auto-review-loop-llm

Automated iterative research paper review runs review, fix, and re-review cycles using an OpenAI-compatible LLM API to address weaknesses until meeting submission readiness thresholds or hitting the maximum round limit.

Yes, state persistence tracks progress across review rounds, allowing you to resume the autonomous review loop seamlessly after interruptions without triggering redundant LLM API cycles.

Yes, the autonomous review loop targets machine learning research projects for top-tier venues including MICCAI, NeurIPS, and ICML, automatically identifying critical weaknesses and implementing minimal fixes.

Yes, you must configure an OpenAI-compatible LLM API via the llm-chat MCP server or environment variables to generate reviewer assessments and run the autonomous review loop.

The best way is using an autonomous review loop to automatically generate reviewer assessments, apply minimal fixes to critical weaknesses, and re-assess readiness for top venue submission.

The autonomous review loop terminates when either the research paper meets submission readiness thresholds or the maximum round limit is reached, stopping further LLM API review cycles.