This paper argues that artificial intelligence now functions as a determinant of health, proposing a novel epidemiological framework to measure and study its population-level effects. It differentiates between ambient and personal AI exposure and discusses the implications for study design, health equity, and AI governance.

This paper surveys artificial intelligence methods for modeling and simulating mixed automated and human traffic, addressing the limitations of existing simulation tools in accurately representing complex driving behaviors. It proposes a comprehensive taxonomy of AI methods, reviews evaluation protocols, and outlines future research directions to bridge the gap between transportation engineering and computer science.

This paper introduces a Markovian framework to audit the reliability and oversight cost of agentic AI systems in organizational workflows before deployment. It reveals the "stochastic gap," where systems may appear state-level supported but possess blind spots in next-step decisions, impacting reliability and increasing human oversight.

This paper investigates the practical barriers to trustworthy AI-driven Cyber Threat Intelligence (CTI) in financial institutions. Through a mixed-methods study, it identifies socio-technical challenges that hinder AI adoption and proposes security-oriented safeguards for effective deployment.

This paper introduces LSAI, a novel large and small AI model codesign framework, to enable agentic robots to perform accurate and real-time environment sensing and estimation with efficient path planning in complex scenarios like search and rescue. It aims to overcome limitations of traditional and singular large AI solutions in multi-robot cooperation by deeply integrating edge and terminal intelligence.

This paper proposes an Integrated Channel Sounding and Communication (ICSC) framework that deeply integrates channel sounding and communication to address limitations of traditional channel modeling in dynamic, complex wireless networks like SAGSIN. ICSC enables real-time acquisition of channel characteristics, intelligent scenario identification, and adaptive waveform optimization to enhance communication performance and establish comprehensive channel model libraries.

This paper introduces a decision-theory framework and a novel blended AI/statistical model for generating tailored, probabilistic seasonal monsoon onset forecasts. The system significantly improves forecast skill at longer lead times, enabling heterogeneous farmers to make more informed agricultural decisions under weather uncertainty, and was operationally deployed to 38 million Indian farmers in 2025.

This paper investigates patenting trends in core AI, traditional robots, and AI-enhanced robots from 1980-2019, revealing distinct growth trajectories and long-run integration patterns across major national innovation systems. It highlights how institutional contexts influence the co-evolution of AI and robotics.

This paper examines how cyber threat intelligence (CTI) needs to adapt for artificial intelligence (AI) systems, detailing AI-specific assets, vulnerabilities, and attack patterns. It reviews existing AI security knowledge, outlines requirements for an AI-oriented threat intelligence knowledge base with concrete indicators of compromise (IoC), and discusses techniques for measuring similarity between IoCs and AI artifacts.

This paper systematically reviews 1,291 studies to understand the impacts of AI on environmental sustainability and human well-being. It highlights a research imbalance, noting that environmental studies often focus narrowly on energy and CO2, while well-being research is largely conceptual and overlooks crucial subjective aspects.