{
  "paper_id": "ATT-0594",
  "content_status": "PASS_WITH_CONDITIONAL_USE_LIMITS_AND_REVIEW_ERRATA",
  "independent_original_pdf_visual_pages": [
    7,
    9,
    19,
    37
  ],
  "independent_original_pdf_text_pages": [
    5,
    6,
    8,
    13,
    17,
    18,
    22,
    25,
    37,
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  ],
  "risk_banner": "Future covariate availability, unequal tuning budgets, and missing seed uncertainty limit deployment and close-ranking claims. No demonstrated leakage or critical method failure was found.",
  "confirmed_paper_findings": [
    {
      "id": "reported_results_and_boundaries",
      "locations": [
        "PDF p7 Fig5",
        "PDF p6 Fig4",
        "PDF p25 Fig13"
      ],
      "finding": "Real-data normalized WQL0.878 improves over Chronos Small; MASE0.911 trails NBEATSx-G0.898, NHiTS0.902 and PatchTSTx0.905. OIB-only real MASE0.898 beats dual-block0.911, and simple-synthetic OIB-onlyWQL0.182 beats dual-block0.735. These are genuine conditional gains, not universal dominance."
    },
    {
      "id": "information_and_budget",
      "locations": [
        "PDF p5 setup",
        "PDF p17 Table2",
        "PDF p18 AppendixG"
      ],
      "finding": "The paper searches three learning rates for proposed models but states no dataset-specific or global tuning for baselines. Dataset descriptors list counts, frequency and forecast lengths without complete feature-level as-of release-time contracts. The latter is a documented information gap, not confirmed future leakage."
    },
    {
      "id": "uncertainty_and_cost",
      "locations": [
        "PDF p18 repeat-run description",
        "PDF p22 Tables4–7",
        "PDF p19 Fig11",
        "PDF p37 Fig17 and p53 Fig33"
      ],
      "finding": "Tables report means over three seeds without corresponding main-comparison variances. Forecast figures label80% Interval, not90%; forecast uncertainty is distinct from repeated-run uncertainty. Fig11 measures mean training plus testing time and updated parameters, not single-call latency or full model storage."
    },
    {
      "id": "README_metric_example",
      "locations": [
        "PDF p5 metrics",
        "https://github.com/amazon-science/chronos-forecasting/blob/chronosx/README.md"
      ],
      "source_blob_sha": "f95b6620f307efaa37b7cde9a73aab7fa8c97065",
      "finding": "The official README example uses np.arange(0.05,1,0.05),19 quantile levels; paper uses0.1 through0.9,9 levels. This is a configuration difference in the example, not proof that paper experiments used wrong quantiles. No code was executed."
    }
  ],
  "review_errors_corrected": [
    "C used invalid scored status for16 dimensions; author confirms rated with same values and null rated judgment ranges.",
    "D accidentally wrote90% for the80% prediction intervals; score unchanged.",
    "B/C appended corrections replacing experimental task-list phrasing with evidence gaps and research questions."
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