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JEPA-Anything: Learning Predictive Models across Different Worlds

58 upvotes · 17 SEP 2026 · Taoyong Cui, Zhongyao Wang, Xinyue Xu et al.

This paper develops a new framework, JEPA-Anything, that enables predictive models to work across different domains and systems, allowing for world modeling and learning from interaction. Practitioners might care about this because it could lead to more generalizable and versatile AI models.

world modelingdomain-agnosticpredictive factorizationorthogonal predictive factorizationjoint-embedding architecturescontinual learning