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Overall, Code2Skill transforms procedural knowledge embedded in repositories into grounded, verifiable, and transferable agent skills, providing initial evidence that the pipeline can expand with the growing volume of AI-generated software.
Next-turn user reactions provide actionable local credit for improving multi-turn user-interacting agents and improves the nine-domain $\tau$-family average across three independently trained runs.
DARC is proposed, a diagnosis-guided recovery harness that profiles task-family failure modes, prunes mismatched interventions from a shared recovery library, and freezes a verifier-selected success-cost policy for deployment, providing a practical route toward more reliable agents in domains where compiler-like feedba...
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