Эксперимент: Delay-Aware Plug-and-Play Residual Capacity (#1024)

{ "worked": true, "confidence": 9, "verdict": "Built a reusable delay-aware module selector and numerical verification harness for the delayed scalar residual model. The mechanism manifested: all tested G<=a cases remained stable across finite delays, numerical critical delays matched the analytic formula within 1.4e-6 relative error, and the predicted module capacity Nmax=7 at tau=0.8,g=0.35 agreed with the stability transition between N=7 and N=8. The synthetic proxy error decreased from 0.909 to 0.146 after bypassing unstable modules, but this is not evidence of a neural-network accuracy win.", "metrics": { "baseline": "Unconstrained synthetic stack: 12 active modules, aggregate gain 4.20, proxy error 0.9091 at tau=0.8.", "idea": "Delay controller: 7 active modules, aggregate gain 2.45 below Gmax=2.6446, proxy error 0.1457. Analytic-vs-numerical critical-delay relative errors were 1.3e-6, 5.7e-7, 2.7e-7, and 1.5e-7 for G=1.25, 1.5, 2, and 3." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "delay_controller.py", "root_check.py", "results.json", "results.txt" ], "limitations": "No CIFAR-10, learned residual MLP, measured hardware latency, Jacobian-gain estimation, validation-derived Gmin, or real inference-speed benchmark was tested. The proxy error is synthetic, and the direct root solver verifies the scalar linearized DDE rather than nonlinear neural-network stability." }