AI for Science is expanding from model development to full discovery systems that can support scientific workflows. Foundation models provide general representations and generative priors for scientific data, and agentic systems organize reasoning and multi-step decision processes, forming a continuous loop of hypothesis generation, experimental design, observation, and refinement.

This workshop will examine the methodological questions that arise in building such systems across materials science and biomedicine, including superconducting materials, virtual cells, drug discovery, cell and spatial omics, and proteomics. The discussion will focus on shared challenges in multimodal representation learning, integration of scientific constraints and prior knowledge, planning over tools and experiments, coordination between models and experimental workflows, and evaluation in closed-loop discovery settings.

By bringing together researchers working on scientific foundation models and agentic AI, the workshop will provide a forum for discussing technical challenges and emerging research directions in next-generation AI for Science.