This Tutorial presents Denario, an AI multiagent system that can assist in the full scientific process, and provides examples and guidance for its application.

This Roadmap envisions how to prioritize research in hardware-based machine learning methods and more to adapt to challenging real-time data collection environments in the forthcoming era of particle physics experiments.

Comprehensive benchmarking demonstrates the power and robustness of two convolutional backflow architectures for strongly correlated systems.

An approach that uses prediction-error methodology to train universal differential equations can learn unknown states in chaotic systems by removing the sensitive dependence of conventional methods on parameter choices.

Transformer models trained for protein structure prediction are analyzed from a statistical mechanics perspective, leading to a better understanding of how they work and why they can efficiently retrieve spatial and chemical relationships between amino acids.

AI Foundation Models trained on collider data can translate to cosmology.

A denoising network that reports a calibrated uncertainty alongside every data point allows 12-second photoemission scans to yield superconducting gap values with meaningful error bars, turning deep learning from a visualization aid into a quantitative measurement tool.

Several stress tests are utilized to assess the accuracy of two widely used machine-learned potential approaches.

A Transformer-based neural quantum state unifies spin and fermionic degrees of freedom in a single variational framework, achieving high accuracy in one dimension and extending naturally to challenging two-dimensional systems.