8 citations · 8 across the 4 of their papers we have counts for
7 papers
HyperReCo: Retrieving and Connecting Evidence with Hypergraph Neural Networks for LLM Multi-hop Reasoning
Zicheng Zhao, Linhao Luo, Junnan Dong +4
Large language models (LLMs) have shown strong capabilities, with retrieval-augmented generation (RAG) supporting complex multi-hop reasoning by retrieving evidence distributed acr…
Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models
Zongji Yu, Wenshui Luo, Yiliu Sun +5
Post-training via Reinforcement Learning (RL) has enabled Large Reasoning Models (LRMs) to achieve strong performance in individual domain. However, real-world applications increas…
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…
CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate
Yiliu Sun, Zicheng Zhao, Sheng Wan +1
Nowadays, single Large Language Model (LLM) struggles with critical issues such as hallucination and inadequate reasoning abilities. To mitigate these issues, Multi-Agent Debate (M…
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…
Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models
Linhao Luo, Zicheng Zhao, Gholamreza Haffari +3
Large language models (LLMs) have demonstrated impressive reasoning abilities, but they still struggle with faithful reasoning due to knowledge gaps and hallucinations. To address…
Graph Stochastic Neural Process for Inductive Few-shot Knowledge Graph Completion
Zicheng Zhao, Linhao Luo, Shirui Pan +2
Knowledge graphs (KGs) store enormous facts as relationships between entities. Due to the long-tailed distribution of relations and the incompleteness of KGs, there is growing inte…