A Dominant Self-Conditioning Direction Drives Repetition in Unconditional Continuous Diffusion Language Models
Continuous diffusion models show repetition because their self-conditioning feedback loop settles on predictable, repeated content. The ACE fix identifies this as a one-dimensional 'repetition axis' and subtracts a single direction from the feedback at every step. This simple method cuts repetition to near human levels while remaining highly efficient.