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    "story_id": "mp-2026-08-27-009",
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    "headline": "Distance Failed to Predict What the Model Would Relearn",
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    "dek": "FRAG scores whether an unlearning update targets forget-critical weights while sparing retain-critical ones.",
    "summary": "FRAG scores whether an unlearning update targets forget-critical weights while sparing retain-critical ones.",
    "body_text": "The authors argue that global weight displacement confuses selective unlearning with random or destructive change. Their training-free Forget-Retain Alignment Gap better separated selective from dense updates, and a pruning method built on the same principle improved relearning robustness in the reported experiments.",
    "why_it_matters": "FRAG scores whether an unlearning update targets forget-critical weights while sparing retain-critical ones.",
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    "importance": 8,
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    "tags": [
      "machine unlearning",
      "relearning",
      "weight selectivity"
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  "sources": [
    {
      "source_id": "source-2026-08-27-009",
      "title": "arXiv preprint 2608.25429",
      "publisher": "arXiv",
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      "published_at": "2026-08-26T02:39:49.000-04:00",
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    "title": "Distance Failed to Predict What the Model Would Relearn",
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