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0 pp across readers — large in absolute terms, especially on Q0
from the top result below
https://arxiv.org/html/2608.02613v1
2 pp, exceeding MemSearch reader scaling (+10
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2412.12213v2
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https://arxiv.org/html/2407.02342v1
Index Terms:
Vehicular Edge Computing, Age of Information, Multi-agent Deep Reinforcement Learning, Federated Learning, Graph Neural Networks
https://arxiv.org/html/2608.02668v1
Sphere pp-Angular Retraction Normalization
https://arxiv.org/html/2608.03529v1
(2024), where a human expert and an automated system might describe information in different terms
https://arxiv.org/html/2607.27188v1
In machine-learning terms, RND recovery is an inverse problem from irregular observations (Arridge et al
https://arxiv.org/html/2608.02813v1
Wind Turbine Generators
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https://arxiv.org/html/2609.02852v1
A linguistically-decodable feature is one that is defined in terms of natural language, and is extracted directly from a model’s natural-language outputs or indirectly via mechanistic probes like sparse autoencoders Bricken et al
https://arxiv.org/html/2608.00458v1
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arXiv:2608
https://arxiv.org/html/2608.03086v1
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arXiv:2608
https://arxiv.org/html/2608.03011v1
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arXiv:2608
https://arxiv.org/html/2410.19947v2
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arXiv:2410
https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems
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https://arxiv.org/html/2607.28410v1
com/zaneopen/PACE
1 Introduction
Reducing execution costs is a central objective for financial institutions (Bertsimas and Lo 1998)
https://arxiv.org/html/2608.03480v1
com Arabic Institute for Translation, Algiers, Algeria CERIST, Algeria Nasredine Semmar CEA LIST, Paris-Saclay, France Hassina Aliane CERIST, Algeria
Abstract
The adoption of large pre-trained multilingual models for neural machine translation (MNMT) faces a major challenge: excessive memory and computational
https://arxiv.org/html/2407.10811v1
II RELATED WORK
II-A Traditional methods
II-B RL methods.
III PRELIMINARIES
IV METHOD
IV-A RL Setup
IV-B Network design
IV-C Training
IV-D Theoretical Analysis
V EXPERIMENT
V-A Towards Real-world Dataset and Simulation
V-B Benchmark
https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction
https://arxiv.org/html/2407.13076v1
Index Terms:
LoRa, multigateway, system energy efficiency, matching-based, multiagent deep reinforcement learning
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/2608.02909v1
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https://arxiv.org/html/2407.04974v1
Through experimental validation, we illlustrate the superiority of our proposed algorithm in terms of performance
https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/pdf/2006.02318
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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/2608.03883
CL journal 1613-0073 1 A arXiv GenPDF (tex2pdf:8def8d8) 2026-08-05T01:19:06+00:00 2026-08-05T01:19:06+00:00 2026-08-05T01:19:12
https://arxiv.org/pdf/2607.27188v1
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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/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
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