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This paper introduces FeF-DLLM, a discrete diffusion language model that eliminates factorization errors by using exact prefix-conditioned factorization and accelerates inference via speculative decoding, achieving significant improvements in accuracy and speed on benchmarks such as GSM8K and MATH.