Core Viewpoint - The emergence of the FAW Audi A5L QianKun Intelligent Driving® version, a fuel vehicle equipped with Huawei's intelligent driving technology, challenges the stereotype that fuel vehicles lack intelligence, showcasing a significant advancement in the automotive industry [1][2][44]. Group 1: Product Features and Performance - The FAW Audi A5L integrates Huawei's QianKun Intelligent Driving with Audi's mechanical quality and Porsche's engine technology, creating a highly capable vehicle [1]. - The vehicle's design includes innovative placement of the lidar system, which is located below the headlights, maintaining traditional aesthetics while enhancing functionality [7]. - During a test drive, the vehicle demonstrated high levels of intelligent driving, with 93% of the journey under intelligent assistance, effectively navigating various urban and highway scenarios [9][39]. Group 2: User Experience and Feedback - The intelligent driving system of the FAW Audi A5L successfully dispelled preconceived notions about fuel vehicles being non-intelligent, providing a comfortable and safe driving experience [41][44]. - Users reported smooth acceleration and steering, with the system effectively managing complex driving situations, such as navigating through blind spots and roundabouts [24][28][39]. Group 3: Market Trends and Implications - The fuel vehicle market remains significant, with 14.67 million units sold in China from January to October, indicating a strong user base that demands intelligent features [45]. - The integration of intelligent driving technology into fuel vehicles is seen as a response to market demands, with the potential for significant growth in this segment [46][56]. - The collaboration between Audi and Huawei represents a shift in the automotive industry, where fuel vehicles can now compete with electric vehicles in terms of intelligence and user experience [57][60]. Group 4: Technological Innovations - The vehicle's architecture includes a six-layer system, with Huawei responsible for the upper layers that enhance intelligent capabilities, while Audi focuses on the lower layers to improve responsiveness [47][49]. - The Vehicle Motion Manager (VMM) plays a crucial role in facilitating communication between hardware and software, ensuring smooth operation of the intelligent driving system [52][54]. - Audi's redesign of the electronic architecture reduces communication delays, allowing for more efficient execution of driving commands, which is essential for the performance of intelligent driving features [50][55].

编辑部 发自 凹非寺 量子位 | 公众号 QbitAI 目前，我们有 三大方向 岗位招聘，希望你是 （或者能成为） 这三个方向的内容专家： 岗位均为全职，工作地点：北京中关村。 岗位面向： 加入我们，你可以获得： 以下是岗位详情： 所有岗位不同能力层级职位均在开放，欢迎结合个人履历和经验申请。 AI产业方向 AI热潮还在汹涌，但如果你还不知道如何参与……那为什么不来 量子位 呢？ 我们是一家以 追踪AI新进展 为核心的内容平台，经过8年积累，目前拥有顶流影响力，广泛且备受认可的产业资源，以及时代风口的最佳观 测和学习生态位。 岗位职责： AI产业方向 ：关注基建层创新，包含芯片、AI Infra、云计算； AI财经方向 ：关注AI领域创投和财报，跟踪产业链资本动向； AI产品方向 ：关注AI在应用和硬件终端方向的进展。 社招：覆盖编辑、主笔、主编各个层级，按能力匹配岗位； 校招：应届毕业生，接受实习且可转正。 站在AI浪潮之巅 ：第一时间接触和了解AI领域最新技术和产品，构建完整的AI认知体系。 玩转AI新工具 ：将各种AI新技术、新工具应用于工作，提升工作效率和创造力。 打造个人影响力 ：通过撰写独家原创内 ...

