Jepa-anything learns predictive models across diverse worlds and tasks
JEPA-Anything: Learning Predictive Models across Different Worlds
Computation and Language
Summary
Making intelligent predictions about the future works well when focused on one kind of data, like images or physical movements. The authors introduce JEPA-Anything, a new method that can learn about many very different worlds using a shared approach. It breaks down predictions into parts, learns each, and combines them to improve accuracy across different fields like biology, weather, and molecular science. This shared method not only predicts better but can also help design new experiments and understand natural laws.
What this means in practice
- •For healthcare data teams: Forecast clinical patient events and intervention outcomes using JEPA-Anything’s predictive models trained on diverse healthcare trajectories.
- •For biotech researchers: Use factor-based predictive models to guide biological interventions and experimental design in cell cultures and organoid systems.
Authors
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
Abstract
World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning principle support world modeling across radically different systems? We introduce JEPA-Anything, a domain-agnostic framework based on orthogonal predictive factorization (OPF). Extending joint-embedding predictive architectures, OPF decomposes latent targets into complementary factors, learns them through dedicated pathways, and recombines them within a shared predictive design. We evaluate JEPA-Anything across seven domains: vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather. Experiments span representation learning, intervention prediction, out-of-distribution generalization, and long-horizon dynamics, including 10 matched dynamics tasks, forecasting of over 1,000 clinical events, and 100-step molecular rollouts across four systems. Against matched JEPA baselines, JEPA-Anything improves reported metrics on all 10 dynamics tasks and reduces single-intervention prediction error on Interventional Pong by 34.8%. It achieves the lowest one-step and 100-step molecular errors among compared methods in all four systems. Beyond prediction, a factor-nominated biological intervention receives experimental support in cell co-cultures, patient-derived organoids, tumor fragments, and mice; latent orbital modes recover the Keplerian scaling exponent with a fitted slope of -1.4991. These results support a common factorized predictive principle across heterogeneous worlds, connecting world modeling with intervention and experimentally grounded scientific discovery. Code: https://github.com/Gen-Verse/JEPA-Anything