{
  "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": "2609.27355",
  "title": "Quantization-Robust Unlearning through the Lens of Retain-Forget Loss Landscapes Interaction",
  "summary": "It replaces standard unlearning fine-tuning, whose forgetting effect degrades after post-training quantization, with curvature-based identification of sensitive weights, noise regularization toward flatter minima, and updates restricted to forget-critical layers. The cost is additional curvature computation, per-layer sensitivity analysis, and extra unlearning-stage optimization rather than any change to inference cost. It can fail if the curvature criterion misidentifies sensitive parameters, if the regularization weight is mis-tuned so forgetting is incomplete or utility drops, and its benefits are unverified outside the MUSE and TOFU benchmarks and the tested quantization settings.",
  "rationale": "The method is relevant only if the reader ships unlearning plus quantized deployment, in which case it is cheap enough to validate on a single GPU but requires workload-specific confirmation before adoption.",
  "technique": "Sensitivity-guided noisy regularization for quantization-robust unlearning",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.27355",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "dimension": "compatibility",
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      "dimension": "security",
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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