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

Multi-agent systems that use language models to evaluate each other's outputs are gaining traction in automated research, code review, and content moderation pipelines. But when one agent's bias influences another's, errors can compound silently across the network. This paper formalizes that risk with the Contagion Networks framework, measuring how systematically biased evaluators propagate their tendencies through interacting agents. The finding that expanding evaluator committees from one to three models cuts effective contagion by over 70% offers a practical design principle. Relevant applications include LLM-as-judge pipelines, automated peer review, multi-agent debate systems, and any architecture where model outputs feed recursively into other models.