# Эксперимент: Residual-to-Symbolic Neural Pruning (#610)

{
  "worked": false,
  "confidence": 8,
  "verdict": "Built a reproducible noisy nonlinear-oscillator experiment with an MLP residual, L1 library projection, coefficient t-statistic gating, and sparse-only/neural rollout baselines. The statistical mechanism showed the expected qualitative behavior: signal t-statistics increased with sample size (observed exponent 0.606 versus predicted 0.5) and null acceptance was low (0.0178 versus predicted 0.0124), but the endpoint ratio was 5.34 versus predicted 4.0. The projected symbolic model improved acceleration rollout RMSE over the neural residual (0.05294 versus 0.06442), yet it was identical to sparse regression alone and admitted spurious v and v3 terms, so the claimed residual-to-symbolic advantage and reliable hidden-state term rejection were not demonstrated.",
  "metrics": {
    "baseline": "Unconstrained neural residual: training RMSE 0.05683; 100-step acceleration rollout RMSE 0.06442. Sparse-only rollout RMSE 0.05294.",
    "idea": "Residual-to-symbolic rollout RMSE 0.05294, selected x3, u, v, v3; t-statistics for v and v3 were 10.13 and 11.93. Mechanism sweep: t scaling exponent 0.606 vs predicted 0.5, 640/40 t ratio 5.34 vs predicted 4.0, null acceptance 0.0178 vs predicted 0.0124."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "The rollout metric is acceleration error rather than full observed-state trajectory error, only one fixed seed was used for the model comparison, no multi-cycle refitting or bootstrap uncertainty was tested, and the latent auxiliary mode was not estimated. The sparse projection was effectively equivalent to direct sparse regression because the final accepted coefficients were refit against the measured residual."
}