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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/list/math.ST/2024-03
00186 [pdf, html, other]
Title: Warped Kernel Estimator for I
https://arxiv.org/list/cs.LG/2024-12
3251-3298
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[1] arXiv:2412
https://arxiv.org/list/astro-ph.SR/new
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[1] arXiv:2608
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
https://arxiv.org/list/cs.OS/new
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New submissions (showing 2 of 2 entries)
[1] arXiv:2608
https://arxiv.org/abs/2609.14858
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Computer Science > Computation and Language
arXiv:2609
https://arxiv.org/list/cs.AR/new
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[1] arXiv:2608
https://arxiv.org/html/2609.03027v1
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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
https://arxiv.org/html/2609.02852v1
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https://arxiv.org/html/2511.06451v1
Spectral safety and discrete Green kernel
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
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
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
https://arxiv.org/list/quant-ph/new
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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
https://arxiv.org/pdf/2005.13905
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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
https://arxiv.org/list/cs.LG/2022-12
2001-2004
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[1] arXiv:2212
https://arxiv.org/abs/2504.02246
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Computer Science > Programming Languages
arXiv:2504
https://arxiv.org/html/2608.03411v1
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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/pdf/2608.02976
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https://arxiv.org/src/2006.00064
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https://arxiv.org/src/2006.01128
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https://arxiv.org/src/2006.02759
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