\begin{table*}[ht]
\centering
\footnotesize
\setlength{\tabcolsep}{3.5pt}
\renewcommand{\arraystretch}{1.1}
\begin{tabular}{l cccccccccc >{\columncolor[gray]{0.95}}c}
\toprule
Methods 
& eng-res & eng-lap & jpn-hot & jpn-fin 
& rus-res & tat-res & ukr-res 
& zho-res & zho-lap & zho-fin 
& \textbf{Avg.} \\
\midrule

\multicolumn{12}{l}{\textit{\textbf{Zero-Shot Learning}}} \\
Kimi K2 Thinking$^\dagger$ 
& 2.3432 & 2.6546 & 2.3294 & 2.3379 
& 2.0630 & 2.3636 & 2.0782 
& 2.6230 & 2.0426 & 2.9662 
& 2.3802 \\

\midrule
\multicolumn{12}{l}{\textit{\textbf{One-Shot Learning}}} \\
Kimi K2 Thinking$^\dagger$ 
& 2.1461 & 2.1893 & 1.7553 & 1.6396 
& 1.7768 & 1.9380 & 1.7805 
& 1.8959 & 1.6440 & 1.9652 
& 1.8731 \\

\midrule
\multicolumn{12}{l}{\textit{\textbf{Supervised Fine-Tuning}}} \\
Qwen-3 14B$^\dagger$       
& 2.6427 & 2.8089 & 2.2906 & 1.8964 
& 2.1528 & 2.6367 & 2.2121 
& 2.0073 & 1.7706 & 1.4707 
& 2.1889 \\

GPT-OSS 120B$^\dagger$     
& \underline{1.4605} & \underline{1.5269} & \underline{0.7188} & \underline{1.0188} 
& \underline{1.4775} & \textbf{1.7153} & \underline{1.5166} 
& \underline{1.0349} & \underline{0.8032} & \underline{0.6511} 
& \underline{1.1924} \\

\midrule
\rowcolor[gray]{0.95}
\textbf{Ours}    
& \textbf{1.2676} & \textbf{1.3098} & \textbf{0.6289} & \textbf{0.8331} 
& \textbf{1.4420} & \underline{1.8596} & \textbf{1.4750} 
& \textbf{0.9256} & \textbf{0.6553} & \textbf{0.4892} 
& \textbf{1.0886} \\

\bottomrule
\end{tabular}
\caption{
Track~A Subtask~1 Results. $\text{RMSE}_{\mathrm{VA}}$ on the official test sets across 10 language--domain pairs.
The \textbf{best} results are in bold, and the \underline{second-best} are underlined.
Baseline results$^\dagger$ from~\cite{lee2026dimabsabuildingmultilingualmultidomain}.
}
\label{tab:main_result}
\end{table*}