Recent advances in LLMs, foundation models, and AI agents have rapidly expanded the role of AI in science. These systems are now being used to read literature, generate hypotheses, write and execute code, design molecules and experiments, interact with simulators, and assist in scientific reasoning. As AI systems become increasingly embedded in scientific workflows, the key question is no longer only what can they do, but how should we build and use them reliably for scientific discovery.
The AI Scientist Summer Workshop brings together researchers across AI and science to discuss the emerging vision of AI systems as scientific collaborators. We aim to examine both the opportunities and limitations of current models, and to ask what scientific problems are best suited for AI agents and foundation models.
Reliability & verification
Producing scientific outputs that are trustworthy and independently checkable.
Evaluation & reproducibility
Benchmarks and protocols for assessing scientific reasoning and reproducing results.
Closed-loop discovery systems
Hypothesis generation, experimental design, and closed-loop autonomous experimentation.
Foundation models for science
Models for molecules, sequences, simulations, and multi-modal scientific data.
Human–AI collaboration
How scientists and AI systems divide work, build trust, and reason together.
Scientific applications
Domains where AI agents and foundation models drive new discovery — biology, chemistry, materials, physics, and beyond.
The workshop is in person, with seats limited to 100 attendees. Register through the form below to attend, and optionally propose a contributed talk or poster for the afternoon session.
