auto-review-loop-minimax

Automated multi-round external review for ML research is orchestrated by triggering an iterative loop that captures feedback and implements fixes via the MiniMax API until a positive assessment threshold is reached.

An interrupted automated research review loop can be resumed because the workflow persists progress in REVIEW_STATE.json and logs details in AUTO_REVIEW.md, ensuring no iterative feedback is lost.

A MiniMax API key or MCP tool is required to run autonomous ML critique cycles, as MiniMax serves as the external reviewer with a fallback mechanism to ensure continuous iterative feedback.

You configure the rigor of an automated multi-round review workflow by adjusting MAX_ROUNDS to limit iterations and POSITIVE_THRESHOLD to set the required acceptance score for stopping the loop.

The best way to track state during iterative ML proposal critiques is using a persistence mechanism that automatically records progress in a JSON state file and logs details in a markdown file.

An automated external review loop for ML research is needed when projects require rigorous critique before submission, utilizing iterative multi-round feedback to accelerate proposal refinement.