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This paper introduces Elmes+, an automated framework for constructing fine-grained evaluation rubrics for LLMs in long-tail educational scenarios, and presents the Edu-330 benchmark covering 330 scenarios across 11 subjects. The framework uses a multi-agent engine and self-evolving module to co-optimize evaluation criteria and test data, revealing multidimensional educational capability differences among top LLMs.