Neural Networks Discover Physical Principles from Observation with an Intuitive World Model

Humans readily grasp physical principles via visual observation, underpinning our ability to model the world and reason about events. In contrast, AI systems often fail to extract consistent physical rules from video, relying on black-box mechanisms that produce implausible outcomes. We introduce the Intuitive World Model (IWM), designed to learn physical principles directly from vision, mimicking human intuition. Drawing inspiration from developmental psychology, IWM utilizes a modular architecture to decompose interactions into force-based dynamics. Through observing objects collide and interact via charges, it identifies intrinsic properties like mass and charge and learns representations analogous to Newton's and Coulomb's laws. This paradigm uniquely allows neural networks to provide explicit explanations for physical phenomena and conduct counterfactual reasoning consistent with learned physical laws. Our work demonstrates that a developmentally-inspired approach empowers AI to acquire explicit, interpretable physical knowledge from observation, mirroring a core aspect of human intelligence.