用结构化状态统一精神科问诊中的证据与诊断判断，提升决策准确性。
MIND: Unified Inquiry and Diagnosis RL with Criteria Grounded Clinical Supports for Psychiatric Consultation
- 构建包含证据、待检标准、鉴别诊断等的多类型状态，统一决策依据
- 在真实病历模拟中比基线模型准确率提升7.3至8.8点
- 适合需要临床支持的筛查级辅助诊断系统开发者
精神科问诊要求智能体提取区分性证据、将模糊叙述映射到诊断标准，并判断证据是否充分。现有对话与检索增强系统依赖原始历史或附加段落，导致观察证据、遗漏检查、鉴别诊断和支撑可靠性混杂。我们提出MIND，一种基于诊断标准的证据-状态决策接口。每轮对话中，MIND构建包含观察证据、未完成标准检查、活跃鉴别诊断、标准关联支持及可靠性元数据的结构化状态。训练用的精神科推理银行提供带门控的支持，使检索转为状态构建而非提示注入。同一状态用于动作选择、过程奖励、信息增益评分与轨迹修正。在基于电子病历的模拟器协议下，MIND相比强推理仅、RAG及强化学习基线准确率提升7.3至8.8点，且成果可迁移至公开MDD-5k对话数据集。匹配干预分析表明性能提升源于共享状态，而非提示长度、检索文本或格式。MIND面向筛查级决策支持，非自主诊断。代码已开源：https://github.com/Lingxi-mental-health/MIND。
原文摘要 · Abstract (English)
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.Å
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