Эксперимент: Excitation-Gated Latent Frame Calibration (#771)

{ "worked": true, "confidence": 8, "verdict": "Built a reproducible 2-D latent-frame calibration MVP with autodiff Jacobians, singular-value excitation scoring, and gated versus ordinary consistency optimization. The mechanism manifested clearly: zero motion gave rank 4 versus rank 5 with motion, the small-motion log-log slope was 0.994 (predicted 1), and sigma_min increased monotonically from 0.501 at T=2 to 0.665 at T=8. However, the toy gated optimizer had effectively identical parameter error to baseline at every excitation level, so this is evidence for the identifiability mechanism rather than a demonstrated training improvement.", "metrics": { "baseline": "Mean final theta error for d={0.03,0.1,0.3,1.0}: {2.29565, 2.23181, 1.83331, 0.18794}.", "idea": "Mean final theta error for d={0.03,0.1,0.3,1.0}: {2.29565, 2.23181, 1.83331, 0.18794}; no practical improvement over baseline in this isolated optimization test. Core checks: rank d=0=4, rank d=1=5; sigma_min/d median=0.641; log-log slope=0.994; T sweep sigma_min={0.5014,0.5054,0.5340,0.6005,0.6650}." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "This is a small analytic calibration experiment, not a GRU/world-model integration. The gating comparison optimizes one calibration vector rather than a learned recurrent network and does not test generalization, real sensor noise models, randomized Jacobian approximations, robust losses, or FLOP/speed tradeoffs. The gate is detached from the Jacobian in the toy optimizer, so the result does not establish end-to-end gated training benefit." }