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 presents our approach for SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. We investigate multiple methodologies, including fine-tuning transformer models and few-shot learning with GPT-4o-mini, incorporating Retrieval-Augmented Generation (RAG) for emotion intensity estimation. Our approach also leverages back-translation for data augmentation and threshold optimization to improve multi-label emotion classification. The experiments evaluate performance across multiple languages, including low-resource settings, with a focus on enhancing cross-lingual emotion detection.