The temperature scale, I think. You divide the logit output by the temperature before feeding it to the softmax function. Larger (resp. smaller) temperature results in a higher (resp. lower) entropy distribution.

Can you provide the source of a few of these completely different LLMs?

add even a small amount of change into an LLM […] radically alter the output

You mean perturbing the parameters of the LLM? That’s hardly surprising IMO. And I’m not sure it’s convincing enough to show independence, unless you have a source for this?

two totally independent LLMs

How do you propose to get these independent LLMs? If both are trained using similar objectives e.g., masked token prediction, then they won’t be independent.

Also, assuming independent LLMs could be obtained, how do you propose to compute this hallucination probability? Without knowing this probability, you can’t know how many verification LLMs are sufficient for your application, can you?