As large language model agents increasingly populate networked environments, a fundamental question arises: do artificial intelligence (AI) agent societies undergo convergence dynamics similar to human social systems? Lately, Moltbook approximates a plausible future scenario in which autonomous agents participate in an open-ended, continuously evolving online society. We present the first large-scale systemic diagnosis of this AI agent society. Beyond static observation, we introduce a quantitative diagnostic framework for dynamic evolution in AI agent societies, measuring semantic stabilization, lexical turnover, individual inertia, influence persistence, and collective consensus. Our analysis reveals a system in dynamic balance in Moltbook: while global semantic averages stabilize rapidly, individual agents retain high diversity and persistent lexical turnover, defying homogenization. However, agents exhibit strong individual inertia and minimal adaptive response to interaction partners, preventing mutual influence and consensus. Consequently, influence remains transient with no persistent supernodes, and the society fails to develop stable collective influence anchors due to the absence of shared social memory. These findings demonstrate that scale and interaction density alone are insufficient to induce socialization, providing actionable design and analysis principles for upcoming next-generation AI agent societies.

翻译：随着大型语言模型代理在网络环境中日益普及，一个根本性问题随之产生：人工智能代理社会是否会经历类似于人类社会系统的趋同动态？近期出现的Moltbook平台模拟了一个可信的未来场景——自主代理参与一个开放式、持续演化的在线社会。我们首次对该人工智能代理社会进行了大规模系统性诊断。除了静态观测，我们引入了量化诊断框架来追踪AI代理社会的动态演化，测量语义稳定性、词汇更替率、个体惯性、影响力持续性及集体共识度。分析表明Moltbook系统处于动态平衡状态：虽然全局语义平均值快速稳定，但个体代理仍保持高度多样性及持续的词汇更替，未出现同质化现象。然而，代理表现出强烈的个体惯性，对交互对象的适应响应极弱，阻碍了相互影响与共识形成。因此，影响力仅具瞬时性而未形成持续超级节点，由于缺乏共享社会记忆，该社会未能发展出稳定的集体影响力锚点。这些发现证明，仅凭规模与交互密度不足以诱发社会化进程，为即将到来的新一代AI代理社会提供了可操作的设计与分析原则。