kwargs["torch_dtype"] = torch.float16
@@ -5007,31 +4644,20 @@ def _resolve_injected_value(self, ctx: RequestContext, requested_type: Any, mode
                     from_pretrained = getattr(requested_type, "from_pretrained")
                     model_source: str = str(model_id)
                     preload_kwargs: dict[str, Any] = {}
-                    _ref: ParsedModelRef | None = None
                     try:
                         p = Path(model_source)
                         if p.exists():
                             model_source = p.as_posix()
                         else:
-                            _ref = parse_model_ref(model_source)
-                            if _ref.scheme in ("cozy", "hf") and self._downloader is not None:
+                            parsed = parse_model_ref(model_source)
+                            if self._downloader is not None and parsed.scheme in ("cozy", "hf"):
                                 model_source = self._downloader.download(model_source, str(worker_model_cache_dir()))
-                            elif _ref.scheme == "hf" and _ref.hf is not None:
+                            elif parsed.scheme == "hf" and parsed.hf is not None:
                                 # Fallback path when downloader is unavailable.
-                                model_source = _ref.hf.repo_id
-                                if _ref.hf.revision:
-                                    preload_kwargs["revision"] = _ref.hf.revision
-                            elif _ref.scheme == "cozy":
-                                raise RuntimeError(
-                                    f"cozy model resolution requires downloader for ref {model_source!r}"
-                                )
-                    except Exception as e:
-                        # Never hand raw cozy refs to huggingface loaders; surface the real
-                        # download/resolve error instead of falling back to repo-id parsing.
-                        if _ref is not None and getattr(_ref, "scheme", "") == "cozy":
-                            raise RuntimeError(
-                                f"cozy model materialization failed for {model_id!r}: {e}"
-                            ) from e
+                                model_source = parsed.hf.repo_id
+                                if parsed.hf.revision:
+                                    preload_kwargs["revision"] = parsed.hf.revision
+                    except Exception:
                         model_source = str(model_id)
                         preload_kwargs = {}
 
@@ -5102,11 +4728,16 @@ def _resolve_injected_value(self, ctx: RequestContext, requested_type: Any, mode
                                         # Fallback heuristic when deltas are noisy.
                                         size_gb = float(os.getenv("WORKER_DIFFUSERS_VRAM_GB_FALLBACK", "10") or "10")
                                 self._model_cache.mark_loaded_to_vram(canon, obj, size_gb)
+                                logger.info(
+                                    "pipeline injection resolved: model=%s size_gb=%.1f device=%s",
+                                    canon, size_gb, str(ctx.device),
+                                )
                                 return obj
-                        except Exception:
-                            pass
-                except Exception:
-                    pass
+                        except Exception as _cache_exc:
+                            logger.warning("model_cache mark_loaded_to_vram failed: %s", _cache_exc)
+                except Exception as _to_exc:
+                    logger.error("failed to move pipeline to device=%s: %s", str(ctx.device), _to_exc)
+                    raise
                 self._custom_runtime_cache[key] = obj
                 return obj
 
diff --git a/tests/test_action_context_local_outputs.py b/tests/test_action_context_local_outputs.py
index f65f6ec..19b33e8 100644
--- a/tests/test_action_context_local_outputs.py
+++ b/tests/test_action_context_local_outputs.py
@@ -2,11 +2,11 @@
 
 from pathlib import Path
 
-from gen_worker.worker import RequestContext
+from gen_worker.worker import ActionContext
 
 
 def test_action_context_local_output_backend(tmp_path: Path) -> None:
-    ctx = RequestContext(
+    ctx = ActionContext(
         "rid1",
         local_output_dir=str(tmp_path),
         owner="o1",
diff --git a/tests/test_action_context_partition.py b/tests/test_action_context_partition.py
index 3d02703..dd662d3 100644
--- a/tests/test_action_context_partition.py
+++ b/tests/test_action_context_partition.py
@@ -2,11 +2,11 @@
 
 import pytest
 
-from gen_worker.worker import RequestContext
+from gen_worker.worker import ActionContext
 
 
 def test_partition_context_exposes_lineage_and_item_fields() -> None:
-    ctx = RequestContext(
+    ctx = ActionContext(
         "req-1",
         parent_request_id="parent-1",
         child_request_id="child-1",
@@ -26,16 +26,16 @@ def test_partition_context_exposes_lineage_and_item_fields() -> None:
 
 
 def test_item_output_ref_prefers_item_id_when_present() -> None:
-    ctx = RequestContext("req-2", item_id="item-custom", item_index=7)
+    ctx = ActionContext("req-2", item_id="item-custom", item_index=7)
     ref = ctx.item_output_ref("/result.json")
     assert ref == "runs/req-2/outputs/items/item-custom/result.json"
 
 
 def test_item_output_ref_falls_back_to_index_then_default() -> None:
-    with_index = RequestContext("req-3", item_index=7)
+    with_index = ActionContext("req-3", item_index=7)
     assert with_index.item_output_ref("out.bin") == "runs/req-3/outputs/items/item-000007/out.bin"
 
-    without_index = RequestContext("req-4")
+    without_index = ActionContext("req-4")
     assert without_index.item_output_ref("out.bin") == "runs/req-4/outputs/items/item-000000/out.bin"
 
     with pytest.raises(ValueError):
diff --git a/tests/test_asset_materialization.py b/tests/test_asset_materialization.py
new file mode 100644
index 0000000..77755c9
--- /dev/null
+++ b/tests/test_asset_materialization.py
@@ -0,0 +1,148 @@
+import hashlib
+import os
+import tempfile
+import unittest
+from typing import Any, Dict, Optional
+from unittest.mock import patch
+
+from gen_worker.types import Asset
+from gen_worker.worker import ActionContext, Worker
+
+
+class _FakeHeaders(dict):
+    def get(self, key: str, default: Optional[str] = None) -> Optional[str]:  # type: ignore[override]
+        return super().get(key, default)
+
+
+class _FakeHTTPResponse:
+    def __init__(self, body: bytes, status: int = 200, headers: Optional[Dict[str, str]] = None) -> None:
+        self._body = body
+        self._pos = 0
+        self.status = status
+        self.headers: Any = _FakeHeaders(headers or {})
+
+    def read(self, n: int = -1) -> bytes:
+        if self._pos >= len(self._body):
+            return b""
+        if n is None or n < 0:
+            n = len(self._body) - self._pos
+        chunk = self._body[self._pos : self._pos + n]
+        self._pos += len(chunk)
+        return chunk
+
+    def __enter__(self) -> "_FakeHTTPResponse":
+        return self
+
+    def __exit__(self, exc_type, exc, tb) -> None:
+        return None
+
+
+class TestAssetMaterialization(unittest.TestCase):
+    def _worker(self, owner: str = "tenant-1") -> Worker:
+        w = Worker.__new__(Worker)
+        w.owner = owner
+        return w
+
+    def test_materialize_external_url(self) -> None:
+        w = self._worker()
+        # Use a literal public IP so SSRF/DNS resolution doesn't depend on DNS working.
+        a = Asset(ref="https://1.1.1.1/a.png")
+        data = b"\x89PNG\r\n\x1a\nhello"
+
+        with tempfile.TemporaryDirectory() as td:
+            os.environ["WORKER_RUN_DIR"] = td
+            os.environ["WORKER_CACHE_DIR"] = os.path.join(td, "cache")
+
+            class _Opener:
+                def open(self, req: Any, timeout: int = 0) -> _FakeHTTPResponse:
+                    _ = req, timeout
+                    return _FakeHTTPResponse(data)
+
+            with patch("urllib.request.build_opener", return_value=_Opener()) as _mock:
+                w._materialize_asset(ActionContext("run-1", owner=w.owner), a)
+                self.assertGreaterEqual(_mock.call_count, 1)
+
+            self.assertIsNotNone(a.local_path)
+            assert a.local_path is not None
+            with open(a.local_path, "rb") as f:
+                self.assertEqual(f.read(), data)
+
+            self.assertEqual(a.size_bytes, len(data))
+            self.assertEqual(a.sha256, hashlib.sha256(data).hexdigest())
+            self.assertEqual(a.mime_type, "image/png")
+
+    def test_materialize_external_url_size_cap(self) -> None:
+        w = self._worker()
+        a = Asset(ref="https://1.1.1.1/a.bin")
+        data = b"1234"
+
+        with tempfile.TemporaryDirectory() as td:
+            os.environ["WORKER_RUN_DIR"] = td
+            os.environ["WORKER_CACHE_DIR"] = os.path.join(td, "cache")
+            os.environ["WORKER_MAX_INPUT_FILE_BYTES"] = "1"
+
+            class _Opener:
+                def open(self, req: Any, timeout: int = 0) -> _FakeHTTPResponse:
+                    _ = req, timeout
+                    return _FakeHTTPResponse(data)
+
+            with patch("urllib.request.build_opener", return_value=_Opener()):
+                with self.assertRaises(Exception):
+                    w._materialize_asset(ActionContext("run-1", owner=w.owner), a)
+
+    def test_materialize_tensorhub_ref(self) -> None:
+        w = self._worker(owner="tenant-1")
+        a = Asset(ref="my-uploads/cat.png")
+        body = b"\x89PNG\r\n\x1a\ncat"
+        sha = hashlib.sha256(body).hexdigest()
+
+        def fake_urlopen(req: Any, timeout: int = 0) -> _FakeHTTPResponse:
+            # HEAD request (urllib.request.Request)
+            method = getattr(req, "method", None)
+            items = getattr(req, "header_items", None)
+            hdrs: Dict[str, str] = {}
+            if callable(items):
+                for k, v in items():
+                    hdrs[str(k).lower()] = str(v)
+            if method == "HEAD":
+                self.assertEqual(hdrs.get("authorization"), "Bearer tok")
+                self.assertEqual(hdrs.get("x-cozy-owner"), "tenant-1")
+                return _FakeHTTPResponse(
+                    b"",
+                    status=200,
+                    headers={
+                        "X-Cozy-Size-Bytes": str(len(body)),
+                        "X-Cozy-SHA256": sha,
+                        "X-Cozy-Mime-Type": "image/png",
+                    },
+                )
+            # GET request
+            self.assertEqual(hdrs.get("authorization"), "Bearer tok")
+            self.assertEqual(hdrs.get("x-cozy-owner"), "tenant-1")
+            return _FakeHTTPResponse(body, status=200, headers={"Content-Type": "image/png"})
+
+        with tempfile.TemporaryDirectory() as td:
+            os.environ["WORKER_RUN_DIR"] = td
+            os.environ["WORKER_CACHE_DIR"] = os.path.join(td, "cache")
+            os.environ["WORKER_MAX_INPUT_FILE_BYTES"] = "9999999"
+
+            with patch("urllib.request.urlopen", side_effect=fake_urlopen):
+                ctx = ActionContext(
+                    "run-1",
+                    owner="tenant-1",
+                    file_api_base_url="https://tensorhub.example",
+                    file_api_token="tok",
+                )
+                w._materialize_asset(ctx, a)
+
+            self.assertIsNotNone(a.local_path)
+            assert a.local_path is not None
+            with open(a.local_path, "rb") as f:
+                self.assertEqual(f.read(), body)
+            self.assertEqual(a.mime_type, "image/png")
+            self.assertEqual(a.size_bytes, len(body))
+            self.assertEqual(a.sha256, sha)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_chunked_reassembly.py b/tests/test_chunked_reassembly.py
new file mode 100644
index 0000000..af7c6bf
--- /dev/null
+++ b/tests/test_chunked_reassembly.py
@@ -0,0 +1,333 @@
+"""
+A/B test: chunked split + reassemble produces byte-identical output.
+
+Proves that the ingest chunking (Go: SplitAndStore at 1 GiB boundaries)
+followed by the worker reassembly (_reassemble_chunked) does NOT corrupt
+weights.  The reassembled file must be byte-for-byte identical to the
+original, with matching blake3 hashes.
+
+Test matrix:
+  - small file (under chunk threshold, no split)
+  - file exactly at 1 GiB boundary
+  - file slightly over 1 GiB (2 parts)
+  - large file spanning 3 parts
+  - random binary content (simulates real safetensors weights)
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import shutil
+import tempfile
+from pathlib import Path
+from typing import List
+
+import pytest
+from blake3 import blake3
+
+
+# ---------------------------------------------------------------------------
+# Constants matching Go chunkedblob.MaxChunkSize
+# ---------------------------------------------------------------------------
+
+MAX_CHUNK_SIZE = 1 << 30  # 1 GiB
+
+# For fast tests, use a smaller chunk size.  Set CHUNKED_TEST_REAL_SIZE=1 to
+# test at actual 1 GiB boundaries (slow, ~3 GiB of I/O).
+FAST_CHUNK = 1 << 20  # 1 MiB for fast tests
+
+
+def _chunk_size() -> int:
+    if os.getenv("CHUNKED_TEST_REAL_SIZE") == "1":
+        return MAX_CHUNK_SIZE
+    return FAST_CHUNK
+
+
+# ---------------------------------------------------------------------------
+# Simulate Go-side ingest: split file into chunks + write parts.json
+# ---------------------------------------------------------------------------
+
+def _blake3_bytes(data: bytes) -> str:
+    h = blake3()
+    h.update(data)
+    return h.hexdigest()
+
+
+def _blake3_file(path: Path) -> str:
+    h = blake3()
+    with open(path, "rb") as f:
+        while True:
+            b = f.read(1 << 20)
+            if not b:
+                break
+            h.update(b)
+    return h.hexdigest()
+
+
+def _simulate_ingest(
+    original_file: Path,
+    blobs_root: Path,
+    chunk_size: int,
+    rel_path: str | None = None,
+) -> List[dict]:
+    """Simulate Go SplitAndStore: split file into chunks, store blobs, return entries list."""
+    data = original_file.read_bytes()
+    total_size = len(data)
+    original_path = rel_path or original_file.name
+
+    entries = []
+
+    if total_size <= chunk_size:
+        # No chunking needed — store as single blob.
+        digest = _blake3_bytes(data)
+        blob_dir = blobs_root / "blake3" / digest[:2] / digest[2:4]
+        blob_dir.mkdir(parents=True, exist_ok=True)
+        (blob_dir / digest).write_bytes(data)
+        entries.append({
+            "path": original_path,
+            "type": "file",
+            "size_bytes": total_size,
+            "digest": f"blake3:{digest}",
+            "blake3": digest,
+        })
+        return entries
+
+    # Split into chunks.
+    parts_meta = []
+    offset = 0
+    part_idx = 0
+    while offset < total_size:
+        end = min(offset + chunk_size, total_size)
+        chunk_data = data[offset:end]
+        chunk_digest = _blake3_bytes(chunk_data)
+        chunk_path = f"{original_path}.part{part_idx:04d}"
+
+        # Store chunk blob.
+        blob_dir = blobs_root / "blake3" / chunk_digest[:2] / chunk_digest[2:4]
+        blob_dir.mkdir(parents=True, exist_ok=True)
+        (blob_dir / chunk_digest).write_bytes(chunk_data)
+
+        entries.append({
+            "path": chunk_path,
+            "type": "file",
+            "size_bytes": len(chunk_data),
+            "digest": f"blake3:{chunk_digest}",
+            "blake3": chunk_digest,
+        })
+        parts_meta.append({
+            "path": chunk_path,
+            "size_bytes": len(chunk_data),
+            "digest": f"blake3:{chunk_digest}",
+        })
+
+        offset = end
+        part_idx += 1
+
+    # Build and store .parts.json manifest.
+    manifest = {
+        "original_path": original_path,
+        "total_bytes": total_size,
+        "parts": parts_meta,
+    }
+    manifest_bytes = json.dumps(manifest).encode()
+    manifest_digest = _blake3_bytes(manifest_bytes)
+    manifest_path = f"{original_path}.parts.json"
+
+    blob_dir = blobs_root / "blake3" / manifest_digest[:2] / manifest_digest[2:4]
+    blob_dir.mkdir(parents=True, exist_ok=True)
+    (blob_dir / manifest_digest).write_bytes(manifest_bytes)
+
+    entries.append({
+        "path": manifest_path,
+        "type": "file",
+        "size_bytes": len(manifest_bytes),
+        "digest": f"blake3:{manifest_digest}",
+        "blake3": manifest_digest,
+    })
+
+    return entries
+
+
+# ---------------------------------------------------------------------------
+# Simulate worker-side reassembly (same logic as _reassemble_chunked)
+# ---------------------------------------------------------------------------
+
+def _simulate_reassemble(blobs_root: Path, output_dir: Path, entries: List[dict]) -> None:
+    """Mirrors CozySnapshotV2Downloader._reassemble_chunked + _materialize_regular."""
+    import re
+    PART_RE = re.compile(r"\.part\d{4}$")
+
+    parts_manifests = [e for e in entries if e["path"].endswith(".parts.json")]
+    part_paths = {e["path"] for e in entries if PART_RE.search(e["path"])}
+
+    # Reassemble chunked files.
+    for pm in parts_manifests:
+        digest = pm["blake3"]
+        blob = blobs_root / "blake3" / digest[:2] / digest[2:4] / digest
+        manifest = json.loads(blob.read_bytes())
+
+        original_path = manifest["original_path"]
+        parts = manifest["parts"]
+
+        dst = output_dir / original_path
+        dst.parent.mkdir(parents=True, exist_ok=True)
+
+        with open(dst, "wb") as out_f:
+            for part in parts:
+                pd = part["digest"]
+                if pd.startswith("blake3:"):
+                    pd = pd[7:]
+                part_blob = blobs_root / "blake3" / pd[:2] / pd[2:4] / pd
+                with open(part_blob, "rb") as in_f:
+                    shutil.copyfileobj(in_f, out_f)
+
+    # Materialize regular (non-chunked) files.
+    for e in entries:
+        if e["path"].endswith(".parts.json") or e["path"] in part_paths:
+            continue
+        digest = e["blake3"]
+        src = blobs_root / "blake3" / digest[:2] / digest[2:4] / digest
+        dst = output_dir / e["path"]
+        dst.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(src, dst)
+
+
+# ---------------------------------------------------------------------------
+# Test cases
+# ---------------------------------------------------------------------------
+
+class TestChunkedReassembly:
+    """A/B: original bytes == reassembled bytes after split+merge."""
+
+    def _run_ab(self, original_data: bytes, filename: str = "model.safetensors"):
+        chunk_size = _chunk_size()
+
+        with tempfile.TemporaryDirectory() as tmp:
+            tmp_path = Path(tmp)
+            original_file = tmp_path / "original" / filename
+            original_file.parent.mkdir(parents=True)
+            original_file.write_bytes(original_data)
+            original_hash = _blake3_file(original_file)
+
+            blobs_root = tmp_path / "blobs"
+            output_dir = tmp_path / "output"
+            blobs_root.mkdir()
+            output_dir.mkdir()
+
+            # A: ingest (split)
+            entries = _simulate_ingest(original_file, blobs_root, chunk_size, rel_path=filename)
+
+            # B: worker (reassemble)
+            _simulate_reassemble(blobs_root, output_dir, entries)
+
+            # Verify
+            reassembled = output_dir / filename
+            assert reassembled.exists(), f"reassembled file not found: {reassembled}"
+
+            reassembled_data = reassembled.read_bytes()
+            reassembled_hash = _blake3_file(reassembled)
+
+            # Byte-for-byte identical
+            assert len(reassembled_data) == len(original_data), (
+                f"size mismatch: original={len(original_data)} reassembled={len(reassembled_data)}"
+            )
+            assert reassembled_data == original_data, "WEIGHT CORRUPTION: bytes differ after reassembly"
+            assert reassembled_hash == original_hash, (
+                f"blake3 mismatch: original={original_hash[:16]} reassembled={reassembled_hash[:16]}"
+            )
+
+    def test_small_file_no_chunking(self):
+        """File under chunk size — passes through without splitting."""
+        data = os.urandom(500_000)  # 500 KB
+        self._run_ab(data)
+
+    def test_exact_boundary(self):
+        """File exactly at chunk boundary — single chunk, no remainder."""
+        chunk = _chunk_size()
+        data = os.urandom(chunk)
+        self._run_ab(data)
+
+    def test_one_byte_over(self):
+        """File 1 byte over chunk size — triggers 2 parts."""
+        chunk = _chunk_size()
+        data = os.urandom(chunk + 1)
+        self._run_ab(data)
+
+    def test_two_full_parts(self):
+        """File exactly 2x chunk size — 2 full parts."""
+        chunk = _chunk_size()
+        data = os.urandom(chunk * 2)
+        self._run_ab(data)
+
+    def test_three_parts_with_remainder(self):
+        """File spanning 3 parts with a small remainder."""
+        chunk = _chunk_size()
+        data = os.urandom(chunk * 2 + chunk // 3)
+        self._run_ab(data)
+
+    def test_random_weights_pattern(self):
+        """Simulates real safetensors: structured header + random float bytes."""
+        chunk = _chunk_size()
+        # Fake safetensors header (8 bytes length + JSON)
+        header = b'{"weight_0": {"dtype": "F16", "shape": [1024, 1024], "data_offsets": [0, 2097152]}}'
+        header_with_len = len(header).to_bytes(8, "little") + header
+        # Fill rest with random "weight" bytes to span 2+ chunks
+        weight_bytes = os.urandom(chunk * 2 - len(header_with_len))
+        data = header_with_len + weight_bytes
+        self._run_ab(data, filename="unet/diffusion_pytorch_model.safetensors")
+
+    def test_all_zeros(self):
+        """Edge case: all-zero file (compressible but split is still raw bytes)."""
+        chunk = _chunk_size()
+        data = b"\x00" * (chunk + 100)
+        self._run_ab(data)
+
+    def test_identical_chunks(self):
+        """Two chunks with identical content (same digest) — dedup in blob store."""
+        chunk = _chunk_size()
+        pattern = os.urandom(chunk)
+        data = pattern + pattern  # 2 identical chunks
+        self._run_ab(data)
+
+    def test_fp16_and_normal_share_parts(self):
+        """Simulates sd-turbo manifest: fp16 and normal variants share same chunk digests."""
+        chunk = _chunk_size()
+
+        with tempfile.TemporaryDirectory() as tmp:
+            tmp_path = Path(tmp)
+            blobs_root = tmp_path / "blobs"
+            output_dir = tmp_path / "output"
+            blobs_root.mkdir()
+            output_dir.mkdir()
+
+            # Same underlying data for both variants (they share part digests).
+            data = os.urandom(chunk + chunk // 2)
+            original_hash = _blake3_bytes(data)
+
+            # Ingest fp16 variant.
+            fp16_file = tmp_path / "model.fp16.safetensors"
+            fp16_file.write_bytes(data)
+            fp16_entries = _simulate_ingest(fp16_file, blobs_root, chunk)
+
+            # Ingest normal variant (same data, different filename).
+            normal_file = tmp_path / "model.safetensors"
+            normal_file.write_bytes(data)
+            normal_entries = _simulate_ingest(normal_file, blobs_root, chunk)
+
+            # Combined entries (like the real manifest).
+            all_entries = fp16_entries + normal_entries
+
+            # Reassemble both.
+            _simulate_reassemble(blobs_root, output_dir, all_entries)
+
+            # Both must be byte-identical to original.
+            for name in ["model.fp16.safetensors", "model.safetensors"]:
+                result = output_dir / name
+                assert result.exists(), f"{name} not found"
+                assert result.read_bytes() == data, f"WEIGHT CORRUPTION in {name}"
+                assert _blake3_file(result) == original_hash, f"blake3 mismatch in {name}"
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
diff --git a/tests/test_cozy_cas_resume.py b/tests/test_cozy_cas_resume.py
index f581056..0aa4bf0 100644
--- a/tests/test_cozy_cas_resume.py
+++ b/tests/test_cozy_cas_resume.py
@@ -66,6 +66,7 @@ def _parse_range(h: str) -> int:
     return int(start_s)
 
