Эксперимент: Centered-Geometry Projection Loss (#1223)

{ "worked": true, "confidence": 8, "verdict": "Built a self-contained centered-geometry projection-loss experiment with Gaussian ranking verification and a matched learned bottleneck comparison. The Gaussian Kendall correlation followed the claimed (2/pi)sqrt(m/d) scaling, and centered training improved Kendall ranking from 0.613 to 0.679 and normalized centered distance MSE from 0.351 to 0.237. However, nearest-neighbor recall decreased from 0.238 to 0.179 and raw distance preservation became much worse, so the benefit is specifically for centered geometry/ranking rather than universal embedding quality.", "metrics": { "baseline": "Kendall tau 0.6129; centered normalized MSE 0.3514; raw normalized MSE 0.7889; Recall@1 0.2375", "idea": "Kendall tau 0.6795; centered normalized MSE 0.2373; raw normalized MSE 1041.7; Recall@1 0.1792", "gaussian_check": "Observed Kendall tau for m={2,4,8,16,32}: {0.097,0.148,0.223,0.320,0.495}; predictions: {0.113,0.159,0.225,0.318,0.450}" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "This is a small synthetic fixed-embedding experiment, not CIFAR-100 or a real sentence-embedding benchmark. It compares current-batch centering rather than a separately tuned EMA baseline, uses one bottleneck size, and does not measure wall-clock overhead or equal-FLOP training." }