# Эксперимент: Curvature-Guided Discrepancy Gradient Accumulation (#1410)

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built the sign-controller MVP and verified the core discrepancy phenomenon numerically: guided signs reduced the median infinity-norm residual from 14.1–32.2 (all-plus) and 15.3–29.1 (random) to 0.83–3.19 across m=4–64, with approximately zero temporal growth exponent versus about 0.48–0.51 for controls. However, in the fixed-seed MLP test the guided method reduced peak residual median from 1.015 to 0.186 but degraded accuracy from 91.9% to 51.8% and increased loss from 0.510 to 0.683, so the proposed adaptation did not produce a useful optimization win.",
  "metrics": {
    "baseline": "Toy all-plus: median ||b||inf 14.10, 22.06, 22.19, 26.58, 32.16 for m=4,8,16,32,64; random: 15.25, 18.89, 23.87, 25.58, 29.12. MLP: peak residual median 1.015, max 2.685, accuracy 0.919, loss 0.510.",
    "idea": "Toy guided: median ||b||inf 0.981, 0.832, 1.669, 1.985, 3.190 for m=4,8,16,32,64; m=32 growth exponent -0.0002 versus 0.482 all-plus and 0.514 random. MLP: peak residual median 0.186, max 0.498, accuracy 0.518, loss 0.683."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "discrepancy.py",
    "results.json",
    "run.log"
  ],
  "limitations": "Only a tiny synthetic 2D classification task and one fixed seed were tested; no CIFAR-10, dropout/microbatch redundancy alternatives, AdamW, FLOP-normalized comparison, multiple seeds, GPU path, clipping-frequency difference, or optimizer-state memory measurement was evaluated. The toy controller used exact enumeration for n=4 and therefore does not validate large-n greedy-search quality."
}