How can LLMs power multi-agent systems?
A Survey on Multi-Generative Agent System: Recent Advances and New Frontiers
This paper surveys recent advances in Multi-Generative Agent Systems (MGASs), which are groups of AI agents powered by Large Language Models (LLMs) that interact within a shared environment. It categorizes MGAS applications into solving complex tasks, simulating specific scenarios (like social dynamics or urban planning), and evaluating/training other generative agents. Key points for LLM-based systems include frameworks for multi-agent reasoning and communication, resource management for efficient scaling, and addressing LLM limitations like hallucination and limited context windows. The paper also highlights the need for better evaluation metrics and benchmarks specifically designed for complex, emergent behaviors in MGASs.