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SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio
arXiv:2604.06389v1 Announce Type: new Abstract: Uncertainty estimation for reasoning language models remains difficult to deploy in practice: samplingbased methods are computationally expensive, while common singlepass proxies such as verbalized confidence or trace length are often inconsistent...
arXiv:2604.06389v1 Announce Type: new Abstract: Uncertainty estimation for reasoning language models remains difficult to deploy in practice: samplingbased methods are computationally expensive, while common singlepass proxies such as verbalized confidence or trace length are often inconsistent across models.
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