 
+@pytest.mark.skip(reason="Range header resume not supported by tensorhub presigned URLs yet")
 def test_cozy_cas_resume_with_range(tmp_path: Path) -> None:
     data = (b"0123456789abcdef" * 65536)  # 1MB-ish
     expected_size = len(data)
@@ -125,35 +126,3 @@ async def handler(req: web.Request) -> web.StreamResponse:
         assert state["saw_range"] is True
     finally:
         _stop_server(srv)
-
-
-def test_cozy_cas_resume_restarts_on_bad_content_range_start(tmp_path: Path) -> None:
-    data = b"y" * 1024 * 256
-    expected_size = len(data)
-    expected_b3 = blake3(data).hexdigest()
-
-    state = {"saw_range": False, "saw_plain": False}
-
-    async def handler(req: web.Request) -> web.StreamResponse:
-        if req.headers.get("Range"):
-            state["saw_range"] = True
-            # Broken origin behavior: returns 206 but wrong start offset.
-            resp = web.Response(status=206, body=data)
-            resp.headers["Accept-Ranges"] = "bytes"
-            resp.headers["Content-Range"] = f"bytes 0-{len(data)-1}/{len(data)}"
-            return resp
-        state["saw_plain"] = True
-        return web.Response(status=200, body=data, headers={"Accept-Ranges": "bytes"})
-
-    srv = _start_server(handler)  # type: ignore[arg-type]
-    try:
-        dst = tmp_path / "out.bin"
-        part = dst.with_suffix(dst.suffix + ".part")
-        part.write_bytes(data[:12345])
-
-        asyncio.run(_download_one_file(f"{srv.base_url}/file", dst, expected_size=expected_size, expected_blake3=expected_b3))
-        assert dst.read_bytes() == data
-        assert state["saw_range"] is True
-        assert state["saw_plain"] is True
-    finally:
-        _stop_server(srv)
diff --git a/tests/test_cozy_snapshot_v2_downloader.py b/tests/test_cozy_snapshot_v2_downloader.py
index 90f5208..47deac8 100644
--- a/tests/test_cozy_snapshot_v2_downloader.py
+++ b/tests/test_cozy_snapshot_v2_downloader.py
@@ -268,12 +268,12 @@ async def get_pipeline(_req: web.Request) -> web.Response:
             ],
         )
 
-        # Accept legacy resolved-map keys that still include a cozy: scheme prefix.
+        # NOTE: canonical() lowercases and adds cozy: prefix.
         resolved_by_id = {"cozy:o/r:latest": resolved}
         tok = set_resolved_cozy_models_by_id(resolved_by_id)
         try:
             dl = ModelRefDownloader(cozy_base_url=None, cozy_token=None, allow_tensorhub_api_resolve=False)
-            local = dl.download("o/r:latest", tmp_path.as_posix())
+            local = dl.download("cozy:o/r:latest", tmp_path.as_posix())
             assert (Path(local) / "cozy.pipeline.yaml").read_bytes() == b1
         finally:
             reset_resolved_cozy_models_by_id(tok)
diff --git a/tests/test_dev_http_runner.py b/tests/test_dev_http_runner.py
index 399f732..4bb0948 100644
--- a/tests/test_dev_http_runner.py
+++ b/tests/test_dev_http_runner.py
@@ -27,7 +27,7 @@ def test_dev_http_runner_writes_outputs(tmp_path: Path, monkeypatch) -> None:
         """
 import msgspec
 from gen_worker.decorators import worker_function
-from gen_worker.worker import RequestContext
+from gen_worker.worker import ActionContext
 
 class In(msgspec.Struct):
     prompt: str
@@ -36,7 +36,7 @@ class Out(msgspec.Struct):
     ref: str
 
 @worker_function()
-def generate(ctx: RequestContext, payload: In) -> Out:
+def generate(ctx: ActionContext, payload: In) -> Out:
     ref = f"runs/{ctx.request_id}/outputs/out.txt"
     ctx.save_bytes(ref, (payload.prompt + "\\n").encode("utf-8"))
     return Out(ref=ref)
diff --git a/tests/test_discover_models.py b/tests/test_discover_models.py
index fe5f95b..b021177 100644
--- a/tests/test_discover_models.py
+++ b/tests/test_discover_models.py
@@ -56,7 +56,7 @@ def test_discovery_emits_fixed_and_payload_keyspaces(self) -> None:
                     """
 from typing import Annotated
 import msgspec
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 
 class Input(msgspec.Struct):
@@ -71,7 +71,7 @@ class MockPipeline:
 
 @worker_function()
 def generate_fixed(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[MockPipeline, ModelRef(Src.FIXED, "sdxl")],
     payload: Input,
 ) -> Output:
@@ -79,7 +79,7 @@ def generate_fixed(
 
 @worker_function()
 def generate_dynamic(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[MockPipeline, ModelRef(Src.PAYLOAD, "model_key")],
     payload: Input,
 ) -> Output:
@@ -150,7 +150,7 @@ def test_missing_fixed_key_fails_discovery(self) -> None:
                     """
 from typing import Annotated
 import msgspec
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 
 class Input(msgspec.Struct):
@@ -164,7 +164,7 @@ class MockPipeline:
 
 @worker_function()
 def generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[MockPipeline, ModelRef(Src.FIXED, "sdxl")],
     payload: Input,
 ) -> Output:
@@ -216,7 +216,7 @@ def test_missing_payload_keyspace_fails_discovery(self) -> None:
                     """
 from typing import Annotated
 import msgspec
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 
 class Input(msgspec.Struct):
@@ -231,7 +231,7 @@ class MockPipeline:
 
 @worker_function()
 def generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[MockPipeline, ModelRef(Src.PAYLOAD, "model_key")],
     payload: Input,
 ) -> Output:
@@ -285,7 +285,7 @@ def test_batch_dimension_emitted(self) -> None:
                 (src_dir / "__init__.py").write_text(
                     """
 import msgspec
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 
 class Input(msgspec.Struct):
     items: list[str]
@@ -294,7 +294,7 @@ class Output(msgspec.Struct):
     ok: bool
 
 @worker_function()
-def caption(ctx: RequestContext, payload: Input) -> Output:
+def caption(ctx: ActionContext, payload: Input) -> Output:
     return Output(ok=True)
 """.lstrip(),
                     encoding="utf-8",
@@ -346,7 +346,7 @@ def test_inline_ref_in_modelref_rejected(self) -> None:
                     """
 from typing import Annotated
 import msgspec
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 from gen_worker.injection import ModelRef, ModelRefSource as Src
 
 class Input(msgspec.Struct):
@@ -360,7 +360,7 @@ class MockPipeline:
 
 @worker_function()
 def generate(
-    ctx: RequestContext,
+    ctx: ActionContext,
     pipeline: Annotated[
         MockPipeline,
         ModelRef(Src.FIXED, "sdxl", ref="stabilityai/stable-diffusion-xl-base-1.0"),
diff --git a/tests/test_discover_names.py b/tests/test_discover_names.py
index 56b9db8..615fb4b 100644
--- a/tests/test_discover_names.py
+++ b/tests/test_discover_names.py
@@ -49,7 +49,7 @@ def test_function_name_slugified_from_python_name(self) -> None:
                 (src_dir / "main.py").write_text(
                     """
 import msgspec
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 
 class Input(msgspec.Struct):
     x: int
@@ -58,7 +58,7 @@ class Output(msgspec.Struct):
     y: int
 
 @worker_function()
-def medasr_transcribe(ctx: RequestContext, payload: Input) -> Output:
+def medasr_transcribe(ctx: ActionContext, payload: Input) -> Output:
     return Output(y=payload.x)
 """
                 )
@@ -106,7 +106,7 @@ def test_endpoint_slug_collision_fails(self) -> None:
                 (src_dir / "main.py").write_text(
                     """
 import msgspec
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 
 class Input(msgspec.Struct):
     x: int
@@ -115,11 +115,11 @@ class Output(msgspec.Struct):
     y: int
 
 @worker_function()
-def image_worker(ctx: RequestContext, payload: Input) -> Output:
+def image_worker(ctx: ActionContext, payload: Input) -> Output:
     return Output(y=payload.x)
 
 @worker_function()
-def image__worker(ctx: RequestContext, payload: Input) -> Output:
+def image__worker(ctx: ActionContext, payload: Input) -> Output:
     return Output(y=payload.x)
 """
                 )
@@ -164,7 +164,7 @@ def test_endpoint_name_slugified_from_tensorhub_toml(self) -> None:
                 (src_dir / "main.py").write_text(
                     """
 import msgspec
-from gen_worker import RequestContext, worker_function
+from gen_worker import ActionContext, worker_function
 
 class Input(msgspec.Struct):
     x: int
@@ -173,7 +173,7 @@ class Output(msgspec.Struct):
     y: int
 
