7 citations · 7 across the 13 of their papers we have counts for
14 papers
DocMaster: A Hierarchical Structure-Aware System for Document Analysis
Ziqi Chen, Yingli Zhou, Fangyuan Zhang +3
Leveraging large language models (LLMs) to analyze complex documents -- such as academic papers, technical manuals, and financial reports -- has emerged as a mainstream and critica…
HETERQA: Benchmarking Record Retrieval over Multiple Heterogeneous Sources
Yaodong Su, Hanchang Li, Quanqing Xu +2
In emerging systems (e.g., social media and e-commerce platforms), data records are often drawn from heterogeneous sources, such as relational tables, text documents, image reposit…
A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications
Yingli Zhou, Wang Shu, Yaodong Su +3
Large language model (LLM)-based agents that reason, plan, and act through tools, memory, and structured interaction are emerging as a promising paradigm for automating complex wor…
H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory via a Hybrid Structure
Jiawei Yu, Yixiang Fang, Xilin Liu +1
Memory data are ubiquitous in Large Language Model (LLM)-based agents (e.g., OpenClaw and Manus). A few recent works have attempted to exploit agents'memory for improving their per…
ASTRA-QA: A Benchmark for Abstract Question Answering over Documents
Shu Wang, Shansong Zhou, Xinyang Wang +3
Document-based question answering (QA) increasingly includes abstract questions that require synthesizing scattered information from long documents or across multiple documents int…
SkillRAE: Agent Skill-Based Context Compilation for Retrieval-Augmented Execution
Xiangcheng Meng, Shu Wang, Yixiang Fang
Large Language Model (LLM)-based agents (e.g., OpenClaw) increasingly rely on reusable skill libraries to solve artifact-rich tasks such as document-centric workflows and data-inte…
LLM+Graph@VLDB'2025 Workshop Summary
Yixiang Fang, Arijit Khan, Tianxing Wu +2
The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing strong interest from both academia and…
Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework
Yanchen Wu, Tenghui Lin, Yingli Zhou +7
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…
Maintaining Leiden Communities in Large Dynamic Graphs
Chunxu Lin, Yumao Xie, Yixiang Fang +3
Community detection is a foundational capability in large-scale industrial graph analytics, powering applications such as fraud-ring discovery, recommendation systems, and hierarch…
BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents
Shu Wang, Yingli Zhou, Yixiang Fang
As an effective method to boost the performance of Large Language Models (LLMs) on the question answering (QA) task, Retrieval-Augmented Generation (RAG), which queries highly rele…
EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora
Fangyuan Zhang, Zhengjun Huang, Yingli Zhou +6
Graph-based Retrieval-Augmented Generation (Graph-RAG) enhances large language models (LLMs) by structuring retrieval over an external corpus. However, existing approaches typicall…
Clue-RAG: Towards Accurate and Cost-Efficient Graph-based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval
Yaodong Su, Yixiang Fang, Yingli Zhou +2
Despite the remarkable progress of Large Language Models (LLMs), their performance in question answering (QA) remains limited by the lack of domain-specific and up-to-date knowledg…
In-depth Analysis of Graph-based RAG in a Unified Framework
Yingli Zhou, Yaodong Su, Youran Sun +8
Graph-based Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs), improving their factual accuracy, adaptab…
ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation
Shu Wang, Yixiang Fang, Yingli Zhou +2
Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs) for solving question-answer (QA) tasks. The state-of-t…