Summary of Foundations and Recent Trends in Multimodal Mobile Agents: a Survey, by Biao Wu et al.
Foundations and Recent Trends in Multimodal Mobile Agents: A Survey
by Biao Wu, Yanda Li, Meng Fang, Zirui Song, Zhiwei Zhang, Yunchao Wei, Ling Chen
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and multimodal interaction. Recent evaluation benchmarks have been developed to capture the static and interactive environments of mobile tasks, offering more accurate assessments of agents’ performance. The paper categorizes these advancements into two main approaches: prompt-based methods, which utilize large language models (LLMs) for instruction-based task execution, and training-based methods, which fine-tune multimodal models for mobile-specific applications. Additionally, the survey explores complementary technologies that augment agent performance. By discussing key challenges and outlining future research directions, this survey offers valuable insights for advancing mobile agent technologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mobile agents are important for completing tasks in complex environments. This paper looks at how these agents work and how they can be improved. It also talks about how we evaluate their performance using special benchmarks. The survey finds that there are two main ways to improve these agents: by giving them instructions or by fine-tuning models for specific tasks. Other technologies, like language models, can also help make the agents better. By talking about challenges and future directions, this paper helps us understand how we can make mobile agent technology better. |
Keywords
» Artificial intelligence » Fine tuning » Prompt