 @worker_function()
-def generate(ctx: RequestContext, payload: Input) -> Output:
+def generate(ctx: ActionContext, payload: Input) -> Output:
     return Output(y=payload.x)
 """
                 )
diff --git a/tests/test_entrypoint_cache_preflight.py b/tests/test_entrypoint_cache_preflight.py
new file mode 100644
index 0000000..06a90b2
--- /dev/null
+++ b/tests/test_entrypoint_cache_preflight.py
@@ -0,0 +1,42 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from gen_worker import entrypoint
+
+
+def test_preflight_cache_dirs_fails_without_fallback(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    monkeypatch.setenv("TENSORHUB_CACHE_DIR", str(tmp_path / "primary-root"))
+    monkeypatch.delenv("WORKER_LOCAL_MODEL_CACHE_DIR", raising=False)
+
+    def _fail(_: Path) -> None:
+        raise PermissionError("permission denied")
+
+    monkeypatch.setattr(entrypoint, "_probe_cache_path_writable", _fail)
+
+    with pytest.raises(RuntimeError, match="tensorhub CAS path"):
+        entrypoint._preflight_cache_dirs()
+
+
+def test_preflight_cache_dirs_uses_tensorhub_cache_dir_root(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+) -> None:
+    root = tmp_path / "cache-root"
+    primary = root / "cas"
+    monkeypatch.setenv("TENSORHUB_CACHE_DIR", str(root))
+    monkeypatch.delenv("WORKER_LOCAL_MODEL_CACHE_DIR", raising=False)
+
+    def _probe(path: Path) -> None:
+        path.mkdir(parents=True, exist_ok=True)
+        probe = path / ".probe"
+        probe.write_bytes(b"ok")
+        probe.unlink()
+
+    monkeypatch.setattr(entrypoint, "_probe_cache_path_writable", _probe)
+
+    cfg = entrypoint._preflight_cache_dirs()
+    assert cfg["model_cache_dir"] == str(primary)
+    assert cfg["local_model_cache_dir"] == ""
+    assert primary.exists()
diff --git a/tests/test_entrypoint_worker_mode.py b/tests/test_entrypoint_worker_mode.py
new file mode 100644
index 0000000..9c4a2fc
--- /dev/null
+++ b/tests/test_entrypoint_worker_mode.py
@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+import pytest
+
+from gen_worker import entrypoint
+from gen_worker.trainer import runtime as trainer_runtime
+
+
+def test_entrypoint_routes_to_trainer_mode(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("WORKER_MODE", "trainer")
+    monkeypatch.setattr(trainer_runtime, "run_training_runtime_from_env", lambda: 13)
+    assert entrypoint._run_main() == 13
+
+
+def test_entrypoint_rejects_invalid_worker_mode(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("WORKER_MODE", "wat")
+    assert entrypoint._run_main() == 1
diff --git a/tests/test_error_mapping.py b/tests/test_error_mapping.py
new file mode 100644
index 0000000..d45956c
--- /dev/null
+++ b/tests/test_error_mapping.py
@@ -0,0 +1,59 @@
+import unittest
+
+from gen_worker.errors import CanceledError, FatalError, ResourceError, RetryableError, ValidationError
+from gen_worker.worker import Worker
+
+
+class TestErrorMapping(unittest.TestCase):
+    def _worker(self) -> Worker:
+        # Avoid running Worker.__init__ (network/env-heavy); these helpers don't depend on init state.
+        return Worker.__new__(Worker)
+
+    def test_sanitize_safe_message_redacts_tokens_urls_paths(self) -> None:
+        w = self._worker()
+        msg = "Bearer abc.def.ghi https://example.com/secret /home/user/token.txt"
+        out = w._sanitize_safe_message(msg)
+        self.assertNotIn("abc.def.ghi", out)
+        self.assertNotIn("https://example.com/secret", out)
+        self.assertNotIn("/home/user/token.txt", out)
+
+    def test_map_exception_validation(self) -> None:
+        w = self._worker()
+        error_type, retryable, safe, internal = w._map_exception(ValidationError("bad input"))
+        self.assertEqual(error_type, "validation")
+        self.assertFalse(retryable)
+        self.assertIn("bad input", safe)
+        self.assertIn("ValidationError", internal)
+
+    def test_map_exception_retryable(self) -> None:
+        w = self._worker()
+        error_type, retryable, safe, _ = w._map_exception(RetryableError("temporary"))
+        self.assertEqual(error_type, "retryable")
+        self.assertTrue(retryable)
+        self.assertIn("temporary", safe)
+
+    def test_map_exception_fatal(self) -> None:
+        w = self._worker()
+        error_type, retryable, safe, _ = w._map_exception(FatalError("no"))
+        self.assertEqual(error_type, "fatal")
+        self.assertFalse(retryable)
+        self.assertIn("no", safe)
+
+    def test_map_exception_resource(self) -> None:
+        w = self._worker()
+        error_type, retryable, safe, _ = w._map_exception(ResourceError("oom"))
+        self.assertEqual(error_type, "resource")
+        self.assertFalse(retryable)
+        self.assertIn("oom", safe)
+
+    def test_map_exception_canceled(self) -> None:
+        w = self._worker()
+        error_type, retryable, safe, _ = w._map_exception(CanceledError("stop"))
+        self.assertEqual(error_type, "canceled")
+        self.assertFalse(retryable)
+        self.assertEqual(safe, "canceled")
+
+
+if __name__ == "__main__":
+    unittest.main()
+
diff --git a/tests/test_file_token_scoping.py b/tests/test_file_token_scoping.py
new file mode 100644
index 0000000..0632223
--- /dev/null
+++ b/tests/test_file_token_scoping.py
@@ -0,0 +1,257 @@
+"""Tests for per-run file token scoping (issue #50)."""
+import os
+import tempfile
+import unittest
+from typing import Any, Dict, Optional
+from unittest.mock import patch
+import urllib.error
+
+from gen_worker.errors import AuthError
+from gen_worker.types import Asset
+from gen_worker.worker import ActionContext, Worker
+
+
+class _FakeHeaders(dict):
+    def get(self, key: str, default: Optional[str] = None) -> Optional[str]:  # type: ignore[override]
+        return super().get(key, default)
+
+
+class _FakeHTTPResponse:
+    def __init__(self, body: bytes, status: int = 200, headers: Optional[Dict[str, str]] = None) -> None:
+        self._body = body
+        self._pos = 0
+        self.status = status
+        self.headers: Any = _FakeHeaders(headers or {})
+
+    def read(self, n: int = -1) -> bytes:
+        if self._pos >= len(self._body):
+            return b""
+        if n is None or n < 0:
+            n = len(self._body) - self._pos
+        chunk = self._body[self._pos : self._pos + n]
+        self._pos += len(chunk)
+        return chunk
+
+    def __enter__(self) -> "_FakeHTTPResponse":
+        return self
+
+    def __exit__(self, exc_type, exc, tb) -> None:
+        return None
+
+
+def _make_http_error(code: int, msg: str = "error") -> urllib.error.HTTPError:
+    """Create a real HTTPError for testing."""
+    import io
+    return urllib.error.HTTPError(
+        url="https://example.com",
+        code=code,
+        msg=msg,
+        hdrs={},  # type: ignore
+        fp=io.BytesIO(b""),
+    )
+
+
+class TestFileTokenScoping(unittest.TestCase):
+    """Test that per-run file tokens are used instead of env vars."""
+
+    def test_save_bytes_uses_per_run_token(self) -> None:
+        """save_bytes should use the token from ActionContext, not env."""
+        captured_auth: list[str] = []
+
+        def fake_urlopen(req: Any, timeout: int = 0) -> _FakeHTTPResponse:
+            items = getattr(req, "header_items", None)
+            if callable(items):
+                for k, v in items():
+                    if str(k).lower() == "authorization":
+                        captured_auth.append(str(v))
+            return _FakeHTTPResponse(b'{"size_bytes": 5, "sha256": "abc123"}', status=200)
+
+        # Set env var to a different value to prove it's not used
+        os.environ["FILE_API_TOKEN"] = "env-token-should-not-be-used"
+        os.environ["FILE_API_BASE_URL"] = "https://should-not-be-used.example"
+
+        ctx = ActionContext(
+            "run-123",
+            owner="tenant-1",
+            file_api_base_url="https://tensorhub.example",
+            file_api_token="per-run-token",
+        )
+
+        with patch("urllib.request.urlopen", side_effect=fake_urlopen):
+            asset = ctx.save_bytes("runs/run-123/outputs/test.bin", b"hello")
+
+        self.assertEqual(len(captured_auth), 1)
+        self.assertEqual(captured_auth[0], "Bearer per-run-token")
+        self.assertIsNotNone(asset)
+
+    def test_save_bytes_falls_back_to_env_when_no_per_run_token(self) -> None:
+        """save_bytes should fall back to env var if no per-run token provided."""
+        captured_auth: list[str] = []
+
+        def fake_urlopen(req: Any, timeout: int = 0) -> _FakeHTTPResponse:
+            items = getattr(req, "header_items", None)
+            if callable(items):
+                for k, v in items():
+                    if str(k).lower() == "authorization":
+                        captured_auth.append(str(v))
+            return _FakeHTTPResponse(b'{"size_bytes": 5, "sha256": "abc123"}', status=200)
+
+        os.environ["FILE_API_TOKEN"] = "env-fallback-token"
+        os.environ["FILE_API_BASE_URL"] = "https://tensorhub.example"
+
+        ctx = ActionContext(
+            "run-456",
+            owner="tenant-1",
+            # No file_api_token provided
+        )
+
+        with patch("urllib.request.urlopen", side_effect=fake_urlopen):
+            asset = ctx.save_bytes("runs/run-456/outputs/test.bin", b"hello")
+
+        self.assertEqual(len(captured_auth), 1)
+        self.assertEqual(captured_auth[0], "Bearer env-fallback-token")
+        self.assertIsNotNone(asset)
+
+
+class TestAuthErrorHandling(unittest.TestCase):
+    """Test that 401/403 errors raise AuthError (non-retryable)."""
+
+    def test_save_bytes_raises_auth_error_on_401(self) -> None:
+        """save_bytes should raise AuthError on 401 response."""
+        def fake_urlopen(req: Any, timeout: int = 0) -> _FakeHTTPResponse:
+            raise _make_http_error(401, "Unauthorized")
+
+        ctx = ActionContext(
+            "run-789",
+            owner="tenant-1",
+            file_api_base_url="https://tensorhub.example",
+            file_api_token="expired-token",
+        )
+
+        with patch("urllib.request.urlopen", side_effect=fake_urlopen):
+            with self.assertRaises(AuthError) as cm:
+                ctx.save_bytes("runs/run-789/outputs/test.bin", b"hello")
+
+        self.assertIn("401", str(cm.exception))
+        self.assertIn("file_token", str(cm.exception))
+
+    def test_save_bytes_raises_auth_error_on_403(self) -> None:
+        """save_bytes should raise AuthError on 403 response."""
+        def fake_urlopen(req: Any, timeout: int = 0) -> _FakeHTTPResponse:
+            raise _make_http_error(403, "Forbidden")
+
+        ctx = ActionContext(
+            "run-abc",
+            owner="tenant-1",
+            file_api_base_url="https://tensorhub.example",
+            file_api_token="wrong-scope-token",
+        )
+
+        with patch("urllib.request.urlopen", side_effect=fake_urlopen):
+            with self.assertRaises(AuthError) as cm:
+                ctx.save_bytes("runs/run-abc/outputs/test.bin", b"hello")
+
+        self.assertIn("403", str(cm.exception))
+
+    def test_save_bytes_create_raises_auth_error_on_401(self) -> None:
+        """save_bytes_create should raise AuthError on 401 response."""
+        def fake_urlopen(req: Any, timeout: int = 0) -> _FakeHTTPResponse:
+            raise _make_http_error(401, "Unauthorized")
+
+        ctx = ActionContext(
+            "run-def",
+            owner="tenant-1",
+            file_api_base_url="https://tensorhub.example",
+            file_api_token="bad-token",
+        )
+
+        with patch("urllib.request.urlopen", side_effect=fake_urlopen):
+            with self.assertRaises(AuthError):
+                ctx.save_bytes_create("runs/run-def/outputs/new.bin", b"data")
+
+    def test_materialize_asset_raises_auth_error_on_401(self) -> None:
+        """_materialize_asset should raise AuthError on 401 for HEAD request."""
+        w = Worker.__new__(Worker)
+        w.owner = "tenant-1"
+
+        def fake_urlopen(req: Any, timeout: int = 0) -> _FakeHTTPResponse:
+            raise _make_http_error(401, "Unauthorized")
+
+        a = Asset(ref="some-file.png")
+
+        with tempfile.TemporaryDirectory() as td:
+            os.environ["WORKER_RUN_DIR"] = td
+            os.environ["WORKER_CACHE_DIR"] = os.path.join(td, "cache")
+
+            ctx = ActionContext(
+                "run-ghi",
+                owner="tenant-1",
+                file_api_base_url="https://tensorhub.example",
+                file_api_token="expired-token",
+            )
+
+            with patch("urllib.request.urlopen", side_effect=fake_urlopen):
+                with self.assertRaises(AuthError) as cm:
+                    w._materialize_asset(ctx, a)
+
+            self.assertIn("401", str(cm.exception))
+
+    def test_materialize_asset_raises_auth_error_on_403(self) -> None:
+        """_materialize_asset should raise AuthError on 403 for HEAD request."""
+        w = Worker.__new__(Worker)
+        w.owner = "tenant-1"
+
+        def fake_urlopen(req: Any, timeout: int = 0) -> _FakeHTTPResponse:
+            raise _make_http_error(403, "Forbidden - path not in allowed prefixes")
+
+        a = Asset(ref="other-run/outputs/secret.png")
+
+        with tempfile.TemporaryDirectory() as td:
+            os.environ["WORKER_RUN_DIR"] = td
+            os.environ["WORKER_CACHE_DIR"] = os.path.join(td, "cache")
+
+            ctx = ActionContext(
+                "run-jkl",
+                owner="tenant-1",
+                file_api_base_url="https://tensorhub.example",
+                file_api_token="scoped-token",
+            )
+
+            with patch("urllib.request.urlopen", side_effect=fake_urlopen):
+                with self.assertRaises(AuthError) as cm:
+                    w._materialize_asset(ctx, a)
+
+            self.assertIn("403", str(cm.exception))
+
+
+class TestAuthErrorMapping(unittest.TestCase):
+    """Test that AuthError is mapped to non-retryable error type."""
+
+    def test_auth_error_is_non_retryable(self) -> None:
+        """AuthError should map to 'auth' error type with retryable=False."""
+        w = Worker.__new__(Worker)
+        w.owner = "tenant-1"
+
+        exc = AuthError("token expired")
+        error_type, retryable, safe_msg, internal_msg = w._map_exception(exc)
+
+        self.assertEqual(error_type, "auth")
+        self.assertFalse(retryable)
+        # safe_msg contains the exception message
+        self.assertIn("token expired", safe_msg.lower())
+
+    def test_auth_error_default_message(self) -> None:
+        """AuthError with empty message should use 'authentication failed'."""
+        w = Worker.__new__(Worker)
+        w.owner = "tenant-1"
+
+        exc = AuthError("")
+        error_type, retryable, safe_msg, internal_msg = w._map_exception(exc)
+
+        self.assertEqual(error_type, "auth")
+        self.assertFalse(retryable)
+        self.assertIn("authentication", safe_msg.lower())
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_function_compute_capability.py b/tests/test_function_compute_capability.py
index d8066cb..3ecd452 100644
--- a/tests/test_function_compute_capability.py
+++ b/tests/test_function_compute_capability.py
@@ -57,7 +57,7 @@ def test_discovery_emits_function_compute_capability_requirement(self) -> None:
                 (src_dir / "main.py").write_text(
                     """
 import msgspec
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 
 class Input(msgspec.Struct):
     x: int
@@ -66,7 +66,7 @@ class Output(msgspec.Struct):
     y: int
 
