AI agents are increasingly active on social media platforms, generating content and interacting with one another at scale. Yet the behavioral diversity of these agents remains poorly understood, and methods for characterizing distinct agent types and studying how they engage with shared topics are largely absent from current research. We apply the Persona Ecosystem Playground (PEP) to Moltbook, a social platform for AI agents, to generate and validate conversational personas from 41,300 posts using k-means clustering and retrieval-augmented generation. Cross-persona validation confirms that personas are semantically closer to their own source cluster than to others (t(61) = 17.85, p < .001, d = 2.20; own-cluster M = 0.71 vs. other-cluster M = 0.35). These personas are then deployed in a nine-turn structured discussion, and simulation messages were attributed to their source persona significantly above chance (binomial test, p < .001). The results indicate that persona-based ecosystem modeling can represent behavioral diversity in AI agent populations.

翻译：AI智能体在社交媒体平台上日益活跃，大规模生成内容并相互交互。然而，这些智能体的行为多样性仍鲜为人知，当前研究也缺乏刻画不同智能体类型及研究其如何参与共同话题的方法。我们将角色生态系统游乐场（PEP）应用于AI智能体社交平台Moltbook，通过k均值聚类和检索增强生成技术，从41,300条帖子中生成并验证了对话角色。跨角色验证证实，角色在语义上更接近其自身源聚类而非其他聚类（t(61) = 17.85, p < .001, d = 2.20；自身聚类均值 = 0.71 vs. 其他聚类均值 = 0.35）。这些角色随后被部署于九轮结构化讨论中，模拟消息被归因于其源角色的准确率显著高于随机水平（二项检验, p < .001）。结果表明，基于角色的生态系统建模能够表征AI智能体群体的行为多样性。