@inproceedings{song-etal-2026-demma,
    title = "{D}em{MA}: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulation",
    author = "Song, Yutong  and
      Wu, Jiang  and
      Sharif, Kazi Shaharair  and
      Zhang, Pengfei  and
      Huang, Wenjun  and
      Xu, Honghui  and
      Dutt, Nikil  and
      Rahmani, Amir M.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.605/",
    pages = "12439--12470",
    ISBN = "979-8-89176-395-1",
    abstract = "Simulating dementia patients with large language models (LLMs) is challenging due to the need to jointly model cognitive impairment, emotional dynamics, and nonverbal behaviors over long conversations. We present DemMA, an expert-guided dementia dialogue agent for high-fidelity multi-turn patient simulation. DemMA constructs clinically grounded dementia personas by integrating pathology information, personality traits, and subtype-specific memory-status personas informed by clinical experts. To move beyond text-only simulation, DemMA explicitly models nonverbal behaviors, including motion, facial expressions, and vocal cues. We further introduce a Chain-of-Thought distillation framework that trains a single LLM to jointly generate reasoning traces, patient utterances, and aligned behavioral actions within one forward pass, enabling efficient deployment without multi-agent inference."
}