AnalysisAI ModelsAugust 28, 2026

Diffusion LLM inference gains speed via new decoding methods

New papers propose parallel decoding, length control, caching, and verification to speed up diffusion language models. Techniques include visual-information-guided parallel decoding, survival-guided length control, affix cache, and prefix-denoising consistency.

How this story unfolded

4 days · 9 reports · from Aug 24

Aug 24
- Is Multimodal Speculative Decoding Ready for Diffusion-Based Parallel Drafting? A Survey and Empirical Diagnosisarxiv.org
- GRAFT: Adaptive DLM-Based Draft Tree Construction with Target-Distilled Edge Scoringarxiv.org
Aug 25
- Improving Few-Step Language Flows with Untied Self-Conditioningarxiv.org
Aug 26
- Serving Masked Diffusion LLMs: Characterization and Design Principles from Real Hardwarearxiv.org
Aug 27
- DCGC: Draft-Conditioned Global Correction for Complex Reasoning with Masked Diffusion Modelsarxiv.org
- Prefix-Denoising Consistency: Test-Time Verification for Diffusion Language Modelsarxiv.org
Aug 28

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Diffusion LLM inference gains speed via new decoding methods

How this story unfolded

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