From the 1 of 14 linked papers with an AI index.
14 papers
ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG
Yongfeng Huang, Yuren Lai, Ruiying Chen +3
Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate thes…
When and Why Does Multi-Agent Debate Fail and Does It Really Underperform?
Yongqiang Chen, Gang Niu, James Cheng +2
The paper examines why multi-agent debate (MAD) often underperforms single-agent methods, identifies flaws in existing competitive and consensus-based MAD protocols, and proposes a…
APeB: Benchmarking Personalization Ability of Large Language Model Agents
Garry Yang, Zizhe Chen, Xinru Chen +9
LLM-powered agents struggle with personalization when users issue raw, underspecified queries. In this setting, agents must infer latent intent, extract preferences from noisy inte…
On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents
Deyu Zou, Yongqiang Chen, Fan Feng +4
Reinforcement learning (RL) has become a de facto paradigm for building LLM-based agents that act, interact, and reason over extended task horizons. However, in active reasoning wh…
SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning
Yongfeng Huang, Ruiying Chen, James Cheng
Retrieval-Augmented Generation (RAG) is widely employed to mitigate risks such as hallucinations and knowledge obsolescence in medical question answering, yet its predominantly sin…
Hista and Numca: Estimate State Value Effectively for LLM Reinforcement Learning
Zizhe Chen, Jiqian Dong, Yizhou Tian +4
Reinforcement learning (RL) refines large language models (LLMs) by directly optimizing model behavior through reward signals. While accurate state value estimation is critical for…