**Overall Verdict:** Strong Accept — The submission is complete, internally consistent, professionally representative, safely isolated, and objectively evaluable through visible scoring and private deterministic replay.

**Completeness Check:**

- Instruction: Complete — The method specification defines equations, attack procedures, stable ordering, intervention policy, retraining, schemas, release gates, CLI behavior, and required deliverables.
- Inputs: Complete — The solver bundle provides valid train, validation, and test tables; six source groups; model and ensemble parameters; ten attack scenarios; policy settings; runtime metadata; and a non-answering triage utility.
- Toolchain: Complete — Python 3.12 and NumPy 2.3.5 are free and obtainable, with compatible version pins and a single-machine CPU workflow requiring no network services.
- Reference output: Complete — Visible and private gold artifacts cover every required schema, and the expert visible outputs match the visible gold files.
- Rubric: Complete — Component weights, visible/private weights, numerical tolerance, pass threshold, partial-credit behavior, schema validation, and deterministic scoring logic are supplied and coherent.

**File and Task Reachability:**

Attachment set: `reachable`. End-to-end task: `reachable`.

Both `input_materials.zip` and `reference_output_and_evaluation_dependencies.zip` were inspected. Every archive member was inventoried and matched its embedded size and SHA-256 manifest. The review covered the method specification; CSV datasets; model, ensemble, attack, policy, task-card, validation, and rubric JSON; pinned runtime files; starter utility; execution wrapper; evaluator lifecycle and scoring source; provenance documentation; expert pipeline; all CSV and Markdown outputs; and every visible/private NPZ member. The archives were readable, unencrypted, bounded, and free of traversal paths, links, device entries, or unexpected executables.

The tables have complete schemas, unique IDs, finite feature values, binary labels, expected record coverage, and balanced source-program coverage. NPZ arrays use non-object numeric or Unicode dtypes and reconcile across attack identities, perturbation norms, defended predictions, reported metrics, intervention coverage, release gates, and decisions. No attachment member was omitted; uploaded programs were reviewed statically and not executed. Independent executable replay was not performed because review policy prohibits executing submitted programs and the review environment did not provide an approved Python runtime.

The completion path is clear: implement the documented CLI, run it on solver-visible inputs, produce the six audit artifacts alongside the reusable pipeline, then undergo exact visible scoring and an isolated unprivileged replay against independently provisioned inputs. Evaluator-only references remain separated from the solver workflow.

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. The evidence identifies the datasets as deterministic clean-room simulations but does not establish AI fabrication or a predetermined defect-removal fixture. No approved public-search capability was available; supplied provenance states that the task is newly authored and cites public research only as scientific context.

**Recommended Changes:**

The submission provides a coherent professional workflow, complete evidence, deterministic references, and strong evaluation isolation. No changes are required.