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30 result(s) for “okapi bm25 site allegrolokalnie pp site www 31 com site does could goode com site:arxiv.org” of 118 matched · 603 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
6 Ingest Overhead: When Does Structured Memory Pay Off
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/2607.29281v2
Empirical Practice
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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/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/2608.00458v1
org perpetual non-exclusive license
arXiv:2608
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/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/html/2608.03703v1
111June 23 and 26 and July 7, 8, 13, 28 and 29, 2026, all first-stage halts; the count runs through July 31
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/html/2609.03159v1
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https://arxiv.org/html/2609.03103v1
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https://arxiv.org/html/2607.27099v1
9 Detailed results for Météo France data at small time scales
License: arXiv
https://arxiv.org/html/2608.03099v1
4 System Success Alone Does Not Isolate Language
5
https://arxiv.org/list/cs.AI/new
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https://arxiv.org/html/2407.00575v3
Gradient play converges to a unique Nash equilibrium (NE) that does not optimize the global objective
https://arxiv.org/html/2609.02852v1
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https://arxiv.org/html/2608.02909v1
This approach is theoretically valid, easy to implement, and does not require new data
https://arxiv.org/html/2410.19947v2
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arXiv:2410
https://arxiv.org/html/2604.05327v4
Variational Preferences
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https://arxiv.org/html/2608.01085v1
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arXiv:2608
https://arxiv.org/html/2607.28847v1
3 Research Question 1: Does Masked Inequality Exist
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/pdf/2608.03883
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/html/2407.10811v1
Recently, employing reinforcement learning (RL) to solve TSC has become a research hotspot since it does not require too much artificial priori and can improve performance significantly through the paradigm of trial and error
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/html/2407.16741v3
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https://arxiv.org/html/2608.00328v2
6 Model definition
References
License: arXiv
https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems
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https://arxiv.org/html/2608.02966v1
Across 189 LLMs and 31,554 items from 14 benchmarks, LLM-NRM predicts held-out LLM-item interactions more accurately than binary Item Response models and conventional nominal-response baselines, and its ability estimates achieve the strongest Spearman correlation of 0