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NP is a category of decision problems - problems with boolean answers. Saying that it's NP-complete to find the tokeniser that produces the fewest symbols is meaningless. You have to convert it to the form "is there a tokenizer that produces fewer than N symbols?" before it even makes sense to ask whether it's NP-complete.

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I fully agree with your final statement, but needing to constrain the problem in an artificial way to prove it's NP-complete doesn't mean the constraint was justified or realistic, because then you've only proved the constrained version of the decision problem is NP-hard.

There might be plenty of perfectly "good" tokenizers (whatever that ends up meaning) that can be found or generated without formulating their design as an NP-complete decision problem. Claiming "tokenization is NP-complete" (paper title) in general seems like an overstatement.

If it's NP-hard to even know whether the answer is bigger or smaller than a certain number, then it's obvious that in a non-formal way, finding the exact answer is at least as hard as NP-hard, whatever that means.

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