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RESCUE: Repairing Language Model Errors to Sparse Circuits via Reinforcement Learning
Large language models (LLMs) exhibit strong general capabilities that mechanistic interpretability has attributed to sparse computational circuits. However, existing circuit studies emphasize preserving functionality or explaining safety, leaving the mechanisms underlying failures across a broader r
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