{
  "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": "2609.31590",
  "title": "AgentWorld: Benchmarking Long-Horizon Collaboration of Multi-agent LLMs",
  "summary": "AgentWorld replaces short-horizon, competitive, or single-agent benchmarks with 100 human-annotated MMORPG tasks requiring 3-20 agents over 50+ interaction rounds, plus a Causal Collaboration Effectiveness metric that attributes outcomes to individual agent actions. It costs nothing in model deployment but requires standing up the sandbox environment and running many API calls per task, which strains a limited cloud budget for routine evaluation. It can fail as a production decision tool because MMORPG task performance may not transfer to the reader's actual domain, and the benchmark itself shows frontier models solve only 52.0% of tasks, so low scores may reflect environment difficulty rather than actionable signal about a specific deployment.",
  "rationale": "This is an evaluation benchmark rather than a production technique, so it is relevant context for assessing multi-agent systems but not something to adopt or test against constrained-infrastructure workloads.",
  "technique": "Long-horizon multi-agent collaboration benchmark (AgentWorld)",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.31590",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 11,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.31590",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "finding": "",
      "interpretation": "",
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      "next_check": "",
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      "dimension": "operations",
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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_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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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": "",
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}