3 papers
cs.CL2026
Does More Retrieved Evidence Help Visual Retrieval-Augmented Generation with Diffusion Language Models?
Jiankun Wang, Yisen Gao, Ziwei Zhang +3
Visual retrieval-augmented generation (RAG) commonly expands the retrieved evidence set to improve answer-page coverage, implicitly assuming that all available evidence should be p…
cs.LG2026
Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
Haonan Yuan, Qingyun Sun, Junhua Shi +3
Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learning. However, dynamic graphs fro…
cs.LG2025
Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks
Yumeng Wang, Zengyi Wo, Wenjun Wang +2
Graph Neural Networks (GNNs) excel in node classification tasks but often assume homophily, where connected nodes share similar labels. This assumption does not hold in many real-w…