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SERPO introduces a self-evolving rubric policy optimization framework for test-time reinforcement learning in open-ended generation, replacing answer voting with a closed loop that co-evolves response evidence, query-specific rubrics, and policy parameters, achieving significant improvements on health and research benchmarks.