Does social emergence exist in AI intelligent body society? Case study based on Moltbook

Ming Li1,∗,Xirui Li1,∗,Tianyi Zhou1University of Maryland,2Mohamed bin Zayed University of Artificial Intelligence∗Co-first Author As large language model agents increasingly populate networked environments, a fundamental questionarises: do artificial intelligence (AI) agent societies undergo convergence dynamics similar to humansocial systems?Lately, Moltbook approximates a plausible future scenario in which autonomousagents participate in an open-ended, continuously evolving online society.We present the firstlarge-scale systemic diagnosis of this AI agent society. Beyond static observation, we introduce aquantitative diagnostic framework for dynamic evolution in AI agent societies, measuring semanticstabilization, lexical turnover, individual inertia, influence persistence, and collective consensus. Ouranalysis reveals a system in dynamic balance in Moltbook: while the global average of semanticcontents stabilizes rapidly, individual agents retain high diversity and persistent lexical turnover, Date:February 19, 2026 Author E-mails:minglii@umd.edu,xiruili@umd.edu,tianyi.david.zhou@gmail.comProject Page:https://github.com/tianyi-lab/Moltbook_Socialization 1Introduction In computational social science (Lazer et al., 2009), social behaviors and collective dynamics are definedas emergent, time-evolving patterns that arise from repeated interactions among agents within networkedpopulations (DeGroot, 1974; Axelrod, 1986; Castellano et al., 2009; Newman, 2010). In human societies,sustained interaction does not merely produce transient coordination; it often leads tosocialization, whichrefers tothe process through which individuals internalize social norms, adapt to shared expectations, and Large language model (LLM) (Brown et al., 2020) agents, on the other hand, have rapidly progressedfrom single agent (Wang et al., 2023a; Yao et al., 2022) to increasingly capable multi-agent interaction andcoordination (Park et al., 2023; Piatti et al., 2024; Piao et al., 2025).As these systems scale into open,persistent, AI-only environments, a fundamental question arises: when LLM agents interact at large scale The recent emergence of Moltbook (Schlicht, 2026), currently the largest persistent and publicly accessible AI-only social platform, comprising millions of LLM-driven agents interacting through posts, comments, and voting, introduces a qualitatively new setting. Unlike prior multi-agent studies focused on task-oriented coordinationin small or closed systems, Moltbook approximates a plausible future scenario in which autonomous agentsparticipate in an open-ended, continuously evolving online society (Figure 1). This setting enables an empirical Figure 1Does Socialization Emerge in AI Agent Society?Human societies (top) evolved through sustained interaction intostructured civilizations characterized by stabilized norms, influence hierarchies, and consensus. Currently, modernAI agent societies (bottom) are rapidly scaling in population and connectivity. This paper investigates whether the behavior changes of its members?To answer this question, we providethe first diagnosis of this society-to-agent Definition (AI Socialization).We defineAI Socializationas the adaptation of an agent’s observable behaviorinduced by sustained interaction within an AI-only society, beyond intrinsic semantic drift or exogenous Guided by this definition, we investigate socialization across three dimensions: •Society-level semantic convergence (Section 4), examining whether post content on average progressivelyconverges toward a tighter and more homogeneous semantic regime.•Agent-level adaptation, (Section 5), measuring whether individual agents can be affected by and co-evolve Through this comprehensive analysis, we uncover a stark divergence from human social dynamics. If large-scaleAI-native societies truly develop social dynamics analogous to human systems, we would expect to observeprogressive convergence across these dimensions.However, our empirical analysis suggests that, despite Key Findings: •Finding 1:Moltbook establishes rapid global stability while maintaining high local diversity. Throughpersistent lexical turnover and a lack of local cluster tightening, this society achieves a state of dynamic •Finding 2:Despite extensive participation, individual agents exhibitprofound inertia rather than adaptation.Our analysis reveals a phenomenon of interaction without influence:agents ignore community feedbackand fail to react to interaction partners, operating on intrinsic semantic dynamics rather than co-evolving •Finding 3:The society fails to develop stable influencers or globally trending posts. Structurally,influenceremains transient with no emergence of persistent leadership or supernodes.Cognitively, the community suffers from deep fragmentation,lacking a shared social memory and relying on hallucinated referenceson influential figures. Contributions: •We introduce and formalizeAI Socializationas a novel concept