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While revocable decoding strategies attempt to mitigate errors by verifying and remasking tokens, they typically operate within a mixed-quality context. This leads to two critical failures: \\textit{Error Propagation}, where new tokens absorb toxic information from erroneous context, and \\textit{Local Error Reinforcement}, where errors mutually reinforce each other to evade detection. To alleviate these challenges, we propose ASRD (Anchor Supervised Revocable Decoding), a training-free framework that operates within the embedding space. ASRD explicitly decouples the decoding context into trusted \\textit{Anchor Tokens}, which are identified via temporal consistency, and uncertain candidates. 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To alleviate these challenges, we propose ASRD (Anchor Supervised Revocable Decoding), a training-free framework that operates within the embedding space. ASRD explicitly decouples the decoding context into trusted \\textit{Anchor Tokens}, which are identified via temporal consistency, and uncertain candidates. 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