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This is a Experimental Study study.

October 20, 2025Open Access

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies

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Findings reveal that some PRNGs labeled 'crush-resistant' may fail statistical tests, affecting ML system reliability.
The study evaluated 896 random streams using TestU01, identifying discrepancies between native and integrated implementations.
Comparative analysis focused on PRNGs like Mersenne Twister, PCG, and Philox against their C implementations.
Challenges to the statistical robustness of PRNGs indicate the necessity for deeper scrutiny in ML applications.

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This analysis compares the statistical quality and reproducibility of PRNGs in ML frameworks, highlighting potential vulnerabilities.

Benjamin A. Antunes (2025) studied this question.

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