A Generalization Theory for JEPA-Based World Models
Joint Embedding Predictive Architectures (JEPAs) have emerged as a promising paradigm for world modeling by predicting future representations rather than reconstructing observations. Despite their empirical success, the theoretical understanding of JEPA-based world models remains limited. In this paper, we develop the first generalization theory for JEPA-based world models. We formulate JEPA pretraining as a conditional spectral graph learning problem and show that the JEPA objective is equivalent to a low-rank factorization of an action-conditioned co-occurrence matrix. Building on this characterization, we establish a connection between JEPA pretraining error and downstream planning reg...
| Authors | Jingyi Cui, Qi Zhang, Hongwei Wen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 25 June 2026 |
| Last updated at source | 29 September 2026 |
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