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VLMs can be taught to *use* latent geometry by training them to read continuous geometric tokens, rather than just reconstructing geometry from RGB.
Action-class miscalibration is a hidden failure mode that can significantly skew performance evaluations of LLMs in multi-turn tool-calling tasks.
On-policy self-distillation can boost diffusion model performance by up to 44% while slashing training time by over 60%.
Ten teams achieved groundbreaking advancements in low-light image enhancement, with improvements that redefine the state-of-the-art in burst photography.
