[2605.00412] Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling

Computer Science > Artificial Intelligence arXiv:2605.00412 (cs) [Submitted on 1 May 2026] Title:Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling Authors:Sen Cui, Jingheng Ma View a PDF of the paper titled Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling, by Sen Cui and Jingheng Ma View PDF HTML (experimental) Abstract:World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement learning. However, current world model research is often dominated by three partially separated routes: 2D video-generative models that emphasize visual future synthesis, 3D scene-centric models that emphasize spatial reconstruction, and JEPA-like latent models that emphasize abstract predictive representations. While each route has made important progress, they still struggle to provide physically reliable, action-controllable, and long-horizon stable predictions for embodied decision making. In this paper, we argue that the bottleneck of world models is no longer only whether they can generate realistic futures, but whether those futures are physically meaningful and useful for action. We propose \emph{Hamiltonian World Models} as a physically grounded perspective on world modeling. The key idea is to encode observations into a structured latent phase space, evolve the latent state through Hamiltonian-inspired dynamics with control, diss...