the secret sauce is the decomposition, i think. how to take the data, and make it into a form that provides meaningful state transitions in the algorithm that reflect the semantics relating two concepts. with natural language, it's a little easier, the numbers are smaller, and tree structured. with visual and volumetric information, precision of sampling becomes the problem. it seems to me thtat this algebraic traversal will work for code, but probably be rubbish at images and 3d and higher dimensional inputs.

I agree the decomposition is the hard part. The substrate is secondary to how you factor the signal into state transitions that preserve semantics. Natural language is easier because: it’s already discretized it’s hierarchical it compresses well Images and volumetric data aren’t “harder” because they’re continuous — they’re harder because we typically treat them as raw pixel manifolds instead of decomposed symbolic structures. If you algebraically traverse raw pixel space, yes — that’s rubbish. But if you decompose visual data into invariant primitives first (edges, topology, object relations, symmetry groups), then the traversal problem becomes discrete again. The mistake most systems make is trying to optimize directly over the continuous field. The approach I’m describing assumes: Decomposition → Canonicalization → Algebraic transition The algebra isn’t meant to replace sampling. It’s meant to operate after structure extraction. So I’d say: It won’t work on raw images. It might work extremely well on structured representations of images. And that’s a very different claim. Code is just the easiest example because it’s already algebraic. The real question isn’t dimensionality. It’s whether you can factor the domain into stable primitives first. That’s where things get interesting.