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arXiv · 2610.22612

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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BibTeXRIS

Madhuparna Das, Soumya Banerjee. 2026-10-15. Proof Perplexity: A Probabilistic Approach to Measuring non-triviality in Machine Generated Proofs. https://arxiv.org/abs/2610.22612

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