The way LLMs lack broader context, have a narrow focus, and hallucinate, strike me as similar to people that have had traumatic brain injuries to their right hemisphere. Those people may hallucinate that the left side (the right hemisphere senses the left side of the body) of their body is made of wood and hinges and can talk to you about it like it is the most natural thing in the world. When the information gets to the left hemisphere to construct language about what they sense, there is a failure of the right to deliver the broader context to the left hemisphere that that's not possible, but they won't bat an eye discussing what they believe.
So, we have a left hemisphere where we do most of our focused thinking, logic, constructing language, etc. and LLMs seem pretty similar to a lot of that. But, we also think without language, thinking does not require language. A lot of thinking is also happening in the right hemisphere and it isn't using formal logic, isn't using narrow focus but rather intuition based on broad contextual and experiential embodied knowledge. And this type of thinking isn't binary, it accommodates paradoxes without issue. LLMs don't currently have anything analogous to this type of knowledge and this type of processing AFAICT.
In addition, that intuition might be tied to a feedback system with the body, for example, our second brain, the gut, provides a lot of control over how our body performs and provides a lot of feedback to the brain about how we feel. In fact, all feelings are sensed in the body (gut feelings, cold feet, weak in the knees, lump in your throat, burning ears, tight fists, etc.). Part of our intuition is based on considering an idea, sensing how we feel about that idea, sensed in various parts of the body, and then bouncing that back and forth across hemispheres to decide.
I wonder, what sort of pattern matching can we build that models embodied feelings. How would you model boredom, hunger, lust, fear, humor, etc? I think that's possible, but I don't know that we'll be able to do that with a normal computer, I think the way the brain works is more analogous to a symphony of simultaneous signals being processed with an emergent thought and less like a single-threaded process assembling words.
Maybe we can enumerate and model the human drivers of behavior and get something closer to what we're calling comprehension here, but token predictors for language are not getting us any closer to human comprehension. The human brain might just be an anticipation machine, but LLMs only deal with one dimension of human behavior, language, and there's little reason to think you can skip modeling everything that leads to human comprehension and still get anything more than just word babel with compounding error rates in predicting words that represent human comprehension.