arXiv AI June 4, 2026 By Boyan Han, Yiwei Wang, Yi Song, Yujun Cai, Chi Zhang

Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models

arXiv:2606. 04535v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally support format-constrained tasks like parseable JSON or reasoning templates.

Summary generated by The Flow from the publisher's feed. 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 AI

Jun 19

Diffusion Language Models: An Experimental Analysis

arXiv:2606. 19475v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized language modeling through autoregressive generation, enabling strong performance across a wide range of tasks.

By Thomas Bertolani, Davide Bucciarelli, Leonardo Zini, Marcella Cornia, Lorenzo Baraldi

llms diffusion benchmarks

arXiv Machine Learning

Aug 4

Just on Time: Token-Level Early Stopping for Diffusion Language Models

arXiv:2602. 11133v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative refinement, a process that is often computationally inefficient because many tokens reach stability long before the final denoising step.

By Zakhar Kohut, Severyn Shykula, Mykola Vysotskyi, Serhii Dmytryshyn, Dmytro Khamula, Michal Zakrzewski, Damian Rynczak, Jacek Ma{\l}ecki, Taras Rumezhak, Volodymyr Karpiv

llms diffusion nlp fine-tuning 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

Jul 22

Robust Reasoning Benchmark

arXiv:2604. 08571v3 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their problem-solving abilities depend on the context and textual formatting.

By Pavel Golikov, Evgenii Opryshko, Gennady Pekhimenko, Mark C. Jeffrey

llms nlp benchmarks

arXiv AI

Jun 15

Residual Context Diffusion Language Models

arXiv:2601. 22954v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel.

By Yuezhou Hu, Harman Singh, Monishwaran Maheswaran, Haocheng Xi, Coleman Hooper, Jintao Zhang, Aditya Tomar, Michael W. Mahoney, Sewon Min, Mehrdad Farajtabar, Kurt Keutzer, Amir Gholami, Chenfeng Xu

llms diffusion benchmarks

Related stories

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

Diffusion Language Models: An Experimental Analysis

Just on Time: Token-Level Early Stopping for Diffusion Language Models

A Survey on Diffusion Language Models

Robust Reasoning Benchmark

Residual Context Diffusion Language Models