{
  "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": "2610.11015",
  "title": "Prompts versus Rules: Auditing and Controlling Speech Naturalness Behaviors in Voice User Simulators",
  "summary": "The method replaces prompt-only control of disfluency, interruption, and backchanneling in LLM-based voice user simulators with a deterministic, model-free injection algorithm, plus an audit of realized rather than configured behaviors. It costs little in compute or infrastructure since it is rule-based, but adds implementation and validation effort for the injection logic and the auditing pipeline. It can fail if the rules encode unnatural placement or distributions for a given language or domain, and its advantage over prompting was shown only on the authors' tau-Voice setup across three models, so transferability to other simulators is unverified.",
  "rationale": "For engineers evaluating voice agents on constrained infrastructure, a deterministic injection layer plus realized-behavior auditing is cheap to adopt and directly addresses unreliable prompt-based control, but its benefits should be validated on the reader's own simulator and speech domain before production reliance.",
  "technique": "Rule-based injection of speech naturalness behaviors",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11015",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 122,
  "citations": 0,
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      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2610.11015",
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    }
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      "dimension": "security",
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    },
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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": "",
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  "equations_source_sha256": "",
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}