Shanghai AI Lab · KnowledgeXLab

Xuemeng Yang

I am currently a young researcher in KnowledgeXLab at Shanghai Artificial Intelligence Laboratory. I earned my Master’s degree from Zhejiang University in 2022, where I was a member of the APRIL Lab under the mentorship of Dr. Yong Liu. Prior to this, I completed my bachelor’s degree at Northwestern Polytechnical University.

My current research focuses on AI agent frameworks. Specifically, I am interested in intelligent agents for industrial software operation, tool utilization, and continuous learning mechanisms. My core objective is to drive Agent technology from theoretical models to solving complex real-world problems. Prior to this, my research focused on autonomous driving.

Feel free to drop me emails (xuemengyang96@gmail.com) if you have similar interests on above topics.

Xuemeng Yang · 杨雪梦

Research Focus

Building agents that learn, act, and evolve within closed-loop environments.

I believe that open-loop execution limits an agent's true potential. To thrive in complex, dynamic scenarios, all agents must operate, learn, and evolve within a continuous closed-loop environment. To achieve this, my research focuses on both agent frameworks and world modeling technologies—breaking the boundaries of static learning to drive the realization of lifelong, self-evolving intelligence.

LLM Agent Frameworks Continuous Learning World Modeling Closed-Loop Simulation Industrial Software AI Cognitive Driving Agents

ICML 2026

Evolver: Self-evolving llm agents through an experience-driven lifecycle

Wu R, ..., Yang X, et al.

arXiv →

ACL 2026 Findings

The Agent's First Day: Benchmarking Learning & Scheduling in Workplace Scenarios

Fu D*, Mei J*, Wu R*, Yang X*, et al.

arXiv →

ACL 2026 Findings

Learning on the Job: An Experience-driven Self-evolving Agent for Long-horizon Tasks (MUSE)

Yang C*, Yang X*, Wen L*, et al.