**Overall Verdict:** Accept — the submission is complete, coherent, objectively evaluable, and end-to-end reachable, with only a negligible artifact-count wording inconsistency.

**Completeness Check:**
- Instruction: Complete — clearly defines the ten latent parameters, partial-observation constraints, validation protocols, controller ranking criterion, and five required artifacts.
- Inputs: Complete — includes 48 training and 18 validation episodes, parameter bounds, a functioning simulator specification, loaders, evaluation utilities, and five distinct controller checkpoints.
- Toolchain: Complete — pins the required open-source Python, MuJoCo, Gymnasium, PyTorch, NumPy, and SciPy stack for Linux.
- Reference output: Complete — provides evaluator-only ground truth, hidden trajectories, deployment scores, expert artifacts, and a structurally valid 100/100 expert report.
- Rubric: Complete — the evaluator implements the stated 35/10/15/35/5 scoring allocation, fatal integrity/schema checks, independent history-aware one-step evaluation, open-loop evaluation, and deterministic controller selection.

**File and Task Reachability:**
The attachment set is `reachable`, and the end-to-end task is `reachable`. I inspected `01_INPUT_MATERIALS__INSTANCE_1.zip` and `02_REFERENCE_OUTPUT__INSTANCE_1.zip`, including all contained files: documentation, source and XML, JSON and CSV schemas, NumPy archives and arrays, PyTorch checkpoint containers, hidden evaluator assets, and expert outputs. Both archives match their manifest sizes, are readable and unencrypted, pass container integrity checks, have modest expansion, and contain no traversal paths, links, devices, or unexpected executables. Numeric arrays have consistent shapes, finite values, contiguous episode timesteps, valid binary masks, and documented measurement ordering; all 19 public inputs match the supplied authoritative hashes. Reference and hidden assets are separated from solver-visible inputs.

No archive members were left unreviewed. Uploaded programs were statically inspected and not executed, as required. The review environment lacked a Python runtime, so the supplied expert run was validated structurally rather than reproduced; exact external package availability was not independently searched. The documented completion path is to install the pinned dependencies, run the submitted calibration CLI against the public task root to generate the five artifacts, and invoke the separate evaluator against those artifacts and the evaluator-only reference directory.

License keys, accounts, tokens, and other credentials are not required from the contributor. Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.

**Special Screening:**
None. External public-identifiability searches were not permitted, so this classification relies on the submitted and locally inspectable evidence.

**Recommended Changes:**
The task is professionally scoped and ready; no material changes are required. Optionally clarify whether diagnostic files are permitted, because the reference script writes `per_episode_estimates.csv` in addition to the five stated artifacts. As defense in depth, execute submitted `calibration.py` inside a network-disabled OS sandbox exposing only the read-only public task and writable output directory.