{
  "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": "2609.39801",
  "title": "RATIO: Reasoning Analysis and Token-level Inference Optimization for Quantized Reasoning Models",
  "summary": "RATIO replaces fixed, predefined overthinking-marker interventions with model-specific penalty tokens identified by comparing a quantized model against its full-precision counterpart (QRBA), with per-token penalties derived from full-precision guidance (TSPD) and no additional training. The cost is a one-time analysis pass requiring access to the full-precision model for calibration, plus a modified decoding loop; penalties must be recomputed per model and quantization configuration. It can fail when a full-precision reference is unavailable (as with API-only models), when the identified overthinking tokens do not transfer across tasks or quantization settings, or when accuracy gains do not replicate on the reader's specific model and workload, since results are benchmark-reported and code availability is prospective.",
  "rationale": "The method is lightweight and training-free, fitting constrained infrastructure, but its gains are model- and quantization-specific and require a full-precision reference plus workload-specific validation before production adoption.",
  "technique": "Quantization-aware overthinking token penalties (RATIO)",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39801",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RATIO replaces fixed, predefined overthinking-marker interventions with model-specific penalty tokens identified by comparing a quantized model against its full-precision counterpart (QRBA), with per-token penalties derived from full-precision guidance (TSPD) and no additional training. The cost is a one-time analysis pass requiring access to the full-precision model for calibration, plus a modified decoding loop; penalties must be recomputed per model and quantization configuration. It can fail when a full-precision reference is unavailable (as with API-only models), when the identified overthinking tokens do not transfer across tasks or quantization settings, or when accuracy gains do not replicate on the reader's specific model and workload, since results are benchmark-reported and code availability is prospective.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.39801",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Up to 9.8 points accuracy improvement and up to 51.3% reduction in chain-of-thought length compared with quantized baselines.",
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      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.39801",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "source_url": "",
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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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,
      "source_excerpt": ""
    }
  ],
  "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": ""
}