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arXiv cs.LG
arXiv cs.LG
5/13/2026
QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization

QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization

Short summary

QuIDE proposes a unified Intelligence Index metric combining compression-accuracy-latency trade-offs for quantized neural networks. Experiments across SimpleCNN, ResNet-18, and Llama-3-8B reveal task-dependent optimal bit-widths: 4-bit for MNIST and LLMs, 8-bit for complex CNNs. The accuracy-gated variant flags unviable low-precision configurations that raw scoring would incorrectly reward.

  • •QuIDE metric unifies compression-accuracy-latency trade-offs into single Intelligence Index score
  • •4-bit quantization optimal for MNIST and large language models; 8-bit best for complex CNN tasks
  • •Accuracy-gated variant prevents deceptive configurations that would otherwise appear efficient

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