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30 result(s) for “okapi bm25 site terms com pp site www postgresql org site www no com site www site:arxiv.org” of 31 matched · 985 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/abs/2608.03883
Because no annotated training data were released for this first edition of the task, we relied on zero-shot experimentation, manual validation, and unsupervised or weakly guided retrieval pipelines
https://arxiv.org/list/cs.CL/new
Prior work enables one-way, scholar-oriented translation from Akkadian to English, but offers no path in the reverse direction: non-specialist users cannot compose new content in cuneiform, and therefore remain passive consumers of ancient culture rather than active participants
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
KGD improves over strong pretrain-transfer baselines by 4-12% on eight public benchmarks and sustains its advantage over a 90-day production stream where baselines show no gains
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2608.03118v1
org perpetual non-exclusive license
arXiv:2608
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/pdf/2608.03157
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https://arxiv.org/html/2608.02813v1
Wind Turbine Generators
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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/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2410.19947v2
org perpetual non-exclusive license
arXiv:2410
https://arxiv.org/html/2608.03655v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2407.00575v3
1 Introduction
1.1 Related Work
1.2 Outline and Notation
2 Problem Formulation
2.1 Applications
2.1.1 Quadratic Global Objective
2.1.2 Weighted Resource Allocation Games
3 Game Control Algorithm
3.1 Performance Guarantees
4 Convergence
https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2607.29281v2
2 Exact Finite Sample Behaviour
Exact Finite Sample Behaviour and No-Data Rules
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/2607.28847v1
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2006.15704
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https://arxiv.org/pdf/2508.04671
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https://arxiv.org/pdf/2608.02918
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https://arxiv.org/pdf/2607.27070v1
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