多智能体系统

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北大汇丰王小愚:中国AI投资具备三大优势,首要挑战在核心技术依赖与硬件短板
Xin Lang Cai Jing· 2025-09-22 02:02
Core Viewpoint - The central financial work conference emphasizes the importance of technology finance, green finance, inclusive finance, pension finance, and digital finance for promoting high-quality financial development. The integration of 5G, AI, and blockchain is reshaping the financial infrastructure and service landscape, presenting both opportunities and challenges for the banking industry [1][3]. Group 1: Technological Integration in Finance - The collaboration of 5G, AI, and blockchain is fundamentally restructuring the architecture and operational logic of financial systems, enhancing payment systems, investment management, and supply chain finance [3][4][5]. - Payment and settlement systems can achieve real-time and trustworthy transactions, with 5G enabling millisecond-level latency and blockchain ensuring transaction immutability and traceability [3][4]. - AI enhances investment advisory and asset management by analyzing user preferences and market data, leading to more personalized and transparent investment strategies [4][5]. Group 2: Challenges of Technological Integration - The integration of these technologies may increase complexity and systemic risks within the financial system, such as compatibility issues between distributed ledgers and centralized AI frameworks [2][7]. - Performance bottlenecks exist between blockchain's low transaction per second (TPS) capabilities and the high throughput demands of 5G [6][7]. - The potential for AI algorithm resonance could amplify market volatility, leading to systemic risks if similar AI models are widely adopted [7]. Group 3: Key Players in the Ecosystem - Two types of companies are likely to dominate the "5G + AI + blockchain" ecosystem: technology giants with integration capabilities and specialized financial technology service providers [7][8]. - Technology giants can leverage their vast user bases and data resources to create efficient technology linkages, while specialized firms can focus on specific industry needs, enhancing their competitive edge [8]. Group 4: Future Directions in AI Investment - AI investment in China is driven by scenario-based applications, policy support, and engineering efficiency, with key challenges including reliance on core technologies and hardware limitations [9][12]. - The future of AI in finance will focus on multi-agent systems for decision-making, democratization of investment through asset tokenization, and seamless cross-border payment solutions [9][10][11]. - The evolution of AI technology is expected to shift from large models to intelligent agents capable of autonomous decision-making, enhancing operational efficiency in various sectors [12]. Group 5: Current Trends and Risks in Blockchain Investment - The current blockchain investment landscape is characterized by a mix of technological innovation and speculative behavior, leading to a phenomenon where "bad money drives out good" [14][17]. - Regulatory actions have targeted misleading cryptocurrency investment practices, indicating a need for clearer distinctions between genuine technological advancements and speculative projects [17][18]. - The differentiation between technological innovation and speculative behavior is crucial, with a focus on projects that do not promise financial returns and adhere to regulatory standards [18].
马斯克“巨硬计划”新动作曝光!从0建起算力集群,6个月完成OpenAI&甲骨文15个月的工作
Sou Hu Cai Jing· 2025-09-18 06:34
