LLM-enabled Multi-Agent Social Interaction, Collaboration and Competition: A Survey
Jingqi Liu , Bin Guo , Yingshan Zhang , Hao Wang , Yahan Pei , Sinuo Zhao , Ying Liu , Yunji Liang , Zhiwen Yu
Recent breakthroughs in Large Language Models (LLMs) revolutionize the field of Natural Language Processing (NLP), driving advancements in comprehension, reasoning, and content generation. These models achieve remarkable performance in specific tasks but encounter limitations in complex social interactions, motivating the development of multi-agent systems that enable dynamic collaboration and competition. LLM-based multi-agent systems, using natural language as the primary medium of interaction, emerge as key enablers of digital social relationships and collective intelligence. At the same time, these systems introduce challenges in modeling agent social behaviors, optimizing collaboration efficiency, and adapting game-theoretic strategies. This survey addresses gaps in the existing literature by systematically exploring the theoretical foundations of LLM-powered social agents, drawing insights from cognitive science and sociology. We first introduce relevant sociological and cognitive theories and propose framework to model social intelligence in multi-agent systems. Based on this framework, we comprehensively review key techniques for social agent construction, collaboration, competition, as well as their application scenarios and evaluation methodologies. Finally, we identify future research directions, with particular emphasis on constructing large-scale cognitive world models and modeling the growth and evolution of multi-agent systems. This work establishes a solid foundation for advancing the understanding of social dynamics in LLM-based multi-agent environments.
Large Language Models / Multi-Agent Systems / Social Interaction / Collective Intelligence
Higher Education Press 2026
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