PRISM: Patient-specific Reasoning and Information Seeking for Multimodal Psychiatric Diagnosis

Multimodal reasoning under partial observability requires acquiring evidence whose decision value justifies its cost. However, predefined prediction pipelines provide limited support for adapting evidence sources and analysis operations to evolving information needs. We formulate psychiatric diagnosis from electronic health records (EHR) and electroencephalography (EEG) as a patient-specific reasoning and information-seeking problem and propose PRISM, a large language model (LLM) agent framework for joint source and analysis selection. First, we construct PsyNexus, a longitudinal multimodal cohort of 2,051 patients with major depressive disorder, bipolar disorder, or schizophrenia, comprising 2,799 encounter-linked EEG recordings and 58,514 EHR encounters. The encounter linkage supports interpreting neural observations within each patient's clinical context. Second, we develop PRISM-Base, an evidence-grounded agent that maintains a patient-level diagnostic state and integrates clinical facts, explicit EEG measurements, and optional learned representations through source-linked tools. Third, we introduce Progressive Evidence Seeking and Reasoning (PESR), a reinforcement learning alignment strategy for resource-efficient evidence acquisition and analysis. PESR evaluates diagnostic refinement with a frozen assessor and progressively introduces evidence grounding, information value, and cost sensitivity, allowing informative acquisition to develop before optimizing resource expenditure. On PsyNexus, PRISM achieves an absolute macro-F1 improvement of 5.1% while reducing inference time by 60% relative to full-evidence multimodal supervised fine-tuning. This work provides a methodological foundation for adaptive multimodal intelligence, supporting the development of evidence-seeking agents that reason and make decisions under uncertainty and resource constraints.