JEPA-Anything: Learning Predictive Models across Different Worlds
JEPA-Anything, a domain-agnostic framework based on orthogonal predictive factorization (OPF), achieves the lowest one-step and 100-step molecular errors among compared methods in all four systems and supports a common factorized predictive principle across heterogeneous worlds.
Tao-Yong Cui, Zhong-Yao Wang, Xin-Yue Xu et al.
· 2 citations