Kavli Affiliate: Blake Sherwin
| First 5 Authors: Andrew Laverick, Kristen Surrao, Inigo Zubeldia, Boris Bolliet, Miles Cranmer
| Summary:
Multi-agent systems (MAS) utilizing multiple Large Language Model agents with
Retrieval Augmented Generation and that can execute code locally may become
beneficial in cosmological data analysis. Here, we illustrate a first small
step towards AI-assisted analyses and a glimpse of the potential of MAS to
automate and optimize scientific workflows in Cosmology. The system
architecture of our example package, that builds upon the autogen/ag2
framework, can be applied to MAS in any area of quantitative scientific
research. The particular task we apply our methods to is the cosmological
parameter analysis of the Atacama Cosmology Telescope lensing power spectrum
likelihood using Monte Carlo Markov Chains. Our work-in-progress code is open
source and available at https://github.com/CMBAgents/cmbagent.
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