Group size effects and collective misalignment in LLM multi-agent systems

• URL: http://arxiv.org/abs/2510.22422v1
• Date: Sat, 25 Oct 2025 19:45:45 GMT
• Title: Group size effects and collective misalignment in LLM multi-agent systems
• Authors: Ariel Flint, Luca Maria Aiello, Romualdo Pastor-Satorras, Andrea Baronchelli,
• Abstract summary: We show that collective bias is a deeper phenomenon than previously assessed.<n>We demonstrate that group size affects the dynamics in a non-linear way.<n>These findings establish group size as a key driver of multi-agent dynamics.
• Score: 0.0874967598360817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent systems of large language models (LLMs) are rapidly expanding across domains, introducing dynamics not captured by single-agent evaluations. Yet, existing work has mostly contrasted the behavior of a single agent with that of a collective of fixed size, leaving open a central question: how does group size shape dynamics? Here, we move beyond this dichotomy and systematically explore outcomes across the full range of group sizes. We focus on multi-agent misalignment, building on recent evidence that interacting LLMs playing a simple coordination game can generate collective biases absent in individual models. First, we show that collective bias is a deeper phenomenon than previously assessed: interaction can amplify individual biases, introduce new ones, or override model-level preferences. Second, we demonstrate that group size affects the dynamics in a non-linear way, revealing model-dependent dynamical regimes. Finally, we develop a mean-field analytical approach and show that, above a critical population size, simulations converge to deterministic predictions that expose the basins of attraction of competing equilibria. These findings establish group size as a key driver of multi-agent dynamics and highlight the need to consider population-level effects when deploying LLM-based systems at scale.

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