JEPA-Anything: Learning Predictive Models across Different Worlds

World models learn internal states for predicting how a system changes. Cells, molecules, physical fields, control environments and clinical trajectories differ radically, yet share one predictive problem: infer unobserved, intervened or future states from the current context. JEPA-Anything is a domain-agnostic framework that builds on joint-embedding predictive architectures and replaces their monolithic target representation with orthogonal predictive factorization, which organises a latent world state into complementary predictive factors, learns each through its own pathway, and recombines them for readout, intervention prediction and rollout. Each domain keeps its own observations and encoders while sharing the predictive core.