You can evaluate the logic for the decipherment step by step and make sure all the claims are justified. But the best test is to try the proposed decipherment against some new text and see if it makes sense. In the case of Linear A and the other remaining undeciphered scripts, there's not a lot of held-out text to test against, so it's tricky.
Egyptian writing had phonetic elements (as does the modern Chinese writing system) but it was not an alphabet in the strict linguistic sense that individual symbols indicated individual phonemes.
A mapping of Chinese characters to integers (like a tokenizer) would not be a dictionary. You’d also need definitions. At best it’s an index to a hypothetical dictionary.
Your model of what AI is good at is wrong. Generative AI is not good at wandering off into novel esoteric abstract corners while maintaining correctness, it is good at things that are close to its training data. I suspect that humans will long outperform AI in the domain of "novel esoteric abstract useless math" whereas AI will outperform humans in the domains of (1) making connections between already-well-understood concepts, things that seem obvious in retrospect but which no human figured out just because of the accidents of what people happened to focus on, and (2) proving things that require long, tedious, intellectually unsatisfying calculations, which would cause a human mathematician to give up for boredom.
As early as last year "AI psychosis" seemed to refer to people going crazy due to talking to ChatGPT too much. That was a useful term for a real phenomenon! Now it seems like it's been taken over to mean "thinking that AI is promising" which is more of a rhetorical bludgeon and less useful as a concept.
China at least has this. The stuff you get at the Ole Supermarket inside a shopping mall is different from the stuff you get from the little store facing the street on the ground floor of your apartment building.