We propose NTNU-SMIL’s system for SemEval-2026 Task 3 Track B Subtask 1 Dimensional Stance Analysis (DimStance). Our approach models target-conditioned valence–arousal regression using sentence-pair encoding, dual regression heads, and a logistic-loss regression formulation. For English and Chinese, we further leverage same-language transfer from Track A and apply lightweight out-of-fold calibration with multi-seed ensembling to reduce cross-lingual scale mismatch. Post-hoc analysis shows that same-language transfer and logistic-loss regression are the main drivers of performance gains, while arousal variance collapse remains a challenge in low-resource settings such as Swahili.