Diffusion Language Model Inference

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26 papers in the last four weeks, up 767% on the four weeks before. 0.3% of all new papers.

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Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs
May 5, 2026Michael Rottoli, Subhankar Roy, Stefano ParaboschiDiffusion Language Model Inference Diffusion Language Model Decoding
Universit`a degli Studi di Bergamo, Italy
Focus on the Core: Empowering Diffusion Large Language Models by Self-Contrast
May 2, 2026Jinyuan Feng, Xin Yu, Yiqun Chen +5Diffusion Language Model Inference Diffusion Language Model Decoding
Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · Renmin University of China +1
DARE: Diffusion Language Model Activation Reuse for Efficient Inference
May 1, 2026Natalia Frumkin, Bokun Wang, Hung-Yueh Chiang +3Self-Attention Diffusion Language Model Inference
Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin · Cornell University
When to Commit? Towards Variable-Size Self-Contained Blocks for Discrete Diffusion Language Models
Apr 27, 2026Danny Wang, Ruihong Qiu, Zi HuangBlockwise Diffusion Language Models Diffusion Language Model Inference
The University of Queensland
$R^2$ -dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction
Apr 21, 2026Zhenbang Du, Kejing Xia, Xinrui Zhong +6Diffusion Language Model Inference Diffusion Model Fine-Tuning
Georgia Institute of Technology · NVIDIA
Reasoning on the Manifold: Bidirectional Consistency for Self-Verification in Diffusion Language Models
Apr 17, 2026Jiaoyang Ruan, Xin Gao, Yinda Chen +5Diffusion Model Alignment Diffusion Language Model Inference
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China · University of Science and Technology of China, Hefei, China +1
DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference
Apr 17, 2026Xiang Xia, Wuyang Zhang, Jiazheng Liu +2Diffusion Language Model Inference Parallel Decoding
University of Science and Technology of China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center
Towards Faster Language Model Inference Using Mixture-of-Experts Flow Matching
Apr 16, 2026Aihua LiNon-Autoregressive Text Generation Flow Matching
Duke University
Just on Time: Token-Level Early Stopping for Diffusion Language Models
Feb 11, 2026Zakhar Kohut, Severyn Shykula, Mykola Vysotskyi +7Diffusion Language Model Inference Diffusion Language Model Decoding
SoftServe Inc.
ILRR: Inference-Time Steering Method for Masked Diffusion Language Models
Jan 29, 2026Eden Avrahami, Eliya NachmaniMasked Diffusion Models Language Model Steering
School of Computer Science, Tel Aviv University, Israel · Department of Electrical and Computer Engineering, Ben Gurion University, Beer Sheva, Israel.
Window-Diffusion: Accelerating Diffusion Language Model Inference with Windowed Token Pruning and Caching
Jan 28, 2026Fengrui Zuo, Zhiwei Ke, Yiming Liu +3Diffusion Language Model Inference Token Pruning
School of Computer Science and Technology, University of Science and Technology of China · Suzhou Institute of Advanced Research, University of Science and Technology of China
Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding
Jan 25, 2026Zhongyu Xiao, Zhiwei Hao, Jianyuan Guo +4Adaptive Inference Blockwise Diffusion Language Models
School of information and Electronics, Beijing Institute of Technology, Beijing · Department of Computer Science, City University of Hong Kong, Hong Kong · School of Computer Science, Wuhan University, Wuhan +2
Systematic Hazard Sampling: Minimal-Variance Inference for Discrete Diffusion and Flow Models
Jan 6, 2026Seunghwan Jang, Wonje Jeung, SooJean HanMarkov Models Diffusion Sampling
Department of EE KAIST Daejeon, South Korea
Sparse-LaViDa: Sparse Multimodal Discrete Diffusion Language Models
Dec 16, 2025Shufan Li, Jiuxiang Gu, Kangning Liu +4Masked Diffusion Models Diffusion Language Model Inference
Adobe · UCLA
A Comprehensive Study on Visual Token Redundancy for Discrete Diffusion-based Multimodal Large Language Models
Nov 19, 2025Duo Li, Zuhao Yang, Xiaoqin Zhang +2Multimodal Large Language Models Diffusion Language Model Inference
CCDS, NTU, Singapore · CCST, ZJUT, China · Terminus AI Lab, UCAS, China
ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs
Oct 6, 2025Wonjun Kang, Kevin Galim, Seunghyuk Oh +8Diffusion Language Model Inference Parallel Decoding
FuriosaAI · Seoul National University · UW-Madison +4
RFG: Self-Improving Diffusion Large Language Models with Reward-Free Guidance
Sep 29, 2025Tianlang Chen, Minkai Xu, Jure Leskovec +1Diffusion Guidance Diffusion Language Model Inference
Stanford University
Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture
Jun 24, 2025Shuchen Xue, Tianyu Xie, Tianyang Hu +5Masked Diffusion Models Decoder-Only Language Models
University of Chinese Academy of Sciences · Academy of Mathematics and Systems Science, Chinese Academy of Sciences · School of Mathematical Sciences, Peking University +2

May 5, 2026, cs.LGJ/K move · Enter open · S save

Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs

Michael Rottoli, Subhankar Roy, Stefano Paraboschi

Universit`a degli Studi di Bergamo, Italy

Diffusion Language Model Inference Diffusion Language Model Decoding

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Diffusion-based Large Language Models (D-LLMs) represent a promising frontier in generative AI, offering fully parallel token generation that can lead to significant throughput advantages and superior GPU utilization over the traditional autoregressive paradigm. However, this parallelism is constrained by the requirement of a fixed-size response length prior to generation. This architectural limitation imposes a severe trade-off: oversized response length results in computational waste on semantically meaningless padding tokens, while undersized response length causes output truncation requiring costly re-computations that introduce unpredictable latency spikes. To tackle this issue, we propose Predict-then-Diffuse, a simple and model-agnostic framework that enables compute-budgeted inference per input query by first estimating the response length and then using it to run inference with D-LLM. At its core lies an Adaptive Response Length Predictor (AdaRLP), which estimates the optimal response length given an input query. As a measure against under-estimating the response length and re-running inference with a higher value, we introduce a data-driven safety mechanism based on a small increase of the predicted length. As a whole, our framework avoids wasting computation on padding tokens, at the same time preserving output quality. Experimental validation on multiple datasets demonstrates that Predict-then-Diffuse significantly reduces computational costs (FLOP) compared to the default D-LLM inference mechanism, while being robust to skewed data distributions.

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