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

arXiv:2608.29029v1 Announce Type: cross Abstract: 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 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 toward clean future representations. This formulation retains the reconstruction-free JEPA framework while replacing point-wise transition regression with stochastic trajectory-level prediction. F-JEPA raises mean success from 86% to 92% under clean observations and from 67% to 86% under noisy conditions, suggesting that conditional flow matching provides a promising alternative to deterministic autoregressive dynamics in JEPA world models.

Related

• Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
• Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
• FF-JEPA: Long-Horizon Planning in World Models with Latent Planners

Source: arXiv cs.AI | 2026-09-01