**Overall Verdict:** Strong Accept — The submission is complete, internally consistent, safely reachable, and supports deterministic end-to-end least-privilege reconstruction and evaluation.

**Completeness Check:**
- Instruction: Complete — Clearly defines the evidence model, generalization requirements, security boundaries, and two output artifacts.
- Inputs: Complete — Includes 276 valid access-log records spanning approximately 88 days, current and boundary policies, SCP, role metadata, reconstruction specification, and 100 visible targets.
- Toolchain: Complete — Requires only freely available Python 3.9+ standard-library tooling on Linux, with no network or cloud account dependency.
- Reference output: Complete — Includes the reference policy, golden deliverables, required and negative contexts, escalation probes, audit grid, expected verdicts, and effective-grant manifest.
- Rubric: Complete — Implements the documented hard gates and 40/30/20/10 weighted scoring contract with deterministic authorization semantics and calibration cases.

**File and Task Reachability:**
The attachment set is `reachable`, and the end-to-end task is `reachable`. I inspected `input.zip` and `output-eval.zip`, including all contained documentation, JSON/JSONL evidence, golden outputs, reference corpora, and Python evaluator sources; no content was left unreviewed. Both archives matched their declared sizes, passed container integrity checks, were unencrypted, contained no unsafe paths or special file types, and expanded to modest sizes. All structured files were valid UTF-8 and parseable, corpus counts and identifiers were consistent, and an independent read-only check reproduced all 908 expected corpus verdicts and all 148 reference effective grants. The submitted self-test was reviewed statically but could not be executed because Python was unavailable in the review environment; this is a reviewer-tooling limitation rather than a submission defect. Solver-visible inputs are separated from evaluator-only references, and the workflow supports producing both required outputs and grading them objectively on one ordinary machine. 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. No affirmative evidence supports an AI-synthetic, public-benchmark, or synthetic-defect-fixture label. Public-benchmark status could not be independently searched because external access was prohibited.

**Recommended Changes:**
The submission provides coherent evidence, reference artifacts, and objective evaluation coverage. No changes are required.