Эксперимент: Zonotope Active Exposure for Sensor-Fusion Networks (#1231)

{ "worked": false, "confidence": 9, "verdict": "Built a deterministic toy sensor-fusion MLP experiment with exact zonotope Minkowski and linear-map checks, support-function margins, and nonlinear probe evaluation. The algebra checks passed at about 2.2e-16 error and the static example had a positive 0.37 certificate, but under the idea's stated shared local Jacobian the probe term Jδ cancels between trusted and attack centers, so the certified margin stayed exactly constant across the full probe budget. The nonlinear network showed a modest 0.0583 separation increase, but this was not captured or optimized by the proposed first-order objective; therefore the claimed active-exposure mechanism was not demonstrated.", "metrics": { "baseline": "No probe: nonlinear trusted/attacked output separation 0.58834; local certified margin 0.33129", "idea": "Best bounded probe: nonlinear separation 0.64665 (+0.05830), but local certified margin remained 0.33129 for every probe, with margin range 0.0" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only a two-input, one-output synthetic regression MLP and one fixed attack hypothesis were tested; no multi-step horizon, training robustness loss, detection-rate benchmark, modality-specific Jacobians, or MNIST/CIFAR sensor-fusion experiment was run. The positive certificate and sampled support-bound check are toy numerical validations, not a deployment-scale robustness evaluation." }