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Amazon researchers introduce Selective Retrieval-Augmented Translation (SRAT), an agentic reinforcement learning framework that learns when to retrieve external evidence, what to retrieve, and how to integrate it for LLM-based machine translation, outperforming never- and always-retrieve baselines.