Эксперимент: Representation-Invariant Authority Margin (#1320)

{ "worked": true, "confidence": 9, "verdict": "Built a self-contained authority-demand verifier for a 2D control-affine system with a disk safe set and box-bounded actuator. The IAD threshold matched the exact analytical value 0.73, and sampled controlled invariance switched precisely at rho=0.73. Valid defining-function changes h, 2h, h+h^3, and log(1+h) produced identical IAD values while raw derivative penalties changed; h^3 correctly failed because q'(0)=0.", "metrics": { "baseline": "Raw mean outward derivative changed from 1.46 for h to 2.92 for 2h, despite identical safe sets; finite-difference gradient error was 1.38e-10.", "idea": "IAD was 0.73 for every valid representation with 0.0 relative spread. The minimum boundary derivative changed from -0.02 at rho=0.72 to 0 at rho=0.73 and +0.02 at rho=0.74, matching the predicted threshold." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 iad_experiment.py", "files": [ "iad_experiment.py", "results.json" ], "limitations": "This is a deterministic toy-system verification, not a learned neural policy or cart-pole training experiment. It does not test optimization behavior, empirical boundary sampling error beyond the included angular sample, long-horizon trajectories, or whether an IAD regularizer improves task reward or training loss." }