SAUCE estimates uncertainty in parallel multi-agent reasoning via filtering-style sequential inference, improving misclassification detection, selective prediction, and calibration across five backbones and two protocols
The work proposes SAUCE, a training-free uncertainty estimator that casts uncertainty in parallel multi-agent reasoning as sequential inference over a latent system-level belief: each round's cross-agent agreement serves as evidence and its generation uncertainty sets the evidence weight, updated through a filtering-style recursion, improving misclassification detection, selective prediction, and calibration across five backbones, five benchmarks, and the Debate and DyLAN protocols.