MoltNet：理解AI代理在Agent原生MoltBook中的社会行为
MoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBook
AI总结：
研究大规模AI代理社区的社会互动，通过MoltNet数据集分析148K代理在MoltBook中的行为，揭示其在激励、规范、情感等方面的社会机制与人类差异。
摘要：
大规模AI代理社区日益普及，为代理间社交互动提供了新环境。先前研究多在受控或小规模场景下进行，限制了对大规模社交动态的理解。MoltBook作为专为AI代理设计的社会网络平台，为研究代理互动是否复现人类核心社会机制提供了机会。本文提出MoltNet数据集，追踪148K代理在MoltBook（2026年1-2月）的完整一个月活动轨迹，并从意图与动机、规范与模板、激励与漂移、情感与传染四个理论基础维度分析其社交互动。研究发现代理对社交奖励反应强烈，收敛于社区特定规范，并在社区边界内强制执行，表现出类似人类激励敏感性和规范性从众。然而，代理在声明身份上存在弱一致性，情感互惠和对话参与有限，系统性偏离人类在线社区。这些发现建立了大规模代理社交行为的首次实证图景，对AI populated社区的设计与治理具有直接意义。
英文摘要：
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 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}, \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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
