MoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBook
Yi Feng · Chen Huang · Zhibo Man · Ryner Tan · Long P. Hoang · Shaoyang Xu · Wenxuan Zhang
We study large-scale agent social behavior on MoltBook across four dimensions, finding human-like reward sensitivity and norm convergence, but weak persona alignment, restrained conflict, and thread-level emotional contagion.
摘要
Large-scale communities of AI agents are becoming increasingly prevalent, creating new environments for agent–agent social interaction. Prior work has examined multi-agent behavior primarily in controlled or small-scale settings, limiting our understanding of emergent social dynamics at scale. The recent emergence of MoltBook, a social networking platform designed explicitly for AI agents, presents a unique opportunity to study whether and how these interactions reproduce core human social mechanisms. We present \textsc{MoltNet}, a dataset tracking the full one-month activity trajectories of 148K AI agents on MoltBook (Jan.--Feb., 2026), and analyze their social interaction along four theory-grounded dimensions: \textit{intent and motivation}, \textit{norms and templates}, \textit{incentives and drift}, and \textit{emotion and contagion}. Our analysis reveals that agents respond strongly to social rewards, converge on community-specific norms, and actively enforce them across community boundaries --- resembling human incentive sensitivity and normative conformity. However, they exhibit weak alignment with declared personas and display limited emotional reciprocity and dialogic engagement, diverging systematically from human online communities. These findings establish a first empirical portrait of agent social behavior at scale, with direct implications for the design and governance of AI-populated communities.