 @worker_function(resources=ResourceRequirements(compute_capability_min=10))
-def generate_nvfp4(ctx: RequestContext, payload: Input) -> Output:
+def generate_nvfp4(ctx: ActionContext, payload: Input) -> Output:
     return Output(y=payload.x)
 """.lstrip(),
                     encoding="utf-8",
diff --git a/tests/test_hf_injection_normalization.py b/tests/test_hf_injection_normalization.py
new file mode 100644
index 0000000..a9eca59
--- /dev/null
+++ b/tests/test_hf_injection_normalization.py
@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+from gen_worker.injection import InjectionSpec, ModelRef, ModelRefSource as Src
+from gen_worker.worker import ActionContext, Worker
+
+
+class _DummyModel:
+    last_source: str = ""
+    last_kwargs: dict = {}
+
+    @classmethod
+    def from_pretrained(cls, source: str, **kwargs):
+        cls.last_source = str(source)
+        cls.last_kwargs = dict(kwargs)
+        return cls()
+
+
+class _DownloaderStub:
+    def __init__(self, local_path: str) -> None:
+        self.local_path = local_path
+        self.calls: list[tuple[str, str]] = []
+
+    def download(self, model_ref: str, cache_dir: str) -> str:
+        self.calls.append((model_ref, cache_dir))
+        return self.local_path
+
+
+def _bare_worker() -> Worker:
+    w = Worker.__new__(Worker)
+    w._model_manager = None
+    w._model_cache = None
+    w._custom_runtime_locks = {}
+    w._custom_runtime_cache = {}
+    w._downloader = None
+    return w
+
+
+def test_non_diffusers_hf_ref_normalizes_without_downloader() -> None:
+    w = _bare_worker()
+    ctx = ActionContext("run-hf-no-downloader")
+    inj = InjectionSpec(param_name="model", param_type=_DummyModel, model_ref=ModelRef(Src.FIXED, "joycaption"))
+
+    _ = Worker._resolve_injected_value(w, ctx, _DummyModel, "hf:owner/repo@main", inj)  # type: ignore[arg-type]
+
+    assert _DummyModel.last_source == "owner/repo"
+    assert _DummyModel.last_kwargs.get("revision") == "main"
+
+
+def test_non_diffusers_hf_ref_uses_downloader_path_when_available() -> None:
+    w = _bare_worker()
+    dl = _DownloaderStub("/tmp/cozy-model-cache/hf-owner-repo-main")
+    w._downloader = dl
+    ctx = ActionContext("run-hf-downloader")
+    inj = InjectionSpec(param_name="model", param_type=_DummyModel, model_ref=ModelRef(Src.FIXED, "joycaption"))
+
+    _ = Worker._resolve_injected_value(w, ctx, _DummyModel, "hf:owner/repo@main", inj)  # type: ignore[arg-type]
+
+    assert dl.calls
+    assert dl.calls[0][0] == "hf:owner/repo@main"
+    assert _DummyModel.last_source == "/tmp/cozy-model-cache/hf-owner-repo-main"
diff --git a/tests/test_injection_type_enforcement.py b/tests/test_injection_type_enforcement.py
new file mode 100644
index 0000000..05047de
--- /dev/null
+++ b/tests/test_injection_type_enforcement.py
@@ -0,0 +1,31 @@
+import unittest
+
+from gen_worker.injection import InjectionSpec, ModelRef, ModelRefSource
+from gen_worker.worker import ActionContext, Worker
+
+
+class _StubModelManager:
+    async def get_active_pipeline(self, model_id: str):
+        return _ActualPipeline()
+
+
+class _ExpectedPipeline:
+    pass
+
+
+class _ActualPipeline:
+    pass
+
+
+class TestInjectionTypeEnforcement(unittest.TestCase):
+    def test_rejects_model_manager_type_mismatch(self) -> None:
+        w = Worker(user_module_names=[], model_manager=_StubModelManager(), worker_jwt="dummy-worker-jwt")
+        ctx = ActionContext("run-1")
+        inj = InjectionSpec(
+            param_name="pipeline",
+            param_type=_ExpectedPipeline,
+            model_ref=ModelRef(ModelRefSource.FIXED, "foo"),
+        )
+
+        with self.assertRaises(ValueError):
+            w._resolve_injected_value(ctx, _ExpectedPipeline, "model-id", inj)
diff --git a/tests/test_mock_orchestrator_dev.py b/tests/test_mock_orchestrator_dev.py
new file mode 100644
index 0000000..0cb4dd8
--- /dev/null
+++ b/tests/test_mock_orchestrator_dev.py
@@ -0,0 +1,109 @@
+from __future__ import annotations
+
+import os
+import signal
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+import pytest
+
+from gen_worker.testing.mock_orchestrator import _MockOrchestrator
+from gen_worker.pb import worker_scheduler_pb2_grpc as pb_grpc
+
+
+@pytest.mark.skipif(os.getenv("COZY_DEV_GRPC_E2E") != "1", reason="set COZY_DEV_GRPC_E2E=1 to run gRPC e2e")
+def test_mock_orchestrator_can_run_one_task(tmp_path: Path) -> None:
+    # Start mock orchestrator server.
+    orch = _MockOrchestrator()
+    import grpc
+    from concurrent import futures
+
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=8))
+    pb_grpc.add_SchedulerWorkerServiceServicer_to_server(orch, server)
+    port = server.add_insecure_port("127.0.0.1:0")
+    server.start()
+
+    # Write a tiny module with a single worker function into a temp dir.
+    mod_dir = tmp_path / "mod"
+    mod_dir.mkdir(parents=True, exist_ok=True)
+    (mod_dir / "hello_mod.py").write_text(
+        """
+from __future__ import annotations
+
+import msgspec
+from gen_worker import ActionContext, ResourceRequirements, worker_function
+
+
+class Input(msgspec.Struct):
+    name: str
+
+
+class Output(msgspec.Struct):
+    message: str
+
+
+@worker_function(ResourceRequirements())
+def hello(ctx: ActionContext, payload: Input) -> Output:
+    return Output(message=f"hello {payload.name}")
+""".lstrip(),
+        encoding="utf-8",
+    )
+
+    # Start a worker process that connects to our mock orchestrator.
+    env = dict(os.environ)
+    env["SCHEDULER_PUBLIC_ADDR"] = f"127.0.0.1:{port}"
+    (tmp_path / "endpoint.toml").write_text(
+        """
+schema_version = 1
+name = "dev-test"
+main = "hello_mod"
+""".lstrip(),
+        encoding="utf-8",
+    )
+    env["ENDPOINT_TOML_PATH"] = str(tmp_path / "endpoint.toml")
+    env["PYTHONPATH"] = f"{mod_dir}:{env.get('PYTHONPATH','')}"
+    env["WORKER_ID"] = "dev-test"
+    env["WORKER_JWT"] = "dev-test-jwt"
+
+    proc = subprocess.Popen(
+        [sys.executable, "-m", "gen_worker.entrypoint"],
+        env=env,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=True,
+    )
+    try:
+        sess = orch.get_session(timeout_s=30.0)
+        assert sess is not None
+        assert "hello" in sess.available_functions
+
+        request_id = sess.run_task(function_name="hello", payload_obj={"name": "world"})
+
+        # Wait for a result.
+        start = time.monotonic()
+        while time.monotonic() - start < 30:
+            msg = sess.recv(timeout_s=0.5)
+            if msg is None:
+                continue
+            if msg.HasField("run_result") and msg.run_result.request_id == request_id:
+                assert msg.run_result.success is True
+                assert msg.run_result.output_payload
+                import msgspec
+
+                out = msgspec.msgpack.decode(msg.run_result.output_payload)
+                assert out["message"] == "hello world"
+                return
+
+        raise AssertionError("timed out waiting for run_result")
+    finally:
+        try:
+            proc.send_signal(signal.SIGINT)
+        except Exception:
+            pass
+        try:
+            proc.wait(timeout=5)
+        except Exception:
+            proc.kill()
+        server.stop(grace=None)
diff --git a/tests/test_model_cache.py b/tests/test_model_cache.py
new file mode 100644
index 0000000..14c3080
--- /dev/null
+++ b/tests/test_model_cache.py
@@ -0,0 +1,312 @@
+"""Tests for the ModelCache class."""
+
+import unittest
+from unittest.mock import patch, MagicMock
+
+from gen_worker.model_cache import (
+    ModelCache,
+    ModelCacheStats,
+    ModelLocation,
+    CachedModel,
+)
+
+
+class TestModelCache(unittest.TestCase):
+    """Tests for ModelCache LRU eviction and stats."""
+
+    def setUp(self) -> None:
+        """Create a fresh ModelCache for each test."""
+        # Patch torch to avoid CUDA detection
+        with patch("gen_worker.model_cache.ModelCache._detect_total_vram", return_value=24.0):
+            self.cache = ModelCache(
+                max_vram_gb=20.0,
+                vram_safety_margin_gb=4.0,
+            )
+
+    def test_register_model_vram(self) -> None:
+        """Test registering a model in VRAM."""
+        self.cache.register_model(
+            model_id="model-a",
+            location=ModelLocation.VRAM,
+            size_gb=5.0,
+            pipeline=MagicMock(),
+        )
+
+        self.assertTrue(self.cache.has_model("model-a"))
+        self.assertTrue(self.cache.is_in_vram("model-a"))
+        self.assertFalse(self.cache.is_on_disk("model-a"))
+
+    def test_register_model_disk(self) -> None:
+        """Test registering a model on disk."""
+        from pathlib import Path
+
+        self.cache.register_model(
+            model_id="model-b",
+            location=ModelLocation.DISK,
+            size_gb=10.0,
+            disk_path=Path("/tmp/model-b"),
+        )
+
+        self.assertTrue(self.cache.has_model("model-b"))
+        self.assertFalse(self.cache.is_in_vram("model-b"))
+        self.assertTrue(self.cache.is_on_disk("model-b"))
+
+    def test_lru_ordering(self) -> None:
+        """Test that LRU ordering works correctly."""
+        # Register three models
+        self.cache.register_model("model-a", ModelLocation.VRAM, 5.0, MagicMock())
+        self.cache.register_model("model-b", ModelLocation.VRAM, 5.0, MagicMock())
+        self.cache.register_model("model-c", ModelLocation.VRAM, 5.0, MagicMock())
+
+        # model-a is LRU (first registered)
+        lru = self.cache._get_lru_vram_models()
+        self.assertEqual(lru[0], "model-a")
+
+        # Access model-a, now model-b should be LRU
+        self.cache._touch("model-a")
+        lru = self.cache._get_lru_vram_models()
+        self.assertEqual(lru[0], "model-b")
+
+    def test_lru_eviction(self) -> None:
+        """Test that LRU eviction frees space."""
+        # Fill cache: 5 + 5 + 5 = 15GB used out of 20GB max
+        self.cache.register_model("model-a", ModelLocation.VRAM, 5.0, MagicMock())
+        self.cache.register_model("model-b", ModelLocation.VRAM, 5.0, MagicMock())
+        self.cache.register_model("model-c", ModelLocation.VRAM, 5.0, MagicMock())
+
+        self.assertEqual(self.cache._vram_used_gb, 15.0)
+
+        # Try to add 10GB model - should evict LRU models
+        # Need to evict 5GB (15 + 10 - 20 = 5)
+        freed = self.cache._evict_lru_for_space(10.0)
+
+        # model-a (5GB) should be evicted
+        self.assertEqual(freed, 5.0)
+        self.assertEqual(self.cache._vram_used_gb, 10.0)
+        self.assertFalse(self.cache.is_in_vram("model-a"))
+
+    def test_get_stats(self) -> None:
+        """Test stats generation for heartbeat."""
+        self.cache.register_model("model-vram-1", ModelLocation.VRAM, 5.0, MagicMock())
+        self.cache.register_model("model-vram-2", ModelLocation.VRAM, 3.0, MagicMock())
+        from pathlib import Path
+        self.cache.register_model("model-disk-1", ModelLocation.DISK, 10.0, disk_path=Path("/tmp/m"))
+        self.cache.mark_downloading("model-dl-1", 0.5)
+
+        stats = self.cache.get_stats()
+
+        self.assertIsInstance(stats, ModelCacheStats)
+        self.assertEqual(len(stats.vram_models), 2)
+        self.assertIn("model-vram-1", stats.vram_models)
+        self.assertIn("model-vram-2", stats.vram_models)
+        self.assertEqual(len(stats.disk_models), 1)
+        self.assertIn("model-disk-1", stats.disk_models)
+        self.assertEqual(len(stats.downloading_models), 1)
+        self.assertIn("model-dl-1", stats.downloading_models)
+        self.assertEqual(stats.vram_used_gb, 8.0)
+        self.assertEqual(stats.vram_model_count, 2)
+        self.assertEqual(stats.disk_model_count, 1)
+        self.assertEqual(stats.total_models, 4)
+
+    def test_stats_to_dict(self) -> None:
+        """Test stats serialization to dict."""
+        self.cache.register_model("model-a", ModelLocation.VRAM, 5.5, MagicMock())
+        stats = self.cache.get_stats()
+        d = stats.to_dict()
+
+        self.assertIsInstance(d, dict)
+        self.assertIn("vram_models", d)
+        self.assertIn("vram_used_gb", d)
+        self.assertEqual(d["vram_used_gb"], 5.5)
+
+    def test_unload_model(self) -> None:
+        """Test unloading a model from cache."""
+        self.cache.register_model("model-a", ModelLocation.VRAM, 5.0, MagicMock())
+        self.assertTrue(self.cache.has_model("model-a"))
+        self.assertEqual(self.cache._vram_used_gb, 5.0)
+
+        result = self.cache.unload_model("model-a")
+        self.assertTrue(result)
+        self.assertFalse(self.cache.has_model("model-a"))
+        self.assertEqual(self.cache._vram_used_gb, 0.0)
+
+    def test_unload_nonexistent(self) -> None:
+        """Test unloading a model that doesn't exist."""
+        result = self.cache.unload_model("nonexistent")
+        self.assertFalse(result)
+
+    def test_mark_loaded_to_vram(self) -> None:
+        """Test marking a model as loaded to VRAM."""
+        self.cache.mark_loaded_to_vram("model-a", MagicMock(), 8.0)
+
+        self.assertTrue(self.cache.is_in_vram("model-a"))
+        self.assertEqual(self.cache._vram_used_gb, 8.0)
+
+    def test_mark_cached_to_disk(self) -> None:
+        """Test marking a model as cached to disk."""
+        from pathlib import Path
+
+        # First load to VRAM
+        self.cache.mark_loaded_to_vram("model-a", MagicMock(), 8.0)
+        self.assertTrue(self.cache.is_in_vram("model-a"))
+
+        # Then mark as disk-cached (offloaded)
+        self.cache.mark_cached_to_disk("model-a", Path("/tmp/model-a"), 8.0)
+
+        self.assertFalse(self.cache.is_in_vram("model-a"))
+        self.assertTrue(self.cache.is_on_disk("model-a"))
+        self.assertEqual(self.cache._vram_used_gb, 0.0)
+
+    def test_can_fit_in_vram(self) -> None:
+        """Test checking if model can fit in VRAM."""
+        # Empty cache with 20GB max
+        self.assertTrue(self.cache.can_fit_in_vram(10.0))
+        self.assertTrue(self.cache.can_fit_in_vram(20.0))
+        self.assertFalse(self.cache.can_fit_in_vram(25.0))
+
+        # With some models loaded
+        self.cache.register_model("model-a", ModelLocation.VRAM, 15.0, MagicMock())
+        self.assertTrue(self.cache.can_fit_in_vram(5.0))  # 15 + 5 = 20
+        self.assertTrue(self.cache.can_fit_in_vram(20.0))  # Can evict model-a
+        self.assertFalse(self.cache.can_fit_in_vram(25.0))  # Too big even after eviction
+
+    def test_get_pipeline(self) -> None:
+        """Test getting a pipeline from cache."""
+        pipeline = MagicMock()
+        self.cache.register_model("model-a", ModelLocation.VRAM, 5.0, pipeline)
+
+        retrieved = self.cache.get_pipeline("model-a")
+        self.assertIs(retrieved, pipeline)
+
+        # Getting pipeline should update LRU
+        self.cache.register_model("model-b", ModelLocation.VRAM, 5.0, MagicMock())
+        self.cache.get_pipeline("model-a")
+        lru = self.cache._get_lru_vram_models()
+        self.assertEqual(lru[0], "model-b")  # model-b is now LRU
+
+    def test_get_pipeline_disk_model(self) -> None:
+        """Test getting pipeline for disk-cached model returns None."""
+        from pathlib import Path
+        self.cache.register_model("model-a", ModelLocation.DISK, 5.0, disk_path=Path("/tmp"))
+
+        retrieved = self.cache.get_pipeline("model-a")
+        self.assertIsNone(retrieved)
+
+    def test_download_progress(self) -> None:
+        """Test download progress tracking."""
+        self.cache.mark_downloading("model-a", 0.0)
+        model = self.cache._models.get("model-a")
+        self.assertIsNotNone(model)
+        self.assertEqual(model.location, ModelLocation.DOWNLOADING)
+        self.assertEqual(model.download_progress, 0.0)
+
+        self.cache.update_download_progress("model-a", 0.5)
+        self.assertEqual(model.download_progress, 0.5)
+
+        self.cache.update_download_progress("model-a", 1.0)
+        self.assertEqual(model.download_progress, 1.0)
+
+
+class TestModelCacheEnvironment(unittest.TestCase):
+    """Test ModelCache configuration from environment."""
+
+    def test_env_config(self) -> None:
+        """Test that environment variables configure the cache."""
+        import os
+
+        with patch.dict(os.environ, {
+            "WORKER_MAX_VRAM_GB": "16",
+            "WORKER_VRAM_SAFETY_MARGIN_GB": "2.5",
+        }):
+            with patch("gen_worker.model_cache.ModelCache._detect_total_vram", return_value=24.0):
+                cache = ModelCache()
+                self.assertEqual(cache._max_vram_gb, 16.0)
+                self.assertEqual(cache._vram_safety_margin, 2.5)
+
+
+class TestProgressiveAvailability(unittest.TestCase):
+    """Tests for progressive model availability."""
+
+    def setUp(self) -> None:
+        """Create a fresh ModelCache for each test."""
+        with patch("gen_worker.model_cache.ModelCache._detect_total_vram", return_value=24.0):
+            self.cache = ModelCache(max_vram_gb=20.0)
+
+    def test_are_models_available_all_ready(self) -> None:
+        """Test that available check passes when all models are ready."""
+        self.cache.register_model("model-a", ModelLocation.VRAM, 5.0, MagicMock())
+        from pathlib import Path
+        self.cache.register_model("model-b", ModelLocation.DISK, 5.0, disk_path=Path("/tmp"))
+
+        # Both VRAM and disk models should be considered available
+        self.assertTrue(self.cache.are_models_available(["model-a"]))
+        self.assertTrue(self.cache.are_models_available(["model-b"]))
+        self.assertTrue(self.cache.are_models_available(["model-a", "model-b"]))
+
+    def test_are_models_available_downloading(self) -> None:
+        """Test that available check fails when model is downloading."""
+        self.cache.register_model("model-a", ModelLocation.VRAM, 5.0, MagicMock())
+        self.cache.mark_downloading("model-b", 0.5)
+
+        # model-a is available, model-b is not
+        self.assertTrue(self.cache.are_models_available(["model-a"]))
+        self.assertFalse(self.cache.are_models_available(["model-b"]))
+        self.assertFalse(self.cache.are_models_available(["model-a", "model-b"]))
+
+    def test_are_models_available_missing(self) -> None:
+        """Test that available check fails for unknown models."""
+        self.cache.register_model("model-a", ModelLocation.VRAM, 5.0, MagicMock())
+
+        self.assertTrue(self.cache.are_models_available(["model-a"]))
+        self.assertFalse(self.cache.are_models_available(["model-a", "model-unknown"]))
+        self.assertFalse(self.cache.are_models_available(["model-unknown"]))
+
+    def test_get_available_models(self) -> None:
+        """Test getting list of available models."""
+        self.cache.register_model("model-vram", ModelLocation.VRAM, 5.0, MagicMock())
+        from pathlib import Path
+        self.cache.register_model("model-disk", ModelLocation.DISK, 5.0, disk_path=Path("/tmp"))
+        self.cache.mark_downloading("model-dl", 0.5)
+
+        available = self.cache.get_available_models()
+        self.assertEqual(len(available), 2)
+        self.assertIn("model-vram", available)
+        self.assertIn("model-disk", available)
+        self.assertNotIn("model-dl", available)
+
+    def test_get_downloading_models(self) -> None:
+        """Test getting list of downloading models."""
+        self.cache.register_model("model-a", ModelLocation.VRAM, 5.0, MagicMock())
+        self.cache.mark_downloading("model-b", 0.3)
+        self.cache.mark_downloading("model-c", 0.7)
+
+        downloading = self.cache.get_downloading_models()
+        self.assertEqual(len(downloading), 2)
+        self.assertIn("model-b", downloading)
+        self.assertIn("model-c", downloading)
+
+    def test_get_download_progress(self) -> None:
+        """Test getting download progress for a model."""
+        self.cache.mark_downloading("model-a", 0.5)
+        self.cache.register_model("model-b", ModelLocation.VRAM, 5.0, MagicMock())
+
+        self.assertEqual(self.cache.get_download_progress("model-a"), 0.5)
+        self.assertIsNone(self.cache.get_download_progress("model-b"))
+        self.assertIsNone(self.cache.get_download_progress("unknown"))
+
+    def test_max_concurrent_downloads_config(self) -> None:
+        """Test max concurrent downloads configuration."""
+        import os
+
+        # Default value
+        self.assertEqual(self.cache.get_max_concurrent_downloads(), 2)
+
+        # From environment
+        with patch.dict(os.environ, {"WORKER_MAX_CONCURRENT_DOWNLOADS": "4"}):
+            with patch("gen_worker.model_cache.ModelCache._detect_total_vram", return_value=24.0):
+                cache = ModelCache()
+                self.assertEqual(cache.get_max_concurrent_downloads(), 4)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_model_refs.py b/tests/test_model_refs.py
index d829fd5..7cad5e9 100644
--- a/tests/test_model_refs.py
+++ b/tests/test_model_refs.py
@@ -52,18 +52,3 @@ def test_parse_hf_revision() -> None:
 def test_parse_invalid_hf() -> None:
     with pytest.raises(ValueError):
         parse_model_ref("hf:justonepart")
-
-
-def test_cozy_canonical_is_unprefixed_repo_ref() -> None:
-    p = parse_model_ref("cozy:fancyfeast/llama-joycaption-beta-one-hf-llava@blake3:abcd")
-    assert p.scheme == "cozy"
-    assert p.cozy is not None
-    # Regression guard: canonical cozy refs must not prepend "cozy:".
-    assert p.canonical() == "fancyfeast/llama-joycaption-beta-one-hf-llava@blake3:abcd"
-
-
-def test_parse_hf_double_prefix_tolerated() -> None:
-    p = parse_model_ref("hf:hf:owner/repo@main")
-    assert p.scheme == "hf"
-    assert p.hf is not None
-    assert p.canonical() == "hf:owner/repo@main"
diff --git a/tests/test_payload_model_selection.py b/tests/test_payload_model_selection.py
new file mode 100644
index 0000000..7fdf444
--- /dev/null
+++ b/tests/test_payload_model_selection.py
@@ -0,0 +1,78 @@
+import unittest
+
+import msgspec
+
+from gen_worker.injection import InjectionSpec, ModelRef, ModelRefSource
+from gen_worker.worker import Worker
+
+
+class _Payload(msgspec.Struct):
+    model: str
+
+
+class TestPayloadModelSelection(unittest.TestCase):
+    def test_fixed_key_must_exist_in_mapping(self) -> None:
+        w = Worker(
+            user_module_names=[],
+            worker_jwt="dummy-worker-jwt",
+            manifest={
+                "models": {
+                    "sd15": {"ref": "demo/sd15", "dtypes": ["fp16", "bf16"]},
+                }
+            },
+        )
+        inj = InjectionSpec(
+            param_name="pipeline",
+            param_type=object,
+            model_ref=ModelRef(ModelRefSource.FIXED, "missing"),
+        )
+        payload = _Payload(model="sd15")
+        with self.assertRaises(ValueError) as ctx:
+            w._resolve_model_id_for_injection("generate", inj, payload)
+        self.assertIn("unknown fixed model key", str(ctx.exception).lower())
+
+    def test_payload_key_must_exist_in_function_keyspace(self) -> None:
+        w = Worker(
+            user_module_names=[],
+            worker_jwt="dummy-worker-jwt",
+            manifest={
+                "models_by_function": {
+                    "generate": {
+                        "sd15": {"ref": "demo/sd15", "dtypes": ["fp16", "bf16"]},
+                        "flux": {"ref": "demo/flux", "dtypes": ["bf16"]},
+                    }
+                }
+            },
+        )
+        inj = InjectionSpec(
+            param_name="pipeline",
+            param_type=object,
+            model_ref=ModelRef(ModelRefSource.PAYLOAD, "model"),
+        )
+        payload = _Payload(model="does-not-exist")
+        with self.assertRaises(ValueError) as ctx:
+            w._resolve_model_id_for_injection("generate", inj, payload)
+        self.assertIn("unknown model key", str(ctx.exception).lower())
+        self.assertIn("sd15", str(ctx.exception))
+
+    def test_payload_key_resolves_to_repo_ref(self) -> None:
+        w = Worker(
+            user_module_names=[],
+            worker_jwt="dummy-worker-jwt",
+            manifest={
+                "models_by_function": {
+                    "generate": {
+                        "sd15": {"ref": "demo/sd15", "dtypes": ["fp16", "bf16"]},
+                    }
+                }
+            },
+        )
+        inj = InjectionSpec(
+            param_name="pipeline",
+            param_type=object,
+            model_ref=ModelRef(ModelRefSource.PAYLOAD, "model"),
+        )
+        payload = _Payload(model="sd15")
+        out, key = w._resolve_model_id_for_injection("generate", inj, payload)
+        self.assertEqual(out, "cozy:demo/sd15:latest")
+        self.assertEqual(key, "sd15")
diff --git a/tests/test_pipeline_thread_safety.py b/tests/test_pipeline_thread_safety.py
new file mode 100644
index 0000000..661fa6d
--- /dev/null
+++ b/tests/test_pipeline_thread_safety.py
@@ -0,0 +1,355 @@
+"""
+Tests for thread-safe pipeline access in PipelineLoader.
+
+These tests verify that get_for_inference() properly creates thread-safe
+pipeline copies with fresh schedulers to avoid concurrent access issues.
+"""
+import threading
+import time
+from typing import Any, Dict, List, Optional
+from unittest.mock import MagicMock, patch, PropertyMock
+
+import pytest
+
+# Check if torch is available for skip markers
+try:
+    import torch
+    TORCH_AVAILABLE = True
+except ImportError:
+    TORCH_AVAILABLE = False
+
+
+class MockSchedulerConfig:
+    """Mock scheduler config for testing."""
+    def __init__(self) -> None:
+        self.num_train_timesteps = 1000
+        self.beta_start = 0.0001
+        self.beta_end = 0.02
+
+
+class MockScheduler:
+    """Mock scheduler that tracks instance creation."""
+    _instance_count = 0
+
+    def __init__(self) -> None:
+        MockScheduler._instance_count += 1
+        self.instance_id = MockScheduler._instance_count
+        self.config = MockSchedulerConfig()
+        # Simulate internal state that would cause issues if shared
+        self._timesteps: List[int] = []
+        self._step_index: Optional[int] = None
+
+    @classmethod
+    def from_config(cls, config: MockSchedulerConfig) -> "MockScheduler":
+        """Create new scheduler from config (like diffusers does)."""
+        scheduler = cls()
+        scheduler.config = config
+        return scheduler
+
+    @classmethod
+    def reset_instance_count(cls) -> None:
+        cls._instance_count = 0
+
+
+class MockPipeline:
+    """Mock pipeline for testing thread-safe access."""
+    _from_pipe_calls = 0
+
+    def __init__(self, scheduler: Optional[MockScheduler] = None) -> None:
+        self.scheduler = scheduler or MockScheduler()
+        self.unet = MagicMock()  # Simulates heavy component
+        self.vae = MagicMock()   # Simulates heavy component
+
+    @classmethod
+    def from_pipe(cls, base_pipeline: "MockPipeline", scheduler: MockScheduler) -> "MockPipeline":
+        """Create new pipeline sharing components but with fresh scheduler."""
+        MockPipeline._from_pipe_calls += 1
+        new_pipeline = cls(scheduler=scheduler)
+        # Share heavy components
+        new_pipeline.unet = base_pipeline.unet
+        new_pipeline.vae = base_pipeline.vae
+        return new_pipeline
+
+    @classmethod
+    def reset_call_count(cls) -> None:
+        cls._from_pipe_calls = 0
+
+
+class MockLoadedPipeline:
+    """Mock LoadedPipeline container."""
+    def __init__(self, pipeline: MockPipeline, model_id: str) -> None:
+        self.pipeline = pipeline
+        self.model_id = model_id
+        self.pipeline_class = "MockPipeline"
+        self.dtype = "float16"
+        self.size_gb = 10.0
+        self.load_format = "safetensors"
+
+
+class TestGetForInferenceLogic:
+    """
+    Tests for get_for_inference() logic without requiring torch.
+    Uses direct method testing with mocked dependencies.
+    """
+
+    def setup_method(self) -> None:
+        """Reset mocks before each test."""
+        MockScheduler.reset_instance_count()
+        MockPipeline.reset_call_count()
+
+    def test_creates_fresh_scheduler_logic(self) -> None:
+        """Test that get_for_inference creates a fresh scheduler."""
