The challenge (as I understand it) is that the vocabulary size is pretty massive — thousands of unique words — and the structure might not be 1:1 with how real language maps. Like, is a “word” in Voynich really a word? Or is it a chunk, or a stem with affixes, or something else entirely? That makes brute-forcing a direct mapping tricky.
That said… using cluster IDs instead of individual word (tokens) and scoring the outputs with something like a language model seems like a pretty compelling idea. I hadn’t thought of doing it that way. Definitely some room there for optimization or even evolutionary techniques. If nothing else, it could tell us something about how “language-like” the structure really is.
Might be worth exploring — thanks for tossing that out, hopefully someone with more awareness or knowledge in the space see's it!