{
  "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": "2609.39367",
  "title": "A Dynamical Theory of LoRA in Continual Learning",
  "summary": "The paper analyzes LoRA dynamics in sequential fine-tuning and proposes freezing hidden units that carry the strongest first-task representations, restricting adaptation to the complementary subspace, replacing naive full LoRA fine-tuning on new tasks. The cost is additional instrumentation to identify and mask task-relevant units per task, plus an initial slowdown in adaptation to the new task inherent to low-rank initialization; the theory also predicts forgetting grows with adapter rank while transfer saturates beyond the task's intrinsic dimensionality. The mechanism is derived from a solvable two-task teacher-student model and only qualitatively reproduced on sequential MNIST, so the masking gains may not transfer to large pretrained models or realistic task sequences.",
  "rationale": "The masking strategy is cheap enough to try on a single GPU, but the evidence is a toy theoretical model plus MNIST, so production adoption requires validation on the reader's actual continual-learning workload.",
  "technique": "Dynamical analysis of LoRA with state-dependent masking for continual learning",
  "family": "treino_eficiente",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39367",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.39367",
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      "source_excerpt": ""
    }
  ],
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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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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}