近日，中国AIoT行业头部企业特斯联与"人形机器人第一股"优必选宣布达成战略合作。依托特斯联搭 建的空间智能感知、推理及行动层面的空间信息处理技术闭环，及优必选在人形机器人领域领先的全栈 式技术自研能力，双方将围绕"空间智能+具身智能"展开深度合作，彼此开放并共享空间智能与具身智 能创新技术，打造以空间智能为底层的多模态训练全新范式，共同推动智能体机器人的全面落地。 特斯联凭借其搭建的三层架构——AIoT领域模型、AIoT基础设施层、AIoT智能体层让机器人看得懂世 界，而优必选这类人形机器人也为空间智能打通了落地的"最后一公里"。二者结合后，人形机器人不仅 能够自主移动、执行精细操作，更作为实时感知与交互的智能终端，在真实物理场景中持续验证、增强 空间推理模型的性能。其在运行过程中所生成的多模态交互数据，持续丰富空间数据资源，进一步反哺 和优化模型的空间推理能力，推动系统持续进化。 据悉，优必选具备行业领先的机器人硬件与控制技术、人工智能技术、机器人与人工智能融合技术以及 机器人操作系统应用框架。优必选科技创始人、董事会主席兼CEO周剑认为，空间智能是机器人与物理 世界交互的重要支撑。机器人要从单一执行工具 ...

Core Viewpoint - World Labs, founded by Stanford professor Fei-Fei Li, has launched a limited preview of its space intelligence model, Marble, which allows users to generate persistent 3D worlds from a single image or text prompt [1][4][11]. Group 1: Product Features - Marble generates expansive, navigable, and controllable 3D worlds that are free for users to explore [5][13]. - The model supports the export of generated worlds as Gaussian point clouds, facilitating integration into downstream projects using the open-source rendering library Spark [14]. - Users can combine multiple generated results to create larger worlds, enhancing the overall experience [15]. Group 2: Technical Advancements - The latest model shows improvements in consistency and style adherence, allowing for richer geometric complexity and more complete 3D scenes compared to previous models [17][20]. - Marble is designed primarily for creating 3D environments rather than focusing on individual objects, which may not cater to users interested in personal subjects like selfies or pets [18]. Group 3: User Experience - Users can navigate and interact within a consistent 3D world, which is a core requirement for many visual creators [17]. - The model allows for diverse style transformations, enabling users to explore various artistic expressions in their 3D worlds [20][21]. Group 4: Future Potential - The model encourages users to think beyond room-sized environments, allowing for the assembly of larger, more imaginative spaces [25].

Group 1: Company Overview - Yidao Information is a provider of smart electronic products and solutions, focusing on product definition and R&D design [2] - The company’s main business segments include rugged smart terminals and consumer smart terminals, with applications in smart manufacturing, transportation, energy exploration, and public utilities [2] Group 2: Business Performance - In the first half of 2025, the company achieved a revenue growth of 19.24% year-on-year [3] - The rugged smart terminal business grew by 24.71%, smart hardware by 16.27%, and XR and AIoT business by 27.60% [3] - R&D expenses increased by 2.71% year-on-year [3] Group 3: R&D Focus - The company is increasing R&D investments in AI, spatial computing, and native hardware to align with the AI+ industry development trend [3] - New R&D projects and patent applications have increased, leading to breakthroughs such as AI industry terminals, AI enterprise workstations, and AI glasses [3] - The establishment of Yidao Research Institute in January 2024 aims to collaborate with universities and research institutions to advance research in AI, perception technology, cloud computing, and spatial intelligence [3]

Group 1 - The core viewpoint of the reports is that Alibaba maintains a "buy" rating, with projected revenues of 1.06/1.18/1.30 trillion yuan and adjusted net profits of 125.6/159.9/190.3 billion yuan for FY2026-FY2028, corresponding to PE ratios of 19.9/15.6/13.1 times [1] - Alibaba's synergy between instant retail and its main e-commerce platform is significant, further enhancing its "big consumption platform" strategy, while its cloud business is expected to benefit from AI applications and the commercialization of AIAgent [1] - Gaode Map, a subsidiary of Alibaba, launched the "Gaode Street Ranking," the world's first ranking based on user behavior, to support offline dining and service consumption, covering over 300 cities in China [1] Group 2 - Following the launch of Taobao Flash Sale, Alibaba has further expanded its offline business with the introduction of Gaode's "Street Ranking," which is expected to create synergies with its home delivery services [2] - The complexity of offline consumption scenarios is lower than that of delivery services, which reduces fulfillment and delivery costs, thus increasing profit margins [2] - Gaode Map's average DAU reached 186 million by July 2025, six times that of Dianping, indicating a strong traffic advantage, and it aims to become a "super app" in the domestic market by integrating various services [2]

