There is a well-known paper related to a statistical zero-knowledge proof about Kolmogorov complexity, but this proof introduced is considered a perfect ZKP
Methodology for comparison: train zstd dictionary on enwik9. Then build my dictionary as most common words in enwik9. Mine does 13% better because of the way I discovered how you can generate dictionary replacement symbols
I used the z-score. How can you claim that the digits of pi are random, yet a random forest classifier predicted better than the distribution probability. Your claim implicitly means "there is no structure." The hard thing to understand is that the classifier didn't see the test set, so what structure did it learn? At the very least this is an interesting question