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Agents trained with Agent-Reflex exhibit a 29.1% improvement on a noise-heavy benchmark, demonstrating robust failure recovery and proactive reflection even with faulty tool feedback.
External agent scaffolding can be discarded after training by distilling evolved decision-making procedures directly into a model's native reasoning traces.