Not sure what this internal state you are referring to is. Are you talking about all the values that come out of each step of the computations?

It would need to be able to form memories like real brains do, by creating new connections between neurons and adjusting their weights in real time in response to stimuli, and having those connections persist. I think that’s a prerequisite to models that are capable of higher-level reasoning and understanding. But then you would need to store those changes to the model for each user, which would be tens or hundreds of gigabytes.

These current once-through LLMs don’t have time to properly digest what they’re looking at, because they essentially forget everything once they output a token. I don’t think you can make up for that by spitting some tokens out to a file and reading them back in, because it still has to be human-readable and coherent. That transformation is inherently lossy.

This is basically what I’m talking about: https://www.comicagile.net/comic/context-switching/

But for every single token the LLM outputs. The fact that it’s allowed to take notes is a mitigation for this context loss, not a silver bullet.