A 1.8-million-network test of the 'networks only differ by parameter symmetry' claim in AI interpretability

A new preprint from ETH Zürich's Mehmet Demir Güven fits about 1.8 million small neural networks to test the field's working explanation for why trained networks look so different: "it's just parameter symmetry."

The standard story says a network's internal "weights" carry redundant symmetries, so reading them for meaning is hard. The new paper, posted to r/MachineLearning with a public repo, splits the question. On MNIST, networks fitted to the same images from shared vs. independent initializations differ by 80.4 accuracy points when a classifier reads their raw weights. Randomizing only each network's exact symmetry group destroys 79.1 of those points. That is a sufficiency result, not a mediation claim.

The picture is uneven across datasets. Orbit-valued reframing recovers 63% of the gap on MNIST, 66% on FashionMNIST, and only 32% on CIFAR-10, per the project README and claims ledger. A new phasor-graded invariant read off raw parameters hits 0.917 at matched capacity, reversing an earlier canonicalization claim the author withdrew after a pre-registration.

The work is a single-author preprint, not peer-reviewed, and one of its headline numbers reversed mid-project. The next test is whether independent groups reproduce the 0.917 phasor-graded quotient on out-of-distribution natural images.