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DemMA: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulation

Paper · 2026 · Yutong Song, Jiang Wu, Kazi Shaharair Sharif et al.

Summary

Realistic dementia patient dialogue is scarce. Privacy and ethics rules restrict real data, and generic LLMs used as simulators tend to slip back into a polished assistant voice. They also ignore differences between subtypes and leave out nonverbal behaviour. DemMA addresses this by building patient profiles in stages: a clinical and demographic background, then subtype-dependent personality traits based on WHO ICF b126, then long- and short-term memories. A pipeline of five LLM agents produces conversations. The agents handle memory-state analysis, dialogue planning, utterance generation, action labelling (motion, facial expression, sound) and validation, and four dementia experts then review the output. The resulting synthetic corpus, DemMA-Dialogue, is used for Chain-of-Thought distillation. A single model learns to produce clinical reasoning, the patient's reply and matching action labels in one pass, which avoids the latency of multi-agent inference. According to the authors, DemMA beats baselines on persona fidelity, clinical validity and educational value. Medical students who studied its dialogues could identify subtypes at better than chance. This provides a privacy-safe resource for caregiver training.

Key points

  • The system covers nine dementia subtypes/stages: Alzheimer's disease at both an early and a mid/late stage (AD-early, AD-mid/late), vascular dementia (VaD), dementia with Lewy bodies (DLB), Parkinson's disease dementia (PDD), behavioural-variant frontotemporal dementia (FTD-bv), and three primary progressive aphasia variants (nfvPPA, svPPA, lvPPA).
  • The authors claim this is the first LLM agent to attach explicit motion, facial-expression and sound labels to text dialogue as stand-ins for nonverbal cues.
  • They release DemMA-Dialogue, described as the first expert-validated synthetic dementia dialogue dataset, checked by 4 domain experts. Code is at github.com/RainieLLM/DemMA.
  • Training uses multi-task SFT with a weighted combination of utterance, planner and action losses. This distils the five-agent workflow into one low-latency model.
  • Stated limitations:
    • The data is entirely synthetic.
    • Action labels are only textual approximations of behaviour.
    • Disease progression and medication effects are not modelled.
    • Part of the evaluation relies on LLM judges.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of California, IrvineIrvine · United States43 works · 36,565 citations🎓Kennesaw State UniversityKennesaw · United States4 works · 2 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksMedicine · 275 worksHealth Informatics · 214 worksPsychiatry and Mental health · 9 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1,685 worksDialogue systems · 654 works

Cite

Cite as 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. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.605

@inproceedings{song2026demma,
  title = {{DemMA: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulation}},
  author = {Yutong Song, Jiang Wu, Kazi Shaharair Sharif, Pengfei Zhang, Wenjun Huang, Honghui Xu, Nikil Dutt and Amir M. Rahmani},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.605},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.605}
}

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