ElicitDoctor: Robust Multi-Turn Evidence Elicitation in Psychiatric Dialogue under Low-Disclosure Patient Responses

Psychiatric dialogue requires evidence elicitation: asking about a relevant topic does not ensure that a patient's reply supports an assessment. We introduce ElicitBench, a controlled benchmark for multi-turn evidence elicitation under low-disclosure responses, constructed from MDD-5K and DAIC-family corpora. It separates dialogue-derived hidden profiles, stateful disclosure control, doctor-visible interaction, and evaluator-side evidence recovery. Its evaluation protocol combines construction-quality checks, full-trajectory patient audits, scoring validation, and matched doctor-policy comparisons. We also propose ElicitDoctor, a question-generation policy trained with immediate visible-dialogue feedback and continuation value through group-relative optimization. The experimental design examines evidence coverage, acquisition efficiency, boundary-respecting behavior, and the relationship between recovered evidence and downstream diagnostic or screening judgment. Together, the benchmark and method provide a framework for studying adaptive inquiry without exposing hidden case evidence to the doctor.