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https://arxiv.org/list/math.ST/new
A general characterization is established through a probability measure obtained by reweighting the distribution of a single observation according to its conditional probability of occupying a prescribed rank
https://arxiv.org/list/cs.DC/new
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Automating GPU kernel optimization remains difficult in practice: generated variants can violate correctness constraints, runtime measurements are noisy, and search often stalls early
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/abs/2608.01651
for Hybrid-Attention Language Models, by Li Wang and 11 other authors
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Abstract:Hybrid-attention large language models combine full attention with recurrent linear attention to reduce long-context inference costs, yet their autoregressive decoding remains memory-bound
https://arxiv.org/abs/2512.09590
09590] On Inhomogeneous Affine Volterra Processes: Stationarity and Applications to the Volterra Heston Model
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https://arxiv.org/list/econ/new
Through simulations, we show that this approach recovers estimates close to the true values, even in relatively small samples, while the standard approach can produce substantial bias in practice
https://arxiv.org/list/cs.NA/new
Nazarov, A new approach to the reaction-diffusion systems modelling, Vestn
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
https://arxiv.org/abs/2504.02246
A key barrier to programmer participation in verification practices is the disconnect of environments and paradigms between programming and verification practices, which limits accessibility and real-time verification
https://arxiv.org/abs/2609.14858
Current systems face a fundamental dilemma: fixed strategies fail to adapt as search spaces scale, while online policy optimization requires navigating vast meta-search spaces under delayed and expensive feedback over long-horizon rollouts
https://arxiv.org/list/astro-ph.SR/new
Villaseñor, Eleonora Zari
Comments: submitted to A&A, comments welcome, 19 pages, 14 figures
Subjects: Solar and Stellar Astrophysics (astro-ph
https://arxiv.org/abs/1202.3424
, dust particles), but may also be 1-d or 2-d periodic arrays of "target unit cells", which can be used to study absorption, scattering, and electric fields around arrays of nanostructures
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.CR/new
We also show that coarse-to-fine label selection can create near-perfect measured correlation without learned dependence
https://arxiv.org/list/astro-ph.IM/new
This geometry offers an efficient compromise among survey coverage, pointed sensitivity, system complexity, and resilience, and can be scaled to larger persistent soft-X-ray monitoring architectures
https://arxiv.org/html/2508.02739v1
However, their application to financial candlestick (K-line) data remains limited, often underperforming non-pre-trained architectures
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/list/quant-ph/new
The suggested interpretation leads to differences in some outcome probabilities compared to previous analyses which, in principle, make it possible to falsify the suggested modifications or the previous analyses (or both)
https://arxiv.org/html/2511.06451v1
From selective scan to a risk-neutral operator layer
https://arxiv.org/html/2508.16279v1
Such advancements have laid a robust foundation for developing powerful LLM-based agent applications that can effectively interface with the world through a variety of tools, to perform diverse and complex tasks with increased autonomy and precision (Qin et al
https://arxiv.org/html/2407.02518v2
com
Abstract
Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements
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.CV/new
To address this limitation, we propose Hunyuan3D-Buffalo 1
https://arxiv.org/html/2407.18010v2
Model misspecifications of this kind can result in vast disparities between the predictions of financial models and the financial phenomena they attempt to model (Simons et al
https://arxiv.org/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/html/2407.05763v1
By proper parameter tuning with linear matrix inequalities, the observer error equation possesses finite/fixed-time stability in the perturbation-free case and input-to-state stability with respect to bounded perturbations