Investment Rating - The report maintains a "Buy" rating for Alibaba Group (09988.HK) [4] Core Views - Alibaba is enhancing its "big consumption platform" strategy with the launch of Gaode's "Street Ranking," which supports offline dining and service consumption [1][2] - The integration of Gaode's services with Alibaba's existing platforms, such as Taobao, is expected to create synergies between in-store and delivery services, potentially increasing profit margins [2][3] - The report anticipates significant revenue growth for Alibaba, projecting revenues of CNY 1.06 trillion, CNY 1.18 trillion, and CNY 1.30 trillion for FY2026, FY2027, and FY2028 respectively [3] Financial Summary - Revenue (CNY million): - FY2024A: 941,168 - FY2025A: 996,347 - FY2026E: 1,058,220 - FY2027E: 1,183,302 - FY2028E: 1,304,354 - Year-over-year growth rates for revenue are projected at 8.34%, 5.86%, 6.21%, 11.82%, and 10.23% for FY2024A to FY2028E [1][3] - Net profit attributable to shareholders (CNY million): - FY2024A: 79,741 - FY2025A: 129,470 - FY2026E: 107,108 - FY2027E: 137,785 - FY2028E: 167,027 - Year-over-year growth rates for net profit are projected at 9.97%, 62.36%, -17.68%, 28.64%, and 21.22% for FY2024A to FY2028E [1][3] - The report highlights a significant increase in the return on equity (ROE), expected to rise from 8.11% in FY2024A to 12.47% in FY2028E [1][3] Market Position - Gaode's average daily active users (DAU) reached 186 million by July 2025, significantly outperforming competitors [2] - The report suggests that Gaode is on track to become a "super app" in the domestic market, integrating various services such as maps, ride-hailing, reviews, and group buying [2]

Core Viewpoint - The limitations of large language models (LLMs) in understanding the physical world are highlighted, emphasizing that language is a generated signal dependent on human input, while the physical world is an objective reality governed by its own laws [1][5][19]. Group 1: Language Models and Their Limitations - Language models operate on a one-dimensional representation of discrete tokens, making them adept at handling written text but inadequate for representing the three-dimensional nature of the physical world [12][14]. - The challenge of spatial intelligence lies in extracting, representing, and generating information from the real world, which is fundamentally different from language processing [17][19]. - Experiments show that LLMs struggle with physical tasks, performing poorly compared to human children and specialized robots [22][28]. Group 2: Experimental Findings - In a test using the Animal-AI environment, LLMs could only complete simple tasks, failing at more complex ones even with additional teaching examples [26][27]. - A tool named ABench-Physics was developed to assess LLMs' physical reasoning abilities, revealing that even the best models achieved only a 43% accuracy rate on basic physics problems [30][34]. - Visual tasks further demonstrated the limitations of LLMs, with human accuracy at 95.7% compared to a maximum of 51% for the models [37][41]. Group 3: Philosophical and Future Considerations - The discussion includes perspectives on whether language can sometimes describe reality better than perception and the potential for AI to develop its own language for understanding the physical world [46][47]. - The ongoing development of models based on physical and multimodal understanding indicates a shift towards addressing these limitations [44].

机器之心报道 机器之心编辑部 香港科技大学 谭平 教 授团队 与地平线（Horizon Robotics）团队 最新发布了一项 3D 场景表征与 大规模重 建新方法 SAIL-Recon ，通过锚点图建立构建场景全局 隐式表征，突破现有 VGGT 基础模型对于大规模视觉定位与 3D 重建的处理能力瓶颈，实现万帧级的场景表征抽取与定位重建， 将空间智能「3D 表征与建模」前 沿推向一个新的高度 。该技术作为 3D 场景表征与重建的一个 基础模型 ，不仅可以用于任意场景中的大规模 3D 重建和空间漫游，也可以为机器人的 3D 空间感 知、自主空间定位与导航提供基础技术支撑。 谭平教授目前为香港科技大学电子与计算机工程系正教授，冯诺依曼人工智能研究院副院长，也是「香港科技大学–比亚迪具身智能联合实验室」主任，长期致力 于 3D 空间智能与具身智能相关的技术前沿研究。 谭平教授创立的 人工智能初创公司「光影焕像」 致力于 3D 和空间智能的核心技术和产品研发，打造 3D 空间智能大脑，推进相关技术在游戏、影视和具身智能 等行业场景的商业化应用。 作者简介： 邓俊源 分别于 2021 年和 2024 年获上海交通大学学 ...

