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This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback
from the top result below

Artificial Intelligence

https://arxiv.org/list/cs.AI/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 Artificial Intelligence New submissions Cross-lists Replacements See recent

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/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 Computer Vision and Pattern Recognition New submissions Cross-lists Replacements

Software Engineering

https://arxiv.org/list/cs.SE/new
PL) Glob patterns, a domain-specific language for structured string matching, are a foundational yet understudied component of modern software development embedded in everything from build scripts and configuration files to web server routes

Sphere Retraction Normalizations

https://arxiv.org/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (), Correspondence: Cheng-Fang Su () “Geometry is not true, it is advantageous

[2006.02924] Scaling Distributed Training with Adaptive Summation

https://arxiv.org/abs/2006.02924
It then introduces a series of case-studies to show Adasum works with multiple frameworks, (TensorFlow and PyTorch), scales multiple optimizers (Momentum-SGD, Adam, and LAMB) to larger batch-sizes while still giving good downstream accuracy

Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

https://arxiv.org/html/2608.02918v1
Miranda is also with LCTI, Télécom Paris, IP Paris, France

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

[2006.15704v1] PyTorch Distributed: Experiences on Accelerating Data

https://arxiv.org/abs/2006.15704v1
Experiences on Accelerating Data Parallel Training, by Shen Li and 10 other authors View PDF Abstract:This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module

[2006.15704] PyTorch Distributed: Experiences on Accelerating Data

https://arxiv.org/abs/2006.15704
15704] PyTorch Distributed: Experiences on Accelerating Data Parallel Training Skip to main content arXiv is now an independent nonprofit

[1910.03771v4] HuggingFace's Transformers: State-of-the-art Natural

https://arxiv.org/abs/1910.03771v4
HuggingFace's Transformers library features carefully crafted model implementations and high-performance pretrained weights for two main deep learning frameworks, PyTorch and TensorFlow, while supporting all the necessary tools to analyze, evaluate and use these models in downstream tasks such as text/token

Decoupling Generation and Selection for Budget-Constrained Faithful

https://arxiv.org/html/2608.03655v1
Summarization Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report

1 Introduction

https://arxiv.org/html/2506.01844v1
We evaluate SmolVLA on a range of both simulated as well as real-world robotic benchmarks and release all code, pretrained models, and training data

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

https://arxiv.org/html/2607.27188v1
net School of Computer ScienceGeorgia Institute of TechnologyUSA , Michael Galarnyk mgalarnyk3@gatech

Train Often, Deploy Selectively: Forward-Gated Model Replacement in

https://arxiv.org/html/2607.28577v1
Once the week’s labels mature, SBS promotes only if mean challenger NLL is at least 10−410^{-4} lower

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https://arxiv.org/pdf/2006.15704
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Distributed, Parallel, and Cluster Computing

https://arxiv.org/list/cs.DC/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 Distributed, Parallel, and Cluster Computing New submissions Cross-lists

When Collaboration Becomes a Trigger:Collective Evidence-Threshold

https://arxiv.org/html/2608.01085v1
Backdoors in Multi-Agent Systems Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv

General Relativity and Quantum Cosmology

https://arxiv.org/list/gr-qc/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 General Relativity and Quantum Cosmology New submissions Cross-lists

Report GitHub Issue

https://arxiv.org/html/2608.03077v1
× Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to Abstract Download PDF

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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

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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2609.18842
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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/2405.20232
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https://arxiv.org/src/2006.00064
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/src/2006.02924
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