When multiple AI agents debate to reach a decision, combining their individual confidence scores into one aggregated score helps you better judge whether the final answer is trustworthy—without sacrificing answer quality.
This paper introduces methods for multiagent systems to produce a single confidence score alongside their final answer. The researchers developed three protocols that combine confidence signals from multiple debating agents using soft voting or Bayesian fusion, making confidence comparable across different models.