**Overall Verdict:** Accept — The revision is complete and internally consistent, with reproducible scoring evidence and a viable offline workflow.

**Completeness Check:**

- Instruction: Complete — Defines the prediction objective, atom indexing, haptic binding convention, restrictions, and required output schema.
- Inputs: Complete — All 100 ligand records align with the template and references; supplied atom maps match the SMILES atom-token sequences.
- Toolchain: Complete — The open-source Python stack supports offline completion on one ordinary machine; scoring requires only the standard library.
- Reference output: Complete — Includes 100 observed answers and 142 accepted sets, with valid index ranges and consistent denticities.
- Rubric: Complete — Exact-set accuracy, independent denticity accuracy, and invalid-row handling are defined. Independent comparisons confirm both controls and the reported 39% atom-set and 51% denticity self-test scores.

**File and Task Reachability:**

Attachments: `reachable`. Task: `reachable`.

Inspected all four attachments: `input_materials.zip`, `reference_output.zip`, `score.txt`, and `predictions.csv`. Archive inspection covered `ENVIRONMENT.md`, `ATTRIBUTION.md`, `TASK.md`, `ligands.csv`, `predictions_template.csv`, `GRADING.md`, `predictions_reference.csv`, `reference_answers.csv`, `score.py`, and `build_equivalent_sets.py`. File types and byte counts match the manifest; both archives are readable, unencrypted, and contain no unsafe paths or special entries. Text and CSV content is valid UTF-8, and no attachment content remains unread.

The completion path is to provision dependencies before network isolation, predict from the solver-visible inputs, produce the required CSV, and evaluate against separately held references. No answer leakage was found in the input archive. Uploaded code was inspected without execution. RDKit molecular validation and equivalence enumeration, external provenance, and the claimed self-test model identity were not independently verified.

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:**

`public_benchmark` — The submission explicitly reuses pydentate test/holdout examples and observed labels from an identified public data release. Strict offline execution mitigates answer retrieval. External verification was unavailable, so current answer retrievability and pretraining exposure remain uncertain.

**Recommended Changes:**

The submission provides consistent data, separated grading assets, and corroborated scoring evidence. No contributor changes are required. Preserve strict network isolation and evaluator-only access to references and self-test predictions.