{
  "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": "2609.29465",
  "title": "SWE-Prometheus: Measuring Engineering Governance Improvements in Real-World Repositories",
  "summary": "SWE-Prometheus replaces issue-to-patch benchmarks (e.g., SWE-bench-style functional checks) with open-ended governance tasks scored on improvement, behavior preservation, evidence quality, and coverage across six dimensions. It costs evaluation compute and teacher-model rating calls rather than training resources, and adds harness complexity for clean-environment probes and behavior gates. It can fail to transfer if the 22-repository public subset does not represent the reader's codebase stack, and teacher-based scoring introduces rating variance despite the reported 57/60 exact agreement on no-op evidence.",
  "rationale": "The benchmark is directly runnable on constrained infrastructure via API-based models and gives the reader a rigorous way to compare coding agents on governance tasks before production adoption, but its small public subset and teacher-rated scoring require workload-specific validation.",
  "technique": "Repository-governance benchmark for coding agents",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29465",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "SWE-Prometheus replaces issue-to-patch benchmarks (e.g., SWE-bench-style functional checks) with open-ended governance tasks scored on improvement, behavior preservation, evidence quality, and coverage across six dimensions. It costs evaluation compute and teacher-model rating calls rather than training resources, and adds harness complexity for clean-environment probes and behavior gates. It can fail to transfer if the 22-repository public subset does not represent the reader's codebase stack, and teacher-based scoring introduces rating variance despite the reported 57/60 exact agreement on no-op evidence.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.29465",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
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    },
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    },
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      "dimension": "latency",
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    },
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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": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "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": ""
}