\label{sec:setup}
\paragraph{Dataset.}
The official DimABSA Track~A dataset \cite{lee2026dimabsabuildingmultilingualmultidomain} covers 6 languages (Chinese, English, Japanese, Russian, Tatar, and Ukrainian) and 4 domains (Hotel, Laptop, Restaurant, and Finance), organized into 10 language--domain pairs for Subtask~1 (DimASR).

In our experiments, we utilize the official training and development splits, and reserve 10\% of the training data as an internal validation set. 

%----------------------------------------------
\paragraph{Evaluation Metrics.}
According to the official task guidelines, Subtask~1 (DimASR) is evaluated by measuring the prediction error in the VA space using Root Mean Squared Error (RMSE). The metric is defined as:
\vspace{-0.2cm}
\begin{equation}
\small
\mathrm{RMSE}_\mathrm{{VA}} =
\sqrt{
\frac{1}{N}
\sum_{i=1}^{N}
\left(V_{p}^{(i)}-V_{g}^{(i)}\right)^2+
\left(A_{p}^{(i)}-A_{g}^{(i)}\right)^2
}
\label{eq:rmse_va}
\end{equation}
where $N$ is the number of instances; $V_p^{}$ and $A_p^{}$ denote the predicted valence and arousal values for an instance; and $V_g^{}$ and $A_g^{}$ denote the corresponding gold values.

%----------------------------------------------
\paragraph{Implementation Details.}
Our models are implemented using PyTorch 2.3 and Hugging Face Transformers 4.46.1.
All experiments are conducted on a single NVIDIA A800 GPU (40GB VRAM).
The model parameters are optimized using AdamW with early stopping based on validation RMSE, where the patience is set to 2. 
To promote model diversity for ensemble, we train multiple models under different hyperparameter configurations. 
Specifically, we vary the batch size in $\{16, 32, 64\}$ and the learning rate within the range of $8\times10^{-6}$ to $3\times10^{-5}$.
All experiments are conducted with a fixed random seed of 42.

%----------------------------------------------
\paragraph{Baselines.}
We compare our method with the official baseline results reported by the task organizers \cite{lee2026dimabsabuildingmultilingualmultidomain}. 
These baselines include LLM-based approaches evaluated in zero-shot and few-shot settings, such as \textbf{Kimi K2 Thinking} \cite{team2025kimi}, as well as supervised fine-tuned LLMs, including \textbf{Qwen-3 14B} \cite{yang2025qwen3} and \textbf{GPT-OSS 120B} \cite{agarwal2025gpt}. 