This paper presents team One and Only’s sys-tem for SemEval-2026 Task 2: PredictingVariation in Emotional Valence and Arousalover Time (Soni et al., 2026). We investigatewhether zero-shot LLM reasoning can replacefine-tuning for ecological affect forecasting bycombining deterministic statistical priors withfrozen LLMs (Gemini 3 Pro, Claude Opus4.6, GPT-5.2). For short-term state changes(Subtask 2A), an OLS mean-reversion anchoris paired with LLM-generated impulses; forlong-term disposition changes (Subtask 2B),a Chain-of-Thought prompt drives direct nu-meric prediction. Our system underperformsfine-tuned approaches on both subtasks. How-ever, post-submission ablation across threeLLMs reveals a task-dependent pattern: CoTreasoning substantially improves dispositionforecasting (rV : −0.185 → +0.129; MAEV :0.899 → 0.422), while uncalibrated LLM im-pulses degrade state-change prediction due tovariance collapse (σpred = 0.41 vs. σgold =1.73). We provide a detailed diagnostic anal-ysis of these failure modes and release allprompts and outputs for reproducibility.