Core Viewpoint - Zhipu AI, regarded as the "Chinese version of OpenAI," is preparing for its IPO on the Hong Kong Stock Exchange, having recently passed the hearing process [2][4]. Company Overview - Founded in 2019, Zhipu AI has raised over 8 rounds of financing, accumulating more than 8.3 billion RMB, with a current valuation of 24.38 billion RMB [3][59]. - The company focuses on the development of Artificial General Intelligence (AGI) and has created a complete system from foundational models to application products [4][5]. Technology and Product Development - Zhipu AI has developed the "GLM" series models, which support multi-modal inputs and outputs, demonstrating strong capabilities in understanding and generating text, images, and more [9]. - The company has released flagship models GLM-4.5 and GLM-4.6, achieving significant recognition in industry benchmarks [10][11]. - Zhipu AI's models have been recognized for their efficiency, with GLM-4.5 ranking third globally in industry standards and first in China [11]. Business Model and Commercialization - Zhipu AI has been implementing a Model as a Service (MaaS) business model since 2021, which has proven to be scalable and flexible, attracting over 2.7 million enterprise and application developers [23][24]. - The company has generated significant revenue from its models, with GLM-4.5/4.6 achieving over 1 billion RMB in income from global developers [25]. Financial Performance - Zhipu AI's revenue has shown rapid growth, with projected revenues of 57.4 million RMB in 2022, 124.5 million RMB in 2023, and 312.4 million RMB in 2024, reflecting a compound annual growth rate of 130% [29]. - The company maintains a high gross margin, with rates of 54.6%, 64.6%, and 56.3% from 2022 to 2024 [34]. Industry Context - The Chinese large language model market is projected to reach 5.3 billion RMB in 2024, with expectations to grow to 101.1 billion RMB by 2030, driven primarily by institutional clients [61]. - The commercialization paths for enterprise-level LLMs are becoming clearer, indicating a promising future for the industry [62].

一凡 发自 凹非寺 量子位 | 公众号 QbitAI 2025年最受资本热捧的具身智能初创公司，在2025年年终完成了"首秀"。 它石智航，成立不到1年拿了17亿元融资后，刚刚交卷，举行了首次技术发布会。 而它石的首秀，也是通过"绣"展现的—— 首秀绣的是正是它石智航的LOGO，它石介绍说这是世界首台会刺绣的机器人。 不只会干针线活，也能下车间： 以及今年各路机器人的基操——跳舞： 这背后的丝滑动作，都是由基于真实数据训练的世界模型输出。在不同场景下执行不同任务，覆盖家庭和车间也意味着，这不仅仅是一 场技术发布会，也是商业化潜力的展现。 它石智航"首绣" 它石智航刚刚展示了两款产品： 工业机器人A系列（下图左二）和通用机器人T系列（下图右一），从下肢就能看出他们的差别。 △ A系列和T系列中间的是它石智航创始人、CEO陈亦伦 它石认为这体现了复杂操作任务的多种能力，亚毫米级精度、双手协同、连续触觉和力觉反馈调节和长时序任务的执行等。 所以为什么展示机器人的刺绣能力？这背后有两个原因： 秀技术和救手艺 。 而背后支撑这一切的，是它石智航对 下一代具身智能Scaling Law 的理解。 首先，是秀技术实力。 它 ...

Core Viewpoint - The article discusses the innovative features and capabilities of the TRON 2 robot developed by Zhujidi Dynamics, highlighting its versatility, performance, and ease of deployment in various tasks and environments [10][44]. Group 1: Product Features - TRON 2 is a multi-form embodied robot that can switch between three core configurations: dual arms, dual legs, and dual wheels, allowing it to adapt to different tasks [10][11]. - The robot features a 7-DoF (Degrees of Freedom) arm design that mimics human arm flexibility, enhancing its ability to perform complex tasks such as precise grabbing and positioning [18][19]. - TRON 2 is equipped with a humanoid spherical wrist structure that allows for high-precision movements in confined spaces, addressing common industry challenges related to end-effector control [20][21]. - The robot has a reach of 70 cm and can perform tasks in a wide range of environments, including high and distant operations [23]. - It supports dual-wheel and dual-leg movement modes, improving its obstacle avoidance and environmental perception capabilities [26][27]. - TRON 2 has a payload capacity of 30 kg and a battery life of up to 4 hours, making it suitable for continuous operation in various scenarios [29]. Group 2: Deployment and Usability - The design of TRON 2 emphasizes ease of deployment, allowing users to set up the robot in just 30 minutes and complete the full process from environment configuration to task execution within 2 hours [36][38]. - The robot comes with a VLA development toolkit that includes example tutorials and preset modules, facilitating integration with mainstream models like Pi 0.5 and ACT [36][38]. - TRON 2 integrates data collection, training validation, and deployment testing into a closed-loop system, enhancing research efficiency and stability [38][40]. Group 3: Company Strategy and Market Position - Zhujidi Dynamics focuses on long-term development in the embodied intelligence sector, prioritizing foundational aspects like motion control and universal platform capabilities over superficial features [44][45]. - The company has attracted significant investment from major players like Alibaba and JD.com, indicating confidence in its strategic direction and product development [45][46]. - TRON 2 represents a culmination of the company's iterative approach to product development, addressing real user needs and enhancing the usability of embodied robots [46][47].

