Researchers have developed a machine learning model that can interpret lunar geology by analyzing topographic, spectral, and geological maps. The model uses a multimodal vision-language approach to generate verifiably grounded interpretations of the moon's surface. While the system is able to accurately describe stratigraphy and terrain, it relies on memorized priors for numeric age dating. However, when given access to published chronologies through an open-book retrieval mechanism, the model can provide accurate age dates. --- Why it matters: This research matters because it demonstrates a potential solution for automating geologic inference in planetary science. By developing a machine learning model that can accurately interpret geological data, researchers may be able to streamline their workflows and make new discoveries more efficiently. Source: https://arxiv.org/abs/2608.09276