GRACE: Gaussian Prototypical Consensus with Perturbation-aware Adaptation for Test-time Prompt Tuning in Graph Foundation Models

This paper studies the problem of test-time prompt tuning for graph foundation models (GFMs), which aims to improve GFMs at test time by optimizing prompts. Existing approaches typically learn prompts from a small number of support data to facilitate GFM adaptation. Despite recent progress, the performance of these approaches is far from satisfactory due to overconfident pseudo-labels of target nodes and thus unstable prompt optimization. Towards this end, we propose a novel approach named $\underline{G}$aussian P$\underline{r}$ototypic$\underline{a}$l $\underline{C}$onsensus with P$\underline{e}$rturbation-aware Adaptation (GRACE) for test-time prompt tuning in GFMs. The core of our GRACE is to improve the stability of test-time prompt tuning from two complementary perspectives, i.e., probabilistic prototype measurement and selective perturbation-aware adaptation. In particular, our GRACE first estimates Gaussian prototypes from support nodes to characterize the representation spaces across different layers in GFMs. More importantly, we aggregate covariance-aware Mahalanobis distances between each target node and its corresponding prototypes via majority voting, thereby filtering out unreliable pseudo-labels. Furthermore, we select target nodes with strong prototypical distance stability under graph perturbations and estimate prompt update directions through state-consistent symmetric perturbations, coupling reliability probing and prompt adaptation within a unified perturbation-aware procedure. Experiments on benchmark datasets validate the effectiveness of against extensive baselines. Our code is available at https://anonymous.4open.science/r/GRACE-03D3.