A single RFM/AGOP direction per concept steers diffusion at a fraction of guidance cost

Wang et al. train a Recursive Feature Machine on flattened U-Net/DiT block activations from forward-noised labeled images, taking the sign-corrected top eigenvectors of its AGOP matrix as one reusable per-concept steering direction [wang-etal-2026-general-and-efficient-steering-of-diffusion-models-a-single-rfm-agop-direction-per-concept-steers-unconditional-and-latent-diffusion-models-at-a-fraction-of-classifier-guidance-cost] Adding this direction during sampling steers unconditional DDPM/ADM U-Nets (CIFAR-10, ImageNet-256, CelebA-HQ), the transformer latent-diffusion SiT-XL/2, and Stable Diffusion 1.5 [wang-etal-2026-general-and-efficient-steering-of-diffusion-models-a-single-rfm-agop-direction-per-concept-steers-unconditional-and-latent-diffusion-models-at-a-fraction-of-classifier-guidance-cost] On CIFAR-10, NA-RFM reaches 96.6% guidance accuracy versus 77.1% and 86.0% baselines, with FID 41.4 versus 73.9 and 41.9 [wang-etal-2026-general-and-efficient-steering-of-diffusion-models-a-single-rfm-agop-direction-per-concept-steers-unconditional-and-latent-diffusion-models-at-a-fraction-of-classifier-guidance-cost] It runs one fixed low-rank direction across the sampling trajectory at a 16x sampling speedup over a training-free-guidance baseline [wang-etal-2026-general-and-efficient-steering-of-diffusion-models-a-single-rfm-agop-direction-per-concept-steers-unconditional-and-latent-diffusion-models-at-a-fraction-of-classifier-guidance-cost]