# Эксперимент: Tensorized concentration mixing (#563)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built a discrete Fourier concentration MVP with sequential tensorized application and dense Kronecker references. The mechanism manifested: contraction held to about 3e-15, tensor application matched dense multiplication at 1e-16 relative error, and Schatten multiplicativity matched within 3.4e-8. Parameter and arithmetic scaling matched the predicted reductions, although tiny-matrix wall-clock timing favored dense BLAS due to overhead; no end-to-end accuracy win was tested.",
  "metrics": {
    "baseline": "At 144 tokens: 20,736 dense parameters; 22.1 microseconds per CPU matvec; largest singular value 1.0000000000000009.",
    "idea": "At 144 tokens: 288 factor parameters, 72x fewer; 101.3 microseconds sequential CPU application; largest singular value 1.0000000000000009. Predicted dense/axis operation ratios were 4.0, 21.33, and 31.25 for the tested 2D/3D/4D sweeps."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 tensorized_mixing.py > results.json",
  "files": [
    "tensorized_mixing.py",
    "results.json",
    "REPORT.md"
  ],
  "limitations": "Only a NumPy toy experiment was run. Learned factors, GPU memory/speed, CIFAR-10 or ViT accuracy, training stability, channel projections, and realistic large-grid performance were not tested."
}