You're kind of confusing different issues. On the one hand, PRNGs aren't a very popular research subject AFAIK. The Blackman/Vigna PRNGs (which are the other r

You're kind of confusing different issues. On the one hand, PRNGs aren't a very popular research subject AFAIK. The Blackman/Vigna PRNGs (which are the other recently popular PRNG family) likewise only got papers on Arxiv, AFAIK; so it's not clear to which peer-reviewed PRNG are you comparing PCGs. We are talking about non-crypto PRNGs here after all, which mostly get tested just experimentally, to check their statistical quality. On the other hand, there is a really weird claim associated with the PCG family: that it is challenging to predict and that it thus "provides many of the benefits provided by CSPRNGs". This is how the PCG designer ends the abstract of her PCG paper: > These functions can be designed to make it difficult for an adversary to discover the generator’s internal state by examining its output, and thus make it challenging to predict. This property, coupled with the ability to easily switch between random streams, provides many of the benefits provided by cryptographically secure generators without the overheads usually associated with those generators. That's baloney. PCGs are not CSPRNGs, but she's deceptively trying to make it sound like they kind of are (have the same benefits). Anyway, a relatively efficient attack has been demonstrated this year: https://hal.inria.fr/hal-02700791/ https://hal.inria.fr/hal-02700791/ Another issue with the "challenging predictability" design goal is that it seems to come at the cost of necessary trade-offs at the expense of speed and/or statistical quality, even though it is of dubious utility (considering that no real guarantee is given, and that a relatively efficient attack has recently been demonstrated): https://github.com/numpy/numpy/issues/16313#issuecomment-726984639 https://github.com/numpy/numpy/issues/16313#issuecomment-726...