Not the weights, the activations, these depend on the input and change every time you evaluate the model. They are not fed back into the next iteration, as is done in an RNN, so information doesn't persist for very long, but it is very much persisted and chewed upon by the various layers as it propagates through the network.
I am not trying to claim that the current crop of LLMs understand in the sense that a human does, I agree they do not, but nothing you have said actually justifies that conclusion or places any constraints on the abilities of future LLMs. If you ask a human to read a joke and then immediately shoot them in the head before it's been integrated into their long term memory they may or may not have understood the joke.