用反馈迭代提升医学论文生成质量，让AI写稿更像人。
FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights
- 分步拆解4287篇医学论文，构建结构化数据集
- 三类智能体协作，通过指标反馈逐轮优化内容
- 人评结果接近人工写作，尤其擅长提炼研究方向
通过大语言模型自动化科学研宄带来机遇，但也面临知识整合与质量保障的挑战。本文提出反馈精炼代理方法（FRAME），通过迭代精炼与结构化反馈提升医学论文生成质量。方法包括：（1）将4287篇医学论文分解为关键研究组件的结构化数据构建方法；（2）集成生成器、评估器与反思器三类代理的架构，实现基于指标反馈的内容质量逐步提升；（3）结合统计指标与人工基准的综合评估框架。实验表明，FRAME在多个模型上均表现优异，相比传统方法平均提升9.91%（使用DeepSeek V3），GPT-4o Mini也取得相当改善。人工评估确认，其生成论文质量可媲美人类撰写，尤其在归纳未来研究方向方面表现突出。本工作为自动化医学论文生成奠定了坚实基础，同时保持严格的学术标准。
原文摘要 · Abstract (English)
The automation of scientific research through large language models (LLMs) presents significant opportunities but faces critical challenges in knowledge synthesis and quality assurance. We introduce Feedback-Refined Agent Methodology (FRAME), a novel framework that enhances medical paper generation through iterative refinement and structured feedback. Our approach comprises three key innovations: (1) A structured dataset construction method that decomposes 4,287 medical papers into essential research components through iterative refinement; (2) A tripartite architecture integrating Generator, Evaluator, and Reflector agents that progressively improve content quality through metric-driven feedback; and (3) A comprehensive evaluation framework that combines statistical metrics with human-grounded benchmarks. Experimental results demonstrate FRAME's effectiveness, achieving significant improvements over conventional approaches across multiple models (9.91% average gain with DeepSeek V3, comparable improvements with GPT-4o Mini) and evaluation dimensions. Human evaluation confirms that FRAME-generated papers achieve quality comparable to human-authored works, with particular strength in synthesizing future research directions. The results demonstrated our work could efficiently assist medical research by building a robust foundation for automated medical research paper generation while maintaining rigorous academic standards.
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