Xue Liu

LLM agents can now autonomously generate complex skills with multi-file dependencies, rivaling human-authored skills, thanks to a co-evolutionary verification process that doesn't need ground truth labels.

Diffusion language models can achieve better reasoning performance by explicitly balancing generation quality and exploration, outperforming methods that prioritize only one.

Even state-of-the-art LLMs struggle to adapt to mid-task changes in long-horizon web navigation, highlighting a critical gap in their ability to handle realistic user interactions.