AINeutralarXiv – CS AI · 3h ago6/10
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Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
Researchers propose Hamiltonian World Models, a physics-grounded approach to generative world modeling that encodes observations into structured latent phase spaces and evolves them through Hamiltonian-inspired dynamics. The framework aims to address limitations in current world models by prioritizing physical accuracy and action-controllability alongside visual realism, with applications to robotics, autonomous driving, and reinforcement learning.