FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights

TL;DR

The paper tackles the difficulty of knowledge synthesis and quality assurance in automated medical writing by introducing FRAME, a Feedback-Refined Agent Methodology that uses iterative, structured feedback to improve output quality.It constructs a robust six-section dataset from 4,287 medical papers and deploys a tripartite Generator-Evaluator-Reflector architecture together with a retrieval-augmented generation pipeline to refine content.Extensive experiments demonstrate robust improvements across multiple models and evaluation metrics, with FRAME achieving an average 9.91% gain and human evaluations showing parity with human-authored work while excelling at future-direction synthesis.The work highlights FRAME's potential to efficiently assist medical research while maintaining rigorous standards, though it notes limitations related to retrieval dependency and offline data, pointing to adaptive retrieval and human-in-the-loop verification as future directions.

Abstract

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.

Figures (4)

• Figure 1: Architecture of the Feedback-Refined Agent Methodology (FRAME). During the training phase, the system generates and accumulates Reflection Reports in a dedicated database, which subsequently guides the formal paper generation process. This iterative training paradigm enables continuous refinement of the generation capabilities through structured feedback mechanisms.
• Figure 2: Overview of the dataset construction process. Core information from academic papers is iteratively extracted and refined through $N$ rounds of Extractor-Checker cycles ($N=3$ in our implementation), resulting in a more structured and concise representation of the content.
• Figure 3: Human vs Model Writing Quality Comparison
• Figure 4: Impact of Training Sample Size on Multi-Dimensional Scoring Metrics