Reminds me of "Universal pre-training by iterated random computation" https://arxiv.org/pdf/2506.20057, with bit less formal approach.

I wonder if there is a closed-form solution for those kinds of initialization methods (call them pre-training if you wish). A solution that would allow attention heads to detect a variety of diverse patterns, yet more structured than random init.

I did a similar project but using 3D fractals I found on shadertoy feeding into ViTs. They are extremely simple iterative functions that produce a ton of scene like complexity.

I have a pet theory that the visual cortex when developing is linked to some kind of mechanism such as this. You just need proteins that create some sort of resonating signal that feed into the neurons as they grow (obviously this is hand-wavy) but similar feedback loops guide nervous system growth in Zebra fish for example.

Eternal question about pre-training, i.e. on what medium this pre-training or pre-pre-training has been performed ... if we assume that the platform is CMOS-based piece of hardware, then the most primitive trainable element would be a two-state learning automaton, that can be assembled out of a couple of dozen transistors. This is effectively a kind of a single cell bacteria. You then need to organize these automata into collectives which can start to exchange tokens and reinforcements between themselves and the training environment, and these collectives do so at a higher level and so on, and that's what can be seen as NCAs. (*) A good source of idea on this approach can be found in the book of M.L. Tsetlin: Automata Theory and Modelling of Biological Systems, 1973 - https://shop.elsevier.com/books/automation-theory-and-modeli...

Can someone ELI5 how this hypothesis could ever be true?

  > "The core hypothesis: what makes language useful for pre-training is its structure, not its semantics."

As a layman, I've always held the intuition that semantics are the only meaningful thing.

"Structure without semantics" = form without function, symmetric/regular noise, right?

My naive bet is on compressing semantics into mediums more expressive/information dense than text. Like how some languages have single words/symbols to represent entire sentence-long concepts.

> The key: since every sequence has a unique latent rule, the model must infer that rule in-context to predict what comes next. This in-context learning ability underpins many of the key reasoning capabilities observed in language models.

This is a remarkable paper. This is the first time I've heard someone training the actual thing we're trying to get this stuff to do!

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> This raises a radical question: Is natural language the only path to intelligence?

Of course not! We have octopi, ravens etc., which in many domain display higher intelligence than frontier AIs.

"Embodied reasoning" (genetic algorithm brute force solving physical tasks for a billion years, to name one solution) is definitely one very practical form of intelligence, although we're taking some shortcuts in replicating it.

I'm wondering if simplified analog tasks like Box2D puzzled would help too (or perhaps even simpler? Hanoi? Block worlds?). I know many companies are using simulations of 3D worlds for that.

What I don't understand is how that can integrate with the LLM (physical intelligence would seem to require specialized circuitry, if only for the latency). But maybe once we have good specialized models, LLMs can be trained on their synthetic data?

Neural cellular automata are interesting because they shift learning from “predict tokens” to “model state evolution.” That feels much closer to a transition-based view of systems, where structure emerges from repeated local updates (transitions) rather than being encoded explicitly.

I'm working on a theoretical/computational framework, the Functional Universe, intended for modeling physical reality as functional state evolution. i would say it could be used to replicate your CA process. Won't link it here to signal my good faith discussing this issue - it's on my GH.

“The long-term vision is: foundation models that acquire reasoning from fully synthetic data, then learn semantics from a small, curated corpus of natural language. This would help us build models that reason without inheriting human biases from inception.”

Honestly, I never thought about reasoning this way, but it's kind of obvious now that someone did it. Very interesting.