Summary of Large Model Based Agents: State-of-the-art, Cooperation Paradigms, Security and Privacy, and Future Trends, by Yuntao Wang et al.
Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
by Yuntao Wang, Yanghe Pan, Zhou Su, Yi Deng, Quan Zhao, Linkang Du, Tom H. Luan, Jiawen Kang, Dusit Niyato
First submitted to arxiv on: 22 Sep 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 paper explores the development of general-purpose intelligent agents powered by large language models (LMs), which will serve as essential tools in production tasks without human intervention. The authors review the current state of LMs, key technologies enabling collaboration, and security challenges during cooperative operations. They discuss foundational principles of LM agents, including architecture, components, enabling technologies, and modern applications. The paper also analyzes security vulnerabilities and privacy risks associated with LM agents in multi-agent settings, proposing future research directions for building robust and secure ecosystems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about super smart computer programs called Large Language Models (LLMs) that can work together without humans telling them what to do. The authors want to know how these LLMs will talk to each other and share information. They look at the current state of LLMs, what makes them tick, and what happens when they work together. The paper also warns about potential problems with keeping LLMs safe and private when they’re working together. |
