Linux driver support ready for Intel Panther Lake's NPU 5
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NVIDIA Blackwell Raises Bar in New InferenceMAX Benchmarks, Delivering Unmatched Performance and Efficiency
NVIDIA Blackwell swept the new SemiAnalysis InferenceMAX v1 benchmarks, delivering the highest performance and best overall efficiency. InferenceMax v1 is the first independent benchmark to measure total cost of compute across diverse models and real-world scenarios. Best return on investment: NVIDIA GB200 NVL72 delivers unmatched AI factory economics — a $5 million investment generates $75
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Microsoft Azure Unveils World’s First NVIDIA GB300 NVL72 Supercomputing Cluster for OpenAI
Microsoft Azure today announced the new NDv6 GB300 VM series, delivering the industry’s first supercomputing-scale production cluster of NVIDIA GB300 NVL72 systems, purpose-built for OpenAI’s most demanding AI inference workloads. This supercomputer-scale cluster features over 4,600 NVIDIA Blackwell Ultra GPUs connected via the NVIDIA Quantum-X800 InfiniBand networking platform. Microsoft’s unique systems approach applied radical engineering
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Incoming: ‘Battlefield 6’ Lands on GeForce NOW at Launch
Lock, load and stream — the battle is just beginning. EA’s highly anticipated Battlefield 6 is set to storm the cloud when it launches tomorrow with GeForce RTX 5080 power, delivering the high-intensity combat and heart-pounding chaos the series is known for. Catch it as part of the six games joining the cloud, including Bethesda’s
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( 7
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Use Amazon SageMaker HyperPod and Anyscale for next-generation distributed computing
In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for distributed AI workloads with high-performance hardware, continuous monitoring, and seamless integration with Ray, the leading AI compute engine, enabling organizations to reduce time-to-market and lower total cost of ownership.
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Customizing text content moderation with Amazon Nova
In this post, we introduce Amazon Nova customization for text content moderation through Amazon SageMaker AI, enabling organizations to fine-tune models for their specific moderation needs. The evaluation across three benchmarks shows that customized Nova models achieve an average improvement of 7.3% in F1 scores compared to the baseline Nova Lite, with individual improvements ranging from 4.2% to 9.2% across different content moderation tasks.
( 47
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Lagrangian neural ODEs: Measuring the existence of a Lagrangian with Helmholtz metrics
arXiv:2510.06367v1 Announce Type: new
Abstract: Neural ODEs are a widely used, powerful machine learning technique in particular for physics. However, not every solution is physical in that it is an Euler-Lagrange equation. We present Helmholtz metrics to quantify this resemblance for a given ODE and demonstrate their capabilities on several fundamental systems with noise. We combine them with a second order neural ODE to form a Lagrangian neural ODE, which allows to learn Euler-Lagrange equations in a direct fashion and with zero additional inference cost. We demonstrate that, using only positional data, they can distinguish Lagrangian and non-Lagrangian systems and improve the neural ODE solutions.
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Chem-NMF: Multi-layer $\alpha$-divergence Non-Negative Matrix Factorization for Cardiorespiratory Disease Clustering, with Improved Convergence Inspired by Chemical Catalysts and Rigorous Asymptotic Analysis
arXiv:2510.06632v1 Announce Type: new
Abstract: Non-Negative Matrix Factorization (NMF) is an unsupervised learning method offering low-rank representations across various domains such as audio processing, biomedical signal analysis, and image recognition. The incorporation of $\alpha$-divergence in NMF formulations enhances flexibility in optimization, yet extending these methods to multi-layer architectures presents challenges in ensuring convergence. To address this, we introduce a novel approach inspired by the Boltzmann probability of the energy barriers in chemical reactions to theoretically perform convergence analysis. We introduce a novel method, called Chem-NMF, with a bounding factor which stabilizes convergence. To our knowledge, this is the first study to apply a physical chemistry perspective to rigorously analyze the convergence behaviour of the NMF algorithm. We start from mathematically proven asymptotic convergence results and then show how they apply to real data. Experimental results demonstrate that the proposed algorithm improves clustering accuracy by 5.6% $\pm$ 2.7% on biomedical signals and 11.1% $\pm$ 7.2% on face images (mean $\pm$ std).
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A General Constructive Upper Bound on Shallow Neural Nets Complexity
arXiv:2510.06372v1 Announce Type: cross
Abstract: We provide an upper bound on the number of neurons required in a shallow
neural network to approximate a continuous function on a compact set with a
given accuracy. This method, inspired by a specific proof of the
Stone-Weierstrass theorem, is constructive and more general than previous
bounds of this character, as it applies to any continuous function on any
compact set.
( 2
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Road Surface Condition Detection with Machine Learning using New York State Department of Transportation Camera Images and Weather Forecast Data
arXiv:2510.06440v1 Announce Type: cross
Abstract: The New York State Department of Transportation (NYSDOT) has a network of roadside traffic cameras that are used by both the NYSDOT and the public to observe road conditions. The NYSDOT evaluates road conditions by driving on roads and observing live cameras, tasks which are labor-intensive but necessary for making critical operational decisions during winter weather events. However, machine learning models can provide additional support for the NYSDOT by automatically classifying current road conditions across the state. In this study, convolutional neural networks and random forests are trained on camera images and weather data to predict road surface conditions. Models are trained on a hand-labeled dataset of ~22,000 camera images, each classified by human labelers into one of six road surface conditions: severe snow, snow, wet, dry, poor visibility, or obstructed. Model generalizability is prioritized to meet the operational needs of the NYSDOT decision makers, and the weather-related road surface condition model in this study achieves an accuracy of 81.5% on completely unseen cameras.
