{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "You already need task-specific training and want to reduce its memory or compute cost.",
    "change": "Change only the training technique on a small controlled run after checking prerequisites.",
    "baseline": "Your current training recipe with the same model, data split, and evaluation.",
    "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": "Held-out quality, peak memory, training time, and full compute cost to the target quality.",
    "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": "2609.24979",
  "title": "LoRA-generating hypernetworks for efficient on-device LLM generative personalization",
  "summary": "The method replaces per-user in-context learning or per-user PEFT fine-tuning with a shared hypernetwork that maps user context tokens to a personalized LoRA via forward passes only, avoiding both prompt-length latency and on-device gradient training. Costs include offline training of the hypernetwork against the target base model, coupling to that specific base model's weights, and the engineering burden of a two-stage deploy-and-synthesize pipeline, none of which the abstract quantifies. Failure modes include poor LoRA synthesis for users whose behavior drifts from training-time context distributions, quality degradation on long-form generation where evaluation is hardest, and obsolescence of the hypernetwork whenever the base model is updated.",
  "rationale": "The technique targets mobile-device personalization, requires an unquantified offline training investment tied to a fixed base model, and presents no numerical results, so a constrained-infrastructure team should monitor it rather than commit resources to replication.",
  "technique": "Hypernetwork-generated LoRA for on-device personalization",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24979",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2609.24979",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
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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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}