Counter argument - generating probabilistic tokens (degree of randomness) is core concept for an LLM.
Set the temperature to zero and that's exactly what you get. The point is the randomness is something applied externally, not a "core concept" for the LLM.
And regardless, turning this into a system that has some notion of strategic consistency or contextual steering seems like a remarkably easy problem. Treating it as one API call in, one deterministic and constrained choice out is wrong.
In some NN implementations, randomness is actually pretty important to keep the gradients from getting stuck at local minima/maxima. Is that true for LLMs, or is it not something that applies at all?
Eg you tend to randomly shuffle your corpus to train on. If you use drop-out (https://en.wikipedia.org/wiki/Dilution_(neural_networks)) you use randomness. You might also randomly perturb your training data. Lots of other sources of randomness that you might want to try.
It's in the first four words! Which parts have you read?
None of that was deterministic and the hardest part was writing efficient monte carlos that could weight each situation and average out a betting strategy close to that from the player's hand history, but throw in randomness in a band consistent with the player's own randomness in a given situation.
And none of it needed to touch on game theory. If it did, it would've been much better. LLMs would have no hope at conceptualizing any of that.