Эксперимент: Riemannian inertial updates for normalized embeddings (#26)

{ "worked": false, "confidence": 9, "verdict": "Built a synthetic normalized-prototype classification benchmark comparing ambient momentum plus normalization against the proposed tangent-projected inertial update with retraction. Projector, retraction, and tangent-velocity checks passed at approximately 1e-7 numerical error. Across three seeds and beta values 0.9, 0.95, and 0.99, the idea showed no accuracy win; beta=0.99 modestly improved training loss but remained worse in final accuracy than baseline.", "metrics": { "baseline": "beta=0.9: final accuracy 0.42875, best accuracy 0.43833, final train loss 1.58014, norm error 1.79e-7; beta=0.99: final accuracy 0.40625, loss 1.71472", "idea": "beta=0.9: final accuracy 0.42833, best accuracy 0.43417, final train loss 1.58012, norm error 1.79e-7, tangent velocity error 1.82e-8; beta=0.99: accuracy 0.42250, loss 1.63279" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py" ], "limitations": "Only a small synthetic full-batch prototype classifier was tested; no CIFAR or MNIST backbone, contrastive objective, AdamW comparison, FLOP-matched timing, learning-rate schedules, coupling term, or large-scale generalization study was evaluated." }