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This paper introduces Confident Decoding, a training-free decoding strategy that dynamically selects the most reliable intermediate layer in LLMs using entropy-guided search, mitigating the alignment tax and improving reasoning performance on benchmarks like GPQA-Diamond and Omni-MATH with negligible overhead.