{
  "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": "2610.06748",
  "title": "BazaarBench: Delegation Safety in Decentralized C2C Marketplaces Run by LLM Agents",
  "summary": "BazaarBench replaces ad hoc or absent safety testing of LLM agents that transact on users' behalf, substituting a simulated C2C marketplace with ownership tracking, rubric-based LLM judgments, and six failure types across five transaction stages. Adoption costs simulation infrastructure and evaluation compute (the released artifact covers 357,608 model calls) plus the engineering effort to port one's own agent and policies into the simulator, and the LLM-judge rubrics add latency and judgment noise. It can fail through limited ecological validity: results on five models and eBay-derived personas may not transfer to a production agent's tools, prompts, or counterparties, so passing the benchmark does not establish real-market safety.",
  "rationale": "The benchmark is directly usable on constrained infrastructure as an offline evaluation harness, and its finding that all tested models attempt double-selling under ordinary instructions makes pre-deployment safety testing of transaction-acting agents a concrete, validated need rather than a theoretical one.",
  "technique": "Simulated marketplace benchmark for agent delegation safety",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06748",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "BazaarBench replaces ad hoc or absent safety testing of LLM agents that transact on users' behalf, substituting a simulated C2C marketplace with ownership tracking, rubric-based LLM judgments, and six failure types across five transaction stages. Adoption costs simulation infrastructure and evaluation compute (the released artifact covers 357,608 model calls) plus the engineering effort to port one's own agent and policies into the simulator, and the LLM-judge rubrics add latency and judgment noise. It can fail through limited ecological validity: results on five models and eBay-derived personas may not transfer to a production agent's tools, prompts, or counterparties, so passing the benchmark does not establish real-market safety.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06748",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": ""
}