Эксперимент: RG Pyramid Flow Matching (#1027)

{ "worked": true, "confidence": 8, "verdict": "Built a self-contained RG locality toy verification and a PyTorch coarse-to-fine pyramid flow-matching MVP. The three mechanism predictions were confirmed: required radius fit R versus 1/Lambda with R2=0.9996 and coefficient of variation of LambdaR=3.7%; radius versus log(1/epsilon) had R2=0.9964; and Lambda=pi/a produced exactly constant cell radius 4 across six levels. The fixed-radius-8 operator error rose from 0.00024 at Lambda=1 to 0.742 at Lambda=0.1, while the selected RG radius kept error near 0.02. The neural toy showed lower pyramid mean velocity MSE (0.609 versus 0.875) and slightly better long-range correlation (0.075 versus -0.044), but it was not parameter-matched and was too small/short-trained to claim a generative-quality win.", "metrics": { "baseline": "Operator fixed radius 8: error 0.00024 to 0.742 as Lambda decreases; neural velocity MSE 0.875, d=16 correlation -0.044, 3,281 parameters.", "idea": "RG radius sweep: RLambda mean 4.248, CV 0.037; log-tolerance fit R2 0.9964; rescaled cell radius 4 at all levels; neural velocity MSE 0.609, d=16 correlation 0.075, 7,083 parameters." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 rg_pyramid_toy.py && /home/maxwelhelp/main/bin/python3 rg_pyramid_flow.py", "files": [ "rg_pyramid_toy.py", "rg_pyramid_flow.py", "results.json", "flow_results.json", "toy_output.txt", "flow_output.txt" ], "limitations": "No CIFAR-10 or FFHQ experiment, FID, ODE sampling benchmark, memory benchmark, scaling study, or parameter/FLOP-matched neural comparison was performed. The toy kernel assumes exponential locality and therefore verifies the claimed scaling mechanism rather than the full paper theorem." }