Web This site Trusted
30 result(s) for “okapi bm25 site hotchips pp site en wikipedia org site www ldac com site:arxiv.org” of 118 matched · 526 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.
Did you mean okapi bm25 site hotchips pp site en wikipedia org site www lac com site:arxiv.org?
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/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2608.03480v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/pdf/2608.02758
F Mb1S9) `U,KPbJ6JR%B\2y88hRc M0d1 {S2OoiC=6@ 5~| ,RXWZYQq@c]L8-g9 5Nû9>%8*{s49afTeNݟF;W˨ *:@\_շ~uIc gy ]}:c x yRWcuz㮾@1e}R'qjN6훻 fwvq5_fj5
https://arxiv.org/pdf/2608.03883
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard
https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv
https://arxiv.org/pdf/2608.03157
4 4C0M8$SH Rpk4>e8Q{ћPp/I1Mc2^Q#T#NrFRKcl w'0I}J U h+`d@Wul `r0= hJlf6'hީ&8bA| kJwԄFLŷJ6Q,'DU {P_GObiwI& zkHF)Ҫ0 ^SogR7C68W l2N *cO 8S
https://arxiv.org/html/2608.03086v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2607.28410v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2608.03154v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.01366v2
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2407.02342v1
org) Wen Chen is with the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China(e-mail: wenchen@sjtu
https://arxiv.org/pdf/2607.26188v1
JMY[n rHbܝD6H E V1ɴeqafkf8L1%4LyxŴsf ˸dC1g*a&W*NG1a' )əRL *a*7JYOgRTys+/lo TC֜-dO}j-:}8k +-tUPe_5ߘ8(=DFJt
https://arxiv.org/pdf/2402.03300
߯jD岻fc;Z̫MfG} q]/ޭmޯo⣩0Z^x]fkn{E+{*ʧypg6;5PVpH8$hmڢ*߄zR:")3qXysO'H)-"}[˺s 3 4{pYdrK+ a }ѫW{ Fvm7344AGc
https://arxiv.org/pdf/2608.03118
Zz|PG-T*1P8Y v k-'Nğ`PM $&] L'|* p7k7mE7nry09U=PP#'#ބv2Z$t;Ʀlr5
https://arxiv.org/src/2006.02759
YT, *ܙLҲ̝{=;3==BiH#;#33:ǥe/Ŋ !I E9V = =bʩf.821?=cfzfZ6?cǎq\e$Nz.5212 ?p7A WR`1 tz=Ë́A*IHJHz585 e
https://arxiv.org/pdf/2608.02851v1
%6G"8/1tx493C`LԒ)c9S18YecܨՌ;4LCς eQ0F3pLVVһL -ѳc8zt4h(SdSLՂ5\ҳaZ*LkC|r*1)3I83Ҥ) hL1F2aYebt,qU1+(nřG.|-;tϾvcynЏ
https://arxiv.org/pdf/2303.12712v5
z$gsh)_:^XGCyrG4NǦpP%ŪB nk 5(zӸ%TQx[^Nwi o[HaWcv};F ~
https://arxiv.org/pdf/2608.03411
6applМe~-y2Gi&pf3KjGKe ,"&:w|ҶP"rfr\xqF=/ܖn*>]zJX-w_0}0(B5&2
https://arxiv.org/pdf/2608.03233
T+jq8 M\c~wD`%::P sC` zzTrH`${0F{kʆƄ 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/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
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/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2608.02668v1
Sphere pp-Angular Retraction Normalization
https://arxiv.org/html/2607.27188v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2607.28577v1
org perpetual non-exclusive license
arXiv:2607