arXiv AI July 17, 2026 By Parikshit Bansal, Sujay Sanghavi

Token Time Continuous Diffusion for Language Modeling

arXiv:2607. 14106v1 Announce Type: cross Abstract: In this paper we introduce token time continuous diffusion (TTCD), a new diffusion language model which (a) operates in continuous space, deterministically mapping Gaussian noise to a final token canvas with no further sampling, and crucially (b) incorporates a new notion of per-token times, with some tokens proceeding from noise to token at a faster rate than others.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding

arXiv:2607. 01775v1 Announce Type: new Abstract: Discrete diffusion models have steadily improved in quality relative to autoregressive (AR) models.

By Marianne Arriola, Volodymyr Kuleshov

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d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation

arXiv:2601. 07568v3 Announce Type: replace-cross Abstract: Diffusion large language models (dLLMs) offer capabilities beyond those of autoregressive (AR) LLMs, such as parallel decoding and random-order generation.

By Yu-Yang Qian, Junda Su, Lanxiang Hu, Peiyuan Zhang, Zhijie Deng, Peng Zhao, Hao Zhang

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Sumi: Open Uniform Diffusion Language Model from Scratch

arXiv:2606. 19005v1 Announce Type: cross Abstract: Diffusion models have become a promising alternative to autoregressive models.

By Mengyu Ye, Keito Kudo, Wataru Ikeda, Ryosuke Matsuda, Keisuke Sakaguchi, Jun Suzuki

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 AI

Jun 10

Optimality of FSQ Tokens for Continuous Diffusion for Categorical Data with Application to Text-to-Speech

arXiv:2606. 09962v1 Announce Type: cross Abstract: Continuous diffusion for categorical data is a framework belonging to the diffusion family and aiming at generating discrete data.

By Vadim Popov, Wenju Gu, Tasnima Sadekova, Georgii Aparin, Assel Yermekova

llms diffusion nlp multimodal

arXiv AI

Jul 7

Training Hybrid Block Diffusion Language Models with Partial Bidirectionality

arXiv:2607. 02805v1 Announce Type: cross Abstract: High-throughput long-context generation is one of the central challenges for large language models.

By Pranshu Chaturvedi, Parth Shroff, Tarun Suresh, Hangoo Kang, Kaiyue Wen

llms diffusion efficiency

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Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding

d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation

Sumi: Open Uniform Diffusion Language Model from Scratch

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

Optimality of FSQ Tokens for Continuous Diffusion for Categorical Data with Application to Text-to-Speech

Training Hybrid Block Diffusion Language Models with Partial Bidirectionality