Relevance Is Not Permission: Localizing and Controlling Metric-Facing Attention Contributions

Abstract:Attention identifies items relevant to a current query, but does not separately determine whether their value contributions support the prediction. We propose Warrant, a unified method for locating and controlling metric-facing attention contributions. Warrant identifies and exposes the item-wise contribution path that reaches the reported metric, then applies current-query-conditioned permission on that same path. Full Warrant improves the primary metric in 27 of 32 model-dataset comparisons across CTDG, MTPP, RAG, STPP, and TKG. Exact item-removal analysis in five representative settings finds near-zero correlation between attention and marginal prediction utility; even the highest-attention item reduces target utility in 43.5-54.4% of examples. Decomposition over the complete benchmark shows that the contributions of path exposure and learned permission vary by task. In a five-seed HotpotQA analysis, the opened path assigns more attention mass to distractors than to gold evidence, whereas learned permission preserves gold contributions, suppresses distractor contributions, and recovers evidence ranking in four of five seeds. These results show why attention-selected contributions must be localized and authorized again on the metric-facing path.

Artificial Intelligence (cs.AI)

arXiv:2606.30139 [cs.AI]

(or arXiv:2606.30139v3 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.30139 Focus to learn more arXiv-issued DOI via DataCite

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