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arXiv · 2509.16241

REAMS: Reasoning Enhanced Algorithm for Maths Solving

Eishkaran Singh·Tanav Singh Bajaj·Siddharth Nayak
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Abstract

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.

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Eishkaran Singh, Tanav Singh Bajaj, Siddharth Nayak. 2025-09-16. REAMS: Reasoning Enhanced Algorithm for Maths Solving. https://arxiv.org/abs/2509.16241

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