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Polymathic

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Deep learning is everywhere in astronomy, but most models live in silos—built for one data type, one survey, and one bespoke task. As a result, they must be retrained whenever these settings change. The AstronomIcal Omnimodal Network (AION-1) changes this: a single foundation model that spans multiple surveys and data types, producing a unified representation that transfers across domains and performs strongly even in low-data regimes. By learning shared astrophysical structure across instruments and modalities, AION-1 not only removes the need for retraining—it enables better performance, faster adaptation, and a deeper physical understanding from fewer examples. AION-1 is a large-scale multimodal foundation model for astronomy that a wide variety of astronomical measurements into a single, unified model spanning everything from nearby stars to distant quasars and galaxies. This approach addresses a longstanding challenge: integrating multiple heterogeneous datasets—spanning various i

Paper Code Deep learning is everywhere in astronomy, but most models live in silos—built for one data type, one survey, and one bespoke task. As a result, they must be retrained whenever these settings change. The AstronomIcal Omnimodal Network (AION-1) changes this: a single foundation model that spans multiple surveys and data types, producing a unified representation that transfers across domains and performs strongly even in low-data regimes. By learning shared astrophysical structure across instruments and modalities, AION-1 not only removes the need for retraining—it enables better perfo

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