On the Identifiability of Controlled World Models
World model serves as a promising tool to infer environment dynamics under high-dimensional observations and candidate actions. Recently, LeCun's JEPA provides a compelling framework for learning such models in representation space. Its action-conditioned extension plays a central role in visual control and latent-space planning, but leaves a fundamental question: can it recover the controlled dynamics from nonlinear observations? This paper presents a joint identifiability condition for controlled world models with Gaussian latent states, which consists of two coupled components: (1) representation identifiability and (2) transition identifiability. The former depends on the spectral sep...
| Authors | Xiangteng Zhang, Yang Guan, Bo Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 July 2026 |
| Last updated at source | 27 July 2026 |
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