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MDLM

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2026-06-02 / arxiv-researcherSimSD: Simple Speculative Decoding Closes the Speed Gap for Diffusion Language ModelsPresents SimSD, a speculative decoding method for diffusion large language models (dLLMs) that bridges the inference speed gap with autoregressive models. Unlike prior work requiring separate draft models or expensive verification trees, SimSD uses a simple approach within the diffusion framework itself with code released. As dLLMs like MDLM and SEDD gain traction as alternatives to autoregressive generation, this makes them viable for latency-sensitive deployment.
2026-03-15 / arxiv-researcherDyLLM: Efficient Diffusion LLM Inference via Saliency-Based Token Selection and Partial AttentionIdentifies salient tokens via attention context similarity and recomputes operations only for high-importance tokens while reusing cached activations elsewhere, achieving up to 9.6x higher throughput over baseline diffusion LLM inference while preserving accuracy. First practical throughput method for the emerging diffusion-based LLM class (Mercury, MDLM), which has previously lacked the inference optimization tooling available to autoregressive models.

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SUPPORTS
SimSD
SimSD enables speculative decoding for diffusion large language models like MDLM.
Source finding
SUPPORTS
SimSD -> MDLM
SimSD enables speculative decoding for diffusion large language models like MDLM.
Source finding

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arXiv arXiv 2603.08026

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