{
  "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": "2610.21069",
  "title": "Probabilistic Optical-Layer Surrogate for Risk-Aware Control-Plane Decision Making",
  "summary": "The method replaces analytical impairment models, deterministic learned predictors, and quantile-regression estimates in optical transport control planes with a conditional generative model that outputs full distributions over OSNR, Q-margin, and feasibility, enabling runtime tail-risk queries without per-decision retraining. The cost is training on validated optical simulator traces plus integration into a network controller, with inference latency claimed as low enough for live decision making but not quantified in the abstract. It can fail through distribution shift between simulator training traces and real hardware behavior, despite the paper's cross-emulator and GNPy validation studies.",
  "rationale": "This is optical transport network engineering, not LLM inference, training, or agent work, so it falls outside the reader's production domain regardless of infrastructure constraints.",
  "technique": "Generative surrogate for optical-layer feasibility estimation",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21069",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces analytical impairment models, deterministic learned predictors, and quantile-regression estimates in optical transport control planes with a conditional generative model that outputs full distributions over OSNR, Q-margin, and feasibility, enabling runtime tail-risk queries without per-decision retraining. The cost is training on validated optical simulator traces plus integration into a network controller, with inference latency claimed as low enough for live decision making but not quantified in the abstract. It can fail through distribution shift between simulator training traces and real hardware behavior, despite the paper's cross-emulator and GNPy validation studies.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21069",
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    }
  ],
  "exposure_map": [
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    },
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      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "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": ""
}