# Exact e0 Distribution Validation This note records the clean distributional validation for the actual FA initial operator erosion theorem. ## Theorem Being Validated Use a one-hidden-layer MLP with Gaussian feedback: ```text x -> W1 -> ReLU -> W2 -> output ``` For fixed forward weights, data, gates, and residual: ```text speed_BP = ||g_output^BP||^2 + ||g_hidden^BP||^2 speed_FA = ||g_output^BP||^2 + e0(B) = 1 - speed_FA / speed_BP ``` Because the hidden FA gradient is linear in Gaussian feedback `B`: ```text = ``` Therefore: ```text ~ Normal(0, sigma_B^2 ||C(W,r)||^2) ``` and: ```text e0(B) ~ Normal( 1 - ||g_output^BP||^2 / speed_BP, sigma_B^2 ||C(W,r)||^2 / speed_BP^2 ) ``` This is an exact conditional distribution for one-hidden-layer Gaussian FA at initialization. It does not involve training horizon `T`. ## Experiment Script: ```text scripts/actual_fa_initial_erosion_distribution.py ``` Run: ```text python scripts/actual_fa_initial_erosion_distribution.py \ --widths 16 32 64 128 \ --init-seeds 3 \ --feedback-samples 4096 \ --torch-threads 8 \ --outdir outputs/actual_fa_initial_erosion_distribution ``` Outputs: ```text outputs/actual_fa_initial_erosion_distribution/e0_distribution_theory_vs_empirical.png outputs/actual_fa_initial_erosion_distribution/e0_distribution_moment_calibration.png outputs/actual_fa_initial_erosion_distribution/e0_distribution_rows.csv ``` ## Results Representative rows: | width | init | theory mean | empirical mean | theory std | empirical std | KS | p-value | |---:|---:|---:|---:|---:|---:|---:|---:| | 16 | 0 | 0.113830 | 0.113413 | 0.027228 | 0.027401 | 0.0150 | 0.314 | | 16 | 1 | 0.113705 | 0.113859 | 0.027261 | 0.027278 | 0.0103 | 0.778 | | 32 | 0 | 0.121730 | 0.121544 | 0.018952 | 0.018693 | 0.0114 | 0.654 | | 32 | 2 | 0.227193 | 0.227691 | 0.031148 | 0.030779 | 0.0166 | 0.206 | | 64 | 0 | 0.031155 | 0.031119 | 0.003652 | 0.003583 | 0.0118 | 0.609 | | 128 | 0 | 0.023004 | 0.022989 | 0.001630 | 0.001610 | 0.0135 | 0.441 | All tested panels show close agreement between theory density and empirical random-feedback histograms. ## Paper Use This is the clean validation figure for the actual FA local erosion theorem. It should replace any tangent-hierarchy figure if the goal is: ```text theory predicts a distribution large random-feedback sampling produces the same distribution ``` Suggested main-text wording: ```text For one-hidden-layer Gaussian FA, the conditional distribution of the initial operator erosion is exactly Gaussian. Figure X compares this predicted density against 4096 random feedback draws per initialization across widths; the empirical histograms and theoretical densities coincide without fitted parameters. ``` This figure belongs to: ```text Contribution 4: actual FA initial operator erosion theorem Contribution 5: distributional empirical validation ``` It does not require a training-time axis.