{
  "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": "2609.24812",
  "title": "MSI-Bench: Evaluating Multi-Speaker Voice Interaction for Collaborative AI Agents",
  "summary": "MSI-Bench replaces ad hoc or single-speaker evaluation of voice agents with 1,152 multi-party multi-turn audio scenes featuring participant context, expected tool calls, and atomic rubrics across memory, instruction following, and reasoning. Adopting it costs benchmark integration effort and access to the evaluated models, and it is an evaluation asset rather than a deployable capability, so it adds no runtime latency or memory to a production system. It can fail to transfer if the reader's domain, languages, or speaker counts differ from the benchmark's scripted scenes, and rubric-based pass rates may not correlate with perceived quality in a specific product.",
  "rationale": "This is an evaluation benchmark, not a deployable method, so it is only directly actionable for a team actively building multi-speaker voice agents; for most constrained-infrastructure practitioners it is a signal that open-weight multi-speaker audio front-ends are not production-ready and that even frontier systems lack reliable speaker-scoped decision making and conversational restraint.",
  "technique": "MSI-Bench multi-speaker voice interaction benchmark",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24812",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "MSI-Bench replaces ad hoc or single-speaker evaluation of voice agents with 1,152 multi-party multi-turn audio scenes featuring participant context, expected tool calls, and atomic rubrics across memory, instruction following, and reasoning. Adopting it costs benchmark integration effort and access to the evaluated models, and it is an evaluation asset rather than a deployable capability, so it adds no runtime latency or memory to a production system. It can fail to transfer if the reader's domain, languages, or speaker counts differ from the benchmark's scripted scenes, and rubric-based pass rates may not correlate with perceived quality in a specific product.",
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    {
      "claim_id": "claim-02",
      "statement": "Best configuration passes all rubrics on 66.8% of English and 54.5% of Mandarin cases; best open-weight configuration reaches 34.0% and 19.3%, indicating multi-speaker voice interaction is far from solved.",
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      "dimension": "operations",
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      "dimension": "compatibility",
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
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  "equations_status": "not_fetched",
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