Satoshi Hayakawa

Masked generative models can break the linear evaluation barrier during parallel decoding without ever needing to fully reconstruct the underlying token dependency graph.

Committed reveal sampling (CRS) significantly lowers generative perplexity by leveraging persistent context, outperforming traditional top-$p$ sampling methods.