How Can LLMs Think Better Under Pressure? Optimizing Inference through Optimal Arousal Search

How can machines perform better under pressure? In human organizations, moderate pressure, such as a tight deadline or a critical meeting, can sharpen focus and accelerate prioritization, while excessive pressure can induce anxiety, overconfidence, and mistakes. Inspired by the Yerkes–Dodson arousal–performance trade-off, we introduce **AgenticOAT**, a stress-augmented inference framework that operationalizes stress as controllable, task-extrinsic linguistic signals injected only through the system prompt. AgenticOAT leaves the original task query unchanged and searches a theory-grounded $6\times 4$ arousal space spanning six Challenge–Hindrance–Threat stressor types and four graded intensity levels to locate behaviorally optimal configurations for each model–task pair. To evaluate stress-induced shifts beyond accuracy, we propose a Behavior Score that jointly captures task performance, verbosity, and hedging, with changes in these dimensions defining Optimal and Sub-Optimal arousal modes. Across 11 LLMs and 5 cognitive task categories, AgenticOAT improves Behavior Score on 50/54 model–task pairs in full-sample search. In the 27 Optimal Mode cases, models achieve an average +6.1 task-performance-point improvement while reducing verbosity by 42% and hedging by 29%. Content-matched controls support an additional contribution from stress framing beyond explicit directives, while held-out evaluation shows positive mean gains on items not used for selection. Quadratic fitting further characterizes an inverted-U pattern in the aggregated Optimal Mode trajectory, with independent held-out analysis supporting stronger negative curvature in this mode. These results support stress-augmented prompting as a behaviorally tunable approach to inference-time LLM optimization while preserving the original task query. The source code will be fully released upon paper acceptance.