Discrete Diffusion Language Models (DLMs) offer a promising non-autoregressive alternative for text generation, yet effective mechanisms for inference-time control remain relatively underexplored. Existing approaches include sampling-level guidance procedures or trajectory optimization mechanisms. In this work, we introduce Iterative Latent Representation Refinement (ILRR), a learning-free framework for steering DLMs using a single reference sequence. ILRR guides generation by dynamically aligning the internal activations of the generated sequence with those of a given reference throughout the denoising process. This approach captures and transfers high-level semantic properties, with a tunable steering scale enabling flexible control over attributes such as sentiment. We further introduce Spatially Modulated Steering, an extension that enables steering long texts using shorter references by regulating guidance intensity across the sequence. Empirically, we demonstrate that ILRR achieves effective attribute steering on LLaDA and MDLM architectures with a minor computational overhead, requiring only one additional parallel forward pass per denoising step. Under the same compute budget, ILRR improves attribute accuracy over comparable baselines by 10$\%$ to 60$\%$ points, while maintaining high generation quality.

翻译：离散扩散语言模型为文本生成提供了一种有前景的非自回归替代方案，但有效的推理时控制机制仍相对缺乏深入探索。现有方法包括采样级引导程序或轨迹优化机制。本文提出迭代潜在表示精炼，这是一种无需学习的引导框架，仅需单个参考序列即可操控离散扩散语言模型。该方法通过在去噪过程中动态对齐生成序列与给定参考序列的内部激活来实现生成引导，从而捕获并迁移高层次语义特征，其可调节的引导强度能灵活控制情感等属性。我们进一步提出空间调制引导扩展技术，通过沿序列调节引导强度，实现使用较短参考文本引导生成长文本。实验表明，ILRR在LLaDA和MDLM架构上以微小计算开销实现了有效的属性引导，每个去噪步骤仅需增加一次并行前向传播。在相同计算预算下，ILRR将属性准确率较可比基线提升10%至60%，同时保持高质量生成结果。