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    "headline": "The Replay Buffer Refreshed Its Old Examples",
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    "dek": "Uniform Herding reselects bounded class exemplars in the model's current representation instead of freezing yesterday's geometry.",
    "summary": "Uniform Herding reselects bounded class exemplars in the model's current representation instead of freezing yesterday's geometry.",
    "body_text": "On a ten-task CIFAR-100 protocol with a 2,000-example active budget, the method reports 44.00 percent final average accuracy and 17.22 percent forgetting, compared with 42.33 percent and 24.87 percent for iCaRL. Removing distillation increased forgetting, while changes to the active budget mattered more than the retrieval budget. Because the end-to-end comparison changes more than exemplar refresh alone, the paper explicitly limits its causal claim to the tested protocol.",
    "why_it_matters": "Uniform Herding reselects bounded class exemplars in the model's current representation instead of freezing yesterday's geometry.",
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    "importance": 7,
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    "tags": [
      "continual learning",
      "replay",
      "catastrophic forgetting",
      "computer vision"
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  "sources": [
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      "source_id": "source-2026-08-14-010",
      "title": "arXiv preprint 2608.13061",
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      "published_at": "2026-08-13T06:25:38.000-04:00",
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    "title": "The Replay Buffer Refreshed Its Old Examples",
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