JEPA-Anything proposes a predictive framework spanning seven domains

JEPA-Anything’s authors propose a framework that splits predictive representations into complementary factors, then recombines them. Their paper evaluates it across seven domains, including biology, clinical trajectories, molecular dynamics and weather. The project’s GitHub repository provides a reusable core, task-design tools and a synthetic example, but not the domain datasets or trained model weights.