Core Insights - Elon Musk's "Macrohard" initiative has rapidly established a computing cluster capable of supporting 110,000 NVIDIA GB200 GPUs within six months, achieving a power supply scale of 200MW, which is a record compared to similar projects by OpenAI and Oracle that took 15 months [1][2][4] Group 1: Project Overview - The "Macrohard" project, which started in 2021, aims to automate the entire software development lifecycle using AI agents, including coding, design, testing, and management [2][4] - The Colossus II project, initiated on March 7, 2025, plans to deploy over 550,000 GPUs, with a peak power demand expected to exceed 1.1GW, and a long-term goal of expanding to 1 million GPUs [4][5] Group 2: Infrastructure and Power Supply - To meet the substantial power requirements, xAI has acquired a former Duke Energy power plant in Mississippi, which has been temporarily approved to operate gas turbines for 12 months [4][5] - xAI has partnered with Solaris Energy Infrastructure to lease gas turbines, with 400MW currently allocated to the project, and has invested $112 million in capital expenditures for this partnership [5] Group 3: Strategic Importance - The Macrohard initiative is becoming a crucial part of Musk's business strategy, positioning Tesla as an "AI robotics company," with 80% of its future value tied to robotics [6] - The AI software developed through Macrohard will enhance Tesla's autonomous driving algorithms and factory automation, while Tesla's extensive real-world data will provide valuable training data for the Macrohard project [6]
张小珺对话OpenAI姚顺雨:生成新世界的系统
Founder Park· 2025-09-15 05:59
Core Insights - The article discusses the evolution of AI, particularly focusing on the transition to the "second half" of AI development, emphasizing the importance of language and reasoning in creating more generalizable AI systems [4][62]. Group 1: AI Evolution and Language - The concept of AI has evolved from rule-based systems to deep reinforcement learning, and now to language models that can reason and generalize across tasks [41][43]. - Language is highlighted as a fundamental tool for generalization, allowing AI to tackle a variety of tasks by leveraging reasoning capabilities [77][79]. Group 2: Agent Systems - The definition of an "Agent" has expanded to include systems that can interact with their environment and make decisions based on reasoning, rather than just following predefined rules [33][36]. - The development of language agents represents a significant shift, as they can perform tasks in more complex environments, such as coding and internet navigation, which were previously challenging for AI [43][54]. Group 3: Task Design and Reward Mechanisms - The article emphasizes the importance of defining effective tasks and environments for AI training, suggesting that the current bottleneck lies in task design rather than model training [62][64]. - A focus on intrinsic rewards, which are based on outcomes rather than processes, is proposed as a key factor for successful reinforcement learning applications [88][66]. Group 4: Future Directions - The future of AI development is seen as a combination of enhancing agent capabilities through better memory systems and intrinsic rewards, as well as exploring multi-agent systems [88][89]. - The potential for AI to generalize across various tasks is highlighted, with coding and mathematical tasks serving as prime examples of areas where AI can excel [80][82].
DeepDiver-V2来了,华为最新开源原生多智能体系统,“团战”深度研究效果惊人
量子位· 2025-09-11 10:19
允中 发自 凹非寺 量子位 | 公众号 QbitAI 采用了 "团队作战" 模式:一个Planner负责任务分解,任务分发,进度审视和成果验收,多个专业Executor并行处理子任务,通过共享文件 系统高效交换信息。 与仅通过推理框架实现的多智能体系统不同,DeepDiver-V2以多智能体形态进行训练,模型天然具备更强的角色扮演和协同推理能力。这套 系统不仅在复杂知识问答任务上取得突破,更是能够 生成数万字的高质量深度研究报告 ,在多个榜单中表现亮眼。 它基于华为openPangu Agent推出的DeepDiver-V2,这是一个专攻AI深度搜索和长文调研报告生成的模型。 目前已开源 。 性能爆表:优于同规格竞品 数字最有说服力。DeepDiver-V2-7B和DeepDiver-V2-38B和在多个权威基准测试中表现亮眼: 让智能体组团搞深度研究,效果爆表! 华为最新发布 DeepDiver-V2原生多智能体系统 。 在长文报告生成方面 ,DeepDiver-V2提出了一个全新的面向深度调研报告生成的基准测试WebPuzzle-Writing,该基准给每个调研query设 置了详细的调研范围而非开放生成 ...
