mlsanityData to 14 Oct 2026

Pre-Registering the Detectable Effect: A Paired-MDE Budget for 4-bit Quantization Benchmarks, with a Pilot Audit

arXiv 2605.28873 ↗First posted 25 May 2026cs.LGcited by 8

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Abstract (arXiv)

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 δ* ≤ (z1-α/2+z1-β)√ρd/m in the paired item count m and the FP16-NF4 disagreement rate ρd. 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 (k=5 splits of n=100), and add a parallel MMLU prompt-template study to put the bound's quantization-noise scale alongside the prompt-noise scale. Assuming ρd=0.10 (an unmeasured planning value), all observed NF4-FP16 deltas fall below the implied MDE, and most cross-split SDs lie within ± 1.5 pp of the binomial reference √p(1-p)/n, so much of the variance reported as "benchmark unreliability" on n=100 subsamples is binomial sampling noise. The single borderline cell (OPT-WinoGrande, |Delta|=3.2 pp) is below the implied MDE at ρd=0.10 but above it at ρd=0.05, illustrating the planning trade-off the bound makes explicit. On MMLU, prompt-template ranges of 2-10 pp meet or exceed the largest observed quantization delta (3.2 pp), so a quantization audit that does not first fix the prompt template absorbs template variance into its noise floor. We complement the bound with a five-line pre-registration template.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 26 of its 45 references that are in the map. First posted on arXiv 25 May 2026.

Builds on

26 of 45 references.

References · 26

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14 more
  1. 2023SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compressioncited by 367
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 8.

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  1. 2026Saturation Makes Quantization Error Additive: A Coverage Model with a Certificate
  2. 2022The case for 4-bit precision: k-bit Inference Scaling Laws
  3. 2026When Local Variance Optimality Is Not Enough: RoPE-Aligned Q/K Rotations for Dynamic 4-Bit Quantisation
  4. 2026A Target-Centric Survey of Quantization-Aware Training
  5. 2026Scale Sensitivity in Low-Bit Post-Training Quantization: Curvature of the Quantization Error Landscape
  6. 2026On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation
  7. 2026When Bits Break Recourse: Counterfactual-Faithful Quantization
  8. 2025BayesQ: Uncertainty-Guided Bayesian Quantization