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扩散语言模型展现出较高的发展潜力，但在代码等形式语言生成任务中，仍难以稳定满足语法约束。针对这一问题，本文提出了针对扩散语言模型约束解码方法 —— LAVE，通过对扩散语言模型的中间输出进行前瞻补全与语法验证，为模型生成过程提供了可靠的语法保障。实验表明，LAVE 能够显著提升多种扩散语言模型生成形式化语言（例如源代码、JSON、化学表达式）的语法正确率，同时改善功能正确率，并保持较低的推理开销。