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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.
Paper2Agent is an automated framework that converts research papers into interactive AI agents to enhance knowledge dissemination and enable complex scientific queries through natural language.