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30 result(s) for “okapi bm25 site www eyespy cy site www is gov site consumer fjc gov site do site:arxiv.org” of 39 matched · 691 ms
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Zep implements three search functions: cosine semantic similarity search (φcos)subscript𝜑cos(\varphi_{\text{cos}})( italic_φ start_POSTSUBSCRIPT cos end_POSTSUBSCRIPT ), Okapi BM25 full-text search (φbm25)subscript𝜑bm25(\varphi_{\text{bm25}})( italic_φ start_POSTSUBSCRIPT bm25 end_POSTSUBSCRIPT )
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https://arxiv.org/html/2501.13956v1
Consequently, additional context is required to provide out-of-domain (OOD) knowledge and reduce hallucinations
https://arxiv.org/html/2608.03048v1
com/JetAstra/PI-Mem
Introduction
The ability to reason over long contexts is increasingly important for large language models (LLMs), supporting applications such as long-document question answering (Gemini Team 2025; Yang et al
https://arxiv.org/html/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/list/cs.CL/new
Many practically important problems are naturally treated as black-box optimization (BBO) problems, in which only objective values are observable, and the functional form is unavailable
https://arxiv.org/pdf/2604.05327v4
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https://arxiv.org/pdf/2607.28577v1
0/mm/ DocumentID URI internal UUID based identifier for all versions and renditions of a document InstanceID URI internal UUID based identifier for specific incarnation of a document VersionID Text internal Document version identifier RenditionClass RenditionClass internal The manner in which a document is rendered
https://arxiv.org/pdf/2210.03493
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https://arxiv.org/pdf/2608.02978
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https://arxiv.org/list/cs.AI/new
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https://arxiv.org/src/2006.00441
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/pdf/2210.00185
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https://arxiv.org/pdf/2608.03709
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https://arxiv.org/src/2006.03720
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https://arxiv.org/pdf/2407.00215
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/src/2006.01598
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https://arxiv.org/src/2006.02833
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2407.00132
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https://arxiv.org/src/2006.03318
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https://arxiv.org/src/2005.14165
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https://arxiv.org/pdf/2006.02833
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https://arxiv.org/src/2006.01318
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https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking
https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs
https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation
https://arxiv.org/list/cs.IR/recent
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