From the 1 of 6 linked papers with an AI index.
6 papers
BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms
Pengyu Wang, Benfeng Xu, Shaohan Wang +5
The paper conducts a controlled scaling study of various retrieval-augmented generation methods and finds that BM25 becomes the most accurate and cost‑effective approach once the c…
FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents
Chiwei Zhu, Benfeng Xu, Mingxuan Du +4
Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research often exceed model context limits…
A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces
Mingxuan Du, Benfeng Xu, Chiwei Zhu +4
Frontier language models have demonstrated strong reasoning and long-horizon tool-use capabilities. However, existing RAG systems fail to leverage these capabilities. They still re…
WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora
Pengyu Wang, Benfeng Xu, Licheng Zhang +4
Graph-based Retrieval-Augmented Generation (GraphRAG) organizes external knowledge as a hierarchical graph, enabling efficient retrieval and aggregation of scattered evidence acros…
Wiki Live Challenge: Challenging Deep Research Agents with Expert-Level Wikipedia Articles
Shaohan Wang, Benfeng Xu, Licheng Zhang +5
Deep Research Agents (DRAs) have demonstrated remarkable capabilities in autonomous information retrieval and report generation, showing great potential to assist humans in complex…
DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generation
Shaohan Wang, Licheng Zhang, Zheren Fu +2
Retrieval-Augmented Generation (RAG) is an effective method to enhance the capabilities of large language models (LLMs). Existing methods typically optimize the retriever or the ge…