Teng Zhang

Achieving near-AO-DMET accuracy while retaining computational efficiency, this new LO-based DMET method transforms how we approach excited states in strongly correlated systems.

Runtime safety checks in Move could be the critical layer that prevents asset loss from undetected verifier bugs in blockchain applications.

RL fine-tuning unlocks a 6x performance gain for in-place trajectory editing in autonomous driving, demonstrating the power of aligning diffusion planners with reinforcement learning.

A surprisingly simple change to the motion latent space—representing each body joint with its own token—dramatically improves text-to-motion generation quality, outperforming monolithic latent vector approaches.