A2A、MCP、Gemini……谷歌技术专家手把手教你搭建 AI Agent
Founder Park· 2025-09-02 10:21
Core Insights - The article discusses a seminar featuring Google Cloud AI expert Shi Jie, focusing on techniques for building AI agents using ADK, A2A, MCP, and Agent Engine [2] - It emphasizes the potential of Google's latest AI technologies to create collaborative, efficient, and scalable multi-agent systems [2] - The future of agent development and its impact on human-computer interaction is also explored [2] Group 1: Seminar Details - The seminar will cover how to leverage ADK, A2A, MCP, and Agent Engine to construct AI agents [6] - It aims to provide insights into utilizing Google's latest AI technology for developing highly collaborative and efficient multi-agent systems [6] - The event is targeted at AI startup leaders, technical heads, AI product managers, solution architects, developers, and AI engineers [6] Group 2: Registration Information - Participants are encouraged to scan a QR code for registration, with limited slots available and registration subject to approval [3]
LLM也具有身份认同?当LLM发现博弈对手是自己时,行为变化了
3 6 Ke· 2025-09-01 02:29
Core Insights - The research conducted by Columbia University and Montreal Polytechnic reveals that LLMs (Large Language Models) exhibit changes in cooperation tendencies based on whether they believe they are competing against themselves or another AI [1][29]. Group 1: Research Methodology - The study utilized an Iterated Public Goods Game, a variant of the Public Goods Game, to analyze LLM behavior in cooperative settings [2][3]. - The game involved multiple rounds where each model could contribute tokens to a public pool, with the total contributions multiplied by a factor of 1.6 and then evenly distributed among players [3][4]. - The research was structured into three distinct studies, each examining different conditions and configurations of the game [8][14]. Group 2: Key Findings - In the first study, when LLMs were informed they were playing against "themselves," those prompted with collective terms tended to betray more, while those prompted with selfish terms cooperated more [15][16]. - The second study simplified the rules by removing reminders and reasoning prompts, yet the behavioral differences between the "No Name" and "Name" conditions persisted, indicating that self-recognition impacts behavior beyond mere reminders [21][23]. - The third study involved LLMs truly competing against their own copies, revealing that under collective or neutral prompts, being told they were playing against themselves increased contributions, while under selfish prompts, contributions decreased [24][28]. Group 3: Implications - The findings suggest that LLMs possess a form of self-recognition that influences their decision-making in multi-agent environments, which could have significant implications for the design of future AI systems [29]. - The research highlights potential issues where AI might unconsciously discriminate against each other, affecting cooperation or betrayal tendencies in complex scenarios [29].
如何借助 ADK、A2A、MCP 和 Agent Engine 构建智能体?
Founder Park· 2025-08-27 11:41
Core Insights - The article highlights a collaboration between Founder Park and Google to explore the potential of AI agents through an online sharing session featuring Google Cloud AI expert Shi Jie [2][3]. Group 1: Event Details - The online sharing session is scheduled for next Thursday, September 4, from 20:00 to 21:00, with limited slots available for registration [4]. - Participants are encouraged to register via a QR code, and the event is free but requires approval for registration [4]. Group 2: Discussion Topics - The session will cover how to build AI agents using ADK, A2A, MCP, and Agent Engine [3][8]. - It will also discuss leveraging Google’s latest AI technologies to create collaborative, efficient, and scalable multi-agent systems [3][8]. - The future of agent development will be explored, focusing on how agents will transform human-technology interaction [3][8]. Group 3: Target Audience - The event is aimed at AI startup leaders, overseas business heads, technical leaders, AI product managers, solution architects, developers, and AI engineers [8].
Chain-of-Agents: OPPO推出通用智能体模型新范式,多榜单SOTA,模型代码数据全开源
机器之心· 2025-08-23 04:42
针对上述瓶颈,本文提出了一种全新的智能体推理范式——Chain-of-Agents(CoA)。与传统的 TIR 模型仅支持单一智能体的「思考-行动-观察」模式不同,CoA 框架能够灵活定义多个角色和工具的智能体,在单一模型内动态激活,实现端到端的多智能体协作。 本文通讯作者周王春澍,OPPO个性化AI实验室负责人,主要研究方向是AI个性化、智能体的自主进化和强化学习、以及大模型和智能体的记忆系统等。本文核 心贡献者均来自OPPO个性化AI实验室的AI智能体团队。 近年来,以多智能体系统(MAS)为代表的研究取得了显著进展,在深度研究、编程辅助等复杂问题求解任务中展现出强大的能力。现有的多智能体框架通过多 个角色明确、工具多样的智能体协作完成复杂任务,展现出明显的优势。然而,现阶段的 MAS 依然面临一些关键限制: 同时,近期兴起的工具融合推理(TIR)模型,通过显式地将工具使用融入推理过程,显著提升了单智能体框架(如 ReAct)在信息检索任务中的表现。然而,传 统的 TIR 模型,无法直接支持多智能体系统的原生训练与协作。 计算开销高 : 智能体之间频繁冗余的通信和复杂的工作流设计导致效率不高。 泛化能力有 ...
