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# Reciprocal Rank Fusion

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arXiv Oct 1, 2026
PatchHolmes lets an agent read all 100 candidate commits at once, lifting patch-retrieval Recall@1 from 32.63% to 59.95%
PatchHolmes is a two-phase patch retrieval system whose first stage fuses BM25 with time decay and Qwen3-Embedding-8B dense retrieval via RRF into a top-100 candidate set, and whose second stage runs a frozen open-weight LLM agent that views all 100 candidates listwise through four tools (list_candidates, read_commit, read_file_diff, submit_answer) and submits a single best commit; on 809 CVEs from GitHubAD it reaches 59.95% Recall@1, 25.34% above the pointwise classifier Favia and 31.40% above IRCoT, adds 27.32% Recall@1 over the retriever's top pick on identical candidates, and transfers unchanged to PatchFinder_top10 to lift Recall@1 from 24.28% to 39.86%.
PatchHolmes is a two-phase patch retrieval system whose first stage fuses BM25 with time decay and Qwen3-Embedding-8B dense retrieval via RRF into a top-100 candidate set, and whose second stage runs a frozen open-weight LLM agent that views all 100 candidates listwise through four tools (list_candidates, read_commit, read_file_diff, submit_answer) and submits a single best commit; on 809 CVEs from GitHubAD it reaches 59.95% Recall@1, 25.34% above the pointwise classifier Favia and 31.40% above IRCoT, adds 27.32% Recall@1 over the retriever's top pick on identical candidates, and transfers unchanged to PatchFinder_top10 to lift Recall@1 from 24.28% to 39.86%.
Stevens Institute of Technology
Guanqun Yang（First author）
PatchHolmes Patch Retrieval Listwise Selection Agentic System Reciprocal Rank Fusion
medRxiv Sep 13, 2026
Hybrid lexical-semantic retrieval over SNOMED CT: combining two retrieval paradigms to facilitate clinical data entry
The work proposes and implements a hybrid retrieval architecture that lets deterministic lexical matching and learned semantic matching coexist over SNOMED CT's own curated descriptions, combining multi-prefix search, BioLORD-2023-M embeddings, an optional BGE cross-encoder for re-ranking, and a local LLM for query normalization, with Reciprocal Rank Fusion and a hierarchy filter; on a search-only linking evaluation over 542 disease mentions from the DisTEMIST corpus (Spanish, zero-shot), semantic search with re-ranking and no LLM query pre-processing reached accuracy@1 of 0.60 and recall@10 of 0.80, and on 12,897 mentions from English real-EHR discharge notes a field-scoped typeahead placed the concept on the top-10 picker list for 73% of mentions (accuracy@1 0.
The work proposes and implements a hybrid retrieval architecture that lets deterministic lexical matching and learned semantic matching coexist over SNOMED CT's own curated descriptions, combining multi-prefix search, BioLORD-2023-M embeddings, an optional BGE cross-encoder for re-ranking, and a local LLM for query normalization, with Reciprocal Rank Fusion and a hierarchy filter; on a search-only linking evaluation over 542 disease mentions from the DisTEMIST corpus (Spanish, zero-shot), semantic search with re-ranking and no LLM query pre-processing reached accuracy@1 of 0.60 and recall@10 of 0.80, and on 12,897 mentions from English real-EHR discharge notes a field-scoped typeahead placed the concept on the top-10 picker list for 73% of mentions (accuracy@1 0.
Alejandro López Osornio（First author）
Hybrid concept retrieval SNOMED CT Reciprocal Rank Fusion Entity Linking

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