Dynamic Closed-Loop Steering for Robust and Interpretable System-2 Reasoning in Large Language Models

Large language models, or LLMs, are moving from fast token generation toward deliberate multi-step reasoning. Scaling test-time compute has become a key way to improve performance on complex tasks because it gives models more opportunity to develop intermediate reasoning before producing an answer. However, unconstrained compute scaling frequently leads to a practical failure mode known as "overthinking." Internally, models become trapped in high-entropy local minima, causing t