I suspect you’ve not interacted with many humans.

They key thing the OP said was "in ways that humans don't".

If you ask a human for a URL to support an argument, they will either send you a URL, which may or may not support the argument, or they won't send a URL.

ChatGPT, before they added a guard to prevent it giving references, would simply make up a plausible looking URL that 404'd. It is a lie in a way humans do not.

Exactly.

I think this can all be boiled down to "memory", and I think the only thing restraining it is that these models don't retrain and store full reweighted snapshots after each move... so basically a storage, not a processing bottleneck. I'd predict the next major innovation in the space will be some type of NN quick diff that lets you restore a memory and personality right into the weights, from a relatively small file without having to go through a whole training process and dialog every time you boot it. Right now it's like getting back to level 8 of Contra... or level 100 of Flappy Bird... but it's going to be a few simple hacks to jump it to the point you want it. Then there's memory, de-falsification, and what we'd describe as "personality".

In Forward Forward Networks, inference and training are the same operation.

can you recommend any papers on this?

I think when the spec is hammered out, we'll know about 12 hours later, because everyone's phone will ring at the same time and the missiles will launch.

Some humans do things like that too...

While I think everyone can agree that the current language models aren't behaving like humans, articulating how seems to be pretty much impossible with our current knowledge, as literally all examples given have been factually incorrect.

Humans totally will lie like that in certain situations. For instance, when held at gunpoint and demanded to answer within 5 seconds, which isn't too dissimilar to the conditions a LLM operates under.