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These observations establish that the gas-water interface of microbubbles is not the site for hydroxyl radical generation (else CL would be recorded in all cases)
from the top result below

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/2608.02612v1
org perpetual non-exclusive license arXiv:2608

Report GitHub Issue

https://arxiv.org/html/2608.02613v1
Then it must make an even harder judgment: even if the answer is known, is it actually appropriate to tell Bob

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

https://arxiv.org/html/2608.03452v1
Prior studies have shown that character-level transformers can reproduce this pattern, but that evidence describes what models produce, not what they represent

Rainfall is rough

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

1 Introduction

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

Reviving Micro Real Rigidities: The Importance of Demand ShocksThe views

https://arxiv.org/html/2608.02817v1
The authors would like to thank Nir Jaimovich (editor) and four anonymous referees, as well as Isaac Baley, Mark Bils, Laura Castillo-Martínez (discussant), Basile Grassi, Susanto Basu, John Haltiwanger, Pete Klenow, Francesco Lippi, Virgiliu Midrigan, Tobias Renkin, Luminita Stevens, Javier Turén, Rosen Valchev

Mathematics

https://arxiv.org/list/math/new
up to 2000 entries per page: fewer | more | all New submissions (showing 219 of 219 entries) [1] arXiv:2608

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

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new
Project Page: this https URL [2] arXiv:2608

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

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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

Instrumentation and Methods for Astrophysics

https://arxiv.org/list/astro-ph.IM/new
In particular, the maximal light deflection of a given Solar System body can be easily estimated for a given observer

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

https://arxiv.org/abs/2608.12700
Those rates come from a single loose test: run the kernel on a few random inputs at one fixed shape and accept it if the output is close to a reference

Stabilization of First-Order Partial Integro-Differential Equations with

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

Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
An analytical model that can evaluate the EE in real time is therefore important for efficiently allocating the transmission parameters

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

Stochastic Games with Minimally Bounded Action Costs

https://arxiv.org/html/2407.18010v2
Since acting at each available opportunity would accumulate prohibitively large costs, the resulting decision problem is one in which players must make strategic decisions about when to execute actions in addition to their choice of action

[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

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https://arxiv.org/html/2608.03411v1
com Abstract Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model’s true epistemic state

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https://arxiv.org/html/2609.03027v1
Report GitHub Issue Description: arXiv is now an independent nonprofit

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

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https://arxiv.org/html/2609.03371v1
Report GitHub Issue Description: arXiv is now an independent nonprofit

Cryptography and Security

https://arxiv.org/list/cs.CR/new
fewer | more | all New submissions (showing 32 of 32 entries) [1] arXiv:2608

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

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

Homogeneous Distributed Observers for Quasilinear Systems

https://arxiv.org/html/2407.05763v1
However, centralized observers for plants modeled by nonlinear systems satisfying Ho¨¨o\ddot{\mathrm{o}}over¨ start_ARG roman_o end_ARGlder condition can be found in the recent literature (see, e

Solar and Stellar Astrophysics

https://arxiv.org/list/astro-ph.SR/new
per page: fewer | more | all New submissions (showing 19 of 19 entries) [1] arXiv:2608

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

Databases

https://arxiv.org/list/cs.DB/new
We propose an architectural pattern for such systems, a trusted kernel with a generative shell, resting on one invariant: a component that can fabricate may influence which question the system answers, never which value it returns