MentalWM-Bench: Evaluating Mental-State Transitions in Video World Models

Video generation models are increasingly used to build dynamic worlds with characters that act and interact. Coherent behavior in these worlds requires more than realistic appearance and motion: events must change characters' emotions and intentions, and these changes must shape subsequent behavior. Mental world modeling captures this connection between events, internal states, and actions. Yet visual fidelity, event occurrence, and a correct final expression alone cannot establish whether an event-driven mental-state transition unfolds in order and persists across interactions. We introduce MentalWM-Bench to evaluate these transitions through observable behavior. Three complementary tasks assess trigger execution, complete transitions, and two-turn continuation across individual, dyadic, and group scenarios. Evidence-based criteria examine events, initial and resulting states, emotion and intent changes, temporal order, and cross-turn continuity, alongside scene and physical fidelity. We evaluate seven video generators and compare automatic assessments with multi-rater human judgments. Correct outcomes can coexist with incomplete transitions, and successful first-turn states do not ensure subsequent preservation. Physical and mental-state scores remain weakly associated within generators. Emotion and intent updates score below endpoints, with lower performance in longer continuations and group interactions. MentalWM-Bench provides a framework for systematically evaluating mental-state dynamics in video, informing the development of next-generation world models with sustained, coherent character interactions.