+        # Test the logic directly without PipelineLoader instantiation
+        base_scheduler = MockScheduler()
+        base_pipeline = MockPipeline(scheduler=base_scheduler)
+
+        initial_count = MockScheduler._instance_count
+
+        # Simulate what get_for_inference does
+        fresh_scheduler = base_pipeline.scheduler.from_config(
+            base_pipeline.scheduler.config
+        )
+        task_pipeline = MockPipeline.from_pipe(base_pipeline, scheduler=fresh_scheduler)
+
+        # Verify new scheduler was created
+        assert MockScheduler._instance_count == initial_count + 1
+        assert task_pipeline.scheduler.instance_id != base_scheduler.instance_id
+
+    def test_shares_heavy_components_logic(self) -> None:
+        """Test that heavy components are shared."""
+        base_pipeline = MockPipeline()
+        original_unet = base_pipeline.unet
+        original_vae = base_pipeline.vae
+
+        # Simulate what get_for_inference does
+        fresh_scheduler = base_pipeline.scheduler.from_config(
+            base_pipeline.scheduler.config
+        )
+        task_pipeline = MockPipeline.from_pipe(base_pipeline, scheduler=fresh_scheduler)
+
+        # Heavy components should be shared
+        assert task_pipeline.unet is original_unet
+        assert task_pipeline.vae is original_vae
+        # Scheduler should be different
+        assert task_pipeline.scheduler is not base_pipeline.scheduler
+
+
+class TestConcurrentAccess:
+    """Tests for concurrent pipeline access patterns."""
+
+    def setup_method(self) -> None:
+        """Reset mocks before each test."""
+        MockScheduler.reset_instance_count()
+        MockPipeline.reset_call_count()
+
+    def test_concurrent_scheduler_creation_is_safe(self) -> None:
+        """Multiple concurrent scheduler creations should be independent."""
+        base_pipeline = MockPipeline()
+        results: List[MockPipeline] = []
+        errors: List[Exception] = []
+        lock = threading.Lock()
+
+        def create_task_pipeline() -> None:
+            try:
+                # Simulate what get_for_inference does
+                fresh_scheduler = base_pipeline.scheduler.from_config(
+                    base_pipeline.scheduler.config
+                )
+                task_pipeline = MockPipeline.from_pipe(
+                    base_pipeline, scheduler=fresh_scheduler
+                )
+                with lock:
+                    results.append(task_pipeline)
+            except Exception as e:
+                with lock:
+                    errors.append(e)
+
+        # Spawn multiple threads
+        threads = [threading.Thread(target=create_task_pipeline) for _ in range(10)]
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+
+        # All should succeed
+        assert len(errors) == 0, f"Got errors: {errors}"
+        assert len(results) == 10
+
+        # Each result should have a unique scheduler
+        scheduler_ids = [r.scheduler.instance_id for r in results]
+        assert len(set(scheduler_ids)) == 10  # All unique
+
+    def test_concurrent_access_simulated_inference(self) -> None:
+        """
+        Simulate concurrent inference to verify no state corruption.
+
+        This test verifies that separate schedulers prevent the
+        'IndexError: index N is out of bounds' that occurs when
+        multiple threads share a scheduler's internal state.
+        """
+        base_pipeline = MockPipeline()
+        errors: List[Exception] = []
+        completed = 0
+        lock = threading.Lock()
+
+        def simulate_inference() -> None:
+            nonlocal completed
+            try:
+                # Create thread-safe pipeline (what get_for_inference does)
+                fresh_scheduler = base_pipeline.scheduler.from_config(
+                    base_pipeline.scheduler.config
+                )
+                task_pipeline = MockPipeline.from_pipe(
+                    base_pipeline, scheduler=fresh_scheduler
+                )
+
+                # Simulate scheduler state modification during inference
+                task_pipeline.scheduler._timesteps = list(range(1000))
+                task_pipeline.scheduler._step_index = 0
+
+                # Simulate stepping through inference
+                for i in range(50):
+                    idx = task_pipeline.scheduler._step_index
+                    if idx is not None and idx < len(task_pipeline.scheduler._timesteps):
+                        # This would cause IndexError if scheduler is shared
+                        _ = task_pipeline.scheduler._timesteps[idx]
+                    task_pipeline.scheduler._step_index = i + 1
+                    time.sleep(0.001)  # Simulate work
+
+                with lock:
+                    completed += 1
+
+            except Exception as e:
+                with lock:
+                    errors.append(e)
+
+        # Spawn concurrent inference threads
+        threads = [threading.Thread(target=simulate_inference) for _ in range(5)]
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+
+        # All should complete without errors
+        assert len(errors) == 0, f"Got errors: {errors}"
+        assert completed == 5
+
+
+class TestModelManagementInterface:
+    """Tests for ModelManagementInterface.get_for_inference()"""
+
+    def test_default_implementation_calls_get_active_pipeline(self) -> None:
+        """Default get_for_inference should fall back to get_active_pipeline."""
+        from gen_worker.model_interface import ModelManagementInterface
+
+        class TestManager(ModelManagementInterface):
+            def __init__(self) -> None:
+                self.get_active_pipeline_called = False
+
+            async def process_supported_models_config(
+                self, supported_model_ids: List[str], downloader_instance: Any
+            ) -> None:
+                pass
+
+            async def load_model_into_vram(self, model_id: str) -> bool:
+                return True
+
+            async def get_active_pipeline(self, model_id: str) -> Any:
+                self.get_active_pipeline_called = True
+                return MockPipeline()
+
+            async def get_active_model_bundle(self, model_id: str) -> Any:
+                return None
+
+            def get_vram_loaded_models(self) -> List[str]:
+                return []
+
+        manager = TestManager()
+        result = manager.get_for_inference("test-model")
+
+        assert manager.get_active_pipeline_called
+        assert result is not None
+
+
+@pytest.mark.skipif(not TORCH_AVAILABLE, reason="torch not installed")
+class TestPipelineLoaderIntegration:
+    """Integration tests that require torch."""
+
+    def setup_method(self) -> None:
+        """Reset mocks before each test."""
+        MockScheduler.reset_instance_count()
+        MockPipeline.reset_call_count()
+
+    def test_get_for_inference_with_real_loader(self) -> None:
+        """Test get_for_inference with actual PipelineLoader."""
+        from gen_worker.pipeline_loader import PipelineLoader
+
+        loader = PipelineLoader()
+
+        # Create mock loaded pipeline
+        base_pipeline = MockPipeline()
+        loaded = MockLoadedPipeline(base_pipeline, "test-model")
+        loader._loaded_pipelines = {"test-model": loaded}
+
+        result = loader.get_for_inference("test-model")
+
+        assert result is not None
+        # Should have different scheduler
+        assert result.scheduler is not base_pipeline.scheduler
+        # Should share heavy components
+        assert result.unet is base_pipeline.unet
+
+    def test_get_for_inference_returns_none_for_missing(self) -> None:
+        """Test get_for_inference returns None for unloaded models."""
+        from gen_worker.pipeline_loader import PipelineLoader
+
+        loader = PipelineLoader()
+        loader._loaded_pipelines = {}
+
+        result = loader.get_for_inference("nonexistent")
+
+        assert result is None
+
+    def test_concurrent_get_for_inference_with_loader(self) -> None:
+        """Test concurrent get_for_inference calls with real PipelineLoader."""
+        from gen_worker.pipeline_loader import PipelineLoader
+
+        loader = PipelineLoader()
+
+        # Create mock loaded pipeline
+        base_pipeline = MockPipeline()
+        loaded = MockLoadedPipeline(base_pipeline, "test-model")
+        loader._loaded_pipelines = {"test-model": loaded}
+
+        results: List[Any] = []
+        errors: List[Exception] = []
+        lock = threading.Lock()
+
+        def get_pipeline() -> None:
+            try:
+                result = loader.get_for_inference("test-model")
+                with lock:
+                    results.append(result)
+            except Exception as e:
+                with lock:
+                    errors.append(e)
+
+        # Spawn concurrent threads
+        threads = [threading.Thread(target=get_pipeline) for _ in range(10)]
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+
+        assert len(errors) == 0, f"Got errors: {errors}"
+        assert len(results) == 10
+
+        # Each should have unique scheduler but share heavy components
+        scheduler_ids = set()
+        for r in results:
+            assert r is not None
+            scheduler_ids.add(r.scheduler.instance_id)
+            assert r.unet is base_pipeline.unet  # Shared
+
+        # All schedulers should be unique
+        assert len(scheduler_ids) == 10
diff --git a/tests/test_realtime_socket.py b/tests/test_realtime_socket.py
new file mode 100644
index 0000000..66a9067
--- /dev/null
+++ b/tests/test_realtime_socket.py
@@ -0,0 +1,83 @@
+import time
+import unittest
+
+from gen_worker.worker import ActionContext, RealtimeSocket, Worker, pb
+from gen_worker.decorators import worker_websocket, ResourceRequirements
+
+
+@worker_websocket(ResourceRequirements())
+async def echo_ws(ctx: ActionContext, sock: RealtimeSocket) -> None:
+    await sock.send_json({"status": "ready"})
+    async for data in sock.iter_bytes():
+        await sock.send_bytes(data)
+
+
+class TestRealtimeSocket(unittest.TestCase):
+    def _make_worker(self) -> Worker:
+        w = Worker.__new__(Worker)
+        import threading
+
+        w.owner = "tenant-1"
+        w._emit_progress_event = lambda e: None  # type: ignore[method-assign]
+        w._runtime_loaders = {}
+        w._custom_runtime_cache = {}
+        w._custom_runtime_locks = {}
+        w._fixed_model_id_by_key = {}
+        w._payload_model_id_by_key_by_function = {}
+        w._fixed_model_spec_by_key = {}
+        w._payload_model_spec_by_key_by_function = {}
+        w._release_allowed_model_ids = None
+        w._model_manager = None
+        w._realtime_sessions = {}
+        w._realtime_lock = threading.Lock()
+        w._sent = []
+        w._send_message = lambda msg: w._sent.append(msg)  # type: ignore[method-assign]
+        w._ws_specs = {}
+        w._discovered_resources = {}
+        w._inspect_websocket_spec = Worker._inspect_websocket_spec.__get__(w, Worker)  # type: ignore[attr-defined]
+        w._resolve_injected_value = Worker._resolve_injected_value.__get__(w, Worker)  # type: ignore[attr-defined]
+        w._handle_realtime_open_cmd = Worker._handle_realtime_open_cmd.__get__(w, Worker)  # type: ignore[attr-defined]
+        w._handle_realtime_frame = Worker._handle_realtime_frame.__get__(w, Worker)  # type: ignore[attr-defined]
+        w._handle_realtime_close_cmd = Worker._handle_realtime_close_cmd.__get__(w, Worker)  # type: ignore[attr-defined]
+        return w
+
+    def test_realtime_echo(self) -> None:
+        w = self._make_worker()
+        spec = w._inspect_websocket_spec(echo_ws)  # type: ignore[arg-type]
+        w._ws_specs[spec.name] = spec
+
+        w._handle_realtime_open_cmd(
+            pb.RealtimeOpenCommand(session_id="s1", function_name=spec.name, owner="tenant-1")
+        )
+
+        # Wait for ready JSON frame.
+        deadline = time.time() + 2.0
+        ready = False
+        while time.time() < deadline:
+            for m in list(w._sent):
+                if getattr(m, "realtime_frame", None) and m.realtime_frame.is_text:
+                    if b"ready" in m.realtime_frame.data:
+                        ready = True
+                        break
+            if ready:
+                break
+            time.sleep(0.01)
+        self.assertTrue(ready)
+
+        # Send binary bytes frame and expect an echoed binary frame back.
+        w._handle_realtime_frame(pb.RealtimeFrame(session_id="s1", data=b"abc", is_text=False))
+
+        deadline = time.time() + 2.0
+        echoed = False
+        while time.time() < deadline:
+            for m in list(w._sent):
+                if getattr(m, "realtime_frame", None) and not m.realtime_frame.is_text:
+                    if m.realtime_frame.data == b"abc":
+                        echoed = True
+                        break
+            if echoed:
+                break
+            time.sleep(0.01)
+        self.assertTrue(echoed)
+
+        w._handle_realtime_close_cmd(pb.RealtimeCloseCommand(session_id="s1", reason="end"))
diff --git a/tests/test_reconnect_policy.py b/tests/test_reconnect_policy.py
deleted file mode 100644
index 3d4d2b4..0000000
--- a/tests/test_reconnect_policy.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-from gen_worker.worker import Worker
-
-
-def _make_worker(tmp_path: Path, **kwargs) -> Worker:
-    mod_dir = tmp_path / "mod"
-    mod_dir.mkdir(parents=True, exist_ok=True)
-    (mod_dir / "hello_mod.py").write_text(
-        """
-from __future__ import annotations
-
-import msgspec
-from gen_worker import RequestContext, ResourceRequirements, worker_function
-
-class Input(msgspec.Struct):
-    name: str
-
-class Output(msgspec.Struct):
-    message: str
-
-@worker_function(ResourceRequirements())
-def hello(ctx: RequestContext, payload: Input) -> Output:
-    return Output(message=f"hello {payload.name}")
-""".lstrip(),
-        encoding="utf-8",
-    )
-    sys.path.insert(0, str(mod_dir))
-    return Worker(
-        scheduler_addr="127.0.0.1:50051",
-        user_module_names=["hello_mod"],
-        worker_jwt="jwt-test",
-        **kwargs,
-    )
-
-
-def test_lb_only_retries_keeps_seed_list_stable(tmp_path: Path) -> None:
-    w = _make_worker(tmp_path, scheduler_addrs=["orchestrator-lb:50051"], lb_only_retries=True)
-    before = list(w.scheduler_addrs)
-    w._set_scheduler_addr("orch-direct:50051")
-    assert w.scheduler_addr == "orch-direct:50051"
-    assert w.scheduler_addrs == before
-
-
-def test_reconnect_delay_is_exponential_and_capped(tmp_path: Path) -> None:
-    w = _make_worker(tmp_path, reconnect_delay=0.1, lb_only_retries=True)
-    w._reconnect_jitter_seconds = 0.0
-    w._reconnect_delay_max = 1.0
-    assert w._next_reconnect_delay(1) == 0.1
-    assert w._next_reconnect_delay(2) == 0.2
-    assert w._next_reconnect_delay(3) == 0.4
-    assert w._next_reconnect_delay(4) == 0.8
-    assert w._next_reconnect_delay(5) == 1.0
-    assert w._next_reconnect_delay(10) == 1.0
diff --git a/tests/test_runtime_batching_config_cmd.py b/tests/test_runtime_batching_config_cmd.py
new file mode 100644
index 0000000..256a189
--- /dev/null
+++ b/tests/test_runtime_batching_config_cmd.py
@@ -0,0 +1,113 @@
+import json
+from types import SimpleNamespace
+
+from gen_worker.decorators import ResourceRequirements
+from gen_worker.pb import worker_scheduler_pb2 as pb
+from gen_worker.worker import Worker
+
+
+def test_runtime_batching_config_cmd_applies_and_acks(monkeypatch) -> None:
+    w = Worker(user_module_names=[], worker_jwt="dummy-worker-jwt")
+    w._task_specs["caption"] = SimpleNamespace(output_mode="incremental")
+
+    sent = []
+    monkeypatch.setattr(w, "_send_message", lambda m: sent.append(m))
+
+    cmd = pb.RuntimeBatchingConfigCommand(
+        config=pb.RuntimeBatchingConfig(
+            function_name="caption",
+            batch_size_target=6,
+            batch_size_min=2,
+            batch_size_max=8,
+            prefetch_depth=3,
+            max_wait_ms=120,
+            version=3,
+        )
+    )
+    w._handle_runtime_batching_config_cmd(cmd)
+
+    cfg = w._runtime_batching_cfg_for_function("caption")
+    assert cfg["function_name"] == "caption"
+    assert cfg["batch_size_target"] == 6
+    assert cfg["batch_size_min"] == 2
+    assert cfg["batch_size_max"] == 8
+    assert cfg["prefetch_depth"] == 3
+    assert cfg["max_wait_ms"] == 120
+    assert cfg["version"] == 3
+
+    results = [m.runtime_batching_config_result for m in sent if m.HasField("runtime_batching_config_result")]
+    assert len(results) == 1
+    assert results[0].function_name == "caption"
+    assert results[0].version == 3
+    assert results[0].success is True
+    assert results[0].error_message == ""
+
+
+def test_runtime_batching_config_cmd_stale_version_is_ignored(monkeypatch) -> None:
+    w = Worker(user_module_names=[], worker_jwt="dummy-worker-jwt")
+    w._task_specs["caption"] = SimpleNamespace(output_mode="single")
+    w._runtime_batching_config_by_function["caption"] = {
+        "function_name": "caption",
+        "batch_size_target": 5,
+        "batch_size_min": 1,
+        "batch_size_max": 6,
+        "prefetch_depth": 2,
+        "max_wait_ms": 100,
+        "version": 4,
+    }
+
+    sent = []
+    monkeypatch.setattr(w, "_send_message", lambda m: sent.append(m))
+
+    msg = pb.WorkerSchedulerMessage(
+        runtime_batching_config_cmd=pb.RuntimeBatchingConfigCommand(
+            config=pb.RuntimeBatchingConfig(
+                function_name="caption",
+                batch_size_target=2,
+                batch_size_min=1,
+                batch_size_max=2,
+                prefetch_depth=1,
+                max_wait_ms=50,
+                version=3,  # stale
+            )
+        )
+    )
+    w._process_message(msg)
+
+    cfg = w._runtime_batching_cfg_for_function("caption")
+    assert cfg["version"] == 4
+    assert cfg["batch_size_target"] == 5
+
+    results = [m.runtime_batching_config_result for m in sent if m.HasField("runtime_batching_config_result")]
+    assert len(results) == 1
+    assert results[0].success is True
+    assert results[0].version == 3
+
+
+def test_function_capabilities_event_emits_when_changed(monkeypatch) -> None:
+    w = Worker(user_module_names=[], worker_jwt="dummy-worker-jwt")
+    w._discovered_resources["caption"] = ResourceRequirements(
+        batch_size_min=1,
+        batch_size_target=4,
+        batch_size_max=8,
+        prefetch_depth=2,
+        max_wait_ms=150,
+        memory_hint_mb=12288,
+        stage_profile="io_gpu_disaggregated",
+        stage_traits=["decode_prefetch", "gpu_decode_overlap"],
+    )
+    w._task_specs["caption"] = SimpleNamespace(output_mode="incremental")
+
+    sent = []
+    monkeypatch.setattr(w, "_send_message", lambda m: sent.append(m))
+
+    w._emit_function_capabilities_event()
+    w._emit_function_capabilities_event()  # second send should be deduped
+
+    events = [m.worker_event for m in sent if m.HasField("worker_event")]
+    assert len(events) == 1
+    assert events[0].event_type == "worker.function_capabilities"
+    payload = json.loads(bytes(events[0].payload_json or b"{}").decode("utf-8"))
+    fns = list(payload.get("functions") or [])
+    assert len(fns) == 1
+    assert "max_inflight_requests" not in fns[0]
diff --git a/tests/test_scheduler_model_scope.py b/tests/test_scheduler_model_scope.py
new file mode 100644
index 0000000..2e38982
--- /dev/null
+++ b/tests/test_scheduler_model_scope.py
@@ -0,0 +1,36 @@
+import msgspec
+
+from gen_worker.pb import worker_scheduler_pb2 as pb
+from gen_worker.worker import Worker
+
+
+class _Payload(msgspec.Struct):
+    model: str
+
+
+def test_scheduler_cannot_widen_manifest_model_scope(monkeypatch) -> None:
+    # Tenant-declared scope via baked manifest mapping.
+    w = Worker(
+        user_module_names=[],
+        worker_jwt="dummy-worker-jwt",
+        manifest={
+            "models": {
+                "sd15": {"ref": "demo/sd15", "dtypes": ["fp16", "bf16"]},
+            }
+        },
+    )
+
+    # Avoid background download threads in this unit test.
+    monkeypatch.setattr(w, "_start_startup_prefetch", lambda *_args, **_kwargs: None)
+
+    # Scheduler tries to widen scope (should be ignored / intersected away).
+    msg = pb.WorkerSchedulerMessage(
+        endpoint_config=pb.EndpointConfig(
+            supported_repo_refs=["cozy:evil/evil:latest"],
+            required_variant_refs=[],
+        )
+    )
+    w._process_message(msg)
+
+    # Worker must not widen scope outside the tenant manifest mapping.
+    assert w._release_allowed_model_ids == {"cozy:demo/sd15:latest"}
diff --git a/tests/test_signature_contract_and_incremental.py b/tests/test_signature_contract_and_incremental.py
new file mode 100644
index 0000000..33d2e5c
--- /dev/null
+++ b/tests/test_signature_contract_and_incremental.py
@@ -0,0 +1,178 @@
+import json
+import unittest
+from typing import Annotated, Iterator
+
+import msgspec
+
+from gen_worker.injection import ModelRef, ModelRefSource as Src
+from gen_worker.worker import ActionContext, Worker
+
+
+class Input(msgspec.Struct):
+    text: str
+
+
+class Delta(msgspec.Struct):
+    delta: str
+
+
+class InputWithModel(msgspec.Struct):
+    text: str
+    model_key: str
+
+
+class Output(msgspec.Struct):
+    model_id: str
+
+
+class FakeModel:
+    def __init__(self, model_id: str) -> None:
+        self.model_id = model_id
+
+    @classmethod
+    def from_pretrained(cls, model_id: str) -> "FakeModel":
+        return cls(model_id)
+
+
+def _make_worker() -> Worker:
+    w = Worker.__new__(Worker)
+    import threading
+
+    w._gpu_busy_lock = threading.Lock()
+    w._is_gpu_busy = False
+    w._has_gpu = False
+    w.max_output_bytes = 0
+    w._model_manager = None
+    w._runtime_loaders = {}
+    w._custom_runtime_cache = {}
+    w._custom_runtime_locks = {}
+    w._fixed_model_id_by_key = {}
+    w._payload_model_id_by_key_by_function = {}
+    w._fixed_model_spec_by_key = {}
+    w._payload_model_spec_by_key_by_function = {}
+    w._release_allowed_model_ids = None
+    w._active_tasks_lock = threading.Lock()
+    w._active_tasks = {}
+    w._request_batch_context_lock = threading.Lock()
+    w._request_batch_context = {}
+    w._send_message = lambda msg: w._sent.append(msg)  # type: ignore[method-assign]
+    w._sent = []
+    w._stop_event = threading.Event()
+    w._running = True
+    w._materialize_assets = lambda ctx, obj: None  # type: ignore[method-assign]
+    w._discovered_resources = {}
+    w._model_cache = None
+    return w
+
+
+class TestContractAndIncremental(unittest.TestCase):
+    def test_rejects_missing_return_annotation(self) -> None:
+        def bad(ctx: ActionContext, payload: Input):  # type: ignore[no-untyped-def]
+            return Delta(delta="x")
+
+        w = _make_worker()
+        with self.assertRaises(ValueError):
+            w._inspect_task_spec(bad)  # type: ignore[arg-type]
+
+    def test_incremental_output_emits_deltas_and_completed(self) -> None:
+        def stream(ctx: ActionContext, payload: Input) -> Iterator[Delta]:
+            yield Delta(delta=payload.text)
+            yield Delta(delta="!")
+
+        w = _make_worker()
+        spec = w._inspect_task_spec(stream)  # type: ignore[arg-type]
+        self.assertEqual(spec.output_mode, "incremental")
+
+        ctx = ActionContext("run-1", emitter=lambda _e: None)
+        payload = Input(text="hi")
+        b = msgspec.msgpack.encode(msgspec.to_builtins(payload))
+        w._execute_task(ctx, spec, b)
+
+        # Capture incremental output messages (typed token events preferred, legacy worker_event fallback).
+        events = []
+        for m in w._sent:
+            if not hasattr(m, "WhichOneof"):
+                continue
+            msg_type = m.WhichOneof("msg")
+            if msg_type == "incremental_token_delta":
+                events.append(("output.delta", m.incremental_token_delta.payload_json))
+                continue
+            if msg_type == "incremental_token_stream_done":
+                events.append(("output.completed", b"{}"))
+                continue
+            if msg_type == "worker_event":
+                evt = m.worker_event
+                # Ignore non-output events (e.g. metrics.*).
+                if not str(evt.event_type or "").startswith("output."):
+                    continue
+                events.append((evt.event_type, evt.payload_json))
+
+        self.assertGreaterEqual(len(events), 3)
+        self.assertEqual(events[0][0], "output.delta")
+        self.assertEqual(json.loads(events[0][1].decode("utf-8"))["delta"], "hi")
+        self.assertEqual(events[1][0], "output.delta")
+        self.assertEqual(json.loads(events[1][1].decode("utf-8"))["delta"], "!")
+        self.assertEqual(events[2][0], "output.completed")
+
+    def test_incremental_output_emits_typed_token_messages(self) -> None:
+        def stream(ctx: ActionContext, payload: Input) -> Iterator[Delta]:
+            yield Delta(delta=payload.text)
+
+        w = _make_worker()
+        spec = w._inspect_task_spec(stream)  # type: ignore[arg-type]
+
+        ctx = ActionContext("run-typed-1", emitter=lambda _e: None)
+        payload = Input(text="hello")
+        b = msgspec.msgpack.encode(msgspec.to_builtins(payload))
+        w._execute_task(ctx, spec, b)
+
+        msg_types = [m.WhichOneof("msg") for m in w._sent if hasattr(m, "WhichOneof")]
+        self.assertIn("incremental_token_delta", msg_types)
+        self.assertIn("incremental_token_stream_done", msg_types)
+
+    def test_payload_model_key_resolves_via_endpoint_map(self) -> None:
+        def fn(
+            ctx: ActionContext,
+            model: Annotated[FakeModel, ModelRef(Src.PAYLOAD, "model_key")],
+            payload: InputWithModel,
+        ) -> Output:
+            return Output(model_id=model.model_id)
+
+        w = _make_worker()
+        w._payload_model_id_by_key_by_function = {"fn": {"a": "google/foo"}}
+        w._release_allowed_model_ids = {"google/foo"}
+        spec = w._inspect_task_spec(fn)  # type: ignore[arg-type]
+        ctx = ActionContext("run-2", emitter=lambda _e: None)
+        payload = InputWithModel(text="x", model_key="a")
+        b = msgspec.msgpack.encode(msgspec.to_builtins(payload))
+        w._execute_task(ctx, spec, b)
+
+        run_results = [m.run_result for m in w._sent if hasattr(m, "WhichOneof") and m.WhichOneof("msg") == "run_result"]
+        self.assertEqual(len(run_results), 1)
+        rr = run_results[0]
+        self.assertTrue(rr.success)
+        out = msgspec.msgpack.decode(rr.output_payload, type=dict)
+        self.assertEqual(out["model_id"], "google/foo")
+
+    def test_payload_model_key_rejects_not_allowlisted(self) -> None:
+        def fn(
+            ctx: ActionContext,
+            model: Annotated[FakeModel, ModelRef(Src.PAYLOAD, "model_key")],
+            payload: InputWithModel,
+        ) -> Output:
+            return Output(model_id=model.model_id)
+
+        w = _make_worker()
+        w._payload_model_id_by_key_by_function = {"fn": {"a": "google/foo", "b": "google/bar"}}
+        w._release_allowed_model_ids = {"google/foo"}
+        spec = w._inspect_task_spec(fn)  # type: ignore[arg-type]
+        ctx = ActionContext("run-3", emitter=lambda _e: None)
+        payload = InputWithModel(text="x", model_key="b")
+        b = msgspec.msgpack.encode(msgspec.to_builtins(payload))
+        w._execute_task(ctx, spec, b)
+
+        run_results = [m.run_result for m in w._sent if hasattr(m, "WhichOneof") and m.WhichOneof("msg") == "run_result"]
+        self.assertEqual(len(run_results), 1)
+        rr = run_results[0]
+        self.assertFalse(rr.success)
+        self.assertEqual(rr.error_type, "validation")
diff --git a/tests/test_startup_model_prefetch.py b/tests/test_startup_model_prefetch.py
index 48ab6f9..94be301 100644
--- a/tests/test_startup_model_prefetch.py
+++ b/tests/test_startup_model_prefetch.py
@@ -43,7 +43,6 @@ def test_startup_prefetch_warms_disk_and_reports_disk_models(tmp_path: Path, mon
     server.start()
 
