\begin{table*}[htbp]
    \centering
    \begin{tabular}{llccccccc}
    \toprule
    \textbf{Model} & \textbf{App.} & \textbf{$n_{icl}$} & \textbf{$n_{prompts}$} & \textbf{$\alpha$} & \textbf{Layer} & \textbf{Dev Acc} & \textbf{Test Acc} \\
    \midrule
    Gemma-3 1B & 1 & 2 & 16 & 7.0 & 22 & 48.50\% & 56.67\% \\
    Gemma-31B & 2 & 4 & 16 & 7.0 & 22 & 49.50\% & 55.00\% \\
    \midrule
    Gemma-3 4B & 1 & 4 & 16 & 6.0 & 22 & 68.75\% & 74.58\% \\
    Gemma-3 4B & 2 & 2 & 16 & 6.0 & 22 & 69.25\% & 74.17\% \\
    \midrule
    LLaMA-3.2-1B & 1 & 8 & 64 & 3.0 & 21 & 49.00\% & 52.92\% \\
    LLaMA-3.2-1B & 2 & 8 & 32 & 3.0 & 21 & 52.25\% & 47.50\% \\
    \midrule
    LLaMA-3.2-3B & 1 & 4 & 64 & 2.0 & 21 & 61.75\% & 63.33\% \\
    LLaMA-3.2-3B & 2 & 8 & 16 & 4.0 & 21 & 63.00\% & 61.25\% \\
    \midrule
    LLaMA-3.1-8B & 1 & 2 & 16 & 2.0 & 21 & 60.75\% & 65.00\% \\
    LLaMA-3.1-8B & 2 & 2 & 16 & 2.0 & 21 & 60.50\% & 64.17\% \\
    \midrule
    Qwen3-4B & 1 & 2 & 32 & 6.0 & 15 & 67.50\% & 66.25\% \\
    Qwen3-4B & 2 & 4 & 16 & 1.0 & 15 & 68.00\% & 67.92\% \\
    \bottomrule
    \end{tabular}
    \caption{Activation steering grid search results. Hyperparameters were selected via grid search on the development set, then optimal $\alpha$ values were determined through separate search. $n_{icl}$ = number of in-context learning examples. $n_{prompts}$ =number of prompts for steering vector computation. $\alpha$ = steering coefficient. Layer = starting layer for steering intervention.}
    \label{tab:steering_grid_results}
\end{table*}