SOURCE-LINKED INTELLIGENCE

Flow-JEPA: Flow Matching for Robust Latent Dynamics in JEPA World Models

arXiv · AI, language, vision and robotics · article · Aug 29, 2026 · UTC

Joint-Embedding Predictive Architectures (JEPAs) have shown strong potential for learning compact predictive representations, and LeWorldModel (LeWM) extends this paradigm to reconstruction-free latent world modeling from pixels. However, its deterministic autoregressive predictor generates future states through repeated one-step transitions, which can accumulate errors and remain sensitive to task-irrelevant visual perturbations. In this work, we propose Flow-JEPA (F-JEPA), a conditional flow matching dynamics model that jointly generates a sequence of future latent states conditioned on the

Read original source ↗ Open in workspace

recordType

paper

region

Global