Candidate-depth-aware hybrid rank fusion
Parent: Concept knowledge architecture strategies · Published reference · snapshot 2026-10-22
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Source-anchored research for Candidate-depth-aware hybrid rank fusion. Run status: BUDGET_EXHAUSTED.
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Measurements and reference values
- OpenSearch's six-dataset comparison reported RRF nDCG@10 averaging 3.86% below its score-based hybrid comparator. (low confidence) [source]
Comparisons and alternatives
- The RRF rank constant differs from a nearest-neighbor count or a candidate-depth limit. (low confidence) [source]
Facts and statements
- RRF adds reciprocal rank contributions from the input rankings; raw score magnitudes do not enter its formula. (high confidence) [source]
- In truncated-list RRF, an item absent from one input list contributes zero from that list. (low confidence) [source]
- Cormack et al. fixed k=60 during a pilot and retained it during later validation. (low confidence) [source]
- Elastic's 2023 BEIR subset experiment favored k=20 and candidate depth 1000 for the tested dense retrievers. (low confidence) [source]
- Bruch et al. found tuned convex score combination superior to RRF in their in-domain and out-of-domain experiments. (low confidence) [source]
- Elasticsearch separates the per-retriever rank window from the final response size. (low confidence) (verified as of 2026-10-22) [source]
- Bruch et al.'s min-max normalization uses the minimum and maximum scores in the retrieved candidate union. (low confidence) [source]
- Bruch et al. compute missing candidate scores and approximate otherwise unknown ranks within the candidate union. (low confidence) [source]
- Increasing the RRF rank constant flattens relative differences between high and low ranks. (low confidence) [source]
- Bruch et al. exclude near-zero score variance from their empirical normalization analysis and identify it as a challenge for some normalizers. (low confidence) [source]
- The original RRF definition models each input ranking as a permutation, with one rank per document. (low confidence) [source]
- The original RRF experiments include a TREC-3 case where CombMNZ outscored RRF, so the paper does not show universal dominance. (low confidence) [source]
- Elasticsearch requires a fixed rank window for stable RRF pagination; changing that window can change previously seen ordering. (low confidence) (verified as of 2026-10-22) [source]
- Exact-term matching depends on indexed field analysis; Elasticsearch's standard text analyzer removes most punctuation and lowercases tokens. (low confidence) (verified as of 2026-10-22) [source]
- Bruch et al.'s normalization rank-equivalence result for one query does not by itself establish equivalent learned weights across all queries. (low confidence) [source]
- Azure documents a fused RRF score bound that increases with the number of query lists being fused. (low confidence) (verified as of 2026-10-22) [source]
- Azure semantic reranking runs after RRF and reports its score separately from the fusion score. (low confidence) (verified as of 2026-10-22) [source]
- The EAHR preprint bounds unread RRF contributions and requests deeper channel ranks while they can change complete-list top-K membership or order. (low confidence) [source]
- EAHR's reported anti-correlated cases exhaust both input lists; its authors do not guarantee a speedup on every request. (low confidence) [source]
Children
- Source-diverse evidence selection (frontier)
- Query-intent routing (frontier)
- Cross-encoder reranking (frontier)
Frontier under this node: Cross-encoder reranking, Query-intent routing, Source-diverse evidence selection