Liang Wang

Robots can now navigate complex environments solely based on initial goals, achieving autonomy without continuous external guidance.

CRANE achieves a remarkable 96.9% Grounded Success in knowledge editing for reasoning MLLMs, overcoming traditional failure modes that plague existing methods.

EAPO enables agents to learn when to forgo tool use, achieving a remarkable 10.45% performance boost while slashing tool calls by over 18%.

Personalizing LLMs through a sociologically grounded framework reveals the hierarchical nature of user behavior, leading to significant performance gains across tasks.

Achieve state-of-the-art results in agentic knowledge base question answering by distilling gold-action policies into on-policy student rollouts, bridging the gap between sparse rewards and weakly supervised intermediate actions.