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In this paper, we propose Aime, a novel framework to overcome the __T35842_____ of large-scale language model (LLM)-based multi-agent systems (MAS). To address the rigidity of existing plan-execution approaches, static agent capabilities, and inefficient communication, Aime presents a dynamic and reactive planning and execution architecture. Its key innovations include a dynamic planning capability based on real-time feedback, a dynamic agent creation capability that generates specialized agents on demand, and a centralized progress management module for system-wide state awareness. It demonstrates superior performance over existing state-of-the-art agents on a variety of benchmarks, including general reasoning, software engineering, and web browsing.
Takeaways, Limitations
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Takeaways:
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A new framework Aime is proposed to overcome the __T35846_____ of the existing plan-execution method of MAS
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Improved adaptability and efficiency through dynamic planning, dynamic agent creation, and centralized progress management.
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Breaking previous best performance records in various fields
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Presenting the possibility of building a more powerful and flexible multi-agent collaboration infrastructure
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Limitations:
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This paper lacks detailed descriptions of Aime's specific implementation details or algorithms.
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Although the range of benchmarks presented is diverse, more extensive experiments and performance evaluations in diverse environments are needed.
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There is a lack of consideration for single point of failure and scalability of a centralized progress management module.
