{
  "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": "2609.38043",
  "title": "UserProxyBench: Evaluating LLM User Simulators for Agent Benchmarks and Training",
  "summary": "The method replaces agent-only scoring in tau-bench-style interactive benchmarks with an additional evaluation layer (UFS) that scores the LLM user simulator's adherence to its private instructions via task-grounded rubrics. It costs an extra rubric-based judging pass per episode, adding LLM-judge inference cost and benchmark instrumentation complexity, and requires access to the task's private user instructions. It can fail if rubric judging is itself unreliable or miscalibrated, and fidelity scores computed on one benchmark family may not transfer to custom in-house agent evaluations.",
  "rationale": "If the reader evaluates or trains agents with LLM-simulated users, unmeasured simulator infidelity can silently distort results, so fidelity scoring is worth validating on their own benchmark workload before trusting comparative agent metrics.",
  "technique": "User-simulator fidelity scoring for agent benchmarks",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.38043",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces agent-only scoring in tau-bench-style interactive benchmarks with an additional evaluation layer (UFS) that scores the LLM user simulator's adherence to its private instructions via task-grounded rubrics. It costs an extra rubric-based judging pass per episode, adding LLM-judge inference cost and benchmark instrumentation complexity, and requires access to the task's private user instructions. It can fail if rubric judging is itself unreliable or miscalibrated, and fidelity scores computed on one benchmark family may not transfer to custom in-house agent evaluations.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.38043",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Varying only the user proxy (agent fixed at GPT-5.5) shifts mean task reward by 15.2 points across 375 tasks; 24.4% of successful episodes contain a user-specification violation, and premature disclosure reduces agent tool calls by 1.06 on average without changing reward.",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.38043",
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    }
  ],
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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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": "",
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}