Thanks, this is a very interesting topic.
What I personally would like to see is some kind of quantization of how the biases that the author talks about (such as insufficient seed volume of a PRNG) affects computed p-values. Specifically, why there must no "cancellation of errors" happen? So far, IIUC, the author only shows theoretical possibility of errors, but what's more interesting is a real effect. When it all boils down to a p-value being less than a certain threshold (choosing which is another pita), it might not matter whether a true p-value is within, say, 2^-16 from the computed.
Perhaps it would make sense to provide LLMs with some strategy guides written in .md files.