{
  "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": "2609.22000",
  "title": "RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents",
  "summary": "RecreationWorld replaces ad hoc, single-modality CUA evaluation with a five-platform benchmark where a running reference application acts as an oracle for hidden behavioral tests. Adoption costs substantial infrastructure: reproducible Ubuntu, macOS, Windows, Android, and Web environments, a unified GUI-plus-code harness, and trajectory generation at scale, none of which fits a single 24 GB GPU or a limited cloud budget. It can fail as a decision signal because reference-grounded assertions may overfit to the frozen test suites, and the low 2.8% full-pass rate indicates the benchmark mainly discriminates frontier-scale agents rather than small-team systems.",
  "rationale": "The framework is a benchmark and training-environment suite whose value depends on multi-platform infrastructure and large-scale trajectory generation that the reader's constrained setup cannot support, so it is worth tracking but not adopting.",
  "technique": "Recreation-grounded hybrid computer-use agent benchmark",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-18",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.22000",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 235,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RecreationWorld replaces ad hoc, single-modality CUA evaluation with a five-platform benchmark where a running reference application acts as an oracle for hidden behavioral tests. Adoption costs substantial infrastructure: reproducible Ubuntu, macOS, Windows, Android, and Web environments, a unified GUI-plus-code harness, and trajectory generation at scale, none of which fits a single 24 GB GPU or a limited cloud budget. It can fail as a decision signal because reference-grounded assertions may overfit to the frozen test suites, and the low 2.8% full-pass rate indicates the benchmark mainly discriminates frontier-scale agents rather than small-team systems.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.22000",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "GPT-6 Astra leads RecreationBench at 58.1% overall but passes all programmatic tests on only 2.8% of tasks; models trained on its trajectories improve on five out-of-distribution coding and hybrid benchmarks (no factor stated).",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.22000",
      "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": ""
}