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20242026
most citedConstructing and Evaluating Declarative RAG Pipelines in PyTerrier

6 citations · 14 across the 12 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CL2025

GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations

Zhaohan Meng, Zaiqiao Meng, Siwei Liu +3

Multi-turn medical question answering (QA) aims to model realistic clinical diagnosis, where a doctor gathers patient information across multiple turns of conversation. Existing mu…

cs.IR2025

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,…

cs.AI2025

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…

cs.IR2025★ 6 cited

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…

cs.SE2025

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…

cs.CL2025

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:…

cs.IR2025

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…