{
  "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": "2610.23033",
  "title": "Distinguishability and Recovery for a Doubly Sparse Spiked Wigner Model",
  "summary": "This is a theoretical random-matrix statistics paper on detecting and recovering a sparse rank-one signal in a spiked Wigner model; it does not replace any component of an LLM production stack. Its cost is irrelevant to the reader because the contribution consists of statistical thresholds, recovery guarantees, and a leave-one-out quadratic thresholding algorithm analyzed asymptotically, not an implementable inference or training optimization. Nothing fails after adoption because there is nothing to adopt for an engineer running one 24 GB GPU or third-party APIs.",
  "rationale": "The paper addresses sparse signal recovery in random matrix models, a topic with no operational connection to deploying, serving, or fine-tuning language models on constrained infrastructure.",
  "technique": "Doubly sparse spiked Wigner detection and recovery",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-16",
  "as_of": "2026-10-19",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.23033",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This is a theoretical random-matrix statistics paper on detecting and recovering a sparse rank-one signal in a spiked Wigner model; it does not replace any component of an LLM production stack. Its cost is irrelevant to the reader because the contribution consists of statistical thresholds, recovery guarantees, and a leave-one-out quadratic thresholding algorithm analyzed asymptotically, not an implementable inference or training optimization. Nothing fails after adoption because there is nothing to adopt for an engineer running one 24 GB GPU or third-party APIs.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.23033",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
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    },
    {
      "dimension": "compute",
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    {
      "dimension": "latency",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}