Author name: Yannis Montreuil, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi

arXiv:2604.09414v2 Announce Type: replace-cross
Abstract: Existing multi-expert learning-to-defer surrogates are statistically consistent, yet they can underfit, suppress useful experts, or degrade as the expert pool grows. We trace these failures to …

arXiv:2604.09414v1 Announce Type: cross
Abstract: Learning-to-Defer routes each input to the expert that minimizes expected cost, but it assumes that the information available to every expert is fixed at decision time. Many modern systems violate this…