# Эксперимент: Time-Shell Long-Horizon Decoder (#452)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a reproducible shell decoder toy experiment and verified three quantitative predictions: cumulative shell sums reproduce later-insertion products to about 1e-14 error, fitted complexity exponents are 1.00 for shell and 2.00 for pairwise, and normalized shell amplification approaches 0.5 while dense amplification grows as K. On the K=32 toy task, shell and pairwise MSE were essentially identical in both ballistic and mixing regimes, while the shell fit used 160 versus 1120 parameters and took 0.00485 versus 0.0183 seconds in the ballistic case. Thus the claimed scalability/stability mechanism manifested, but no accuracy advantage was observed.",
  "metrics": {
    "baseline": "Pairwise decoder: ballistic MSE 0.0009228337, mixing MSE 0.9994222, 1120 parameters, fit time 0.0183 s / 0.0354 s.",
    "idea": "Time-shell decoder: ballistic MSE 0.0009228338, mixing MSE 0.9994222, 160 parameters, fit time 0.00485 s / 0.00741 s; product identity max error <=1.1e-14; complexity exponents 1.00 vs 2.00."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "The decoder comparison is a small NumPy linear toy, not a trained recurrent/SSM or Transformer model. It does not measure GPU memory, wall-clock autoregressive inference, connected third-order residual collapse, variable horizon ordering, or a genuine advection PDE; the pairwise baseline is a feature-rich linear comparator rather than full temporal attention."
}