UNFOLD: Patient-State Prediction and Progressive Profile Disclosure for Long-Horizon Patient Simulation

Long-horizon patient simulation requires more than profile consistency: a simulated patient may remain factually faithful while exhibiting implausible behavioral changes or revealing personal information prematurely, repeatedly, or excessively. We frame these trajectory-level failures as two coupled temporal controls: patient-state prediction, which models how attitude, emotion, and topic evolve with interaction, and progressive profile disclosure, which controls when patient-specific information becomes available. We introduce UNFOLD, a psychotherapy patient simulator that predicts the next behavioral state from structured interaction history and controls access to independently addressable profile evidence before response generation. On 8,397 held-out patient turns, UNFOLD improves attitude, emotion, and topic prediction over a supervised text-encoder reference. Across 30 matched long-horizon simulations, it produces broader, more gradual, and less repetitive information trajectories than full-profile simulators, with differences persisting under longer baseline horizons. Mechanism-targeted ablations isolate the roles of state control, progressive selection, and the disclosure bottleneck, while blinded therapist raters prefer UNFOLD overall in pairwise evaluation. Together, these results suggest that long-horizon simulation should treat both behavioral state and information availability as explicit time-varying controls, a principle that may extend to interactive systems combining evolving behavior with persistent information.