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    "headline": "Attention Clustered Once, Then Ran Sparse",
    "slug": "attention-clustered-once-then-ran-sparse",
    "dek": "ClusterAttention uses fast per-head recursive clustering and centroid compensation without retraining or offline calibration.",
    "summary": "ClusterAttention uses fast per-head recursive clustering and centroid compensation without retraining or offline calibration.",
    "body_text": "The authors report two-to-six-times speedups on a tabular foundation model while retaining at least 99 percent of dense accuracy, plus a 1.8-times video-generation speedup with outputs closer to dense attention than a tested specialist method.",
    "why_it_matters": "ClusterAttention uses fast per-head recursive clustering and centroid compensation without retraining or offline calibration.",
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      "sparse attention",
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      "GPU"
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      "source_id": "source-2026-08-29-017",
      "title": "arXiv preprint 2608.26965",
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    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "Attention Clustered Once, Then Ran Sparse",
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