Эксперимент: Level-Adaptive Replay Memory (#507)

{ "worked": true, "confidence": 8, "verdict": "Built a controlled level-adaptive replay-memory simulator with empirical rho estimation, M-fold escape-rate calculation, neighbor probing, and fixed-window baselines. The core mechanism manifested: the exact escape formula matched Monte Carlo within 0.0017, monotone memory improvement held for 100% of tested increments, and increasing stale-data penalty shifted the optimal depth from m=16 to m=3-5, with observed optima within one depth of prediction. The adaptive controller did not win the matched toy comparison (65 escapes versus 73 for fixed m=2), so the evidence supports the mechanism but not a performance improvement.", "metrics": { "baseline": "Fixed windows over 80 rounds: m=1: 60 escapes, m=2: 73, m=4: 69, m=8: 43, m=16: 0; best fixed m=2.", "idea": "Adaptive controller: 65/80 escapes, mean depth 3.75, final depth 6. Formula Monte Carlo absolute error <=0.0017; monotone improvement fraction 1.0; stale-penalty predicted/observed optima: 0.0: 16/15, 0.005: 8/8, 0.015: 5/5, 0.03: 3/4, 0.05: 3/3, 0.08: 3/3. Low-rho r/(M*rho) ranged 0.985-1.000 for M=1-16." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "final_run.txt" ], "limitations": "This is a synthetic Bernoulli level-escape simulator, not a neural generator, diffusion/VAE, molecular benchmark, or policy-optimization task. It does not test wall-clock speed, gradient/FLOP budgets, distributional level profiles, confidence intervals for online adaptation, or robustness across many random seeds; the controller uses a simple neighbor-probing policy and fixed toy stale-data curve." }