12 papers
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…
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Zhishang Xiang, Zerui Chen, Yunbo Tang +5
Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always ben…
SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search
Yunbo Tang, Chengyi Yang, Shiyu Liu +4
Agentic search enables LLMs to solve complex multi-hop questions through iterative reasoning and external search. Despite the effectiveness, these systems often suffer from a criti…
MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation
Chuanjie Wu, Zhishang Xiang, Yunbo Tang +3
Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge. Although effecti…
LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning
Zerui Chen, Qinggang Zhang, Zhishang Xiang +5
Graph-based Retrieval-Augmented Generation (GraphRAG) advances flat document retrieval by structuring knowledge as relational graphs, enabling more coherent and effective reasoning…
BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search
Shiyu Liu, Yongjing Yin, Jianhao Yan +7
RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies op…