{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "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": "2610.07781",
  "title": "Quantization Effects on Tool-Failure Recovery Vary Across Prompts and Evaluation Designs",
  "summary": "This replaces ad hoc robustness evaluation (single prompt, fault-screened task subsets, implicit scoring policy) with a protocol requiring matched-task comparison, full-pipeline success reporting, stated scoring policies, and task-level uncertainty quantification. It costs additional evaluation runs across multiple prompts and scoring variants, plus the effort to rerun logs under different executor leniency settings; it requires no new infrastructure beyond what an engineer already uses for offline benchmarking. It can fail if treated as a one-time checklist: the paper shows conclusions reverse across prompts and scoring policies, so a deployment decision based on a single configuration remains unreliable even after adopting the protocol partially.",
  "rationale": "The paper's recommendations are directly actionable for an engineer choosing between 8-bit and 4-bit deployment of open models on a single 24 GB GPU, since the demonstrated result reversals mean any existing single-configuration comparison of quantized variants is not decision-grade.",
  "technique": "Evaluation protocol for quantized tool-use agents",
  "family": "avaliacao_de_agente",
  "recommendation": "adotar",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.07781",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This replaces ad hoc robustness evaluation (single prompt, fault-screened task subsets, implicit scoring policy) with a protocol requiring matched-task comparison, full-pipeline success reporting, stated scoring policies, and task-level uncertainty quantification. It costs additional evaluation runs across multiple prompts and scoring variants, plus the effort to rerun logs under different executor leniency settings; it requires no new infrastructure beyond what an engineer already uses for offline benchmarking. It can fail if treated as a one-time checklist: the paper shows conclusions reverse across prompts and scoring policies, so a deployment decision based on a single configuration remains unreliable even after adopting the protocol partially.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.07781",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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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": "",
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}