AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,807 papers
BLOCK VECTOR Research Map The BLOCK VECTOR Research Map is a visual guide to the Stable Authority Boundary (SAB), its formalization, core principles, applications, and related research. The collection examines a common systems problem: a machine, AI agent, autonomous system, or distributed system may remain technically...
Understanding the long-term drivers of settlement location requires disentangling ecological constraints from historically contingent choices. Here I apply a Random Forest machine-learning framework, coupled with explainable AI techniques, to investigate settlement dynamics in Latium vetus (central Italy) from the Earl...
Description The preceding papers in this series argued that explainability cannot ground accountability in probabilistic AI systems, that hallucination is the expected epistemic consequence of forced answer generation, and that accountable AI therefore requires a formal system language of non-decision, decision boundar...
This dataset accompanies a mixed-methods pilot study of an eight-week intervention (22 October – 17 December 2025). In the intervention, 54 university EFL students in China used freely available large language model chatbots for low-stakes speaking practice. The chatbots used were DeepSeek, Doubao, ChatGPT, Kimi Chat a...
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The protocol emphasizes several key components, including the preparation of standardized healthcare datasets, the development of stable machine learning models, the evaluation of predictive performance, the generation of clinically relevant explanations, and the statistical assessment of reproducibility across repeate...
Both social design features and chatbot design features shaped adoption-related beliefs, with peer-oriented and family-oriented framing and AI-powered chatbots showing particular promise.
This review synthesizes current evidence across these domains, highlights methodological limitations, and proposes a research agenda through which AI-augmented NHP experimentation can become a bridge from preclinical data complexity to clinical precision in xenotransplantation.
Large language models are increasingly used to generate and evaluate online content, yet it remains unclear whether the qualities they associate with higher engagement match what real users respond to. We study this question using 1.17 million answers to 25,978 questions from Zhihu, Quora, and Reddit, comparing real pl...
Causal Sufficient and Necessary Regional Explanations (SNRE), a framework that learns an input region and output region such that membership in A is both sufficient and necessary for the model output to fall in B, is proposed.
Lighter than ViT, which is effective in capturing long-range dependencies, the proposed CNN-integrated CCT has also proven efficient in breast cancer classification by capturing long-range dependencies among breast tissue regions.
It is found that aggregate pairwise comparisons transfer strongly across evaluation settings, including from randomly generated tasks to clinically meaningful tasks and across datasets, and at the same time, clinically meaningful tasks exhibit greater task-method interaction.
It is shown that fostering deep trust in AI, as now required by regulatory frameworks worldwide, will unlock its full potential and transform the pace of adoption across mission-critical domains.
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
GPT-Lab
· gpt-lab.eu
Oct 30, 2026
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
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