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Fusing volumetric and surface brain MRI data in a self-supervised autoencoder uncovers substantially more heritable genetic loci than unimodal approaches.
Even the best vision-language models struggle with reliable evaluation of computer-using agents, but OS-Shepherd models offer a low-cost solution that matches their performance.
Book-level organization of synthetic data boosts language model performance by over 1% compared to traditional methods, highlighting the power of structured content.
OpenMobile proves that high-performing mobile agents can be trained on entirely synthetic, open-source data, closing the gap with closed-source models and enabling broader research.