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PreprintCC BY 4.0arXiv

Proof Perplexity: A Probabilistic Approach to Measuring non-triviality in Machine Generated Proofs

  • Madhuparna Das
  • Soumya Banerjee
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Abstract

In this article, we propose a benchmark to quantify the non-triviality and novelty of machine-generated mathematical proofs compared to existing literature. Our core methodology uses a probabilistic measure to evaluate the non-triviality of the underlying mathematical arguments in comparison to the current \texttt{MATHLIB} library. Finally, we apply this benchmark to the recent proof generated by Claude on the complex zeros of the Riemann zeta function to test for non-trivial steps.

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Cite

BibTeXRIS
BibTeX
@misc{das2026proof,
  title = {{Proof Perplexity: A Probabilistic Approach to Measuring non-triviality in Machine Generated Proofs}},
  author = {Madhuparna Das and Soumya Banerjee},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.22612},
  eprint = {2610.22612},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.22612},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Proof Perplexity: A Probabilistic Approach to Measuring non-triviality in Machine Generated Proofs
AU  - Madhuparna Das
AU  - Soumya Banerjee
PY  - 2026
DA  - 2026/10/15
PB  - arXiv
DO  - 10.48550/arxiv.2610.22612
UR  - https://arxiv.org/abs/2610.22612
AB  - In this article, we propose a benchmark to quantify the non-triviality and novelty of machine-generated mathematical proofs compared to existing literature. Our core methodology uses a probabilistic measure to evaluate the non-triviality of the underlying mathematical arguments in comparison to the current \texttt{MATHLIB} library. Finally, we apply this benchmark to the recent proof generated by Claude on the complex zeros of the Riemann zeta function to test for non-trivial steps.
ER  -