We invite submissions that explore how core advancements in artificial intelligence are accelerating progress in science. This call focuses on transformative AI innovations that enable new modes of inquiry, hypothesis generation, and experimentation across scientific disciplines.

We especially welcome work in the following areas:

Foundational Models: Research on large-scale, pre-trained models that serve as general-purpose engines for scientific reasoning, prediction, and simulation
Long-Term Memory Mechanisms: Innovations in memory architectures that enable persistent knowledge representation, context retention, and lifelong learning in AI systems
Synthetic Data Generation: Novel techniques for creating high-fidelity synthetic datasets that augment or replace empirical data in research pipelines
Research Process Automation: AI tools and frameworks that automate experimental design, data analysis, literature synthesis, or other components of the scientific workflow
We encourage submissions from researchers working at the intersection of AI and the physical or life sciences, including but not limited to biology, chemistry, physics, medicine, and engineering. Selected papers will be presented at the conference, where authors will join a dynamic community shaping the future of AI-accelerated science.