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Over 1,500 submissions revealed stark differences in model performance across diverse domains, highlighting the challenges of generalizing egocentric video understanding.
LLMs can retain 10x more of their original capabilities after fine-tuning, simply by using a dynamically adjusted "anchor" to constrain distributional drift during training.
