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This paper introduces the EDU-CIRCUIT-HW dataset for evaluating multimodal large language models on real-world university-level STEM handwritten solutions, revealing significant recognition limitations and proposing a hybrid approach that combines automated recognition with minimal human oversight to enhance grading robustness.