ADAR: Abductive Distance-Aware Retrieval for Implicit-Preference Dialogue Memory

Retrieving a user's implicit preferences requires finding supporting dialogue turns that may share few words with the current request. We study how evidence annotations available during training can teach a compact model to search for such indirect support without annotations at inference. We introduce ADAR, a dialogue search agent trained on restricted-oracle demonstrations: a teacher uses annotated evidence to explore search trajectories, filters reject paths with overt leakage, incorrect answers, or missing evidence, and the student learns from the resulting observable traces. During search, an Abductive Distance Score (ADS) summarizes lexical coverage and semantic competition as interpretable feedback alongside retrieved text. Under a shared backbone, ADAR improves accuracy over the strongest evaluated search baseline by 14.94 percentage points on PersonaMem-v2, 4.38 on held-out LongMemEval, and 2.63 on LoCoMo. Withholding numerical ADS feedback from the same checkpoint while preserving autonomous stopping lowers accuracy on all three datasets. These results support converting source evidence annotations into search supervision that improves preference answering and transfers to new dialogue histories. Our code is available at https://anonymous.4open.science/r/adar27-E72B.