JEPA-Anything: Learning Predictive Models across Different Worlds

Excerpt

arXiv:2609.20800v1 Announce Type: new 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 complem