Эксперимент: Target-Law Neural Stopping (#976)

{ "worked": true, "confidence": 8, "verdict": "Built a differentiable survival-mixture stopping-policy MVP for Brownian trajectories and verified its core equations numerically. Survival slopes matched predicted values to below 5e-16, mixture mass error was 2.2e-16, and discretization error halved when the time step was halved. The learned policy reduced toy target-law feature MSE from 0.3263 to 0.000548 while reducing expected compute from 3.0 to 1.147 time units.", "metrics": { "baseline": "Fixed terminal-time Brownian sample: feature MSE 0.3263046; compute 3.0 time units.", "idea": "Learned differentiable survival mixture: feature MSE 0.0005478; expected compute 1.1467 time units. Survival-slope error <=4.5e-16, mixture-mass error 2.2e-16, and discretization errors for dt 0.2, 0.1, 0.05, 0.025 were 0.09335, 0.04607, 0.02288, 0.01140." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only a small CPU toy Brownian experiment was tested. The fitted hazard is time-dependent rather than state/path-dependent, no real diffusion or neural sampler was used, the discrepancy is handcrafted feature MSE rather than MMD or FID, and no multiple-seed statistical study was performed." }