This paper describes our system to SemEval-2026 Task 3 Track A Subtask 1 on Dimensional Aspect Sentiment Regression (DimASR). We propose a lightweight and resource-efficient system built entirely on multilingual pre-trained encoders, without relying on LLMs or external corpora. 
We adopt joint multilingual and multi-domain training to facilitate cross-lingual transfer and alleviate data sparsity, introduce a bounded regression transformation that improves training stability while constraining predictions within the valid range, and employ an adaptive ensemble strategy via subset search to reduce prediction variance.
Experimental results demonstrate that our system achieves strong and consistent performance, ranking 1st on \textit{zho-res}, 2nd on \textit{zho-lap}, and 3rd on \textit{jpn-hot}, with all remaining datasets placed within the top half of participating teams\footnotemark.

\footnotetext{Our code is available at \url{https://github.com/huangly312/SemEval2026-task3-DimASR}.}