Эксперимент: Eigenmode-Targeted Hidden-State Actuator Selection (#1142)

{ "worked": false, "confidence": 9, "verdict": "The toy system verified accurate eigenpairs and strong resolvent growth near a unit eigenvalue. Modal coordinate selection improved target gain over random by 1.54x to 1.78x across three seeds, but missed the stated 2x target; the literal inverse-footprint ranking was worse than random, so the overall idea is not a demonstrated win.", "metrics": { "baseline": "Random k=2 mean gains: 2.915, 16.904, 15.036.", "idea": "Modal gains: 4.475, 29.718, 26.810; mean ratio 1.692x. Literal score mean ratio 0.420x." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only an 8D linear system was tested. No trained nonlinear RNN, defense, pruning, optimization, FLOP match, or larger statistical evaluation was performed." }