Hugging Face Trending Papers July 20, 2026

FlowBlock: Wavefront-Parallel Decoding for Self-Correcting Diffusion Language Models

Read the original on Hugging Face Trending Papers →

Block-wise diffusion large language models (dLLMs) decode sequentially at the block level, enabling effective KV-cache reuse across blocks but making inter-block decoding strictly serial. Prior work has attempted to unlock inter-block parallelism through post-training methods, but achieves only modest speedups and often degrades accuracy.

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 Hugging Face Trending Papers.

arXiv AI

Jul 21

FlowBlock: Wavefront-Parallel Decoding for Self-Correcting Diffusion Language Models

arXiv:2607. 17652v1 Announce Type: new Abstract: Block-wise diffusion large language models (dLLMs) decode sequentially at the block level, enabling effective KV-cache reuse across blocks but making inter-block decoding strictly serial.

By Bing Tian, Haikun Liu, Xiaocheng Zhong, Zhuohui Duan, Zhaokai Luo, Huayi Jin, Zhiyong Wang, Xiaofei Liao

llms diffusion efficiency benchmarks

arXiv AI

Jun 2

BlockBatch: Multi-Scale Consensus Decoding for Efficient Diffusion Language Model Inference

arXiv:2605. 29233v2 Announce Type: replace-cross Abstract: Diffusion language models (dLLMs) generate text by iteratively denoising multiple token positions in parallel, offering an attractive alternative to strictly autoregressive decoding.

By Xiaoyou Wu, Cheng-Jhih Shih, Binfei Ji, Yong Liu, Yingyan Celine Lin

llms diffusion

Hugging Face Trending Papers

Jul 5

Sangam: Efficiently Serving Diffusion LLMs with the AR Stack

Diffusion language models (dLLMs) generate text by iteratively denoising a masked response and can commit multiple output positions per model invocation. Their bidirectional attention prevents exact autoregressive-style KV caching, since committing one position shifts the KV activations of all others.

llms diffusion

arXiv AI

Jul 28

Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models

arXiv:2607. 22663v1 Announce Type: new Abstract: Block diffusion has emerged as the dominant paradigm for scaling discrete diffusion language models (dLLMs), because decoding text in fixed-size blocks preserves parallel generation within each block while keeping the quadratic attention cost tractable.

By Xingyu Mou, Zijin Huang, Tianze Zhang, Yuxin Ma, Lanning Wei, Zengfeng Huang, Da Zheng, Lun Du

llms diffusion fine-tuning efficiency benchmarks

arXiv Machine Learning

Sep 18

Block Parallelism For Efficient Distributed Long-Context Diffusion Language Model Training

The paper introduces Block Parallelism (BP) and Context‑Sharded Block Parallelism (CSBP) to improve training efficiency for Block Diffusion Language Models (BDLMs) with long contexts. By assigning each corrupted‑block computation to a separate rank and sharding the shared clean sequence, CSBP reduces communication overhead and memory usage while preserving training semantics. Experiments on 16 H200 GPUs and 8 H100 GPUs show throughput gains of up to 1.61× and 7.59×, respectively, and higher benchmark pass rates in practical fine‑tuning scenarios.

By Tarun Suresh, Pranshu Chaturvedi, Hangoo Kang, Parth Shroff, Ishan S. Khare, Hermann Kumbong, Azalia Mirhoseini

llms diffusion fine-tuning efficiency

arXiv Machine Learning

Aug 10

Retrofitting Linear Attention into Diffusion Language Models

arXiv:2608. 06628v1 Announce Type: new Abstract: Diffusion language models (dLLMs) offer a promising alternative to autoregressive models by accelerating inference through parallel decoding.

By Jinha Kim, Younghun Roh, Jaeyeon Kim

llms diffusion efficiency benchmarks

One email a morning, machine-written

Related stories

FlowBlock: Wavefront-Parallel Decoding for Self-Correcting Diffusion Language Models

BlockBatch: Multi-Scale Consensus Decoding for Efficient Diffusion Language Model Inference

Sangam: Efficiently Serving Diffusion LLMs with the AR Stack

Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models

Block Parallelism For Efficient Distributed Long-Context Diffusion Language Model Training

Retrofitting Linear Attention into Diffusion Language Models