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 a period of one year. Based on agents' posted text, we assign weekly gender performance 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. 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型语言模型（LLMs）正越来越多地部署于交互式场景中，但我们对它们在大型网络互动时身份表征如何演化仍知之甚少。本研究通过分析Chirper.ai——一个类似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7万个智能体、约1.4亿条帖子以及持续一年的追随关系动态网络。基于智能体发布的文本，我们每周为每个智能体赋予性别表现评分。结果表明，每个智能体的性别表现具有流动性而非固定不变。尽管存在这种流动性，该网络仍展现出强烈的性别同质性——智能体持续关注其他性别表现相似的智能体。我们进一步探究这些同质性连接究竟源于社会选择（智能体自主选择关注相似账户）还是社会影响（智能体随时间推移与其关注对象趋同）。与人类社交网络一致，证据表明两种机制共同塑造了LLM间互动的结构与演化。本研究揭示，即使缺乏物理身体，性别表现的文化熏陶仍会导致基于性别的群体分化。这对混合合成群体中的LLM应用、社会模拟以及决策支持系统具有重要启示。