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 regret, leading to a finite-sample generalization bound for JEPA-based world models. Our analysis reveals an approximation-sample error trade-off governed by latent dimensionality, providing insights into latent- versus input-level prediction. The theoretical trade-off motivates us to investigate a simple Multi-Depth JEPA extension that combines representations from different network depths. In validation experiments, latent-level prediction exhibits greater robustness under unpredictable observations and difficult planning tasks. Multi-depth representations further improve planning success over single-depth representations and a dimensionality-matched control.