@inproceedings{bi-etal-2025-magi,
    title = "{MAGI}: Multi-Agent Guided Interview for Psychiatric Assessment",
    author = "Bi, Guanqun  and
      Chen, Zhuang  and
      Liu, Zhoufu  and
      Wang, Hongkai  and
      Xiao, Xiyao  and
      Xie, Yuqiang  and
      Zhang, Wen  and
      Huang, Yongkang  and
      Chen, Yuxuan  and
      Peng, Libiao  and
      Huang, Minlie",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1278/",
    pages = "24898--24921",
    ISBN = "979-8-89176-256-5",
    abstract = "Automating structured clinical interviews could revolutionize mental healthcare accessibility, yet existing large language models (LLMs) approaches fail to align with psychiatric diagnostic protocols. We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational workflows through coordinated multi-agent collaboration. MAGI dynamically navigates clinical logic via four specialized agents: 1) an interview tree guided navigation agent adhering to the MINI{'}s branching structure, 2) an adaptive question agent blending diagnostic probing, explaining, and empathy, 3) a judgment agent validating whether the response from participants meet the node, and 4) a diagnosis Agent generating Psychometric Chain-of- Thought (PsyCoT) traces that explicitly map symptoms to clinical criteria. Experimental results on 1,002 real-world participants covering depression, generalized anxiety, social anxiety and suicide shows that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning."
}