93 citations · 93 across the 5 of their papers we have counts for
6 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…
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…
On the Intrinsic Limits of Transformer Image Embeddings in Non-Solvable Spatial Reasoning
Siyi Lyu, Quan Liu, Feng Yan
Vision Transformers (ViTs) excel in semantic recognition but exhibit systematic failures in spatial reasoning tasks such as mental rotation. While often attributed to data scale, t…
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…
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
Tianhe Ren, Shilong Liu, Ailing Zeng +14
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM). This integration enables the detection and seg…