{
  "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": "2610.06251",
  "title": "Shared Stopping Decisions Change Answers in HQQ Cache Quantization",
  "summary": "The fix replaces HQQ's shared cross-request average-error stopping criterion with either a fixed iteration budget or request-local stopping when quantizing the KV cache. It costs no additional memory or latency beyond the original iteration budget, but the paper states neither repair has an established quality advantage and request-local stopping remains sensitive to synthetic padding at the tensor level. After adoption, natural rebatching can still change answers, so request-independence is not fully guaranteed even with the fix.",
  "rationale": "If the reader already uses or plans HQQ four-bit KV-cache quantization in batched serving, applying fixed-iteration or request-local stopping is a low-cost change that removes a demonstrated source of batch-dependent answers, but it requires validation on their workload since quality effects are unestablished.",
  "technique": "Fixed or request-local stopping in HQQ KV-cache quantization",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06251",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "source_url": "https://arxiv.org/abs/2610.06251",
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    }
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    {
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  "risks": [],
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  "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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}