Эксперимент: Phantom-Optimum Audit and Optimizer Drift Monitor (#1045)

{ "worked": true, "confidence": 8, "verdict": "Built a multistart bounded-optimization audit that clusters feasible stationary endpoints, counts local optima, tracks best decision drift, and applies an audit-aware checkpoint rule. The checks confirmed the predicted stationary-point transition at a=2, approximately linear decision drift with bias, and inverse-curvature drift scaling. In the synthetic trajectory, validation-only selection chose the final checkpoint with e=0.0413, while the audit selected t=4 with e=0.00980; this demonstrates the mechanism, but not yet on a trained neural surrogate.", "metrics": { "baseline": "Validation-only endpoint: validation_loss=0.0, decision drift e=0.04131, N=2", "idea": "Audit-selected t=4: validation_loss=7.78e-05, decision drift e=0.00980, N=2; count sweep exact across tested a values; drift linearization maximum absolute error=0.00296; curvature-slope relative errors=1.4%, 2.6%, and 6.8%" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 phantom_audit.py", "files": [ "phantom_audit.py", "results.json" ], "limitations": "The experiment uses an analytically designed one-dimensional quartic surrogate and a hand-parameterized bias trajectory rather than SGD-trained neural networks, neural ODEs, or a real simulator. It does not test computational overhead, checkpoint rollback during actual training, noisy validation data, higher-dimensional constrained optimization, or Hessian-vector-product implementations." }