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JEPA-Anything: Learning Predictive Models across Different Worlds

arXiv cs.CL LLMs & Foundation Models Taoyong Cui, Zhongyao Wang, Xinyue Xu, Weiyang Liu, Zhaochen Yu, Yuying Zhang, Qiang Gao, Mengyue Yang, Wanli Ouyang, Pheng Ann Heng, Yingcheng Wu, Zhenfei Yin, Ling Yang 2026-09-17

TL;DR - JEPA-Anything is a domain-agnostic world-modeling framework that factorizes latent prediction targets into complementary components. It improves predictive performance across seven heterogeneous domains and links learned representations to interventions and experimentally supported scientific findings.

Orthogonal predictive factorization extends JEPA by learning target factors through dedicated pathways and recombining them in a shared predictive architecture.
The framework outperforms matched JEPA baselines on all 10 dynamics tasks and reduces single-intervention prediction error in Interventional Pong by 34.8%.
It achieves the lowest reported one-step and 100-step molecular-dynamics errors among compared methods across four systems.
Learned factors produced a biologically nominated intervention supported in multiple experimental models and recovered Keplerian scaling with a fitted exponent of -1.4991.

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