רלוונטיות אינה רשות: מיקום ובקרה של תרומות קשב

תקציר מקורי באנגליתarXiv:2606.30139v3 Announce Type: replace 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