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Training paradigms dictate orientation selectivity in vision transformers, revealing that early layers excel at encoding low-level features while deeper layers transition to semantic representations.
CLAE enables real-time behavior adaptation in robots without retraining, allowing for unprecedented flexibility in dynamic environments.
