It's a prediction algorithm that walks a high-dimensional manifold, in that sense all application of knowledge it just "search", so yes, you're fundamentally correct but still fundamentally wrong since you think this foundational truth is the end and beginning of what LLMs do, and thus your mental model does not adequately describe what these tools are capable of.

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jvanderbot 4 hours ago | parent [-]

Me? My mental model? I gave an analogy for Claude not a explanation for LLMs.

But you know what? I was mentally thinking of both deep think / research and Claude code, both of which are literally closed loop. I see this is slightly off topic b/c others are talking about the LLM only.

	▲	soulofmischief 4 hours ago \| parent [-]
		Sorry, I should have said "analogy" and not "mental model", that was presumptuous. Maybe I also should have replied to the GP comment instead. Anyway, since we're here, I personally think giving LLMs agency helps unlock this latent knowledge, as it provides the agent more mobility when walking the manifold. It has a better chance at avoiding or leaving local minima/maxima, among other things. So I don't know if agentic loops are entirely off-topic when discussing the latent power of LLMs.