{
  "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": "2609.37233",
  "title": "DatalogBench: Evaluating Large Language Models on Text-to-Datalog Synthesis",
  "summary": "DatalogBench is a benchmark, not a deployable technique; it replaces ad hoc or example-based assessment of LLM-generated Datalog with execution-grounded grading against a mutation-validated oracle across 136 tasks. Adopting it costs only benchmark integration effort and LLM inference, but it consumes engineering time without itself improving any production system. Its findings can mislead if generalized beyond Datalog synthesis, and the reported agent gains are model-dependent with recursion-related semantic errors unresolved.",
  "rationale": "The paper is an evaluation artifact whose actionable signal for a constrained-infrastructure team is limited to the observation that coding agents outperform direct prompting for logic synthesis, which warrants awareness rather than adoption or dedicated testing.",
  "technique": "Execution-graded text-to-Datalog synthesis benchmark",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.37233",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DatalogBench is a benchmark, not a deployable technique; it replaces ad hoc or example-based assessment of LLM-generated Datalog with execution-grounded grading against a mutation-validated oracle across 136 tasks. Adopting it costs only benchmark integration effort and LLM inference, but it consumes engineering time without itself improving any production system. Its findings can mislead if generalized beyond Datalog synthesis, and the reported agent gains are model-dependent with recursion-related semantic errors unresolved.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.37233",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Direct prompting peaks at 68.4% exact match; coding agents reach up to 83.8% and nearly eliminate compile-time failures, leaving semantic errors concentrated in recursive tasks. No multiplicative factor is stated.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.37233",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
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      "dimension": "compute",
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      "source_page": null,
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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",
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      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": ""
}