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TAC-Time is proposed, a unified framework that transforms textual information into additional temporal channels that preserves temporal continuity and periodic structures while remaining efficient and scalable and identifies predictive textual signals whose correlation-aware alignment yields partial forecasting improvements.
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.
