Session

DescriptionThe landscape of AI for science has shifted dramatically. Beyond building ever-larger models from scratch, the scientific community is increasingly leveraging powerful pre-trained models—adapting them through fine-tuning, domain specialization, and integration into complex AI systems combining reasoning models, knowledge graphs, agentic workflows, and connections to simulators and instruments. These shifts highlight a full stack of interdependent challenges for developers, scientists, and HPC centers, from strategies and infrastructure for sharing data and computing, to frameworks orchestrating multiple models and agents, to methods and tools for ensuring safety, alignment, and rigorous evaluation of scientific reasoning and skills. Progress in any one area both depends upon and accelerates the others. This workshop will highlight advances across this interconnected landscape, emphasizing openness, international collaboration, and community-driven efforts, featuring peer-reviewed papers and invited talks spanning shared infrastructure, open model development, agentic AI systems for science, evaluation and safety, and driving challenge applications.