{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time budget.",
    "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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2609.15079",
  "title": "Translating the Translator: Decomposing the Cost of English-Forced Inter-Agent Communication",
  "summary": "Native-language routing replaces the default English-forced inter-agent communication pattern common in frameworks such as LangChain and AutoGen, including the extra back-translation agent needed to return output to the user's language. It reduces token usage and latency by removing a translation round-trip, and costs nothing beyond ensuring the chosen model has adequate capability in the user's language. It can fail when the deployed model is substantially weaker in the target language than in English, and the evidence comes from a single 8B model on an extraction-answer task, so gains may not transfer to stronger models or other task types.",
  "rationale": "The recommendation is a configuration-level design choice with no infrastructure cost, it eliminates a translation step rather than adding one, and the measured accuracy penalty of English routing is large and statistically robust for typologically distant languages.",
  "technique": "Native-language routing in multi-agent LLM pipelines",
  "family": "orquestracao_multiagente",
  "recommendation": "adotar",
  "published": "2026-09-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.15079",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Native-language routing replaces the default English-forced inter-agent communication pattern common in frameworks such as LangChain and AutoGen, including the extra back-translation agent needed to return output to the user's language. It reduces token usage and latency by removing a translation round-trip, and costs nothing beyond ensuring the chosen model has adequate capability in the user's language. It can fail when the deployed model is substantially weaker in the target language than in English, and the evidence comes from a single 8B model on an extraction-answer task, so gains may not transfer to stronger models or other task types.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.15079",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Forcing inter-agent communication through English reduces Exact Match accuracy by 13.0 percentage points (Spanish) to 30.6 percentage points (Hindi) versus native-language multi-agent execution, statistically significant after Bonferroni correction, on Aya-23-8B across 300 samples per language.",
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      "source_url": "https://arxiv.org/abs/2609.15079",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
      "source_url": "",
      "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_formula",
  "core_kind": "evaluation_protocol",
  "equations_fetched_at": "2026-10-05",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
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}