Warranted Attention: Learning What to Pass from Attention to Prediction

Relevance of information read by attention does not guarantee that its contribution benefits the current prediction. We propose Warrant, which learns how strongly attention-derived item contributions should be transmitted under the current query. Warrant applies learned item-wise permission before aggregation without renormalization, jointly controlling relative allocation and total transmission mass. Across backbones, we construct interfaces connecting these con- tributions to prediction scores or states and compare against ungated models on the same paths. On three CyGNet datasets, ungated paths reduce MRR, whereas learned permission partially or almost fully recovers the losses. In a 5-seed HotpotQA/RoBERTa experiment, distractors receive lower permission than gold support, while mean Support MRR rises from .9111 to .9141 and the unsupported selection rate falls from .2271 to .2215. Performance comparisons and contribution- level interventions across five task families reveal both the effects and limits of selective control. The results support learning the strength of contributions transmitted to prediction separately from attention relevance