Core Conclusions - The non-farm employment in the U.S. unexpectedly increased by 119,000 in September, significantly exceeding the market expectation of 50,000, while the unemployment rate rose to 4.4%, the highest since 2021 [5][6] - The price of antimony has rebounded significantly, presenting potential investment opportunities in antimony-related sectors [2][4] Domestic Market Overview - The Shanghai Composite Index closed at 3,836.77, with a slight increase of 0.05%, while the Shenzhen Component Index rose by 0.37% to 12,585.08 [3] - The CSI 300 Index decreased by 0.12% to 4,448.05, indicating a mixed performance across major indices [3] International Market Overview - The Dow Jones Industrial Average closed at 46,448.27, up by 0.44%, while the S&P 500 and Nasdaq saw increases of 1.55% and 2.69%, closing at 6,705.12 and 22,872.01 respectively [3] Industry Insights - The Federal Reserve's October meeting minutes revealed significant disagreement among policymakers regarding the potential for a rate cut in December, with a 10 to 2 vote to lower the federal funds rate by 25 basis points [4] - The Congo has extended the ban on artisanal mining trade in conflict-affected provinces, impacting global supply chains for tin, tantalum, and tungsten, which are critical for electronics and aerospace industries [7] Market Trends - The North Exchange saw an average daily trading volume of 17.91 billion yuan, a decrease of 16.2% week-on-week, with the North Exchange 50 index dropping by 9.04% [8] - The top five gainers included Dapeng Industrial (up 1211.1%) and Beikang Testing (up 289.6%), while the largest losers were Luqiao Information (down 23.2%) and Taipeng Intelligent (down 19.8%) [8] Investment Recommendations - The North Exchange's policy support is expected to benefit specialized and innovative enterprises, with a focus on the net subscription status of thematic funds and the liquidity recovery opportunities from the launch of the "specialized and innovative" index funds [10] - The current market adjustment may provide a window for medium to long-term investment opportunities, particularly in high-growth sectors that have been undervalued [10]

Core Insights - Luma AI has raised $900 million in Series C funding to accelerate its development towards multimodal AGI, which can simulate reality and assist humans in the physical world [1][3][4] - The partnership with HUMAIN aims to build Project Halo, a 2 GW AI supercluster in Saudi Arabia, which will support the training of large-scale world models [3][4][5] - The collaboration is expected to unlock significant opportunities across various sectors, including entertainment, marketing, education, and robotics, potentially worth trillions [1][4] Company Overview - Luma AI is focused on creating multimodal general intelligence capable of generating, understanding, and manipulating the physical world [8] - The flagship model, Ray3, has been successfully deployed in studios, advertising agencies, and brands, including integration with Adobe's global products [7][8] - HUMAIN, a PIF company, provides comprehensive AI capabilities across four core areas: next-generation data centers, high-performance infrastructure, advanced AI models, and transformative AI solutions [9] Funding and Infrastructure - The $900 million funding round was led by HUMAIN, with participation from AMD Ventures and previous investors like Andreessen Horowitz and Matrix Partners [1][3] - The Project Halo supercluster will represent a significant leap in multimodal AI infrastructure, enabling the training of peta-scale multimodal data [5][6] - Luma AI plans to expand its leadership in entertainment and advertising into simulation, design, and robotics with the new funding [7] Strategic Goals - The partnership aims to create AI systems that can learn from vast amounts of data, estimated at quadrillions of tokens, to enhance understanding and simulation of reality [5][6] - HUMAIN's investment philosophy emphasizes building a complete value chain to support the next wave of AI development [5] - The collaboration is set to establish new benchmarks for how capital, computing power, and capabilities can be integrated in the AI sector [5]

