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arXiv · 2512.24119

GeoBench: Rethinking Multimodal Geometric Problem-Solving via Hierarchical Evaluation

Yuan Feng·Yue Yang·Xiaohan He·Jiatong Zhao·Jianlong Chen·Zijun Chen·Daocheng Fu·Qi Liu·Renqiu Xia·Bo Zhang·Junchi Yan
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Abstract

Geometric problem solving constitutes a critical branch of mathematical reasoning, requiring precise analysis of shapes and spatial relationships. Current evaluations of geometric reasoning in vision-language models (VLMs) face limitations, including the risk of test data contamination from textbook-based benchmarks, overemphasis on final answers over reasoning processes, and insufficient diagnostic granularity. To address these issues, we present GeoBench, a hierarchical benchmark featuring four reasoning levels in geometric problem-solving: Visual Perception, Goal-Oriented Planning, Rigorous Theorem Application, and Self-Reflective Backtracking. Through six formally verified tasks generated via TrustGeoGen, we systematically assess capabilities ranging from attribute extraction to logical error correction. Experiments reveal that while reasoning models like OpenAI-o3 outperform general MLLMs, performance declines significantly with increasing task complexity. Key findings demonstrate that sub-goal decomposition and irrelevant premise filtering critically influence final problem-solving accuracy, whereas Chain-of-Thought prompting unexpectedly degrades performance in some tasks. These findings establish GeoBench as a comprehensive benchmark while offering actionable guidelines for developing geometric problem-solving systems.

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Yuan Feng, Yue Yang, Xiaohan He, Jiatong Zhao, Jianlong Chen, Zijun Chen, Daocheng Fu, Qi Liu, Renqiu Xia, Bo Zhang, Junchi Yan. 2025-12-30. GeoBench: Rethinking Multimodal Geometric Problem-Solving via Hierarchical Evaluation. https://arxiv.org/abs/2512.24119

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