{
  "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": "2610.18139",
  "title": "Revisiting Error Feedback for Model Quantization",
  "summary": "Replaces the fixed-order error-feedback coefficients used in GPTQ/LDLQ-style post-training quantization with coefficients optimized via a metric-dependent trace problem, attained by a geometric mean decomposition of the Hessian. Cost is limited: it runs as a modification to an existing one-shot PTQ procedure on a single GPU, with added complexity from computing downstream-sensitive metrics, and QAT variants require additional training budget. What can fail: the paper itself shows that minimizing the trace bound does not guarantee better finite-level (actual bit-width) quantization accuracy, so gains may not transfer to a specific model and task without validation.",
  "rationale": "It is a low-cost drop-in refinement of GPTQ/LDLQ feasible on constrained infrastructure, but the paper's own finding that trace optimality need not improve practical accuracy makes workload-specific validation necessary before adoption.",
  "technique": "Optimized causal error-feedback coefficients for quantization",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-10-13",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.18139",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
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      "dimension": "compatibility",
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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