13 citations · 27 across the 17 of their papers we have counts for
17 papers
EdgeMem: LLM-Free Agent Memory Construction and Retrieval via Evidence-Preserving Multi-Anchor Hypergraph
Zeyang Cui, Jiannong Cao, Zhiyuan Wen +3
Agent memory allows LLM agents to use earlier interactions when answering new queries. Existing methods often compress interaction histories into summaries or other LLM-generated r…
Evaluating LLMs on Conversational Text-to-SQL under Chain Ambiguity and Intent Drift
Yujia Liu, Jiayan Lin, Zijin Hong +6
Recent advances in large language models (LLMs) have established conversational text-to-SQL as a practical interface between users and databases, often involving multiple turns of…
Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence
Yuyuan Feng, Zhishang Xiang, Chaobin Yang +32
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit…
Slides2MindMap: Reconstructing Cognitively Efficient Knowledge Hierarchies from Lecture Slides
Yuzhi Wang, Rongjun Ye, Shengyuan Chen +5
Generating mind maps from lecture slides can help learners efficiently assimilate fragmented knowledge, promising substantial benefits for intelligent education. However, dedicated…
Zero-Mem: Zero-Token Memory Operations for LLM Agents
Yilin Xiao, Zhehan Zhu, Yujing Zhang +8
LLM agents need memory to act consistently over long interactions, yet many systems use additional LLM calls to operate that memory. Generating intermediate records and mediating t…
Graph-based Agent Memory: Taxonomy, Techniques, and Applications
Chang Yang, Chuang Zhou, Yilin Xiao +15
Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), whe…
Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
Su Dong, Qinggang Zhang, Yilin Xiao +3
Large language models (LLMs) often struggle with knowledge-intensive tasks due to hallucinations and outdated parametric knowledge. While Retrieval-Augmented Generation (RAG) addre…
LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora
Luyao Zhuang, Shengyuan Chen, Yilin Xiao +5
Retrieval-Augmented Generation (RAG) is widely used to mitigate hallucinations of Large Language Models (LLMs) by leveraging external knowledge. While effective for simple queries,…
You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures
Shengyuan Chen, Chuang Zhou, Zheng Yuan +6
Large language models (LLMs) often suffer from hallucination, generating factually incorrect statements when handling questions beyond their knowledge and perception. Retrieval-aug…
LAG: Logic-Augmented Generation from a Cartesian Perspective
Yilin Xiao, Chuang Zhou, Yujing Zhang +5
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet exhibit critical limitations in knowledge-intensive tasks, often generating…
When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation
Zhishang Xiang, Chuanjie Wu, Qinggang Zhang +4
Graph retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) with external knowledge. It leverages graphs to model…
Automated Heterogeneous Network learning with Non-Recursive Message Passing
Zhaoqing Li, Maiqi Jiang, Shengyuan Chen +3
Heterogeneous information networks (HINs) can be used to model various real-world systems. As HINs consist of multiple types of nodes, edges, and node features, it is nontrivial to…
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
Qinggang Zhang, Shengyuan Chen, Yuanchen Bei +9
Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need fo…
Graph Cross-Correlated Network for Recommendation
Hao Chen, Yuanchen Bei, Wenbing Huang +3
Collaborative filtering (CF) models have demonstrated remarkable performance in recommender systems, which represent users and items as embedding vectors. Recently, due to the powe…
Neuro-Symbolic Entity Alignment via Variational Inference
Shengyuan Chen, Zheng Yuan, Qinggang Zhang +3
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. Existing methods can be categorized into symbolic and neural models. Symbolic…
Entity Alignment with Noisy Annotations from Large Language Models
Shengyuan Chen, Qinggang Zhang, Junnan Dong +3
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. While existing methods heavily rely on human-generated labels, it is prohibit…
Structure Guided Large Language Model for SQL Generation
Qinggang Zhang, Hao Chen, Junnan Dong +3
Recent advancements in large language models (LLMs) have shown promise in bridging the gap between natural language queries and database management systems, enabling users to inter…