JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts

Zheyuan Zhang; Suyu Ye; Nakul Agarwal; Hossein Nourkhiz Mahjoub; Ehsan Moradi Pari; Daniel Khashabi; Tianmin Shu; Vaishnav Tadiparthi

arXiv · 2610.00722

JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts

Zheyuan Zhang·Suyu Ye·Nakul Agarwal·Hossein Nourkhiz Mahjoub·Ehsan Moradi Pari·Daniel Khashabi·Tianmin Shu·Vaishnav Tadiparthi

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Abstract

World models enable agents to plan by predicting future states of the environment, but their predictions can become unreliable when test-time dynamics differ from those seen during training. We present JEPA-TTT, which adapts the latent dynamics predictor of a pretrained action-conditioned Joint-Embedding Predictive Architecture world model throughout test time. Self-supervised updates accumulate across episodes, while the visual encoder and reward head remain fixed, preserving the pretrained representation and task objective. Planning requires neither a goal image nor online environment reward. JEPA-TTT uses dense replay, which forms prediction windows at every temporal offset, retains them in a growing buffer, and samples minibatches from that buffer for predictor updates. Across eight dynamics shifts in four continuous-control environments, JEPA-TTT improves planning on every shift. After 500 test-time episodes, it reduces autoregressive latent prediction error by 83% on average and improves planning performance by 153% over the frozen JEPA world model. These results show that persistent self-supervised test-time training can adapt a pretrained latent world model under changed dynamics.

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BibTeX RIS

Zheyuan Zhang, Suyu Ye, Nakul Agarwal, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Daniel Khashabi, Tianmin Shu, Vaishnav Tadiparthi. 2026-09-30. JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts. https://arxiv.org/abs/2610.00722

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