This submission proposes a hierarchical framework for longitudinal affect modeling, specifically designed for predicting variations in emotional valence and arousal over time. The system utilizes a DeBERTa-v3 encoder backbone optimized with a differentiable Concordance Correlation Coefficient (CCC) Loss for affect assessment (Subtask 1). This approach prioritizes capturing the "shape" and trend of emotional trajectories over absolute point-wise accuracy, yielding a significant performance gain over standard Mean Squared Error.For state change forecasting (Subtask 2A), the framework employs a Transformer-based temporal forecaster with positional encoding to account for inter-subject variability in emotional baselines. Disposition profiling (Subtask 2B) is addressed using a deep attention network that aggregates historical embeddings to identify emotionally informative essays. Experimental results from the official competition indicate that aligning the loss function with evaluation metrics and utilizing task-specific temporal modeling are essential for robust performance in longitudinal emotion recognition.