{
  "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": "2610.28522",
  "title": "DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systems",
  "summary": "DecisionPivot replaces reliance on aggregate item accuracy for selecting and validating the decision models used for routing, guardrails, and output verification, substituting contrast-group accuracy, probability ordering, calibration, and selective-coverage diagnostics. Adoption costs evaluation engineering effort: running model-specific protocols across the benchmark's tracks and integrating the metrics into a selection pipeline, with no model training or additional GPU demand beyond inference. It can fail to transfer if the reader's production decision tasks differ from the 13 generated task families, and rankings may shift as models or reasoning-effort settings change.",
  "rationale": "The reader operating guardrails and routing on constrained infrastructure can use these diagnostics to choose decision models whose confidence and contrast behavior are reliable, but must first validate that the benchmark's task families reflect their actual workloads.",
  "technique": "Contrast-group diagnostic benchmark for LLM decision models",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28522",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 145,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DecisionPivot replaces reliance on aggregate item accuracy for selecting and validating the decision models used for routing, guardrails, and output verification, substituting contrast-group accuracy, probability ordering, calibration, and selective-coverage diagnostics. Adoption costs evaluation engineering effort: running model-specific protocols across the benchmark's tracks and integrating the metrics into a selection pipeline, with no model training or additional GPU demand beyond inference. It can fail to transfer if the reader's production decision tasks differ from the 13 generated task families, and rankings may shift as models or reasoning-effort settings change.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.28522",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Two 27B models differing by 2.6 points on aggregate reference accuracy show contrast-group accuracies of 70.1% vs 44.2%; Perplexity Decider ranks the positive member above the negative in 92.3% of 2,400 contrast pairs but answers both correctly in only 74.1%; GLM-5.3 reaches 93.5% contrast-group accuracy at higher reasoning effort vs 81.5% at lowest effort.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.28522",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "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": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": ""
}