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30 result(s) for “okapi bm25 site terms com pp site learn ca site brockley blogspot com site www ibc site:arxiv.org” of 40 matched · 641 ms

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Did you mean okapi bm25 site terms com pp site learn ca site brockley blogspot com site www inc site:arxiv.org?

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The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
from the top result below

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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

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https://arxiv.org/src/2006.15704
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https://arxiv.org/src/2006.02893
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https://arxiv.org/pdf/2607.26188v1
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https://arxiv.org/src/2005.13905
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https://arxiv.org/pdf/2006.02759
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https://arxiv.org/src/2405.20232
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https://arxiv.org/pdf/2608.03883
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2006.01354
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https://arxiv.org/pdf/2607.27070v1
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https://arxiv.org/src/2006.01331
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https://arxiv.org/pdf/2407.01722
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2608.03233
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https://arxiv.org/pdf/2006.01402
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https://arxiv.org/src/2006.01318
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https://arxiv.org/src/2006.02055
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https://arxiv.org/src/2006.02464
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1 Introduction

https://arxiv.org/html/2501.13956v1
com Abstract We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark

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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

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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/src/2212.07841
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Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Artificial Intelligence New submissions Cross-lists Replacements See recent

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https://arxiv.org/pdf/2006.02464
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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/pdf/2608.02615
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[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

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

Computation and Language

https://arxiv.org/list/cs.CL/new
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

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