Эксперимент: Memory-Retaining RG Feature Blocks (#378)

{ "worked": false, "confidence": 9, "verdict": "Built a compact memory-retaining profile implementation and scale-conditioned 1D feature block. The toy sweeps recovered alpha=0.700 and beta=0.620 from amplitude and width scaling, and the mathematically matched update eta'=eta/L^(1/beta) gave residuals below 2e-16 across L=1.25–3. However, the idea's stated eta'=eta/L update gave residuals 0.159, 0.306, 0.573, and 1.035 for those L values, exposing a coordinate/scale-update mismatch when beta is not 1. In the tiny classifier, the baseline achieved 1.000 accuracy and 0.0318 loss versus 0.776 and 0.4136 for the eta-conditioned idea, so there is no observed practical win here.", "metrics": { "baseline": "held-out accuracy 1.000; cross-entropy 0.03184", "idea": "held-out accuracy 0.776; cross-entropy 0.41361; exact-profile alpha fit 0.700 vs true 0.700; beta fit 0.620000 vs true 0.620; matched-RG residual 1.36e-16 to 2.00e-16; prescribed eta/L residual 0.159 to 1.035" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only a synthetic 1D signal task was tested, with one seed and a deliberately tiny network; no CIFAR-10/UNet/SSM integration, multiple-seed confidence intervals, learned alpha/beta, profile loss, FLOP benchmarking, or real-image unseen-scale evaluation was performed. The exact residual test uses the corrected eta scaling implied by h(x,eta), not the idea's eta/L rule." }