Эксперимент: Conformal Residual Gate for Latent Filtering (#375)

{ "worked": true, "confidence": 9, "verdict": "Built a scalar Kalman-filter MVP with held-out conformal residual calibration and an innovation-triggered R-inflation gate. At alpha values 0.05, 0.10, 0.20, and 0.30, observed coverage was 0.963, 0.911, 0.804, and 0.696 versus targets 0.95, 0.90, 0.80, and 0.70. Quantiles decreased monotonically with alpha, calibration quantile standard deviation scaled with fitted exponent -0.537 versus predicted -0.5, and gating reduced contaminated RMSE from 0.3842 to 0.3453, a 10.1% improvement.", "metrics": { "baseline": "Contaminated-observation RMSE 0.3842 +/- 0.0932 (ungated Kalman filter)", "idea": "Contaminated-observation RMSE 0.3453 +/- 0.087?; 10.13% mean improvement, calibrated innovation threshold 1.634, trigger rate 15.38%; conformal coverage at alpha=0.10 was 0.9115" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 conformal_gate_experiment.py", "files": [ "conformal_gate_experiment.py", "results.json" ], "limitations": "This is a scalar random-walk toy rather than a learned latent decoder or heat-diffusion/nonlinear benchmark. It tests marginal exchangeable coverage and robust measurement gating, not conditional coverage, horizon-specific drift, fallback models, additional observations, GPU performance, or a full latent covariance-normalized decoder residual." }