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.

This paper explores the use of a lightweight and scalable EfficientNet architecture, combined with global jet features, for the computationally inexpensive yet competitive classification of top-quark jets. It aims to address the high computational demands of current state-of-the-art jet tagging methods like Transformers and GNNs.