{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.20788",
  "title": "Sliced Wasserstein Sketches with Error Certificates",
  "summary": "The method replaces storing and comparing full empirical distributions (or large equal-mass quantizations) in sliced Wasserstein pipelines with optimal free-weight M-atom quantizations per projection, costing O(LM) comparison time and the implementation complexity of the quantization plus residual-energy bookkeeping. It adds certified error bounds and covariance recovery from residual energies, but equal-mass sketches can converge more slowly than the O(M^{-1}) rate for optimal sketches. It can fail when the finite (2+delta) moment assumption does not hold, when too few projections are used for covariance recovery, or when the workload does not actually compare probability distributions, making the machinery irrelevant.",
  "rationale": "For engineers whose production systems compare probability distributions via sliced Wasserstein distances, this offers storage reduction with computable error certificates at negligible infrastructure cost, but the gain is workload-specific and unquantified, so it warrants a targeted pilot rather than blanket adoption.",
  "technique": "Optimal sliced Wasserstein sketching with error certificates",
  "family": "outro",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20788",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "statement": "Experiments show optimal sketches substantially reduce storage while preserving the classification performance of uncompressed sliced distances on document, point-cloud, and flow-cytometry benchmarks; no multiplicative factor is stated in the abstract.",
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      "dimension": "compatibility",
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      "dimension": "security",
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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