Key result
Mersenne Twister and PCG yield different streams across R and Python unlike identical XorShift128+ sequences.
Why the study?
Differences in PRNG implementation across programming languages can lead to inconsistencies in collaborative, cross-language workflows.
Highlights inconsistencies in pseudo-random number generation across R and Python, emphasizing the need for tools like the reticulate package to ensure exact consistency in cross-language workflows.
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PRNG differences across languages may undermine reproducibility in simulations; leaves open need for standardized cross-platform validation.
Dumnich et al. (2026) studied this question. Pseudo-random number generators (MTA, PCG, XorShift128+) vs. Cross-language comparison (R vs Python) was evaluated on Sequence consistency and distribution equivalence. Mersenne Twister and PCG produce different integer streams across R and Python despite statistically indistinguishable distributions, whereas XorShift128+ sequences would be identical.
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