The "2nd Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'26)" workshop focuses on advancing graph machine learning (GML) techniques in the context of large-scale foundation models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex d...
Qing-Yun Sun, Zi-Wei Zhang, Xing-Cheng Fu et al.· Proceedings of the 32nd ACM...· 0 citations
Visual retrieval-augmented generation (RAG) commonly expands the retrieved evidence set to improve answer-page coverage, implicitly assuming that all available evidence should be passed to the generator. We show that this assumption does not hold for diffusion language models (DLMs): retrieving more pages increases ans...
Jiankun Wang, Yi-Sen Gao, Ziwei Zhang et al.· 0 citations
This work proposes HiGram, an evolving hierarchical graph memory framework with path-level localization and rewriting, which organizes the memory into coarse-to-fine architecture composed of upper-level nodes and MemoryUnits, thereby reducing the amount of irrelevant information during retrieval.
Xiawei Yue, Boran Wang, Xiaoqing Zhang et al.· 0 citations
GTAlign is proposed, a surprisingly simple yet effective Graph-to-Table Alignment framework for text-free Graph Foundation Model, and a community-guided continual pre-training, where pseudo-labels derived from graph community are used to construct few-shot prediction episodes.
Chunyu Hu, Tianyin Liao, Ge Lan et al.· arXiv.org· 0 citations
DualG-MRAG is proposed, a Dual-tier framework that introduces a decoupled architecture comprising Macro-reasoning and Micro-matching Graphs for Multimodal RAG to suppress retrieval noise by isolating global structural reasoning from fine-grained evidence matching, and introduces a dynamic programming decoding mechanism...
This work introduces Evolutionary Rule Abstraction to distill generalized logic from optimization trajectories, overcoming the opacity of traditional evolution, and designs a Counterfactual Validation Module to strictly verify the causal reliability of abstracted rules.
Zhongyu Xing, Hanwen Luo, Maozu Guo et al.· Proceedings of the 32nd ACM...· 0 citations
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