{
  "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": "2609.30798",
  "title": "Evaluating Real-Time Voice Agents: From Component Quality to Grounded Outcomes",
  "summary": "The TRG reporting standard replaces single-number component metrics (latency, turn-prediction accuracy, claimed task success) with a joint characterization of timing, post-disruption recovery, and backend-state-verified outcomes, plus a conditional multiparty axis. It costs little in infrastructure: it requires instrumenting agents to verify real backend state rather than trusting agent self-reports, adding evaluation-engineering effort rather than GPU or API spend. It can fail if teams lack access to backend state for verification, if deployment workloads are strictly dyadic so the multiparty axis is wasted effort, or if the survey's corpus of 38 sources misses relevant systems and its architecture conclusions age quickly in a fast-moving field.",
  "rationale": "The TRG standard and its state-verified outcome evaluation are directly adoptable on constrained infrastructure since they change how agents are measured, not how they are trained or served.",
  "technique": "TRG timing-recovery-grounded voice agent evaluation",
  "family": "avaliacao_de_agente",
  "recommendation": "adotar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.30798",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The TRG reporting standard replaces single-number component metrics (latency, turn-prediction accuracy, claimed task success) with a joint characterization of timing, post-disruption recovery, and backend-state-verified outcomes, plus a conditional multiparty axis. It costs little in infrastructure: it requires instrumenting agents to verify real backend state rather than trusting agent self-reports, adding evaluation-engineering effort rather than GPU or API spend. It can fail if teams lack access to backend state for verification, if deployment workloads are strictly dyadic so the multiparty axis is wasted effort, or if the survey's corpus of 38 sources misses relevant systems and its architecture conclusions age quickly in a fast-moving field.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.30798",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "No quantified improvement factor; the paper claims that no fully self-hostable end-to-end speech system yet meets production constraints, while a chunked cascade independently achieves state-of-the-art duplex behaviour, and that evaluation is shifting from component metrics to backend-state-verified outcomes.",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.30798",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": ""
}