LLM algorithm does not need to be probabilistic, we add probability to make it more interesting, but in principle it should return the same tokens for the same input (which is often desired, and we had temperature=0 for that, even if for practical reasons it was not always working that way). But it is not the point. My point is that LLM inference follows an easy algorithm, but what we get is something different, because weights are part of the algorithm and they are not easily interpretable. So if I ask LLM to write me an essay on a given subject I know what it is technically doing, but writing it as an algorithm different than "convert this text to tokens and than perform billions of simple operations on them to get the next token and repeat" seems hopeless. Because the black box of weights is what matters. In this meaning it is not an algorithm we can find in a book on algorithms. Of course it's a computational process, but the question is, if it is qualitatively different to the one in our heads.