{
  "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": "2609.40097",
  "title": "AutoDataBench: A Data-centric Testbed for Accelerating Auto Research",
  "summary": "AutoDataBench replaces general auto-research benchmarks by isolating data-related agent capabilities (diagnosis, organization, construction) while holding training framework, hyperparameters, and compute fixed; it is an evaluation testbed, not a production method. Adopting it costs the compute to run its three curated optimization tasks under task-specific resource budgets, plus engineering effort to integrate with an evaluation pipeline; the paper also suggests reusing its agent trajectories for mid-training, which costs fine-tuning compute. It can fail to transfer: performance on curated data tasks may not predict an agent's data-handling ability on a proprietary pipeline, and the mid-training benefit is reported only for downstream coding performance, not general workloads.",
  "rationale": "This is a controlled benchmark for measuring LLM data intelligence, relevant only if the team needs to evaluate or mid-train agents for data engineering tasks, and it offers no direct production efficiency gain for constrained infrastructure.",
  "technique": "Data-centric benchmark for LLM data intelligence",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.40097",
  "independent_implementations": 1,
  "total_implementations": 3,
  "stars": 123,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "AutoDataBench replaces general auto-research benchmarks by isolating data-related agent capabilities (diagnosis, organization, construction) while holding training framework, hyperparameters, and compute fixed; it is an evaluation testbed, not a production method. Adopting it costs the compute to run its three curated optimization tasks under task-specific resource budgets, plus engineering effort to integrate with an evaluation pipeline; the paper also suggests reusing its agent trajectories for mid-training, which costs fine-tuning compute. It can fail to transfer: performance on curated data tasks may not predict an agent's data-handling ability on a proprietary pipeline, and the mid-training benefit is reported only for downstream coding performance, not general workloads.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.40097",
      "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",
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      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
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      "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": ""
}