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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
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https://arxiv.org/list/cs.AI/new
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https://arxiv.org/list/cs.SE/new
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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
https://arxiv.org/list/cs.CV/new
Project Page: this https URL
[2] arXiv:2608
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
https://arxiv.org/html/2608.02918v1
Biscainho are with SMT, DEL/Poli & PEE/COPPE, Federal University of Rio de Janeiro, Brazil (corresponding e-mail: wallace
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
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/pdf/2006.15704
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https://arxiv.org/abs/2006.15704v1
Experiences on Accelerating Data Parallel Training, by Shen Li and 10 other authors
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Abstract:This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module
https://arxiv.org/abs/2006.15704
15704] PyTorch Distributed: Experiences on Accelerating Data Parallel Training
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arXiv is now an independent nonprofit
https://arxiv.org/abs/1910.03771v4
03771v4] HuggingFace's Transformers: State-of-the-art Natural Language Processing
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arXiv is now an independent nonprofit
https://arxiv.org/html/2608.03655v1
org perpetual non-exclusive license
arXiv:2608
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
https://arxiv.org/html/2607.28577v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/list/cs.DC/new
Several components are the subject of pending United States patent applications by the first author and are presented here as candidate open architectural principles
[2] arXiv:2608
https://arxiv.org/html/2608.01085v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2607.27188v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/list/gr-qc/new
We observe that each emitted graviton carries away an angular momentum of $\dot{J}/\dot {N}\simeq 2\hbar$, not only for elliptical orbits (as has already been pointed out by Page [arXiv:2409
https://arxiv.org/html/2608.03077v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/pdf/2006.02318
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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
https://arxiv.org/pdf/2609.18842
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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/src/2104.08691
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https://arxiv.org/src/2006.02924
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2006.00064
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