{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted assistant is too expensive because its model uses most of the GPU memory.",
    "change": "Try the paper's supported lower-precision weights on a copy of your serving setup.",
    "baseline": "The same model at its original precision, on the same GPU.",
    "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": "Peak memory, task pass rate, latency, and total serving cost at the same context and load.",
    "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": "2609.31371",
  "title": "Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness",
  "summary": "This is a benchmark study rather than a new method; it informs the choice between full-precision LLM deployments and low-bit quantized variants for log anomaly detection, replacing accuracy-only model selection with a performance-efficiency-robustness comparison. Adoption costs are limited to revalidating a quantized model on the reader's own log data, since the findings come from three public datasets whose structural and semantic noise profiles may differ from production logs. What can fail is transfer: adaptation-strategy rankings and scaling gains varied across datasets in the study itself, and robustness under label or semantic noise at production perturbation levels is not guaranteed.",
  "rationale": "The quantization finding is directly actionable on a 24 GB GPU, but the paper's own cross-dataset variability requires workload-specific validation before production adoption.",
  "technique": "Empirical study of LLM log anomaly detection",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.31371",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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