arXiv Machine Learning By Kehan Wen, Ziming Li, Siyuan Luo, Fan Shi

JEPA-x: Cross-Predictive Physics Grounding for Forecastable Latent Dynamics

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JEPA-x is a cross‑predictive physics grounding method that aligns visual latent dynamics with privileged physical trajectories. By treating visual observations and physical states as two views of the same action‑conditioned trajectory and sharing a predictor, it forces the model to learn a common transition rule for both modalities. The physical branch is only used during training, so deployment incurs no extra cost, and the approach significantly reduces rollout drift and boosts control success across a multi‑task suite.

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