Tao-Yong Cui

Predictive world models, not just static feedback, unlock substantial gains in recursive self-improvement across diverse domains, from code to scientific discovery.

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.

Orthogonal JEPA achieves superior predictive performance by factorizing latent states, allowing for more efficient learning in complex systems.

Operation laundering in vision encoders can be effectively mitigated, revealing hidden boundaries in learned assignments that traditional methods obscure.

Finally, a neural interatomic potential that accurately models long-range electrostatic interactions without sacrificing SO(3) equivariance or energy-force consistency.