arXiv cs.AI· Alan Sorani, Ashvni Narayanan, Michael Shalyt, Tali Monderer, Rotem Kalisch, Hila Barkan, Elyasheev Leibtag, Shachar Weinbaum, Carsten Schneider, John Campbell, Ido Kaminer·· 14 小时前AI 评分42
Ramanujan Challenge 结果：面向多格式证明的 AI 辅助评估流水线
Ramanujan Challenge Results: AI-Assisted Evaluation Pipeline for Multi-Format Proofs
AI 导读
Ramanujan Challenge 提交分析显示，不同 AI 模型、计算机代数系统（CAS）与形式化工具均取得部分正确结果，其中包括与官方解答概念不同的正确证明，以及对一项已公开开放猜想给出的证明。
正文 · 原文
Authors:Alan Sorani, Ashvni Narayanan, Michael Shalyt, Tali Monderer, Rotem Kalisch, Hila Barkan, Elyasheev Leibtag, Shachar Weinbaum, Carsten Schneider, John Campbell, Ido Kaminer
Abstract:As AI's mathematical capabilities rapidly improve, meaningful benchmarks are shifting from university-level problems and math olympiads to research-level mathematics. The Ramanujan Challenge is a benchmark comprised of research-level problems in number theory, including unpublished results and open conjectures. It presented concrete formulas for mathematical constants whose correctness can be checked numerically, while their proofs may require non-obvious mathematics. Here, we analyze the Challenge submissions, measuring the relative success of different AI models, Computer Algebra Systems (CAS), and formal tools. We find correct submissions that are conceptually distinct from the official solutions, and a proof of one of the published open conjectures. Evaluating the submissions exposed a second challenge: judging complex proofs reliably at scale. We introduce an AI-assisted evaluation pipeline for natural language proofs, CAS code, and Lean formalizations that assesses semantic and logical correctness. Compared with the conventional one-shot LLM judgment, our pipeline reduces false-positive verdicts on natural language proofs from 23% to 6% and improves judgment accuracy on supporting code from 46% to 58%. This approach provides a methodology for evaluating research-level mathematics at scale, with potential applications across mathematics and AI for science.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Symbolic Computation (cs.SC); Number Theory (math.NT) |
| Cite as: | arXiv:2610.40551 [cs.AI] |
| (or arXiv:2610.40551v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40551 arXiv-issued DOI via DataCite |
Submission history
From: Ashvni Narayanan [view email]
[v1]
Thu, 29 Oct 2026 17:39:04 UTC (301 KB)
来源：arXiv cs.AI · arxiv.org