正文： 梅开四度， 国内领先通用具身智能企业[极佳视界]完成亿元级A1轮融资！ 近日，Physical AI（物理AI）领域头部创企 [极佳视界 ]宣布完成 新一轮亿元级A1轮融资，本轮融资由华为哈 勃、华控基金联合投资 。 值的注意的是，公司于8月28日刚完成Pre-A、Pre-A+两轮数亿元融资，其中 Pre-A轮融资由国中资本领投，紫峰 资本、老股东 PKSHA Algorithm Fund跟投；Pre-A+轮融资由中金资本、广州产投、一村淞灵、华强资本投资； 以及于今年2月份完成由 普超资本、合鼎共资本、上海天使会投资联合投资的 数千万天使++轮融资。 温馨提示 ： 点击下方图片，查看运营团队最新原创报告（共235页） 说明： 欢迎约稿、刊例合作、行业交流 ， 行业交流记得先加入 "机器人头条"知识星球 ，后添加（ 微信号：lietou100w ）微 信； 若有侵权、改稿请联系编辑运营（微信：li_sir_2020）； 有关科技大厂入局具身赛道（大模型赋能、投资和自研）更多详细梳理、解读，已放到知识星 球"机器人头条"（点击后方链接，加入星球查看） ： 【 原创】多家顶尖科技大厂，进军人形机器人整机制 ...

Core Viewpoint - The roundtable forum highlighted the importance of funding and data in advancing embodied intelligence, with participants discussing various strategies for utilizing a hypothetical budget of 10 billion yuan to drive development in the field [1][53]. Group 1: Funding and Investment Strategies - Participants expressed differing opinions on how to allocate 10 billion yuan for the advancement of embodied intelligence, with suggestions including investing in research institutions and building data engines [1][54][56]. - The CEO of Accelerated Evolution emphasized the need for collaboration, suggesting that 10 billion yuan may not be sufficient without partnerships [1][53]. - The focus on creating the largest self-evolving data flywheel was proposed as a key investment area [54]. Group 2: Data Challenges and Solutions - A significant discussion point was the scarcity of data, with varying opinions on the importance of real-world data versus synthetic data [2][29]. - The emphasis was placed on the necessity of high-quality, diverse data collected from real-world scenarios to enhance model training [30][32][36]. - The use of simulation data was also highlighted as a means to accelerate the development of embodied intelligence before sufficient real-world data can be gathered [43][44]. Group 3: World Models and Predictive Capabilities - The forum participants agreed on the critical role of world models in embodied intelligence, particularly in enabling robots to predict and plan actions based on future goals [5][12]. - There was a consensus that training data for these models should primarily come from the robots themselves to ensure relevance and effectiveness [5][12]. - The discussion included the potential for a unified architecture in embodied intelligence models, contrasting with the current fragmented approaches [7][15][27]. Group 4: First Principles and Decision-Making - Participants shared their foundational principles guiding decision-making in the development of embodied intelligence, emphasizing the importance of data scale and quality [48][49][51]. - The need for a physical world foundation model that accurately represents complex physical interactions was highlighted as essential for future advancements [26][27]. - The concept of a closed-loop model for embodied intelligence was proposed, contrasting with the open-loop nature of current language models [10][11].

Core Viewpoint - Xiaomi is making significant investments in intelligent driving technology, focusing on safety, comfort, and efficiency, with safety being the top priority in their development strategy [4][7]. Development Progress - Xiaomi's intelligent driving has progressed through several versions: from high-precision maps for highway NOA (version 24.3) to urban NOA (version 24.5), and moving towards light map and no map versions (version 24.10) [7]. - The company is advancing through three stages of intelligent driving: 1.0 (rule-driven), 2.0 (data-driven), and 3.0 (cognitive-driven), with a focus on VLA (Vision Language Architecture) for the next production phase [7][10]. World Model Features - The world model introduced by Xiaomi has three essential characteristics: diversity in generated scenarios, multimodal input and output, and interactive capabilities that influence vehicle behavior [8][9]. - The world model is designed to enhance model performance through cloud-based data generation, closed-loop simulation, and reinforcement learning, rather than direct action outputs from the vehicle [10]. VLA and Learning Models - VLA is described as an enhancement over end-to-end learning, integrating high-level human knowledge (traffic rules, values) into the driving model [13]. - Xiaomi's development roadmap includes various model training stages, from LLM pre-training to embodied pre-training, with recent advancements in MiMo and MiMo-vl models [13]. Community and Knowledge Sharing - The "Automated Driving Heart Knowledge Sphere" community aims to provide a comprehensive platform for learning and sharing knowledge in the field of autonomous driving, with over 4,000 members and plans to expand [15][26]. - The community offers resources such as technical routes, video tutorials, and Q&A sessions to assist both beginners and advanced learners in the autonomous driving sector [27][30].

