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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.
U4D reveals that leveraging spatial uncertainty can drastically enhance the quality of LiDAR scene synthesis, achieving unprecedented fidelity and coherence.
Robots can reliably hand over objects to humans by actively probing grasps, achieving a 30% improvement over passive methods.
