While semantic ID-based generative retrieval enables efficient end-to-end modeling in industrial applications, these methods face a persistent trade-off. On one hand, data-rich head items often suffer from ID collisions, which blur their distinct features and degrade downstream tasks. On the other hand, data-sparse tail items especially cold-start items are prone to semantic fragmentation during quantization; they are often mapped as isolated discrete points, which severely hinders their ability to generalize. To address this issue, we propose the Anchored Curriculum with Sequential Adaptive Quantization ($SA^2CRQ$) framework. The framework introduces Sequential Adaptive Residual Quantization (SARQ) to dynamically allocate code lengths based on item path entropy, assigning longer, discriminative IDs to head items and shorter, generalizable IDs to tail items. To mitigate data sparsity, the Anchored Curriculum Residual Quantization (ACRQ) component utilizes a frozen semantic manifold learned from head items to regularize and accelerate the representation learning of tail items. Experimental results from a large-scale industrial search system and multiple public datasets indicate that $SA^2CRQ$ yields consistent improvements over existing baselines, particularly in cold-start retrieval scenarios.

翻译：摘要：基于语义ID的生成式检索虽然能在工业应用中实现高效端到端建模，但这类方法面临持续的权衡挑战。一方面，数据丰富的高频项目常出现ID冲突问题，这会模糊其特征独特性并降低下游任务性能；另一方面，数据稀疏的长尾项目（尤其是冷启动项目）在量化过程中易产生语义碎片化，常被映射为孤立离散点，严重阻碍其泛化能力。针对该问题，我们提出锚定课程式序贯自适应量化框架（$SA^2CRQ$）。该框架引入序贯自适应残差量化（SARQ）机制，根据项目路径熵动态分配编码长度，为高频项目分配更长的区分性ID，为长尾项目分配更短的泛化性ID。为缓解数据稀疏问题，锚定课程式残差量化（ACRQ）组件利用从高频项目学得的冻结语义流形，对长尾项目的表示学习进行正则化与加速。大规模工业搜索系统及多个公开数据集的实验表明，$SA^2CRQ$相较于现有基线方法持续取得性能提升，尤其在冷启动检索场景中表现更为显著。