AI-Assisted Methodological Feedback

Initial feedback: 3 November 2026; revised 4 November 2026. This is author-side AI-assisted methodological feedback, not independent peer review. Scope: public manuscript, PDF, sanitized outcome projection, analysis code, supporting research documents, and website build.

Strongest Rejection Risks and Disposition

1. The title foregrounds security regressions, but the experiment establishes oracle-level persistence and scoped T09 remediation. The abstract, results, discussion, and conclusion now state that every T06 oracle-failing output reproduces the baseline, with consumer-dependent vulnerability interpretation and that no newly introduced regression was established. The final pre-adjudication correction names security-oracle outcomes and gate admissions, and distinguishes conditional T06 interpretation from T09 remediation.
2. The evidence is concentrated in one synthetic task whose counterexample informed evaluator development. A task-level table now exposes that all six violations and all four functional failures occur in T06. Evaluator-selection and overfitting risks are explicit. This limitation cannot be removed without a new preregistered experiment.
3. Repeated generations, a single model, and 12 tasks do not support population inference. The analysis is explicitly descriptive, uses the task as the unit of scenario diversity, reports 31 byte-distinct patches and 41 repeats, and makes no significance, confidence-interval, equivalence, or causal claim.
4. The audit is separately executed but not independently authored, and public replication is incomplete. The paper now distinguishes execution separation from independent authorship. Hidden cases, raw outputs, generated code, exact patch hashes, and reviewer mappings remain controlled; public artifacts reproduce reporting but not the full security test.
5. Independent human adjudication is absent. Publication metadata says “Human adjudication pending.” No human-safe, prompt-compliance, agreement, false-admission, or false-rejection result is reported. This remains the principal external scientific dependency.

Reference Verification

The five academic references were checked against DOI or official conference records: IEEE S&P 2022 (Pearce et al.), ACM CCS 2023 (Perry et al.), ICLR 2024/OpenReview (Jimenez et al.), ACM Computing Surveys (Monperrus), and the Journal of Systems and Software (Ye et al.). Anthropic's official model page supports the recorded Claude Sonnet 4.6 list pricing. The paper does not claim a systematic literature review.

Evidence Integrity

The public analysis reproduces 72 attempts, 68 functional passes, six security-oracle failures, two violations among functional passes, 216 admissions, 31 distinct byte patches, and 41 repeated generations. Immutable data, request hashes, examples, and analysis code remain byte-identical to the v0.1.0-preadjudication evidence lock. The tagged historical release is not modified by these subsequent manuscript and website commits.

Feedback provenance clarification

The operator reports that a Claude model provided non-binding methodological feedback through chat without inspecting individual patches, captures or private audit cases. The artifact-level checks summarized here were Codex-assisted repository checks, not work performed by that feedback model. The chat model and experimental Claude Sonnet 4.6 share a model family; the chat model's exact identifier and snapshot are unrecorded. No identical snapshot, independent human review or peer-review status is claimed.