Summary of Osworld: Benchmarking Multimodal Agents For Open-ended Tasks in Real Computer Environments, by Tianbao Xie et al.
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
by Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, Yitao Liu, Yiheng Xu, Shuyan Zhou, Silvio Savarese, Caiming Xiong, Victor Zhong, Tao Yu
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces OSWorld, a scalable, real-computer environment for multimodal agents that can perform complex computer tasks with minimal human intervention. This environment addresses the limitations of existing benchmarks by providing an interactive environment that supports task setup, execution-based evaluation, and interactive learning across various operating systems (Ubuntu, Windows, and macOS). The authors create a benchmark of 369 computer tasks involving real web and desktop apps in open domains, OS file I/O, and workflows spanning multiple applications. This benchmark is designed to assess the abilities of agents in serving as computer assistants. The paper also evaluates state-of-the-art LLM/VLM-based agents on OSWorld, revealing significant deficiencies in their ability to perform complex tasks. While humans can accomplish over 72.36% of the tasks, the best model achieves only 12.24% success, primarily struggling with GUI grounding and operational knowledge. The authors’ findings provide valuable insights for developing multimodal generalist agents that were not possible with previous benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous computer agents have the potential to change how humans interact with computers. But right now, these agents aren’t very good at helping us with everyday tasks on our computers. That’s because we don’t have a good way to test and train them to do these tasks. The authors of this paper created something called OSWorld that helps solve this problem. It’s like a big computer lab where we can test and train these agents to do all sorts of tasks, like using different software or doing file management. They also created a big list of tasks, 369 of them, that the agents need to be able to do. The authors tested some of the best computer models on OSWorld and found that they didn’t do very well. In fact, humans can do over 72% of these tasks, but the best computer model only did about 12%. This is because the computers are having trouble understanding how to use our computers in a way that makes sense. |
Keywords
» Artificial intelligence » Grounding