浙江大学ReLER团队 投稿 量子位 | 公众号 QbitAI 尽管扩散模型在单图像生成上已经日渐成熟，但 当任务升级为高度定制化的多实例图像生成 （Multi-Instance Image Generation, MIG） 时 ，挑战随之显现： 如何在实现空间布局控制的同时，保持多主体身份与参考图像高度一致？ 现有方法在面对需要宏观的布局控制和微观的身份注入的复杂任务时 常常陷入两难 。 能显式控制布局的方法，往往无法利用参考图像来对实例进行定制。 而能以参考图像为指导的方法，则难以实现对布局的精确控制，且在实例数量增加时面临着严重的身份信息丢失问题。 为解决这一制约自定义图像生成的技术瓶颈， 浙江大学ReLER团队发布基于DiT的新框架ContextGen 。 该框架通过分层解耦上下文，解决布局控制与身份保真度的难题，并在多项关键指标上取得了SOTA突破。 机制创新：布局与身份的协同控制 ContextGen的核心在于提出了双重上下文注意力机制，将复杂的全局控制和局部注入任务，并在DiT的不同层级进行部署。 Contextual Layout Anchoring (CLA)：宏观布局锚定 CLA机制将包含 ...

编辑部 发自 凹非寺 量子位 | 公众号 QbitAI AI热潮还在汹涌，但如果你还不知道如何参与……那为什么不来 量子位 呢？ 我们是一家以 追踪AI新进展 为核心的内容平台，经过8年积累，目前拥有顶流影响力，广泛且备受认可的产业资源，以及时代风口的最佳观 测和学习生态位。 目前，我们有 三大方向 岗位招聘，希望你是 （或者能成为） 这三个方向的内容专家： 岗位均为全职，工作地点：北京中关村。 岗位面向： 加入我们，你可以获得： 以下是岗位详情： 所有岗位不同能力层级职位均在开放，欢迎结合个人履历和经验申请。 AI产业方向 岗位职责： AI产业方向 ：关注基建层创新，包含芯片、AI Infra、云计算； AI财经方向 ：关注AI领域创投和财报，跟踪产业链资本动向； AI产品方向 ：关注AI在应用和硬件终端方向的进展。 任职要求： AI财经商业方向 岗位职责： 任职要求： AI产品方向 岗位职责： 社招：覆盖编辑、主笔、主编各个层级，按能力匹配岗位； 校招：应届毕业生，接受实习且可转正。 站在AI浪潮之巅 ：第一时间接触和了解AI领域最新技术和产品，构建完整的AI认知体系。 玩转AI新工具 ：将各种AI新技术 ...

Core Viewpoint - QQ Browser has successfully transformed into an AI browser, leveraging Tencent's self-developed large model capabilities to enhance user experience across various scenarios, including AI search, browsing, learning, and office tasks [2][57]. Group 1: Transformation and Features - QQ Browser has shifted its product direction towards AI, introducing the QBot intelligent agent and achieving comprehensive AI integration [2][3]. - The browser has ranked highly in multiple authoritative lists in the AI Agent sector, indicating its strong performance in the industry [3]. - The evolution of QQ Browser over the past fifteen years reflects a consistent logic of simplifying complex capabilities and returning control to users [8][57]. Group 2: User Experience and AI Integration - The upgraded QQ Browser interface prioritizes AI functionality, allowing users to seamlessly switch between traditional search engines and AI dialogue [14][15]. - The AI+Mini Window feature integrates over ten AI capabilities, enhancing user efficiency without disrupting browsing flow [18][20]. - Key functionalities like webpage summarization, mind mapping, and translation are designed to assist users in managing lengthy content and improving reading efficiency [23][25][29]. Group 3: Agent Capabilities - The QBot Agent Center consolidates various agents capable of completing tasks, addressing traditional browser limitations [34]. - The AI Video Assistant offers features such as multi-language subtitle generation and content summarization, enhancing the video viewing experience [36][38]. - The AI Subscription Assistant efficiently aggregates and tracks relevant information, significantly reducing the time spent on manual searches [41][42]. Group 4: Mobile Expansion and Ecosystem - QQ Browser's AI capabilities have expanded to mobile platforms, providing comprehensive document handling and educational tools tailored for students [51][53]. - The integration with Tencent's ecosystem allows users to access various services without switching applications, streamlining the user experience [55]. - The shift towards AI in browsers reflects a broader industry trend of moving from simple information retrieval to task completion [56].

