AdaR让图模型在测试时自己调整推理步数，不用重新训练，效果比传统图神经网络好。
本文提出AdaR，一种自适应递归图模型，解决预训练图模型与固定架构不匹配问题。理论分析证明步骤依赖是自适应递归收敛的充要条件。AdaR通过编码归一化步骤信息和表示-目标关系实现自适应推理，并利用梯度监督信号确保递归收敛。在归纳和直推设置下，AdaR在多项图学习中优于现有强基线模型。
Adaptive Recurrent Message Passing for Test Time Computing on Graphs
Pre-trained foundation models have demonstrated remarkable success in many domains, enabling a unified backbone to generalize across diverse downstream tasks. However, extending this paradigm to graph learning remains challenging due to the intrinsic mismatch between graph data and fixed architectural designs. In this work, we show that this limitation can be overcome via recurrent graph models. To achieve this, we conduct a systematic theoretical analysis, rigorously deriving step dependence as a necessary and sufficient condition for an adaptively convergent recurrent process. Building on this foundation, we propose AdaR, an Adaptive Recurrent graph model, empowering flexible test-time computing on various downstream tasks without changing model parameters. To enable adaptive inference, AdaR explicitly encodes normalized step information and representation-target relations into the recurrent updates. To ensure convergence of the recurrent process, AdaR employs gradient-based supervision signals that guide representation updates throughout the recurrence. Empirical results demonstrate that AdaR consistently outperforms strong baselines in both inductive and transductive settings.