{
  "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": "2610.23343",
  "title": "How Finite-Rank Bottlenecks Shape the Low-Rank Adaptation Landscape",
  "summary": "The paper does not propose a method to replace anything; it provides non-NTK theory explaining why low-rank LoRA updates suffice, showing that optimal update rank is bounded by a 'bottleneck rank' induced by frozen activations and by attention-specific invariances, not by sample size. It costs nothing to apply directly, since it introduces no new algorithm, but acting on it requires computing or estimating bottleneck ranks of frozen feature maps, which is nontrivial engineering. It can fail as practical guidance because the rank bounds are upper bounds under trace-norm or perturbed objectives, so the theoretically sufficient rank may still exceed what a given task needs, and empirical LoRA rank selection remains heuristic.",
  "rationale": "This is a theoretical result with no deployable artifact or measured performance gain, so the reader should track it as guidance for choosing LoRA rank rather than adopt or benchmark it in production.",
  "technique": "Bottleneck-rank theory for LoRA rank selection",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-10-16",
  "as_of": "2026-10-19",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.23343",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 4,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
      "baseline": "",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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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": ""
}