{
  "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": "2610.30635",
  "title": "What the Specification Did Not Say: Specification-Oracle Gaps in Test-Graded Coding-Agent Benchmarks",
  "summary": "The method replaces blind trust in test-graded coding-agent benchmark scores with a static audit that flags tests depending on names, paths, or literals absent from the task specification, so benchmark results measure agent capability rather than recall of upstream implementation details. It costs an additional analysis pass over benchmark tasks (audit precision 89.2%, recall 92.9% held out) and adds tooling complexity without improving any agent's performance. It can fail by misclassifying dependencies in roughly 8-11% of cases, by not transferring to benchmarks whose construction it was not validated on, and by being unnecessary if the reader's evaluation tasks are already written or screened for their tests.",
  "rationale": "For an engineer choosing or interpreting coding-agent benchmarks on a constrained budget, running a cheap static audit before trusting leaderboard scores is practical validation work whose value depends on which benchmarks the reader actually uses.",
  "technique": "Static audit of specification-oracle gaps in coding-agent benchmarks",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-22",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30635",
  "independent_implementations": 4,
  "total_implementations": 5,
  "stars": 136,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces blind trust in test-graded coding-agent benchmark scores with a static audit that flags tests depending on names, paths, or literals absent from the task specification, so benchmark results measure agent capability rather than recall of upstream implementation details. It costs an additional analysis pass over benchmark tasks (audit precision 89.2%, recall 92.9% held out) and adds tooling complexity without improving any agent's performance. It can fail by misclassifying dependencies in roughly 8-11% of cases, by not transferring to benchmarks whose construction it was not validated on, and by being unnecessary if the reader's evaluation tasks are already written or screened for their tests.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.30635",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "finding": "",
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      "next_check": "",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "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",
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      "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": ""
}