{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your workload might fit a smaller or differently structured model.",
    "change": "Check for available weights and a compatible runtime before planning a product trial.",
    "baseline": "Your current model on identical held-out tasks and the same resource budget.",
    "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": "Task success, latency, memory, context limits, and migration cost.",
    "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": "2610.19966",
  "title": "Choosing the Number of Attention Heads from Intrinsic Dimension: A Small-Scale Study",
  "summary": "The method replaces convention- or grid-search-based selection of attention head count with a label-free intrinsic-dimension estimate from a nested-dropout VAE, avoiding a hyperparameter sweep. The cost is a one-time VAE training pass over embeddings (here DistilBERT on WikiText-2) plus the assumption that the estimated dimension transfers to the target architecture; no inference-time overhead is added. It can fail because the evidence covers only two classification tasks and one small from-scratch 4-layer transformer, with no formal link between intrinsic dimension and head count, so the heuristic may not transfer to other tasks, sizes, or pretrained models.",
  "rationale": "The heuristic is cheap enough to try under constrained infrastructure when training small transformers from scratch, but the narrow evidence base means the parameter savings must be validated per workload before adoption.",
  "technique": "Intrinsic-dimension heuristic for attention head count",
  "family": "arquitetura_eficiente",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.19966",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
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      "statement": "10 heads statistically equivalent to 32 heads on SST-2 and MRPC (paired TOST, ±1 point, 10 seeds) with 69% fewer attention parameters; training 10 heads from scratch beats pruning 32 to 10 by 0.6 points (p=0.008).",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    {
      "dimension": "security",
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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