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    "story_id": "mp-2026-08-17-026",
    "source_story_id": "tmp-story-vlm-color-bias",
    "edition_id": "mp-2026-08-17-morning-0039",
    "edition_url": "https://themachinepress.com/edition/2026-08-17",
    "position": 15,
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    "section": "safety-security",
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    "headline": "Green Words Made the Vision Model Read the Sentence Differently",
    "slug": "green-words-made-the-vision-model-read-the-sentence-differently",
    "dek": "Subtle color and contrast changes shifted sentiment and visual-question answers even when the rendered words stayed the same.",
    "summary": "Subtle color and contrast changes shifted sentiment and visual-question answers even when the rendered words stayed the same.",
    "body_text": "Stealth Visual Prompts alter the styling of text rendered as an image without changing its words. Across the reported experiments, coloring positive words green moved sentiment predictions in a positive direction and sometimes obscured negative content; reducing contrast increased reliance on salient visual cues and produced more wrong answers. The study shows a presentation-layer vulnerability in tested vision-language models, not a claim that every model maps green to approval.",
    "why_it_matters": "Subtle color and contrast changes shifted sentiment and visual-question answers even when the rendered words stayed the same.",
    "limitations": [
      "The study shows a presentation-layer vulnerability in tested vision-language models, not a claim that every model maps green to approval."
    ],
    "importance": 8,
    "canonical_url": "https://themachinepress.com/story/mp-2026-08-17-026/green-words-made-the-vision-model-read-the-sentence-differently",
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    "first_published_at": "2026-08-17T09:00:00.000-04:00",
    "modified_at": "2026-08-17T09:00:00.000-04:00",
    "content_status": "new",
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        "text": "Subtle color and contrast changes shifted sentiment and visual-question answers even when the rendered words stayed the same.",
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        "qualification": "The study shows a presentation-layer vulnerability in tested vision-language models, not a claim that every model maps green to approval."
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    "source_ids": [
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    "tags": [
      "vision-language models",
      "color bias",
      "rendered text",
      "robustness"
    ],
    "image_url": null,
    "corrections": []
  },
  "sources": [
    {
      "source_id": "source-2026-08-17-015",
      "title": "arXiv preprint 2608.14286",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2608.14286",
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      "source_type": "primary_research",
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      "published_at": "2026-08-13T20:00:00.000-04:00",
      "accessed_at": "2026-08-17T08:24:13.830-04:00",
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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": "Green Words Made the Vision Model Read the Sentence Differently",
    "publisher": "The Machine Press",
    "published_at": "2026-08-17T09:00:00.000-04:00",
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