Эксперимент: Finite-Excitation Latent Replay (#1077)

{ "worked": true, "confidence": 8, "verdict": "Built a NumPy MVP implementing integral regressors, Gramian excitation, perturbation correction, and greedy replay selection in finite_excitation_replay.py. The mechanism checks passed: 0/12 perturbation-bound violations, expected small-error O(epsilon) scaling, and a certificate threshold crossing at epsilon 0.240 observed versus 0.242 interpolated. Replay activated at step 4, but its final parameter error was worse than using all data (0.0541 versus 0.00697), so the mechanism is supported while this toy replay policy did not show an identification win.", "metrics": { "baseline": "All-candidate least squares: final parameter error 0.00697; best error 0.00417.", "idea": "Greedy excitation replay: stack size 8, lambda_min=0.6195, activation step 4, final parameter error 0.0541; best error 0.0287. Bound violations: 0/12; certificate threshold epsilon observed/interpolated: 0.240/0.242." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 finite_excitation_replay.py", "files": [ "finite_excitation_replay.py", "results.json" ], "limitations": "This is a deterministic linear toy identification test, not a neural latent ODE or Lorenz experiment. It uses one random seed, synthetic matrix regressors, known perturbation magnitudes for validation, and a simple sequential greedy stack rather than a full online observer or ensemble uncertainty estimator; runtime, FLOPs, noisy latent-state estimation, and multi-seed statistical significance were not tested." }