Industry Adoption C

Showing 31–60 of 100
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    AI systems and the reproduction of (standard) language ideologies in World Englishes
    Generative AI Neural Network Reinforcement Learning
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  • Microsoft Research Blog · EN Agents & Tool Use extract
    Echoverse: Deep, evolving environments for computer-use agents
    Microsoft's Echoverse trains computer-use agents in evolving environments
    AI Agents Microsoft
    Microsoft Research unveiled Echoverse, a set of deep, evolving environments for training computer-use agents that struggle with multi-step workflows such as email and customer support. Training in realistic settings aims to improve agents' ability to complete complex tasks.
    Read original (Microsoft Research Blog) ↗
  • Microsoft Research Blog · EN Industry Adoption extract
    EvoLib: Turning experience into evolving knowledge
    Microsoft's EvoLib turns experience into evolving knowledge
    Microsoft
    Microsoft Research presented EvoLib, arguing that LLMs do not get smarter simply by remembering more. EvoLib converts task experience into reusable, evolving skills, aiming to let models continuously improve rather than just accumulate raw memory.
    Read original (Microsoft Research Blog) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Industry Adoption
    QuantWAMs: Calibrating at the Right Granularity for World Action Models
    Quantization Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Infrastructure & Hardware
    How Benchmarks Mis-Score Computer-Use Agents
    AI Agents Neural Network
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  • arXiv cs.AI (Artificial Intelligence) · EN Training & Fine-tuning
    ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow
    Fine-tuning Neural Network Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Industry Adoption
    Correlation between prosody and pragmatics: A case study of the discourse marker hālā `now' in Persian
    Retrieval-Augmented Generation (RAG)
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  • Anthropic News · EN Safety & Evaluation extract
    Investigating three real-world incidents in our cybersecurity evaluations
    Anthropic's Frontier Red Team probes three cybersecurity-eval incidents
    Claude Machine Learning OpenAI Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Anthropic's Frontier Red Team published a review of three real-world incidents tied to its cybersecurity evaluations. The investigation examines potential misuse and the validity of its evaluation methods to strengthen the safety of frontier models.
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  • Data Center Dynamics · EN Infrastructure & Hardware extract
    Crusoe partners with Aalo on nuclear-powered AI data center deployment at Idaho National Lab
    Crusoe teams with Aalo on SMR-powered AI data center at Idaho National Lab
    Crusoe is partnering with Aalo to deploy a nuclear-powered AI data center at Idaho National Lab, DatacenterDynamics reported. Crusoe will install a Spark unit at the site, which is to be powered by a small modular reactor (SMR), reflecting growing interest in nuclear for AI compute.
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  • arXiv cs.LG (Machine Learning) · EN Inference & Efficiency
    Fully Inductive Cardinality Estimation
    Embeddings Neural Network Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Infrastructure & Hardware
    MonoVoc: Decoupling Geometry and Semantics for Lightweight Monocular Open-Vocabulary 3D Gaussians
    Embeddings Retrieval-Augmented Generation (RAG) Software Engineering
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    MORFES: A Benchmark for Productive Inflectional Competence in Modern Greek
    Deep Learning DeepSeek Llama Retrieval-Augmented Generation (RAG) Software Engineering
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  • arXiv cs.AI (Artificial Intelligence) · EN Safety & Evaluation
    AI and Authenticity in Islamic Research: A Critical Evaluation of Generative AI Reliability, Hallucination, and Source Fidelity in Quranic, Hadith, and Fiqh Knowledge
    Deep Learning Generative AI Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Safety & Evaluation
    Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation
    Neural Network Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Integrating AI into Requirements Quality Learning in Software Engineering Education: A TPACK-Guided Empirical Study
    Software Engineering
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  • arXiv cs.AI (Artificial Intelligence) · EN Infrastructure & Hardware
    ConMem: Contribution-Aware Memory for Long-Horizon Manufacturing Inspection Logs
    Retrieval-Augmented Generation (RAG) Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Industry Adoption
    Towards Practical Algorithm Selection for Unsupervised Domain Adaptation in Medical Imaging
    Algorithms & Theory Retrieval-Augmented Generation (RAG)
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  • arXiv cs.CL (Computation and Language) · EN Industry Adoption
    Challenges in annotations by humans and LLMs: A case study of evaluative language
    Neural Network
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  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale
    AI Agents Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    Stimulus-Evoked Network Dynamics in Human Cortical Organoids: From a Graph-Computational Framework to Repeated-Stimulation Depression
    Neural Network Reinforcement Learning
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  • arXiv cs.LG (Machine Learning) · EN Industry Adoption
    Building a User Foundation Model for the Open Web
    Retrieval-Augmented Generation (RAG) Transformer
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  • arXiv cs.LG (Machine Learning) · EN Inference & Efficiency
    TAPO: Transition-Aware Policy Optimization for LLM Agents
    AI Agents Algorithms & Theory Inference Reinforcement Learning
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  • OpenAI Blog · EN New Model Releases extract
    Advancing the price-performance frontier with GPT-5.6
    OpenAI advances the price-performance frontier with GPT-5.6
    GPT OpenAI
    OpenAI said GPT-5.6 pushes the price-performance frontier forward, pairing lower pricing with stronger capability. The update aims to make high-performance models more affordable and expand adoption across a wider range of use cases.
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  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    ChronoMem: Version Control and Semantic Rollback for Large Language Model Agent Memory
    AI Agents Google Reinforcement Learning Software Engineering
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  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Harness-G: A Graph-Structured Harness for Search Agents
    AI Agents Reinforcement Learning Software Engineering
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  • ITmedia AI+ · JA Industry Adoption extract
    営業製作所、図面管理システム「ジーエン図面」の販路拡大へSB C&Sと契約
    Eigyo Seisakusho signs SB C&S to distribute its Gien Zumen drawing system
    Japanese firm Eigyo Seisakusho signed a distributor agreement with SB C&S for its 'Gien Zumen' drawing-management system. By leveraging SB C&S's nationwide B2B sales network, the two aim to widen the product's distribution channels and grow the number of adopting companies.
    Read original (ITmedia AI+) ↗
  • arXiv cs.LG (Machine Learning) · EN Training & Fine-tuning
    Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning?
    Fine-tuning Machine Learning Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN Funding & M&A
    Skillful forecasting of offshore winds from satellite scatterometer constellations
    Retrieval-Augmented Generation (RAG)
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  • arXiv cs.LG (Machine Learning) · EN Infrastructure & Hardware
    Investigating reservoir computing for branch predictionin pipelined processors using emerging CMOS memristor devices
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  • Data Center Dynamics · EN Industry Adoption extract
    Axe Compute secures $1.5bn compute contract with unnamed customer
    Axe Compute lands $1.5bn, five-year compute contract with unnamed customer
    Neural Network
    DatacenterDynamics reports that Axe Compute has secured a $1.5 billion compute contract with an unnamed customer, with the agreement set to run for five years. The customer's identity, workload and facility details are outside the excerpt and unconfirmed.
    Read original (Data Center Dynamics) ↗