Core Viewpoint - The article emphasizes that 2026 will be a pivotal year for technology leaders, with unprecedented speed in transformation, innovation, and risk driven by artificial intelligence (AI) and a highly interconnected world [2]. Group 1: AI Supercomputing Platforms - AI supercomputing platforms integrate various computing paradigms to manage complex workloads, enhancing performance and innovation potential [5]. - By 2028, over 40% of leading companies will adopt hybrid computing architectures for critical business processes, a significant increase from the current 8% [6]. - The technology is already driving innovation across industries, significantly reducing drug modeling time in biotech and lowering portfolio risks in financial services [7]. Group 2: Multi-Agent Systems - Multi-agent systems consist of multiple AI agents that interact to achieve complex individual or collective goals, enhancing automation and collaboration [9]. - These systems allow for modular design, improving efficiency and adaptability in business processes [9]. Group 3: Domain-Specific Language Models (DSLM) - DSLMs are trained on specialized datasets for specific industries, providing higher accuracy and compliance compared to generic large language models (LLMs) [11]. - By 2028, over half of generative AI models used by enterprises will be domain-specific [12]. - Context is crucial for the success of AI agents based on DSLMs, enabling them to make informed decisions even in unfamiliar scenarios [13]. Group 4: AI Security Platforms - AI security platforms provide unified protection mechanisms for third-party and custom AI applications, helping organizations monitor AI activities and enforce usage policies [13]. - By 2028, over 50% of enterprises will utilize AI security platforms to safeguard their AI investments [15]. Group 5: AI-Native Development Platforms - AI-native development platforms enable rapid software development, allowing non-technical experts to create applications with AI assistance [17]. - By 2030, 80% of enterprises will transform large software engineering teams into smaller, more agile teams empowered by AI [17]. Group 6: Confidential Computing - Confidential computing reshapes how enterprises handle sensitive data by isolating workloads in trusted execution environments [18]. - By 2029, over 75% of business workloads processed in untrusted environments will be secured through confidential computing [18]. Group 7: Physical AI - Physical AI empowers machines and devices with perception, decision-making, and action capabilities, providing significant benefits in automation and safety-critical industries [19]. Group 8: Proactive Cybersecurity - Proactive cybersecurity is becoming a trend as organizations face increasing threats, with predictions that by 2030, proactive defense solutions will account for half of enterprise security spending [23]. Group 9: Geopolitical Data Migration - Geopolitical risks are prompting companies to migrate data and applications to sovereign or regional cloud services, enhancing control over data residency and compliance [26]. - By 2030, over 75% of enterprises in Europe and the Middle East will migrate virtual workloads to solutions that mitigate geopolitical risks, up from less than 5% in 2025 [26].

Core Viewpoint - The article discusses the evolution and differentiation between AI Agents and Agentic AI, highlighting their respective roles in automating tasks and collaborating on complex objectives, with a focus on the advancements since the introduction of ChatGPT in November 2022 [2][10][57]. Group 1: Evolution of AI Technology - The development of AI technology has progressed from early expert systems like MYCIN to modern AI Agents and Agentic AI, marking a significant paradigm shift in capabilities [10][11]. - ChatGPT's release in November 2022 is identified as a pivotal moment that catalyzed the evolution of AI Agents, transitioning from passive responders to more autonomous systems capable of executing multi-step tasks [12][24]. - The introduction of frameworks like AutoGPT and BabyAGI in 2023 signifies the formal establishment of AI Agents, which integrate LLMs with external tools to perform complex tasks [12][24]. Group 2: Characteristics of AI Agents - AI Agents are defined as modular systems driven by LLMs and LIMs, designed for task automation, filling the gap where generative AI lacks execution capabilities [13][16]. - Three core features distinguish AI Agents from traditional automation scripts: autonomy, task-specificity, and reactivity [16][17]. - The integration of tools allows AI Agents to overcome limitations of static knowledge and hallucination issues, enabling them to perform real-time data retrieval and processing [19][20]. Group 3: Agentic AI and Multi-Agent Collaboration - Agentic AI represents a shift towards multi-agent collaboration, where multiple AI Agents work together to achieve complex goals, enhancing system-level intelligence [24][27]. - The architecture of Agentic AI includes dynamic task decomposition and shared memory, facilitating efficient collaboration among specialized agents [33][36]. - Real-world applications of Agentic AI demonstrate its advantages in various fields, such as healthcare and agriculture, where multiple agents coordinate to optimize processes [37][38]. Group 4: Challenges and Future Directions - Both AI Agents and Agentic AI face challenges, including causal reasoning deficits and coordination issues among multiple agents [48][50]. - Proposed solutions include enhancing retrieval-augmented generation (RAG), implementing causal modeling, and establishing shared memory architectures to improve collaboration and decision-making [49][53]. - The future roadmap emphasizes the need for deeper causal reasoning, transparency in decision-making, and ethical governance to ensure the responsible deployment of AI technologies [56][59].

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].

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 ...

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 ...