In this paper, we present a lightweight and resource-efficient system for Subtask 1 (Dimensional Aspect Sentiment Regression) of SemEval-2026 Task 3 Track~A. Without relying on LLMs, external corpora, or data augmentation, our approach leverages joint multilingual training and an adaptive ensemble strategy.

Extensive experiments across 10 language--domain pairs demonstrate that, in dimensional sentiment regression, less is more: a carefully designed PLM-based system remains highly competitive against strong LLM-based baselines.
