auto-review-loop-minimax

Automated multi-round external review is orchestrated by coordinating iterative feedback cycles via an external validator. The workflow applies stateful loop control to converge on a final evaluation decision for ML research.

Stateful loop control works by persisting per-round state to a JSON file and logging progress to a Markdown file. This mechanism ensures traceability and allows the review process to resume or converge across multiple evaluation rounds.

Yes, curl can be used as a fallback for API integration when the primary MCP tool is unavailable. This ensures continuous API access to the external reviewer during the automated evaluation workflow.

The best way to log persistent state is by writing per-round data to a state JSON file and a review Markdown file. This provides complete traceability and records the iterative feedback for each evaluation round.

Yes, the autonomous review loop supports primary MCP tool usage for external validation. It integrates with MCP to manage the iterative feedback and improvement cycles for ML research evaluation.

You need an autonomous external reviewer when applying iterative review scenarios to ML research evaluation. It is required when external validators provide feedback and you must converge on a final decision through multiple rounds.