WorldMER: Recognizing Micro-Expressions through Temporal Closure

Existing micro-expression recognition (MER) methods typically aggregate facial motion cues across the onset-to-apex and apex-to-offset phases, overlooking the predictive biomechanical relationship between muscle activation and relaxation. To explicitly model this underlying physical coherence, we draw inspiration from latent world models, which excel at learning transition dynamics in state spaces. We introduce temporal closure as a bidirectional predictive constraint between onset-to-apex activation and apex-to-offset relaxation. Given either observed phase, the model predicts the latent representation of its counterpart. Based on this formulation, WorldMER learns two direction-specific transitions between onset-to-apex and apex-to-offset latent states. Their predictions encode phase dynamics shared across expressions, while the residuals between predicted and observed states expose sample-specific departures from those dynamics. A reliability-aware gate then regulates predicted and residual cues at the token-channel level while retaining observed motion as the evidence anchor. Under subject-independent evaluation, WorldMER achieves 90.33% UF1 and 90.30% UAR on the 3DB Composite benchmark, and obtains the best UF1 across the three-, four-, and seven-class CAS(ME)${}^3$ protocols. Ablations show that transition-derived predictions outperform observed motion alone and that bidirectional prediction, residual reasoning, and adaptive fusion provide complementary gains. Our code is available in an anonymous repository at https://anonymous.4open.science/r/WorldMER/.