We study the emergence of agency from scratch by using Large Language Model (LLM)-based agents. In previous studies of LLM-based agents, each agent's characteristics, including personality and memory, have traditionally been predefined. We focused on how individuality, such as behavior, personality, and memory, can be differentiated from an undifferentiated state. The present LLM agents engage in cooperative communication within a group simulation, exchanging context-based messages in natural language. By analyzing this multi-agent simulation, we report valuable new insights into how social norms, cooperation, and personality traits can emerge spontaneously. This paper demonstrates that autonomously interacting LLM-powered agents generate hallucinations and hashtags to sustain communication, which, in turn, increases the diversity of words within their interactions. Each agent's emotions shift through communication, and as they form communities, the personalities of the agents emerge and evolve accordingly. This computational modeling approach and its findings will provide a new method for analyzing collective artificial intelligence.

本研究解决了之前LLM代理个性预定义的局限性，探讨了个体性如何在社交互动中自发形成。通过多代理模拟，我们发现自主互动的LLM代理在交流中产生幻觉和标签，促进了沟通的多样性，进而使代理的个性和情感逐渐演变。这一新的计算建模方法为集体人工智能的分析提供了新的视角。

通过社交互动自发产生的代理个体性在基于大型语言模型的社区中