most citedHyperRAG: Reasoning N-ary Facts over Hypergraphs for Retrieval Augmented Generation

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment

Xizhe Zhang, Jingfeng Zhang, Meng-Fen Chiang

Multimodal entity alignment (MMEA) is fundamental to integrating heterogeneous knowledge graphs (KGs), yet existing paradigms remain critically vulnerable to incomplete modalities…

cs.AI2026

Evo-PI: Aligning Medical Reasoning via Evolving Principle-Guided Supervision

Xianda Zheng, Huan Gao, Meng-Fen Chiang +3

Despite recent progress, the reasoning capabilities of large multimodal language models (MLLMs) remain fundamentally constrained by static supervision, where fixed prompts, rules,…

cs.CL2026

HyperPatch: Sequential Knowledge Editing Under n-ary Structural Drift

Yu-Kai Chan, Wen-Sheng Lien, Dong-Ting Yao +4

Large Language Models (LLMs) rely on Knowledge Editing (KE) to maintain temporal validity, yet real-world knowledge is inherently n-ary. We demonstrate that in non-stationary envir…

cs.CL2026★ 1 cited

HyperRAG: Reasoning N-ary Facts over Hypergraphs for Retrieval Augmented Generation

Wen-Sheng Lien, Yu-Kai Chan, Hao-Lung Hsiao +5

Graph-based retrieval-augmented generation (RAG) methods, typically built on knowledge graphs (KGs) with binary relational facts, have shown promise in multi-hop open-domain QA. Ho…

cs.AI2025

Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts

Xianda Zheng, Zijian Huang, Meng-Fen Chiang +4

Explicit knowledge conflicts, occurring when retrieved contexts contain contradictory information, pose a fundamental challenge for Large Language Models (LLMs) as they integrate i…