logbook.json

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  "schema_version": 2,
  "title": "Reproduction: Unsupervised Neural Langevin Sampler for Mixed Integer Linear Programming",
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  "space_id": "junwatu/repro-unsupervised-neural-langevin-sampler-for-mixed-integer-linear-programming",
  "paper": {
    "openreview_id": "R3za7ajCwo"
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  "tags": [
    "icml2026-repro",
    "paper-R3za7ajCwo"
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  "updated_at": "2026-07-23T02:29:27+00:00",
  "root": {
    "slug": "index",
    "title": "Reproduction: Unsupervised Neural Langevin Sampler for Mixed Integer Linear Programming",
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        "slug": "executive-summary",
        "title": "Executive summary",
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      },
      {
        "slug": "claim-1-the-unsupervised-langevin-sampler-achieves-100-feasibility-on-milps-without-expensive-post-processing",
        "title": "Claim 1: The unsupervised Langevin sampler achieves 100% feasibility on MILPs without expensive post-processing",
        "file": "pages/claim-1-the-unsupervised-langevin-sampler-achieves-100-feasibility-on-milps-without-expensive-post-processing/page.md",
        "children": []
      },
      {
        "slug": "claim-2-the-method-matches-or-outperforms-supervised-solvers-on-benchmark-datasets-for-mixed-integer-linear-programming",
        "title": "Claim 2: The method matches or outperforms supervised solvers on benchmark datasets for mixed integer linear programming",
        "file": "pages/claim-2-the-method-matches-or-outperforms-supervised-solvers-on-benchmark-datasets-for-mixed-integer-linear-programming/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      }
    ]
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