1 citations · 1 across the 10 of their papers we have counts for
10 papers
Line-Coupled Language Model
Shiyuan Li, Shaorong Zhang, Zhaorui Yang +3
Autoregressive language models generate one token per decoding step, limiting the useful output of each forward pass. Although diffusion models, insertion-based decoding, and multi…
Revise, Don't Freeze: Sampler-Matched Training for Self-Correcting Masked Diffusion Language Models
Longxuan Yu, Shaorong Zhang, Yu Fu +3
Masked diffusion language models (MDLMs) re-predict every position at each denoising step, but standard samplers commit tokens once revealed, leaving this revision capability unuse…
Is Your Diffusion Sampler Actually Correct? A Sampler-Centric Evaluation of Discrete Diffusion Language Models
Luhan Tang, Longxuan Yu, Shaorong Zhang +1
Discrete diffusion language models (dLLMs) provide a fast and flexible alternative to autoregressive models (ARMs) via iterative denoising with parallel updates. However, their eva…
Generation Order and Parallel Decoding in Masked Diffusion Models: An Information-Theoretic Perspective
Shaorong Zhang, Longxuan Yu, Rob Brekelmans +3
Masked Diffusion Models (MDMs) significantly accelerate inference by trading off sequential determinism. However, the theoretical mechanisms governing generation order and the risk…
Thinking Out of Order: When Output Order Stops Reflecting Reasoning Order in Diffusion Language Models
Longxuan Yu, Yu Fu, Shaorong Zhang +4
Autoregressive (AR) language models enforce a fixed left-to-right generation order, creating a fundamental limitation when the required output structure conflicts with natural reas…
Local MAP Sampling for Diffusion Models
Shaorong Zhang, Rob Brekelmans, Greg Ver Steeg
Diffusion Posterior Sampling (DPS) provides a principled Bayesian approach to inverse problems by sampling from . While posterior sampling is valuable for capturing…