2 papers
cs.LG2026
Why Depth Matters in Parallelizable Sequence Models: A Lie Algebraic View
Gyuryang Heo, Timothy Ngotiaoco, Kazuki Irie +2
Scalable sequence models, such as Transformer variants and structured state-space models, often trade expressivity power for sequence-level parallelism, which enables efficient tra…
cs.LG2025
Any-Order Flexible Length Masked Diffusion
Jaeyeon Kim, Lee Cheuk-Kit, Carles Domingo-Enrich +5
Masked diffusion models (MDMs) have recently emerged as a promising alternative to autoregressive models over discrete domains. MDMs generate sequences in an any-order, parallel fa…