{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A product assistant retrieves plausible documents but gives unsupported answers.",
    "change": "Change one retrieval or ranking step while keeping the document snapshot fixed.",
    "baseline": "Your current retrieval pipeline with identical questions and answer model.",
    "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": "Supported answers, retrieval misses, correct abstentions, index cost, and response latency.",
    "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": "2610.01139",
  "title": "Do Multilingual Encoders Produce Language-Consistent Semantic IDs?",
  "summary": "The paper evaluates, rather than replaces, the standard practice of fitting residual quantizers on multilingual encoder embeddings to produce semantic IDs for generative retrieval. The cost is the same as baseline SID pipelines, but the evaluation reveals a quality risk: language-balanced quantizer fitting reduces cross-lingual first-code agreement (Spanish drops from 28.3% to 6.6%, versus 67.6% under an English-only fit). What can fail after adoption is cross-lingual retrieval consistency: translated items receive different SIDs than their source, degrading generative retrieval in multilingual catalogs, and the quantizer offers no selective robustness to translation-induced movement.",
  "rationale": "For a small team shipping multilingual generative retrieval, this is an actionable diagnostic: do not assume multilingual encoders yield language-consistent SIDs, and validate fitting-mixture choices on your own catalog before production.",
  "technique": "Language-consistent semantic IDs via multilingual encoders",
  "family": "recuperacao_e_rag",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01139",
  "independent_implementations": 3,
  "total_implementations": 3,
  "stars": 27,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.01139",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "operations",
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      "dimension": "compatibility",
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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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    },
    {
      "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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  "exposures_source_sha256": ""
}