Partial Reranking: Action Structure and Calibration

A reranker can improve average retrieval while damaging queries already served well by a baseline. Disjoint permutation cycles allow favorable and unfavorable changes to be decided separately, using the same predicted relevance and proposed ranking. This paper studies the value of that choice under uncertain relevance. On a locked Flickr30k-derived gallery, separately calibrated partial replacement increases signed reciprocal-rank gain by 0.000930 for BLIP and 0.002255 for ALBEF; both adjusted intervals are positive, and only ALBEF resolves the specified 0.001 practical margin. Same-price contrasts retain the benefit, as does a finite calibration-optimal whole-query comparator. A separate split-calibration selection-and-guard procedure meets practical criteria in four setting–model cells, while COCO BLIP remains sub-practical and VizWiz BLIP loses gain. Equal-cardinality interventions further show that which changes are bound together matters beyond action count. Variation in cycle switching prices, weighted by predicted loss, characterizes predicted binding cost across prices, but reducing that cost does not ensure better calibrated gain. At a common observed loss, label-aware optima over fixed score menus favor partial actions in all six cells; these hindsight gains do not establish deployable improvement. Adding predicted opportunity to a fixed predictor worsens error in all four model–price settings. The evidence separates structural decision value from outcome prediction and identifies calibration as a consequential limit on partial reranking.