{
  "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": "2609.19083",
  "title": "A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings",
  "summary": "The SLE kernel replaces the requirement that a distance measure be conditionally negative definite for Gaussian Process and kernel-method use: it embeds each input into a sparse |D|-dimensional vector via compactly supported bump functions centered at training points, then applies a standard PSD kernel in that embedding space, guaranteeing PSD for arbitrary distances such as geodesic or Wasserstein. The cost is an embedding whose dimension grows with training-set size, the design of compact-support bump functions per input space, and reliance on sparsity to keep kernel matrices well-conditioned and tractable. It can fail if distances concentrate so that landmarks overlap heavily (destroying sparsity and conditioning), if bump-support radii are poorly calibrated to the distance scale, or on very large datasets where the |D|-dimensional embedding and O(n^3) GP inference become impractical without further approximation.",
  "rationale": "This is a classical kernel-methods contribution, not an LLM-systems technique, but it runs on a single GPU or CPU and is directly useful if the reader deploys Gaussian Processes on non-Euclidean data, so small-scale validation on their specific distance measure is warranted rather than blind adoption.",
  "technique": "Sparse Landmark Embedding kernel for arbitrary distances",
  "family": "outro",
  "recommendation": "testar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19083",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
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      "dimension": "compatibility",
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    {
      "dimension": "security",
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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  "equations_source_sha256": "",
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}