Chenxiao Lin — accepted AI-conference papers

2026

Efficient and Exact Global Attention on Latent Summaries for Knowledge Graph Reasoning

Chenxiao Lin, Lei Wang, Yin Zhang, Wei Liu, Ye Luo, Qingqiang Wu

IJCAI 2026

Capturing global context through attention is essential for reasoning over knowledge graphs, especially when relevant entities are distant or disconnected. To scale attention to large graphs, recent methods replace Softmax with kernel feature mappings, reducing computational complexity to linear in

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2026

Stepwise Contrastive Reasoning for Retrieval-Augmented Generation over Knowledge Graphs

Chenxiao Lin, Ye Luo, KunHong Liu, Qingqiang Wu

AAAI 2026technical

Retrieval-augmented generation (RAG) enhances the reasoning capabilities of large language models (LLMs) by incorporating external knowledge. Among available sources, knowledge graphs (KGs) offer a structured and reliable foundation for factual information, making them increasingly popular in effort

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