Эксперимент: Følner-Gated Message Passing (#830)

{ "worked": true, "confidence": 6, "verdict": "Built a reproducible Følner-gated six-layer message-passing prototype with frontier expansion monitoring, EMA gating, pooled skips, exact graph sanity checks, and a four-graph classification comparison. The core mechanism manifested: cycle ratios matched 1+2/m exactly, tree-ball ratios matched the exact expansive-growth calculation, and the sigmoid gate's 0.5 boundary matched r=1+delta=1.35 for all tested slopes. The mini-experiment did not show a general accuracy or speed win: gating activated on all finite graphs because the sampled frontier expanded, and accuracy was usually lower than baseline; therefore this is a mechanism-validation success, not evidence of a practical model improvement.", "metrics": { "baseline": "Validation accuracy: cycle 0.967, grid 0.600, random3 0.333, tree3 0.379; mean training time 0.50 s; gate=1.0 by definition.", "idea": "Validation accuracy: cycle 0.533, grid 0.533, random3 0.467, tree3 0.483; mean training time 0.97 s; gate mean approximately 0 on every graph. Exact checks: cycle maximum absolute ratio error 0; tree maximum error 0; gate boundary observed 1.35 versus predicted 1.35 for slopes 2,4,8,16." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py", "files": [ "run_experiment.py", "results.json", "run.log" ], "limitations": "The implementation uses dense adjacency matrices and a global pooled skip rather than sparse minibatch graph sampling or learned local pooling. It does not measure true activation memory, FLOPs, peak GPU memory, confidence intervals, fixed pooling, or radius-3 labels as specified. The auxiliary EMA persistence approximation disagreed with the discrete recurrence for r=1.6 and r=2.0, so it was not used to claim a win." }