This paper describes the system developed by team for SemEval-2026 Task 2, Subtask 1: Longitudinal Affect Assessment. Our goal is to predict Valence and Arousal from ecological essays and feeling words over time. We propose an efficient hybrid framework that uses quantized 7B-scale language models as deterministic meta-feature extractors and combines them with an ensemble of DeBERTa, RoBERTa, and DistilBERT encoders. The system is designed to run on a single consumer-grade RTX 5060 Ti (16GB) GPU while remaining competitive. To bridge discrete supervision and continuous evaluation, we train the model as an ordinal classification problem and decode class probabilities into continuous scores through expected-value decoding. Our best system achieved an overall V&A average of 0.587, with per-dimension composite correlations of 0.647 for Valence and 0.527 for Arousal, ranking 3rd out of 31 teams. The results show that lightweight SLM-derived priors and multi-encoder fusion provide a strong performance–efficiency trade-off, especially for Arousal, where contextual anchoring is crucial.