{
  "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": "2609.39542",
  "title": "Comparative study of adapting pre-trained models for driving behavior video captioning",
  "summary": "The method replaces task-specific model training from scratch with adaptation of a pre-trained video-language model (SpaceTimeGPT, VideoLLaVA) via full fine-tuning, LoRA, or prompt engineering for driving-behavior captioning. Full fine-tuning costs the most memory and is likely beyond a single 24 GB GPU without aggressive optimization, while LoRA reduces trainable parameters and fits constrained hardware at some risk of lower ceiling; prompting costs nothing but showed limitations in the study. Failures include overfitting to the narrow BDD-X domain, weak generalization to other driving footage, and metric gains that may not reflect caption correctness or safety relevance.",
  "rationale": "LoRA adaptation of a pre-trained video LLM is feasible on one 24 GB GPU, but the paper reports only partial metric improvements and notes limitations, so the reader should validate quality on their own driving data before production adoption.",
  "technique": "LoRA and full fine-tuning for video LLM captioning",
  "family": "treino_eficiente",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39542",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "dimension": "compatibility",
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      "dimension": "security",
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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