SemEval-2026 Task 3 (DimABSA) includes Dimensional Aspect Sentiment Quadruplet Extraction (DimASQP), which requires extracting structured tuples—aspect term, aspect category, and opinion term—together with continuous valence–arousal (VA) values from reviews (Yu et al., 2026a). In this work, we participate in Track A, Subtask 3. We describe NYCU Speech Lab’s submission for the Chinese Restaurant and Laptop domains. Our system is a post-processing ensemble over heterogeneous architectures: LoRA/QLoRA fine-tuned decoder-only LLMs, a fine-tuned encoder-only model, and (optionally) prompted API-based LLMs. To improve robustness under the continuous F1 (cF1) metric, we use validation-calibrated weighted voting for tuple selection and weighted VA fusion for numerical aggregation, with strict output validation to enforce task constraints. Experiments on a held-out validation split show consistent gains over single models and clarify the precision–recall trade-offs induced by the voting threshold. On the organizers’ released (tentative) test leaderboard snapshot, our submission ranks first in both domains.