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auto-review-loop-minimax

Automate iterative research paper review and weakness remediation using the MiniMax API.

2|Updated Aug 12, 2025
Search Tags:#research paper#state persistence#conference submission#paper improvement#autonomous review#MiniMax API#ML review
One-click install
npx skills add https://github.com/goupup-ai/miccai25 --skill auto-review-loop-minimax-goupup-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: auto-review-loop-minimax
Source: https://github.com/goupup-ai/miccai25/tree/main/ARIS/skills/auto-review-loop-minimax
Command: npx skills add https://github.com/goupup-ai/miccai25 --skill auto-review-loop-minimax-goupup-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually iterating research paper reviews, addressing weaknesses, and re-submitting for top-tier conferences like MICCAI, NeurIPS, or ICML is extremely time-consuming and often lacks consistent, rigorous feedback from senior reviewers.

Core Features & Use Cases

  • Autonomous Multi-Round Review Loop: Uses the MiniMax API to get senior ML researcher feedback, automatically implements fixes for identified weaknesses, runs required experiments, and re-reviews until the paper meets submission standards or the maximum round limit is reached.
  • State Persistence: Survives context compaction by saving loop state to a JSON file, so long-running review cycles can be resumed without losing progress.
  • Flexible API Integration: Supports both MCP tool and curl fallback for MiniMax API calls, with no dependency on Codex MCP.
  • Use Case: A researcher submitting a vertebrae segmentation paper to MICCAI 2025 can use this skill to get iterative, constructive feedback, implement fixes for model weaknesses, and receive a clear readiness verdict for submission.

Quick Start

Use the auto-review-loop-minimax skill to run an autonomous review loop for your MICCAI 2025 vertebrae segmentation paper to get iterative feedback and fixes until it is ready for submission.

Frequently Asked Questions about auto-review-loop-minimax

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate iterative review for a machine learning conference submission?▼

An autonomous review loop automates iterative paper review for machine learning conferences by using the MiniMax API to generate senior reviewer feedback, implement fixes, run experiments, and re-review until meeting a stop condition or maximum round limit.

How does state persistence work during long-running paper improvement cycles?▼

State persistence saves the ongoing review loop state to a JSON file, allowing long-running paper improvement cycles to survive context compaction and resume without losing progress.

Can I use the MiniMax API for autonomous ML review without Codex MCP?▼

Yes, autonomous ML review supports flexible MiniMax API integration using both MCP tool and curl fallback, requiring no dependency on Codex MCP to execute the review pipeline.

What is the stop condition for an automated research paper review loop?▼

The stop condition for an automated research paper review loop is determined by the reviewer score and verdict indicating submission readiness, or by reaching the maximum round limit.

Does this automated review pipeline support experiments for medical imaging papers like MICCAI submissions?▼

Yes, the automated review pipeline supports medical imaging papers like MICCAI submissions by autonomously running required experiments and implementing fixes for identified model weaknesses during the iterative review cycle.

Why does context compaction break long-running ML review loops and how is it handled?▼

Context compaction erases intermediate progress in long-running ML review loops, handled by saving state to a JSON file to ensure the pipeline remains stateless per round while preserving overall loop progress.

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