# Эксперимент: Transverse Synchrony Training (#952)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built and ran a deterministic transverse-synchrony toy system with shared forcing, analytic transverse multiplier a-k, numerical exponent fitting, parameter sweeps, and a small torch controller comparison. The predicted stability boundary k*=a-1=0.15 was observed at 0.14875 (error 0.00125), stability classification agreement was 100%, and measured log-residual slopes matched the analytic transverse exponent with maximum absolute error 0.00076. The mechanism manifested clearly, but the synchrony-trained controller did not beat the task-only baseline because this toy task loss is itself the synchrony residual.",
  "metrics": {
    "baseline": "final k=0.28862; test tail MSE=2.2581e-7",
    "idea": "final k=0.28862; test tail MSE=2.2581e-7; identical task performance",
    "mechanism_checks": "boundary predicted 0.15 vs observed 0.14875; classification agreement 1.0; maximum exponent-slope error 0.00076; gain multipliers exactly matched at k=0.2, 0.3, 0.4, 0.5"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 transverse_sync_experiment.py",
  "files": [
    "transverse_sync_experiment.py",
    "results.json"
  ],
  "limitations": "The experiment is a scalar linear discrete-time system rather than a nonlinear GRU/neural ODE or Duffing/pendulum model. It does not test behavior equivalence beyond shared-input state synchronization, learned transverse Jacobian penalties, continuous-time QR Lyapunov estimation, noise robustness, or a meaningful task objective distinct from synchrony."
}