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SPRINT tackles the overlooked challenge of few-shot class-incremental learning on tabular data, achieving state-of-the-art results by smartly exploiting unlabeled data and cheap storage.
LLMs face a Scale-Dependent Safety Divergence: larger reasoning models achieve safety by adding nuance, whereas capacity-constrained models revert to deleting text.
