# Эксперимент: Fading-Memory Habituation Gate (#606)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built and ran a reproducible fading-memory habituation gate verification. The three quantitative predictions were confirmed: steady-state gain matched 1/(1+beta*s) with maximum absolute error 2.0e-14, recovery half-indices exactly matched ceil(log(0.5)/log(rho)) for all tested rho, and fitted decay slopes matched log(rho) to below 2e-16. The pole sweep showed decay for rho<1, constant state at rho=1, and growth for rho>1; the controlled comparison showed repeated-pulse gain 0.363 versus baseline 1.0, with recovery to 0.824 after withdrawal, so the mechanism clearly manifested, though no trained-transformer or task-accuracy win was established.",
  "metrics": {
    "baseline": "Unmodified gain: repeated pulse 1.000, post-withdrawal 1.000, novel pulse 1.000.",
    "idea": "Nonlinear gate (rho=0.9, beta=2): repeated pulse gain 0.3627, post-withdrawal gain 0.8240, novel pulse after reset 0.8333. Matched linear state gate: repeated 0.4144 and post-withdrawal 0.9288."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 habituation_experiment.py",
  "files": [
    "habituation_experiment.py",
    "results.json",
    "run_output.txt"
  ],
  "limitations": "Only analytical/numerical toy dynamics and a controlled pulse-sequence comparison were tested. No causal transformer training, language-modeling accuracy, inference latency, KV-cache integration, or robustness/generalization evaluation was performed."
}