arXiv Machine Learning September 10, 2026 By Mingqi Yang, Zidong Guo, Jihui Yang, Wenming Zuo

LoGIC: Budgeted Context Construction for Node-Level Graph In-Context Learning with Tabular Foundation Models

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llms

arXiv Machine Learning

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Message Passing Does More with Less for In-Context Learning on Graphs

arXiv:2609.37057v1 Announce Type: new Abstract: Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeat...

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arXiv Machine Learning

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arXiv:2509. 21489v4 Announce Type: replace Abstract: Graph foundation models face several fundamental challenges including transferability across diverse domains and data scarcity, which calls into question the very feasibility of creating such models.

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llms fine-tuning benchmarks

arXiv AI

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning

arXiv:2510. 04567v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs).

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arXiv AI

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KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning

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arXiv Machine Learning

Jul 21

Node4All: Learning Node Representation Beyond Datasets

arXiv:2607. 17272v1 Announce Type: new Abstract: Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning.

By Dooho Lee, Jaemin Yoo

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Message Passing Does More with Less for In-Context Learning on Graphs

GraphPFN: A Prior-Data Fitted Graph Foundation Model

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Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning

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