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Operators can replace or upgrade their instruments and distribute longitude coverage across a network
from the top result below

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https://arxiv.org/html/2609.03371v1
Operators can replace or upgrade their instruments and distribute longitude coverage across a network

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
We demonstrate that it can self-assemble functional circuits from scratch and rapidly re-route logic around permanent, previously unseen hardware faults

Condensed Matter

https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries

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https://arxiv.org/html/2609.02852v1
However, various strands of evidence indicate that an LLM’s externalized linguistic outputs and mechanistically-extracted linguistic features can be an unreliable lens for understanding internal model computation

Quantum Physics

https://arxiv.org/list/quant-ph/new
While the mathematical structure of matrix product states (MPS) is well understood through the MPS fundamental theorem, an analogous understanding for mixed-state tensor networks remains largely absent: if two purification tensors generate the same density matrix, how are they related

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new
CV) Fine-grained wildlife classification in aerial imagery is limited not only by model performance, but also by unreliable labels: animals occupy few pixels, key visual cues vary seasonally, and modality-specific evidence can be ambiguous

Software Engineering

https://arxiv.org/list/cs.SE/new
Yet it remains unclear when further evolution helps, how successful and failed trajectories shape revision, and whether extra test-time computation can recover the same gains

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https://arxiv.org/html/2607.22511v2
A statement can be free of sorry and still be mathematically uninformative: a definition may reduce to True; or an unproven step may be introduced as an axiom that the kernel accepts

Stabilization of First-Order Partial Integro-Differential Equations with

https://arxiv.org/html/2608.02851v1
org perpetual non-exclusive license arXiv:2608

[2608.12700] A Contract-Grade Verifier for LLM-Generated GPU Kernels

https://arxiv.org/abs/2608.12700
A kernel can pass that test and still be silently wrong

[2204.04218] Multimodal Multi-Head Convolutional Attention with Various

https://arxiv.org/abs/2204.04218
In many situations, computed tomography (CT) or magnetic resonance imaging (MRI) techniques capture several scans (modes) during a single investigation, which can jointly be used (in a multimodal fashion) to further boost the quality of super-resolution results

Rainfall is rough

https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license arXiv:2607

Numerical Analysis

https://arxiv.org/list/cs.NA/new
all New submissions (showing 18 of 18 entries) [1] arXiv:2608

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https://arxiv.org/html/2609.03027v1
EPIC can achieve ��(N​log�N)\mathcal{O}(N\log N) computational scaling for dense aperture arrays and has been demonstrated on the Long Wavelength Array (LWA) station in Sevilleta (USA) using Graphics Processing Unit (GPU)

Statistics Theory

https://arxiv.org/list/math.ST/new
If \(T_\star\) is the selected physical trajectory length and \(a_\star=\sqrt m\,T_\star\), a terminal-depth transfer argument yields restricted conductance and warm-start mixing without lazifying either kernel

1 Introduction

https://arxiv.org/html/2511.06451v1
Spectral safety and discrete Green kernel

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

Mathematics

https://arxiv.org/list/math/new
Their finite iterates can develop rapidly increasing numbers of linear pieces, making them a natural test case for the expressive power of deep neural networks

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

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

Reviving Micro Real Rigidities: The Importance of Demand ShocksThe views

https://arxiv.org/html/2608.02817v1
expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. The authors are grateful for the invaluable feedback they received on an earlier draft from Jón

Machine Learning Dec 2022

https://arxiv.org/list/cs.LG/2022-12
2001-2004 Showing up to 50 entries per page: fewer | more | all [1] arXiv:2212

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https://arxiv.org/src/2608.03898
+™ï€¤D9ŽÿÒ™[S5ªîP�àü~ç6Í‹~÷ð´_ÿ«ð‡ïó1ñqšMýÄçîÓl¶Ú?X-»Ñlší†ƒ~–Õrì˜ù�Ö³õIã„GŒý)•ôòU¢æ�XˆàU­òz,òYÞƒ»ÁUÀñGÿ¿*zeµ©ÊI¨"ñ0Í�'#9ª�âdM”ž¿Õò Ì…{µQ,UHC¬¼KäBTß¼²M»•Oõj)ìZo»\Ø+WÍæ˜;Ò}çÞXïEO™íd;ÿjqýåŸ�¸F£à;Í¡íßùšý7Ú­vnÿ »Ùl´

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

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https://arxiv.org/pdf/2508.11152
document was published isbn Text external ISBN for the publication in which the document was published doi Text external Digital Object Identifier for the document url URL external URL at which the document can be found byteCount Integer internal Approximate file size in octets pageCount Integer internal Number of

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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2609.18842
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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