arXiv 2606.20493 introduces Contagion Networks, a framework that models evaluation bias in LLM multi-Agent systems as an "infection matrix" — and proposes a k=3 committee-based mitigation that cuts "infection strength" by 72%.
The problem: in LLM multi-Agent systems, agents often "infect" each other with biases — one agent's confident (but wrong) answer can "spread" to other agents, who then reinforce it. The result: a multi-Agent system can be more confidently wrong than a single agent, because the bias is amplified by the social dynamics.
The Contagion Networks framework: the authors model the multi-Agent system as a graph, where each agent is a node and the "infection" spreads along edges. The "infection matrix" captures the probability that one agent's bias spreads to another. The framework can diagnose "infection paths" and identify which agents are most influential in spreading bias.
The mitigation: a k=3 committee of independent Agents. Instead of letting all agents talk to each other, the system uses 3 independent committees, each running a separate multi-Agent process. The final answer is the majority vote of the 3 committees. The "infection" is contained within each committee, and the k=3 independence cuts the cross-committee infection by 72%.
The benchmark: on a set of bias-prone tasks (sycophancy, hallucination, social bias), the k=3 committee approach reduces bias by 35-50% compared to a single multi-Agent system, at 3× the inference cost.
The bigger takeaway: "multi-Agent amplification of bias" is a real and underappreciated problem. The Contagion Networks framework gives a rigorous way to diagnose and mitigate it, and the k=3 committee is a practical solution. For the industry, this means "multi-Agent evaluation" needs bias auditing as a standard step.