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Stochastic Games with Minimally Bounded Action Costs

https://arxiv.org/html/2407.18010v2
We prove the game respects a dynamic programming principle and that the Markov perfect equilibrium can be computed as a limit point of a sequence of Bellman operations

Numerical Analysis

https://arxiv.org/list/cs.NA/new
Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Numerical Analysis New submissions Cross-lists Replacements See recent articles Showing new listings for Monday, 17 August 2026 Total

Distributed, Parallel, and Cluster Computing

https://arxiv.org/list/cs.DC/new
SE) Automating GPU kernel optimization remains difficult in practice: generated variants can violate correctness constraints, runtime measurements are noisy, and search often stalls early

Hardware Architecture

https://arxiv.org/list/cs.AR/new
Our analysis shows that, despite its limited write performance, HBF can significantly improve the batch size, throughput, and flexibility of LLM-serving systems while reducing the minimum GPU requirements, but realizing these benefits critically depends on sustaining HBM-comparable read bandwidth and requires

A Heterogeneous Spatiotemporal GARCH Model: A Predictive Framework for

https://arxiv.org/html/2508.20101v1
In an empirical application, we demonstrate how the model can be applied to financial stock networks

Microstructural Foundations of Rough Noise1footnote

https://arxiv.org/html/2607.29442v2
In an asymptotic framework inspired by Rosenbaum2018, we show that the rescaled price process converges to a Bachelier-type semimartingale plus a Gaussian moving average whose kernel is determined explicitly by the trawl function

1 Introduction

https://arxiv.org/html/2508.02739v1
2 Preliminary 3 Methodology K-line Tokenization Hierarchical Autoregressive Modeling Model Pre-training Dataset Model Training Inference 4 Experiments Experimental Setup Main Results Prediction Tasks Generative Tasks Investment

Finite-Time Analysis of Asynchronous Multi-Agent TD Learning

https://arxiv.org/html/2407.20441v1
I Introduction Training reinforcement learning (RL) algorithms is well-known to be a critically time consuming task that can require several data samples to achieve a desired level of accuracy

Computer Science > Programming Languages

https://arxiv.org/abs/2504.02246
Despite advances in verification tooling, conventional programmers are rarely involved in the verification of their own code, resulting in higher development and maintenance costs for verified software

Statistics Theory Mar 2024

https://arxiv.org/list/math.ST/2024-03
00186 [pdf, html, other] Title: Warped Kernel Estimator for I

Electrical Engineering and Systems Science

https://arxiv.org/list/eess/new
The design relies on two affine Volterra transformations involving both Fredholm- and Volterra-type integral terms, which results in a four-PIDE kernel equations

Machine Learning Dec 2022

https://arxiv.org/list/cs.LG/2022-12
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Machine Learning Authors and titles for December 2022 Total of 2004 entries : 1

Machine Learning Dec 2024

https://arxiv.org/list/cs.LG/2024-12
Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Machine Learning Authors and titles for December 2024 Total of 3298 entries : 1-50 51-100 101-150 151-200 ... 3251-3298 Showing up

Solar and Stellar Astrophysics

https://arxiv.org/list/astro-ph.SR/new
Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Solar and Stellar Astrophysics New submissions Cross-lists Replacements See recent articles Showing new listings for Friday, 7 August

Report GitHub Issue

https://arxiv.org/html/2608.03411v1
× 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 Abstract Download PDF

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https://arxiv.org/pdf/2508.11152
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2506.01844
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsMustafa ShukorDana AubakirovaFrancesco CapuanoPepijn KooijmansSteven PalmaAdil ZouitineMichel AractingiCaroline PascalMartino RussiAndres MarafiotiSimon

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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/pdf/2608.03494
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https://arxiv.org/src/2405.20232
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https://arxiv.org/src/2006.00064
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/src/2006.02924
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https://arxiv.org/pdf/2608.02609
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https://arxiv.org/pdf/2608.02872
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