In nonconvex Burer--Monteiro low-rank matrix sensing of $U^\ast \in \mathbb{R}^{V \times D}$ under power-law covariance $Σ= \mathrm{diag}(j^{-s})_{j=1}^V$, the precision-wall approximation predicts that Adam stalls when the smallest non-gauge Hessian eigenvalue drops below its denominator floor, giving $s^\ast(V,D) = [\log(1/\varepsilon_{\mathrm{eff}}) - \log V]/\log(VD) + δ_{\mathrm{Adam}}$. If $\varepsilon_{\mathrm{eff}}$ were set by $\varepsilon_0$, lowering $\varepsilon_0$ from $10^{-10}$ to $10^{-12}$ would raise $s^\ast$ by $0.95\text{--}1.33$. Instead, across six values of $\varepsilon_0$ two decades apart, $10^{-2}$ to $10^{-12}$, the boundary follows the $\varepsilon_0$ slope down to $10^{-6}$, bends near $10^{-8}$, and is flat below $10^{-8}$, while warm-started L-BFGS and Gauss--Newton reach machine precision on stalled instances.
We trace this saturation to a self-generated dynamical floor