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.

The paper explores human-AI collaboration in mathematical research through a case study on Hermite quadrature error estimation, demonstrating AI's capabilities in algebraic manipulation and proof exploration while emphasizing the critical need for human verification and strategic direction to mitigate errors and ensure novel discovery.

This paper introduces "symmetrical reality," a new paradigm for human-machine interaction that moves beyond human-centric views. It proposes a framework where humans and advanced AI entities coexist symbiotically, capable of symmetrically perceiving and interacting across both physical and virtual worlds.

This paper argues that modularity is a fundamental computational principle underlying both natural and artificial intelligence. It reviews how modularity provides significant advantages in efficiency, generalization, and robustness across diverse fields like engineering, neuroscience, and AI, suggesting it as a core design principle for future intelligent systems.

This paper presents a vision for self-driving particle accelerators that operate autonomously with minimal human intervention. It proposes future facilities be designed through AI co-design, where AI jointly optimizes accelerator components and science applications from inception to maximize performance and enable autonomous operation.

The paper presents a community vision for integrating Artificial Intelligence across the entire experimental lifecycle in particle physics to accelerate discovery. It outlines grand challenges and proposes a national-scale collaboration to enable transformative breakthroughs and secure U.S. leadership in AI-powered fundamental science.