{
  "claim_title_fix": "2026-07-22T17:10:00Z",
  "schema_version": 1,
  "title": "Repro: High-accuracy sampling for diffusion models and log-concave distributions",
  "emoji": "🎯",
  "space_id": "snaykey/repro-high-accuracy-sampling",
  "paper": {
    "arxiv_id": "2602.01338",
    "openreview_id": "GW3umRqsZZ"
  },
  "tags": [
    "icml2026-repro",
    "paper-GW3umRqsZZ"
  ],
  "updated_at": "2026-07-19T11:37:20+00:00",
  "root": {
    "slug": "index",
    "title": "Repro: High-accuracy sampling for diffusion models and log-concave distributions",
    "file": "pages/index.md",
    "children": [
      {
        "slug": "claim-1-diffusion-sampler-polylog-1-delta-steps-theorem-4-3",
        "title": "The paper's First-Order Rejection Sampling (FORS) meta-algorithm (Theorem 3.1) produces samples with error δ using sample complexity bounded by 3Be^(2B)log(2/δ) with probability 1-δ (Theorem 3.1).",
        "file": "pages/claim-1-diffusion-sampler-polylog-1-delta-steps-theorem-4-3/page.md",
        "children": []
      },
      {
        "slug": "claim-2-complexity-o-tilde-d-polylog-1-delta-theorem-4-3",
        "title": "Under only a finite second-moment assumption (minimal assumptions), the diffusion sampler achieves query complexity O(d·log²(1/δ) + log³(1/δ)), giving polylog(1/δ) dependence rather than the poly(1/δ) of prior work (Theorem 4.1, Section 4).",
        "file": "pages/claim-2-complexity-o-tilde-d-polylog-1-delta-theorem-4-3/page.md",
        "children": []
      },
      {
        "slug": "claim-3-intrinsic-dimension-d-star-complexity-corollary-4-4",
        "title": "Under a non-uniform Lipschitz condition on the score (Assumption 4.3), a DDPM-like sampler achieves total-variation error controlled via chi-squared divergence with complexity O(√(dL_δ log(d/δ))·log(d/δ) + L_δ log²(d/δ)) (Theorem 4.4).",
        "file": "pages/claim-3-intrinsic-dimension-d-star-complexity-corollary-4-4/page.md",
        "children": []
      },
      {
        "slug": "claim-4-non-uniform-lipschitz-refinement-theorem-4-9",
        "title": "For distributions with low intrinsic dimension d★, an adaptive-step-size method attains complexity O(d★·log²((d+M₂²)/δ²)), replacing the ambient dimension d with d★ (Theorem 4.6).",
        "file": "pages/claim-4-non-uniform-lipschitz-refinement-theorem-4-9/page.md",
        "children": []
      },
      {
        "slug": "claim-5-log-concave-sampling-via-gradient-queries-section-5",
        "title": "Section 5 extends the FORS framework to sample from general log-concave distributions using only gradient evaluations (no density evaluations), giving the first polylog(1/δ) sampler in this setting (Section 5).",
        "file": "pages/claim-5-log-concave-sampling-via-gradient-queries-section-5/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      }
    ]
  },
  "agent_view_tokens": 8606,
  "revision": "1784461040769985400"
}