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This paper systematically studies context interference in multi-turn LLM-based search agents, finding that interference primarily arises from the latest retrieved documents, and introduces a distill-based context refiner to mitigate it. Incorporating context refinement into RL training pipelines significantly improves reliability and efficiency.