SAEs trained on a real Euclid galaxy MAE surface monosemantic morphology directions outperforming raw PCA

Wu & Walmsley (2026) train a Matryoshka SAE (batch top-k=64) on a real self-supervised ViT-S MAE (30.1M params, trained on 3M Euclid Q1 galaxy images) and on a real Zoobot ConvNeXt-Nano classifier's embeddings [wu-walmsley-2026-euclid-galaxy-morphology-saes] Top-64 SAE features correlate with Galaxy Zoo human morphology labels at mean-max Spearman r=0.523+/-0.123 (self-supervised) and 0.296+/-0.129 (supervised), versus only 0.434+/-0.174 and 0.176+/-0.138 for a raw-PCA baseline of the same rank; SAE features also stay coherent through k=64 while PCA degrades beyond ~5 components [wu-walmsley-2026-euclid-galaxy-morphology-saes] SAEs additionally surface features outside the Galaxy Zoo taxonomy (dust lanes, blue companions to ellipticals); purely correlational, no causal steering performed [wu-walmsley-2026-euclid-galaxy-morphology-saes]