Эксперимент: Coverage-Controlled Adaptive Time Sampling (#1111)

{ "worked": true, "confidence": 9, "verdict": "Built a deterministic coverage-controlled sampler with longest-gap midpoint refinement and numerical verification of the tube-radius law. The three predicted mechanisms manifested: radius slope Gamma/2 and intercept q matched to below 9e-16 relative error, the safety transition occurred around h_c=2(epsilon-q)/Gamma for every tested Gamma, and required uniform interval counts matched ceil(T/h_c) exactly. Adaptive refinement preserved the target radius in all mini-cases, but did not provide a speedup because retained irregular anchors caused 13–19 evaluations versus 5–14 for a freshly generated uniform grid.", "metrics": { "baseline": "Uniform endpoint grid: predicted and observed interval counts matched exactly; mini-case evaluations were [14, 10, 6, 5], with radii [0.1769, 0.2111, 0.3000, 0.3500].", "idea": "Adaptive longest-gap refinement: all 4 cases passed the epsilon coverage bound, with evaluations [19, 16, 14, 13] and radii [0.1745, 0.2111, 0.2412, 0.2641]. Mean evaluation reduction versus uniform was -0.973." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 coverage_sampling.py", "files": [ "coverage_sampling.py", "results.json", "run_output.txt" ], "limitations": "This is a toy geometric verification, not a trained neural ODE, conformal calibration, sensor-acquisition study, or wall-clock benchmark. Gamma and q were treated as known constants; state-dependent regularity, model error, stochastic observations, and physical trajectory accuracy were not tested. The baseline was a freshly constructed uniform grid, whereas adaptive sampling retained the initial irregular anchors, so the experiment does not establish an inference-time speedup." }