The Ramanujan Challenge For AI
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Abstract (arXiv)
To help evaluate the mathematical skills of current AI systems, we present a set of formulas for fundamental mathematical constants. These problems are attractive for AI evaluation because they are concrete and can be checked numerically to arbitrary precision, yet proving them may require non-obvious mathematics. Mathematical constants such as π, e, Catalan's constant, and special values of the Riemann zeta function have fascinated mathematicians for centuries. The search for formulas evaluating mathematical constants has produced some of the most beautiful mathematics in the field, especially in cases that yield irrationality proofs or fast convergence rates. Ramanujan's legacy is emblematic of this tradition. The list we provide contains two types of problems: formulas whose proofs are known to the authors but will remain encrypted for a short initial period; and formulas that are not yet proven. We are curious to see the achievements of AI in both cases.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 27 Jun 2026.
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References · 8
- 2024GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Modelscited by 550
- 2024FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AIcited by 243
- 2026First Proofcited by 32
- 2025RealMath: A Continuous Benchmark for Evaluating Language Models on Research-Level Mathematicscited by 31
- 2026LemmaBench: A Live, Research-Level Benchmark to Evaluate LLM Capabilities in Mathematicscited by 10
- 2026Riemann-Bench: A Benchmark for Moonshot Mathematicscited by 8
- 2023Algorithm-assisted discovery of an intrinsic order among mathematical constantscited by 5
- 2025ASyMOB: Algebraic Symbolic Mathematical Operations Benchmarkcited by 5
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