world models

Multimodal Agents: From Automation toward Open-Ended Self-Improvement

Mingchen Zhuge, Ph.D. Student, Computer Science

May 9, 17:30 - 19:30

B4 R5220; Zoom Meeting 91489077683

AI agents coding Multi-agent systems world models recursive self-improvement LLM Deep Reinforcement Learning

This thesis presents practical methodologies for building scalable multimodal agents that move from narrow automation toward open-ended self-improvement.

Mingchen Zhuge

Ph.D. Student, Computer Science

AI agents coding Multi-agent systems world models recursive self-improvement LLM Reinforcement Learning

Mingchen Zhuge's research focuses on scalable multimodal agent systems, including code generation, agent swarms, agentic societies and economies, recursive self-improvement, open-ended evaluation, multimodal reasoning, and neural computers.