Company Overview - Yidao Information is a provider of smart electronic products and solutions, focusing on product definition and R&D design [2] - The company’s main business segments include rugged smart terminals and consumer smart terminals, with applications in smart manufacturing, transportation, energy exploration, and public utilities [2] Financial Performance - In the first half of 2025, the company achieved a revenue growth of 19.24% year-on-year [3] - Business segment growth rates: - Rugged smart terminals: 24.71% - Smart hardware: 16.27% - XR and AIoT: 27.60% [3] - R&D expenses increased by 2.71% year-on-year [3] Profitability Insights - The company's gross margin fluctuates due to the diverse product range, with a stable gross margin maintained between 14% and 20%, higher than the industry average [3] Future Development Directions - The company aims to make cutting-edge technology more accessible, focusing on competitive product solutions and services [3] - Plans to deepen customer engagement and expand product offerings to enhance operational scale [3] - Establishment of Yidao Digital (Research Institute) to invest in AI, perception technology, and spatial computing, fostering innovation and talent development [3] - Emphasis on adapting R&D for XR, AIoT, and domestically controlled products to seize opportunities in digital China and Industry 4.0 [3]

Group 1 - The core update of Gaode Map's subway navigation service aims to enhance commuting efficiency and comfort through the use of "spatial intelligence" technology, analyzing multiple data dimensions such as passenger flow and subway operation status [1] - The new version features a "real-time subway arrival prediction" function, allowing users to view the estimated arrival times of current and next trains, along with a detailed weekday and weekend departure schedule [1] - The "smart reminder" function alerts passengers two stops before their destination with pop-up notifications and vibrations, even when the phone is locked, to prevent overshooting their stop [1] Group 2 - The updated app integrates payment and navigation, enabling users to access the "boarding code" for gate entry while planning their subway route, currently available in 51 cities including Beijing, Shenzhen, and Chengdu [1] - The "subway station guidance" feature provides clear navigation for transfers or exits using text and images, addressing the complexities of subway station environments [1] - The update also optimizes carriage selection by indicating "cold/cool carriages," allowing passengers to choose based on their temperature preferences, covering 44 cities including Beijing, Shanghai, and Guangzhou [2] - The system provides information on the "fastest transfer carriage and door" to help passengers save walking time within the station [2]

Core Insights - AIGC technology is evolving from text and image generation to more complex 3D space and video domains, facing challenges in understanding physical world structures and maintaining temporal consistency in video creation [2][6] - Spatial intelligence is identified as a crucial bridge for AI to transition from the digital to the physical world, requiring AI to learn the "language" of space [2][9] Model Developments - The newly released models, SpatialLM 1.5 and SpatialGen, address the challenges of 3D scene generation and video creation, with SpatialLM 1.5 focusing on structured generation through "spatial language" and SpatialGen ensuring spatial coherence across multiple perspectives [3][4] - SpatialLM 1.5 encodes spatial relationships as "language," allowing for end-to-end generation of 3D scenes based on user input, producing structured scripts with physical parameters [4][5] Data and Training - The scarcity of high-quality 3D data is a significant bottleneck for spatial intelligence development, with over 4.41 billion 3D models and 500 million structured 3D scenes available by mid-2025 [5] - The company leverages its platform, CoolJia, to accumulate data that enhances the training of spatial understanding and generation models, creating a feedback loop between tools, data, and models [5] Video Generation Innovations - Current AI video generation tools struggle with spatial logic due to their reliance on 2D image sequences, leading to issues like object distortion and inconsistency [6][7] - SpatialGen overcomes these limitations by using a 3D Gaussian scene as an intermediary, allowing for the generation of images from any perspective while maintaining object consistency across frames [6][7] Market Strategy and Ecosystem - The company emphasizes open-sourcing its models and data to foster collaboration and innovation in the spatial intelligence market, aiming to expand the ecosystem rather than monopolize it [9][10] - The open-source strategy has garnered international attention, with the company releasing the world's first 3D Gaussian dataset, which has implications for various industries, including autonomous driving [9][10] Differentiation and Future Directions - The company's focus on interactive functional scenes differentiates it from other models that may lack spatial consistency, positioning it for industrial applications [10][11] - By providing a new path for industrial software development, the company aims to create "AI-native" design tools that bypass traditional complex geometric algorithms [11]