Эксперимент: Global-statistics context shortcut (#974)

{ "worked": true, "confidence": 9, "verdict": "Built masked sequencewise global normalization and a local-convolution labeler benchmark in experiment.py. The autograd Jacobian matched the paper formula to numerical precision, influence scaled exactly as 2/n, and influence scaled linearly with gamma including zero influence at gamma=0. The shortcut was not a performance win in this toy task: it was slower and less accurate than the local baseline for all tested run lengths.", "metrics": { "baseline": "Local k=9 CNN: accuracy 0.7731, 0.8822, 0.9417 for run lengths 2, 8, 32; parameters 338.", "idea": "Global-statistics k=9 CNN: accuracy 0.7719, 0.8672, 0.8697 for run lengths 2, 8, 32; parameters 370. Recorded CPU times were 55.6s, 46.8s, and 66.3s versus baseline 45.4s, 18.9s, and 18.4s.", "math": "Exact Jacobian off-diagonal prediction error 0; diagonal error 2.2e-16. Deterministic n sweep gave |coefficient|*n = 2.0 for n=16, 32, 64, and 128. Gamma sweep was exactly linear: 0, 0.00583, 0.01166, and 0.02333 for gamma=0, 0.5, 1, and 2." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "CUDA convolution failed and the script correctly fell back to CPU. The benchmark used one seed, a synthetic setup where local input already exposed the label, and fixed training steps rather than carefully matched FLOPs. Padding-mask behavior was implemented but not separately benchmarked, so throughput results are indicative only." }