{
  "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": "2610.09183",
  "title": "Q-PACE: Dynamic Precision Allocation for Quantization-Aware Training",
  "summary": "Q-PACE replaces static or heuristic layer-precision assignments in mixed-precision quantization-aware training with a second-order sensitivity model that periodically recomputes per-layer curvature coefficients and reassigns precision under a memory budget. The cost is the added implementation complexity of curvature estimation and periodic perturbation-based recalibration during training, plus QAT's inherent training-time overhead relative to post-training quantization. It can fail if sensitivity estimates drift between recalibrations, if the second-order approximation misranks layers for architectures or tasks unlike those evaluated, or if the reader only performs inference-time quantization, in which case a training-time method offers no benefit.",
  "rationale": "The method targets QAT, which the reader can run at small scale on a single 24 GB GPU, but the claimed gains are only demonstrated up to 4B parameters and require workload-specific validation of the recalibration overhead and sensitivity stability before production adoption.",
  "technique": "Dynamic per-layer precision allocation for QAT",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.09183",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Q-PACE replaces static or heuristic layer-precision assignments in mixed-precision quantization-aware training with a second-order sensitivity model that periodically recomputes per-layer curvature coefficients and reassigns precision under a memory budget. The cost is the added implementation complexity of curvature estimation and periodic perturbation-based recalibration during training, plus QAT's inherent training-time overhead relative to post-training quantization. It can fail if sensitivity estimates drift between recalibrations, if the second-order approximation misranks layers for architectures or tasks unlike those evaluated, or if the reader only performs inference-time quantization, in which case a training-time method offers no benefit.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.09183",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Achieves comparable loss to existing mixed-precision QAT recipes at substantially lower total memory budgets, on models up to 4B parameters; no numeric factor is stated in the abstract.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.09183",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "finding": "",
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    },
    {
      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}