Эксперимент: Dissipation–Memory Budget for Stochastic RNNs (#1028)

{ "worked": false, "confidence": 9, "verdict": "Built a three-state nonequilibrium stochastic Markov hidden-state MVP with entropy-production and autocorrelation-time estimators, plus activity/frequency sweeps and a budget-based rate controller. Two quantitative predictions were confirmed: entropy production scales as activity^1.000 (predicted +1) and memory time as activity^-1.000 (predicted -1); Monte Carlo estimates matched exact sigma=0.5493 and tau=0.6667 within about 5%. However, the central kappa=1 budget inequality was not supported: epsilonsigma/(tauv^2) ranged from 0.054 to 0.819, and the controller helped only at high frequency while worsening low-frequency tracking, so the full proposed mechanism is not validated.", "metrics": { "baseline": "Fixed rate=1 tracking MSE at frequencies 0.03, 0.1, 0.3: 0.01575, 0.14104, 0.38923", "idea": "Budget controller with predeclared kappa=0.5 selected rates 0.5, 1, 4 and produced MSE 0.05818, 0.14104, 0.08954; budget ratio min/median/max was 0.054/0.768/0.819; observed activity exponents were sigma +1.000 and tau -1.000" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py && /home/maxwelhelp/main/bin/python3 analyze_results.py", "files": [ "run_experiment.py", "analyze_results.py", "results.json", "run_output.txt", "diagnostics.txt" ], "limitations": "No trained GRU or neural stochastic RNN was tested; the tracking experiment uses an analytically simulated first-order decoder rather than learned finite-state transition parameters. The budget prefactor kappa was not fitted, and only a small three-state cycle, sinusoidal targets, finite-duration Monte Carlo estimates, and a limited rate/frequency grid were evaluated." }