{
  "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": "2609.24799",
  "title": "When Quantization Preserves Accuracy but Not Evidence: Explanation-Aware Post-Training Quantization for Medical LLMs",
  "summary": "The method replaces the generic reconstruction/perplexity calibration objective in transformation-based PTQ (OSTQuant at W4A4KV4) with an explanation-aware objective that uses an offline faithfulness cache built from full-precision teacher rationales to preserve evidence tokens and evidence-conditioned answer behavior. The cost is an additional offline teacher-rationale caching step plus modified optimization, while model size, inference latency, and serving cost remain those of standard W4A4KV4 PTQ; it targets preservation of FP behavior rather than accuracy gains. It can fail when the deployment task lacks extractable rationales or teacher rationales are unfaithful, and the abstract reports no quantified improvement, so gains are validated only on three medical multiple-choice benchmarks with 7B-8B models.",
  "rationale": "For a constrained-infrastructure team already using or considering 4-bit PTQ, this is a low-cost modification worth testing only if their production task exposes rationales that users inspect, since the paper shows accuracy preservation does not imply rationale preservation but provides no quantified improvement factor.",
  "technique": "Explanation-aware post-training quantization objective",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24799",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "source_url": "https://arxiv.org/abs/2609.24799",
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    {
      "dimension": "reproducibility",
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  "risks": [],
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  "open_questions": [],
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  "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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}