REAMS: Reasoning Enhanced Algorithm for Maths Solving
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909798300123136 |
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| author | Singh, Eishkaran Bajaj, Tanav Singh Nayak, Siddharth |
| author_facet | Singh, Eishkaran Bajaj, Tanav Singh Nayak, Siddharth |
| contents | The challenges of solving complex university-level mathematics problems, particularly those from MIT, and Columbia University courses, and selected tasks from the MATH dataset, remain a significant obstacle in the field of artificial intelligence. Conventional methods have consistently fallen short in this domain, highlighting the need for more advanced approaches. In this paper, we introduce a language-based solution that leverages zero-shot learning and mathematical reasoning to effectively solve, explain, and generate solutions for these advanced math problems. By integrating program synthesis, our method reduces reliance on large-scale training data while significantly improving problem-solving accuracy. Our approach achieves an accuracy of 90.15%, representing a substantial improvement over the previous benchmark of 81% and setting a new standard in automated mathematical problem-solving. These findings highlight the significant potential of advanced AI methodologies to address and overcome the challenges presented by some of the most complex mathematical courses and datasets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_16241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | REAMS: Reasoning Enhanced Algorithm for Maths Solving Singh, Eishkaran Bajaj, Tanav Singh Nayak, Siddharth Computation and Language Artificial Intelligence Programming Languages The challenges of solving complex university-level mathematics problems, particularly those from MIT, and Columbia University courses, and selected tasks from the MATH dataset, remain a significant obstacle in the field of artificial intelligence. Conventional methods have consistently fallen short in this domain, highlighting the need for more advanced approaches. In this paper, we introduce a language-based solution that leverages zero-shot learning and mathematical reasoning to effectively solve, explain, and generate solutions for these advanced math problems. By integrating program synthesis, our method reduces reliance on large-scale training data while significantly improving problem-solving accuracy. Our approach achieves an accuracy of 90.15%, representing a substantial improvement over the previous benchmark of 81% and setting a new standard in automated mathematical problem-solving. These findings highlight the significant potential of advanced AI methodologies to address and overcome the challenges presented by some of the most complex mathematical courses and datasets. |
| title | REAMS: Reasoning Enhanced Algorithm for Maths Solving |
| topic | Computation and Language Artificial Intelligence Programming Languages |
| url | https://arxiv.org/abs/2509.16241 |