Summary of Crab: Cross-environment Agent Benchmark For Multimodal Language Model Agents, by Tianqi Xu et al.
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents
by Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Anjie Yang, Zhaoxuan Jin, Jianbo Deng, Philip Torr, Bernard Ghanem, Guohao Li
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a new benchmark framework for Multimodal Language Models (MLMs) called Crab, which enables the development of autonomous agents that can perform tasks in various interactive environments such as websites, desktop computers, or mobile phones. The existing benchmarks are limited by their focus on a single environment and lack of detailed evaluation methods. The Crab framework addresses these limitations with a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction. It supports multiple devices and can be easily extended to any environment with a Python interface. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces the Crab Benchmark-v0, which includes 120 tasks in computer desktop and mobile phone environments. The authors evaluated four advanced MLMs using different single and multi-agent system configurations on this benchmark. The results show that a single agent with GPT-4o achieves the best completion ratio of 38.01%. The framework code, agent code, and task datasets are publicly available at this URL. |
Keywords
» Artificial intelligence » Gpt
