BandRank: Interval-Anchored ListMLE for Threshold-Transferable Pointwise Reranking

Pointwise neural rerankers are widely deployed for their $\mathcal{O}(N)$ inference efficiency, yet learning them effectively involves balancing within-query discrimination against cross-query score consistency. Hard pointwise targets discard distinctions between relevance grades, whereas listwise ranking objectives are shift-invariant and lack an absolute anchor, leading to cross-query scale drift that degrades global thresholding. We propose **BandRank**, an interval-anchored ListMLE training framework. BandRank couples ListMLE with a smooth two-sided softplus penalty defined over query-shared score bands, encouraging predictions to remain within grade-specific intervals while ListMLE governs the relative ordering. To evaluate cross-query score reliability, we examine threshold transfer using transferred balanced accuracy (tr. BA) under frozen validation cuts. On the MultiVENT 2.0 video benchmark, BandRank improves on the previous state of the art by 7.6 points in nDCG@5 and 5.1 points in Recall@5, while improving tr. BA over a pointwise BCE baseline by up to 6.1 points. Furthermore, BandRank transfers thresholds stably even under calibration-data scarcity, and the same objective and band layout carry over to visual document (MMDocIR) and text passage (TREC DL) reranking. Code is available at https://anonymous.4open.science/r/ICLR2027-13167-7F87.