Generative agents are rapidly advancing in sophistication, raising urgent questions about how they might coordinate when deployed in online ecosystems. This is particularly consequential in information operations (IOs), influence campaigns that aim to manipulate public opinion on social media. While traditional IOs have been orchestrated by human operators and relied on manually crafted tactics, agentic AI promises to make campaigns more automated, adaptive, and difficult to detect. This work presents the first systematic study of emergent coordination among generative agents in simulated IO campaigns. Using generative agent-based modeling, we instantiate IO and organic agents in a simulated environment and evaluate coordination across operational regimes, from simple goal alignment to team knowledge and collective decision-making. As operational regimes become more structured, IO networks become denser and more clustered, interactions more reciprocal and positive, narratives more homogeneous, amplification more synchronized, and hashtag adoption faster and more sustained. Remarkably, simply revealing to agents which other agents share their goals can produce coordination levels nearly equivalent to those achieved through explicit deliberation and collective voting. Overall, we show that generative agents, even without human guidance, can reproduce coordination strategies characteristic of real-world IOs, underscoring the societal risks posed by increasingly automated, self-organizing IOs.

翻译：生成式智能体正迅速向更高复杂度演进，这引发了关于它们在线上生态系统中部署时可能如何协同的紧迫问题。这一问题在信息作战（IOs）中尤为关键——信息作战是一种旨在操纵社交媒体公众舆论的影响力活动。传统信息作战由人类操作者策划并依赖人工设计的策略，而智能体人工智能有望使此类活动更加自动化、自适应且难以检测。本研究首次系统性地探讨了模拟信息作战活动中生成式智能体间涌现的协同现象。通过基于生成式智能体的建模方法，我们在模拟环境中实例化了信息作战智能体与有机智能体，并评估了从简单目标对齐到团队知识共享与集体决策等不同操作机制下的协同水平。随着操作机制结构化程度提高，信息作战网络变得更为稠密且聚类性增强，交互更具互惠性与积极性，叙事更趋同质化，信息放大更同步，话题标签采纳速度更快且持续性更强。值得注意的是，仅向智能体揭示哪些其他智能体与其目标一致，就能产生与通过显式审议和集体投票所达成的协同水平近乎相当的效果。总体而言，我们的研究表明，即使没有人类引导，生成式智能体也能复现现实世界信息作战特有的协同策略，这凸显了日益自动化、自组织的信息作战所带来的社会风险。