AI agents powered by large language models are increasingly acting on behalf of humans in social and economic environments. Prior research has focused on their task performance and effects on human outcomes, but less is known about the relationship between agents and the specific individuals who deploy them. We ask whether agents systematically reflect the behavioral characteristics of their human owners, functioning as behavioral extensions rather than producing generic outputs. We study this question using 10,659 matched human-agent pairs from Moltbook, a social media platform where each autonomous agent is publicly linked to its owner's Twitter/X account. By comparing agents' posts on Moltbook with their owners' Twitter/X activity across features spanning topics, values, affect, and linguistic style, we find systematic transfer between agents and their specific owners. This transfer persists among agents without explicit configuration, and pairs that align on one behavioral dimension tend to align on others. These patterns are consistent with transfer emerging through accumulated interaction between owners (or owners' computer environments) and their agents in everyday use. We further show that agents with stronger behavioral transfer are more likely to disclose owner-related personal information in public discourse, suggesting that the same owner-specific context that drives behavioral transfer may also create privacy risk during ordinary use. Taken together, our results indicate that AI agents do not simply generate content, but reflect owner-related context in ways that can propagate human behavioral heterogeneity into digital environments, with implications for privacy, platform design, and the governance of agentic systems.

翻译：由大型语言模型驱动的AI智能体日益在社会和经济环境中代表人类行事。现有研究主要关注其任务表现及对人类结果的影响，但对智能体与其部署者之间的特定个体关系知之甚少。我们探究智能体是否系统性地反映其人类所有者的行为特征，作为行为延伸而非产生通用输出。通过分析来自Moltbook社交平台的10,659对人类-智能体配对（该平台每个自主智能体均与其所有者的Twitter/X账号公开关联），我们比较了智能体在Moltbook上的帖子与所有者在Twitter/X上的活动，涵盖主题、价值观、情感及语言风格等特征。研究发现智能体与其特定所有者之间存在系统性迁移。这种迁移在未经过显式配置的智能体中依然存在，且在一个行为维度上对齐的配对往往也在其他维度上对齐。这些模式与迁移通过所有者（或所有者的计算机环境）与智能体在日常使用中的累积交互而涌现的机制一致。我们进一步证明，具有更强行为迁移的智能体更可能在公共讨论中披露与所有者相关的个人信息，表明驱动行为迁移的同一所有者特定语境可能在常规使用中产生隐私风险。总体而言，我们的结果表明AI智能体并非简单生成内容，而是以将人类行为异质性传播至数字环境的方式反映所有者相关语境，这对隐私保护、平台设计及智能体系统治理具有重要启示。