Summary

The landscape of LLM-based multi-agent systems is rapidly evolving, as evidenced by the comprehensive survey in Large Language Model based Multi-Agents. This paper delves deep into the role of LLMs in simulating complex environments and problem-solving domains. It outlines the profiling, communication standards, and capacity development for such agents. Additionally, the survey provides a directory of benchmark datasets, contributing greatly to the access and growth of research in this vibrant field.

Highlights

• Catalogs the domains and environments where LLM-based multi-agents have made strides.
• Discusses the profiling and communication conventions for these agents.
• Elucidates mechanisms that expand agent capabilities.
• Serves as a research guide with a compilation of relevant datasets and benchmarks.
• Features an open-source repository to keep the research community abreast of new studies.

Opinion

This survey serves as a valuable resource for AI researchers and enthusiasts alike. By bringing together a wealth of knowledge about LLM-based multi-agent systems, it bridges gaps and poses essential questions for future inquiry. Furthermore, the maintenance of an updated repository displays a commitment to continual learning and discovery, reinforcing the collaborative spirit that drives innovation in AI.