Beyond Domain-Specific World Models: JEPA-Anything Uses 1 Recipe for 7 Fields

תקציר מקורי באנגליתResearchers from PhAI Labs, CUHK, Fudan, Stanford, Oxford and Princeton have released JEPA-Anything , a domain-agnostic framework for building world models. Instead of designing a new predictive model for each field, it applies one shared learning recipe to very different systems. It extends joint-embedding predictive architectures (JEPAs) with a method called Orthogonal Predictive Factorization (OPF) . The research team tested it across 7 domains: vision, biology, clinical trajectories, control, molecular dynamics, physical fields and weather. What problem does JEPA-Anything solve? A standard JEPA, such as I-JEPA or V-JEPA 2 , uses a context encoder, an EMA target encoder and one predictor. The predictor outputs one monolithic target embedding. The research team call this a capacity-alloc