发表机构
The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出智能体图令牌推理，将图令牌化融入推理过程，通过三阶段训练实现，在七个图领域评估中大幅优于基线，可零样本迁移至未见领域，推动图分析向智能体范式发展。
AI 中文摘要
图模型在科学和工业领域中对关系数据进行建模，从引用网络到产品联合购买图均是如此。由于许多此类图的节点带有丰富的文本信息，越来越多的研究将大型语言模型（LLM）应用于图分析。其中最具图原生特性的方法使用图令牌：图编码器将图视图（如节点、其k跳邻域或集群）压缩为一段连续令牌，该令牌共同编码了节点属性和拓扑结构，可直接被模型读取。然而，现有方法以静态单次的方式使用图令牌：它们在模型见到目标前就对一个预定义的图视图进行编码，且从不修改，导致模型的逐步推理能力未被利用。我们提出智能体图令牌推理，将图令牌化重新定义为推理过程的一部分。在每一步，模型选择要编码的图视图及其粒度；按需调用图编码器以生成对应的图令牌；并将生成的令牌块拼接至运行上下文。因此，模型在图令牌空间中逐步推理，且其读取的令牌依赖于轨迹。我们通过三阶段训练流程实现这一方法：（i）自监督任务，教导模型读取异构图令牌；（ii）带有图令牌一致性正则化器的令牌鲁棒轨迹阶段；（iii）偏好优化，奖励图令牌证据与节点文本证据一致的轨迹。在覆盖七个图领域的评估中，我们的模型大幅优于大量基线方法，且无需针对每个目标微调即可零样本迁移至未见领域。更广泛而言，本研究将基于LLM的图分析从静态图令牌编码器推向图原生智能体范式。
英文摘要
Graphs model relational data throughout science and industry, from citation networks to product co-purchase graphs. Because the nodes of many such graphs carry rich text, a growing line of work applies large language models (LLMs) to graph analysis. The most graph-native of these methods use graph tokens: a graph encoder compresses a graph view, such as a node, its k-hop neighbourhood, or a cluster, into a short block of continuous tokens that jointly encodes node attributes and topology and is read directly by the model. Existing methods, however, use graph tokens in a static single-shot manner: they encode one predefined graph view before the model has even seen the target and never revise it, leaving the model's step-by-step reasoning ability unused. We introduce agentic graph token reasoning, which recasts graph tokenization as part of the reasoning process itself. At each step, the model chooses which graph view to encode and at what granularity; a graph encoder is invoked on demand to materialise the corresponding graph tokens; and the resulting block is spliced into the running context. The model thus reasons step by step in the graph token space, and the tokens it reads are trajectory-dependent. We realise this with a three-stage training pipeline: (i) self-supervised tasks that teach the model to read heterogeneous graph tokens, (ii) a token-robust trajectory stage with a graph-token consistency regulariser, and (iii) preference optimisation that rewards trajectories in which the graph-token evidence and the node-text evidence agree. Across evaluations spanning seven graph domains, our models outperform a broad set of baselines by a large margin and transfer zero-shot to unseen domains without any per-target fine-tuning. More broadly, this work pushes LLM-based graph analysis from static graph-token encoders towards a graph-native agent paradigm.
