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    "story_id": "mp-2026-09-06-007",
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    "headline": "One Video Editor Learned Six Kinds of Change Without Training",
    "slug": "one-video-editor-learned-six-kinds-of-change-without-training",
    "dek": "EditVid combines sparse causal memory, token correspondence and latent blending for instruction- and reference-guided edits.",
    "summary": "EditVid combines sparse causal memory, token correspondence and latent blending for instruction- and reference-guided edits.",
    "body_text": "The framework supports style transfer, attribute changes, object insertion, part edits and subject replacement without task-specific training. On FiVE, the authors report 78.16 FiVE-Acc versus 58.95 for the strongest evaluated training-free baseline, with competitive IVEBench results. A user study preferred EditVid overall in 51.8 percent of comparisons against seven methods. Those numbers reflect the chosen benchmarks and comparisons, not a blanket claim of identity-safe or artifact-free editing.",
    "why_it_matters": "EditVid combines sparse causal memory, token correspondence and latent blending for instruction- and reference-guided edits.",
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      "Those numbers reflect the chosen benchmarks and comparisons, not a blanket claim of identity-safe or artifact-free editing."
    ],
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        "qualification": "Those numbers reflect the chosen benchmarks and comparisons, not a blanket claim of identity-safe or artifact-free editing."
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    "tags": [
      "video editing",
      "identity preservation",
      "generative media"
    ],
    "image_url": null,
    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-09-06-007",
      "title": "arXiv preprint 2609.04190",
      "publisher": "arXiv",
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      "published_at": "2026-09-03T13:59:01.000-04:00",
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    "name": "The Machine Press",
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    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "One Video Editor Learned Six Kinds of Change Without Training",
    "publisher": "The Machine Press",
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