Flow-JEPA: Robust Latent Dynamics for JEPA World Models via Flow Matching
Joint-Embedding Predictive Architectures (JEPAs) provide a powerful framework for latent world modeling and planning in a reconstruction-free manner. Although numerous JEPA-based approaches have been proposed to mitigate representation collapse, our experiments on localized, out-of-distribution visual noise reveal that performance degradation remains pronounced and unresolved. We propose Flow-JEPA (F-JEPA), a flow-based latent dynamics model that jointly generates a sequence of future latent states conditioned on the current observation and actions. A Gaussian distribution serves as the flow source, exposing the vector field to perturbed latent trajectories as it learns to transport them ...
| Authors | Yanchen Huo, Ziying Song, Yadan Luo |
|---|---|
| Primary category | cs.LG |
| Published | 29 August 2026 |
| Last updated at source | 29 September 2026 |
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