# Эксперимент: Digital-Recurrence Lyapunov Monitor (#935)

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built a bounded state-recurrence monitor with exact/grid-hash detection, finite-difference Lyapunov slopes, restart averaging, collapse ratio, and alarm logic. The logistic-map sanity check found float32 recurrence at tau=4686 with period 4344, while float64 had no recurrence through 20,000 steps. However, C(N) rose to about 0.91 by N=512 in both float32 and float64, well before float32 recurrence, indicating separation saturation rather than recurrence collapse; therefore the claimed effect was not validated.",
  "metrics": {
    "baseline": "float64: no exact recurrence through 20,000 steps; C(512)=0.913 and C(2048)=0.977",
    "idea": "float32: first recurrence tau=4686, period=4344; C(512)=0.914 and C(8192)=0.980; float16 recurrence tau=37, period=1 but finite-difference perturbation was unrepresentable"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 digital_recurrence.py > results.json",
  "files": [
    "digital_recurrence.py",
    "results.json",
    "README.md"
  ],
  "limitations": "Only a deterministic logistic-map toy system was tested, not an RNN, neural simulator, or training controller. The Lyapunov estimator uses raw finite-difference separation slopes without renormalization, so chaotic-distance saturation confounds the collapse ratio. No intervention effectiveness, wall-clock overhead, approximate hashing, or held-out threshold calibration was evaluated."
}