This paper presents our system for SemEval 2026 Task 2 (Subtask 1), modeling affect assessment as a longitudinal trajectory. We evaluate a tripartite affective framework of escalating contextual complexity, spanning zero-context feature extraction, latent temporal modeling via LSTM, and explicit semantic reasoning via the Teacher-Guided Clinical Reasoning Agent utilizing in-context learning. Our results show that robust static extraction outperforms explicit sequence modeling. Specifically, Matryoshka-distilled embeddings (Jasper) paired with XGBoost provided the best balance of speed and accuracy when utilizing the full training corpus (Valence composite r = 0.654, a 17.4% improvement compared with the baseline), mitigating the severe overfitting observed on partitions of the dataset. Additionally, we uncover a distinct agentic advantage: although the reasoning agent trailed mathematical regressors in tracking high-frequency fluctuations, its SOTA psychological profiling yielded the highest Between-User Valence correlation (r = 0.725), demonstrating its efficacy in user-level affective profiling. Finally, a persistent "arousal bottleneck" confirms the limitations of text-only modeling for physiological activation.

This paper explores multilingual emotion classification across binary classification, intensity estimation, and cross-lingual detection tasks. To address linguistic variability and limited annotated data, we evaluate various deep learning approaches, including transformer-based embeddings and traditional classifiers. After extensive experimentation, language-specific embedding models were selected as the final approach, given their superior ability to capture linguistic and cultural nuances. Experiments on high- and low-resource languages demonstrate that this method significantly improves performance, achieving competitive macro-average F1 scores. Notably, in languages such as Tigrinya and Kinyarwanda for cross-lingual detection task, our approach achieved a second-place ranking, driven by the incorporation of advanced preprocessing techniques. Despite these advances, challenges remain due to limited annotated data in underrepresented languages and the complexity of nuanced emotional expressions. The study highlights the need for robust, language-aware emotion recognition systems and emphasizes future directions, including expanding multilingual datasets and refining models.