Эксперимент: Spectral Burn-In and Retrieval Switch (#396)

{ "worked": true, "confidence": 8, "verdict": "Built and ran a reproducible scalar contraction verification plus a noisy Lorenz-63 observer/retrieval switch in run_experiment.py. The core mechanism manifested: predicted burn-in exactly matched observed recurrence steps, log(1/epsilon) slopes agreed with 1/|log r| within 2.2%, and the stability boundary occurred at r=1. Adaptive switching detected 79.4% of injected mismatch steps while reducing retrieval rate to 35.9%, but it did not improve accuracy in this toy implementation: adaptive RMSE was 5.09 versus 0.75 for always-observer and 14.40 for always-retrieve.", "metrics": { "baseline": "Always-observer: RMSE 0.7497, normalized cost 1.00, retrieval rate 0%; always-retrieve: RMSE 14.3996, normalized cost 7.00, retrieval rate 100%.", "idea": "Adaptive switch: RMSE 5.0887, normalized cost 3.153, retrieval rate 35.88%; mismatch-step recall 79.44%, non-mismatch switch rate 25.54%. Scalar math: burn-in step error 0 for all tested r and epsilon; log-scaling relative slope error 0.05%-2.17%; observed stability boundary r=1." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py", "files": [ "run_experiment.py", "results.json", "run_output.txt" ], "limitations": "The Lorenz observer uses a hand-designed fixed contraction proxy rather than 3-10 Jacobian-vector power iterations, and retrieval uses nearest scalar observation rather than a learned latent delay-window index. No trained neural state-space model, real retrieval system, FLOP profiling, epsilon sweep in the learned model, or repeated random seeds was tested. The adaptive policy is evidence for the switching mechanism and math, not a demonstrated end-to-end accuracy or speed win." }