{
  "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": "2609.39489",
  "title": "Towards Robust Time Series Learning via Capacity-Centric Modulation",
  "summary": "SACM replaces uniform, fixed dropout regularization with sample-wise dropout probabilities computed from spectral sparsity of internal activations, integrated into existing backbones without architectural changes. It costs only training-time modifications and added implementation complexity; the inference pipeline is unchanged and deterministic. It can fail when the spectral-sparsity signal misestimates sample reliability on a given dataset or backbone, and gains may not transfer outside the reported benchmark suite, since improvements vary across tasks (17% F1 in anomaly detection versus low single digits elsewhere).",
  "rationale": "The method requires no extra hardware, foundation models, or inference cost, making it feasible on a single 24 GB GPU, but the heterogeneous, task-dependent gains require validation on the reader's own time-series workloads before production adoption.",
  "technique": "Sample-Adaptive Capacity Modulation (spectral-sparsity dropout)",
  "family": "treino_eficiente",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39489",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2609.39489",
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      "claim_id": "claim-02",
      "statement": "Across 301 dataset-backbone pairs: 6.7% average forecasting MSE reduction, +3.04% classification accuracy, +17.05% point-adjusted F1 for anomaly detection, with zero test-time overhead.",
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      "dimension": "latency",
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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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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    },
    {
      "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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}