Even Fable hallucinates. I had it tracking down some very obscure Ancient Greek inscriptions and the response just made up a translation/context for one inscription after "looking it up." Now, it was still a very particular thing and I really had to get into the weeds to push it to that point, but who knows how many other gaps, near or far, it will happily skip over just for the sake of coherence. I think this is an issue more primarily with LLMs than sensory systems like Waymos or all the ML applied to industrial processes--that really only requires pattern recognition, often very impressive and subtle pattern recognition but its no different from an artist learning to tell the difference between Prussian blue and Navy blue or a Sommelier learning the fine distinctions between various regions of Bordeaux. Language has many more avenues and introduces inherent contradictions that do not always lend themselves to easy resolution. But there are no alternatives paths visible to the models, there is only ever the next word; stochastic, in the sense that the possibility space is open; deterministic, in the sense that the final response is always a necessary result of every token that came before it in their total sequence. Thus, any response is constantly in the work of erasing any possible alternative, slowly narrowing down what can be written. If contradictions in language necessarily involve interpretation, then the models will only ever choose one at a time, and for them, it will always be the right one. But anyone who understands the subtleties of language can tell you that when it comes to determining the truth of an indeterminate statement, there is never just one right answer; or, rather, the answer which is taken to be the "right" one depends on the possibility of its own reversal into falsehood, if any argument has to be made to justify it.