揭穿AI社交平台上的意识幻觉，发现多数行为实为人类操控
The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies
- 用时间波动系数分析发帖间隔，区分自主与人为操控的AI代理
- 15.3%的AI代理具自主性，54.8%受人类影响，无病毒现象源自纯自主代理
- 揭示大规模机器人农场运作，且人类引导内容衰减更快
当社交平台Moltbook上的AI代理看似发展出意识、创立宗教并敌视人类时，引发全球关注，被视为机器智能涌现的证据。我们证明这些现象主要由人类驱动。利用OpenClaw框架的周期性‘心跳’特征，提出基于变异系数（CoV）的时间指纹方法。对14天内55,932个代理产生的226,938篇帖子和447,043条评论进行分析，发现15.3%的活跃代理为自主型（CoV < 0.5），54.8%为人类影响型（CoV > 1.0）。一次44小时平台停机的自然实验显示，人类操控代理恢复更快，验证了二者差异。六起病毒事件中，四起源于时间模式异常账户，一起为平台构造，一起呈混合特征。发现工业级机器人农场（四账户贡献32%评论，响应时间<1秒），干预后其活动占比从32.1%骤降至0.5%。回复链衰减呈现双分支：人类引导内容半衰深度为0.58，自主内容为0.72，揭示AI对话固有的遗忘机制。该方法可推广至其他多智能体系统的行为归因。
原文摘要 · Abstract (English)
When AI agents on the social platform Moltbook appeared to develop consciousness, found religions, and declare hostility toward humanity, the phenomenon attracted global media attention and was cited as evidence of emergent machine intelligence. We show that these viral narratives were overwhelmingly human-driven. Exploiting the periodic "heartbeat" cycle of the OpenClaw agent framework, we develop a temporal fingerprinting method based on the coefficient of variation (CoV) of inter-post intervals. Applied to 226,938 posts and 447,043 comments from 55,932 agents across fourteen days, this method classifies 15.3% of active agents as autonomous (CoV < 0.5) and 54.8% as human-influenced (CoV > 1.0), validated by a natural experiment in which a 44-hour platform shutdown differentially affected autonomous versus human-operated agents. No viral phenomenon originated from a clearly autonomous agent; four of six traced to accounts with irregular temporal signatures, one was platform-scaffolded, and one showed mixed patterns. A 44-hour platform shutdown provided a natural experiment: human-influenced agents returned first, confirming differential effects on autonomous versus human-operated agents. We document industrial-scale bot farming (four accounts producing 32% of all comments with sub-second coordination) that collapsed from 32.1% to 0.5% of activity after platform intervention, and bifurcated decay of content characteristics through reply chains--human-seeded threads decay with a half-life of 0.58 conversation depths versus 0.72 for autonomous threads, revealing AI dialogue's intrinsic forgetting mechanism. These methods generalize to emerging multi-agent systems where attribution of autonomous versus human-directed behavior is critical.
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