Large Model Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends

• URL: http://arxiv.org/abs/2409.14457v1
• Date: Sun, 22 Sep 2024 14:09:49 GMT
• Title: Large Model Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
• Authors: Yuntao Wang, Yanghe Pan, Quan Zhao, Yi Deng, Zhou Su, Linkang Du, Tom H. Luan,
• Abstract summary: Large Model (LM) agents, powered by large foundation models such as GPT-4 and DALL-E 2, represent a significant step towards achieving Artificial General Intelligence (AGI) This paper provides a comprehensive survey of the state-of-the-art in LM agents, focusing on the architecture, cooperation paradigms, security, privacy, and future prospects.
• Score: 25.029148345440902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Model (LM) agents, powered by large foundation models such as GPT-4 and DALL-E 2, represent a significant step towards achieving Artificial General Intelligence (AGI). LM agents exhibit key characteristics of autonomy, embodiment, and connectivity, allowing them to operate across physical, virtual, and mixed-reality environments while interacting seamlessly with humans, other agents, and their surroundings. This paper provides a comprehensive survey of the state-of-the-art in LM agents, focusing on the architecture, cooperation paradigms, security, privacy, and future prospects. Specifically, we first explore the foundational principles of LM agents, including general architecture, key components, enabling technologies, and modern applications. Then, we discuss practical collaboration paradigms from data, computation, and knowledge perspectives towards connected intelligence of LM agents. Furthermore, we systematically analyze the security vulnerabilities and privacy breaches associated with LM agents, particularly in multi-agent settings. We also explore their underlying mechanisms and review existing and potential countermeasures. Finally, we outline future research directions for building robust and secure LM agent ecosystems.

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