Category
2,182 papers
It is concluded that future research should prioritize interdisciplinary collaboration, robust regulatory frameworks, and continuous monitoring to promote the ethical use of AI.
Policy drift takes shape through a nonlinear differential equation - framed within the policy state space - with support from Lyapunov stability concepts alongside bifurcation methods alongside bifurcation methods, and the Intent Drift Rate appears: a concrete number per dialogue turn built as the time-based change in...
This conceptual paper theorizes agentic workflows systems, where an AI agent or agents proactively perceive, plan, act and reflect throughout the entire software development lifecycle (SDLC); its implications for end to end software engineering automation are discussed.
AI-driven cybersecurity in the software engineering field is discussed, where machine learning, deep learning, natural language processing, and reinforcement learning can be applied throughout the software development lifecycle to provide increased security.
Reach audiences
Advertise in front of researchers, engineers, and readers.
Intent-Based Security (IBS), a structured approach built on foundational ideas from access control, zero-trust models, and principal-agent dynamics, shows why trusting identities fails against invisible threats.
A new scientific object – the Autonomous Recovery Efficiency Score (ARES) – is introduced – a quantitative measure of autonomous resilience, as well as a supporting foundation for future autonomous self-healing AI agentic infrastructure.
The speed of cloud computing and artificial intelligence, which have transformed the way software applications are designed and deployed. The cloud-based AI systems provide a scalable, adaptable, and cost-efficient solution to build intelligent systems capable of processing large amounts of data and running complicated...
Autonomous multi-agent artificial intelligence (AI) systems have emerged as a rapidly evolving field that revolutionizes the way autonomous systems can make decisions together, collaborate on tasks, and learn, thereby opening new paradigms for distributed decision-making, task execution, and adaptive learning. The comp...
Explainability in Large Language Model (LLM)–assisted PLC programming is essential for industrial adoption, where engineers must understand, validate, and maintain generated control logic under strict safety and standardization constraints. Existing explainability-oriented prompting approaches such as Chain of Note (Co...
AI delegation can remove the comparison evidence needed to establish whether automation continues to improve outcomes. This paper develops the counterfactual preservation principle: sustained claims of current comparative benefit require a continuing source of identification whose temporal relevance remains defensible....
AI delegation can remove the comparison evidence needed to establish whether automation continues to improve outcomes. This paper develops the counterfactual preservation principle: sustained claims of current comparative benefit require a continuing source of identification whose temporal relevance remains defensible....
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
