AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,181 papers
A modular, extensible framework for benchmarking explainable AI methods, including experiment orchestration, metric evaluation, statistical analysis, and reproducibility artifacts for tabular post-hoc explanation studies.
English DescriptionASAN is a conceptual architecture for a large-scale AI system: a directory-routed, energy-aware multi-agent Mixture-of-Experts (MoE) framework. Every node is a specialist agent; missing specialists are created on demand (autopoiesis); a temporary “RAM mode” integrates knowledge across specialists; an...
Read or download the cumulative English edition (4,211 pages). Complete cumulative English LaTeX · Complete editable source projects (ZIP). The cumulative LaTeX contains the complete text of the nine native documents, with a splitter and all dependencies in the ZIP. Each volume also has its own direct LaTeX download. N...
Osteoporosis is a major global health burden, with considerable morbidity, mortality, and healthcare costs. Artificial intelligence (AI) has opened new avenues for osteoporosis screening, bone mineral density quantification, fracture-risk prediction, and clinical decision support, but progress remains fragmented across...
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With the advent of Generative Artificial Intelligence (GenAI), the talent acquisition process is transforming, impacting every stage from sourcing jobs to creating job descriptions to parsing candidate resumes to assessing skills and even the interview process. This process can be enhanced by using large language model...
Generative artificial intelligence (GenAI)is rapidly reconfiguring advertising from message delivery and optimization to relationship-like exchanges between consumers and brands. While existing work has explained these interactions through social response accounts and interpersonal relationship theories, this lens risk...
Enterprise database environments have grown into sprawling, heterogeneous estates spanning on-premises systems, Oracle Cloud Infrastructure, Amazon Web Services, and Microsoft Azure, placing an unsustainable operational burden on human database administrators (DBAs). Traditional automation, rooted in rule-based monitor...
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23109457. Abstract: The abstract contains wordings that does not clearly described the methodology and the major knowledge gap. A review abstract should preferably follow a structur...
THINKIT is a package of coordinated AI instructions and workflows for scientific research. It supports physical understanding and modelling, literature assessment, research-project development, and scientific writing, while keeping the researcher in control of the scientific questions, hypotheses, scope, and substantiv...
Artificial intelligence (AI) has accelerated scientific prediction and design, but accurate predictions and promising structures alone do not explain how scientific systems behave. Using molecular science as our primary lens, we envision a progression from property prediction through structure generation toward a new p...
This project hosts the complete, signed edition (Version 1.12) of Retroactive Completion: Questions, Answers, Causes, and the Underdetermined World — an interdisciplinary monograph of roughly 76,000 words in eight Parts and six appendices — and serves as the work's public home for the record: timestamped registrations,...
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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