Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocols
Computational mental health screening using multimodal speech and text has shown great promise. However, existing models often assume all clinical speech protocols carry equivalent evidentiary validity. In reality, heterogeneous protocols, from free interviews to fixed reading tasks, support fundamentally different evidence. Forcing uniform reasoning flattens these boundaries, causing models to hallucinate symptoms from irrelevant text or overclaim support. Even advanced long chain-of-thought LLMs fail to resolve this issue, as free-form reasoning can exacerbate boundary violations. To address
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- PossiblePossibly related (embedding) · 51%Towards AI-augmented decision making in psychiatry →
- PossiblePossibly related (embedding) · 49%Clinical AI needs safeguards against hallucinations, data leaks and overreliance, review finds - Medical Xpress →
- PossiblePossibly related (embedding) · 47%Independent Clinical Evaluation of General-Purpose LLM Responses to Signals of Suicide Risk - The Association for the Advancement of Artificial Intelligence →
- LinkedLinked via arxiv author · 85%Chengyuan Gao →
“Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocols”
- LinkedLinked via arxiv author · 85%Jiang Wu →
“Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocols”
- LinkedLinked via arxiv author · 85%Tao Lu →
“Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocols”
- LinkedLinked via arxiv author · 85%Jiayan Guo →
“Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocols”
- LinkedLinked via arxiv author · 85%Mingkun Xu →
“Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocols”