Author

Jia-Xing Li

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Sep 2026

Generalizing on Diverse Shifts: A Unified Topology-Aware Reweighting Algorithm for Graphs.

Topology-Aware Dynamic Reweighting (TAR+), a model-agnostic framework for node classification under strict graph distribution shifts and related robustness stressors, is proposed, supporting the broad applicability of topology-aware reweighting across heterogeneous graph learning conditions.

Jia-Xing Li, Jia-Shuo Liu, Wei-Huang Zheng et al. · IEEE Transactions on Pattern... · 0 citations

#machine learning Preprint Sep 2026

Learning from Failures: Heterogeneous Graph Memory for Small Language Model Tool-Using Agents

This paper proposes FRESH, a Failure-aware Retrieval framework over Experience-Structured Heterogeneous graphs, which transforms historical successes and failures into structured external experience for tool-using agents and consistently improves task success and tool-use reliability over no-memory agents and represent...

Jia-Xing Li, Lei Song, Rui Dong et al. · 0 citations

Preprint Aug 2026

NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning

This study offers a practical recipe for node-level JEPA-style latent prediction on graphs, and clarifies when structural conditioning helps representation learning.

Ting-He Zhang, Jian Xu, Jia-Heng Chen et al. · 0 citations