{
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  "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": "2610.30823",
  "title": "Neural Spectral Capacity with Residual Context for Heterogeneous Resource Allocation within Transformers",
  "summary": "ResNSC replaces position-independent per-layer spectral capacity scoring (NSC) and its dynamic-programming width allocator with a residual-history-aware scoring function that jointly allocates residual and feed-forward widths under a parameter budget, requiring no training data or instantiated weights for evaluation. The cost is implementation complexity: the scoring and augmented DP allocator must be integrated into an architecture-selection or pruning pipeline, and the method only ranks candidates, so actual training or fine-tuning is still required to realize gains. It can fail if the statistical residual-state assumptions do not hold for the target architecture family, if the finite candidate set excludes the true optimum, or if perplexity/task gains at 121M-374M and LLaMA-7B pruning do not transfer to the reader's models and budgets.",
  "rationale": "The heuristic is cheap to compute, the code is public, and the pruning use case fits a single-GPU budget, but the gains are demonstrated only at specific scales and require workload-specific validation before production adoption.",
  "technique": "Residual-aware spectral capacity for width allocation",
  "family": "arquitetura_eficiente",
  "recommendation": "testar",
  "published": "2026-10-22",
  "as_of": "2026-10-23",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30823",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
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