Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
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 worl...
| Authors | Sen Cui, Jingheng Ma |
|---|---|
| Primary category | cs.AI |
| Published | 1 May 2026 |
| Last updated at source | 2 July 2026 |
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