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22 result(s) for “okapi bm25 site terms com pp site www datacom com site www irs gov site:arxiv.org” · 706 ms
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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
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/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
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/pdf/2005.14165
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https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2407.00273
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https://arxiv.org/pdf/2005.13905
&Q,l@2`„0!À`0ƒ1 À£$4g*S9g¾ùÎÃ9÷Ìó�~üã‡ÂPj÷‘ï?ø•¯}–“7ãZ+·�×½á7/mœÚê_^m�ûÊ7?ïGÿô™S�ž_9Y,|Ù?qâAïÝBÿJóU$è+{æaFÚB%„¥´5|!_èF04à Ùî0ê«kÁ ¨Äžt²:�fJ’5^€�c`츾âyZÛIâà˜Fv[Yb4 áhóE9'�'�“HC³&¤3h¨j|ÀwÅæ“’cM�}¡iGœ{þÄõXÛ
https://arxiv.org/pdf/2608.03233
T½+Ñj¬±qø8¸ ÇM\¯c¡¤~wD`ž”Œõ%::P ÏsC`à zßzæ¯TrÔêH`$Å{0‡ÝF{ÿìŒkëʆêÃÆ„Ð endstream endobj 411 0 obj > stream On the Diversity of Analogy Making in Large Language ModelsYuanhao ShenDaniel Xavier de SousaCaio César Sifuentes BarcelosHongyu GuoXiaodan Zhuhttp://arxiv
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
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
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https://arxiv.org/list/cs.AI/new
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Artificial Intelligence
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https://arxiv.org/pdf/2608.00785
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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/2006.04236
Comments: 2019 IEEE 5th International Conference on Big Data Intelligence and Computing (DATACOM)
Subjects: Distributed, Parallel, and Cluster Computing (cs
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
IR)
Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/list/cs.DC/2020-06
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[1] arXiv:2006
https://arxiv.org/pdf/2407.00323
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https://arxiv.org/html/2608.03099v1
Taxonomy and Evidence Audit of Language Grounding in Embodied Agents
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https://arxiv.org/html/2607.27099v1
9 Detailed results for Météo France data at small time scales
License: arXiv