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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].
基于奖励驱动和自组织演化机制,全新框架ReSo重塑复杂推理任务中的智能协作
机器之心· 2025-04-27 10:40
本文由上海人工智能实验室,悉尼大学,牛津大学联合完成。第一作者周恒为上海 ailab 实习生和 Independent Researcher 耿鹤嘉。通讯作者为上海人工智能实验 室青年科学家白磊和牛津大学访问学者,悉尼大学博士生尹榛菲,团队其他成员还有 ailab 实习生薛翔元。 ReSo 框架( Re ward-driven & S elf- o rganizing)为复杂推理任务中的多智能体系统(MAS)提供了全新解法,在处理复杂任务时,先分解生成任务图,再为每个 子任务匹配最佳 agent。将任务图生成与奖励驱动的两阶段智能体选择过程相结合,该方法不仅提升了多智能体协作的效率,还为增强多智能体的推理能力开辟了 新路径。 研究背景:LLM 推理能力的掣肘与突破口 近年来, 增加推理时间(Inference Time Scaling) 被广泛认为是提升大语言模型(Large Language Models, LLMs)推理能力的重要途径之一。一方面,通过在训 练后阶段引入强化学习与奖励模型,可优化单一模型的推理路径,使其在回答前生成中间步骤,表现出更强的逻辑链构建能力;另一方面,也有研究尝试构建 多 智能体 ...
巨头抢滩AI智能体,资本沸腾了
投中网· 2025-03-12 04:49
锌财经 . 以下文章来源于锌财经 ,作者路世明 关注新商业,关注新经济。一家由人民网投资的数字化媒体机构。 将投中网设为"星标⭐",第一时间收获最新推送 AI Agent的竞争既充满想象力,又暗藏风险。 作者丨路世明 编辑丨 大风 来源丨锌财经 Manus的出现,激起了科技与资本市场的双重震荡,一时间AI Agent相关概念股集体大涨,阿里、谷 歌、微软等科技巨头密集发布智能体研发计划...... 而在这场热潮的背后,是AI技术从"被动应答"向"主动执行"的范式跃迁。 尽管市场的评价褒贬不一,但不能否认,Manus的突破性在于,它首次验证了通用型AI Agent在复杂 场景下的商业化可行性。 传统的大语言模型虽能生成文本,却难以闭环执行任务,而Manus通过"规划-验证-执行"的架构,将 AI大模型的认知能力转化为生产力工具。 根据麦肯锡等多份权威报告,在多元化需求驱动下,AI Agent市场呈爆发式增长态势,2024年全球 AI Agent市场规模约为51亿美元,预计2030年将飙升至471亿美元,复合年增长率高达44.8%。 然而,这场"智能体浪潮"并非坦途。技术瓶颈与商业野心的碰撞,让AI Agent的 ...