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29 result(s) for “okapi bm25 site terms com pp site www postgresql org site www profelis com to site:arxiv.org” · 962 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
Instead, it falls on local, personal agents to effectively and safely maintain/utilize such dense person-person (PP) and person-agent (PA) interaction histories
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/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/list/cs.CL/new
Accepted to C3NLP @ ACL 2026
Subjects: Computation and Language (cs
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2608.02668v1
Both prove to be members of a one-parameter family of angular retractions, pp-SpheretNorm, whose rotation angle saturates rather than growing without bound
https://arxiv.org/html/2410.19947v2
1 From Gaussian Copula Dependence to Structural Covariance
4
https://arxiv.org/pdf/2407.02510
5gýů{Bå{Bw~2Tä�CfU^1Ižo-YÛòà�nã�b$òæ¡"ÎñS(VS 8››Ùß`�¼w¢%FžLC Z"Îü,™ÜÞ—¡ˆ â)"«ä2ý¹>¾¡©›#û÷¨ìQê\ùZ3„/C]Î-…5m…\‚Ó¹ºws¯pas³Ãº|Ï`-8Æã8ÉlW ŠYdo§¬lœ%f³as€•Ç¢ÕìéuŒ#µâ�pŒ>옦¢¤b$€Ñ9Ø}˜ÃÔÐFLŒç&–çœ/îÇ–ƒB×ÿ¡@ºæ~‘up’…žæÐù`8#æÂ\Po´ÈÈçໞ zÐTO²" á3�Ë܃ˈ¼—�ðèiÖäà®R‰õÈЂI�ÁW]j¡Ùr q¼u[
https://arxiv.org/pdf/2508.04671
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https://arxiv.org/abs/2608.03883
Abstract:This paper describes our submissions to eRisk 2026 Task 3, ADHD Symptom Sentence Ranking
https://arxiv.org/abs/2104.08663
To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval
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/2608.03118v1
From SQL Errors to Concept Gaps: An AI-Powered Knowledge Graph Analytics Platform for Diagnostic Feedback © 2026 IEEE
https://arxiv.org/list/cs.AI/new
To bridge this gap, we introduce a novel and comprehensive Interactive SEmantic Enrichment system (ISEE)
https://arxiv.org/html/2608.02813v1
strategies often treat the DC source as an unconstrained link, creating mismatches when applied to the wind turbine generators (WTGs)
https://arxiv.org/html/2608.03655v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2607.27099v1
3 From heavy-tailed critical Hawkes to rough fractional processes
1
https://arxiv.org/pdf/2508.07408
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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
IR)
Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed
https://arxiv.org/html/2608.03067v1
CL] 04 Aug 2026
Activation-Guided Neuron Intervention to Induce Alzheimer’s-Related Computational Language Phenotypes in a Large Language Model
Rui He1,*, Ercong Nie2, Hong Jiang3, Iris E
https://arxiv.org/html/2407.00575v3
Learning to Control Unknown Strongly Monotone Games
1 Introduction
1
https://arxiv.org/pdf/2608.03157
ÄÌ4ºû4àC0ÎøMÈ8$SÙH‹‘Rp¬Æk4>¢eñ8ŠQ�{Ñ›Pp/ªIŠö1˜œñšMõŠc2ââ…^ø¼Q#ïT#ÑáNrFRÂK“àclƒ©‘w'�0I»ƒŸ}¢”ˆŸJüœãþUÞÁñ™h+`d@«WÙul“`¯r0=ÍÑhJl“ŒfÇ6'è‘ÏÅhÞ©&8bAŸƒ|¢ˆ šk³JˆžwÔ„ÔîØF�¹À´ÿù’LÅ·J6Q‰¶�‘†,'ø²¯ÎÄDÓUÁ {P½‰™_GObiw‚I®µ‰& zàk‚ýÑ�¾¥HÂF)Òª0^SÑogRÁ€¨“�ƒœ7™C¦¸ÈÁÑ6¿Ø8â¯W— lÀŸ2N *cåOÕ䂨8S
https://arxiv.org/html/2608.03099v1
1 Specification: Committing the Agent to a Task
4
https://arxiv.org/html/2607.29281v2
2 Implementation
5 How to Produce Recommendations
5
https://arxiv.org/html/2607.28847v1
2 Bounding the Impact of Fairness Regularisation
6 From Diagnosis to Remedy: Actionable Explanations
6
https://arxiv.org/pdf/2608.02918
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https://arxiv.org/pdf/2006.15704
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