{
  "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": "2610.31511",
  "title": "DuoCook: An Overcooked-Inspired Benchmark for Visual Planning and Coordination",
  "summary": "DuoCook replaces ad hoc or text-only agent evaluation with a standardized Unreal Engine benchmark of 20 cooking maps measuring centralized two-agent coordination, order completion, delivery precision, action acceptance, and API cost. Adopting it costs setup of the Unreal environment plus per-run VLM API expenditure, and adds no capability to the reader's own system since it is an evaluation harness, not a method. It can fail as a proxy: top-down grid visuals and engine-executed navigation abstract away the perception and low-level control problems of real deployments, and with best-model order completion at 26.2% the task may be too hard to discriminate among mid-tier models.",
  "rationale": "This is a research benchmark for comparing VLMs on coordination, not a production technique, so it is worth monitoring for model selection signals but not something to integrate into the reader's stack.",
  "technique": "Overcooked-style VLM coordination benchmark",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-10-22",
  "as_of": "2026-10-23",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.31511",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 122,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DuoCook replaces ad hoc or text-only agent evaluation with a standardized Unreal Engine benchmark of 20 cooking maps measuring centralized two-agent coordination, order completion, delivery precision, action acceptance, and API cost. Adopting it costs setup of the Unreal environment plus per-run VLM API expenditure, and adds no capability to the reader's own system since it is an evaluation harness, not a method. It can fail as a proxy: top-down grid visuals and engine-executed navigation abstract away the perception and low-level control problems of real deployments, and with best-model order completion at 26.2% the task may be too hard to discriminate among mid-tier models.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.31511",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Strongest evaluated model (gpt-6-astra) achieves 26.2% mean order completion; notebook memory ablation shows mixed effects, positive for Claude and Qwen, negative for GPT and Gemini.",
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      "source_url": "https://arxiv.org/abs/2610.31511",
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    }
  ],
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "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": "",
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}