让扩散模型生成时自动满足约束条件，提升可行解质量。
Search-Augmented Masked Diffusion Models for Constrained Generation
- 在去噪过程中融入搜索优化，动态调整生成方向
- 生物设计与符号推理任务中约束满足率显著提升
- 无需训练即可增强生成结果的可行性，适合严格约束场景
离散扩散模型通过迭代去噪生成序列，为结构化符号生成提供非自回归替代方案。但标准训练目标基于似然，仅匹配数据分布，缺乏原生机制在推理时施加硬约束或优化不可导属性。本文提出无训练的神经符号推理框架SearchDiff，将有指导的搜索直接嵌入逆向去噪过程。每步去噪生成的预测构成候选集，并在用户指定属性下优化，生成修正后的逆向转移，引导采样趋向高概率且可行的解。在生物设计与符号推理任务中的实验表明，SearchDiff显著提升约束满足度与属性符合度，持续优于离散扩散和自回归基线模型。
原文摘要 · Abstract (English)
Discrete diffusion models generate sequences by iteratively denoising samples corrupted by categorical noise, offering an appealing alternative to autoregressive decoding for structured and symbolic generation. However, standard training targets a likelihood-based objective that primarily matches the data distribution and provides no native mechanism for enforcing hard constraints or optimizing non-differentiable properties at inference time. This work addresses this limitation and introduces Search-Augmented Masked Diffusion (SearchDiff), a training-free neurosymbolic inference framework that integrates informed search directly into the reverse denoising process. At each denoising step, the model predictions define a proposal set that is optimized under a user-specified property satisfaction, yielding a modified reverse transition that steers sampling toward probable and feasible solutions. Experiments in biological design and symbolic reasoning illustrate that SearchDiff substantially improves constraint satisfaction and property adherence, while consistently outperforming discrete diffusion and autoregressive baselines.
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