Эксперимент: Finite-Excitation Orthogonal Gradient Memory (#1038)

{ "worked": true, "confidence": 9, "verdict": "Built and numerically verified finite-excitation orthogonal-gradient memory. The discrete stability boundary occurred at alpha=2, full-direction contraction began exactly when k=d independent directions were available, and MGS achieved the predicted conditioning-independent decay slope. In the controlled ill-conditioned replay test, raw replay became unstable while MGS remained stable, though this is not evidence of a broad neural-network training win.", "metrics": { "baseline": "Raw replay on ill-conditioned features: late log-error slope +1.5065; error at step 10 6.96e5. On well-conditioned features: slope -1.6094.", "idea": "MGS memory: max |Q^TQ-I| 2.83e-10; stable at alpha=1.99 and divergent at 2.01; full excitation at k=8; late log-error slope -1.6094 for both well- and ill-conditioned features." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "REPORT.md" ], "limitations": "Tested only deterministic NumPy linear-head dynamics. No MNIST/MLP, Adam integration, minibatch feature refresh, label noise, nonlinear backbone, compute comparison, or generalization evaluation was tested." }