LLMs have loops. The output is fed back in for the next prediction cycle. How is that not the same thing?

Wish I had a great answer for you but I don't. It certainly allows for more thought-like LLMs with the reasoning type models. I guess the best answer is that the loop only happens at a single discrete place and doesn't carry any of the internal layer context across.

Another answer might be, how many comments did you read today and not reply too? Did you write a comment by putting down a word and then deciding what the next one should be? Or did you have a full thought in mind before you even began typing a reply?

So, how is it not the same thing? Because it isn't

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adastra22 2 days ago | parent [-]

It feels like the same thing to me…

	▲	MadnessASAP a day ago \| parent [-]
		With more coffee on me, another way to put it would be to say the neural networks in a LLM use dense layers where every neuron takes the output from every neuron in the previous layer and outputs to every neuron in the next layer. A brain doesn't have layers and uses sparse connections, any neuron can connect to any other neuron (but not ever other neuron). You can recreate this structure on a computer but how do you decide where your inputs and outputs are? How do you train it? Since it never halts how do you know when to take the output? There's a reason CS loves its graphs directed and acyclic, they're a lot easier to reason about that way.