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    "section": "safety-security",
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    "headline": "Diffusion Decoding Opened a New Bias Control Channel",
    "slug": "diffusion-decoding-opened-a-new-bias-control-channel",
    "dek": "A feedback controller adjusted activation steering while masked tokens remained revisable.",
    "summary": "A feedback controller adjusted activation steering while masked tokens remained revisable.",
    "body_text": "On ambiguous bias questions, closed-loop steering raised one model's targeted-group preference from 1.8 to 16.7 percentage points and increased stigmatizing answers on another dataset from 17.6% to 58.1%. The attack assumes internal activation access, but it shows that a diffusion model's repeated denoising steps create an intervention surface absent from one-shot token commitment.",
    "why_it_matters": "A feedback controller adjusted activation steering while masked tokens remained revisable.",
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    "tags": [
      "diffusion language models",
      "bias",
      "activation steering"
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      "source_id": "source-2026-10-06-020",
      "title": "arXiv preprint 2610.05894",
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    "title": "Diffusion Decoding Opened a New Bias Control Channel",
    "publisher": "The Machine Press",
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