[mapontosevenths]

I agree, but maybe for different reasons. I think Karpathy is right. We need models that reason, not models that memorize.

Karpathy calls it a "Cognitive Core", and it's essentially a small model that learns to reason and look up the data it needs as opposed to a giant model that memorizes all the data in the world and tries to process large chunks of it all at once with every thought. I think it will be based on the thing that grokking, the lottery ticket hypothesis, and the universal weight subpspace hypothesis all point to.

Eventually someone will figure out how to build it and the entire economy that we've now built on top of the wacky idea that nothing can possibly ever get more efficient will collapse overnight.

Sometimes I wonder how much Nvidia would pay someone not to release a thing like that, and then I wonder if that's already happened.

[CuriouslyC]

The theorem you want to pay attention to is the no free lunch theorem. The important thing to understand there is that the larger models give you "free lunch" in the sense that you can approximate more different systems accurately at the cost of model size. If there was a Karpathy style universal solver, it wouldn't be very smart unless we scaled it up.

This isn't to say that there aren't a fair amount of wasted parameters in current LLMs, but then we already kinda knew that since you can quantize models down to 3-4 bits per weight often times with minimal loss.

[mapontosevenths]

> If there was a Karpathy style universal solver, it wouldn't be very smart unless we scaled it up.

I think that the scaled up version is actually still really valuable.

Imagine being able to just add more compute as needed for any given problem until it's solved by just adding more copies of a single universal layer, without more training. Or being able to burn the individual core into silicon and just loop it as needed.

I tried to build exactly that in my personal lab once, but hit a wall made of my own incompetence and budget.

The idea was to find the parts of the manifold that did generic reasoning and then scale as needed by repeating them. It worked within individual layers (I could make the model score higher on benchmarks by repeating the reasoning extracts within individual layers), but i could never get the interfaces between layers to work again after I'd done that. I suppose it needed traing to "heal" the interface again after my brain surgery, but I didnt have the compute to manage it and moved on to the next project

I'm sure that someone who actually gets paid to do these things will figure out some version of it eventually though, because I know it can be done.

[SwellJoe]

I don't think LLMs get us there, at least based on my understanding of them. You don't get the reasoning at a very high level without a similarly large parameter size.

Though, I don't think we're really at the maximum density of intelligence per byte. Small models of today are approaching the frontier of maybe 2-3 years ago. Gemma 4 and Qwen 3.6 in their dense versions feel quite capable, give them a search tool and they'll research a topic and often give a pretty good explanation of it. They understand more and hallucinate less than GPT-3, GPT-3.5, and maybe even GPT-4 (the first probable trillion parameter model), and their tool capabilities opened the door for agentic use.

But, those small models are still smaller thinkers. It's not a cognitive core that can do anything if given a way to look up the data, though they're more capable than a lot of folks realize.

Also, there's no reliable way to look up data, anyway. The web is poison, approaching the point where naive web search will most likely return misinformation or intentional disinformation. The source of all human knowledge must be viewed as adversarial, now, which makes having thinky machines that search the web and do stuff with the results a risky business.