{
  "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": "2610.23338",
  "title": "SETAREH Transform: A Sorting-Assisted Haar-Wavelet Sparsification Method for Approximate Inner-Product Computation",
  "summary": "The method replaces exact full-dimension inner products between a fixed weight vector and successive input vectors with a truncated computation in a permuted Haar-wavelet domain, retaining only a subset of transformed coefficient-input pairs. It costs the per-vector permutation and Haar transform computation, storage or transmission of permutation metadata, and a controllable but nonzero approximation error. It can fail if the retained-pair budget needed for acceptable accuracy negates the memory-traffic savings, if energy does not concentrate after sorting on real input distributions, or if metadata overhead dominates at moderate vector dimensions.",
  "rationale": "The paper offers only a preliminary approximation result on a synthetic baseline and one small regression task, with no quantified speed or memory factor and no demonstrated relevance to standard LLM or vision serving workloads on a single-GPU setup.",
  "technique": "Haar-wavelet sorted sparsification for inner products",
  "family": "esparsidade_e_poda",
  "recommendation": "observar",
  "published": "2026-10-16",
  "as_of": "2026-10-19",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.23338",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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}