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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 )
from the top result below
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/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
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/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/list/cs.AI/new
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/src/2205.11916v3
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/src/2006.01048
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https://arxiv.org/src/2608.03994
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https://arxiv.org/pdf/2608.02615
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https://arxiv.org/pdf/2508.04671
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https://arxiv.org/src/2303.12712
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https://arxiv.org/src/2205.11916v1
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https://arxiv.org/pdf/2104.00446
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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2608.01651v1
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https://arxiv.org/pdf/2608.03883
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2006.04122
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/abs/2608.03883
arXiv is committed to these values and only works with partners that adhere to them
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
arXiv is committed to these values and only works with partners that adhere to them