{
  "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": "2610.03356",
  "title": "ReFract: Benchmarking Perspective Awareness in Language Model Agents with Text World Models",
  "summary": "ReFract replaces generic task-success evaluation of agents with a 150-entry benchmark in which the same query demands different actions depending on the user's role, using text world models to simulate operating environments. Adopting it costs only evaluation infrastructure and prompt/agent runs against a text simulator, but it provides no mitigation method, only measurement. It can fail as an adoption signal because 150 entries in industrial maintenance may not transfer to the reader's domain, and passing the benchmark does not guarantee safe role calibration on unrepresented scenarios.",
  "rationale": "The paper is a diagnostic benchmark rather than a deployable technique, so a constrained team should run it as a low-cost evaluation harness if they ship role-facing agents, and treat results as workload-specific evidence rather than proof of safety.",
  "technique": "Perspective-awareness benchmark for role-calibrated LLM agents",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-02",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.03356",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 345,
  "citations": null,
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.03356",
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      "source_excerpt": ""
    }
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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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      "source_page": null,
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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",
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  "equations_fetched_at": "",
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}