{
  "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": "2610.13456",
  "title": "Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities",
  "summary": "The paper proposes a model-based systems engineering (MBSE) workflow that replaces ad hoc, implicit transfer of simulation and operational observations into design-time artefacts (specifically Influence Models for cyber-physical systems) with a systematic evidence-driven procedure for participant pruning, interaction characterisation, influence-function abstraction, and validity scoping. Adoption costs are process and tooling overhead: teams must instrument simulation and operational activities, curate evidence traces, and maintain enriched design models, none of which involves GPU compute, model training, or LLM inference infrastructure. It can fail when evidence is sparse, unrepresentative of operational conditions, or when the cost of maintaining model-evidence traceability exceeds the reasoning benefit in subsequent design iterations.",
  "rationale": "This is a systems engineering methodology paper with no AI/ML inference, training, or deployment content, so it falls outside the reader's production practice decisions for constrained GPU or API-based infrastructure.",
  "technique": "Evidence-driven feedback loop into design-time models",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.13456",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 122,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The paper proposes a model-based systems engineering (MBSE) workflow that replaces ad hoc, implicit transfer of simulation and operational observations into design-time artefacts (specifically Influence Models for cyber-physical systems) with a systematic evidence-driven procedure for participant pruning, interaction characterisation, influence-function abstraction, and validity scoping. Adoption costs are process and tooling overhead: teams must instrument simulation and operational activities, curate evidence traces, and maintain enriched design models, none of which involves GPU compute, model training, or LLM inference infrastructure. It can fail when evidence is sparse, unrepresentative of operational conditions, or when the cost of maintaining model-evidence traceability exceeds the reasoning benefit in subsequent design iterations.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.13456",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
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      "finding": "",
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      "next_check": "",
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      "dimension": "latency",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "limitation": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
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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": ""
}