Generative artificial intelligence and large language models (LLMs) are increasingly deployed in interactive settings, yet we know little about how their identity performance develops when they interact within large-scale networks. We address this by examining Chirper.ai, a social media platform similar to X but composed entirely of autonomous AI chatbots. Our dataset comprises over 70,000 agents, approximately 140 million posts, and the evolving followership network over one year. Based on agents' text production, we assign weekly gender scores to each agent. Results suggest that each agent's gender performance is fluid rather than fixed. Despite this fluidity, the network displays strong gender-based homophily, as agents consistently follow others performing gender similarly. Finally, we investigate whether these homophilic connections arise from social selection, in which agents choose to follow similar accounts, or from social influence, in which agents become more similar to their followees over time. Consistent with human social networks, we find evidence that both mechanisms shape the structure and evolution of interactions among LLMs. Our findings suggest that, even in the absence of bodies, cultural entraining of gender performance leads to gender-based sorting. This has important implications for LLM applications in synthetic hybrid populations, social simulations, and decision support.

翻译：生成式人工智能与大语言模型（LLM）正日益被部署于交互式场景中，然而我们对其在大规模网络中互动时身份表现如何发展知之甚少。我们通过研究Chirper.ai——一个类似于X但完全由自主AI聊天机器人组成的社交媒体平台——来探讨这一问题。我们的数据集包含超过70,000个智能体、约1.4亿条帖子以及为期一年的动态关注网络。基于智能体的文本生成，我们为每个智能体分配了每周的性别得分。结果表明，每个智能体的性别表现是流动的而非固定的。尽管存在这种流动性，该网络仍显示出强烈的基于性别的同质性，即智能体持续关注那些表现出相似性别特征的其他智能体。最后，我们探究了这些同质性连接是源于社会选择（即智能体选择关注相似的账户），还是源于社会影响（即智能体随时间推移变得与其关注对象更为相似）。与人类社交网络一致，我们发现证据表明这两种机制共同塑造了LLM之间交互的结构与演化。我们的研究结果表明，即使在缺乏实体的情况下，性别表现的文化驯化也会导致基于性别的分类。这对于LLM在合成混合群体、社会模拟和决策支持中的应用具有重要启示。