Lei Jiang

Explicit multi-timescale predictions in LM-X not only enhance robot manipulation success rates but also provide intrinsic explanations for control decisions, transforming how we understand robotic action generation.

Traditional LLMs may excel at semantics, but they miss the mark on capturing the unique workflows of individual culinary creators, revealing a critical gap in procedural generation.

EvoTS-Agent not only outperforms existing models in financial change-point detection but does so with a flawless execution success rate across multiple datasets.

A stealthy backdoor attack can exploit circuit cutting in VQAs, amplifying energy output while evading detection.