[voxleone]

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.

[troelsSteegin]

from https://voxleone.github.io/FunctionalUniverse/pages/executiv..., "The Functional Universe models reality as a history built from irreversible transitions, with time emerging from the accumulation of causal commitments rather than flowing as a primitive parameter." Is it fair to say that time is simply a way of organizing a log file on a dynamic reality? I interpreted "composition of transitions" as a system of processes. I think the hard modeling problem is interpreting interactions between processes - that transitions don't simply compose, that observed transitions may be confounded views of more complex transitions. I gather NCA would be granular enough to overcome that.

[voxleone]

That’s a very good objection, and it’s pointing at a real pressure point in our framework.

Short answer: it’s close, but incomplete. It’s not that time organizes a log of reality; rather, reality is the accumulation of committed transitions. What you’re calling a ‘log’ it’s the ontological structure itself.

I gather you're basically saying: what we see as a transition ≠ what’s actually happening at the fundamental level. This is a legitimate and deep problem.

You’re right that observed transitions may not compose cleanly. In the Functional Universe, composition is a property of fundamental transitions. What we observe are often coarse-grained projections of many underlying transitions, which can obscure compositional structure.