Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914188304056320 |
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| author | Chervonyi, Yuri Trinh, Trieu H. Olšák, Miroslav Yang, Xiaomeng Nguyen, Hoang Menegali, Marcelo Jung, Junehyuk Kim, Junsu Verma, Vikas Le, Quoc V. Luong, Thang |
| author_facet | Chervonyi, Yuri Trinh, Trieu H. Olšák, Miroslav Yang, Xiaomeng Nguyen, Hoang Menegali, Marcelo Jung, Junehyuk Kim, Junsu Verma, Vikas Le, Quoc V. Luong, Thang |
| contents | We present AlphaGeometry2 (AG2), a significantly improved version of AlphaGeometry introduced in (Trinh et al., 2024), which has now surpassed an average gold medalist in solving Olympiad geometry problems. To achieve this, we first extend the original AlphaGeometry language to tackle problems involving movements of objects, and problems containing linear equations of angles, ratios, and distances. This, together with support for non-constructive problems, has markedly improved the coverage rate of the AlphaGeometry language on International Math Olympiads (IMO) 2000-2024 geometry problems from 66% to 88%. The search process of AG2 has also been greatly improved through the use of Gemini architecture for better language modeling, and a novel knowledge-sharing mechanism that enables effective communication between search trees. Together with further enhancements to the symbolic engine and synthetic data generation, we have significantly boosted the overall solving rate of AG to 84% on all geometry problems over the last 25 years, compared to 54% previously. AG2 was also part of the system that achieved the silver-medal standard at IMO 2024 https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/. Finally, we report progress towards using AG2 as a part of a fully automated system that reliably solves geometry problems from natural language input. Code: https://github.com/google-deepmind/alphageometry2. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03544 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2 Chervonyi, Yuri Trinh, Trieu H. Olšák, Miroslav Yang, Xiaomeng Nguyen, Hoang Menegali, Marcelo Jung, Junehyuk Kim, Junsu Verma, Vikas Le, Quoc V. Luong, Thang Artificial Intelligence Machine Learning We present AlphaGeometry2 (AG2), a significantly improved version of AlphaGeometry introduced in (Trinh et al., 2024), which has now surpassed an average gold medalist in solving Olympiad geometry problems. To achieve this, we first extend the original AlphaGeometry language to tackle problems involving movements of objects, and problems containing linear equations of angles, ratios, and distances. This, together with support for non-constructive problems, has markedly improved the coverage rate of the AlphaGeometry language on International Math Olympiads (IMO) 2000-2024 geometry problems from 66% to 88%. The search process of AG2 has also been greatly improved through the use of Gemini architecture for better language modeling, and a novel knowledge-sharing mechanism that enables effective communication between search trees. Together with further enhancements to the symbolic engine and synthetic data generation, we have significantly boosted the overall solving rate of AG to 84% on all geometry problems over the last 25 years, compared to 54% previously. AG2 was also part of the system that achieved the silver-medal standard at IMO 2024 https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/. Finally, we report progress towards using AG2 as a part of a fully automated system that reliably solves geometry problems from natural language input. Code: https://github.com/google-deepmind/alphageometry2. |
| title | Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2 |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.03544 |