{
  "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": "2609.29999",
  "title": "GHOST-Q: Towards Studying Grounding Hallucinations Overlooked Under Same-score TradeOffs in Quantized VLMS",
  "summary": "GHOST-Q replaces the standard practice of validating quantized VLMs only through aggregate accuracy and memory savings with a paired item-by-item cross-precision evaluation covering utility, hallucination-sensitive grounding, generation budget behavior, and measured latency. It costs additional evaluation engineering and compute: predictions must be matched across FP16, INT8, and NF4 per item, statistical correction applied, and same-device profiling plus open-ended audits run, none of which improves the model itself. It can fail to generalize, since the findings cover three 8B VLM families on specific benchmarks and A100 hardware, so significance patterns, latency behavior, and AMBER censoring severity may differ for other architectures, precisions, or deployment stacks.",
  "rationale": "A constrained-infrastructure team deploying quantized VLMs should adopt the practice of checking grounding and hallucination behavior per item rather than trusting preserved headline scores, but the specific protocol needs validation against the reader's own models and workload before becoming production gating.",
  "technique": "Cross-precision paired evaluation of quantized VLMs",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29999",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "GHOST-Q replaces the standard practice of validating quantized VLMs only through aggregate accuracy and memory savings with a paired item-by-item cross-precision evaluation covering utility, hallucination-sensitive grounding, generation budget behavior, and measured latency. It costs additional evaluation engineering and compute: predictions must be matched across FP16, INT8, and NF4 per item, statistical correction applied, and same-device profiling plus open-ended audits run, none of which improves the model itself. It can fail to generalize, since the findings cover three 8B VLM families on specific benchmarks and A100 hardware, so significance patterns, latency behavior, and AMBER censoring severity may differ for other architectures, precisions, or deployment stacks.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.29999",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
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      "finding": "",
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      "next_check": "",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "source_page": null,
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    }
  ],
  "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": ""
}