arXiv AI June 2, 2026 By Hyundong Jin, Yo-Sub Han

EPIC: Efficient and Parallel Inference under CFG Constraints for Diffusion Language Models

arXiv:2606. 00722v1 Announce Type: cross Abstract: Controlling language model outputs is essential for ensuring structural validity, reliability, and downstream usability, and diffusion language models are no exception.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 3

Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models

arXiv:2601. 12247v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) present a promising non-sequential paradigm for text generation, distinct from standard autoregressive (AR) approaches.

By Miao Li, Hanyang Jiang, Sikai Cheng, Hengyu Fu, Yuhang Cai, Baihe Huang, Tinghan Ye, Xuanzhou Chen, Pascal Van Hentenryck

llms diffusion benchmarks

arXiv Computation and Language

Aug 28

Dependency-Aware Revocable Decoding for Efficient Diffusion Large Language Model Inference

The paper introduces Dependency-Aware Revocable Decoding (DARD), a training‑free framework for diffusion large language models that separates tokens into masked, candidate, and unmasked states. DARD verifies candidate tokens using a selective context that excludes less reliable tokens and adaptively regulates their influence on subsequent decoding. Experiments on 12 textual and multimodal benchmarks across three open‑source dLLMs show that DARD improves the speed‑quality Pareto frontier, achieving a 2.71× speedup and a 4.35‑point CIDEr gain over Saber on Flickr30K.

By Wooje Park, Insu Lee, Minyoung Noh, Jaeyun Jang, Sungmin Lee, Kyuhong Shim, Byonghyo Shim

llms diffusion multimodal benchmarks

arXiv Computation and Language

Aug 25

Lookahead-then-Verify: Reliable Constrained Decoding for Diffusion LLMs under Context-Free Grammars

arXiv:2602.00612v3 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) have demonstrated promising generative capabilities and are increasingly used to produce formal languages d...

By Yitong Zhang, Yongmin Li, Yuetong Liu, Jia Li, Xiaoran Jia, Zherui Li, Ge Li

llms diffusion benchmarks

arXiv Machine Learning

Jun 5

A Survey on Diffusion Language Models

arXiv:2508. 10875v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm.

By Tianyi Li, Mingda Chen, Bowei Guo, Zhiqiang Shen

llms diffusion nlp efficiency multimodal benchmarks

arXiv AI

Jun 4

Supportive Token Revealing for Fast Diffusion Language Model Decoding

arXiv:2606. 04236v1 Announce Type: cross Abstract: Discrete diffusion language models can generate text efficiently by updating multiple masked positions in parallel, but this parallelism introduces a quality-latency trade-off.

By Giries Abu Ayoub, Mario Barbara, Llu\'is Pastor-P\'erez, Tanja Bien, Aneesh Barthakur, Alaa Maalouf, Loay Mualem

llms diffusion benchmarks

arXiv Machine Learning

Jun 16

Mean-Field Parallel Decoding for Discrete Diffusion Language Models

arXiv:2606. 15805v1 Announce Type: new Abstract: Discrete diffusion language models enable parallel token generation, offering a pathway to low-latency decoding.

By Tamim Zoabi, Ameen Ali, Liran Ringel, Lior Wolf

llms diffusion benchmarks

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Related stories

Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models

Dependency-Aware Revocable Decoding for Efficient Diffusion Large Language Model Inference

Lookahead-then-Verify: Reliable Constrained Decoding for Diffusion LLMs under Context-Free Grammars

A Survey on Diffusion Language Models

Supportive Token Revealing for Fast Diffusion Language Model Decoding

Mean-Field Parallel Decoding for Discrete Diffusion Language Models