HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908890211287040 |
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| author | Wang, Erik Y. Motwani, Sumeet Roggeveen, James V. Hodges, Eliot Jayalath, Dulhan London, Charles Ramakrishnan, Kalyan Cipcigan, Flaviu Torr, Philip Abate, Alessandro |
| author_facet | Wang, Erik Y. Motwani, Sumeet Roggeveen, James V. Hodges, Eliot Jayalath, Dulhan London, Charles Ramakrishnan, Kalyan Cipcigan, Flaviu Torr, Philip Abate, Alessandro |
| contents | Can AI make progress on important, unsolved mathematical problems? Large language models are now capable of sophisticated mathematical and scientific reasoning, but whether they can perform novel research is still widely debated and underexplored. We introduce HorizonMath, a benchmark of over 100 predominantly unsolved problems spanning 8 domains in computational and applied mathematics, paired with an open-source evaluation framework for automated verification. Our benchmark targets a class of problems where discovery is hard, requiring meaningful mathematical insight, but verification is computationally efficient and simple. Because these solutions are unknown, HorizonMath is immune to data contamination, and most state-of-the-art models score near 0%. Existing research-level benchmarks instead rely on formal proof verification or manual review, both of which are expensive to scale. Using this platform, we find two problems for which GPT 5.4 Pro proposes solutions that improve on the best-known published results, representing potential novel contributions (pending expert review). We release HorizonMath as an open challenge and a growing community resource, where correct solutions to problems in the unsolved problem classes could constitute novel results in the mathematical literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15617 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification Wang, Erik Y. Motwani, Sumeet Roggeveen, James V. Hodges, Eliot Jayalath, Dulhan London, Charles Ramakrishnan, Kalyan Cipcigan, Flaviu Torr, Philip Abate, Alessandro Machine Learning Can AI make progress on important, unsolved mathematical problems? Large language models are now capable of sophisticated mathematical and scientific reasoning, but whether they can perform novel research is still widely debated and underexplored. We introduce HorizonMath, a benchmark of over 100 predominantly unsolved problems spanning 8 domains in computational and applied mathematics, paired with an open-source evaluation framework for automated verification. Our benchmark targets a class of problems where discovery is hard, requiring meaningful mathematical insight, but verification is computationally efficient and simple. Because these solutions are unknown, HorizonMath is immune to data contamination, and most state-of-the-art models score near 0%. Existing research-level benchmarks instead rely on formal proof verification or manual review, both of which are expensive to scale. Using this platform, we find two problems for which GPT 5.4 Pro proposes solutions that improve on the best-known published results, representing potential novel contributions (pending expert review). We release HorizonMath as an open challenge and a growing community resource, where correct solutions to problems in the unsolved problem classes could constitute novel results in the mathematical literature. |
| title | HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.15617 |