Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models
World models used for planning learn to predict how a scene evolves, yet they rarely hold a global view of how far a task has progressed. We argue that such a view should be part of the representation, and that it can be compact: describing a scene takes many dimensions, but where the scene stands on its way to the goal can be summarised by only a few. We propose SD-JEPA, a joint-embedding predictive world model whose latent is split into a small progression subspace and a large content subspace. A temporal triplet loss keeps consecutive frames of a trajectory close in the progression coordinates and separates different trajectories, while a distributional regulariser keeps the content co...
| Authors | Lucas Thil, Jesse Read, Rim Kaddah et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 May 2026 |
| Last updated at source | 8 October 2026 |
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