Auto Review Loop: Autonomous Research Improvement

Auto Review Loop: Autonomous Research Improvement

Autonomously iterate: review → implement fixes → re-review, until the external reviewer gives a positive assessment or MAX_ROUNDS is reached.

Context: $ARGUMENTS

Constants

• MAX_ROUNDS = 4
• POSITIVE_THRESHOLD: score >= 6/10, or verdict contains "accept", "sufficient", "ready for submission"
• REVIEW_DOC: review-stage/AUTO_REVIEW.md (cumulative log) (fall back to ./AUTO_REVIEW.md for legacy projects)
• OUTPUT_DIR = review-stage/ — All review-stage outputs go here. Create the directory if it doesn't exist.
• REVIEWER_MODEL = gpt-5.5 — Model used via a secondary Codex agent. Must be an OpenAI model (e.g., gpt-5.5, o3, gpt-4o)
• REVIEWER_BACKEND = codex — Default: Codex reviewer agent at xhigh reasoning. Override with --reviewer: oracle-pro only when the user explicitly requests Oracle; if Oracle is unavailable, warn and fall back to Codex xhigh.
• HUMAN_CHECKPOINT = false — When true, pause after each round's review (Phase B) and present the score + weaknesses to the user. Wait for user input before proceeding to Phase C. The user can: approve the suggested fixes, provide custom modification instructions, skip specific fixes, or stop the loop early. When false (default), the loop runs fully autonomously.
• COMPACT = false — When true, (1) read EXPERIMENT_LOG.md and findings.md instead of parsing full logs on session recovery, (2) append key findings to findings.md after each round.
• REVIEWER_DIFFICULTY = medium — Controls adversarial depth: medium uses normal Codex xhigh review through spawn_agent / send_input; hard adds Reviewer Memory and Debate Protocol; nightmare adds direct repository-reading adversarial verification by an independent reviewer.

💡 Override: /auto-review-loop "topic" — compact: true, human checkpoint: true, difficulty: hard

Claude-Aligned Reviewer Memory and Debate

For difficulty: hard and difficulty: nightmare, maintain review-stage/REVIEWER_MEMORY.md.

• Before each reviewer call, prepend the full REVIEWER_MEMORY.md contents under ## Your Reviewer Memory (persistent across rounds).
• Tell the reviewer to check whether prior suspicions were genuinely addressed or merely sidestepped.
• Require a Memory update section in the reviewer response.
• After Phase B, copy the Memory update into REVIEWER_MEMORY.md before writing REVIEW_STATE.json.
• In nightmare, launch an additional fresh adversarial reviewer with direct repository/file-reading instructions. It should read NARRATIVE_REPORT.md or review-stage/AUTO_REVIEW.md for the author's claims, then verify those claims against code, logs, result files, and paper drafts instead of trusting executor summaries.

Instructions

In hard and nightmare modes, the reviewer must actively look for omissions, unsupported claims, cherry-picked evidence, metric mistakes, and weaknesses the executor may have downplayed.

For difficulty: hard and nightmare, use the Debate Protocol after a critical review:

1. Codex writes a concise rebuttal with evidence, not spin.
2. Send the rebuttal to the same reviewer via send_input.
3. The reviewer rules which objections are resolved, unresolved, or newly discovered.
4. Only mark a concern resolved when the reviewer accepts the rebuttal.