FRAME : Feedback-Refined Agent Methodology for Enhancing Medical Research Insights (2025)

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.

## introduction the traditional academic research paradigm relies on human researchers to gather knowledge, formulate hypotheses, and evaluate findings through peer review. while this process has driven significant technological advances, it is inherently limited by human cognitive constraints and timeintensive workflows, with studies showing an average publication cycle of 21.9 months @xcite . the emergence of large language models (llms), particularly since gpt-3.5 @xcite , has introduced unprecedented capabilities in natural language processing, from text generation to complex reasoning tasks @xcite @xcite . advanced techniques like chainof-thought (cot) @xcite and frameworks such as langchain @xcite and metagpt @xcite have further enhanced llms' ability to handle sophisticated multiagent collaboration. current applications of llms in academic research fall into two categories. the first focuses on specific subtasks like code generation @xcite , idea formulation @xcite , and paper review @xcite . while effective within their domains, these applications cannot encompass the entire research process. the second category employs collaborative multi-agent systems for comprehensive research tasks, exemplified by autosurvey's automated literature review pipeline @xcite . however, these applications have primarily focused on computational domains where experiments can be simulated. the application of llms to support end-to-end academic research, particularly in medical research, faces two significant challenges. first, current large language models primarily rely on factual knowledge (e.g., understanding that colds can be caused by either bacteria or viruses) rather than learning from previous generation failures (e.g., recognizing that a previously unsuccessful attempt at generating a cold-related paper failed to adequately consider bacterial influences). this limitation hinders the models' ability to iteratively improve their output quality through experience-based learning (wang et @xcite . second, current paper generation models almost lack robust evaluation mechanisms, depending primarily on subjective agent assessments without rigorous benchmarks against human-authored papers. these limitations underscore the need for methods that ensure scientific validity while maintaining the academic standards characteristic of peer-reviewed publications. drawing inspiration from adversarial learning principles, we propose a novel approach to enhance the quality of llm-generated academic papers, as shown in figure 1 . our feedback-refined agent methodology (frame) reimagines the dynamic interplay between content generation and quality assessment through a feedback-driven iterative process. unlike traditional neural network approaches that rely on gradient-based parameter updates, frame implements a structured refinement cycle where specialized agents work in concert to progressively improve content quality. this methodology addresses the fundamental challenge of continuous self-improvement in llm-based research systems through three key mechanisms: (1) specialized agents assume distinct roles in content generation and quality evaluation; (2) the feedback process targets logical coherence and academic rigor through structured assessment; and our contributions are summarized as follows: ## feedback-refined agent methodology (frame) to enable our agent to craft high-quality medical papers on par with human standards, it is insufficient to merely furnish the agent with a wealth of background knowledge. instead, the agent must be capable of explicitly learning from each deficiency and updating its generation strategy in subsequent iterations. drawing inspiration from adversarial learning principles, we have developed a feedback-driven iterative methodology known as the feedback-refined agent methodology (frame), whose architecture is illustrated in figure 1 . this approach emphasizes continuous improvement through structured feedback loops, where specialized agents work in concert to progressively enhance content quality while maintaining rigorous academic standards. ## experiment our experimental evaluation comprises four key components. first, we systematically evaluate the impact of frame on text generation quality by comparing outputs from two state-of-the-art language models (deepseek v3 and gpt-4o mini) with and without our method. second, we conduct a rigorous human evaluation where 20 randomly selected frame-enhanced papers generated by deepseek v3 are compared against humanauthored counterparts through expert assessments by medical professionals. third, we investigate the generalizability of frame by evaluating its performance on models released prior to our test set creation, ensuring no potential data contamination. finally, we examine the influence of training dataset scale on model performance by conducting systematic experiments with varying dataset sizes, providing insights into the data efficiency of our approach. ## conclusion our feedback-refined agent methodology (frame) demonstrates significant improvements in medical paper generation, with deepseek v3 and gpt-4o mini showing average performance gains of 9.91% across 40 evaluation dimensions, while qwen 1.5 32b achieves a 3.8% improvement. human evaluations reveal that frame-generated papers achieve comparable quality to human-authored works (92.80% vs 92.88%), particularly excelling in conclusion synthesis. the proposed tripartite training architecture and structured dataset construction method effectively address key challenges in medical research automation, though limitations in retrieval dependency and offline dataset usage suggest potential for future enhancements through adaptive generator-evaluator-reflector retrieval strategies and dynamic learning from external sources.