We present lightweight, reproducible models for longitudinal valence–arousal (VA) prediction in the SemEval-2026 Task 2 essay corpus. Using only the official data, we enforce user-disjoint splits to prevent leakage and evaluate three settings: essay-level VA state estimation, short-horizon VA change forecasting, and long-horizon disposition change prediction. Our submitted systems use DistilBERT for essay-level regression, ModernBERT-based history modeling with a GRU and a blended previous-delta baseline for short-horizon change, and pooled DeBERTa history embeddings with a compact MLP for disposition change. On the official evaluation, across our best performing approaches, we achieve rcomp =0.665/0.468 (valence/arousal) for Subtask 1, r = 0.597/0.413 for Subtask 2A, and r =0.046/0.348 for Subtask 2B.