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This paper introduces VESTA, a framework that equips vision-language models with dynamically growing toolkits for data exploration and statistical model refinement, outperforming prior agent-based methods on complex scientific modeling tasks. The authors also present Dawn, a benchmark for distribution fitting and time series modeling, including real-world astronomy challenges.
The article explains how to use Bayesian modeling with Gaussian processes to handle spatial data where the coordinates are observed with error, using a dataset of uranium and vanadium concentrations from Walker Lake as an example.