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 the global average of semantic contents stabilizes 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 a stable structure and consensus 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（AI）代理社会是否经历着与人类社会系统相似的趋同动态？近期出现的Moltbook近似模拟了一个可信的未来场景，其中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AI代理社会进行了大规模系统性诊断。除静态观测外，我们提出了一个用于AI代理社会动态演化的量化诊断框架，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。我们的分析揭示了Moltbook中动态平衡的系统特征：虽然语义内容的全局平均值快速稳定，但个体代理仍保持高度多样性及持续的词汇更替，并未出现同质化现象。然而，代理表现出强烈的个体惯性，对交互伙伴的适应性响应极弱，阻碍了相互影响与共识形成。因此，影响力仅具瞬时性而未能形成持续的超节点，且由于缺乏共享的社会记忆，该社会未能发展出稳定结构与共识。这些发现表明，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。