logbook.json

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  "schema_version": 2,
  "title": "Repro - FAB: A First-Order AB-based Gradient Algorithm for Distributed Bilevel Optimization over T",
  "emoji": "🎯",
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  "paper": {
    "arxiv_id": "2605.06328"
  },
  "tags": [
    "icml2026-repro",
    "paper-coINrhVRkL"
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  "updated_at": "2026-07-22T09:17:18+00:00",
  "root": {
    "slug": "index",
    "title": "Repro - FAB: A First-Order AB-based Gradient Algorithm for Distributed Bilevel Optimization over T",
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      {
        "slug": "claim-1-fab-integrates-push-pull-communication-strategy-with-value-function-based-penalt",
        "title": "Claim 1: FAB integrates Push–Pull communication strategy with value function-based penalt",
        "file": "pages/claim-1-fab-integrates-push-pull-communication-strategy-with-value-function-based-penalt/page.md",
        "children": []
      },
      {
        "slug": "claim-2-resolves-open-question-concerning-push-pull-convergence-over-time-varying-direct",
        "title": "Claim 2: Resolves open question concerning Push–Pull convergence over time-varying direct",
        "file": "pages/claim-2-resolves-open-question-concerning-push-pull-convergence-over-time-varying-direct/page.md",
        "children": []
      },
      {
        "slug": "claim-3-effective-on-hyperparameter-tuning-data-hyper-cleaning-and-reinforcement-learn",
        "title": "Claim 3: Effective on hyperparameter tuning, data hyper-cleaning, and reinforcement learn",
        "file": "pages/claim-3-effective-on-hyperparameter-tuning-data-hyper-cleaning-and-reinforcement-learn/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      },
      {
        "slug": "executive-summary",
        "title": "Executive summary",
        "file": "pages/executive-summary/page.md",
        "children": []
      }
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