Эксперимент: Differentiable Euler-density morphology loss (#1080)

{ "worked": true, "confidence": 8, "verdict": "Implemented differentiable multi-threshold Euler-density morphology loss with exact hard-threshold reference and a small denoising integration. The mechanism checks passed: temperature reduction monotonically approached the hard statistic, constant-field cancellation matched the predicted zero bulk density, and iid Bernoulli measurements matched q−2q²+q⁴ within 0.00018. The denoising experiment showed only a negligible morphology improvement, so the mathematical mechanism is validated but a meaningful ML quality win was not demonstrated.", "metrics": { "baseline": "Denoising MSE 0.00597470; hard Euler-curve MAE 0.00132996; soft target MSE 0.00031454.", "idea": "Denoising MSE 0.00597462; hard Euler-curve MAE 0.00132935; soft target MSE 0.00031418. Temperature sweep mean absolute error decreased from 0.02207 at T=0.20 to 0.00394 at T=0.01; iid Bernoulli observed 0.119827 versus predicted 0.120006." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py", "files": [ "run_experiment.py", "results.json", "REPORT.md" ], "limitations": "Only a tiny synthetic 16x16 denoising task and 80 optimization steps were tested; no CIFAR-10, diffusion, GAN, FID, connected-component, hole-count, equal-FLOP, or GPU comparison was performed. The observed practical improvement is too small to claim generalization." }