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https://arxiv.org/pdf/2607.26188v1
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Optimizing Age of Information in Vehicular Edge Computing with Federated

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

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https://arxiv.org/pdf/2608.04008
Ù6¨¦ãþ ¬>Ÿ–£þÇÈŒ6=RBmL”õ·LXƇÄM¤_³|¼èÉöVõN‡eÑï]Vƒñ¨Ð[ÁRóªÒ{‚ñ5X68$T§å÷ž…­`=�ªŒì™"ÿ03[‚Rô/u‰ Cƒ*ÌJ¬³sI)u–8ƒW“qÿMI|�WGÏ0hù©Z5+ÆÞ§l*¢ pƒair 3÷zÝ } ÃBx×�ŠJ ê±ß£²ºóÛ¡jÞy Ń£‹ŠE"eSPL0¯Š}O;ï®�¾kã;ï_žþEØëø¢æ»³6ó"®)´º†sºr„›\#÷¢êU¥xtôƒ÷’Q‚ é í÷2~'åw�grã‰xô¢W]ÇÕpp*þc …¾ÿ)>VÕåô‡NçbQWŒ'ç�óZŸ]

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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

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https://arxiv.org/pdf/2608.02851v1
%ß6ùõ�ÃG"¥8/1tx4œ93CŠŒ`“LÔ’)¦­cš9S1Ã8‚YÆeí˜cܨšÕŒ;ͯ˜¨4LCÏ‚ eQ™0F3®˜p†ÞÑLVVÒ»L ²Ì-“ÊѳcÒ8z®™t4¯¨˜ªhÁ™¢È(Sš¹dSLÕ‚ž5Ó\Ò³aZ*úµLkC|rÚ*ÄÀ´«1)3•¤I83ÜÒ¤‚) —ÌhL®˜1F2‚ŒµœœaÆYÁ¤e¦¶Šœb¶²ätÍ,§qU1+(nÅ™•ôG‘.Òð|û-»ÿ±;tãÀþϾÙvc»yn†çîÐ�½¨¸

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https://arxiv.org/pdf/2608.03411
” ý6appÂl‘Мe~Ž-yè�2GiìÂ�‘‡&pf3ÒK·¤—jGÒKeòÁ ,õ"&:w|Ò¶öÝP"´rf�r\úÂxÝqF¢Ë=/Ü–ºƒÛn¦Èâ™îÆ*>Ó]zJX-éw_îö0}óó0°(B²5àû×±&›ì2 �

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https://arxiv.org/pdf/2608.03233
T½+Ñj¬±qø8¸ ÇM\¯c¡¤~wD`ž”Œõ%::P ÏsC`à zßzæ¯TrÔêH`$Å{0‡ÝF{ÿìŒkëʆêÃÆ„Ð 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

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

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

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

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

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

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

1 Introduction

https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands Slack http://bit

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
The implemented contract fixes evidence non-overwrite, branch isolation, grounded multi-subject timers, guarded state change, and declaration-ranked event ordering over time and space; an external runner still decides whether evidence becomes an authoritative move

Herding, Momentum, and Reversal in China’s A-Share Market: An Agent

https://arxiv.org/html/2607.27063v1
In practice, however, limits to arbitrage and correlated non-fundamental demand can allow prices to depart from fundamental values (De Long et al

Report GitHub Issue

https://arxiv.org/html/2609.02852v1
Report GitHub Issue Description: arXiv is now an independent nonprofit

Probing Character-level Transformers for the Spanish L-shaped Morphome

https://arxiv.org/html/2608.03452v1
It is decodable above every surface baseline, survives instances in which every form shows the same stem, and probes trained on alternating instances still classify non-alternating ones

Mapping the City Through the Lens of Language Models

https://arxiv.org/html/2608.02971v1
The clearest shared tendency favours urban profiles with larger developed area, faster recent growth, greater mapped infrastructure and non-residential capacity, and less sparse form

Report GitHub Issue

https://arxiv.org/html/2609.03103v1
Report GitHub Issue Description: arXiv is now an independent nonprofit

Activation-Guided Neuron Intervention to Induce Alzheimer’s-Related

https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit!

What Language Does and What the Evidence Supports: A Functional Role

https://arxiv.org/html/2608.03099v1
We define five non-exclusive functional roles for language: Specification, Embodied Representation, Action Orchestration, Grounding Regulation, and Execution Coupling

1. Introduction

https://arxiv.org/html/2608.02909v1
Introduction The increasing availability of large datasets, whether from traditional or non-standard data sources (e

Optimal Execution with Passive Market Impact

https://arxiv.org/html/2607.28323v1
This generates a trade-off between higher fill intensity and larger accumulated impact on the one hand, and lower impact but greater non-execution risk on the other

Learning to Control Unknown Strongly Monotone Games

https://arxiv.org/html/2407.00575v3
Due to the convex and compact action sets, our slower time-scale is a fixed point iteration with a non-expansive mapping instead of the stronger contractive mapping that allows for a O​(t−1)O(t^{-1}) convergence rate [7, 8]

Report GitHub Issue

https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic

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https://arxiv.org/pdf/2102.13654
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https://arxiv.org/pdf/2205.01882v5
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Producing Policy Recommendations: from Statistical Decision Theory to

https://arxiv.org/html/2607.29281v2
The National Bureau of Economic Research identifies as its main objective ‘conducting and disseminating independent, cutting-edge, non-partisan research that advances economic knowledge and informs policy makers and the business community’

You’ve Got to be Efficient: Ambiguity, Misspecification and

https://arxiv.org/html/2604.05327v4
Variational Preferences Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML?

Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
Transmission Parameter Allocation in Multi-Gateway LoRa Networks I INTRODUCTION II RELATED WORK III SYSTEM MODEL III-A The Traffic Pattern of LoRa Networks III-B Capture Effect and Quasi-Orthogonality between SFs III-C Packet Delivery

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https://arxiv.org/pdf/2608.02817
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Finance-Informed Neural Network: Learning the Geometry of Option Pricing

https://arxiv.org/html/2412.12213v2
Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to