Эксперимент: Fisher-floor-corrected DSM (#1046)

{ "worked": true, "confidence": 9, "verdict": "Built and ran a reproducible Fisher-floor DSM toy experiment for a 1D Gaussian-mixture prior with exact posterior floors and a finite memory-bank estimator. The decomposition residual was 0.39%, alpha scaling matched the predicted quadratic law within 4.7%, alpha=0 gave exactly zero floor, and bank error scaled with slope -0.461 versus the predicted -0.5. Pooled checkpoint rank correlation with ideal score error improved from 0.615 for raw DSM to 0.937 for corrected DSM, while the cross-schedule loss offset fell from 1.680 to -0.019; this supports schedule comparability but does not establish improved sample quality.", "metrics": { "baseline": "Raw DSM pooled checkpoint-vs-ideal rank correlation 0.615; mean cross-schedule loss offset 1.680.", "idea": "Corrected DSM pooled rank correlation 0.937; mean cross-schedule offset -0.019; decomposition relative error 0.0039; alpha quadratic-fit maximum relative error 0.0468; bank log-log error slope -0.461 versus -0.5 predicted; alpha=0 floor 0.0." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 fisher_floor_experiment.py", "files": [ "fisher_floor_experiment.py", "results.json" ], "limitations": "Only a low-dimensional synthetic Gaussian-mixture experiment was tested. No MNIST/CIFAR diffusion network, training-speed or FLOP comparison, FID, or real checkpoint-selection experiment was performed. The finite-bank estimator has sampling noise and can produce slightly negative corrected empirical losses." }