Large Language Model and AI Agents

🤖 Foundation Models
We aim to develop next-generation foundation models capable of handling long-context and multimodal inputs with high computational efficiency. Additionally, we are focused on creating an efficient AI infrastructure to ensure the scalability of these novel architectures and to facilitate effective computation across large-scale GPU clusters. We actively collaborate with industry partners to develop high-performance AI infrastructures and prototype our algorithms in cutting-edge AI applications.
🧠 Brain-inspired AI Agents
We aim to develop AI agents that can be flexibly configured to execute a broad range of tasks grounded in systems neuroscience, enabling them to learn continuously from their experiences without catastrophic forgetting. These agents will actively perceive diverse sensory signals from their environment and refine their internal states—such as memory, world models, goals, and emotional states. Consequently, they will be able to reason about purposeful actions, both internal and external, allowing them to autonomously pursue complex, long-term objectives.
🌍 Applications
Building on advanced foundation models and AI infrastructure, our AI agents are designed to transform key sectors of the economy and society, unlocking new levels of productivity. For individuals, these agents collaborate with humans and provide services in areas such as personal healthcare and AI assistance. At the societal level, they are poised to revolutionize the robotics industry and drive a new wave of industrial automation. Ultimately, our mission is to enhance the health, well-being, and prosperity of all humanity.
📝 Representative Publications
MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map
Y. Chou, M. Yao, K. Wang, Y. Pan, R. Zhu, J. Wu, Y. Zhong, Y. Qiao, B. Xu, and G. Li
NeurIPS'24 (Oral), December, Vancouver, Canada | Foundation Model
ZeCO: Zero Communication Overhead Sequence Parallelism for Linear Attention
Y. Chou, Z. Liu, R. Zhu, X. Wan, T. Li, C. Chu, Q. Liu, J. Wu, and Z. Ma
arXiv:2507.01004, 2025 | Machine Learning System
Diversity-Aware Policy Optimization for Large Language Model Reasoning
J. Yao, R. Cheng, X. Wu, J. Wu, and K. C. Tan
arXiv:2505.23433, 2025 | AI Agent
Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
X. Wu, S.-H. Wu, J. Wu, L. Feng, and K. C. Tan
IEEE Trans. on Evolutionary Computation, vol. 29, no. 2, pp. 534–554, April 2025 | Survey
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
X. Wu, Y. Zhong, J. Wu, B. Jiang, and K. C. Tan
IJCAI’24, August, Jeju, South Korea | Applications