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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
Linux provides several ways to observe and respond: Pressure Stall Information (PSI) can detect memory-related task stalls, zram can provide compressed in-memory swap space, and cgroup v2 can request memory reclamation within a selected control group
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
https://arxiv.org/list/cs.LG/2022-12
2001-2004
Showing up to 50 entries per page: fewer | more | all
[1] arXiv:2212
https://arxiv.org/abs/2609.14858
Our key insight is that accumulated discovery history can serve as a replay simulator over the realized search space
https://arxiv.org/html/2508.20101v1
In an empirical application, we demonstrate how the model can be applied to financial stock networks
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
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/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
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/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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New submissions (showing 19 of 19 entries)
[1] arXiv:2608
https://arxiv.org/abs/2608.02851v1
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/abs/2608.01651
We present Bole, a kernel--runtime co-design that enables efficient tree speculation for hybrid-attention LLMs
https://arxiv.org/list/math.ST/2024-03
00186 [pdf, html, other]
Title: Warped Kernel Estimator for I
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/abs/2504.02246
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Computer Science > Programming Languages
arXiv:2504
https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv
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
https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2608.02872
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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/2006.00064
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https://arxiv.org/pdf/2608.02609
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/src/2104.08691
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