Predictively Sufficient Multimodal Social World Modeling

Learning latent states that support future evolution remains an open problem for multimodal interactive systems. Existing approaches typically optimize representations for current semantic understanding or direct prediction, without explicitly defining what information a latent state should preserve to remain predictive of future dynamics. In this work, we investigate this problem through the principle of predictive sufficiency, which defines a predictive state as a latent variable that retains future-relevant information from historical multimodal observations while reducing redundant dependence on the observed history. Based on this principle, we propose Predictively Sufficient Multimodal Social World Modeling (PSM-WM), a framework that jointly learns predictive latent states and their stochastic temporal evolution through probabilistic state inference, event-conditioned transitions, and multi-step consistency optimization. Beyond forecasting performance, we introduce state-centric evaluations to measure whether learned representations satisfy predictive state properties, including predictive closure, future semantic alignment, and transition consistency. Experiments on benchmarks demonstrate that PSM-WM improves future multimodal interaction modeling and learns latent states with stronger predictive properties and robustness. These results indicate that predictive sufficiency provides an effective principle for constructing future-oriented multimodal states and highlights the importance of state construction in multimodal world modeling.