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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

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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/pdf/2608.02854v1
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https://arxiv.org/pdf/2608.00426
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https://arxiv.org/pdf/2507.17609
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Statistics Theory Mar 2024

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

Machine Learning Dec 2024

https://arxiv.org/list/cs.LG/2024-12
3251-3298 Showing up to 50 entries per page: fewer | more | all [1] arXiv:2412

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

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

Operating Systems

https://arxiv.org/list/cs.OS/new
| all New submissions (showing 2 of 2 entries) [1] arXiv:2608

Computer Science > Computation and Language

https://arxiv.org/abs/2609.14858
--> Computer Science > Computation and Language arXiv:2609

Hardware Architecture

https://arxiv.org/list/cs.AR/new
more | all New submissions (showing 4 of 4 entries) [1] arXiv:2608

Report GitHub Issue

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

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

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

1 Introduction

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

Report GitHub Issue

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

Report GitHub Issue

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

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

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

Quantum Physics

https://arxiv.org/list/quant-ph/new
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 Quantum Physics New submissions Cross-lists Replacements See recent articles

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

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https://arxiv.org/pdf/2005.13905
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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

Computer Science > Programming Languages

https://arxiv.org/abs/2504.02246
--> Computer Science > Programming Languages arXiv:2504

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https://arxiv.org/pdf/2608.03898
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https://arxiv.org/src/2608.03994
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https://arxiv.org/src/2006.03720
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https://arxiv.org/src/2006.00064
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