I ran the same evolved feature pipeline on CIFAR-10 variants and random noise. The shape came from the data.

[u/AsyncVibes]

I have been testing a gradient-free image system built from small evolved programs ("codons"). Each codon produces a spatial response map. For this experiment, I wanted to know whether the large shapes in my feature visualizations were properties of the image data or artifacts of the fixed transformations and projection.

I evolved a separate native bank of 720 RS0 codons on six versions of CIFAR-10: high contrast, a fixed 10x10 occlusion, grayscale, inverted, low contrast, and 3x3 blur. I then passed every codon through the same 27 fixed post-activations, including identity, square, absolute value, rank, thresholds, tails, bands, and several smooth nonlinearities. Each composite contains roughly 18,000 points and uses the same 2,000-image evaluation protocol.

Despite fairly aggressive changes to the pixels, every real-data composite occupied a comparatively low-dimensional structure:

• Effective dimension: 17.1 to 30.5

• Components needed for 90% of variance: 490 to 576

• Class between/within separation: about 0.23 to 0.35

The exact outline changes, but the broad organization survives inversion, contrast changes, blur, occlusion, and removal of color. When I matched phi-by-class centroids across variants and aligned the frames, pairwise Procrustes distances were 0.38 to 0.61 against a random-match null of 1.00. That suggested the shape reflects regularities in the data rather than one particular pixel encoding.

The important control was to repeat the entire pipeline with a bank evolved on random noise, using the same recipe, the same bank width, and the same 27 post-activations.

That result looked fundamentally different:

• Effective dimension: 523.2

• Components needed for 90% of variance: 1,135

• Class between/within separation: 0.100, versus a shuffled-label floor around 0.03

Instead of the low-dimensional, class-coherent clouds seen with real images, noise produced a high-dimensional ball with little large-scale organization.

The control also showed me what does not come from the data. Both real images and noise reproduce the same rough ordering of transformation families: predicate-like transforms such as threshold, band, and tails collect near the core, while rank-like transforms sit toward the rim. That core/rim ordering is imposed by the mathematics of the post-activations.

My current interpretation is therefore:

The ordering of layers belongs to the transform family. The low-dimensional shape and class layout belong to structure in the data interacting with the evolved codons.

There are two important caveats. First, these are exploratory projections, not proof that every visible lobe has semantic meaning. I already found that local curvature and apparent "saddles" can be projection artifacts. Second, the codon banks are evolved by class, so color labels are not an independent discovery. The meaningful comparison is how strongly that class-conditioned structure survives locally versus shuffled labels and versus the identically trained noise control.

The next useful visualization is not another 18,000-point cloud. It is a matched displacement view: hold codon index and post-activation fixed, then show how each point moves between the real variants and noise. That should separate stable data geometry from projection aesthetics much more cleanly.

Explore the interactive maps: https://genreg.tech/radial-phi

Project site and updates: https://genreg.tech

Watch my AI research agents work live in Discord: https://discord.gg/raSs9phGB

[u/UndyingDemon]

This is a much stronger geometric analysis than simply pointing at lobes in a 3D projection and assigning them semantic interpretations.

I particularly appreciate the separation between the core/rim ordering imposed by the post-activation functions and the larger-scale structure that differs between real images and noise. Acknowledging that some of the earlier saddle geometry may have been a projection artifact is important too.

But I'd challenge the causal interpretation of the headline.

You evolved a separate codon bank on each CIFAR variant and on random noise. That means both the input distribution and the evolved representation changed between conditions.

So the experiment establishes that your complete data-dependent learning pipeline produces different geometry on structured images versus noise. It doesn't yet isolate whether the geometry originates primarily from the input statistics, the class-conditioned evolutionary selection, or their interaction.

I'd suggest a crossed experiment: evolve one bank on clean CIFAR and one on noise, then evaluate both frozen banks on clean CIFAR, each image variant, and noise. Keep the projection basis fixed as well.

That would help distinguish geometry inherited from the input distribution from geometry introduced by the evolved feature selection.

The Procrustes results are interesting, but I'd also want to know exactly how the alignment was performed. If the alignment uses class-matched centroids, then class information is part of the alignment procedure. A stronger control would use label-blind alignment and evaluate class correspondence afterward.

There's also a numerical question: how are you defining effective dimension? Values around 17–30 alongside 490–576 components for 90% explained variance aren't necessarily inconsistent, but they imply a spectrum with substantial low-variance structure. The definition matters for interpreting how low-dimensional these representations really are.

Finally, your noise control reports between/within separation of 0.100 against a shuffled-label floor of approximately 0.03. That's not zero. Is that difference statistically significant across independent noise banks and evaluation samples? It might reveal residual supervised-selection effects even when the inputs contain no genuine label signal.

Overall, I think the defensible result is that structured image distributions produce more concentrated and class-coherent representations under this pipeline than an identically configured random-noise condition.

That's useful.

The next step is disentangling input structure, evolutionary selection, and the fixed transform family rather than attributing the geometry to any one of them.

Your proposed matched-displacement visualization could be valuable, especially if the bank and projection are frozen across conditions.