6 citations · 8 across the 3 of their papers we have counts for
6 papers
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…
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:…
KEIR @ ECIR 2025: The Second Workshop on Knowledge-Enhanced Information Retrieval
Zihan Wang, Jinyuan Fang, Giacomo Frisoni +4
Pretrained language models (PLMs) like BERT and GPT-4 have become the foundation for modern information retrieval (IR) systems. However, existing PLM-based IR models primarily rely…
On the Structural Memory of LLM Agents
Ruihong Zeng, Jinyuan Fang, Siwei Liu +1
Memory plays a pivotal role in enabling large language model~(LLM)-based agents to engage in complex and long-term interactions, such as question answering (QA) and dialogue system…