How can LLMs build better educational multi-agent systems?
AI Agent for Education: von Neumann Multi-Agent System Framework
This paper proposes a new framework, called von Neumann Multi-Agent System Framework (vNMF), for designing and understanding multi-agent AI systems in education, especially those powered by Large Language Models (LLMs). It models each agent like a von Neumann computer with a control unit, logic unit, memory, and input/output, enabling well-defined operations like task decomposition, self-reflection, memory processing, and tool use. The framework emphasizes how LLMs, combined with techniques like Chain-of-Thought (CoT), Tree of Thoughts (ToT), and Multi-Agent Debate, enable agents to collaborate and improve their performance over time, benefiting both the agents (swarm intelligence) and the students (knowledge construction). It highlights the importance of tool use for complex problem-solving and interaction with the learning environment.