{
  "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": "2609.23668",
  "title": "Fast Graph Laplacian Estimation using Effective Resistance",
  "summary": "The method replaces iterative sparsity-regularized maximum-likelihood estimation of graph Laplacians for Gaussian Markov random fields with a non-iterative estimator that uses effective resistance as a regularizer followed by sparsification. The cost is reduced edge and weight recovery accuracy relative to iterative approaches; compute requirements are low and fit constrained infrastructure easily. It can fail in the underdetermined regime (fewer samples than nodes) or when the Gaussian Markov random field assumption does not hold, producing inaccurate topology estimates.",
  "rationale": "This is a graph signal processing method for inferring network topology from node observations, which falls outside the LLM inference, training, and agent workflows that define the reader's production practice.",
  "technique": "Non-iterative Laplacian estimation via effective resistance",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-20",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.23668",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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