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    "headline": "Agnostic Learning Reached the Known Statistical Limit",
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    "dek": "A new learner matches the lower-bound shape for every fixed best-in-class risk, up to universal constants.",
    "summary": "A new learner matches the lower-bound shape for every fixed best-in-class risk, up to universal constants.",
    "body_text": "For binary hypothesis classes with finite VC dimension, the authors construct an agnostic PAC learner whose excess-risk guarantee adapts to the best achievable error in the class. They say its sample complexity matches established lower bounds up to universal constants for every fixed optimal risk, settling the rate rather than optimizing the very large displayed constant. This is a theoretical learning result and does not imply a practical training algorithm for modern foundation models.",
    "why_it_matters": "A new learner matches the lower-bound shape for every fixed best-in-class risk, up to universal constants.",
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    "tags": [
      "learning theory",
      "PAC learning",
      "sample complexity"
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      "title": "arXiv preprint 2608.06363",
      "publisher": "arXiv",
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