Эксперимент: Weakest-Direction Information Margin for Latent-State Training (#1078)

{ "worked": true, "confidence": 9, "verdict": "Built a self-contained linear latent-state MVP implementing posterior curvature, weakest-eigenvalue margin, soft-min approximation, and mismatch-induced MAP displacement. The mechanism manifested: log-log displacement versus margin had slope -1.00, normalized sensitivity was exactly 1, and equal-trace curvature produced a 50x weakest-direction shift difference. Under equal observation-energy allocation, margin optimization increased the minimum curvature from 0.05 to 1.568 and reduced the tested weak-direction shift from 2.00 to 0.0395, while trace stayed 4.1.", "metrics": { "baseline": "Trace-oriented allocation: trace(H)=4.1, lambda_min(H)=0.05, tested MAP shift=2.000.", "idea": "Smooth-min/margin allocation: trace(H)=4.1, lambda_min(H)=1.5678, tested MAP shift=0.03949; math sweep slope=-1.000 and inverse-margin normalized shift in [1.0,1.0]." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "README.txt", "results.json" ], "limitations": "This is a deterministic two-dimensional linear mechanism verification, not a GRU or neural world-model training study. The baseline is a trace-oriented sensor allocation under a fixed energy budget rather than a trained neural estimator; stochastic observations, explicit transition-matrix mismatch sweeps, learned encoders, CUDA execution, and runtime/FLOP comparisons were not tested." }