GPUs, training clusters, MLOps, and deployment
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
I keep seeing people focus heavily on prompt optimization. But in practice, a lot of failures I’ve observed don’t come from the prompt it...
Hi everyone : ) I just released a new research prototype It’s a lossless BF16 compression format that stores weights in 12 bits by replac...
Jack Dorsey's recent layoffs at Block raise concerns about AI productivity claims, highlighting the slow and often invisible integration ...
OpenAI secures $110 billion in private funding, led by Amazon, Nvidia, and SoftBank, marking a significant milestone in AI infrastructure...
OpenAI secures $110 billion in new investments from Amazon, Nvidia, and Softbank, enhancing its market position and partnerships while pr...
The article discusses recent fixes for the AMD XDNA Ryzen AI driver in Linux 7.0-rc2, highlighting improvements and updates that enhance ...
The article discusses the forensic intelligence system that maps risk exposures related to enterprise AI transitions, highlighting a $2.5...
A 3rd-year IIT Roorkee student specializing in machine learning seeks paid internships, highlighting their experience with MLOps and RAG ...
The Pentagon is advancing its efforts to develop AI tools aimed at enhancing cyber operations against China, focusing on improving nation...
Tessera introduces an innovative protocol for AI-to-AI knowledge transfer, enabling models to share learned knowledge without direct arch...
Amazon is advancing its AI capabilities with custom Trainium chips, designed to outperform traditional GPUs and reduce costs for machine ...
![[2511.05898] Q$^2$: Quantization-Aware Gradient Balancing and Attention Alignment for Low-Bit Quantization](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents Q$^2$, a novel framework addressing gradient imbalance in low-bit quantization for complex visual tasks, enhancing per...
The Tool Decathlon introduces a benchmark for evaluating language agents on diverse, realistic, and complex tasks, highlighting significa...
AgentHub proposes a registry for AI agents that enhances discoverability, verifiability, and reproducibility, addressing gaps in current ...
This article presents a physics-based framework for synthesizing CCD noise in astronomical imaging, addressing noise limitations in curre...
The paper presents a metric called Intelligence per Watt (IPW) to evaluate the efficiency of local AI models compared to centralized clou...
The paper presents CAST, a dynamic tree decoding approach that enhances inference efficiency in large language models by considering infe...
This paper presents a novel framework for secure and reversible face anonymization using diffusion models, addressing challenges in image...
This article evaluates the diversity and quality of content generated by large language models (LLMs), highlighting the trade-offs betwee...
The paper presents Geometric Autoencoders for Bayesian Inversion (GABI), a novel framework for uncertainty quantification in engineering,...
This paper presents MixCache, a novel caching framework designed to enhance the efficiency of text-to-video diffusion models, significant...
LayerT2V presents a novel framework for multi-layer video generation, enabling the creation of editable video layers that enhance profess...
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