MedCEG: Reinforcing Verifiable Medical Reasoning with Critical Evidence Graph

Linjie Mu, Yannian Gu, Zhongzhen Huang, Yakun Zhu, Shaoting Zhang, Xiaofan Zhang

Keywords: Medical Reasoning; Medical Large Language Model

Abstract:

Large language models with reasoning capabilities have demonstrated impressive performance across a wide range of domains. In clinical applications, a transparent, step-by-step reasoning process provides physicians with strong evidence to support decision-making. While reinforcement learning has effectively enhanced reasoning performance in medical contexts, the clinical reliability of these reasoning processes remains limited because their accuracy and validity are often overlooked during training. To address this gap, we propose MedCEG, a framework that augments medical models with clinically valid reasoning pathways by explicitly supervising the reasoning process through a Critical Evidence Graph (CEG).
We curate a dataset of challenging clinical cases and algorithmically construct a CEG for each sample to represent a high-quality verifiable reasoning pathway.
To guide the reasoning process, we introduce a Clinical Reasoning Procedure Reward, which evaluates Node Coverage, Structural Correctness, and Chain Completeness, thereby providing a holistic assessment of reasoning quality. Experimental results show that MedCEG achieves competitive performance across multiple medical benchmarks while generating more clinically sound reasoning chains, offering a promising step towards more reliable medical AI reasoning.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 4151