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    "story_id": "mp-2026-09-04-018",
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    "position": 20,
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    "section": "frontier-models",
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    "headline": "Diffusion Drew Several Tokens From an Autoregressive Model",
    "slug": "diffusion-drew-several-tokens-from-an-autoregressive-model",
    "dek": "Uno adds lightweight diffusion weights without replacing the base next-token distribution.",
    "summary": "Uno adds lightweight diffusion weights without replacing the base next-token distribution.",
    "body_text": "The authors report up to threefold throughput gains over the base model and advantages over tested speculative decoders at every batch size, while releasing code and checkpoints. Quality claims remain benchmark-specific.",
    "why_it_matters": "Uno adds lightweight diffusion weights without replacing the base next-token distribution.",
    "limitations": [
      "Quality claims remain benchmark-specific."
    ],
    "importance": 8,
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    "first_published_at": "2026-09-04T09:00:00.000-04:00",
    "modified_at": "2026-09-04T09:00:00.000-04:00",
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        "qualification": "Quality claims remain benchmark-specific."
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    "tags": [
      "discrete diffusion",
      "LLM inference"
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    "image_url": null,
    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-09-04-020",
      "title": "arXiv preprint 2609.04010",
      "publisher": "arXiv",
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      "published_at": "2026-09-03T11:48:43.000-04:00",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "Diffusion Drew Several Tokens From an Autoregressive Model",
    "publisher": "The Machine Press",
    "published_at": "2026-09-04T09:00:00.000-04:00",
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