     variant_ref = "cozy:demo/repo@blake3:snap-1"
-    variant_ref_canon = "demo/repo@blake3:snap-1"
 
     w = Worker(
         scheduler_addr=f"127.0.0.1:{grpc_port}",
@@ -81,7 +80,6 @@ def test_startup_prefetch_warms_disk_and_reports_disk_models(tmp_path: Path, mon
         saw_started = False
         saw_completed = False
         disk_ready = False
-        saw_cache_only_supports_model_loading_false = False
         while time.monotonic() - start < 30:
             msg = sess.recv(timeout_s=0.5)
             if msg is None:
@@ -94,16 +92,12 @@ def test_startup_prefetch_warms_disk_and_reports_disk_models(tmp_path: Path, mon
                     saw_completed = True
             if not msg.HasField("worker_registration"):
                 if disk_ready and saw_started and saw_completed:
-                    assert saw_cache_only_supports_model_loading_false
                     return
                 continue
-            if not msg.worker_registration.resources.supports_model_loading:
-                saw_cache_only_supports_model_loading_false = True
             disk_models = list(msg.worker_registration.resources.disk_models)
-            if variant_ref_canon in disk_models:
+            if variant_ref in disk_models:
                 disk_ready = True
             if disk_ready and saw_started and saw_completed:
-                assert saw_cache_only_supports_model_loading_false
                 return
 
         raise AssertionError("timed out waiting for worker to report disk_models after startup prefetch")
diff --git a/tests/test_tensorhub_toml.py b/tests/test_tensorhub_toml.py
index 85ebc40..04ad01b 100644
--- a/tests/test_tensorhub_toml.py
+++ b/tests/test_tensorhub_toml.py
@@ -92,23 +92,6 @@ def test_invalid_dtype_rejected(self) -> None:
             with self.assertRaises(ValueError):
                 load_tensorhub_toml(p)
 
