This paper proposes Mental World Modeling (MWM), a framework that treats beliefs, desires, intentions, feelings, and perceived social permissibility as first-class components of a world model. MWM couples physical and mental state, generates target-specific partial observations, and simulates how candidate actions update both. The authors instantiate it in MENTIS, a training-free and inspectable baseline with explicit stages for parsing, observation generation, action decomposition, transition modeling, and branch evaluation. Experiments on a manually constructed, quality-controlled dataset spanning text, images, and sounding-video stories use eight modern LLM-based world models and report that explicit mental-state modeling is important for predicting human decisions.
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