Эксперимент: Gauge-Patched Local Experts (#508)

{ "worked": true, "confidence": 9, "verdict": "Built a compact Gauge-Patched Local Experts MVP with orthogonal 2D transitions, Holder regularity checks, small-angle patch residual analysis, and a matched two-expert optimization sweep. All three quantitative predictions were confirmed: norm error was 2.2e-16, the observed Holder log-log slope was -0.7496 versus predicted -0.75, and the residual slope was 1.9995 versus predicted 2.0. The patch penalty reduced overlap disagreement from 0.7203 to 0.0800 at lambda=1 and to 0.00163 at lambda=10, but increased task MSE, so this demonstrates the mechanism rather than an overall accuracy win.", "metrics": { "baseline": "lambda_patch=0: task MSE 2.88e-15, overlap MSE 0.7203", "idea": "lambda_patch=1: task MSE 0.0800, overlap MSE 0.0800; lambda_patch=10: task MSE 0.1633, overlap MSE 0.00163; predicted/observed slopes: Holder -0.75/-0.7496, small-angle residual 2.0/1.9995" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 gauge_patched_experts.py", "files": [ "gauge_patched_experts.py", "results.json" ], "limitations": "Only a 2D analytic toy and two linear local experts were tested; no graph neural network, learned transition MLP, matrix exponential parameterization beyond the SO(2) analytic equivalent, unseen-rotation classification, cocycle triples, GPU scaling, or equal-FLOP multi-expert benchmark was evaluated." }