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AutoSaddler achieves up to 10% performance improvement in LLM agents by automatically optimizing harnesses based on execution failure signals.
Token-level attribution struggles to pinpoint the causes of LLM failures in realistic settings, suggesting current interpretability tools may not be up to the task of debugging complex model behaviors.
