{
  "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": "2610.01702",
  "title": "Task-Oriented Rank Adaptation for Continual Learning in Text Classification",
  "summary": "TORA replaces naive strategies of either training isolated LoRA adapters per task or blindly merging/reusing adapters in continual text classification, by routing each new task to the most structurally compatible existing adapter (Boosting) or isolating it (Shielding) using a single geometric similarity threshold. The cost is a routing decision procedure on top of standard LoRA training, with modest added complexity and no extra model capacity, since LoRA adapters themselves are cheap on a 24 GB GPU; the abstract reports reduced training time on compatible tasks without a quantified factor. Failure modes include a miscalibrated similarity threshold causing negative transfer on tasks judged compatible, and limited applicability outside sequential text classification, since the evidence covers 15 text benchmarks only.",
  "rationale": "The method is feasible on a single 24 GB GPU and directly addresses a production-relevant continual-learning scenario, but the abstract provides no quantified gains and only one hyperparameter (the threshold) separates beneficial transfer from harmful routing, so workload-specific validation is required before adoption.",
  "technique": "Geometric routing of LoRA adapters for continual learning",
  "family": "treino_eficiente",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01702",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 121,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "TORA replaces naive strategies of either training isolated LoRA adapters per task or blindly merging/reusing adapters in continual text classification, by routing each new task to the most structurally compatible existing adapter (Boosting) or isolating it (Shielding) using a single geometric similarity threshold. The cost is a routing decision procedure on top of standard LoRA training, with modest added complexity and no extra model capacity, since LoRA adapters themselves are cheap on a 24 GB GPU; the abstract reports reduced training time on compatible tasks without a quantified factor. Failure modes include a miscalibrated similarity threshold causing negative transfer on tasks judged compatible, and limited applicability outside sequential text classification, since the evidence covers 15 text benchmarks only.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.01702",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": ""
}