两篇新研究论文探讨了图基础模型（GFMs）的挑战和潜力。第一篇论文《图基础模型何时能够迁移？一种以数据为中心的理论》研究了影响GFM迁移能力的图域属性，并提出了一个理论框架来分解输出变化并指导数据整理。第二篇论文《图是跨数据模态的基石》介绍了G-Substrate，一个旨在跨异构模态和任务组织图结构学习的框架，其目标是积累结构规律而不是反复重构它们。 AI
排序理由 两篇在arXiv上发表的学术论文，讨论了图基础模型的理论方面。
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arXiv:2605.29828v1 Announce Type: new Abstract: Graph foundation models (GFMs) aim to reuse a single backbone across diverse graph domains, yet their transfer is often uneven and can exhibit negative transfer. While most prior work improves transfer through architectural or adapt…
arXiv:2601.22384v2 Announce Type: replace-cross Abstract: Graphs provide a natural representation of relational structure that arises across diverse domains. Despite this ubiquity, graph structure is typically learned in a modality- and task-isolated manner, where graph represent…