{
  "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": "2610.28555",
  "title": "Learning Computational Structure for Sample-Efficient Prediction",
  "summary": "LeWi replaces fully dense MLP connectivity with wiring learned jointly with weights, where each neuron has a low-dimensional embedding and only nearby neurons connect. It adds embedding parameters and a locality-constrained training procedure, with modest implementation complexity over a standard dense network. It can fail on targets dominated by a single high-arity function, where dense networks perform better, and the evidence comes from controlled synthetic benchmarks and world-model prediction tasks rather than standard production workloads.",
  "rationale": "The sample-efficiency gains are plausible and reproducible on small-scale tasks within a 24 GB budget, but the advantage is task-structure-dependent, so it warrants workload-specific validation before production adoption.",
  "technique": "Learned Wiring: jointly trained connectivity via neuron embeddings",
  "family": "arquitetura_eficiente",
  "recommendation": "testar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28555",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 217,
  "citations": null,
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  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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