Recent progress in language and vision foundation models demonstrates the importance of discrete token interfaces that transform complex inputs into compact sequences for large-scale modeling. Extending this paradigm to graphs requires a tokenization scheme that handles non-Euclidean structures and multi-scale dependencies efficiently. Existing approaches to graph tokenization, linearized, continuous, and quantized, remain limited in adaptability and efficiency. In particular, most current quantization-based tokenizers organize hierarchical information in fixed or task-agnostic ways, which may either over-represent or under-utilize structural cues, and lack the ability to dynamically reweight contributions from different levels without retraining the encoder. This work presents a hierarchical quantization framework that introduces a self-weighted mechanism for task-adaptive aggregation across multiple scales. The proposed method maintains a frozen encoder while modulating information flow through a lightweight gating process, enabling parameter-efficient adaptation to diverse downstream tasks. Experiments on benchmark datasets for node classification and link prediction demonstrate consistent improvements over strong baselines under comparable computational budgets.

翻译：语言与视觉基础模型的最新进展表明，离散标记接口对于将复杂输入转换为紧凑序列以进行大规模建模至关重要。将这一范式扩展到图数据需要一种能够高效处理非欧几里得结构和多尺度依赖关系的标记化方案。现有的图标记化方法，无论是线性化、连续化还是量化方法，在适应性和效率方面仍存在局限。特别是，当前大多数基于量化的标记器以固定或任务无关的方式组织层次信息，这可能导致结构线索的过度表征或利用不足，并且缺乏在不重新训练编码器的情况下动态重新加权不同层次贡献的能力。本文提出了一种层次化量化框架，该框架引入了一种自加权机制，用于跨多尺度的任务自适应聚合。所提出的方法保持编码器冻结，同时通过轻量级的门控过程调节信息流，从而能够以参数高效的方式适应多样化的下游任务。在节点分类和链接预测基准数据集上的实验表明，在可比较的计算预算下，该方法相对于强基线模型取得了持续的性能提升。