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Auteur principal:	Khrulev, Ruslan
Format:	Preprint
Publié:	2025
Sujets:	Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 68T07, 97D50 I.2.7; I.4; K.3.1
Accès en ligne:	https://arxiv.org/abs/2507.22958
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author	Khrulev, Ruslan
author_facet	Khrulev, Ruslan
contents	This paper introduces a novel benchmark, EGE-Math Solutions Assessment Benchmark, for evaluating Vision-Language Models (VLMs) on their ability to assess hand-written mathematical solutions. Unlike existing benchmarks that focus on problem solving, our approach centres on understanding student solutions, identifying mistakes, and assigning grades according to fixed criteria. We compile 122 scanned solutions from the Russian Unified State Exam (EGE) together with official expert grades, and evaluate seven modern VLMs from Google, OpenAI, Arcee AI, and Alibaba Cloud in three inference modes. The results reveal current limitations in mathematical reasoning and human-rubric alignment, opening new research avenues in AI-assisted assessment. You can find code in https://github.com/Karifannaa/Auto-check-EGE-math
format	Preprint
id	arxiv_https___arxiv_org_abs_2507_22958
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	CHECK-MAT: Checking Hand-Written Mathematical Answers for the Russian Unified State Exam Khrulev, Ruslan Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 68T07, 97D50 I.2.7; I.4; K.3.1 This paper introduces a novel benchmark, EGE-Math Solutions Assessment Benchmark, for evaluating Vision-Language Models (VLMs) on their ability to assess hand-written mathematical solutions. Unlike existing benchmarks that focus on problem solving, our approach centres on understanding student solutions, identifying mistakes, and assigning grades according to fixed criteria. We compile 122 scanned solutions from the Russian Unified State Exam (EGE) together with official expert grades, and evaluate seven modern VLMs from Google, OpenAI, Arcee AI, and Alibaba Cloud in three inference modes. The results reveal current limitations in mathematical reasoning and human-rubric alignment, opening new research avenues in AI-assisted assessment. You can find code in https://github.com/Karifannaa/Auto-check-EGE-math
title	CHECK-MAT: Checking Hand-Written Mathematical Answers for the Russian Unified State Exam
topic	Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 68T07, 97D50 I.2.7; I.4; K.3.1
url	https://arxiv.org/abs/2507.22958

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