{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted model uses more compute or memory than your workload can justify.",
    "change": "Check for a usable pruned checkpoint and runtime support before attempting model surgery.",
    "baseline": "The original unpruned model at the same precision, context, and load.",
    "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 quality, actual GPU memory, serving throughput, and preparation 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": "2609.20483",
  "title": "Scaling Fourier-Based Sparse Matrix Analysis on GPUs",
  "summary": "The method replaces dense cuFFT-based spectral analysis of large sparse matrices (e.g., GNN adjacency matrices) with a lossless binary-sparse FFT pipeline, optionally followed by sampled-grid (Elastic BS-FFT) or density-map spatial compression. It costs implementation complexity for a custom sparse FFT pipeline, and the compression variants trade spectral fidelity (up to 11.56% feature error) for computation time. It can fail if downstream uses require exact spectra (only BS-FFT is lossless), if matrices are not binary/sparse enough to benefit, or if the workload is outside graph spectral analysis where the technique has no application.",
  "rationale": "This is a specialized GPU kernel technique for spectral analysis of graph adjacency matrices, which is only relevant if the reader runs GNN workloads that need spectral signatures, so it should be monitored rather than adopted for general LLM production.",
  "technique": "Binary-sparse FFT for spectral analysis of sparse matrices",
  "family": "esparsidade_e_poda",
  "recommendation": "observar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.20483",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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