arXiv AI June 8, 2026 By Peiyuan Li, Yongqi Huang, Jitao Zhao, Dongxiao He, Di Jin, Weixiong Zhang

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts

arXiv:2605. 15888v2 Announce Type: replace-cross Abstract: Heterogeneous Graph Prompt Learning (HGPL)has emerged as a promising paradigm for bridging the gap between the objectives of pre-training foundation models and their downstream applications in heterogeneous graph settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Sep 23

Graph Domain Adaptation Does Not End with Representation Learning

arXiv:2609.25692v1 Announce Type: new Abstract: Graph domain adaptation (GDA) transfers knowledge from a labeled source graph to an unlabeled target graph under shifts in both node attributes and gra...

By Ziqian Liu, Yongxue Xu, Enze Zhang, Jiaqi Zhang, Hao Wang, Maolin Wang

fine-tuning benchmarks safety

arXiv AI

Sep 2

Task-Specific Prompt with Global Context for Multi-Task Graph Pre-Training

The paper introduces TPGC, a dual-prior prompt initialization method for multi-task graph pre-training. It first uses a Task-Prior Injection Module to pre-train prompts on an auxiliary graph, then a Structure-Prior Injection Module to embed global structural context into layer-wise prompt vectors. Experiments on six node and graph classification benchmarks show that TPGC outperforms state‑of‑the‑art baselines in few‑shot settings while requiring fewer tunable parameters and less runtime.

By Zhiyang Qiu, Yangtao Wang, Xiaocui Li, Yanzhao Xie, Siyuan Chen, Wensheng Zhang

rag benchmarks safety

arXiv Machine Learning

Aug 21

GraphPFN: A Prior-Data Fitted Graph Foundation Model

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.

By Dmitry Eremeev, Oleg Platonov, Gleb Bazhenov, Artem Babenko, Liudmila Prokhorenkova

llms fine-tuning benchmarks

arXiv Machine Learning

Aug 18

Self-Routed Tensor Adapters for Parameter-Efficient Universal Visual Adaptation

arXiv:2608. 16384v1 Announce Type: cross Abstract: Universal visual representations require adaptation mechanisms that adapt across heterogeneous domains without fragmenting knowledge into domain-specific modules.

By Suraj Yadav

fine-tuning benchmarks

arXiv Machine Learning

Jun 25

Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts

arXiv:2606. 25665v1 Announce Type: new Abstract: Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target data during training.

By Tien-Hung Nguyen, Tien-Dat Tran, M. -Duong Nguyen, Kok-Seng Wong

benchmarks safety

arXiv Machine Learning

Sep 10

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

arXiv:2609.05955v1 Announce Type: new Abstract: Tabular foundation models have become powerful graph learners. Systems such as G2T-FM and GraphPFN encode each node as a feature row and make predictio...

By Mingqi Yang, Zidong Guo, Jihui Yang, Wenming Zuo

llms

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Related stories

Graph Domain Adaptation Does Not End with Representation Learning

Task-Specific Prompt with Global Context for Multi-Task Graph Pre-Training

GraphPFN: A Prior-Data Fitted Graph Foundation Model

Self-Routed Tensor Adapters for Parameter-Efficient Universal Visual Adaptation

Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts

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