KAOS: Large Model Multi-Agent Operating System

Abstract: The intelligent interaction model based on large models reduces the differences in user experience across various system platforms but faces challenges in multi-agent collaboration and resource sharing. To demonstrate a uniform user experience across different foundational software platforms and address resource coordination management challenges, this paper proposes KAOS, a multi-agent operating system based on the open-source Kylin. The research method involves empowering agents with large models to serve applications. First, by introducing management role agents and vertical multi-agent collaboration to construct or replace typical application software. Second, by studying system-level shared resource scheduling strategies to enhance user experience and optimize resource utilization. And finally, by validating the efficiency and superiority of the large model multi-agent operating system through real applications and scoring intelligence. The feasibility of this system is demonstrated, providing a new perspective for the development of multi-agent operating systems. Experimental results show significant advantages of multi-agent collaboration in various application scenarios.

• The paper introduces KAOS, a system that unifies multi-agent collaboration to deliver consistent application performance across platforms.
• It outlines innovative management role agents and vertical resource scheduling to optimize system-level resource utilization.
• Experimental results validate KAOS’s effectiveness in enhancing user interaction and resource management in practical, real-world scenarios.

The paper "KAOS: Large Model Multi-Agent Operating System" introduces an innovative approach to harmonizing user experience across different foundational software platforms by leveraging large model multi-agent systems. This work addresses the growing need for seamless user interaction and efficient resource utilization in diverse computing environments.

To tackle the inherent challenges of multi-agent collaboration and resource sharing, the authors propose the KAOS system, built upon the open-source Kylin operating system. The core concept revolves around empowering agents with large models to effectively serve applications, thereby minimizing user experience discrepancies across platforms.

Key Components and Methodologies:

1. Management Role Agents and Vertical Collaboration:
   • The paper introduces management role agents to oversee the functioning of various application agents.
   • Vertical multi-agent collaboration is utilized to construct or replace typical application software, ensuring that these apps function cohesively within the multi-agent system.
2. System-Level Resource Scheduling:
   • The authors examine strategies for system-level shared resource scheduling to optimize resource allocation and enhance overall user experience.
   • Efficient resource management is critical to prevent conflicts and ensure that the system operates smoothly even when handling complex tasks.
3. Validation Through Real Applications:
   • The system’s efficiency and superiority are validated through practical applications and intelligence scoring.
   • Experimental results demonstrate substantial advantages of multi-agent collaboration, particularly in scenarios requiring high levels of interaction and resource management.

Conclusions:

• The research indicates that KAOS significantly advances the development of multi-agent operating systems by providing a cohesive user experience and optimized resource utilization.
• The experimental outcomes validate the feasibility and effectiveness of the proposed system, showcasing its potential in real-world applications.

In summary, this paper offers a comprehensive examination of how large model multi-agent systems can be deployed to enhance user experience and resource management across different software platforms. By introducing innovative strategies for agent collaboration and resource scheduling, the KAOS system represents a substantial leap forward in the field of multi-agent operating systems.