auto-review-loop-minimax

Automating iterative peer review involves orchestrating a multi-round external review workflow that repeatedly assesses your research artifacts, parses feedback into ranked minimum fixes, and applies reviewer-driven updates to code and experiments until submission-ready.

State persistence saves progress to REVIEW_STATE.json, allowing the automated research review loop to resume seamlessly after context compaction or interrupted runs without losing prior reviewer feedback or experiment outputs.

The best way to integrate MiniMax API for ML model evaluation is using an MCP tool when available, automatically falling back to a direct curl-based API call to send rigorous reviewer instructions for iterative paper-quality assessment across changing experiments.

Yes, you can resume experiment automation after context compaction because the workflow persists progress in REVIEW_STATE.json and appends raw reviewer responses verbatim to AUTO_REVIEW.md, ensuring no data is lost during long runs.

Yes, to run automated paper iteration you need to set the MINIMAX_API_KEY in your environment or within ~/.claude/settings.json, enabling the workflow to call MiniMax for rigorous reviewer instructions and feedback parsing.

You should use an autonomous review loop for ML or research projects needing iterative paper-quality assessment across changing code, experiments, and results, systematically converting external reviewer feedback into ranked minimum fixes until submission-ready.