{
  "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": "2610.20353",
  "title": "Beyond Expert Rankings: Contrast Calibration for Mixed-Precision MoE Quantization",
  "summary": "It replaces rank-only or heuristic expert bit-width assignment with a storage-budget allocator that preserves expert rankings but recalibrates their gradient weights toward an information-theoretic contrast target while scoring gate, up, and down projections jointly. The cost is an offline calibration and search pipeline: model-specific power-transform fitting, per-expert error estimation across three configurations, combination with spectra/variance/precision-decay priors, and added deployment metadata rather than a simple uniform quantizer. It can fail if the contrast target misweights experts for a specific workload, if the joint gate/up/down error estimate does not transfer to production data, or in the minority of settings where perplexity does not improve despite better KL and token agreement.",
  "rationale": "For a small team serving or compressing MoE models on a 24 GB GPU or API budget, the method is relevant and plausibly useful, but its benefits are allocation-quality claims that require workload-specific validation before replacing an existing mixed-precision policy.",
  "technique": "Contrast-calibrated mixed-precision MoE quantization",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20353",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 81,
  "citations": 0,
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      "statement": "Reports the lowest native-to-quantized KL divergence and highest top-token agreement in all nine model/settings against five allocation baselines, and the lowest perplexity in seven of nine settings.",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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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": [],
  "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": "",
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}