SOLIDGEO: Measuring Multimodal Spatial Math Reasoning in Solid Geometry
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916783808577536 |
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| author | Wang, Peijie Yang, Chao Li, Zhong-Zhi Yin, Fei Ran, Dekang Tian, Mi Ji, Zhilong Bai, Jinfeng Liu, Cheng-Lin |
| author_facet | Wang, Peijie Yang, Chao Li, Zhong-Zhi Yin, Fei Ran, Dekang Tian, Mi Ji, Zhilong Bai, Jinfeng Liu, Cheng-Lin |
| contents | Geometry is a fundamental branch of mathematics and plays a crucial role in evaluating the reasoning capabilities of multimodal large language models (MLLMs). However, existing multimodal mathematics benchmarks mainly focus on plane geometry and largely ignore solid geometry, which requires spatial reasoning and is more challenging than plane geometry. To address this critical gap, we introduce SolidGeo, the first large-scale benchmark specifically designed to evaluate the performance of MLLMs on mathematical reasoning tasks in solid geometry. SolidGeo consists of 3,113 real-world K-12 and competition-level problems, each paired with visual context and annotated with difficulty levels and fine-grained solid geometry categories. Our benchmark covers a wide range of 3D reasoning subjects such as projection, unfolding, spatial measurement, and spatial vector, offering a rigorous testbed for assessing solid geometry. Through extensive experiments, we observe that MLLMs encounter substantial challenges in solid geometry math tasks, with a considerable performance gap relative to human capabilities on SolidGeo. Moreover, we analyze the performance, inference efficiency and error patterns of various models, offering insights into the solid geometric mathematical reasoning capabilities of MLLMs. We hope SolidGeo serves as a catalyst for advancing MLLMs toward deeper geometric reasoning and spatial intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SOLIDGEO: Measuring Multimodal Spatial Math Reasoning in Solid Geometry Wang, Peijie Yang, Chao Li, Zhong-Zhi Yin, Fei Ran, Dekang Tian, Mi Ji, Zhilong Bai, Jinfeng Liu, Cheng-Lin Computational Geometry Geometry is a fundamental branch of mathematics and plays a crucial role in evaluating the reasoning capabilities of multimodal large language models (MLLMs). However, existing multimodal mathematics benchmarks mainly focus on plane geometry and largely ignore solid geometry, which requires spatial reasoning and is more challenging than plane geometry. To address this critical gap, we introduce SolidGeo, the first large-scale benchmark specifically designed to evaluate the performance of MLLMs on mathematical reasoning tasks in solid geometry. SolidGeo consists of 3,113 real-world K-12 and competition-level problems, each paired with visual context and annotated with difficulty levels and fine-grained solid geometry categories. Our benchmark covers a wide range of 3D reasoning subjects such as projection, unfolding, spatial measurement, and spatial vector, offering a rigorous testbed for assessing solid geometry. Through extensive experiments, we observe that MLLMs encounter substantial challenges in solid geometry math tasks, with a considerable performance gap relative to human capabilities on SolidGeo. Moreover, we analyze the performance, inference efficiency and error patterns of various models, offering insights into the solid geometric mathematical reasoning capabilities of MLLMs. We hope SolidGeo serves as a catalyst for advancing MLLMs toward deeper geometric reasoning and spatial intelligence. |
| title | SOLIDGEO: Measuring Multimodal Spatial Math Reasoning in Solid Geometry |
| topic | Computational Geometry |
| url | https://arxiv.org/abs/2505.21177 |