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University of Michigan -Ann Arbor, University of California San Diego
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LLM-guided program discovery gets a serious boost: evolving programs across related tasks isn't just faster, it also beats overfitting, especially when data is scarce.
Achieve interpretability in continual learning without sacrificing accuracy: CI-CBM outperforms existing interpretable methods by 36% while matching black-box model performance.
