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20232026
most citedReasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning

40 citations · 93 across the 25 of their papers we have counts for

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5 papers · 1 filter

cs.MA2025

The Social Cost of Intelligence: Emergence, Propagation, and Amplification of Stereotypical Bias in Multi-Agent Systems

Thi-Nhung Nguyen, Linhao Luo, Amardeep Kaur +5

Bias in large language models (LLMs) remains a persistent challenge, often leading to stereotyping and unfair treatment across social groups. While prior work has mainly focused on…

cs.IR2025

Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning

Junnan Dong, Siyu An, Yifei Yu +6

Graph retrieval-augmented generation (GraphRAG) has effectively enhanced large language models in complex reasoning by organizing fragmented knowledge into explicitly structured gr…

cs.AI2025

G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge

Linhao Luo, Zicheng Zhao, Junnan Liu +9

Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented generation (RAG) mitigates this by inc…

cs.CL2025

SituatedThinker: Grounding LLM Reasoning with Real-World through Situated Thinking

Junnan Liu, Linhao Luo, Thuy-Trang Vu +1

Recent advances in large language models (LLMs) demonstrate their impressive reasoning capabilities. However, the reasoning confined to internal parametric space limits LLMs' acces…

cs.IR2025

GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation

Linhao Luo, Zicheng Zhao, Gholamreza Haffari +3

Retrieval-augmented generation (RAG) has proven effective in integrating knowledge into large language models (LLMs). However, conventional RAGs struggle to capture complex relatio…