{
  "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": "2609.18591",
  "title": "Recursive Reasoning or Statistical Extrapolation? In-Context Learning in Multi-Agent Interdependent Decision-Making",
  "summary": "The paper replaces the assumption that longer interaction history improves LLM agent decisions in strategic multi-agent settings with a rational expectations equilibrium diagnostic that distinguishes genuine recursive belief reasoning from statistical extrapolation. It costs only the effort of constructing history-disruption experiments around an existing game or workflow; no additional inference, training, or hardware is required. Its scope is limited to stylized incomplete-information games, so conclusions may not transfer to concrete production tasks such as negotiation or pricing agents, and adopting it does not itself yield any decision-quality improvement.",
  "rationale": "The finding is a cautionary diagnostic rather than a deployable method: a constrained-infrastructure team should validate with this style of disruption test before paying the token cost of long-context histories for agents in strategically interdependent settings.",
  "technique": "REE benchmark for probing ICL in strategic agents",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18591",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2609.18591",
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    },
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      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "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": "",
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}