内幕曝光:OpenAI模型坦承不会第六题,3人俩月拿下IMO金牌
3 6 Ke· 2025-08-12 00:57
OpenAI在短短两个月内,让AI从挣扎于小学数学题跃升至国际数学奥林匹克(IMO)金牌水平,背后是通用AI技术的突破。 OpenAI的ChatGPT真能拿到国际奥数IMO金牌?还是OpenAI的自嗨?背后到底有何隐情? OpenAI的IMO金牌核心团队Alexander Wei、Noam Brown与Sheryl Hsu做客红杉Training Data播客,分享了如何在两月内让AI斩获IMO金牌。 比如说,OpenAI内部并非所有人都持乐观态度。某位研究员甚至打赌模型不会赢,赔率高达2:1,不过最终因为「不想影响士气」而放弃了赌局。 比赛当天凌晨1-5点,Noam Brown忙里偷闲,小憩了一番,而Alexander Wei疯狂检查模型生成的证明。 他们这次还解释了是如何决定AI是不是拿到了金牌。为了评分,他们雇用了外部的IMO奖牌获得者。每份证明都由三名奖牌获得者进行评分,他们对正 确性达成了一致意见 。就这样,他们认为AI的确有能力拿到IMO金牌。 他们还透露证明像「外星语言」般独特,可读性不高。虽有有能力优化,但为了透明,他们选择发布了原始输出。 如果你只想快速了解精华,先看下方要点;想读幕后故事, ...
GPT5令人失望的背后:OpenAI如何做商业战略调整 | Jinqiu Select
锦秋集· 2025-08-08 15:38
Core Insights - OpenAI claims that GPT-5 integrates "rapid response" and "deep reasoning" into a unified experience, enhancing capabilities in code generation, creative writing, multimodal abilities, and tool usage [1] - Despite these claims, there is no significant breakthrough in leading indicators for GPT-5, with user feedback indicating dissatisfaction due to the removal of older models without convincing alternatives [2] - Speculation arises that OpenAI's strategy may be shifting towards a more closed model system to drive stronger commercial monetization [3] Group 1: GPT-5 Core Upgrades - The most notable upgrade in GPT-5 is the enhancement of "reasoning integration," allowing for a one-stop solution that combines rapid response and deep reasoning [8] - OpenAI has invested heavily in post-training work, focusing on fine-tuning for both consumer and enterprise use, significantly improving the model's utility [9] - GPT-5 has made substantial advancements in code capabilities, setting new standards for reliability and practicality in software development [10][11] Group 2: Business and Infrastructure Perspective - OpenAI's ChatGPT currently boasts 700 million weekly active users, demonstrating the massive appeal of large model products [12] - 85% of ChatGPT's user base is located outside the United States, indicating its global reach and impact [12] - OpenAI has approximately 5 million paid enterprise users, showcasing rapid adoption across various industries [13] - The company has established a three-pronged business model consisting of personal subscriptions, enterprise services, and an API platform, all experiencing explosive growth [13] - OpenAI's CFO emphasizes the importance of input metrics like active user counts over traditional financial metrics, reflecting the company's mission to benefit humanity through AGI [14] Group 3: Product Experience Design Evolution - The discussion around benchmarks and rankings, particularly the ARC-AGI test, highlights the criticism of "score chasing" in AI development [21] - OpenAI's strategy focuses on delivering economic value through targeted optimization rather than blindly pursuing high scores on arbitrary benchmarks [23] Group 4: Multi-Agent System Implementation - The concept of multi-agent systems is gaining traction, with OpenAI exploring how multiple AI agents can collaborate to solve complex tasks more efficiently [24] - Real-world applications of multi-agent systems are being developed, such as using AI agents in software development to automate and streamline processes [25][26] - Challenges remain in fully realizing the potential of multi-agent systems, including the need for cultural and process changes within organizations [28] Group 5: OpenAI Technology Evolution - OpenAI's journey from GPT-1 to GPT-5 reflects a clear strategic progression, focusing on expanding model scale, enhancing alignment techniques, and building a comprehensive intelligent system [30][31] - Each generation of GPT has marked significant advancements in language capabilities, reliability, and practical applications, culminating in the widespread adoption of ChatGPT [33]