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Showing 211–240 of 326
  • Publickey · JA Agents & Tool Use
    OpenAIやAnthropicなどAIベンダごとのAPIの違いを吸収し統合する「Agent Router」、Linux Foundation傘下で業界標準へ
    Envoy AI Gateway joins AAIF as 'Agent Router', a unified LLM and MCP gateway
    AI Agents Anthropic Machine Learning Model Context Protocol (MCP) OpenAI
    The Linux Foundation's Agentic AI Foundation announced at AGNTCon+MCPCon Japan 2026 that the open-source Envoy AI Gateway has joined AAIF and been renamed Agent Router. It exposes OpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI and many other providers through one OpenAI-compatible API, and adds an MCP gateway, fallback and token quotas, metrics-based routing and observability. Already at v1.1 with 11 public adopters, AAIF aims to make it the de facto industry standard.
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  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Sequential Adapter Stacking for Cross-Lingual Low-Resource ASR
    Fine-tuning Speech Processing
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  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    Look Before You Leap: Factual Decoding with Internal Attribution Signals
    Inference Machine Learning Retrieval-Augmented Generation (RAG) Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data
    Deep Learning Neural Network Reinforcement Learning
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  • Data Center Dynamics · EN Developer Tools
    Villagers tie 5G technician to pole in protest over poor cellular coverage in India
    Indian villagers tie 5G technician to tower over poor coverage
    Retrieval-Augmented Generation (RAG)
    Villagers in Hardauli, Uttar Pradesh tied a technician working for Reliance Jio to a cell tower on September 10, protesting two years without 5G service. Video of the incident spread online, prompting a police investigation; the Times of India reports five residents were taken into custody.
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  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Are LLMs Good Financial User Simulators? A Preliminary Study
    Retrieval-Augmented Generation (RAG)
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  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Solving Finite-sum Coupled Compositional Optimization via Multi-block-Single-probe Estimator
    Algorithms & Theory
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  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Data storytelling meets interpretable machine learning: Decoding AI decisions for non-experts without revealing sensitive data and model details
    Machine Learning
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  • Data Center Dynamics · EN Infrastructure & Hardware
    DayOne breaks ground on data center in Tokyo, Japan
    DayOne breaks ground on 42MW Kodaira data center, its second in Tokyo
    APAC operator DayOne broke ground on Phase I of its Kodaira Data Center in Tokyo, delivering 15MW initially and 42MW at full build. It follows an 80MW Fuchu campus being developed with Gaw Capital, whose 18MW first phase is due core-and-shell by mid-2027. DayOne was spun out of GDS in 2025.
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  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Knowledge-Enriched Structured EHR Features for 30-Day Hospital Readmission Prediction on MIMIC-IV
    Embeddings
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  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    New Conditions for Philosophers to Catch the Wave of Citizen Deliberation in the Age of Artificial Intelligence in advance
    Deep Learning Neural Network
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  • arXiv cs.LG (Machine Learning) · EN Inference & Efficiency
    Backward SDEs-based Diffusion for Physics-Constrained Generation
    Deep Learning Inference Reinforcement Learning
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  • Data Center Dynamics · EN Infrastructure & Hardware
    Sponsored: Data center growth is reshaping the insurability question
    Marsh: data center growth is reshaping the insurability question
    Marsh's Michael Mathews argues that the scale of data center development is changing how risk is understood and capacity structured. A $500 million project no longer stands out, city-scale builds are common, and a $10 billion 2020 facility could now cost $14-15 billion, with generation assets extending exposure beyond property losses.
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  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models
    Neural Network Retrieval-Augmented Generation (RAG)
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  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    RESKILL: Explicit Failure Attribution and Structured Repair for Interactive Language Agents
    AI Agents Deep Learning Retrieval-Augmented Generation (RAG) Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis
    Machine Learning Reinforcement Learning
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  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Projection-Free Multi-level Algorithms for Stochastic Constrained Compositional Optimization
    Algorithms & Theory
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  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    Don't Send What You Don't Need: Question-Guided Token Pruning as a Privacy Defense for Vision-Language Models
    Computer Vision Deep Learning Embeddings Inference Software Engineering
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  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    Benchmarking Intra-Patient 3D Deformable Multimodal Image Registration
    Deep Learning Retrieval-Augmented Generation (RAG) Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization
    Embeddings Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    CiteShade: Citation Laundering in Multi-Source Retrieval-Augmented Generation and Its Counterfactual Defense
    Neural Network Retrieval-Augmented Generation (RAG) Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Kaininja: Extending Native 3D Generators to the Part Level
    Neural Network
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  • arXiv cs.CL (Computation and Language) · EN Multimodal
    Human-Grounded Calibration for Long-Text Image-Text Congruence in Vision-Language Models
    Computer Vision Embeddings Machine Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Potential of Artificial Intelligence Algorithms for Identification of Relevant Diagnostic and Prognostic Biomarkers of Early-Stage Liver Cancer
    Algorithms & Theory Deep Learning Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    FedLTLib: A Comprehensive Benchmark for Federated Long-Tail Learning
    Algorithms & Theory Deep Learning Meta Retrieval-Augmented Generation (RAG) Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Training & Fine-tuning
    Beyond AI Literacy: A Structured Review and Exploratory Meta-Analysis of Measures for Competent Generative-AI Use
    AI Agents Machine Learning Meta Neural Network
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  • arXiv cs.LG (Machine Learning) · EN Training & Fine-tuning
    Multi-View Molecular Representation Learning with Hierarchical Graphs and Contextualized Fingerprints
    Embeddings Fine-tuning Retrieval-Augmented Generation (RAG)
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  • IEEE Spectrum (AI section) · EN Inference & Efficiency
    How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
    OpenAI used its own LLMs to design the Jalapeño chip, RTL to tapeout in 9 months
    Inference NVIDIA OpenAI Quantization Reinforcement Learning
    IEEE Spectrum details how OpenAI designed Jalapeño, its first in-house AI accelerator unveiled Aug 25 (13.4 PFLOPS 4-bit, 232 GB memory, up to 3.6x lower latency than Nvidia's GB300). A team of under 100 paired internal LLMs with the XLS high-level synthesis flow, going from concept to first silicon in under 20 months and RTL to tapeout in nine. After first silicon, AI-written kernels rose from 0.31% to 88.94% of theoretical peak in about 40 hours; Broadcom handled physical design.
    Read original (IEEE Spectrum (AI section)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Can We Trust the Judges? Validation of Factuality Evaluation Methods via Answer Perturbation
    Meta Retrieval-Augmented Generation (RAG) Software Engineering
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  • arXiv cs.CL (Computation and Language) · EN Multimodal
    Don't Count the Edits, Judge by the Outcome Alone: Reward-Based Evaluation for Grammatical Error Correction
    Neural Network Reinforcement Learning
    Read original (arXiv cs.CL (Computation and Language)) ↗