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20,518 papers
Post-training reasoning LLMs often requires making a tradeoff between costly supervision and compute inefficiency: supervised finetuning typically requires reasoning traces, which is data inefficient since in many applications one only has prompt-answer tuples and lacks the groundtruth chain-of-thought (CoT) sequence....
Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs, without explicitly requiring their representations to preserve how the underlying signals evolve. We introduce forecast grounding, a pretraining principle that requires language-aligned time-ser...
This study proposes an edge computing-based artificial intelligence (AI) vision inspection system for manufacturing sites and validates it through a soldering inspection case study. While AI-based vision inspection offers higher accuracy compared with conventional rule-based methods, it suffers from increased computati...
The increasing availability of artificial intelligence (AI) tools presents new opportunities and challenges for teaching and learning in higher education. This study examined lecturers’ awareness of and attitudes towards the use of AI tools at the University of Nigeria, Nsukka. A descriptive survey design was adopted....
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This report is a ECOOM-beleidsrapport, which is a type of policy report. The content of this ECOOM-beleidsrapport reports on the "Adoption and use of Artificial Intelligence in Flemish companies" by using the data from the AI Survey 2025, conducted by ECOOM Innovation. More information on the work of this research grou...
Abstract. Data-driven artificial intelligence (AI) weather models are increasingly positioned as alternatives or complements to physics-based numerical weather prediction (NWP) systems, yet systematic model evaluation studies that compare both paradigms under controlled experimental conditions remain limited. Here we p...
Solid electrolytes (SEs) are central to next-generation metal batteries, yet their discovery remains constrained by fragmented data, limited transferability of simulations, and slow experimental iteration. Unlike catalysis, where surface reactivity dominates, SEs require simultaneous optimization of bulk ion transport,...
The 2D Crystal Consortium Materials Innovation Platform (2DCC-MIP) is a NSF supported national user facility focused on advancing the synthesis of 2D materials, monolayers, surfaces, and interfaces. The need for the facility to organize and share data with users led to the development of an internal data management and...
Abstract The development of new drugs is a tedious, time-consuming, and expensive process for which the average costs are estimated to be up to around $2.5 billion. The first step in this long process is the design of the new drug, for which de novo drug design, assisted by artificial intelligence, has blossomed in rec...
The next generation of particle physics experiments will face an era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing these data for scientific discovery demands real-time inference and decision-making, intelligent dat...
A position paper arguing that closed source has failed as a protection, for science first. Large language models now decompile binaries into re-executable source; black-box queries extract parts of production models; distillation copies capabilities at industrial scale; and the largest 2026 trade-secret dispute in arti...
Abstract ADNOC has deployed an AI enabled Production System Optimization (AiPSO) solution to digitally transform production operations and maximize asset performance across thousands of wells, multiple production networks, and reservoirs. AiPSO addresses legacy surveillance approaches, delayed diagnostics, and continuo...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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