{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A narrow, repeated product task may not need your largest model.",
    "change": "Check whether a released student model fits the task before generating training data.",
    "baseline": "Your current model and an off-the-shelf small model on the same held-out tasks.",
    "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": "Student quality, serving cost, teacher calls, training cost, and failures on unusual inputs.",
    "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": "2610.09063",
  "title": "Multi-Label Topic Assignment via LLM Distillation: A Comparative Analysis of Generative vs. Discriminative Student Models",
  "summary": "The method replaces manual annotation with LLM-generated labels distilled into small student models, and replaces larger teacher inference at serving time with 1B-8B generative or discriminative classifiers. The cost is an offline teacher-labeling and distillation pipeline, plus the finding that the correct architecture is data-dependent: discriminative models win on structured reviews, generative models on conversational data, so a single architecture choice will not transfer across domains. It can fail if the LLM oracle's label errors propagate into the student, if the production taxonomy or label distribution drifts from the distilled training set, or if the reader's UGC is structured (where a cheaper discriminative baseline is sufficient) rather than conversational.",
  "rationale": "The approach fits constrained infrastructure since only small students are served, but the data-dependent architecture trade-off requires validating generative versus discriminative students on the reader's own data before committing.",
  "technique": "LLM-oracle distillation into small generative classifiers",
  "family": "destilacao",
  "recommendation": "testar",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.09063",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "statement": "A 1B generative student surpasses discriminative baselines (DeBERTa-V3, ModernBERT) on multi-turn conversational data, while discriminative models suffer a 35% Macro-F1 drop when the label set expands to 112 topics.",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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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": "",
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}