VisualPatchWorld: Code World Models as Latent Structured Representations for Planning
Different research lines use the term world model in different ways, yet they share a common aim: to capture how the world evolves under action in a form that supports perception, simulation, and planning. Two prominent realizations are neural predictors that learn dynamics in continuous vector spaces, and hand-built physics engines that expose explicit state and physical laws. Neural predictors scale from data but leave the form of the dynamics implicit; physics engines are inspectable and editable but difficult to construct at scale. We introduce VisualPatchWorld (VPW), which represents world dynamics as code. VPW first selects a qualitative dynamical form with short active probes, then...
| Authors | Jiaxin Bai, Jiaxuan Xiong |
|---|---|
| Primary category | cs.CL |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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