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Freshness: newest result fetched 26 d ago · median age of these results 26 d · 1 of 30 carry a fetch time
https://arxiv.org/html/2607.28294v1
A second scheme preserves the realized count and sets the number of events in the bootstrap world as n∗=n​(T)n^{\ast}=n(T)
https://arxiv.org/html/2407.20441v1
However, in real-world multi-agent systems, inter-node communication imposes strong constraints, such as limited link capacity and transmission delays [4, 5, 6]
https://arxiv.org/pdf/2608.00785
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https://arxiv.org/html/2608.03573v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02943v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/pdf/2608.02854v1
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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/html/2407.15077v2
2 Problem Definition
3 Method
3.1 Monotonic Improvement of B2MAPO scheme
3.2 B2MAPO Framework
3.2.1 Batch Sequence Generation Layer
3.2.2 Batch by Batch Policy Optimization Layer
4 DAG-based B2MAPO Algorithm
4.1 Hierarchical DAG
https://arxiv.org/html/2407.04974v1
This attribute enhances its convenience and practicality for real-world applications
https://arxiv.org/list/cs.CL/new
LG)
Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed
https://arxiv.org/pdf/2609.18842
€Í[¯·{øMCçalÍÙÿïŽ7ü¦‘ð€ˆ¶–]üýV²iP:Àõ¶âÿ¿Ë#nPöYõÛòˆ
https://arxiv.org/pdf/2608.03833
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https://arxiv.org/pdf/2608.04008
Òo@©,m • UÒ*í‡Û[h¿/Ãx€?pówX_ÃbÙïÇa·]ønöý:,P5ðí·þ%\ñyC@?g{hu.ˆ†ir…‡² è|OY,¾Ä=;„åÔ]a¢s—gìèÒ~¾ÆíCWj\Š¥FÕ )%Í^ã›M†�ϧ;œ0çÌ|rŽÿfWñy+úÔîŠÎœ¾¢,vú„#¯6¹pòë�ýóæmåY)gn)+ùx*TEçf¦a)“ð8{W=áÖÖŽø$}•²J¯º¨«+뺰«*(½ ®ÂÐ\[Y; > endobj 14 0 obj >
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
https://arxiv.org/list/cs.CG/new
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New submissions (showing 1 of 1 entries)
[1] arXiv:2608
https://arxiv.org/html/2608.03452v1
Probing five architectures, twelve trained models each, under lemma-disjoint cross-validation with controls and surface baselines, we show that the models encode the L-shaped class itself, not just its visible alternations
https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing)
D
https://arxiv.org/list/cs.DS/new
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New submissions (showing 7 of 7 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.AR/new
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New submissions (showing 4 of 4 entries)
[1] arXiv:2608
https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3
https://arxiv.org/list/cs.SY/new
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New submissions (showing 8 of 8 entries)
[1] arXiv:2608
https://arxiv.org/src/2006.02759
}†‘û{Ø\»†�fUaÅbTMV ¼•~À8yœš–ˆÆ9}r'e´‘¢ÉŽ]y±DÔ)u³V5ºh㾕z±'&]Ñ'“”ÆPÄÊ0 ´á7N* ¡¼iPÿÎååà3œèqS [÷F@ub¨{@ºÊzêó«èiÍXs™
https://arxiv.org/html/2607.24970v2
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https://arxiv.org/html/2607.29442v2
1footnoteFootnotefootnotesFootnotes1footnote 1We are grateful to Carsten Chong for fruitful discussions on rough noise. We benefitted from comments by and discussions with Kim Christensen, Mikko Pakkanen, Bent Jesper Christensen, and Mikkel
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2608.02909v1
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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/2407.08254v2
1 Introduction
2 Related Work
3 Problem Formulation
3.1 Multi-agent planning with attrition
4 Attritable MCTS with Regret Minimization
4.1 Overview of the A-MCTS algorithm
4.2 Regret Matching For Cooperative Coordination
4.3 Analysis and
https://arxiv.org/html/2407.06328v2
I Introduction
II Preliminaries and Problem Description
II-A Task Allocation Game Model
II-B Learning Rule, Stochastic Alarm Clock, and Evolutionary Dynamics Model
II-C Payoff Vector Estimation
III Convergence Analysis and Revision Rate
https://arxiv.org/pdf/2006.02602
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