GPUs, training clusters, MLOps, and deployment
Asking from a technical standpoint because I feel like the term is doing a lot of work in coverage of this space right now. Genuine real-...
I recently updated my FlashAttention-PyTorch repo so it now includes educational implementations of FA1, FA2, FA3, and FA4 in plain PyTor...
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
![[2509.22211] LogiPart: Local Large Language Models for Data Exploration at Scale with Logical Partitioning](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
LogiPart introduces a scalable framework for data exploration using local large language models, enhancing the efficiency of taxonomic di...
The paper presents a novel framework for premise verification in large language models (LLMs) to reduce hallucinations by using retrieval...
The paper introduces the Intermittent Semi-Working Mask (ISM), a novel masking paradigm for Large Language Models (LLMs) that enhances mu...
The paper discusses the critical energy concerns associated with High-Performance Computing (HPC) systems and applications, emphasizing t...
This paper examines the shortcomings of speech recognition models in accurately transcribing high-stakes utterances, particularly U.S. st...
The paper introduces MARS, a Modular Agent designed for automated AI research, emphasizing cost-aware planning and reflective memory to e...
This paper presents a theoretical framework establishing a Fano-style accuracy upper bound for single-pass reasoning in multi-hop questio...
The paper presents learning-based approaches to dynamic targeting for Earth observation satellites, demonstrating improved scientific dat...
MultiSHAP introduces a Shapley-based framework for explaining interactions in multimodal AI models, enhancing interpretability and trustw...
This paper presents a novel method for simulation-based inference that is robust to outliers and simplifies computation by eliminating th...
OpenAgentSafety introduces a modular framework for evaluating AI agent safety in real-world tasks, addressing critical vulnerabilities in...
Grappa introduces a gradient-only communication framework for scalable training of Graph Neural Networks (GNNs), improving speed and accu...
FlowSteer introduces an end-to-end reinforcement learning framework for automating workflow orchestration, addressing challenges like man...
This article presents CARL-XRay, a novel continual learning framework for chest radiograph classification that adapts to new datasets wit...
The paper introduces mini-vec2vec, an efficient method for aligning text embedding spaces using linear transformations, significantly imp...
The paper presents a novel approach, Bridge, for parallel scaling in LLM inference that generates interdependent responses, enhancing acc...
This paper explores vulnerabilities in diffusion language models (DLMs) related to priming attacks and proposes a novel safety alignment ...
This article introduces quantum agnostic learning protocols for depth-3 circuits, showcasing a quantum agnostic boosting method that enha...
The paper presents LSMART, an open-source simulator for evaluating Multi-Agent Path Finding (MAPF) algorithms in Automated Guided Vehicle...
The paper presents a novel framework, A LoD of Gaussians, for ultra-large-scale scene reconstruction and rendering using Gaussian splatti...
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