**Overall Verdict:** Accept — The revision is complete, safely reachable, professionally grounded, and objectively scorable, with only negligible packaging and validation-hardening issues.

**Completeness Check:**

- Instruction: Complete — The classification construct, two permitted labels, word-ID constraints, and exact JSONL deliverable are explicit.
- Inputs: Complete — Fifty test cases contain 10,312 sequential words with valid offsets; 20 disjoint worked examples contain five complete feedback events and gold labels.
- Toolchain: Complete — Python 3.11+, UTF-8 JSONL, Linux, and a standard-library evaluator provide a practical single-machine workflow.
- Reference output: Complete — Sealed gold labels, chronological process evidence, provenance records, deterministic derivation logic, controls, and audit documentation cover all test cases.
- Rubric: Complete — Exact-document accuracy is unambiguous, with word accuracy, per-label F1, macro-F1, and boundary F1 as secondary diagnostics.

**File and Task Reachability:**

Attachment classification: reachable. Task classification: reachable.

All four attachments were inspected: `genius_word_provenance_ale_50_v1_participant.zip`, `genius_ai_supported_writing_50_v2_gpt55_selftest.zip`, `selftest_primary_outcome_0_of_50.png`, and `genius_word_provenance_ale_50_v1_reference_and_provenance.zip`. Their byte counts and actual formats match the manifest. The ZIPs are readable, unencrypted, CRC-valid, bounded in expansion, and contain only safe relative regular-file and directory entries. UTF-8 JSON/JSONL content parses successfully; schemas, counts, offsets, labels, controls, scorer logic, provenance, and representative records were inspected. The 1600×1600 PNG is readable and visually agrees with the self-test results.

The three self-test outputs each contain all 50 cases and 10,312 labels. Their independently recomputed exact-document and word-accuracy results match the submitted reports. The claimed blindness is procedural rather than filesystem-enforced, but this is adequately disclosed.

One internal manifest digest differs for the empty-control score because two zero-valued label fields were serialized in the opposite order; the content is semantically identical and all metrics agree. The scorer also does not reject additional unknown case objects once all required cases are valid, although it correctly rejects missing, duplicate, extra, or invalid word IDs within scored cases. Neither issue obstructs completion or objective scoring.

The end-to-end path is complete: inspect the participant cases and worked examples, fill the supplied prediction template, write `predictions.jsonl`, and evaluate it against the sealed gold with the supplied scorer. No attachment content remained materially unreviewed, and there were no material tooling limitations.

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 affirmatively states that the student texts were previously published under CC BY 4.0. Risk is substantially mitigated because the process traces, selected-case provenance, and exact word labels are non-public and evaluator-sealed; public essay retrieval would not reveal the protected answers.

**Recommended Changes:**

The submission already provides unusually strong participant/evaluator separation, process-backed labels, controls, provenance documentation, and reproducible self-test evidence. No material changes are required. Optional hardening would be to regenerate the internal manifest only after final packaging, make label-field serialization deterministic, and have the scorer reject unknown or additional case objects so it enforces the stated exactly-50-object contract literally.