# Эксперимент: IMM Stale-Feedback Detector (#977)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built an IMM stale-feedback detector for candidate delays 0..4 using exact Gaussian innovation likelihoods, Markov mode mixing, posterior updates, and alarms. The mechanism manifested: posterior normalization error was 4.4e-16, persistent delay-2 reached median posterior 0.96 versus 0.006 for no-delay, and cumulative innovation log-likelihood slope divided by empirical KL was 0.99-1.01 across noise sweeps. Detection time increased from 6 to 23 steps as KL decreased, but the practical control benchmark produced no alarm and no improvement over baseline, so this supports the detector math rather than a demonstrated neural-training win.",
  "metrics": {
    "baseline": "Delayed quadratic SGD final losses at learning rates 0.8/1.0/1.2: 4.66e-27, 0, and 1.24e-3; median maximum absolute state was 2.0; no IMM intervention occurred.",
    "idea": "IMM toy: delay-2 final posterior 0.961, no-delay posterior 0.0057, and median alarm time 8.5 steps at noise 0.03; KL-slope ratios were 0.991-0.999 in the main sweep; median alarm times were 6, 8.5, 10.5, and 23 steps for noise 0.015, 0.03, 0.06, and 0.12."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 imm_experiment.py > results.json && /home/maxwelhelp/main/bin/python3 mini_control.py > control_results.json",
  "files": [
    "imm_experiment.py",
    "mini_control.py",
    "results.json",
    "control_results.json"
  ],
  "limitations": "No MLP, GRU, asynchronous worker, gradient-age rejection, or GPU training experiment was completed. The successful verification assumes a known scalar linear process and known delayed state history; online estimation of A, Q, R and IMM performance under neural monitor noise were not tested. The control benchmark did not trigger an alarm, so learning-rate mitigation remains unvalidated."
}