# CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation Source: [CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation]() Collection index: [llms.txt]() Data updated: 2026-08-14T05:00:13.408Z Authors: Zhipeng Song, Yizhi Zhou, Xiangyu Kong, Jiulong Jiao, Xuezhou Ye, Chunqi Gao, Xueqing Shi, Yu Wang, Yuhang Zhou, Heng Qi Published: 2026-05-06T04:51:28.000Z ## Abstract Retrieval-augmented generation (RAG) relies on evidence ranking to determine what information is exposed to the generator, yet existing retrieval and reranking methods primarily estimate query--document relevance. Relevance, however, is not equivalent to generator-side usefulness: a relevant passage may introduce ambiguity or distraction, whereas a lower-ranked passage may stabilize the generator's answer. We present CAR (Confidence-Aware Reranking), a training-free rank-correction framework that uses query-only answer stability as a control and measures each candidate by the change it induces in sampled-answer semantic stability. This controlled contrast estimates a document's marginal contribution to generator behavior without treating semantic stability as relevance or calibrated correctness. CAR converts these confidence changes into coarse precedence constraints and returns the feasible ranking with minimum Kendall distance from the baseline, preserving existing pairwise preferences unless generator-side evidence supports reversing them. Experiments on NQ, HotpotQA and FEVER across sparse and dense retrievers, seven ranking methods and three generator families show robust improvements. In the BM25-centered main analysis, CAR achieves a \\textbf{+5.53\\% mean relative NDCG@5 gain}; on the fixed NQ-answerable downstream evaluation, it improves token-level F1 by \\textbf{+0.43 points}, with ranking and generation gains strongly aligned across rankers ($\\rho = 0.93$). These results position CAR as a deployment-friendly, generator-aware correction layer that complements relevance while preserving informative prior rankings. CAR requires neither task-specific training nor access to model internals such as logits or hidden states, making it applicable to black-box LLMs through generated outputs alone. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Document Reranking on NQ](): Document Reranking; NQ; NDCG@5: 50.979. - [Document Reranking on SCID](): Document Reranking; SCID; NDCG@5: 13.919. - [Document Reranking on COVID](): Document Reranking; COVID; NDCG@5: 71.038. - [Document Reranking on AVG](): Document Reranking; AVG; NDCG@5: 54.373. - [Document Reranking on NQ (test)](): Document Reranking; NQ (test); NDCG@5: 8.339. - [End-to-end Generation on NQ (test)](): End-to-end generation; NQ (test); F1 Score: 15.818. - [Document Reranking on FEVER](): Document Reranking; FEVER; NDCG@5: 81.556. ## Original sources and implementation [Original paper]() [PDF]()