做推荐系统的可以看看，InforID 用固定预算自适应分配语义槽容量，比手动调 ID 结构更省事，准确率还更高。
InforID 是面向并行生成推荐的自适应语义目标构建框架，解决现有语义 ID 方法中 ID 结构固定、容量分配不均的问题。它通过固定容量预算在候选语义槽间分配，同时确定有效 ID 长度和每个槽的码本大小。实验表明，在相近容量预算下，InforID 提升了推荐准确率，并保持一步并行预测。该方法避免了均匀扩展语义槽带来的冗余容量问题。
Adaptive Semantic Capacity Allocation for Parallel Generative Recommendation
Autoregressive semantic ID recommenders are constrained by expensive beam-search decoding, which limits the practical length of item identifiers. Parallel generation methods alleviate this bottleneck by predicting all semantic ID tokens simultaneously, enabling longer IDs. However, existing semantic ID methods still rely on manually predefined and homogeneous ID structures, where both the number of semantic slots and the codebook size of each slot are treated as fixed hyperparameters. This ignores the heterogeneous capacity demands of different semantic subspaces and may allocate prediction capacity to slots with limited utility. We show that uniformly expanding semantic slots can provide limited gains, indicating redundant capacity in homogeneous semantic IDs. We propose InforID, a lightweight adaptive semantic target construction framework for parallel generative recommendation. InforID allocates a fixed capacity budget across candidate semantic slots, thereby jointly determining the effective ID length and slot-specific codebook sizes. Experiments demonstrate improved recommendation accuracy under comparable capacity budgets while preserving one-step parallel prediction.