# Эксперимент: Masked Observability Preconditioner (#766)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built a diagonal masked-observability preconditioner and reproducible toy/MLP experiment in experiment.py. The mechanism manifested: the norm bound held (maximum observed ratio 0.848 <= 1), predicted contraction factors matched measured values to numerical precision, and observed stability boundaries were within about 1% of 2(lambda+q)/q. Excitation-dependent noise drift also fell strongly (mean theta^2 499.0 at q=0, 44.5 at q=1, 13.7 at q=4); however, the MLP did not beat Adam on validation MSE.",
  "metrics": {
    "baseline": "SGD: validation MSE 0.08090, parameter drift 1.332; Adam: validation MSE 0.06873, parameter drift 4.006.",
    "idea": "Masked diagonal preconditioner: validation MSE 0.07988, parameter drift 1.324; it slightly beat SGD and substantially reduced drift versus Adam, but was worse than Adam in MSE. Toy bound ratio=0.848, contraction predicted/measured=[0.9619/0.9619, 0.8667/0.8667, 0.6000/0.6000, 0.3600/0.3600]. Stability predicted/observed=[42.0/42.30, 12.0/12.05, 4.0/3.98, 2.5/2.45]."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json",
    "run_output.txt"
  ],
  "limitations": "Only a small synthetic regression MLP was tested, using a diagonal sensitivity proxy rather than exact Jacobian Gramians or a full neural-network training benchmark. No MNIST, structured-mask generalization study, multiple random seeds, FLOP/speed profiling, rank-8 approximation, or persistent-coordinate drift decomposition was evaluated."
}