{
  "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": "2609.29906",
  "title": "Spatio-temporally complementary feature propagation on graphs for longitudinal AADT estimation",
  "summary": "The method replaces reliance on dense physical sensor coverage by propagating sparse loop-detector measurements over a directed road graph, using flow-ratio matrices instead of a binary adjacency matrix and solving a Poisson energy minimization with residues, anchored by a spatially complete but temporally sparse macroscopic transportation model. It costs little computationally (convergence in minutes on standard hardware) but requires two specific inputs: loop detector data and a calibrated city-scale transportation model with intersection turn ratios. It can fail where turn-ratio estimates are inaccurate, where the macroscopic model is systematically biased, or in cities lacking either data source, and the sub-10% error is validated on a single city.",
  "rationale": "This is a transportation-engineering graph method tied to domain-specific sensor and traffic-model data, with no bearing on LLM training, inference, or agent systems on constrained infrastructure.",
  "technique": "Graph feature propagation for AADT estimation",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29906",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces reliance on dense physical sensor coverage by propagating sparse loop-detector measurements over a directed road graph, using flow-ratio matrices instead of a binary adjacency matrix and solving a Poisson energy minimization with residues, anchored by a spatially complete but temporally sparse macroscopic transportation model. It costs little computationally (convergence in minutes on standard hardware) but requires two specific inputs: loop detector data and a calibrated city-scale transportation model with intersection turn ratios. It can fail where turn-ratio estimates are inaccurate, where the macroscopic model is systematically biased, or in cities lacking either data source, and the sub-10% error is validated on a single city.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.29906",
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    },
    {
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      "statement": "Normalized mean absolute error below 10% for network-wide AADT estimation in Zurich, with convergence within minutes.",
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      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.29906",
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    }
  ],
  "exposure_map": [
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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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": "",
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}