Эксперимент: Disorder-Controlled Basin Merging (#418)

{ "worked": true, "confidence": 9, "verdict": "Built a ternary mean-field disorder simulator with stabilized softmax relaxation, finite-N annealed random fields, basin-spread measurement, and a tanh baseline. The slope prediction matched 1/R to 8.3e-13 relative error, the observed crossover was R=0.7997 versus predicted Rc=sqrt(2/pi)=0.7979 (absolute error 0.00183), and the relaxed Jacobian matched theory to 5.1e-13. Initial-condition spread was large below Rc (MSD 1.88 at R=0.45) and essentially zero above it (5.6e-5 at R=0.8 and below numerical precision by R=0.9); the ternary finite-N check gave |m|≈0.835 at R=0.6 and m≈-0.001 at R=1.0, versus persistent ±0.815 for the saturated tanh baseline. This confirms the proposed mechanism in the RFIM-like toy regime, but does not establish a downstream neural-network accuracy or optimization win.", "metrics": { "baseline": "Tanh mean-field coupling 1.4: final m from starts -1,+1 = [-0.81453, +0.81453], persistent two-basin behavior.", "idea": "RF ternary/RFIM-like map: Rc predicted 0.79788, observed 0.79971; pairwise final-m MSD 1.8765 at R=0.45, 0.0000561 at R=0.8, ~0 at R>=0.9; ternary finite-N final m=0.8348 at R=0.6 and -0.00115 at R=1.0." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 disorder_basin.py", "files": [ "disorder_basin.py", "results.json" ], "limitations": "Only the mean-field/RFIM-like toy regime was tested (K=Delta=H=0); no MNIST, RNN/MLP training, gradient-stability sweep, FLOP/speed measurement, 32-seed task experiment, or comparison against a trained optimizer was performed." }