aiqbench analysis
The paper benchmarks AI models, computer algebra systems, and formal tools on research-level number theory problems, and reports that some submissions are correct but conceptually distinct from the official solutions. Its more transferable result is an AI-assisted judging pipeline that cuts false positives on natural-language proofs from 23% to 6%, though judgment accuracy on code only rises from 46% to 58%, so reliability at scale remains limited. Readers should treat the results as one benchmark's evidence, not a general measure of AI mathematical ability.
Ramanujan Challenge Results: AI-Assisted Evaluation Pipeline for Multi-Format Proofs
arXiv:2610.40551v1 Announce Type: new 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 The Ramanujan Challenge is a benchmark comprised of research-level problems in number theory, including unpublished results and open conjectures.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.
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 The Ramanujan Challenge is a benchmark comprised of research-level problems in number theory, including unpublished results and open conjectures.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.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Symbolic Computation (cs.SC); Number Theory (math.NT)
arXiv:2610.40551 [cs.AI]
(or arXiv:2610.40551v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.40551 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 288 words