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30 result(s) for “okapi bm25 site terms com pp site www datacom com site bookaweb com site www thieves site:arxiv.org” of 52 matched · 694 ms
Scope: Nishi’s own index (our crawl + Common Crawl) — not the whole live web. Some sites block all automated crawlers (anti-bot walls), so their pages may be absent here even though we never filter lawful content.
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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/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/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/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
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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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/list/cs.IR/new
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Information Retrieval
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https://arxiv.org/list/cs.DC/2020-06
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Distributed, Parallel, and Cluster Computing
Authors and titles for June 2020
https://arxiv.org/list/cs.AI/new
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Artificial Intelligence
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https://arxiv.org/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (),
Correspondence: Cheng-Fang Su ()
“Geometry is not true, it is advantageous
https://arxiv.org/html/2607.28410v1
com/zaneopen/PACE
1 Introduction
Reducing execution costs is a central objective for financial institutions (Bertsimas and Lo 1998)
https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction
https://arxiv.org/pdf/2608.03157
4 4C0M8$SH Rpk4>e8Q{ћPp/I1Mc2^Q#T#NrFRKcl w'0I}J U h+`d@Wul `r0= hJlf6'hީ&8bA| kJwԄFLŷJ6Q,'DU {P_GObiwI& zkHF)Ҫ0 ^SogR7C68W l2N *cO 8S
https://arxiv.org/html/2608.02813v1
Wind Turbine Generators
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Why HTML?
https://arxiv.org/html/2608.03529v1
(2024), where a human expert and an automated system might describe information in different terms
https://arxiv.org/html/2608.00458v1
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https://arxiv.org/html/2609.03027v1
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arXiv:2609.03027v1 [astro-ph.IM] 02 Sep 2026
a]Space & Astronomy, Commonwealth Scientific and Industrial Research Organisation (CSIRO), P. O. Box 1130, Bentley, WA 6102
https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
https://arxiv.org/html/2608.03011v1
via Disentangled Audio Infilling
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https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems
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https://arxiv.org/html/2609.03103v1
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Optimizing Field Geometry for Modular Micropore-Optics Soft X-ray Time-Domain Surveys
Nicholas E. White Affiliation: The George Washington University, Washington, DC, USA
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2608.03099v1
Taxonomy and Evidence Audit of Language Grounding in Embodied Agents
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https://arxiv.org/html/2608.03655v1
Summarization
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Why HTML? Report
https://arxiv.org/html/2410.19947v2
affect college major choice?1footnote 11footnote 1We are grateful for the valuable feedback from seminar participants at the National University of Singapore, the University of Notre Dame, the University of Pennsylvania, Mississippi State