3DGenR1团队 投稿 量子位 | 公众号 QbitAI 在大语言模型和文生图领域，强化学习 （RL） 已成为提升模型思维链与生成质量的关键方法。 但当我们将目光转向更为复杂的文本到3D生成时，这套方法还会还管用吗？ 近期，一项由 西北工业大学、北京大学、香港中文大学、上海人工智能实验室、香港科技大学合作 开展 的研究系统性探索了这一重要问 题。 论文链接： https://arxiv.org/pdf/2512.10949 代码链接： https://github.com/Ivan-Tang-3D/3DGen-R1 强化学习是否能够用于Text-to-3D生成，以加强3D自回归模型的逐步推理与生成过程？ 在LLM推理和2D文生图中，RL已经证明可以显著提升CoT推理能力和生成质量。但 3D物体更长、更稠密、更具几何约束 。 因此相关方向研究常面临这几个问题： Progressive Investigation：四个层次拆解Text-to-3D+RL 1. Reward设计层 1. 奖励如何同时刻画语义对齐、几何一致性和视觉质量？ 2. 现有RL算法是否适合自回归式3D生成？ 3. 缺乏专门考察"3D推理能力 ...

Core Viewpoint - Achieving AGI requires a balanced approach of technological innovation and scaling, with both aspects being equally important [2][55]. Group 1: Path to AGI - Demis Hassabis outlines a realistic path to AGI, emphasizing that 50% of efforts should focus on model scaling and 50% on scientific breakthroughs [5]. - The success of AlphaFold demonstrates AI's potential to solve fundamental scientific problems, with ongoing research expanding into materials science and nuclear fusion [5][9]. - Current AI models rely heavily on human knowledge, and the next goal is to develop autonomous learning capabilities similar to AlphaZero [5][27]. Group 2: AI Performance and Limitations - AI exhibits a "jagged intelligence" phenomenon, performing well in complex tasks like the International Mathematical Olympiad but struggling with basic logical problems [5][19]. - The need for models to improve self-reflection and verification capabilities is highlighted, as current systems often provide incorrect answers when uncertain [5][57]. - The introduction of confidence mechanisms is necessary to address the hallucination problem, where models generate plausible but incorrect responses [5][56]. Group 3: World Models and Simulation - World models enhance understanding of physical dynamics and sensory experiences, which language models struggle to convey [5][69]. - The use of simulation environments for training AI agents can lead to infinite task generation and complex behavior training, potentially aiding in the exploration of life and consciousness origins [5][80]. - The Genie project exemplifies the potential of interactive world models, which could be applied in robotics and general assistance [5][70]. Group 4: Commercialization and Social Risks - The commercialization of AI poses social risks, and there is a need to avoid the pitfalls of social media's focus on user engagement [5][101]. - Building AI personas that support scientific reasoning and personalized feedback is essential to prevent echo chambers [5][105]. Group 5: Scaling and Innovation - Despite discussions of scaling challenges, the release of Gemini 3 indicates that significant progress continues to be made [5][50]. - The combination of top-tier research capabilities and infrastructure, such as TPUs, positions the company favorably for ongoing innovation and scaling [5][54]. Group 6: Future of AI and AGI - The integration of various projects, including Gemini and world models, is crucial for developing a unified system that could serve as a candidate for AGI [5][114]. - The potential societal impacts of AGI necessitate proactive planning for labor transitions and economic adjustments, similar to lessons learned from the Industrial Revolution [5][118].