{
  "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": "2610.30339",
  "title": "Spike-Aware Gradient-Based Mixed-Precision Quantization of Spiking Neural Networks for Vision Applications",
  "summary": "Replaces Hessian-based mixed-precision bit allocation (e.g., HAWQ-V2) for spiking neural networks with first-order gradient information, avoiding costly second-order computation over temporally unrolled timesteps. The cost is a quantization-specific search and calibration pipeline plus mixed-precision deployment support, which standard inference hardware handles poorly without custom kernels. It can fail for the reader because the method applies only to SNN workloads: on conventional ANN/LLM serving there is no benefit, and mixed bit-widths often yield no real speed or energy gain on commodity GPUs without specialized dataflow hardware.",
  "rationale": "The technique targets SNN deployment with temporal unrolling, a niche the reader is unlikely to serve, and its gains only materialize on neuromorphic or custom mixed-precision hardware, so it warrants monitoring rather than adoption.",
  "technique": "Gradient-based mixed-precision quantization for SNNs",
  "family": "quantizacao",
  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30339",
  "independent_implementations": 3,
  "total_implementations": 3,
  "stars": 4,
  "citations": 0,
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    {
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      "source_url": "https://arxiv.org/abs/2610.30339",
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    },
    {
      "claim_id": "claim-02",
      "statement": "On ResNet-34/ImageNet, achieves 73.30% accuracy at 4.78x compression, matching HAWQ-V2 while reducing bit-allocation memory 2.31x and runtime 100x.",
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    },
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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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    },
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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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}