{
  "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": "2610.20217",
  "title": "Continual pretraining Prostate MRI ViT Encoder for task alignment",
  "summary": "The method replaces direct fine-tuning of a pretrained ViT encoder by inserting an intermediate LoRA continual-pretraining stage on a related task (prostate MRI segmentation) to better align pretrained representations with downstream objectives. It costs an additional training stage, though LoRA keeps it feasible on a single 24 GB GPU, plus the engineering overhead of managing two-stage adaptation. It can fail to deliver gains, as improvement appeared in only half the evaluated tasks, and benefits may not transfer outside the prostate MRI domain or to tasks dissimilar from the continual-pretraining objective.",
  "rationale": "LoRA continual pretraining is cheap enough for constrained infrastructure, but the paper's mixed results (gains in only 3 of 6 tasks within one medical domain) mean the reader must validate it on their own workload before adopting it as standard practice.",
  "technique": "LoRA-based continual pretraining for task alignment",
  "family": "treino_eficiente",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20217",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "source_url": "https://arxiv.org/abs/2610.20217",
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    }
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      "dimension": "compatibility",
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    {
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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