Web This site Trusted
22 result(s) for “okapi bm25 site terms com pp site www datacom com site paying systems site www irs site:arxiv.org” · 774 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.
Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)
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/list/cs.IR/new
IR)
Large language models (LLMs) have emerged as promising listwise rerankers for recommender systems, but their reliability under equivalent candidate permutations remains unclear
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/list/cs.AI/new
Biological systems, in contrast, exhibit adaptive plasticity, maintaining function through dynamic re-organisation around damage
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/html/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.
https://arxiv.org/pdf/2005.14165
{괙֭ƭ[ux~ t Y;i;w~~ \^^puWWW^__ ggg{/_;- 5S;wzrrAR
https://arxiv.org/pdf/2407.00273
&/iM˗Hr2k 6Ih?yT@ؔ'%PsRS?I~V}3R&tHq+֤c H ox)W/1?)Sk;[zo?y]rU:ȟ@>"=Ic? '民ӟ..P78ߛg8MHd %IC-G2brk98 V7ۼn68@p
https://arxiv.org/pdf/2005.13905
&Q,l@2`0!`01 $4g*S9g9~Pj?}7Z+7/m_^m7?GS_9Y,|?qAB JU$+ {aFB%5|!_F04 0k Ğt:fJ5^c`츾yZ IFv[Yb4 hE9''HC&3hj|w擒cM}iG{X
https://arxiv.org/pdf/2608.03233
, ZJJ,_Slb g!)s4c%eq$H 2Bb3H`G3 Ib 3$JƅgO ۿ77{(̨wxn3zmfԫ%b[+,Tq= 4b4& `[zUmgY6qzכS ?u3yx2]:R[VA2AfЪ/*YqQEeG
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
We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark
https://arxiv.org/list/cs.IR/recent
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
Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/list/cs.DC/2020-06
Rodriguez, Rajkumar Buyya
Comments: submitted as book chapter to Knowledge Management in Development of Data-Intensive Software Systems (KMDDIS)
Subjects: Distributed, Parallel, and Cluster Computing (cs
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/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/pdf/2608.00785
*" 4X]滉` Yd)$w(P\we$S IGRǼ%:VͶg{*Ɗъ$ vbyxZ1phN"l̀ >= 9͟^ 11>" mS;wn * j[Gx.ڛ7pi
https://arxiv.org/html/2608.03153v1
Quantile regression estimates show a pronounced pattern by 2016–2019: the gap has effectively closed at the 10th percentile but remains approximately 5% at the 90th, consistent with [Goldin (2014)]’s hypothesis about high-paying positions (“greedy jobs”)
https://arxiv.org/pdf/2407.00323
hm^AjDTpLho9Y@& ׯ{eOb7?|}7f~š߬7|נ٫_7?bz~OtG3U.=V7? `8Ga_Ѭ! ɝj` w; ejnx]Pr9 \~ W?N} v]]7BW[k