Query Adaptive Rank Fusion: A Training Free per Query Weighting Scheme for Sparse-Dense Hybrid Retrieval

Abstract

: Reciprocal Rank Fusion (RRF) is the standard recipe for combining a sparse and a dense retriever in retrieval augmented generation, but it assigns both components a fixed and equal weight on every query. This is a strong assumption, since a query of rare specific tokens is best served by the sparse retriever while a paraphrastic query (one composed largely of common words that admits many surface forms of the same intent) is best served by the dense one. We show on four BEIR corpora that equal weight RRF fails to surpass its stronger component on any of them, and we propose Query Adaptive Rank Fusion (QARF), a training free variant whose fusion weight is a closed form function of standard corpus IDF statistics already available at index build time. QARF operates in two regimes selected automatically by the data: per query weighting on corpora with a broad IDF distribution, and automatic corpus level recalibration of RRF on corpora where almost every query is more specific than the mean. Using a BM25 + E5 large backbone, QARF improves over its own RRF baseline on all four corpora by + 1 . 74, + 5 . 12, + 8 . 61, and + 2 . 97 NDCG@10 points on NFCorpus, SciFact, FiQA, and ArguAna, matches pure E5 within one point on SciFact and FiQA, and exceeds it on ArguAna while retaining the hybrid’s wider candidate pool. End to end evaluation with Gemma 3 1B shows that the retrieval gain transfers downstream as a lower abstain rate on FiQA (4 . 6% → 3 . 2%, the lowest of any retriever); on the other corpora the rate stays within noise of RRF, so we report commit rate as a per corpus companion metric rather than a uniform claim.