Эксперимент: Entropy-Annealed Feature Particle Layer (#40)

{ "worked": false, "confidence": 9, "verdict": "Built an entropy-annealed particle feature layer with projected output weights, cosine temperature annealing, Gaussian Langevin diffusion, and teacher-direction diagnostics. The mathematical checks passed: predictor error was 5.96e-8 and sampled noise variance was within 1.3% of 2ηλ. However, on the two-index ReLU teacher task the particle model reached test MSE 0.1159 versus 0.000136 for the equally wide Adam baseline; it preserved higher feature diversity but had zero particles strongly aligned with teacher directions, so the claimed practical effect was not demonstrated.", "metrics": { "baseline": "test MSE 0.0001355; final train MSE 0.0000654; mean teacher alignment 0.8967; aligned fraction 87.5%; weight diversity 1.1638; runtime 1.34s", "idea": "test MSE 0.115886; final train MSE 0.09726; mean teacher alignment 0.2828; aligned fraction 0%; weight diversity 1.4009; runtime 1.32s" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 particle_experiment.py", "files": [ "particle_experiment.py", "results.json" ], "limitations": "Only a small synthetic two-index ReLU regression task was tested; CIFAR-10, single-index regression, temperature sweeps, alternative step sizes, exact non-Adam Langevin discretization, and larger particle populations were not tested." }