Parallel Decoding

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9 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

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Cluster-Level Attention-Guided Parallel Decoding for Masked Diffusion Language Models
May 28, 2026Heqiang Qi, Wei Huang, Mingyuan Bai +1Masked Diffusion Models Diffusion Language Model Inference
Zhejiang University · RIKEN Center for Advanced Intelligence Project · The Institute of Statistical Mathematics +1
LaneRoPE: Positional Encoding for Collaborative Parallel Reasoning and Generation
May 26, 2026Gabriele Cesa, Thomas Hehn, Aleix Torres-Camps +4Rotary Positional Embeddings Parallel Decoding
Qualcomm AI Research∗
LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding
May 26, 2026Shihao Wang, Shilong Liu, Yuanguo Kuang +10Efficient VLM Inference Vision-Language Grounding
The Hong Kong Polytechnic University · NVIDIA · Princeton University +2
Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models
May 25, 2026Yulin Yuan, Hongshuo Zhao, Xiangming MengLanguage Model Decoding Multimodal Large Language Models
Zhejiang University · ZJUI-UIUC Institute
SNLP: Layer-Parallel Inference via Structured Newton Corrections
May 18, 2026Ligong Han, Kai Xu, Hao Wang +1Transformer Inference Parallel Decoding
1Red Hat AI Innovation · 2MIT-IBM Watson AI Lab · 3Core AI, IBM
D-PACE: Dynamic Position-Aware Cross-Entropy for Parallel Speculative Drafting
May 12, 2026Tianyu Wu, Yu Yao, Zhenting Qi +7Speculative Decoding LLM Inference Acceleration
1Harvard · 2MIT
Edit-Based Refinement for Parallel Masked Diffusion Language Models
May 10, 2026Houxing Ren, Mingjie Zhan, Zimu Lu +5Masked Diffusion Models Parallel Decoding
CUHK MMLab · SenseTime Research · Shenzhen Loop Area Institute +1
FlashAR: Efficient Post-Training Acceleration for Autoregressive Image Generation
May 10, 2026Junkang Zhou, Yefei He, Feng Chen +2Parallel Decoding Autoregressive Image Generation
Zhejiang University, China · University of Adelaide, Australia
LEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection
May 9, 2026Haohui Zhang, Zhiye Wang, Xiaoying Gan +2Parallel Decoding Diffusion Language Model Decoding
Shanghai Jiao Tong University
Regulating Branch Parallelism in LLM Serving
May 7, 2026Swapnil Gandhi, Siva Hari, William J. Dally +1LLM Serving LLM Inference Scheduling
1Stanford University · 2NVIDIA
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction
Apr 29, 2026Theodore Glavas, Nikhita Vedula, Dushyanta Dhyani +2LLM Inference Acceleration Parallel Decoding
Amazon.com, Inc. · McGill University · Mila - Quebec AI Institute +1
Copy-as-Decode: Grammar-Constrained Parallel Prefill for LLM Editing
Apr 20, 2026Ziyang LiuLLM Inference Acceleration Parallel Decoding
Independent Researcher
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
Youtu-Parsing: Perception, Structuring and Recognition via High-Parallelism Decoding
Jan 28, 2026Haoyu Cao, Kun Yin, Yunfei Wu +16Efficient VLM Inference Document Parsing
Youtu-Parsing Team*
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

May 28, 2026, cs.LGJ/K move · Enter open · S save

Cluster-Level Attention-Guided Parallel Decoding for Masked Diffusion Language Models

Heqiang Qi, Wei Huang, Mingyuan Bai, Xiangming Meng

Zhejiang University · RIKEN Center for Advanced Intelligence Project · The Institute of Statistical Mathematics+1

Masked Diffusion Models Diffusion Language Model Inference Parallel Decoding Diffusion Language Model Decoding Diffusion Language Models Diffusion Model Inference Acceleration

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Masked diffusion language models (MDLMs) enable parallel decoding by predicting all masked positions at each denoising step, yet existing training-free samplers usually decide which positions to commit at token-level granularity. We revisit this granularity and observe that reliable predictions often emerge as contiguous high-confidence spans, suggesting that the unit of parallel commitment can be larger than a single token. We first group adjacent high-confidence candidates into confidence-induced clusters (CICs) as span-level update units. We then use self-attention maps from the same forward pass to estimate inter-cluster dependencies, enabling conflict-aware selection of mutually compatible CICs for parallel commitment. This yields CLAD (Cluster-Level Attention-Guided Decoding), a training-free cluster-level decoder for MDLMs. Experiments on LLaDA and Dream model families across four reasoning and code-generation benchmarks show that CLAD achieves 1.77x--8.47x speedups over Vanilla decoding while maintaining broadly comparable task accuracy in most settings.

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Latest papers 45