{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "You already need task-specific training and want to reduce its memory or compute cost.",
    "change": "Change only the training technique on a small controlled run after checking prerequisites.",
    "baseline": "Your current training recipe with the same model, data split, and evaluation.",
    "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": "Held-out quality, peak memory, training time, and full compute cost to the target quality.",
    "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": "2609.20156",
  "title": "QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization",
  "summary": "QUALS replaces naive random sampling of pretraining corpora with pattern quantization (vector quantization plus uniform binning) and learnability-based sampling weight calibration for training time series foundation models. Its cost is the corpus analysis and reweighting pipeline added to a large-scale pretraining workflow that itself requires massive multi-domain data and compute far beyond a 24 GB GPU. For this reader it can fail simply by being irrelevant: the method only matters when pretraining a foundation model from scratch, which constrained infrastructure and budget preclude, and its unverified gains do not transfer to fine-tuning or API-based use.",
  "rationale": "The technique targets foundation-model pretraining corpus construction, which the reader cannot perform and gains nothing from when consuming existing forecasting models or APIs.",
  "technique": "Balanced data curation for time series pretraining",
  "family": "treino_eficiente",
  "recommendation": "nao_aplica",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.20156",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "source_url": "https://arxiv.org/abs/2609.20156",
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      "dimension": "compatibility",
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      "dimension": "security",
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    {
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      "dimension": "reproducibility",
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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",
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