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2,176 papers
Artificial intelligence is commonly described as a property of models: larger architectures, more data, and increasingly capable procedures for prediction, generation, and control. This paper begins from a different object: the network. Before contemporary generative AI existed, the Internet had already assembled sever...
The convergence of Metaverse technologies and Generative AI (GenAI) is reshaping immersive digital education. This study presents a scoping review of Metaverse and GenAI-enabled education following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search of ACM Digital Library, IEEE Xplore,...
Traffic of 3 popular Generative AI mobile apps (ChatGPT, Copilot and Gemini), labeled with both the app and the generative activity performed. MIRAGE-GenAI-2025 includes the traffic generated by human experimenters using 3 popular Generative AI chatbots via their mobile apps: ChatGPT, Microsoft Copilot and Google Gemin...
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Conventional maternal remote patient monitoring has relied upon using immutable ranges for detection of conditions on maternal telemetry. However, this has created extreme alert fatigue in clinicians. The newly proposed Agentic AI Maternal Triage Framework combines patient-reported outcome data with quantitative teleme...
Digital systems rely on compact codes to identify people, places, transactions, objects, and machine states, yet such codes are often difficult for humans to remember, communicate, compare, or verify. The Consonant-Vowel (CV) Mnemonic method addresses this problem by mapping every two-digit value from 00 to 99 to a pro...
This research repository contains the data, scoring matrices, statistical analyses, and supplementary research materials associated with the study "Preliminary Psychometric Calibration of Large Language Models for Multidimensional L2 Academic Writing Assessment: The LICA Framework." The study introduces and preliminari...
This scoping review maps generative AI applications in workplace-based assessment (WBA) and analyzes validity evidence through Downing’s framework. Following JBI methodology and PRISMA-ScR guidelines, four databases were searched (2022-February 2026) with dual-AI screening and human adjudication. Data were mapped to Do...
Against the backdrop of digital and intelligent transformation, generative AI tools including drawing software, 3D modeling systems and rendering engines have been widely adopted in university design teaching. AI streamlines design workflows, improves teaching efficiency, diversifies creative expression, and breaks the...
Current paradigms in generative artificial intelligence predominantly formalize prompt engineering as a prescriptive, additive methodology: the operator attempts to maximize output quality by linearly accumulating explicit descriptive attributes, structural constraints, and negative exclusions. In this paper, we demons...
SARK implements source-anchored residual kernels for recalibrating a complete source-known electromyographic gesture vocabulary from target-day trials of only a subset of gestures. Eleven meta-learned scalars define a rule that uses each participant's source class geometry and requested cross-day residuals. This versio...
This paper introduces the framework of the Cognitive Exoskeleton (인지적 외골격 체계) to redefine human epistemic agency in the era of advanced artificial intelligence. Conventional perspectives oscillate between techno-dystopian anxieties regarding intellectual atrophy and naive instrumentalism that reduces AI to a me...
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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