5 papers
Benchmarking Hybrid Deep Research Across Database Querying and Web Search
Ruofan Wu, Peiran Xu, Xiaolong Li +9
While autonomous agents have made significant strides in "deep research" by iteratively navigating the open web to synthesize information, real-world problem-solving is rarely conf…
ArcticSwarm: Deferring Early Consensus in Long-Horizon Multi-Agent Research
Soyoung Yoon, Boyi Liu, Yite Wang +6
Multi-agent systems have shown strong performance in domains with reliable verifiers such as coding, where multi-parallel candidate generation selected by a verifier is effective.…
DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science
Fan Shu, Yite Wang, Ruofan Wu +4
The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need for accurate benchmarking. There are two majo…
ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering
Ruofan Wu, Youngwon Lee, Fan Shu +5
Retrieval-Augmented Generation (RAG) systems are increasingly diverse, yet many suffer from monolithic designs that tightly couple core functions like query reformulation, retrieva…
Agentic Verification for Ambiguous Query Disambiguation
Youngwon Lee, Seung-won Hwang, Ruofan Wu +5
In this work, we tackle the challenge of disambiguating queries in retrieval-augmented generation (RAG) to diverse yet answerable interpretations. State-of-the-arts follow a Divers…