\begin{table}[t]
\centering\small
\caption{Sampling-strategy baselines. All three adapters are trained with identical QLoRA hyperparameters on the same underlying problem set; the only difference is which problems are drawn. Accuracy in \%; 95\% bootstrap CI in brackets; $p$-value from paired bootstrap against the quantized baseline.}
\label{tab:baselines}
\begin{tabular}{@{}lccc@{}}
\toprule
Sampling strategy & Acc (\%) & 95\% CI & $p$ \\
\midrule
\textbf{Silver bullet} (ours) & 75.4 & [71.4, 79.2] & 0.001$^{**}$ \\
Failed only (no type balancing) & 74.0 & [70.0, 77.8] & 0.009$^{**}$ \\
Random (no diagnosis) & 73.6 & [69.6, 77.4] & 0.028$^{*}$ \\
\midrule
\textit{Quantized baseline (no restoration)} & 69.4 & -- & -- \\
\bottomrule
\end{tabular}
\end{table}