( 3
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Multi-hop Deep Joint Source-Channel Coding with Deep Hash Distillation for Semantically Aligned Image Retrieval
arXiv:2510.06868v1 Announce Type: cross
Abstract: We consider image transmission via deep joint source-channel coding (DeepJSCC) over multi-hop additive white Gaussian noise (AWGN) channels by training a DeepJSCC encoder-decoder pair with a pre-trained deep hash distillation (DHD) module to semantically cluster images, facilitating security-oriented applications through enhanced semantic consistency and improving the perceptual reconstruction quality. We train the DeepJSCC module to both reduce mean square error (MSE) and minimize cosine distance between DHD hashes of source and reconstructed images. Significantly improved perceptual quality as a result of semantic alignment is illustrated for different multi-hop settings, for which classical DeepJSCC may suffer from noise accumulation, measured by the learned perceptual image patch similarity (LPIPS) metric.
( 2
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Bit-Level Discrete Diffusion with Markov Probabilistic Models: An Improved Framework with Sharp Convergence Bounds under Minimal Assumptions
arXiv:2502.07939v2 Announce Type: replace-cross
Abstract: This paper introduces Discrete Markov Probabilistic Models (DMPMs), a novel discrete diffusion algorithm for discrete data generation. The algorithm operates in discrete bit space, where the noising process is a continuous-time Markov chain that flips labels uniformly at random. The time-reversal process, like the forward noise process, is a jump process with its intensity governed by a discrete analogue of the classical score function. Crucially, this intensity is proven to be the conditional expectation of a function of the forward process, underlining theoretical alignment with score-based generative models. We establish convergence bounds for the algorithm under minimal assumptions, ensuring robustness and efficiency, which we demonstrate through experiments on low-dimensional Bernoulli-distributed datasets and high-dimensional binary MNIST data. The results highlight competitive performance in generating discrete structures compared to the state-of-the-art. This work bridges theoretical foundations and practical applications, advancing the development of effective and theoretically grounded discrete generative modeling.
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360-LLaMA-Factory: Plug & Play Sequence Parallelism for Long Post-Training
arXiv:2505.22296v2 Announce Type: replace-cross
Abstract: Adding sequence parallelism into LLaMA-Factory, we open-sourced 360-LLaMA-Factory at https://github.com/Qihoo360/360-LLaMA-Factory. 360-LLaMA-Factory has received wide recognition and used in models such as Light-R1 arXiv:2503.10460, TinyR1 arXiv:2503.04872, Kaggle AIMO math models and also in large companies' training frameworks. This technical report delves deeper into the different sequence parallel modes behind 360-LLaMA-Factory and discusses our implementation insights.
( 2
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An Investigation of Robustness of LLMs in Mathematical Reasoning: Benchmarking with Mathematically-Equivalent Transformation of Advanced Mathematical Problems
arXiv:2508.08833v2 Announce Type: replace-cross
Abstract: In this paper, we introduce a systematic framework beyond conventional method to assess LLMs' mathematical-reasoning robustness by stress-testing them on advanced math problems that are mathematically equivalent but with linguistic and parametric variation. These transformations allow us to measure the sensitivity of LLMs to non-mathematical perturbations, thereby enabling a more accurate evaluation of their mathematical reasoning capabilities. Using this new evaluation methodology, we created PutnamGAP, a new benchmark dataset with multiple mathematically-equivalent variations of competition-level math problems. With the new dataset, we evaluate multiple families of representative LLMs and examine their robustness. Across 18 commercial and open-source models we observe sharp performance degradation on the variants. OpenAI's flagship reasoning model, O3, scores 51.5% on the originals but drops by 4.7 percentage points on surface-renaming variants, and by 12.9 percentage points on parametric variants, while smaller models fare far worse. Overall, the results show that the proposed new evaluation methodology is effective for deepening our understanding of the robustness of LLMs and generating new insights for further improving their mathematical reasoning capabilities.
( 3
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A General Constructive Upper Bound on Shallow Neural Nets Complexity
arXiv:2510.06372v1 Announce Type: new
Abstract: We provide an upper bound on the number of neurons required in a shallow
neural network to approximate a continuous function on a compact set with a
given accuracy. This method, inspired by a specific proof of the
Stone-Weierstrass theorem, is constructive and more general than previous
bounds of this character, as it applies to any continuous function on any
compact set.
( 2
min )
Bit-Level Discrete Diffusion with Markov Probabilistic Models: An Improved Framework with Sharp Convergence Bounds under Minimal Assumptions
arXiv:2502.07939v2 Announce Type: replace
Abstract: This paper introduces Discrete Markov Probabilistic Models (DMPMs), a novel discrete diffusion algorithm for discrete data generation. The algorithm operates in discrete bit space, where the noising process is a continuous-time Markov chain that flips labels uniformly at random. The time-reversal process, like the forward noise process, is a jump process with its intensity governed by a discrete analogue of the classical score function. Crucially, this intensity is proven to be the conditional expectation of a function of the forward process, underlining theoretical alignment with score-based generative models. We establish convergence bounds for the algorithm under minimal assumptions, ensuring robustness and efficiency, which we demonstrate through experiments on low-dimensional Bernoulli-distributed datasets and high-dimensional binary MNIST data. The results highlight competitive performance in generating discrete structures compared to the state-of-the-art. This work bridges theoretical foundations and practical applications, advancing the development of effective and theoretically grounded discrete generative modeling.
( 2
min )
