arXiv Machine Learning

Recursive Scaling in Masked Diffusion Models

arXiv:2606. 18022v1 Announce Type: new Abstract: Masked diffusion models (MDMs) have recently emerged as a promising paradigm for sequence generation.

arXiv Machine Learning

Jun 17

Learn from Your Mistakes: Self-Correcting Masked Diffusion Models

arXiv:2602. 11590v3 Announce Type: replace Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models, enabling parallel token generation while achieving competitive performance.

By Yair Schiff, Omer Belhasin, Roy Uziel, Guanghan Wang, Marianne Arriola, Gilad Turok, Ran Zilberstein, Michael Elad, Volodymyr Kuleshov

diffusion benchmarks

arXiv AI

Jul 20

DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone

arXiv:2511. 15927v4 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) generation, yet their reliance on Transformer backbones limits inference efficiency due to quadratic attention or KV-cache overhead.

By Vaibhav Singh, Oleksiy Ostapenko, Pierre-Andr\'e No\"el, Eugene Belilovsky, Torsten Scholak

llms diffusion efficiency

arXiv Machine Learning

Aug 6

Esoteric Language Models: A Family of Any-Order Diffusion LLMs

arXiv:2506. 01928v5 Announce Type: replace-cross Abstract: Diffusion-based language models offer a compelling alternative to autoregressive (AR) models by enabling parallel and controllable generation.

By Subham Sekhar Sahoo, Zhihan Yang, Yash Akhauri, Johnna Liu, Deepansha Singh, Zhoujun Cheng, Zhengzhong Liu, Eric Xing, John Thickstun, Arash Vahdat

llms diffusion

arXiv Machine Learning

Jun 29

VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing

arXiv:2606. 28301v1 Announce Type: new Abstract: Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize downstream rewards.

By Kijung Jeon, Thuy-Duong Vuong, Molei Tao

diffusion benchmarks

arXiv AI

Jun 9

Simple Self-Conditioning Adaptation for Masked Diffusion Models

arXiv:2604. 26985v2 Announce Type: replace-cross Abstract: Masked diffusion models (MDMs) generate discrete sequences by iterative denoising under an absorbing masking process.

By Michael Cardei, Huu Binh Ta, Ferdinando Fioretto

diffusion

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

arXiv Machine Learning

Jun 5

Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training

arXiv:2602. 10314v2 Announce Type: replace Abstract: Masked Diffusion Models (MDMs) have emerged as a promising approach for generative modeling in discrete spaces.

By Jaeyeon Kim, Jonathan Geuter, David Alvarez-Melis, Sham Kakade, Sitan Chen

diffusion

arXiv Machine Learning

Aug 10

Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement

arXiv:2507. 08390v5 Announce Type: replace Abstract: Discrete diffusion models have recently emerged as strong alternatives to autoregressive language models, matching their performance through large-scale training.

By Meihua Dang, Jiaqi Han, Minkai Xu, Kai Xu, Akash Srivastava, Stefano Ermon

llms diffusion

arXiv AI

Jul 14

MetaState: Persistent Working Memory Enhances Reasoning in Discrete Diffusion Language Models

arXiv:2603. 01331v3 Announce Type: replace-cross Abstract: Discrete diffusion language models (dLLMs) generate text by iteratively denoising a masked sequence.

By Kejing Xia, Mingzhe Li, Lixuan Wei, Zhenbang Du, Xiangchi Yuan, Dachuan Shi, Qirui Jin, Wenke Lee

llms diffusion benchmarks

arXiv Machine Learning

Jun 29

Masked Language Flow Models

arXiv:2606. 27617v1 Announce Type: cross Abstract: Masked Diffusion Models (MDMs) promise fast, parallel language generation, but their reverse transition factorises across token positions -- an approximation that breaks down in the few-step sampling regime where parallel generation ought to provide the greatest efficiency gains.

By Iskander Azangulov, Kianoosh Ashouritaklimi, Leo Zhang, Simon Vary, Patrick Rebeschini

llms diffusion

arXiv Machine Learning

Jul 23

Multi-Mask Diffusion Language Models for Few-Step Generation

arXiv:2607. 19686v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) are a promising family of language generators, but achieving high-quality few-step generation remains challenging.

By Sijin Chen, Yinuo Ren, Heyang Zhao, Ziheng Cheng, Quanquan Gu, Lexing Ying

llms diffusion efficiency

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

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