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2,752 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.
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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 design, deployment, and performance of cloud-based AI systems, especially scalability, system intelligence, and efficiency, and the challenges, such as latency problems in distributed computing systems, data privacy and security, and resource allocation, are indicated.
This study offers new means to design a resilient multi-agent system that is able to resist emergent threats by formalizing trust as a dynamic security vector and introduces an Agentic Trust Dynamics Theory as a conceptual theory to model the evolution, loss, and impact of trust on emerging security vulnerabilities in...
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...
Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as agronomic recomme...
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....
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Professor Ali Jadbabaie describes how AI, digital transformation, climate change, and complex geopolitics are forcing engineers and policymakers to rethink how infrastructure is designed, built, and managed.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
A Blog post by Liquid AI on Hugging Face
In Compton Lecture, the distinguished legal scholar warns of potential “loss of the Enlightenment” while suggesting ways innovations can meet our civic and cultural needs.
Generative AI
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