[jvanderbot]

What would you add?

To me it's "search" like a missile does "flight". It's got a target and a closed loop guidance, and is mostly fire and forget (for search). At that, it excels.

I think the closed loop+great summary is the key to all the magic.

[bitwize]

Which is kind of funny because my standard quip is that AI research, beginning in the 1950s/1960s, and indeed much of late 20th century computer tech especially along the Boston/SV axis, was funded by the government so that "the missile could know where it is". The DoD wanted smarter ICBMs that could autonomously identify and steer toward enemy targets, and smarter defense networks that could discern a genuine missile strike from, say, 99 red balloons going by.

[soulofmischief]

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.

[jvanderbot]

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]

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.