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New framework quantifies bias propagation in multi-agent LLM systems

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a framework called Contagion Networks to quantify how biases spread within multi-agent systems where large language models act as evaluators. Experiments using DeepSeek-chat demonstrated that evaluator biases consistently propagate between agents, even when using the same underlying model. The study identified mitigation strategies, such as increasing the size of the evaluator committee, which significantly reduced bias propagation. AI

IMPACT Provides a method to understand and mitigate bias propagation in multi-agent LLM systems, crucial for reliable AI deployment.

RANK_REASON The cluster contains an academic paper detailing a new framework and experimental results related to LLM evaluator bias.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework quantifies bias propagation in multi-agent LLM systems

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zewen Liu ·

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

    arXiv:2606.20493v1 Announce Type: cross 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 …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Zewen Liu ·

    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 control…

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