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539 papers
This study investigates how employees in a energy company understand AI adoption and identifies areas where AI and LLMs-based agentic workflows could assist daily activities, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection.
This is one of the first reviews to integrate peer-reviewed and grey literature on vibe coding under a single documented protocol and is strongest for prototyping and user-interface work and weakest for production, data-intensive, and safety-critical use, and tool visibility does not imply effectiveness.
A variability-preserving imputation method is introduced that augments linear interpolation with locally adaptive stochastic noise, retaining physiological dynamics essential for accurate forecasting in short-term Heart Rate Variability forecasting.
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An experience report from a small full-stack team that applied contextual prompting and explicit architectural constraints to build a multi-project agent learning platform designed for sustained, production-oriented use and an academic retrieval-augmented generation system is presented.
Overall, the results suggest that epic-organized generation can improve perceived Gherkin quality while maintaining comparable semantic coverage, although broader replication is needed before generalizing this finding.
This study investigates how employees in a energy company understand AI adoption and identifies areas where AI and LLMs-based agentic workflows could assist daily activities, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection.
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
The workshop culminated in the collaborative development of a research roadmap that pinpoints actionable directions for future work, including both immediate solutions and ambitious long-term goals.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
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.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab. Experimental results provide important feedback, helping researchers sharpen future research directions. The post Introducing Q…
A Blog post by Multiverse Computing on Hugging Face
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