LoGIC: Budgeted Context Construction for Node-Level Graph In-Context Learning with Tabular Foundation Models
Tabular foundation models have become powerful graph learners. Systems such as G2T-FM and GraphPFN encode each node as a feature row and make predictions through in-context learning (ICL), with labeled rows serving as the prompt. Current protocols employ the complete training table as context, causing attention to scale quadratically with the labeled pool and introducing preprocessing and memory bottlenecks. We investigate context construction for node-level graph ICL: which labeled nodes and auxiliary unlabeled nodes should constitute the prompt for specified queries. We formulate this allocation in terms of two resources: a labeled-context budget for predictive evidence and an unlabeled...
| Authors | Mingqi Yang, Zidong Guo, Jihui Yang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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