This paper describes our system for the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA). We participate in Track A (DimABSA) and Track B (DimStance), both of which involve Subtask 1 – predicting continuous valence–arousal (VA) scores for given text–aspect pairs in English and Chinese.Our system combines pre-trained multilingual transformers with aspect-marker input encoding and dual regression heads for VA prediction, trained with a 5-fold cross-validation ensemble. We select XLM-RoBERTa-large as the backbone for Track A and mDeBERTa-v3-base for Track B based on systematic model comparison on the development sets. On the official test sets, our system substantially outperforms the organizer-provided baselines across all language domain settings. On the unofficial postevaluation leaderboard, the system achieves strong results on Chinese subsets, ranking 1st on zho-env (Track B) and 2nd on zho-fin (Track A).