Screening Updaters for Multimodal Recommender Adaptation

A recommender often starts from one deployed reference model but still has several ways to update it. Those candidate updaters share the same reference predictions, yet they may not be able to correct the same part of the reference error. We study which updater is worth trying when several updater mechanisms share one reference. A held-out local correction score fits the reference residual in a finite tangent sketch on one earlier cohort and evaluates that learned direction on disjoint users before full adaptation. The score is not a new linearization estimator; it is a screening signal for same-reference updater selection. Across three multimodal recommendation sources, it improves on fixed development rankings, adapter-aware transfer baselines, and short nonlinear trials at the primary scale. Supporting 24- and 48-candidate searches show where screening becomes useful as candidate count or updater scale increases: the score narrows the search before a shortlist receives explicit nonlinear trials. The same measurement also predicts later Brier and ranking gains across time blocks. Preservation is a downstream decision after updater selection: replay keeps more average improvement, while an explicit task-loss requirement reduces positive group degradation when measured correction is large. Capacity, locality, and timing checks define the setting in which this staged updater-search procedure is most useful.