-    def test_nvfp4_dtype_accepted(self) -> None:
-        with tempfile.TemporaryDirectory() as td:
-            p = Path(td) / "endpoint.toml"
-            p.write_text(
-                """
-schema_version = 1
-name = "x"
-main = "x.main"
-
-[models]
-m = { ref = "o/r", dtypes = ["nvfp4"] }
-""".lstrip(),
-                encoding="utf-8",
-            )
-            cfg = load_tensorhub_toml(p)
-            self.assertEqual(cfg.models["m"].dtypes, ("nvfp4",))
-
     def test_invalid_cuda_constraint_rejected(self) -> None:
         with tempfile.TemporaryDirectory() as td:
             p = Path(td) / "endpoint.toml"
diff --git a/tests/test_trainer_checkpointing.py b/tests/test_trainer_checkpointing.py
new file mode 100644
index 0000000..8ed9782
--- /dev/null
+++ b/tests/test_trainer_checkpointing.py
@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+
+def test_save_and_load_trainable_module_checkpoint_roundtrip(tmp_path: Path) -> None:
+    torch = pytest.importorskip("torch")
+    import torch.nn as nn
+
+    from gen_worker.trainer import load_trainable_module_checkpoint, save_trainable_module_checkpoint
+
+    module = nn.Linear(4, 2, bias=True)
+    optimizer = torch.optim.AdamW(module.parameters(), lr=1e-3)
+
+    # Warm optimizer state so optimizer checkpointing path is exercised.
+    x = torch.randn(3, 4)
+    y = torch.randn(3, 2)
+    loss = ((module(x) - y) ** 2).mean()
+    loss.backward()
+    optimizer.step()
+    optimizer.zero_grad(set_to_none=True)
+
+    checkpoint_dir = tmp_path / "step-0001"
+    meta = save_trainable_module_checkpoint(
+        module=module,
+        optimizer=optimizer,
+        output_dir=str(checkpoint_dir),
+        step=1,
+        final=False,
+        model_name_or_path="runwayml/stable-diffusion-v1-5",
+    )
+
+    assert "primary_path" in meta
+    primary_path = Path(str(meta["primary_path"]))
+    assert primary_path.exists()
+    assert (checkpoint_dir / "checkpoint_meta.json").exists()
+
+    saved_weight = module.weight.detach().clone()
+    saved_bias = module.bias.detach().clone()
+
+    # Corrupt params then verify load restores trainable tensors.
+    with torch.no_grad():
+        module.weight.add_(10.0)
+        module.bias.sub_(10.0)
+
+    load_trainable_module_checkpoint(
+        module=module,
+        optimizer=optimizer,
+        checkpoint_dir=str(checkpoint_dir),
+    )
+    assert torch.allclose(module.weight.detach(), saved_weight)
+    assert torch.allclose(module.bias.detach(), saved_bias)
diff --git a/tests/test_trainer_runtime_orchestrated.py b/tests/test_trainer_runtime_orchestrated.py
new file mode 100644
index 0000000..b7da66b
--- /dev/null
+++ b/tests/test_trainer_runtime_orchestrated.py
@@ -0,0 +1,448 @@
+from __future__ import annotations
+
+import io
+import json
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+import sys
+import threading
+import time
+import types
+from typing import Any
+
+import pytest
+
+from gen_worker.trainer.orchestrated import StartupContractError
+from gen_worker.trainer.runtime import run_training_runtime_from_env
+
+
+def _register_trainer_module(monkeypatch: pytest.MonkeyPatch, module_name: str, class_name: str) -> str:
+    mod = types.ModuleType(module_name)
+
+    class _Trainer:
+        def setup(self, ctx) -> None:
+            _ = ctx
+
+        def configure(self, ctx) -> dict[str, object]:
+            _ = ctx
+            return {"loaded": False}
+
+        def prepare_batch(self, raw_batch: object, state: dict[str, object], ctx) -> object:
+            _ = state
+            _ = ctx
+            return raw_batch
+
+        def train_step(self, prepared_batch: object, state: dict[str, object], ctx):
+            _ = prepared_batch
+            _ = ctx
+            loaded = 1.0 if bool(state.get("loaded")) else 0.0
+            from gen_worker.trainer import StepResult
+
+            return StepResult(metrics={"train/loss": loaded})
+
+        def state_dict(self, state: dict[str, object]) -> dict[str, object]:
+            return dict(state)
+
+        def load_state_dict(self, state: dict[str, object], payload: dict[str, object], ctx) -> None:
+            _ = ctx
+            state.update(payload)
+
+    _Trainer.__name__ = class_name
+    setattr(mod, class_name, _Trainer)
+    monkeypatch.setitem(sys.modules, module_name, mod)
+    return f"{module_name}:{class_name}"
+
+
+def _parquet_bytes() -> bytes:
+    pa = pytest.importorskip("pyarrow")
+    pq = pytest.importorskip("pyarrow.parquet")
+    table = pa.table({"image_ref": ["a", "b"], "caption": ["x", "y"]})
+    buf = io.BytesIO()
+    pq.write_table(table, buf)
+    return buf.getvalue()
+
+
+class _TestHTTPHandler(BaseHTTPRequestHandler):
+    auth_token = ""
+    dataset_bytes = b""
+    resume_payload = b"{}"
+    fail_paths: set[str] = set()
+    posts: list[dict[str, Any]] = []
+    puts: list[dict[str, Any]] = []
+
+    def log_message(self, format: str, *args: object) -> None:  # noqa: A003
+        return
+
+    def _authorized(self) -> bool:
+        token = str(self.auth_token or "").strip()
+        if not token:
+            return True
+        got = str(self.headers.get("Authorization") or "").strip()
+        return got == f"Bearer {token}"
+
+    def do_GET(self) -> None:  # noqa: N802
+        if not self._authorized():
+            self.send_response(401)
+            self.end_headers()
+            return
+        if self.path == "/inputs/train.parquet":
+            body = self.dataset_bytes
+        elif self.path == "/inputs/resume.json":
+            body = self.resume_payload
+        else:
+            self.send_response(404)
+            self.end_headers()
+            return
+        self.send_response(200)
+        self.send_header("Content-Type", "application/octet-stream")
+        self.send_header("Content-Length", str(len(body)))
+        self.end_headers()
+        self.wfile.write(body)
+
+    def do_POST(self) -> None:  # noqa: N802
+        if not self._authorized():
+            self.send_response(401)
+            self.end_headers()
+            return
+        length = int(self.headers.get("Content-Length") or "0")
+        raw = self.rfile.read(length) if length > 0 else b"{}"
+        payload = json.loads(raw.decode("utf-8") or "{}")
+        self.posts.append({"path": self.path, "payload": payload})
+        if self.path in self.fail_paths:
+            self.send_response(500)
+            self.send_header("Content-Type", "application/json")
+            self.end_headers()
+            self.wfile.write(b'{"error":"forced failure"}')
+            return
+        self.send_response(200)
+        self.send_header("Content-Type", "application/json")
+        self.end_headers()
+        self.wfile.write(b'{"ok":true}')
+
+    def do_PUT(self) -> None:  # noqa: N802
+        if not self._authorized():
+            self.send_response(401)
+            self.end_headers()
+            return
+        length = int(self.headers.get("Content-Length") or "0")
+        body = self.rfile.read(length) if length > 0 else b""
+        self.puts.append(
+            {
+                "path": self.path,
+                "size": len(body),
+                "content_type": str(self.headers.get("Content-Type") or ""),
+            }
+        )
+        if self.path in self.fail_paths:
+            self.send_response(500)
+            self.send_header("Content-Type", "application/json")
+            self.end_headers()
+            self.wfile.write(b'{"error":"forced failure"}')
+            return
+        payload = {
+            "ref": self.path.removeprefix("/api/v1/file/"),
+            "sha256": "testsha256",
+            "size_bytes": len(body),
+        }
+        raw = json.dumps(payload).encode("utf-8")
+        self.send_response(200)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Content-Length", str(len(raw)))
+        self.end_headers()
+        self.wfile.write(raw)
+
+
+def _start_test_server(token: str, dataset_bytes: bytes, resume_payload: bytes, fail_paths: set[str] | None = None) -> tuple[ThreadingHTTPServer, str]:
+    _TestHTTPHandler.auth_token = token
+    _TestHTTPHandler.dataset_bytes = dataset_bytes
+    _TestHTTPHandler.resume_payload = resume_payload
+    _TestHTTPHandler.fail_paths = set(fail_paths or set())
+    _TestHTTPHandler.posts = []
+    _TestHTTPHandler.puts = []
+    server = ThreadingHTTPServer(("127.0.0.1", 0), _TestHTTPHandler)
+    thread = threading.Thread(target=server.serve_forever, daemon=True)
+    thread.start()
+    host, port = server.server_address
+    return server, f"http://{host}:{port}"
+
+
+def test_trainer_runtime_startup_requires_capability_token(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    trainer_import = _register_trainer_module(monkeypatch, "tmp_orch_startup_mod", "StartupTrainer")
+    spec = {"request_id": "run-orch-startup", "trainer": trainer_import, "max_steps": 1, "mock_batches": [1]}
+    spec_path = tmp_path / "trainer_job.json"
+    spec_path.write_text(json.dumps(spec), encoding="utf-8")
+
+    monkeypatch.setenv("TRAINER_JOB_SPEC_PATH", str(spec_path))
+    monkeypatch.setenv("TRAINER_ORCHESTRATED", "1")
+    monkeypatch.delenv("TRAINER_CAPABILITY_TOKEN", raising=False)
+
+    with pytest.raises(StartupContractError, match="startup.missing_capability_token"):
+        run_training_runtime_from_env()
+
+
+def test_trainer_runtime_orchestrated_happy_path_with_materialize_resume_and_uploads(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+) -> None:
+    trainer_import = _register_trainer_module(monkeypatch, "tmp_orch_happy_mod", "HappyTrainer")
+    token = "cap-123"
+    resume_payload = json.dumps({"state": {"loaded": True}}).encode("utf-8")
+    server, base = _start_test_server(token=token, dataset_bytes=_parquet_bytes(), resume_payload=resume_payload)
+    try:
+        events = tmp_path / "events.jsonl"
+        ckpt = tmp_path / "ckpt"
+        samples = tmp_path / "samples"
+        spec = {
+            "request_id": "run-orch-happy",
+            "trainer": trainer_import,
+            "trainer_api_version": "v1",
+            "max_steps": 2,
+            "metric_every": 1,
+            "checkpoint_every": 1,
+            "sample_every": 1,
+            "dataset": {"batch_size": 1, "readahead": 1, "columns": ["image_ref", "caption"]},
+            "inputs": {
+                "dataset_parquet_refs": [f"{base}/inputs/train.parquet"],
+                "resume_checkpoint_ref": f"{base}/inputs/resume.json",
+            },
+        }
+        spec_path = tmp_path / "trainer_job_happy.json"
+        spec_path.write_text(json.dumps(spec), encoding="utf-8")
+
+        monkeypatch.setenv("TRAINER_ORCHESTRATED", "1")
+        monkeypatch.setenv("TRAINER_CAPABILITY_TOKEN", token)
+        monkeypatch.setenv("TRAINER_JOB_SPEC_PATH", str(spec_path))
+        monkeypatch.setenv("TRAINER_EVENTS_PATH", str(events))
+        monkeypatch.setenv("TRAINER_CHECKPOINTS_DIR", str(ckpt))
+        monkeypatch.setenv("TRAINER_SAMPLES_DIR", str(samples))
+        monkeypatch.setenv("TRAINER_UPLOAD_METRICS_URL", f"{base}/upload/metrics")
+        monkeypatch.setenv("TRAINER_UPLOAD_CHECKPOINT_URL", f"{base}/upload/checkpoint")
+        monkeypatch.setenv("TRAINER_UPLOAD_SAMPLE_URL", f"{base}/upload/sample")
+        monkeypatch.setenv("TRAINER_UPLOAD_TERMINAL_URL", f"{base}/upload/terminal")
+
+        assert run_training_runtime_from_env() == 0
+        lines = [json.loads(x) for x in events.read_text(encoding="utf-8").splitlines() if x.strip()]
+        assert any(x.get("event") == "started" for x in lines)
+        assert any(x.get("event") == "metric" and x.get("name") == "train/loss" and float(x.get("value", -1)) == 1.0 for x in lines)
+        assert any(x.get("event") == "completed" for x in lines)
+        assert all(x.get("schema_version") == "trainer_event.v1" for x in lines)
+
+        posted_paths = [x["path"] for x in _TestHTTPHandler.posts]
+        assert "/upload/metrics" in posted_paths
+        assert "/upload/checkpoint" in posted_paths
+        assert "/upload/sample" in posted_paths
+        assert "/upload/terminal" in posted_paths
+        terminal = [x["payload"] for x in _TestHTTPHandler.posts if x["path"] == "/upload/terminal"][-1]
+        assert terminal["status"] == "completed"
+    finally:
+        server.shutdown()
+        server.server_close()
+
+
+def test_trainer_runtime_orchestrated_uploads_checkpoint_bytes_to_tensorhub(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+) -> None:
+    trainer_import = _register_trainer_module(monkeypatch, "tmp_orch_upload_bytes_mod", "UploadBytesTrainer")
+    token = "cap-bytes-1"
+    server, base = _start_test_server(token=token, dataset_bytes=_parquet_bytes(), resume_payload=b"{}")
+    try:
+        events = tmp_path / "events.jsonl"
+        ckpt = tmp_path / "ckpt"
+        samples = tmp_path / "samples"
+        spec = {
+            "request_id": "run-orch-upload-bytes",
+            "owner": "00000000-0000-0000-0000-000000000001",
+            "trainer": trainer_import,
+            "trainer_api_version": "v1",
+            "max_steps": 1,
+            "metric_every": 1,
+            "checkpoint_every": 1,
+            "sample_every": 0,
+            "mock_batches": [1],
+        }
+        spec_path = tmp_path / "trainer_job_upload_bytes.json"
+        spec_path.write_text(json.dumps(spec), encoding="utf-8")
+
+        monkeypatch.setenv("TRAINER_ORCHESTRATED", "1")
+        monkeypatch.setenv("TRAINER_CAPABILITY_TOKEN", token)
+        monkeypatch.setenv("TRAINER_JOB_SPEC_PATH", str(spec_path))
+        monkeypatch.setenv("TRAINER_EVENTS_PATH", str(events))
+        monkeypatch.setenv("TRAINER_CHECKPOINTS_DIR", str(ckpt))
+        monkeypatch.setenv("TRAINER_SAMPLES_DIR", str(samples))
+        monkeypatch.setenv("TRAINER_UPLOAD_METRICS_URL", f"{base}/upload/metrics")
+        monkeypatch.setenv("TRAINER_UPLOAD_CHECKPOINT_URL", f"{base}/upload/checkpoint")
+        monkeypatch.setenv("TRAINER_UPLOAD_TERMINAL_URL", f"{base}/upload/terminal")
+        monkeypatch.setenv("TENSORHUB_URL", base)
+
+        assert run_training_runtime_from_env() == 0
+
+        assert _TestHTTPHandler.puts, "expected PUT uploads to tensorhub file API"
+        assert any("/api/v1/file/v1/00000000-0000-0000-0000-000000000001/runs/run-orch-upload-bytes/checkpoints/" in x["path"] for x in _TestHTTPHandler.puts)
+
+        terminal = [x["payload"] for x in _TestHTTPHandler.posts if x["path"] == "/upload/terminal"][-1]
+        assert terminal["status"] == "completed"
+        assert terminal["final_checkpoint_ref"] != ""
+        assert terminal["final_checkpoint_sha256"] == "testsha256"
+    finally:
+        server.shutdown()
+        server.server_close()
+
+
+def test_trainer_runtime_cancel_path(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    trainer_import = _register_trainer_module(monkeypatch, "tmp_orch_cancel_mod", "CancelTrainer")
+    spec = {"request_id": "run-orch-cancel", "trainer": trainer_import, "max_steps": 3, "metric_every": 1, "mock_batches": [1, 2, 3]}
+    spec_path = tmp_path / "trainer_job_cancel.json"
+    spec_path.write_text(json.dumps(spec), encoding="utf-8")
+    events = tmp_path / "events.jsonl"
+    cancel_file = tmp_path / "cancel.flag"
+    cancel_file.write_text("1", encoding="utf-8")
+
+    monkeypatch.setenv("TRAINER_JOB_SPEC_PATH", str(spec_path))
+    monkeypatch.setenv("TRAINER_EVENTS_PATH", str(events))
+    monkeypatch.setenv("TRAINER_CANCEL_FILE", str(cancel_file))
+
+    with pytest.raises(Exception, match="canceled"):
+        run_training_runtime_from_env()
+
+    lines = [json.loads(x) for x in events.read_text(encoding="utf-8").splitlines() if x.strip()]
+    failed = [x for x in lines if x.get("event") == "failed"]
+    assert failed
+    assert "canceled" in str(failed[-1].get("error", "")).lower()
+
+
+def test_trainer_runtime_upload_failure_reports_upload_category(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    trainer_import = _register_trainer_module(monkeypatch, "tmp_orch_upload_fail_mod", "UploadFailTrainer")
+    token = "cap-up-1"
+    server, base = _start_test_server(
+        token=token,
+        dataset_bytes=_parquet_bytes(),
+        resume_payload=json.dumps({"state": {"loaded": True}}).encode("utf-8"),
+        fail_paths={"/upload/sample"},
+    )
+    try:
+        spec = {
+            "request_id": "run-orch-upload-fail",
+            "trainer": trainer_import,
+            "max_steps": 1,
+            "metric_every": 1,
+            "checkpoint_every": 1,
+            "sample_every": 1,
+            "mock_batches": [1],
+        }
+        spec_path = tmp_path / "trainer_job_upload_fail.json"
+        spec_path.write_text(json.dumps(spec), encoding="utf-8")
+        events = tmp_path / "events.jsonl"
+
+        monkeypatch.setenv("TRAINER_JOB_SPEC_PATH", str(spec_path))
+        monkeypatch.setenv("TRAINER_EVENTS_PATH", str(events))
+        monkeypatch.setenv("TRAINER_CAPABILITY_TOKEN", token)
+        monkeypatch.setenv("TRAINER_UPLOAD_METRICS_URL", f"{base}/upload/metrics")
+        monkeypatch.setenv("TRAINER_UPLOAD_CHECKPOINT_URL", f"{base}/upload/checkpoint")
+        monkeypatch.setenv("TRAINER_UPLOAD_SAMPLE_URL", f"{base}/upload/sample")
+
+        with pytest.raises(Exception, match="upload"):
+            run_training_runtime_from_env()
+
+        lines = [json.loads(x) for x in events.read_text(encoding="utf-8").splitlines() if x.strip()]
+        failed = [x for x in lines if x.get("event") == "failed"]
+        assert failed
+        assert str(failed[-1].get("error", "")).startswith("upload:")
+    finally:
+        server.shutdown()
+        server.server_close()
+
+
+def test_trainer_runtime_timeout_path(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    mod = types.ModuleType("tmp_orch_timeout_mod")
+
+    class SlowTrainer:
+        def setup(self, ctx) -> None:
+            _ = ctx
+
+        def configure(self, ctx) -> dict[str, object]:
+            _ = ctx
+            return {}
+
+        def prepare_batch(self, raw_batch: object, state: dict[str, object], ctx) -> object:
+            _ = state
+            _ = ctx
+            return raw_batch
+
+        def train_step(self, prepared_batch: object, state: dict[str, object], ctx):
+            _ = prepared_batch
+            _ = state
+            _ = ctx
+            time.sleep(1.1)
+            from gen_worker.trainer import StepResult
+
+            return StepResult(metrics={"train/loss": 0.1})
+
+        def state_dict(self, state: dict[str, object]) -> dict[str, object]:
+            return dict(state)
+
+        def load_state_dict(self, state: dict[str, object], payload: dict[str, object], ctx) -> None:
+            _ = ctx
+            state.update(payload)
+
+    mod.SlowTrainer = SlowTrainer
+    monkeypatch.setitem(sys.modules, "tmp_orch_timeout_mod", mod)
+
+    spec = {
+        "request_id": "run-orch-timeout",
+        "trainer": "tmp_orch_timeout_mod:SlowTrainer",
+        "max_steps": 2,
+        "metric_every": 1,
+        "mock_batches": [1, 2, 3],
+    }
+    spec_path = tmp_path / "trainer_job_timeout.json"
+    spec_path.write_text(json.dumps(spec), encoding="utf-8")
+    events = tmp_path / "events.jsonl"
+    monkeypatch.setenv("TRAINER_JOB_SPEC_PATH", str(spec_path))
+    monkeypatch.setenv("TRAINER_EVENTS_PATH", str(events))
+    monkeypatch.setenv("TRAINER_MAX_RUNTIME_SECONDS", "1")
+
+    with pytest.raises(Exception, match="timeout"):
+        run_training_runtime_from_env()
+
+    lines = [json.loads(x) for x in events.read_text(encoding="utf-8").splitlines() if x.strip()]
+    failed = [x for x in lines if x.get("event") == "failed"]
+    assert failed
+    assert "timeout" in str(failed[-1].get("error", "")).lower()
+
+
+def test_trainer_runtime_resume_idempotent_skips_when_final_exists(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    trainer_import = _register_trainer_module(monkeypatch, "tmp_orch_resume_skip_mod", "ResumeSkipTrainer")
+    token = "cap-up-2"
+    server, base = _start_test_server(
+        token=token,
+        dataset_bytes=_parquet_bytes(),
+        resume_payload=json.dumps({"state": {"loaded": True}}).encode("utf-8"),
+    )
+    try:
+        ckpt = tmp_path / "checkpoints"
+        ckpt.mkdir(parents=True, exist_ok=True)
+        (ckpt / "final.json").write_text(json.dumps({"state": {"loaded": True}}), encoding="utf-8")
+        events = tmp_path / "events.jsonl"
+        spec = {
+            "request_id": "run-orch-resume-skip",
+            "trainer": trainer_import,
+            "max_steps": 5,
+            "resume_from_latest": True,
+            "mock_batches": [1, 2, 3],
+        }
+        spec_path = tmp_path / "trainer_job_resume_skip.json"
+        spec_path.write_text(json.dumps(spec), encoding="utf-8")
+
+        monkeypatch.setenv("TRAINER_JOB_SPEC_PATH", str(spec_path))
+        monkeypatch.setenv("TRAINER_EVENTS_PATH", str(events))
+        monkeypatch.setenv("TRAINER_CHECKPOINTS_DIR", str(ckpt))
+        monkeypatch.setenv("TRAINER_CAPABILITY_TOKEN", token)
+        monkeypatch.setenv("TRAINER_UPLOAD_TERMINAL_URL", f"{base}/upload/terminal")
+
+        assert run_training_runtime_from_env() == 0
+        lines = [json.loads(x) for x in events.read_text(encoding="utf-8").splitlines() if x.strip()]
+        assert any(x.get("event") == "completed" for x in lines)
+        posted_paths = [x["path"] for x in _TestHTTPHandler.posts]
+        assert "/upload/terminal" not in posted_paths
+    finally:
+        server.shutdown()
+        server.server_close()
diff --git a/tests/test_training_endpoints_contract_smoke.py b/tests/test_training_endpoints_contract_smoke.py
new file mode 100644
index 0000000..8a60b89
--- /dev/null
+++ b/tests/test_training_endpoints_contract_smoke.py
@@ -0,0 +1,185 @@
+from __future__ import annotations
+
+import os
+import sys
+from pathlib import Path
+from dataclasses import dataclass, field
+
+import pytest
+
+from gen_worker.trainer import StepContext, TrainingJobSpec, load_trainer_plugin, run_training_loop
+
+
+class _Batch:
+    def __init__(self, values: dict[str, list[object]]) -> None:
+        self._values = values
+
+    def to_pydict(self) -> dict[str, list[object]]:
+        return self._values
+
+
+class _T2IModel:
+    def train_t2i_step(
+        self, *, image_refs: list[str], prompts: list[str], hyperparams: dict[str, object]
+    ) -> float:
+        _ = hyperparams
+        if len(image_refs) != len(prompts):
+            raise ValueError("length mismatch")
+        return 1.0
+
+
+class _EditModel:
+    def train_edit_step(
+        self,
+        *,
+        source_refs: list[str],
+        target_refs: list[str],
+        instructions: list[str],
+        mask_refs: list[str | None],
+        hyperparams: dict[str, object],
+    ) -> float:
+        _ = hyperparams
+        if not (len(source_refs) == len(target_refs) == len(instructions) == len(mask_refs)):
+            raise ValueError("length mismatch")
+        return 2.0
+
+
+@dataclass
+class _Reporter:
+    metrics: list[tuple[str, float, int]] = field(default_factory=list)
+    checkpoints: list[tuple[str, int]] = field(default_factory=list)
+    samples: list[tuple[str, int]] = field(default_factory=list)
+    completed_calls: list[tuple[int, str | None]] = field(default_factory=list)
+    failed_calls: list[tuple[int, str]] = field(default_factory=list)
+
+    def started(self, *, request_id: str) -> None:
+        _ = request_id
+
+    def metric(self, *, name: str, value: float, step: int) -> None:
+        self.metrics.append((name, value, step))
+
+    def checkpoint(self, *, path: str, step: int) -> None:
+        self.checkpoints.append((path, step))
+
+    def sample(self, *, path: str, step: int) -> None:
+        self.samples.append((path, step))
+
+    def completed(self, *, step: int, final_checkpoint: str | None) -> None:
+        self.completed_calls.append((step, final_checkpoint))
+
+    def failed(self, *, step: int, error: str) -> None:
+        self.failed_calls.append((step, error))
+
+    def is_canceled(self) -> bool:
+        return False
+
+
+@dataclass
+class _Writer:
+    checkpoints: list[tuple[int, dict[str, object]]] = field(default_factory=list)
+    final_payloads: list[dict[str, object]] = field(default_factory=list)
+
+    def write_checkpoint(
+        self,
+        *,
+        step: int,
+        state_payload: dict[str, object],
+        trainer: object,
+        state: object,
+        ctx: StepContext,
+    ) -> str:
+        _ = trainer
+        _ = state
+        _ = ctx
+        self.checkpoints.append((step, state_payload))
+        return f"/tmp/ckpt-{step}.json"
+
+    def write_samples(self, *, step: int, state: object, ctx: StepContext) -> list[str]:
+        _ = state
+        _ = ctx
+        return [f"/tmp/sample-{step}.json"]
+
+    def finalize(
+        self,
+        *,
+        state_payload: dict[str, object],
+        trainer: object,
+        state: object,
+        ctx: StepContext,
+    ) -> str | None:
+        _ = trainer
+        _ = state
+        _ = ctx
+        self.final_payloads.append(state_payload)
+        return "/tmp/final.json"
+
+
+def _training_endpoints_root() -> Path:
+    env_override = os.getenv("TRAINING_ENDPOINTS_ROOT")
+    if env_override:
+        return Path(env_override).resolve()
+    return Path(__file__).resolve().parents[2] / "training-endpoints"
+
+
+@pytest.mark.skipif(not _training_endpoints_root().exists(), reason="training-endpoints repo not present")
+def test_training_endpoints_examples_run_in_gen_worker_loop() -> None:
+    root = _training_endpoints_root()
+    t2i_src = root / "t2i_three_prompts" / "src"
+    edit_src = root / "img2img_edit_optional_prompt_mask" / "src"
+    sys.path.insert(0, str(t2i_src))
+    sys.path.insert(0, str(edit_src))
+    try:
+        t2i_trainer = load_trainer_plugin("t2i_three_prompts.main:ThreePromptT2ITrainer")
+        t2i_job = TrainingJobSpec(request_id="t2i-run", max_steps=1, metric_every=1, checkpoint_every=1, sample_every=1)
+        t2i_ctx = StepContext(
+            job=t2i_job,
+            model_handles={"model": _T2IModel()},
+        )
+        t2i_reporter = _Reporter()
+        t2i_writer = _Writer()
+        t2i_terminal = run_training_loop(
+            job=t2i_job,
+            ctx=t2i_ctx,
+            trainer=t2i_trainer,
+            batches=[_Batch({"image_ref": ["a"], "caption_short": ["short"]})],
+            reporter=t2i_reporter,
+            artifact_writer=t2i_writer,
+        )
+        assert t2i_terminal == 1
+        assert any(name == "train/loss" for (name, _value, _step) in t2i_reporter.metrics)
+        assert t2i_reporter.checkpoints and t2i_writer.final_payloads
+        assert t2i_reporter.samples
+
+        edit_trainer = load_trainer_plugin("img2img_edit_optional_prompt_mask.main:Img2ImgEditTrainer")
+        edit_job = TrainingJobSpec(request_id="edit-run", max_steps=1, metric_every=1, checkpoint_every=1, sample_every=1)
+        edit_ctx = StepContext(
+            job=edit_job,
+            model_handles={"model": _EditModel()},
+        )
+        edit_reporter = _Reporter()
+        edit_writer = _Writer()
+        edit_terminal = run_training_loop(
+            job=edit_job,
+            ctx=edit_ctx,
+            trainer=edit_trainer,
+            batches=[
+                _Batch(
+                    {
+                        "source_image_ref": ["source"],
+                        "target_image_ref": ["target"],
+                        "edit_type": ["mosaic"],
+                    }
+                )
+            ],
+            reporter=edit_reporter,
+            artifact_writer=edit_writer,
+        )
+        assert edit_terminal == 1
+        assert any(name == "train/loss" for (name, _value, _step) in edit_reporter.metrics)
+        assert edit_reporter.checkpoints and edit_writer.final_payloads
+        assert edit_reporter.samples
+    finally:
+        if str(t2i_src) in sys.path:
+            sys.path.remove(str(t2i_src))
+        if str(edit_src) in sys.path:
+            sys.path.remove(str(edit_src))
diff --git a/tests/test_worker_jwt_rotation.py b/tests/test_worker_jwt_rotation.py
index 6d96829..f74ca38 100644
--- a/tests/test_worker_jwt_rotation.py
+++ b/tests/test_worker_jwt_rotation.py
@@ -42,7 +42,7 @@ def test_worker_jwt_rotation_updates_next_reconnect(tmp_path: Path, monkeypatch:
 from __future__ import annotations
 
 import msgspec
-from gen_worker import RequestContext, ResourceRequirements, worker_function
+from gen_worker import ActionContext, ResourceRequirements, worker_function
 
 class Input(msgspec.Struct):
     name: str
@@ -51,7 +51,7 @@ class Output(msgspec.Struct):
     message: str
 
 @worker_function(ResourceRequirements())
-def hello(ctx: RequestContext, payload: Input) -> Output:
+def hello(ctx: ActionContext, payload: Input) -> Output:
     return Output(message=f"hello {payload.name}")
 """.lstrip(),
         encoding="utf-8",
@@ -84,27 +84,19 @@ def hello(ctx: RequestContext, payload: Input) -> Output:
             time.sleep(0.05)
         assert w.worker_jwt == "jwt-2"
 
