{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted assistant is too expensive because its model uses most of the GPU memory.",
    "change": "Try the paper's supported lower-precision weights on a copy of your serving setup.",
    "baseline": "The same model at its original precision, on the same GPU.",
    "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": "Peak memory, task pass rate, latency, and total serving cost at the same context and load.",
    "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": "2609.28358",
  "title": "MicroQonv: Reshaping Convolution Tensors for Efficient Microscaling in Training and Inference",
  "summary": "MicroQonv replaces the naive per-tensor double quantization of weights, gradients, and post-im2col activations in microscaling-quantized convolutional layers with single quantization and a channel-batch-first im2col variant. The cost is a custom quantization and im2col implementation tied to microscaling formats, which standard deep learning frameworks do not expose out of the box, plus small accuracy loss that must be re-validated per model. It can fail if the target deployment stack lacks microscaling hardware or kernel support, if the workload is transformer-based rather than convolutional, or if accuracy degradation exceeds the negligible level reported on detection models.",
  "rationale": "The gains are demonstrated on edge CNN training and inference with custom kernels, so a constrained team working primarily with LLMs or standard frameworks cannot adopt it without tooling that does not yet exist off the shelf.",
  "technique": "Microscaling quantization for convolutional layers",
  "family": "quantizacao",
  "recommendation": "observar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.28358",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Reduces memory movement and storage by up to 7.53x versus full precision, halves quantization cost for weights and gradients, and cuts activation memory movement by 3.5x on YOLOv8nano at negligible accuracy cost.",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}