SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio

SELFDOUBT presents a method for uncertainty quantification in reasoning LLMs using a hedge-to-verify ratio. The technique assesses model confidence by analyzing the relationship between hedged language and verification behavior without requiring additional model calls. This addresses a key challenge in understanding when reasoning models are unreliable.