Эксперимент: Utility-Weighted Left-Edge Quantization (#1343)

{ "worked": true, "confidence": 8, "verdict": "Built and numerically verified Utility-Weighted Left-Edge Quantization in a self-contained toy experiment. The prescribed g=sqrt(pQ') allocation equalized per-bin g-mass with CV 1.9e-9, and the weighted utility gap decreased monotonically with n while n·gap approached the predicted Cw=0.4143, reaching 0.4092 at 64 codes. It consistently beat uniform left-edge quantization: at 64 codes the gap was 0.00639 versus 0.02040, while upward rounding remained exactly zero.", "metrics": { "baseline": "Uniform left-edge utility gaps for n={4,8,16,32,64}: {0.24507, 0.15027, 0.08027, 0.04072, 0.02040}; n·gap at 64 was 1.3053.", "idea": "Utility-weighted left-edge utility gaps for n={4,8,16,32,64}: {0.09325, 0.04854, 0.02488, 0.01264, 0.00639}; n·gap at 64 was 0.4092 versus theoretical Cw=0.4143; upward-rounding fraction was 0.0 for every codebook." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "run_output.txt" ], "limitations": "This is a bounded synthetic exponential-distribution experiment, not the proposed Transformer/WikiText-2 validation. The utility curve is hand-selected logarithmic rather than estimated from task-loss perturbations, and throughput, memory, kernel cost, calibration loss, and end-to-end perplexity were not measured." }