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Distilling large models into smaller ones can silently sacrifice crucial capabilities like safety and uncertainty awareness, even if headline metrics stay the same.
Extracting and clustering independent components from EEG data reveals stable neural signatures of emergency braking, enabling more accurate braking intensity prediction.
iTAG flips the script on causally-annotated text generation, achieving both natural language and high annotation accuracy by treating the causal graph as the target and iteratively refining concept selection.
