@inproceedings{li-etal-2025-knowledge-aware,
    title = "Knowledge-Aware Co-Reasoning for Multidisciplinary Collaboration",
    author = "Li, Xurui  and
      Wanghaijiao  and
      Song, Kaisong  and
      Zhu, Rui  and
      Tang, Haixu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.687/",
    pages = "13615--13631",
    ISBN = "979-8-89176-332-6",
    abstract = "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."
}