arXiv Machine Learning

Diffusion-MF: Approximate Structured Diffusion for Sequence Labelling

arXiv:2606. 18856v3 Announce Type: replace-cross Abstract: We introduce Diffusion-MF, a discrete diffu- sion sequence labeller that places a linear-chain conditional random field (LCRF) inside the denoising loop.

arXiv Machine Learning

Jun 18

Approximate Structured Diffusion for Sequence Labelling

arXiv:2606. 18856v1 Announce Type: cross Abstract: Sequence labelling, a core task of Natural Language Processing (NLP), consists in assigning each token of an input sentence a label.

By Nicolas Floquet, Joseph Le Roux, Nadi Tomeh

diffusion nlp

arXiv Machine Learning

Jun 10

A Continuous-Time Markov Chain Framework for Insertion Language Models

arXiv:2606. 10199v1 Announce Type: new Abstract: Insertion Language Models (ILMs) offer several advantages over left-to-right generation and mask-based generation.

By Dhruvesh Patel, Benjamin Rozonoyer, Soumitra Das, Tahira Naseem, Tim G. J. Rudner, Andrew McCallum

llms diffusion

Hugging Face Trending Papers

Jul 27

Rethinking the Generation Order of Block Diffusion Language Models

Diffusion language models enable flexible arbitrary-order generation, but existing sampling methods are mostly designed for early masked diffusion models (MDMs). In this work, we study sampling for recent block diffusion language models (BDLMs).

llms diffusion

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 Machine Learning

Jul 21

Semi-Supervised Conditional Diffusion via Label Augmentation

arXiv:2607. 16685v1 Announce Type: cross Abstract: Conditional diffusion models have become a powerful and flexible framework for learning complex conditional distributions from labeled data.

By Jin Su, Yuan Gao, Yong Zhou, Jian Huang

diffusion benchmarks

arXiv AI

Jun 2

DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention

arXiv:2603. 08026v2 Announce Type: replace-cross Abstract: Masked diffusion language models enable parallel token decoding, providing a promising alternative to the sequential nature of autoregressive generation.

By Younjoo Lee, Seungkyun Dan, Junghoo Lee, Jaiyoung Park, Jung Ho Ahn

llms diffusion efficiency benchmarks

arXiv Machine Learning

Jun 2

DAPD: Dependency-Aware Parallel Decoding via Attention for Diffusion LLMs

arXiv:2603. 12996v2 Announce Type: replace Abstract: Parallel decoding for Diffusion LLMs (dLLMs) is difficult because each denoising step provides only token-wise marginal distributions, while unmasking multiple tokens simultaneously requires accounting for inter-token dependencies.

By Bumjun Kim, Dongjae Jeon, Moongyu Jeon, Albert No

llms diffusion

arXiv AI

Jun 9

Unified Energy for Invariant and Independent Decoding in Diffusion Language Models

arXiv:2606. 09159v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) enable parallel text generation by iteratively denoising a full sequence, offering attractive flexibility compared to auto-regressive (AR) decoding.

By Yuchen Yan, Minkai Xu, Zaiquan Yang, Yatao Bian

llms diffusion

arXiv Machine Learning

Jun 25

Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding

arXiv:2601. 17917v3 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) offer a compelling paradigm for natural language generation, leveraging parallel decoding and bidirectional attention to achieve superior global coherence compared to autoregressive models.

By Zhongyu Xiao, Zhiwei Hao, Jianyuan Guo, Yong Luo, Jia Liu, Jie Xu, Han Hu

llms diffusion efficiency

arXiv Machine Learning

Jul 21

DiFA: Inference-Time Forward-Process Alignment for Diffusion Models

arXiv:2607. 17972v1 Announce Type: new Abstract: The prevailing inference framework for diffusion models formulates generation fundamentally as a problem of numerical integration.

By Shigui Li, Delu Zeng

diffusion safety

arXiv AI

Jun 2

IDLM: Inverse-distilled Diffusion Language Models

arXiv:2602. 19066v2 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have recently achieved strong results in text generation.

By David Li, Nikita Gushchin, Dmitry Abulkhanov, Eric Moulines, Ivan Oseledets, Maxim Panov, Alexander Korotin

llms diffusion efficiency

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