Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916746252779520 |
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| author | Cho, Seunghyuk Qin, Zhenyue Liu, Yang Choi, Youngbin Lee, Seungbeom Kim, Dongwoo |
| author_facet | Cho, Seunghyuk Qin, Zhenyue Liu, Yang Choi, Youngbin Lee, Seungbeom Kim, Dongwoo |
| contents | Plane geometry problem solving (PGPS) has recently gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. Despite the growing interest in PGPS, the research community still lacks a comprehensive overview that systematically synthesizes recent work in PGPS. To fill this gap, we present a survey of existing PGPS studies. We first categorize PGPS methods into an encoder-decoder framework and summarize the corresponding output formats used by their encoders and decoders. Subsequently, we classify and analyze these encoders and decoders according to their architectural designs. Finally, we outline major challenges and promising directions for future research. In particular, we discuss the hallucination issues arising during the encoding phase within encoder-decoder architectures, as well as the problem of data leakage in current PGPS benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey Cho, Seunghyuk Qin, Zhenyue Liu, Yang Choi, Youngbin Lee, Seungbeom Kim, Dongwoo Computer Vision and Pattern Recognition Machine Learning Plane geometry problem solving (PGPS) has recently gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. Despite the growing interest in PGPS, the research community still lacks a comprehensive overview that systematically synthesizes recent work in PGPS. To fill this gap, we present a survey of existing PGPS studies. We first categorize PGPS methods into an encoder-decoder framework and summarize the corresponding output formats used by their encoders and decoders. Subsequently, we classify and analyze these encoders and decoders according to their architectural designs. Finally, we outline major challenges and promising directions for future research. In particular, we discuss the hallucination issues arising during the encoding phase within encoder-decoder architectures, as well as the problem of data leakage in current PGPS benchmarks. |
| title | Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2505.14340 |