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      <h1>Multi-Block Diffusion Language Models</h1>
      <p class="subtitle">A training recipe, paradigm definition, and runnable inference path for practical Multi-Block Diffusion Language Models.</p>
      <p class="authors"><a class="author-link" href="https://github.com/drewjin">Yijie Jin</a><sup>1</sup>, Jiajun Xu<sup>2</sup>, Yuxuan Liu<sup>1</sup>, Chenkai Xu<sup>1</sup>, Yi Tu<sup>3</sup>, Jiajun Li<sup>3</sup>, Dandan Tu<sup>3</sup>, Xiaohui Yan<sup>3</sup>, Kai Yu<sup>1</sup>, Pengfei Liu<sup>1</sup>, <a class="author-link" href="https://thudzj.github.io/">Zhijie Deng</a><sup>1,†</sup></p>
      <p class="affiliations"><sup>1</sup>Shanghai Jiao Tong University &nbsp; | &nbsp; <sup>2</sup>Xi'an Jiao Tong University &nbsp; | &nbsp; <sup>3</sup>Huawei</p>
      <p class="author-note"><sup>†</sup>Corresponding Author</p>
      <p class="hero-links">
        <a href="https://arxiv.org/abs/2606.29215v1">Paper</a>
        <span aria-hidden="true">·</span>
        <a href="https://github.com/SJTU-DENG-Lab/mbd-lms">MBD-LMs</a>
        <span aria-hidden="true">·</span>
        <a href="https://github.com/SJTU-DENG-Lab/Diffulex/tree/mbd-lms">Reproduce</a>
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        <a href="https://github.com/SJTU-DENG-Lab/Diffulex/tree/main">Diffulex</a>
        <span aria-hidden="true">·</span>
        <a href="https://huggingface.co/collections/SJTU-DENG-Lab/mbd-lms">HuggingFace Weights &amp; Data</a>
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    <section id="featured-video" class="video-section">
      <div class="section-kicker video-kicker">Featured Demo</div>
      <h2>Watch Diffulex Decode in Motion</h2>
      <p class="section-lede">From vanilla LLaDA2-Mini to MBD-LLaDA2-Mini to MBD-LLaDA2-Mini-DMax — the <strong>same prompts, same model backbone</strong>, massive decoding speedup. All on a single A100. See the full progression across four model variants on the videos page.</p>

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          <span>Featured Diffulex trace</span>
          <h3>MBD-LLaDA2-Mini-DMax Demo</h3>
          <p>This selected trace uses <strong>MBD-LLaDA2-Mini-DMax</strong>, the fastest model we trained, running on a <strong>single NVIDIA A100-SXM4-80GB</strong> GPU through the Diffulex engine.</p>
          <p class="video-note"><strong>Playback note.</strong> The demo videos pass through a Streamlit frontend, which can consume much of the engine-side throughput advantage. Use the aggregate TPS numbers on the <a href="diffulex.html">Diffulex page</a> to judge the actual engine path.</p>
          <a class="button secondary" href="videos.html">See All 16 Demos</a>
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      <h2>Explore the Project</h2>
      <p class="section-lede">MBD-LMs spans three parts: demonstrated decoding results, the model-side paradigm, and the inference engine that makes it runnable.</p>
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        <a class="landing-nav-card videos" href="videos.html">
          <span class="card-kicker">Demo Videos</span>
          <h2>Watch Diffulex in Motion</h2>
          <p>16 traces across four model variants — LLaDA2-Mini, MBD-LLaDA2, MBD-LLaDA2-DMax, and DiffusionGemma — all generated by the Diffulex inference engine.</p>
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        <a class="landing-nav-card mbd" href="mbd-lms.html">
          <span class="card-kicker">Method</span>
          <h2>MBD-LMs Paradigm</h2>
          <p>MultiBD formulation, MultiTF post-training, Block Buffer runtime, interactive decode traces, full evaluation results, and throughput analysis.</p>
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        <a class="landing-nav-card diffulex" href="diffulex.html">
          <span class="card-kicker">Inference Engine</span>
          <h2>Diffulex Engine</h2>
          <p>The runnable inference path for block-style diffusion LMs. GSM8K benchmarks at mainstream-engine throughput, with MultiBD, prefix caching, and CUDA Graph support.</p>
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      Multi-Block Diffusion Language Models. Train with <a href="https://github.com/SJTU-DENG-Lab/mbd-lms">mbd-lms</a>; reproduce with <a href="https://github.com/SJTU-DENG-Lab/Diffulex/tree/mbd-lms">Diffulex mbd-lms</a>; develop with <a href="https://github.com/SJTU-DENG-Lab/Diffulex/tree/main">Diffulex main</a>.
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