{
  "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": "2610.31066",
  "title": "Dual Exam: Controlled Evaluation of Multimodal Agents and World Models",
  "summary": "Dual Exam replaces ad hoc or single-component agent evaluation with a controlled benchmark that scores action selection and state evolution separately across 1,088 agent items and 400 world-model cases. It costs only evaluation compute and API calls to the listed configurations, which fits a single-GPU or API-budget team, but requires implementing the harness and interpreting non-comparable indices. It can fail to predict real coupled deployments: the 150-case pilot recorded zero task successes, and the authors state its value for predicting coupled success or sustained reciprocal learning remains unestablished.",
  "rationale": "A constrained team can usefully adopt component-level diagnosis before deploying multimodal agents, but the benchmark's predictive validity for production behavior is unproven, so it should be piloted on the team's own workloads rather than trusted as a selection criterion.",
  "technique": "Dual-track benchmark for agents and world models",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-22",
  "as_of": "2026-10-23",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.31066",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 132,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Dual Exam replaces ad hoc or single-component agent evaluation with a controlled benchmark that scores action selection and state evolution separately across 1,088 agent items and 400 world-model cases. It costs only evaluation compute and API calls to the listed configurations, which fits a single-GPU or API-budget team, but requires implementing the harness and interpreting non-comparable indices. It can fail to predict real coupled deployments: the 150-case pilot recorded zero task successes, and the authors state its value for predicting coupled success or sustained reciprocal learning remains unestablished.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.31066",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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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_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
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      "limitation": "",
      "next_check": "",
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      "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": ""
}