JEPA-Anything: Learning Predictive Models across Different Worlds
Researchers introduce JEPA-Anything, a domain-agnostic predictive world-modeling framework using orthogonal predictive factorization, validated across seven domains including biology and weather.
JEPA-Anything extends joint-embedding predictive architectures through orthogonal predictive factorization (OPF), which decomposes latent targets into complementary factors learned via dedicated pathways. It is evaluated across seven domains: vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather. Against matched JEPA baselines it improves metrics on all 10 dynamics tasks, cuts Interventional Pong single-intervention error by 34.8%, and achieves the lowest one-step and 100-step molecular errors across four systems. A factor-nominated biological intervention received experimental support in cell co-cultures, patient-derived organoids, tumor fragments, and mice.