Large Language Model– tag –
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2026 AI Chip Market Sees 44.6% Surge in Cloud Provider Custom ASIC Shipments, Challenging NVIDIA’s GPU Dominance
AIMultiple International Overview In the 2026 AI chip market, while NVIDIA's GPUs remain central, custom ASIC shipments from cloud providers are surging 44.6% year-over-year, reshaping the market structure. AIMultiple's analysis shows AS... -
Domo Unveils AI Agent Platform for Data-Driven Teams, Competing with Salesforce Agentforce and Google Vertex AI Agent Builder
Domo USA Overview Domo has launched an AI agent platform for data-driven teams, enabling the creation of autonomous programs capable of reasoning, acting, and completing tasks. This platform can execute API calls and database queries, ad... -
OpenAI and Broadcom Unveil LLM-Optimized AI Chip “Jalapeño,” Significantly Outperforming State-of-the-Art in Performance per Watt
OpenAI / Broadcom USA Overview OpenAI and Broadcom have announced "Jalapeño," a custom AI chip specifically designed for Large Language Model (LLM) inference. This chip significantly surpasses current state-of-the-art products in perform... -
AtomGPT.org Launches Open-Access Agentic AI Platform ‘AGAPI-Agents’ to Accelerate Materials Design
The Journal of Physical Chemistry Letters USA Overview AtomGPT.org has launched 'AGAPI-Agents,' an open-access agentic AI platform integrating open-source Large Language Models (LLMs) with scientific tools and databases for accelerated m... -
Kyushu University Pioneers Explainable AI and ChatGPT-Powered ‘Human-in-the-Loop’ for Rapid AEM Material Discovery
九州大学ニュース Japan Overview Researchers at Kyushu University have developed a novel 'Human-in-the-Loop' framework that efficiently synthesizes explainable AI, ChatGPT, and expert human knowledge to accelerate the development of anion... -
RSC Paper Proposes ‘Digital Materials Ecosystem’ for Autonomous Discovery, Integrating Databases and AI Agents
RSC Publishing UK Overview A paper from RSC Publishing proposes a 'Digital Materials Ecosystem,' a new materials research paradigm integrating data, theory, and automation. This ecosystem combines reliable material databases, physical fr... -
Hugging Face Showcases LLM Application Preprints in Scientific Discovery, Emphasizing Autonomous Agents in Materials, Biology, and Chemistry
Hugging Face USA Overview Hugging Face's Daily Papers featured several arXiv preprints on the application of Large Language Models (LLMs) in scientific discovery. These papers focus on benchmarking LLMs across biology, chemistry, materia... -
Tech Science Press Journal Features Paradigm Evolution in Materials Science Driven by AI, ML, and Generative Models
Tech Science Press International Overview The latest issue of Tech Science Press's journal 'CMC' (Vol. 88, No. 2, 2026) highlights how AI, machine learning (ML), and generative models are evolving the scientific paradigm of materials sci... -
Scientific Generative Language Model LOGOS Integrates Disparate Natural Science Tasks into a Unified Framework, Achieving High Accuracy
arXiv International Overview The scientific generative language model, LOGOS, has been introduced, integrating disparate tasks across natural sciences into a single autoregressive framework. LOGOS encodes diverse scientific objects and t... -
LLM-Based Autonomous Agent PhyNex Achieves Automated Discovery in Computational Physics, Including Semiconductor Dielectric Spectra Prediction
arXiv International Overview An autonomous agent, PhyNex, has been developed to accelerate scientific discovery in computational physics. By combining LLM-guided search with domain-specific computational tools, PhyNex systematically expl...
