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This paper proposes MASA, a framework that adapts skills to each LLM backbone without modifying weights, using hierarchical evolution and a model-conditioned rewriter, achieving gains of up to 25.8 points over baselines.
SkillAdaptor is a training-free step-level skill adaptation framework with explicit failure attribution for LLM agents, improving performance on WebShop, PinchBench, and Claw-Eval.