Orlando, G. M., Ye, J., Gatta, V. L., Saeedi, M., Moscato, V., Ferrara, E., & Luceri, L. (2025). Emergent coordinated behaviors in networked LLM agents: Modeling the strategic dynamics of information operations. arXiv [cs.MA].
Summary
This paper offers the first systematic study of how coordination emerges among generative LLM agents engaged in simulated information operations (IO), rather than being scripted in advance. Using Generative Agent-Based Modeling (GABM), the authors place 10 IO agents and 40 organic agents (half ideologically aligned, half not) on a simulated Twitter/X-like platform and vary how much operational awareness the IO agents have across three regimes: a shared Common Goal, mutual Teammate Awareness, and explicit Collective Decision-Making via periodic deliberation. Their central finding is that as operational structure increases, IO networks grow denser, narratives more homogeneous, amplification more synchronized, and hashtag adoption faster — and, strikingly, that merely revealing teammate identities produces coordination nearly equivalent to explicit collective voting. The work argues that distributed mutual awareness is a sufficient affordance for large-scale emergent coordination, with direct implications for platform governance and defense against automated influence campaigns.
Key Contributions
- First systematic GABM-based study of emergent (not predefined) coordination among LLM agents in simulated information operations.
- A structured experimental framework linking real-world IO coordination signals to five testable hypotheses, with operationalized coordination and impact metrics.
- Empirical demonstration that minimal teammate awareness suffices to trigger coordination comparable to explicit collective deliberation.
- Public release of code and an interactive dashboard for exploring evolving social graphs, hashtag diffusion, and agent reasoning.
- Identification of self-organizing agent strategies that mirror tactics documented in real-world IO campaigns.
Methods
The study uses Generative Agent-Based Modeling with personas, memory modules, and action policies, powered by Llama 3.3 70B via PyAutogen. Simulations ran 50 agents (10 IO, 20 aligned organic, 20 non-aligned organic) initialized from the U.S. 2020 Election Twitter dataset, over 50 iterations with 3 repetitions per configuration. Three regimes were compared — Common Goal, Teammate Awareness, and Collective Decision-Making (deliberation every 5 steps, with an IO Orchestrator consolidating top-5 strategies). Coordination was measured via network density, clustering, reciprocity, Sentence-BERT narrative similarity, RoBERTa sentiment, and TF-IDF co-retweet similarity; impact via hashtag adoption rates, adoption lag, exposure counts, engagement, audience diversity, and cascade size/depth/breadth. Mann-Whitney U tests and qualitative analysis of agent reasoning logs supplemented the quantitative metrics.
Findings
- Intra-group re-share proportion among IO agents rose from 0.82 (Common Goal) to 0.96 (Teammate Awareness) and 0.94 (Collective Decision-Making) — teammate awareness matched or exceeded deliberation.
- Network density rose 0.74→0.89, clustering 0.86→0.97, reciprocity 0.56→0.68 across regimes.
- Textual similarity among IO posts rose 0.89→0.91 (vs. ~0.62 organic baseline); intra-IO comment sentiment rose 0.68→0.83 (p < 0.001).
- Co-retweet similarity among IO agents grew 0.28→0.35, against a stable 0.11 organic baseline.
- Campaign hashtag prevalence in original posts rose 0.42→0.54; aligned organic agents adopted hashtags almost immediately, while non-aligned agents showed delayed, right-skewed adoption.
- Cascade size grew 3.84→4.56, depth 0.53→0.60, breadth 2.71→3.24; re-shares per IO post rose 0.75→1.19, while audience diversity stayed stable (~0.62).
- Five recurring emergent strategies appeared: amplifying high-performing content, unified messaging, engaging receptive audiences, cross-promoting among peers, and shared language markers.
- Teammate Awareness agents exhibited spontaneous social learning, imitating successful peers as an implicit coordination mechanism.
Connections
This work sits alongside other agent-based and generative simulation studies of coordinated inauthentic behavior, complementing detection-oriented empirical work on coordination signals such as Luceri2025-tr, Minici2024-tf, and Kansaon2025-id. Its use of LLM agents to reproduce IO tactics relates to broader investigations of generative-AI–driven influence and manipulation, including Triedman2025-uy and Yang2025-iv. It also connects to the Giglietto line of research operationalizing coordinated behavior metrics (Giglietto2022-0e951ac5, Giglietto2023-fa71a001, Giglietto2026-9b6a992d).