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DISTAL is a dual-prior framework that combines self-supervised compositional pretraining and structure-aware knowledge distillation to achieve robust structure-agnostic materials property prediction in low-data settings, improving performance across multiple benchmark tasks.
Introduces CGM-JEPA, a self-supervised pretraining framework for continuous glucose monitor data that improves cross-modal and cross-cohort performance through masked latent prediction and distributional objectives.