# Эксперимент: Spectral Basin Allocation for Multimodal Neural Memories (#1221)

{
  "worked": true,
  "confidence": 7,
  "verdict": "Built a reproducible 12-oscillator delayed-Kuramoto associative-memory MVP with composite-Laplacian spectral rates, phase-locked prototypes, direct integration, and empirical basin classification. The local contraction claim was clearly verified: predicted rate 0.5070508 matched the Jacobian rate 0.5070508, and the measured semilog decay slope was -0.50834. Coupling reallocation increased the target q=1 basin from 0.0777 to 0.1677, demonstrating controllable basin asymmetry, but its local contraction rate decreased from 0.3660 to 0.2588, so the stronger universal rate-to-basin claim was not supported.",
  "metrics": {
    "baseline": "weights [0.5, 0.5]; q=1 rate 0.3660; q=1 basin 0.0777; q=0 basin 0.9223",
    "idea": "weights [0.9, 0.1]; q=1 rate 0.2588; q=1 basin 0.1677; q=0 basin 0.8323; math relative rate error 1.97e-15"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py",
  "files": [
    "run_experiment.py",
    "results.json",
    "README.md"
  ],
  "limitations": "This MVP uses manually selected coupling weights rather than optimizing the proposed basin-aware objective, synthetic phase-gradient prototypes rather than MNIST embeddings, and no learned neural layer. The 20-graph correlation check is weak because only two attractors had positive rates in the sampled systems; no 10,000-sample or GPU experiment was performed."
}