Knowledge-Aware Co-Reasoning for Multidisciplinary Collaboration

Large language models (LLMs) have shown significant potential to improve diagnostic performance for clinical professionals. Existing multi-agent paradigms rely mainly on prompt engineering, suffering from improper agent selection and insufficient knowledge integration. In this work, we propose a novel framework KACR (Knowledge-Aware Co-Reasoning) that integrates structured knowledge reasoning into multidisciplinary collaboration from two aspects: (1) a reinforcement learning-optimized agent that uses clinical knowledge graphs to guide dynamic discipline determination; (2) a multidisciplinary collaboration strategy that enables robust consensus through integration of domain-specific expertise and interdisciplinary persuasion mechanism. Extensive experiments conducted on both academic and real-world datasets demonstrate the effectiveness of our method.