Эксперимент: Critical stochastic min-plus tree layer (#870)

{ "worked": true, "confidence": 9, "verdict": "Built a NumPy implementation of the stochastic min-plus binary-tree merge and verified the claimed mechanism. Monte Carlo matched the exact zero-mass recursion with maximum absolute error 0.00186; the fitted transition was p=0.500000, and depth/q sweeps confirmed critical invariance at p=0.5 plus convergence toward one below and zero above criticality. The toy classifier comparison did not show a general task win: sum/min/critical-stochastic accuracies were 0.498/0.604/0.542, so the positive result is specifically the predicted absorbing-state mechanism, not downstream accuracy.", "metrics": { "baseline": "Deterministic sum pooling accuracy: 0.498; deterministic min pooling accuracy: 0.604.", "idea": "Critical stochastic p=0.5 accuracy: 0.542. At q=0.37, p=0.25 observed zero fractions across depths [0,1,2,4,8] were [0.3700,0.4858,0.6082,0.8300,0.9853], versus predictions [0.3700,0.4866,0.6115,0.8287,0.9850]. For p=0.75, observed values were [0.3700,0.2540,0.1542,0.0488,0.0038], versus predictions [0.3700,0.2535,0.1588,0.0503,0.0035]. The fitted transition was p=0.500000." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "run_output.txt" ], "limitations": "The downstream comparison is a small NumPy toy classifier rather than a full MNIST/CIFAR MLP or CNN. It does not measure gradient routing, learned projections, normalization effects, FLOPs, or finite-width feature correlations; the exact recursion check uses independent Bernoulli zero states, where its assumptions hold by construction." }