{
  "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": "2610.00498",
  "title": "Heteroskedastic Canonical Polyadic Tensor Decomposition",
  "summary": "HCP replaces standard CP-ALS squared-error decomposition with a model that jointly estimates a low-rank mean tensor and a low-rank precision tensor for entrywise non-constant noise. The cost is an alternating block-coordinate ascent routine with the same leading-order factor-update complexity as CP-ALS, plus the added state and estimation burden of the precision tensor. It can fail when the low-rank precision assumption is wrong, when data are insufficient to identify both tensors, or in small-sample regimes where variance estimation adds instability rather than accuracy.",
  "rationale": "This is classical tensor decomposition for noisy multiway data such as EEG, not a technique for training, serving, or agentic use of foundation models, so it lies outside the reader's production scope.",
  "technique": "Heteroskedastic CP tensor decomposition",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00498",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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    {
      "claim_id": "claim-01",
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    },
    {
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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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}