2 papers
cs.AI2026
REFLEX: Rethinking MoE Inference as Refinement-Aware Compute Allocation in Diffusion Language Models
Xiang Xia, Cheng Yan, Yiming Zhang +3
Mixture-of-experts (MoE) models increase parameter capacity by activating only a small subset of experts for each token. This conditional-computation paradigm has enabled autoregre…
cs.LG2026
DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference
Xiang Xia, Wuyang Zhang, Jiazheng Liu +2
Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive language generation due to their potential for parallel decoding and global refinement of…