@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T DemMA: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulation
%A Song, Yutong
%A Wu, Jiang
%A Sharif, Kazi Shaharair
%A Zhang, Pengfei
%A Huang, Wenjun
%A Xu, Honghui
%A Dutt, Nikil
%A Rahmani, Amir M.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F song-etal-2026-demma
%X 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.
%U https://aclanthology.org/2026.findings-acl.605/
%P 12439-12470
Markdown (Informal)
[DemMA: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulation](https://aclanthology.org/2026.findings-acl.605/) (Song et al., Findings 2026)
ACL
- Yutong Song, Jiang Wu, Kazi Shaharair Sharif, Pengfei Zhang, Wenjun Huang, Honghui Xu, Nikil Dutt, and Amir M. Rahmani. 2026. DemMA: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 12439–12470, San Diego, California, United States. Association for Computational Linguistics.