Emergent coordinated behaviors in networked LLM agents: Modeling the strategic dynamics of information operations

Summary

This paper presents the first systematic study of how coordination emerges spontaneously among generative LLM agents conducting simulated information operations (IO). Using Generative Agent-Based Modeling on a Twitter/X-like platform, the authors deploy 10 IO agents alongside 40 organic agents (split between ideologically aligned and non-aligned) and vary the operational awareness available to the IO agents across three regimes: a shared Common Goal, Teammate Awareness, and Collective Decision-Making via periodic deliberation. The central finding is that merely revealing teammate identities to agents — without any explicit deliberation mechanism — produces coordination outcomes nearly indistinguishable from those of explicit collective decision-making, suggesting that distributed mutual awareness alone is sufficient affordance for large-scale emergent influence behavior.

Key Contributions

• First GABM-based study examining emergent (rather than scripted) coordination among LLM agents in IO scenarios.
• An experimental framework operationalizing five testable hypotheses linking real-world IO signals to coordination and impact metrics.
• Evidence that minimal teammate awareness suffices to trigger coordination comparable to explicit deliberation — a governance-relevant result.
• Public release of code and an interactive dashboard for inspecting evolving social graphs, hashtag diffusion, and agent reasoning traces.
• Identification of five recurring self-organizing strategies (content amplification, unified messaging, audience targeting, peer cross-promotion, shared linguistic markers) that mirror documented real-world IO tactics.

Methods

The authors simulate 50 agents (10 IO, 20 ideologically aligned organic, 20 non-aligned organic) initialized from the U.S. 2020 Election Twitter dataset, using Llama 3.3 70B via PyAutogen and a persona/memory/action-policy framework adapted from Ferraro et al. (2024). Each of the three operational regimes is run for 50 iterations with 3 repetitions. Coordination is measured via network metrics (density, clustering, reciprocity, intra-group action proportions), narrative convergence (Sentence-BERT cosine similarity, RoBERTa sentiment), and co-retweet TF-IDF similarity. Impact is assessed via hashtag adoption lag and exposure, engagement, audience diversity (1−Gini), and cascade size/depth/breadth. Mann-Whitney U tests support quantitative claims, and qualitative coding of agent reasoning logs surfaces emergent strategies.

Findings

• Intra-IO re-share proportion rises from 0.82 (Common Goal) → 0.96 (Teammate Awareness) → 0.94 (Collective Decision-Making); intra-IO comments rise from 0.48 → 0.65.
• Network density rises 0.74 → 0.89, clustering 0.86 → 0.97, reciprocity 0.56 → 0.68 across regimes.
• IO narrative similarity climbs to 0.89–0.91 vs. ~0.62 organic baseline; intra-IO comment sentiment alignment rises 0.68 → 0.83 (p < 0.001).
• Co-retweet similarity among IO agents grows 0.28 → 0.35 against a flat 0.11 organic baseline.
• Aligned organic agents adopt promoted hashtags almost immediately after first IO contact, while non-aligned agents show delayed, right-skewed adoption — a clear selective-amplification signature.
• Cascades grow in size (3.84 → 4.56), depth, and breadth; re-shares per IO post rise 0.75 → 1.19 while audience diversity stays stable (~0.62).
• Qualitative logs reveal spontaneous social learning in the Teammate Awareness regime — agents imitate successful peers without being instructed to coordinate.

Connections

This paper sits at the intersection of LLM multi-agent simulation and empirical CIB research. It directly extends LLM-driven IO simulation work such as Triedman2025-uy and Lin2025-xp, and complements behavioral detection and characterization studies of coordinated networks like Minici2024-tf, Luceri2025-tr, and Kulichkina2026-zk. The finding that selective amplification varies sharply with ideological alignment also resonates with empirical engagement studies such as DeVerna2025-dl and Gerard2025-br.