{"ID":2870642,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16241","arxiv_id":"2509.16241","title":"REAMS: Reasoning Enhanced Algorithm for Maths Solving","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.","short_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, high...","url_abs":"https://arxiv.org/abs/2509.16241","url_pdf":"https://arxiv.org/pdf/2509.16241v1","authors":"[\"Eishkaran Singh\",\"Tanav Singh Bajaj\",\"Siddharth Nayak\"]","published":"2025-09-16T21:09:48Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.PL\"]","methods":"[]","has_code":false}