Pre-Registering the Detectable Effect: A Paired-MDE Budget for 4-bit Quantization Benchmarks, with a Pilot Audit
arXiv:2605.28873v1 Announce Type: new Abstract: This is a planning-method note with an unpaired pilot audit. We adapt the classical paired-binary sample-size calculation (Miettinen, 1968) to quantization benchmarks, giving a conservative minimum detectable effect (MDE) bound $\delta^{*} \le (z_{1-\alpha/2}+z_{1-\beta})\sqrt{\rho_d/m}$ in the paired item count $m$ and the FP16-NF4 disagreement rate $\rho_d$. The bound turns "how reliable is my quantization claim?" into a one-line budget a benchmark designer can commit to before running. We illustrate the bound on four models and four benchmarks
The proliferation of quantized AI models necessitates robust benchmarking to ensure reliability and consistency, especially as resource constraints become more prominent.
This development provides a standardized and conservative method for evaluating the reliability of quantization claims, fostering trust and accelerating the adoption of efficient AI models.
Benchmark designers can now pre-register a minimum detectable effect, turning model evaluation from a subjective assessment into a quantifiable, budgetary commitment.
- · AI hardware manufacturers
- · Quantization researchers
- · Developers of efficient AI models
- · Cloud computing providers
- · Developers of unreliable quantized models
- · Inefficient AI systems
Improved reliability and comparability of quantized AI model benchmarks will become standard practice.
Faster adoption and deployment of smaller, more power-efficient AI models due to increased confidence in their performance.
Reduced compute and energy footprints for AI infrastructure, impacting operational costs and sustainability efforts.
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