# Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations arXiv: https://arxiv.org/abs/2510.25003 ยท HTML: https://mlsanity.com/paper/2510.25003/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. - Builds on 39 of its 63 references that are in the map. - First posted on arXiv 28 Oct 2025. ## Placed in - LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/) ## Builds on (most cited first, 30 of 39) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020) - [Simulating Opinion Dynamics with Networks of LLM-based Agents](https://mlsanity.com/paper/2311.09618/) (2023) - [Emergent social conventions and collective bias in LLM populations](https://mlsanity.com/paper/2410.08948/) (2024) - [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014) - [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020) - [Cultural Evolution of Cooperation among LLM Agents](https://mlsanity.com/paper/2412.10270/) (2024) - [Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign](https://mlsanity.com/paper/1802.04291/) (2018) - [BotSim: LLM-Powered Malicious Social Botnet Simulation](https://mlsanity.com/paper/2412.13420/) (2024) - [How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare](https://mlsanity.com/paper/2506.06299/) (2025) - [Generative Agent-Based Modeling: Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence](https://mlsanity.com/paper/2309.11456/) (2023) - [An Army of Me: Sockpuppets in Online Discussion Communities](https://mlsanity.com/paper/1703.07355/) (2017) - [#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election](https://mlsanity.com/paper/2010.00600/) (2020) - [On the Detection of Disinformation Campaign Activity with Network Analysis](https://mlsanity.com/paper/2005.13466/) (2020) - [Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter](https://mlsanity.com/paper/2310.09884/) (2023) - [Leveraging Large Language Models to Detect Influence Campaigns in Social Media](https://mlsanity.com/paper/2311.07816/) (2023) - [Language-Driven Opinion Dynamics in Agent-Based Simulations with LLMs](https://mlsanity.com/paper/2502.19098/) (2025) - [Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeek](https://mlsanity.com/paper/2506.12349/) (2025) - [When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems](https://mlsanity.com/paper/2507.14660/) (2025) - [Unveiling Coordinated Groups Behind White Helmets Disinformation](https://mlsanity.com/paper/2003.01313/) (2020) - [Coordinating Narratives and the Capitol Riots on Parler](https://mlsanity.com/paper/2109.00945/) (2021) - [The Spread of Propaganda by Coordinated Communities on Social Media](https://mlsanity.com/paper/2109.13046/) (2021) - [Can Generative Agent-Based Modeling Replicate the Friendship Paradox in Social Media Simulations?](https://mlsanity.com/paper/2502.05919/) (2025) - [Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior](https://mlsanity.com/paper/1902.02765/) (2019) - [Tracking Fringe and Coordinated Activity on Twitter Leading Up To the US Capitol Attack](https://mlsanity.com/paper/2302.04450/) (2023) - [Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X During the 2024 U.S. Presidential Election](https://mlsanity.com/paper/2411.01852/) (2024) - [Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts](https://mlsanity.com/paper/2305.11867/) (2023) - [Exposing Cross-Platform Coordinated Inauthentic Activity in the Run-Up to the 2024 U.S. Election](https://mlsanity.com/paper/2410.22716/) (2024) - [Are LLM-Powered Social Media Bots Realistic?](https://mlsanity.com/paper/2508.00998/) (2025) - [Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure](https://mlsanity.com/paper/2311.05724/) (2023) ## Built on by (most cited first, 6 of 6) - [Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits](https://mlsanity.com/paper/2605.18890/) (2026) - [Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibility](https://mlsanity.com/paper/2602.04674/) (2026) - [Indirect tipping: a social attack surface in AI agent populations](https://mlsanity.com/paper/2609.25194/) (2026) - [How Far Will They Go? Red-Teaming Online Influence with Large Language Models](https://mlsanity.com/paper/2605.22880/) (2026) - [IO Factory: Simulating AI-Enabled Influence Campaigns at Scale](https://mlsanity.com/paper/2608.10920/) (2026) - [Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study](https://mlsanity.com/paper/2608.11649/) (2026) ## Abstract (arXiv) 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.