5 papers
Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement
Shenao Chen, Yidan Xu, Xiangmin Han +5
Modern Multimodal Retrieval-Augmented Generation (M-RAG) systems are fundamentally limited by the binary connectivity paradigm of traditional simple graphs, which fails to capture…
SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs
Yidan Xu, Xiangmin Han, Rundong Xue +1
Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge. Existing methods, however, largely treat semantics from lar…
MisEdu-RAG: A Misconception-Aware Dual-Hypergraph RAG for Novice Math Teachers
Zhihan Guo, Yuting Lu, Jionghao Lin
Novice math teachers often encounter students' mistakes that are difficult to diagnose and remediate. Misconceptions are especially challenging because teachers must explain what w…
Hyper-KGGen: A Skill-Driven Knowledge Extractor for High-Quality Knowledge Hypergraph Generation
Rizhuo Huang, Yifan Feng, Rundong Xue +5
Knowledge hypergraphs surpass traditional binary knowledge graphs by encapsulating complex n-ary atomic facts, providing a more comprehensive paradigm for semantic representation.…
Cog-RAG: Cognitive-Inspired Dual-Hypergraph with Theme Alignment Retrieval-Augmented Generation
Hao Hu, Yifan Feng, Ruoxue Li +5
Retrieval-Augmented Generation (RAG) enhances the response quality and domain-specific performance of large language models (LLMs) by incorporating external knowledge to combat hal…