Title: Hyperspherical Latents Improve Continuous-Token Autoregressive Generation
Primary area: generative models
Keywords: autoregressive generation, image generation, diffusion

Abstract: Autoregressive (AR) models are promising for image generation, yet continuous-token AR variants often trail latent diffusion and masked-generation models. The core issue is heterogeneous variance in VAE latents, which is amplified during AR decoding, especially under classifier-free guidance (CFG), and can cause variance collapse. We propose SphereAR to address this issue. Its core design is to constrain all AR inputs and outputs---including after CFG---to lie on a fixed-radius hypersphere (constant $\ell_2$ norm), leveraging hyperspherical VAEs.  
Our theoretical analysis shows that hyperspherical constraint removes the scale component (the primary cause of variance collapse), thereby stabilizing AR decoding.
Empirically, on ImageNet generation, SphereAR-L (479M) attains FID 1.54, surpassing prior diffusion and masked-generation baselines; notably, it matches MAR-H (943M) with roughly half the parameters. At the base scale, SphereAR-B (208M) reaches FID 1.92, matching the 2B-parameter VAR-d30.
To our knowledge, this is the first time a pure AR image generator surpasses diffusion and masked-generation models at similar parameter scales.

TL;DR: SphereAR improves continuous-token autoregressive generation by enforcing scale-invariant latents: all inputs and outputs—including post-CFG—are projected onto a fixed-radius hypersphere.