To predict human actions accurately, AI systems need to model mental states (beliefs, desires, intentions) as core components of world models, not just physical scene dynamics.
This paper introduces Mental World Modeling (MWM), a framework that extends traditional world models to track not just physical scenes but also the hidden mental states of agents—what they believe, want, and intend.