Infrastructure & Hardware

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Showing 31–60 of 220
  • Data Center Dynamics · EN Infrastructure & Hardware
    Sabey DC to pilot HVO fuel as diesel replacement at its data center in Austin, Texas
    Sabey DC to pilot HVO fuel at its Austin data center
    Sabey DC will pilot HVO fuel as a diesel replacement at its data center in Austin, Texas. If the pilot succeeds, the company plans to expand it to additional sites.
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  • Data Center Dynamics · EN Infrastructure & Hardware
    A taxonomy of American data center opposition: Politics, misinformation, and conspiracy theories
    A taxonomy of US data center opposition: politics, misinformation
    An article categorizing American data center opposition into politics, misinformation, and conspiracy theories, as the industry faces a tide of misinformation on social media.
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  • Data Center Dynamics · EN Infrastructure & Hardware
    PowerBank plans 2MW modular data center in Ontario, Canada
    PowerBank plans 2MW modular data center in Ontario
    PowerBank plans a 2MW modular data center in Ontario, Canada, and has signed its first lease with an AI cloud partner.
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  • arXiv cs.LG (Machine Learning) · EN Inference & Efficiency
    Text as an Index for Zero-Shot Cross-Domain Knowledge Tracing
    Embeddings Inference Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    AI evaluators favor job applications written by certain AI models, independent of candidate quality
    AI Agents Deep Learning Meta
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  • arXiv cs.AI (Artificial Intelligence) · EN Infrastructure & Hardware
    Dynamical Memory Transformer: Selective Recurrent Memories with Constant Cost Attention
    Inference Transformer
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  • NVIDIA Developer Blog · EN Infrastructure & Hardware
    How to Check and Configure NVIDIA GPUDirect Storage for Your Platform
    NVIDIA explains how to check and configure GPUDirect Storage
    Neural Network NVIDIA Retrieval-Augmented Generation (RAG)
    NVIDIA published a developer guide on checking and configuring GPUDirect Storage (GDS) for a given platform. GDS speeds up GPU access to storage in on-premises and cloud environments by offering a portable, efficient data path between storage and the GPU.
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  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Overcoming the Representational Limitations of Amplitude Encoding for Quantum Neural Networks
    Neural Network
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  • arXiv cs.LG (Machine Learning) · EN Safety & Evaluation
    What Standard Evaluations Hide About Detectors of Hijacked LLM Agents: Rare Attacks, Unseen Tools and Benchmark Transfer
    AI Agents Embeddings Retrieval-Augmented Generation (RAG)
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  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    Multimodal In-Context Learning with Explicit Rule Induction
    Fine-tuning Machine Learning Retrieval-Augmented Generation (RAG) Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    Latent-Lens: Visual Perception in Small Language Models Through Latent Communication
    Computer Vision Embeddings Llama Software Engineering
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  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Simple Features Beat Most AI Control Monitors
    Claude Neural Network
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  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    GraphNIAH: A Benchmark for Long-Context Reasoning over Real-World Graphs
    Deep Learning Gemini GPT Neural Network Software Engineering
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  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    When Steering Shifts Discourse: Identifying and Intervening on Discourse-Level Changes due to Activation Steering in LLMs
    Inference
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  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion
    Fine-tuning Google Llama Mistral Retrieval-Augmented Generation (RAG)
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  • arXiv cs.CL (Computation and Language) · EN Agents & Tool Use
    BlindToChange: AI Agents Can Find Changes When Asked But Do They Look When Needed?
    AI Agents Deep Learning Neural Network Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    SparseGDN: Chunk Sparse in Linear Attention
    Retrieval-Augmented Generation (RAG)
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  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    Comparative Evaluation of LLM-Based Review Paradigms for Academic Paper Quality Assessment
    Neural Network
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  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    What We Keep of What We Forget: Replacement Operators and Routing Semantics in Training-Free MoE Expert Pruning
    DeepSeek Mixture of Experts (MoE) Quantization
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  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    SERIAL-7: Deriving, Realizing, and Preserving Machine-Verifiable Outputs Across Representations
    Software Engineering
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  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    From Transcription to Reformulation: Defining and Benchmarking Grounded Generative Speech Recognition
    Speech Processing
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  • ITmedia AI+ · JA Infrastructure & Hardware
    東京と台北を「17ミリ秒」で結ぶIOWN アンリツCTOが語るデータセンター相互接続と日本の勝機
    Anritsu CTO on IOWN linking Tokyo and Taipei in 17 ms
    Anritsu measurement-business CTO Jonathan Borrill discusses IOWN, which links Tokyo and Taipei in 17 milliseconds. He talks about data center interconnection and Japan's chance to lead again in optical communications.
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  • ITmedia AI+ · JA Infrastructure & Hardware
    なぜ「半導体の供給はデータセンター優先」に見えるのか AIブームの実像、東京エレクトロン デバイス幹部が明かす
    Tokyo Electron Device exec rejects 'data center first' chip view
    Amid the AI boom, chip shortages are widely discussed, and some say supply is being prioritized for data centers. An executive at Tokyo Electron Device rejects that view and explains what the AI boom actually looks like from the supply side.
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  • ITmedia AI+ · JA Infrastructure & Hardware
    物理ソルバーからMODSIM、AIへ SIMULIAが描く次世代の設計/シミュレーション
    Dassault's SIMULIA keynote: physics solvers, MODSIM and AI as a 3-layer strategy
    NVIDIA
    Dassault Systèmes held SIMULIA Community Conference Japan 2026, where its keynote outlined a three-layer SIMULIA strategy of physics solvers, MODSIM and AI. It presented an engineer-collaborating AI 'virtual companion', generative design and an NVIDIA partnership as its vision for the future of design and simulation.
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  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Propose-Filter-Release: Scaling Noise to Typical Sensitivity via Private Filtering
    Deep Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    Rethinking Neural Thickets Without Weight Perturbations
    Inference Neural Network
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  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    Read, Allocate, Route: Role-Coupled Tokenization for Pixel-Space Generative Transformers
    Transformer
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  • arXiv cs.CL (Computation and Language) · EN Safety & Evaluation
    ShallowDefend: Harnessing Shallow Safety Alignment for Multi-Turn Defender
    Neural Network Retrieval-Augmented Generation (RAG) Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    Testimony from Timelines: Evidence-Grounded Significance Testing of Temporal Clinical Hypotheses
    Software Engineering
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  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races
    Inference Retrieval-Augmented Generation (RAG) Software Engineering
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