Speaker
Description
Recent advances in large language and reasoning models have established such systems as valuable tools for assisting the scientific process. A key development has been the combination of these models into multi-agent systems that collaborate to perform a range of tasks, including writing, debugging, and executing scientific code, as well as conducting literature reviews and data analysis. While such systems have shown great promise in accelerating scientific research, considerable work remains to make them efficient, trustworthy, and secure.
In this talk, I will present my recent work on developing and applying multi-agent systems to such tasks as CMB and cosmological simulation analysis. I will share recent results from exploring a variety of multi-agent system architectures and discuss how different design patterns affect their efficiency, accuracy, and security. I will also describe my experience with integrating these systems with classical machine learning tools commonly used in astrophysics and cosmology. Finally, as an enthusiastic practitioner, I will reflect on the current limitations of AI agents in supporting the scientific process and highlight what I believe is still missing.