arXiv Machine Learning July 7, 2026 By Byoungkwon Kim, Minhyuk Sung

Tensor-Train Joint Modeling for Few-Step Discrete Diffusion

Read the original on arXiv Machine Learning →

arXiv:2607. 03788v1 Announce Type: new Abstract: Discrete diffusion promises orders-of-magnitude faster generation than autoregressive (AR) models for sequential discrete data, yet its full potential of few-step generation has remained out of reach due to a fundamental structural limitation.

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 Machine Learning.

arXiv AI

Jul 21

Breaking the Factorization Barrier in Diffusion Language Models

arXiv:2603. 00045v3 Announce Type: replace-cross Abstract: Diffusion language models theoretically allow for efficient parallel generation but are practically hindered by the ``factorization barrier'': the assumption that simultaneously predicted tokens are independent.

By Ian Li, Zilei Shao, Benjie Wang, Rose Yu, Guy Van den Broeck, Anji Liu

llms diffusion reinforcement-learning

arXiv Machine Learning

Jul 9

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling

arXiv:2607. 06841v1 Announce Type: cross Abstract: Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process.

By Robert Gruhlke, Julius Berner, David Sommer, Lorenz Richter

diffusion efficiency

arXiv Machine Learning

Jun 3

Bayesian Tensor Decomposition with Diffusion Model Prior

arXiv:2606. 03212v1 Announce Type: new Abstract: Low-rank tensor decomposition (TD) is usually effective on clean, fully observed data, but it often degrades under severe missingness or noise.

By Zerui Tao, Qibin Zhao

diffusion efficiency benchmarks safety

arXiv Machine Learning

Jun 2

Consistent Diffusion Language Models

arXiv:2605. 00161v2 Announce Type: replace Abstract: Diffusion language models (DLMs) are an attractive alternative to autoregressive models because they promise sublinear-time, parallel generation, yet practical gains remain elusive as high-quality samples still demand hundreds of refinement steps.

By Hasan Amin, Yuan Gao, Yaser Souri, Subhojit Som, Ming Yin, Rajiv Khanna, Xia Song

llms diffusion efficiency

arXiv Computation and Language

4d ago

E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models

arXiv:2609.37533v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically...

By Arseny Ivanov, Alexander Kolesov, Alexander Korotin, Ivan Oseledets, Mikhail Goncharov

llms diffusion multimodal benchmarks

arXiv Computation and Language

3d ago

Distribution Matching Distillation for Continuous Diffusion Language Models

arXiv:2609.40235v1 Announce Type: cross Abstract: Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (...

By Paul Le Van Kiem, Dario Shariatian, Umut Simsekli, Alain Durmus

llms diffusion efficiency

One email a morning, machine-written

Related stories

Breaking the Factorization Barrier in Diffusion Language Models

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling

Bayesian Tensor Decomposition with Diffusion Model Prior

Consistent Diffusion Language Models

E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models

Distribution Matching Distillation for Continuous Diffusion Language Models