This paper describes TeleAI’s system for SemEval-2026 Task 3, Track A, Subtask 1 (DimASR), which focuses on predicting continuous Valence-Arousal (VA) scores for specific aspects in text. We frame this task as an end-to-end regression problem and propose a robust framework utilizing Qwen2.5-7B as the feature extraction backbone, combined with parameter-efficient fine-tuning via LoRA. To enhance model generalization and mitigate domain shifts, we primarily leverage multilingual and multi-domain mixed training. Furthermore, our system integrates several optimization and robustness techniques to stabilize continuous score prediction, including R-Drop-style consistency regularization, embedding-level PGD adversarial training, Smooth L1 (Huber) loss, sigmoid-based output interval mapping, and post-hoc linear calibration. Our comprehensive ablations demonstrate that the combination of joint training and robustness regularizations substantially reduces the official evaluation metric, $RMSE{VA}$. The proposed system achieves highly competitive performance across multiple language and domain settings, demonstrating the efficacy of applying lightweight LLM adaptation for dimensional aspect-based sentiment analysis.