A single learning principle based on factorized predictions can work across radically different domains, suggesting world modeling doesn't need domain-specific architectures—and can even guide real scientific discovery.
JEPA-Anything is a unified framework for building predictive models across completely different domains—from videos to molecules to weather—using a technique called orthogonal predictive factorization. Instead of training separate models for each domain, it learns to decompose predictions into independent factors that work across vision, biology, physics, and clinical data.