Эксперимент: Two-Channel Fractal Renormalization Network (#637)

{ "worked": true, "confidence": 8, "verdict": "Built the exact Sierpinski ratio recursion check and a runnable PyTorch two-channel ternary block with cubic neutral mixing and bounded defect routing. The mechanism manifested: divergence occurred below the predicted q=x/z=1 boundary, the q=1.2 marginal scaling gave mean n(R*-R)=2.097 versus predicted 2.4, and q=0.8 showed a double-log growth increment of 0.977 versus predicted 1.0. The toy product task favored the fractal structure only when the target matched the product mechanism; against a linear mean-pooling readout it was worse, so no general accuracy win is claimed.", "metrics": { "baseline": "Toy regression mean-pooling plus linear readout MSE = 9.4605", "idea": "Exact ternary product/fractal recursion MSE = 62.6550 on the generic product toy evaluation; mechanism checks: boundary predicted q=1.0 and observed finite-depth transition between 0.95 and 0.975, q=1.2 marginal statistic 2.097 vs 2.4 predicted, q=0.8 double-log increment 0.977 vs 1.0 predicted, analytic Jacobian finite-difference max error 2.96e-12" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py && /home/maxwelhelp/main/bin/python3 smoke_test.py", "files": [ "experiment.py", "fractal_block.py", "smoke_test.py", "results.json" ], "limitations": "Did not run the proposed CIFAR-10 six-level CNN, matched-parameter training comparison, Jacobian regularization during learning, active-path scaling, or a full paper-scale partition-function implementation. The toy neural block’s Jacobian sweep used a scalar simplified defect map and did not include RMS normalization in the analytic stability check." }