8 citations · 21 across the 6 of their papers we have counts for
9 papers
A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
Jinyuan Fang, Yanwen Peng, Xi Zhang +12
Recent advances in large language models have sparked growing interest in AI agents capable of solving complex, real-world tasks. However, most existing agent systems rely on manua…
Am I on the Right Track? What Can Predicted Query Performance Tell Us about the Search Behaviour of Agentic RAG
Fangzheng Tian, Jinyuan Fang, Debasis Ganguly +2
Agentic Retrieval-Augmented Generation (RAG) is a new paradigm where the reasoning model decides when to invoke a retriever (as a "tool") when answering a question. This paradigm,…
EvoAgentX: An Automated Framework for Evolving Agentic Workflows
Yingxu Wang, Siwei Liu, Jinyuan Fang +1
Multi-agent systems (MAS) have emerged as a powerful paradigm for orchestrating large language models (LLMs) and specialized tools to collaboratively address complex tasks. However…
Constructing and Evaluating Declarative RAG Pipelines in PyTerrier
Craig Macdonald, Jinyuan Fang, Andrew Parry +1
Search engines often follow a pipeline architecture, where complex but effective reranking components are used to refine the results of an initial retrieval. Retrieval augmented ge…
SEW: Self-Evolving Agentic Workflows for Automated Code Generation
Siwei Liu, Jinyuan Fang, Han Zhou +2
Large Language Models (LLMs) have demonstrated effectiveness in code generation tasks. To enable LLMs to address more complex coding challenges, existing research has focused on cr…
KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation
Jinyuan Fang, Zaiqiao Meng, Craig Macdonald
Iterative retrieval-augmented generation (iRAG) models offer an effective approach for multi-hop question answering (QA). However, their retrieval process faces two key challenges:…