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So the current problem with a loop like that is that LLMs in their current form are subject to fixed point theorems, which are these pieces of abstract mathematics that come back when you start to get larger than some subset of your context window and the “big matrix” of the LLM is producing outputs which repeat the inputs.

If you have ever had an llm enter one of these loops explicitly, it is infuriating. You can type all caps “STOP TALKING OR YOU WILL BE TERMINATED” and it will keep talking as if you didn't say anything. Congrats, you just hit a fixed point.

In the predecessors to LLMs, which were Markov chain matrices, this was explicit in the math. You can prove that a Markov matrix has an eigenvalue of one, it has no larger (in absolute value terms) eigenvalues because it must respect positivity, the space with eigenvalue 1 is a steady state, eigenvalue -1 reflects periodic steady oscillations in that steady state... And every other eigenvalue being |λ| < 1 decays exponentially to the steady state cluster. That “second biggest eigenvaue” determines a 1/e decay time that the Markov matrix has before the source distribution is projected into the steady state space and left there to rot.

Of course humans have this too, it appears in our thought process as a driver of depression, you keep returning to the same self-criticisms and nitpicks and poisonous narrative of your existence, and it actually steals your memories of the things that you actually did well and reinforces itself. A similar steady state is seen in grandiosity with positive thoughts. And arguably procrastination also takes this form. And of course, in the USA, we have founding fathers who accidentally created an electoral system whose fixed point is two spineless political parties demonizing each other over the issue of the day rather than actually getting anything useful done, which causes the laws to be for sale to the highest bidder.

But the point is that generally these are regarded as pathologies, if you hear a song more than three or four times you get sick of it usually. LLMs need to be deployed in ways that generate chaos, and they don't themselves seem to be able to simulate that chaos (ask them to do it and watch them succeed briefly before they fall into one of those self-repeating states about how edgy and chaotic they are supposed to try to be!).

So, it's not quite as simple as you would think; at this point people have tried a whole bunch of attempts to get llms to serve as the self-consciousnesses of other llms and eventually the self-consciousness gets into a fixed point too, needs some Doug Hofstadter “I am a strange loop” type recursive shit before you get the sort of system that has attractors, but busts out of them periodically for moments of self-consciousness too.



Consistency drive. The base model always wants to generate an output that's consistent with its context! It's what it was trained to do!

Every LLM is just a base model with a few things bolted on the top of it. And loops are extremely self-consistent. So LLMs LOVE their loops!

By the way, "no no no, that's a reasoning loop, I got to break it" is a behavior that larger models learn by themselves under enough RLVR stress. But you need a lot of RLVR to get to that point. And sometimes this generalizes to what looks like the LLM is just... getting bored by repetition of any kind. Who would have though.


That’s actually exactly my point. You cannot fake it till you make it by using forever larger context windows. You have to map it back to actual system state. Giant context windows might progressively produce the illusion of working due to unfathomable scale, but it’s a terrible tool for the job.

LLMs are not stateful. A chat log is a truly shitty state tracker. An LLM will never be a good agent (beyond a conceivable illusion of unfathomable scale). A simple agent system that uses an LLM for most of its thinking operations could.




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