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    <abstract>Psychiatric consultation requires agents to elicit discriminative evidence, map uncertain narratives to diagnostic criteria, and decide when evidence suffices. Existing dialogue and retrieval-augmented systems condition policies on raw histories or appended passages, leaving observed evidence, missing checks, differentials, and support reliability entangled. We introduce MIND, a criteria-grounded evidence-state decision interface. At each turn, MIND constructs a typed state containing observed evidence, unresolved criterion checks, active differentials, criterion-linked supports, and reliability metadata. A training-split Psychiatric Reasoning Bank supplies gated supports, turning retrieval into state construction rather than prompt injection. The same state conditions action selection, process rewards, information-gain scoring, and trajectory rectification. Under EMR-grounded simulator protocols, MIND improves accuracy by 8.8 and 7.3 points over strong inference-only, RAG, and RL baselines, with gains transferring to public MDD-5k dialogues. Matched interventions attribute these gains to the shared state rather than prompt length, retrieval text, or formatting. MIND targets screening-level decision support, not autonomous diagnosis. Code is available at https://github.com/Lingxi-mental-health/MIND.</abstract>
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%0 Conference Proceedings
%T MIND: An Evidence-State Decision Interface for Criteria-Grounded Psychiatric Inquiry and Diagnosis
%A Li, Guoyi
%A Xu, Shihao
%A Ma, Jiatong
%A Yao, Zhongjiang
%A Han, Yunyun
%A Chen, Jianhua
%A Deng, Yafeng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-mind
%X Psychiatric consultation requires agents to elicit discriminative evidence, map uncertain narratives to diagnostic criteria, and decide when evidence suffices. Existing dialogue and retrieval-augmented systems condition policies on raw histories or appended passages, leaving observed evidence, missing checks, differentials, and support reliability entangled. We introduce MIND, a criteria-grounded evidence-state decision interface. At each turn, MIND constructs a typed state containing observed evidence, unresolved criterion checks, active differentials, criterion-linked supports, and reliability metadata. A training-split Psychiatric Reasoning Bank supplies gated supports, turning retrieval into state construction rather than prompt injection. The same state conditions action selection, process rewards, information-gain scoring, and trajectory rectification. Under EMR-grounded simulator protocols, MIND improves accuracy by 8.8 and 7.3 points over strong inference-only, RAG, and RL baselines, with gains transferring to public MDD-5k dialogues. Matched interventions attribute these gains to the shared state rather than prompt length, retrieval text, or formatting. MIND targets screening-level decision support, not autonomous diagnosis. Code is available at https://github.com/Lingxi-mental-health/MIND.
%U https://aclanthology.org/2026.emnlp-main.1417/
%P 32348-32378

Markdown (Informal)

[MIND: An Evidence-State Decision Interface for Criteria-Grounded Psychiatric Inquiry and Diagnosis](https://aclanthology.org/2026.emnlp-main.1417/) (Li et al., EMNLP 2026)

• MIND: An Evidence-State Decision Interface for Criteria-Grounded Psychiatric Inquiry and Diagnosis (Li et al., EMNLP 2026)

ACL

• Guoyi Li, Shihao Xu, Jiatong Ma, Zhongjiang Yao, Yunyun Han, Jianhua Chen, and Yafeng Deng. 2026. MIND: An Evidence-State Decision Interface for Criteria-Grounded Psychiatric Inquiry and Diagnosis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 32348–32378, Budapest, Hungary. Association for Computational Linguistics.