Beyond Semantic Relevance: Learning Decision Authority over Retrieved Precedents

Large language models increasingly use retrieved examples and cases to support prediction, but semantic relevance alone does not determine whether a past decision should transfer to a new query. This limitation is especially important in case-augmented reasoning, where retrieved examples carry historical decisions that can directly steer the current prediction. We formulate this problem as decision-authority learning: determining when a retrieved precedent is applicable enough for its prior decision to influence the current query. A key challenge is that outcome supervision cannot distinguish valid from invalid precedent transfer when both lead to the same correct label, a failure mode we call analogical reward aliasing. To address this, we construct contrastive intervention groups (CIGs) that approximately match retrieval relevance while varying applicability, and introduce Grounded Analogical Reward Optimization (GARO) together with its intervention-grouped variant, IG-GARO, to separate task correctness from precedent-transfer quality. A separate utility model controls when the trained specialist should replace Query-only. On natural similarity-matched COLD pairs, IG-GARO improves authority discrimination accuracy from 73.6% to 87.6%. The full system reaches 88.1 Macro-F1 while invoking the specialist on 19.7% of queries, and achieves the highest Macro-F1 point estimate across all evaluated moderation and legal settings.