activity
20242026
most citedSpider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks

Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong +33

Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of f…

cs.CL2025

VideoAgentTrek: Computer Use Pretraining from Unlabeled Videos

Dunjie Lu, Yiheng Xu, Junli Wang +12

Training computer-use agents requires massive amounts of GUI interaction data, but manually annotating action trajectories at scale is prohibitively expensive. We present VideoAgen…

cs.AI2025

Scaling Computer-Use Grounding via User Interface Decomposition and Synthesis

Tianbao Xie, Jiaqi Deng, Xiaochuan Li +12

Graphical user interface (GUI) grounding, the ability to map natural language instructions to specific actions on graphical user interfaces, remains a critical bottleneck in comput…

cs.AI2024★ 2 cited

Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?

Ruisheng Cao, Fangyu Lei, Haoyuan Wu +20

Data science and engineering workflows often span multiple stages, from warehousing to orchestration, using tools like BigQuery, dbt, and Airbyte. As vision language models (VLMs)…

cs.CL2024★ 1 cited

EVOR: Evolving Retrieval for Code Generation

Hongjin Su, Shuyang Jiang, Yuhang Lai +5

Recently the retrieval-augmented generation (RAG) has been successfully applied in code generation. However, existing pipelines for retrieval-augmented code generation (RACG) emplo…