Эксперимент: Complementary-Channel Switched Latent Observer (#776)

{ "worked": true, "confidence": 9, "verdict": "Built a readable switched latent observer experiment using exact matrix exponentials, complementary rank-deficient channels, cycle spectral-radius sweeps, decay-rate checks, dwell-time sweeps, and a noisy residual-fusion comparison. The mechanism manifested strongly: the observed stability boundary was gain 0.8 versus the predicted 0.8, observed log-error slopes matched predicted log spectral radii to numerical precision, and gain 1.2 was stable for balanced dwell times even though both individual channel transitions had spectral radius 1.2214. The switched noisy MSE was 0.0432 versus 3.09e27 for the deliberately non-cycle-aware residual baseline, but this is not evidence of a general learned-model win.", "metrics": { "baseline": "Residual-only missing-channel fusion MSE 3.0946e27 at gain=1.2, noise std=0.15, 80 cycles, 200 trials", "idea": "Switched observer MSE 0.04324 under the same toy setup; cycle rho=0.81873 at tau1=tau2=0.5; individual channel rhos=1.22140 each. Boundary: predicted gain 0.8, observed grid gain 0.8. Decay slopes: predicted/observed -0.05/-0.05, -0.10/-0.10, -0.20/-0.20 for gains 0.9, 1.0, 1.2. Dwell sweep: cycle rho=0.92312 at (0.4,0.6) and 0.5 dwell split, but 1.04081 at (0.3,0.7), matching the predicted transition." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only a 2D diagonal linear toy system was tested; no trained RNN/SSM, nonlinear latent task, FLOP or wall-clock comparison, gain optimization, random switching, or realistic modality noise/missingness was evaluated. The residual-fusion baseline is a simple non-cycle-aware estimator and is not a competitive modern neural baseline." }