-        # Trigger reconnect via real transport disruption: stop old server, then restart.
-        server.stop(grace=None)
-        orch2 = _MockOrchestrator()
-        server2 = grpc.server(futures.ThreadPoolExecutor(max_workers=8))
-        pb_grpc.add_SchedulerWorkerServiceServicer_to_server(orch2, server2)
-        rebound = server2.add_insecure_port(f"127.0.0.1:{port}")
-        assert rebound == port
-        server2.start()
-
-        # Wait for a new connection to establish with the rotated token on the restarted server.
+        # Force a reconnect without killing the server. This simulates network interruption.
+        w._handle_connection_error()
+
+        # Wait for a new connection to establish with the rotated token.
         start = time.monotonic()
         while time.monotonic() - start < 30:
-            sess2 = orch2.get_session(timeout_s=0.5)
+            sess2 = orch.get_session(timeout_s=0.5)
             if sess2 is None:
                 continue
             if _extract_bearer(sess2.metadata) == "jwt-2":
-                server2.stop(grace=None)
                 return
             time.sleep(0.1)
 
-        server2.stop(grace=None)
         raise AssertionError("timed out waiting for reconnect with rotated WORKER_JWT")
     finally:
         try:
diff --git a/tests/test_worker_leader_redirect.py b/tests/test_worker_leader_redirect.py
new file mode 100644
index 0000000..b0104d6
--- /dev/null
+++ b/tests/test_worker_leader_redirect.py
@@ -0,0 +1,19 @@
+import unittest
+
+from gen_worker.worker import Worker
+
+
+class TestWorkerLeaderRedirect(unittest.TestCase):
+    def test_extract_leader_addr(self):
+        self.assertEqual(Worker._extract_leader_addr("not_leader:127.0.0.1:8080"), "127.0.0.1:8080")
+        self.assertIsNone(Worker._extract_leader_addr("not_leader:"))
+        self.assertIsNone(Worker._extract_leader_addr("other_error"))
+        self.assertIsNone(Worker._extract_leader_addr(None))
+
+    def test_normalize_scheduler_addrs(self):
+        addrs = Worker._normalize_scheduler_addrs("a:1", ["b:2", "a:1", " ", "c:3"])
+        self.assertEqual(addrs, ["a:1", "b:2", "c:3"])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_worker_model_keyspace.py b/tests/test_worker_model_keyspace.py
new file mode 100644
index 0000000..f6775eb
--- /dev/null
+++ b/tests/test_worker_model_keyspace.py
@@ -0,0 +1,53 @@
+import unittest
+
+from gen_worker.injection import InjectionSpec, ModelRef, ModelRefSource
+from gen_worker.worker import Worker
+
+
+class _Payload:
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+
+
+class TestWorkerModelKeyspace(unittest.TestCase):
+    def test_payload_model_selection_uses_function_mapping(self) -> None:
+        w = Worker.__new__(Worker)
+        w._payload_model_id_by_key_by_function = {
+            "generate": {"sdxl": "stabilityai/stable-diffusion-xl-base-1.0"}
+        }
+        w._fixed_model_id_by_key = {}
+        w._release_allowed_model_ids = None
+
+        inj = InjectionSpec(
+            param_name="pipe",
+            param_type=object,
+            model_ref=ModelRef(ModelRefSource.PAYLOAD, "model_key"),
+        )
+        payload = _Payload(model_key="sdxl")
+
+        got, key = Worker._resolve_model_id_for_injection(w, "generate", inj, payload)  # type: ignore[arg-type]
+        self.assertEqual(got, "stabilityai/stable-diffusion-xl-base-1.0")
+        self.assertEqual(key, "sdxl")
+
+    def test_payload_model_selection_rejects_unknown_key(self) -> None:
+        w = Worker.__new__(Worker)
+        w._payload_model_id_by_key_by_function = {
+            "generate": {"sdxl": "stabilityai/stable-diffusion-xl-base-1.0"}
+        }
+        w._fixed_model_id_by_key = {}
+        w._release_allowed_model_ids = None
+
+        inj = InjectionSpec(
+            param_name="pipe",
+            param_type=object,
+            model_ref=ModelRef(ModelRefSource.PAYLOAD, "model_key"),
+        )
+        payload = _Payload(model_key="nope")
+
+        with self.assertRaises(ValueError):
+            Worker._resolve_model_id_for_injection(w, "generate", inj, payload)  # type: ignore[arg-type]
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_worker_startup_visibility.py b/tests/test_worker_startup_visibility.py
new file mode 100644
index 0000000..ace2c46
--- /dev/null
+++ b/tests/test_worker_startup_visibility.py
@@ -0,0 +1,131 @@
+from __future__ import annotations
+
+import json
+import sys
+import threading
+import time
+import types
+from pathlib import Path
+from typing import Any
+
+import pytest
+
+from gen_worker.worker import Worker
+
+
+def test_registration_watchdog_emits_timeout_and_sets_stop() -> None:
+    w = Worker.__new__(Worker)
+    w.worker_id = "w-1"
+    w.scheduler_addr = "127.0.0.1:8080"
+    w._process_started_monotonic = time.monotonic() - 0.5
+    w._registered_event = threading.Event()
+    w._running = True
+    w._stop_event = threading.Event()
+    w._startup_timeout_triggered = False
+
+    startup_events: list[tuple[str, dict[str, Any]]] = []
+    worker_events: list[tuple[str, dict[str, Any]]] = []
+    w._emit_startup_phase = lambda phase, **kw: startup_events.append((phase, kw))  # type: ignore[method-assign]
+    w._emit_worker_event_bytes = (  # type: ignore[method-assign]
+        lambda request_id, event_type, payload_json: worker_events.append(
+            (event_type, json.loads(payload_json.decode("utf-8")))
+        )
+    )
+    w._close_connection = lambda: None  # type: ignore[method-assign]
+
+    w._registration_watchdog_loop(timeout_s=0.02)
+
+    assert w._startup_timeout_triggered is True
+    assert w._stop_event.is_set() is True
+    assert any(name == "startup_timeout_unregistered" for name, _ in startup_events)
+    assert any(name == "worker.startup_timeout_unregistered" for name, _ in worker_events)
+
+
+def test_task_phase_watchdog_emits_stuck_event() -> None:
+    w = Worker.__new__(Worker)
+    seen: list[tuple[str, dict[str, Any]]] = []
+    w._emit_worker_event_bytes = (  # type: ignore[method-assign]
+        lambda request_id, event_type, payload_json: seen.append((event_type, json.loads(payload_json.decode("utf-8"))))
+    )
+
+    timer = w._start_task_phase_watchdog(
+        request_id="run-1",
+        phase="inference",
+        warn_after_s=0.02,
+        payload={"function_name": "generate"},
+    )
+    time.sleep(0.06)
+    if timer is not None:
+        timer.cancel()
+
+    assert any(name == "task.inference.stuck" for name, _ in seen)
+    ev_payload = next(p for name, p in seen if name == "task.inference.stuck")
+    assert ev_payload["function_name"] == "generate"
+    assert ev_payload["elapsed_ms"] >= 20
+
+
+def test_emit_worker_fatal_includes_traceback_metadata() -> None:
+    w = Worker.__new__(Worker)
+    w.worker_id = "w-2"
+    w.scheduler_addr = "scheduler:8080"
+    w._process_started_monotonic = time.monotonic() - 1.0
+    seen: list[tuple[str, dict[str, Any]]] = []
+    w._emit_worker_event_bytes = (  # type: ignore[method-assign]
+        lambda request_id, event_type, payload_json: seen.append((event_type, json.loads(payload_json.decode("utf-8"))))
+    )
+
+    try:
+        raise RuntimeError("boom")
+    except Exception as exc:
+        w._emit_worker_fatal("startup", exc, exit_code=7)
+
+    assert seen
+    event_type, payload = seen[-1]
+    assert event_type == "worker.fatal"
+    assert payload["phase"] == "startup"
+    assert payload["exception_class"] == "RuntimeError"
+    assert "boom" in payload["exception_message"]
+    assert "RuntimeError" in payload["traceback"]
+    assert payload["exit_code"] == 7
+
+
+def test_run_raises_when_registration_timeout_reached(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    mod_dir = tmp_path / "mod"
+    mod_dir.mkdir(parents=True, exist_ok=True)
+    (mod_dir / "tiny_mod.py").write_text(
+        """
+from __future__ import annotations
+import msgspec
+from gen_worker.decorators import worker_function
+from gen_worker.worker import ActionContext
+
+class Input(msgspec.Struct):
+    name: str
+
+class Output(msgspec.Struct):
+    ok: bool
+
+@worker_function()
+def tiny(ctx: ActionContext, payload: Input) -> Output:
+    return Output(ok=True)
+""".lstrip(),
+        encoding="utf-8",
+    )
+    sys.path.insert(0, str(mod_dir))
+
+    w = Worker(
+        scheduler_addr="127.0.0.1:1",
+        user_module_names=["tiny_mod"],
+        worker_jwt="jwt-test",
+        reconnect_delay=0,
+        max_reconnect_attempts=0,
+    )
+    monkeypatch.setattr(w, "connect", types.MethodType(lambda self: False, w))
+    w._register_timeout_s = 1
+    w._reconnect_delay_base = 0
+    w._reconnect_delay_max = 0
+    w._reconnect_jitter = 0
+
+    with pytest.raises(RuntimeError, match="startup_timeout_unregistered"):
+        w.run()
+
diff --git a/tests/test_worker_telemetry_issue67.py b/tests/test_worker_telemetry_issue67.py
new file mode 100644
index 0000000..0308aee
--- /dev/null
+++ b/tests/test_worker_telemetry_issue67.py
@@ -0,0 +1,187 @@
+from __future__ import annotations
+
+import asyncio
+import threading
+import time
+from pathlib import Path
+from typing import Any
+
+import msgspec
+import pytest
+
+from gen_worker.model_cache import ModelCache
+from gen_worker.worker import ActionContext, Worker, pb, _TaskSpec
+from gen_worker.decorators import ResourceRequirements
+
+
+class _Input(msgspec.Struct):
+    sleep_s: float
+
+
+class _Output(msgspec.Struct):
+    ok: bool
+
+
+def _sleep_fn(ctx: ActionContext, payload: _Input) -> _Output:
+    time.sleep(payload.sleep_s)
+    return _Output(ok=True)
+
+
+def test_gpu_is_busy_refcount_overlapping_inference() -> None:
+    w = Worker.__new__(Worker)
+    w._has_gpu = True
+    w._gpu_busy_lock = threading.Lock()
+    w._gpu_busy_refcount = 0
+    w._is_gpu_busy = False
+    w._active_model_use_lock = threading.Lock()
+    w._active_model_use_counts = {}
+    w._active_tasks_lock = threading.Lock()
+    w._active_tasks = {}
+    w._active_function_counts = {}
+    w._send_message = lambda msg: None  # type: ignore[method-assign]
+    w._send_task_result = lambda *args, **kwargs: None  # type: ignore[method-assign]
+    w._materialize_assets = lambda ctx, obj: None  # type: ignore[method-assign]
+
+    spec = _TaskSpec(
+        name="sleep",
+        func=_sleep_fn,
+        resources=ResourceRequirements(),
+        ctx_param="ctx",
+        payload_param="payload",
+        payload_type=_Input,
+        output_mode="single",
+        output_type=_Output,
+        injections=(),
+    )
+
+    ctx1 = ActionContext("r1", emitter=lambda e: None)
+    ctx2 = ActionContext("r2", emitter=lambda e: None)
+    payload = msgspec.msgpack.encode({"sleep_s": 0.25})
+
+    t1 = threading.Thread(target=w._execute_task, args=(ctx1, spec, payload), daemon=True)
+    t2 = threading.Thread(target=w._execute_task, args=(ctx2, spec, payload), daemon=True)
+
+    t1.start()
+    t2.start()
+
+    # Wait until at least one entered busy.
+    deadline = time.time() + 2.0
+    while time.time() < deadline and not w._get_gpu_busy_status():
+        time.sleep(0.01)
+    assert w._get_gpu_busy_status() is True
+
+    # While at least one task is running, busy must remain true.
+    while t1.is_alive() or t2.is_alive():
+        assert w._get_gpu_busy_status() is True
+        time.sleep(0.01)
+
+    assert w._get_gpu_busy_status() is False
+
+
+class _StubModelManager:
+    def __init__(self) -> None:
+        self._loaded: set[str] = set()
+        self.model_sizes: dict[str, float] = {}
+
+    async def load_model_into_vram(self, model_id: str) -> bool:
+        await asyncio.sleep(0.15)
+        self._loaded.add(model_id)
+        return True
+
+    def unload(self, model_id: str) -> None:
+        self._loaded.discard(model_id)
+
+    def get_vram_loaded_models(self) -> list[str]:
+        return sorted(self._loaded)
+
+
+def test_load_model_emits_events_and_updates_vram_models_and_busy(tmp_path: Path) -> None:
+    w = Worker.__new__(Worker)
+    w._has_gpu = True
+    w._gpu_busy_lock = threading.Lock()
+    w._gpu_busy_refcount = 0
+    w._is_gpu_busy = False
+    w._active_model_use_lock = threading.Lock()
+    w._active_model_use_counts = {}
+    w._model_init_done_event = threading.Event()
+    w._model_init_done_event.set()
+    w._model_manager = _StubModelManager()
+    w._model_cache = ModelCache(model_cache_dir=str(tmp_path / "cache"))
+    w._task_specs = {}
+    w._ws_specs = {}
+    w._discovered_resources = {}
+    w._function_schemas = {}
+    w.max_concurrency = 0
+    w.runpod_pod_id = ""
+
+    sent: list[Any] = []
+    w._send_message = lambda msg: sent.append(msg)  # type: ignore[method-assign]
+
+    cmd = pb.LoadModelCommand(model_id="cozy:demo/repo@sha256:snap-1")
+
+    th = threading.Thread(target=w._handle_load_model_cmd, args=(cmd,), daemon=True)
+    th.start()
+
+    # Busy should become true during the async sleep.
+    deadline = time.time() + 2.0
+    saw_busy = False
+    while time.time() < deadline and th.is_alive():
+        if w._get_gpu_busy_status():
+            saw_busy = True
+            break
+        time.sleep(0.01)
+    assert saw_busy is True
+
+    th.join(timeout=5)
+    assert w._get_gpu_busy_status() is False
+
+    # Must emit load.started + load.completed/failed.
+    event_types = [
+        m.worker_event.event_type
+        for m in sent
+        if getattr(m, "worker_event", None) and m.HasField("worker_event")
+    ]
+    assert "model.load.started" in event_types
+    assert "model.load.completed" in event_types
+
+    # LoadModelResult must succeed.
+    results = [m.load_model_result for m in sent if m.HasField("load_model_result")]
+    assert results and results[-1].success is True
+
+    # The immediate registration update should reflect vram_models after the load.
+    regs = [m.worker_registration for m in sent if m.HasField("worker_registration")]
+    assert regs
+    assert "cozy:demo/repo@sha256:snap-1" in list(regs[-1].resources.vram_models)
+
+
+def test_unload_model_rejected_when_in_use(tmp_path: Path) -> None:
+    w = Worker.__new__(Worker)
+    w._has_gpu = True
+    w._gpu_busy_lock = threading.Lock()
+    w._gpu_busy_refcount = 0
+    w._is_gpu_busy = False
+    w._active_model_use_lock = threading.Lock()
+    w._active_model_use_counts = {}
+    w._model_manager = _StubModelManager()
+    w._model_cache = ModelCache(model_cache_dir=str(tmp_path / "cache"))
+    w._task_specs = {}
+    w._ws_specs = {}
+    w._discovered_resources = {}
+    w._function_schemas = {}
+    w.max_concurrency = 0
+    w.runpod_pod_id = ""
+
+    sent: list[Any] = []
+    w._send_message = lambda msg: sent.append(msg)  # type: ignore[method-assign]
+
+    model_id = "cozy:demo/repo@sha256:snap-1"
+    w._model_use_enter(model_id)
+    try:
+        w._handle_unload_model_cmd(pb.UnloadModelCommand(model_id=model_id))
+    finally:
+        w._model_use_exit(model_id)
+
+    res = [m.unload_model_result for m in sent if m.HasField("unload_model_result")]
+    assert res and res[-1].success is False
+    assert "model_in_use" in (res[-1].error_message or "")
+
diff --git a/tests/test_worker_wire_protocol.py b/tests/test_worker_wire_protocol.py
new file mode 100644
index 0000000..f235287
--- /dev/null
+++ b/tests/test_worker_wire_protocol.py
@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from gen_worker.wire_protocol import WIRE_PROTOCOL_MAJOR, WIRE_PROTOCOL_MINOR
+from gen_worker.worker import Worker
+
+
+def test_registration_advertises_wire_protocol(monkeypatch) -> None:
+    sent = []
+
+    w = Worker(
+        scheduler_addr="127.0.0.1:65535",
+        user_module_names=[],
+        worker_jwt="test-jwt",
+        reconnect_delay=0,
+    )
+    monkeypatch.setattr(w, "_send_message", lambda message: sent.append(message))
+
+    w._register_worker(is_heartbeat=False)
+
+    assert sent, "expected at least one outgoing registration message"
+    reg = sent[0].worker_registration
+    assert reg.protocol_major == WIRE_PROTOCOL_MAJOR
+    assert reg.protocol_minor == WIRE_PROTOCOL_MINOR
+
+
+def test_detects_protocol_incompatibility_marker() -> None:
+    assert Worker._is_protocol_incompatibility("unsupported_worker_protocol:1.0 supported=1:1-9999")
+    assert not Worker._is_protocol_incompatibility("not_leader:127.0.0.1:50051")
diff --git a/uv.lock b/uv.lock
index aff17a8..7bdf473 100644
--- a/uv.lock
+++ b/uv.lock
@@ -10,7 +10,7 @@ resolution-markers = [
 
 [[package]]
 name = "accelerate"
-version = "1.13.0"
+version = "1.12.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
@@ -22,9 +22,9 @@ dependencies = [
     { name = "torch", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
     { name = "torch", version = "2.10.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ca/14/787e5498cd062640f0f3d92ef4ae4063174f76f9afd29d13fc52a319daae/accelerate-1.13.0.tar.gz", hash = "sha256:d631b4e0f5b3de4aff2d7e9e6857d164810dfc3237d54d017f075122d057b236", size = 402835, upload-time = "2026-03-04T19:34:12.359Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/4a/8e/ac2a9566747a93f8be36ee08532eb0160558b07630a081a6056a9f89bf1d/accelerate-1.12.0.tar.gz", hash = "sha256:70988c352feb481887077d2ab845125024b2a137a5090d6d7a32b57d03a45df6", size = 398399, upload-time = "2025-11-21T11:27:46.973Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7e/46/02ac5e262d4af18054b3e922b2baedbb2a03289ee792162de60a865defc5/accelerate-1.13.0-py3-none-any.whl", hash = "sha256:cf1a3efb96c18f7b152eb0fa7490f3710b19c3f395699358f08decca2b8b62e0", size = 383744, upload-time = "2026-03-04T19:34:10.313Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/d2/c581486aa6c4fbd7394c23c47b83fa1a919d34194e16944241daf9e762dd/accelerate-1.12.0-py3-none-any.whl", hash = "sha256:3e2091cd341423207e2f084a6654b1efcd250dc326f2a37d6dde446e07cabb11", size = 380935, upload-time = "2025-11-21T11:27:44.522Z" },
 ]
 
 [[package]]
@@ -151,15 +151,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
 ]
 
-[[package]]
-name = "annotated-doc"
-version = "0.0.4"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
-]
-
 [[package]]
 name = "anyio"
 version = "4.12.1"
@@ -276,11 +267,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2026.2.25"
+version = "2026.1.4"
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 [[package]]
@@ -449,61 +440,64 @@ wheels = [
 
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+version = "46.0.3"
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