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 ...
| Authors | Junshu Sun, Wanxing Chang, Qingming Huang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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