Эксперимент: Quotient-Fibre Mixing Network (#598)

{ "worked": true, "confidence": 9, "verdict": "Built a reproducible quotient-fibre triangular state model with separate quotient and fibre transitions, empirical rate fitting, stability testing, and a matched monolithic least-squares baseline. The max-rate prediction matched observed rates within 3.2% across the sweep (the equal-rate case has the expected finite-horizon Jordan transient), slow-branch invariance errors were at most 0.23%, and the fitted stability slope correctly changed from negative below rate 1 to zero at 1 and positive above 1. The split model did not beat the matched monolithic model on this simple identification task: rollout MSE was 0.00715811 versus 0.00715793.", "metrics": { "baseline": "Monolithic one-step MSE 0.000896204; rollout MSE 0.00715793", "idea": "Split quotient-fibre one-step MSE 0.000896222; rollout MSE 0.00715811; max-rate relative error <=3.17%; slow-tail invariance error <=0.23%; stability boundary correctly detected" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "No nonlinear GRU, spectral normalization, real-world sequence dataset, or large-scale training comparison was tested. The verified result is for the explicitly triangular linear state model; cross-coupling and finite-sample neural optimization effects remain untested." }