想知道大模型评估偏差怎么在智能体间传播？这篇论文用DeepSeek-chat做了实验，告诉你委员会投票能降72%传播，实用。
论文提出Contagion Networks框架，测量评估偏差在多智能体LLM系统中的传播。使用DeepSeek-chat在3个智能体实验中，设定三种评估偏差画像（结构化、平衡、基于证据），测得Cross-Agent Contagion Matrix Gamma_3中偏差传播系数gamma在0.157至0.352之间。发现同质模型智能体传播系数比先前跨模型结果弱3-5倍（MM-EPC约0.85-1.3），处于抑制区。将评估委员会从k=1增至k=3可将有效传播降低72.4%。
Contagion Networks: Evaluator Bias Propagation in Multi-Agent LLM Systems
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.