“Researchers propose SELFDOUBT, a novel approach to quantify uncertainty in reasoning language models by analyzing the ratio of hedging language to verification attempts. This addresses a critical deployment challenge for both open-source and proprietary LLMs, offering a practical, computationally efficient alternative to expensive sampling methods that works even when internal model probabilities are inaccessible.”