Эксперимент: ISS-Gated Positive Neural State Module (#886)

{ "worked": true, "confidence": 9, "verdict": "Built a readable positive mass-action module with generic reaction stoichiometry, RK4 integration, positivity flooring, free-energy computation, and an ISS residual. The three required mechanism predictions manifested: Euler stability boundary 1.951 versus predicted 2.0, disturbance-energy log-log slope 1.973 versus 2.0, and nominal free-energy decay rate -1.964 versus -2.0; the direct mass-action formula check had absolute error 0.0. The sequence comparison was mixed: the positive module had worse training MSE but better long noisy-horizon MSE than the tanh baseline, so the stability mechanism is supported but a general accuracy win is not established.", "metrics": { "baseline": "tanh RNN: train MSE 0.00701; long noisy-horizon MSE 0.00850", "idea": "positive mass-action state: train MSE 0.00941; long noisy-horizon MSE 0.00749; Euler boundary 1.951 vs 2.0 predicted; V amplitude slope 1.973 vs 2.0; nominal log-V rate -1.964 vs -2.0; mass-action field error 0.0" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 iss_experiment.py", "files": [ "iss_experiment.py", "results.json" ], "limitations": "Only a scalar birth-death mass-action system and a small random-search sequence demo were tested. No trained GRU, multidimensional complex-balanced network, learned diagonal rescaling, explicit ISS-penalty optimization, GPU implementation, or large real sequence benchmark was evaluated." }