Contagion Networks: Evaluator Bias Propagation in Multi-Agent LLM Systems

• URL: http://arxiv.org/abs/2606.20493v1
• Date: Thu, 18 Jun 2026 17:09:34 GMT
• Title: Contagion Networks: Evaluator Bias Propagation in Multi-Agent LLM Systems
• Abstract summary: We introduce Contagion Networks, a formal framework for measuring how evaluator biases spread across interacting agents.<n>We find that evaluator biases consistently propagate between agents even within the same underlying model.<n>We show that increasing evaluator committee size from k=1 to k=3 reduces effective contagion by 72.4%.
• Score: 1.0152838128195467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When large language models serve as evaluators in multi-agent systems, their systematic evaluation biases propagate through the agent network. We introduce Contagion Networks, a formal framework for measuring how evaluator biases spread across interacting LLM agents. In a controlled 3-agent experiment using DeepSeek-chat with three distinct evaluator bias profiles (structured, balanced, evidence-based), we measure the Cross-Agent Contagion Matrix Gamma_3 and find that evaluator biases consistently propagate between agents (gamma in [0.157, 0.352]), even within the same underlying model. We identify three propagation regimes governed by the spectral radius rho(Gamma_N), and demonstrate that homogeneous-model agents produce contagion coefficients 3-5x weaker than cross-model coefficients observed in prior work (MM-EPC: gamma approx 0.85-1.3), placing them in the suppression regime. We show that increasing evaluator committee size from k=1 to k=3 reduces effective contagion by 72.4%, providing an actionable mitigation strategy. We release the open-source Contagion Network experimental framework.

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