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ICML 2025 | 多智能体的ChatGPT时刻?上交MAS-GPT实现工作流一键生成
机器之心· 2025-07-05 02:46
Core Viewpoint - The article discusses the introduction of MAS-GPT, a new generative design paradigm for Multi-Agent Systems (MAS), which simplifies the process of creating MAS to a single query input, making it as easy as interacting with ChatGPT [2][9]. Group 1: Introduction of MAS-GPT - MAS-GPT is a collaborative effort from institutions like Shanghai Jiao Tong University and Oxford University, aiming to facilitate the development of MAS as a step towards achieving Artificial General Intelligence (AGI) [2][3]. - The system allows users to generate a complete and executable MAS with just one query, significantly streamlining the process [2][12]. Group 2: Challenges in Existing MAS Methods - Current MAS methods face three fundamental issues: lack of adaptability, high costs, and low generalization capabilities, which hinder their widespread application [5][7]. - Existing systems require extensive manual input and multiple rounds of LLM calls, making them inefficient and costly [7]. Group 3: MAS-GPT's Solution - MAS-GPT transforms the design of MAS into a language generation task, allowing for the automatic generation of MAS from user queries [9][10]. - The generated MAS is presented in Python code, eliminating the need for manual coding [9]. Group 4: Performance and Evaluation - MAS-GPT has been tested against over ten existing methods across eight benchmark tasks and five mainstream models, demonstrating superior performance [16]. - It achieved an average accuracy improvement of 3.89% over the strongest baseline and maintained robust performance on unseen tasks [17]. Group 5: Cost Efficiency and Compatibility - MAS-GPT operates at nearly half the inference cost compared to other systems like DyLAN and GPTSwarm while delivering better results [18]. - The MAS generated by MAS-GPT shows strong compatibility and consistent performance across different LLMs [20]. Group 6: Future Potential and Community Engagement - MAS-GPT has significant potential for future development, with the ability to generate novel MAS structures and adapt to new tasks [24][25]. - The MASWorks community aims to connect researchers globally, fostering collaboration and knowledge sharing in the MAS field [30][31].