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Achieving 100x parameter efficiency, LoopWM redefines how we approach long-horizon world modeling by introducing iterative latent depth as a new scaling axis.
Resolving semantic conflicts between synonymous prompts and across categories dramatically improves the stability and accuracy of open-vocabulary semantic segmentation, all without requiring any additional training.
Achieve SOTA in cross-domain graph few-shot learning by adaptively aligning diverse graph features into a shared semantic space.
Zooming out to mixed-magnification views in computational pathology unlocks cancer-specific improvements in biomarker prediction, suggesting that spatial context is more crucial than previously thought.
