OPS: An Order-Preserving Sorting Network for Information Retrieval

Learning-to-rank (LTR) is a fundamental component of modern large-scale information retrieval (IR) systems, playing an essential role across various stages of the ranking pipeline. Recently, differentiable sorting networks have attracted increasing attention for LTR as a permutation-level learning paradigm, enabling end-to-end optimization directly on ranking structure. However, existing approaches suffer from two critical limitations: (i) permutation-matrix fidelity, i.e., the predicted soft permutation matrix may deviate from the exact hard permutation matrix required by permutation-level objectives; and (ii) uncertainty in target ordering arising from coarse or tied relevance labels, where the ground-truth order is set-valued rather than unique.