{
  "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": "2610.26437",
  "title": "R2-GP: Variance-Calibrated Priors for Additive Gaussian Processes",
  "summary": "R2-GP replaces independent priors on component amplitudes in Matérn-kernel additive Gaussian process models with a single global R2 prior that allocates each component's variance contribution after rescaling by length-scale and observed inputs. It costs a reparameterisation of the prior and, for large datasets, adoption of Hilbert space kernel approximations, adding modelling complexity without reducing compute. It can fail if the chosen global R2 prior is misspecified, if the spectral approximation degrades kernel fidelity at small length-scales, or in regimes where the independent-prior baseline was already adequate.",
  "rationale": "The technique is a Bayesian statistical modelling contribution relevant only if the reader deploys GP-based additive models, which is orthogonal to LLM-centric production work and requires no GPU but also solves no stated infrastructure constraint.",
  "technique": "Variance-calibrated R2 prior for additive GPs",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-10-19",
  "as_of": "2026-10-20",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.26437",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "source_url": "https://arxiv.org/abs/2610.26437",
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    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    {
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  "risks": [],
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  "open_questions": [],
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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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