Core Viewpoint - The article discusses the emerging industry revolution driven by embodied intelligence in the AI era, highlighting the diverse perspectives of top practitioners in the field regarding the allocation of significant funding for its development [5][6]. Group 1: Funding Allocation and Perspectives - During a roundtable forum, participants were asked how they would allocate 10 billion yuan to advance embodied intelligence, revealing varying strategies and priorities among industry leaders [5][6]. - Some participants emphasized the need for collaboration and building data ecosystems, while others focused on addressing data bottlenecks and creating self-evolving data systems [7][68]. Group 2: Data Challenges and Solutions - A significant discussion point was the "data scarcity" issue, with differing opinions on the importance of real-world data versus synthetic data for training models [9][10]. - Participants highlighted the necessity of high-quality, diverse data collected from real-world scenarios to enhance model performance, with some advocating for a combination of real and synthetic data [43][44][50]. Group 3: World Models and Embodied Intelligence - The concept of world models was debated, with some experts agreeing on their importance for embodied intelligence, while others suggested that they are not a mandatory foundation [14][17]. - The need for predictive capabilities in robots was emphasized, suggesting that training data must come from the robots' own experiences to be effective [16][18]. Group 4: Future Model Architectures - There was a consensus that embodied intelligence requires a unique model architecture distinct from existing large language models, with some advocating for a vision-first or action-first approach [19][20][21]. - The idea of a unified model that integrates various elements such as vision, action, and language was discussed, with the potential for a closed-loop system that allows for real-time feedback and adjustment [22][24][25]. Group 5: Long-term Vision and Data Collection - Participants expressed that the development of a powerful embodied intelligence model would depend on accumulating vast amounts of real-world data through practical applications and interactions [27][60]. - The importance of creating a "data flywheel" through the deployment of robots in real environments was highlighted as a means to gather diverse and extensive data [50][51][56].

Core Viewpoint - The article discusses the limitations of current AI language models, emphasizing that while they are advanced in processing language, they lack true understanding of the physical world, which is essential for achieving genuine intelligence [5][6][7]. Group 1: Limitations of Current AI Models - Current AI language models, like ChatGPT and Google's Gemini, excel at predicting the next word based on statistical patterns but fail to understand basic physical concepts [6][7]. - The analogy of a scholar in a dark room illustrates that while these models can generate coherent text, they lack real-world experience and understanding [7][13]. - AI's reliance on language statistics rather than physical interactions leads to nonsensical outputs, highlighting the need for a deeper understanding of the world [8][13]. Group 2: The Concept of Spatial Intelligence - To advance AI, it is crucial to develop "spatial intelligence," which involves understanding and interacting with the physical world without relying solely on language [8][14]. - The article posits that true intelligence requires the ability to predict physical interactions and outcomes, akin to how humans learn through experience [14][15]. - Examples from child development and scientific discovery illustrate how spatial interactions lead to a deeper understanding of cause and effect [9][11]. Group 3: Future Directions for AI - The future of AI may shift from predicting the next word to predicting the next frame of the world, integrating physical laws and spatial reasoning [14][17]. - Developing a "world model" that incorporates spatial data and physical interactions could revolutionize AI capabilities, allowing for more accurate simulations and predictions [15][17]. - The article mentions ongoing efforts to extract spatial information from 2D videos to train AI models, indicating a significant area of research [17][18]. Group 4: Practical Applications and Opportunities - The emergence of AI with spatial intelligence could lead to practical applications in robotics, enhancing their ability to navigate and interact with real-world environments [20][21]. - Potential use cases include virtual scene generation for design, therapy, and educational purposes, showcasing the versatility of AI in various fields [21][22]. - The ability to convert imagination into tangible reality presents significant opportunities for innovation and entrepreneurship [22][23].

11月23日，雷军发文总结小米端到端辅助驾驶HAD增强版的升级点。纵向加减速更舒适，旁车加塞时 可提前预判减速，及时跟车提速，行车更舒适安全。横向变道更丝滑，在变道并线、借道绕行时表现更 自然流畅。路况理解能力提升，在多车道复杂大路口能提前看懂导航信息，优化走对路、选对道的能 力。 此外，雷军还强调，辅助驾驶不是自动驾驶，驾驶时仍需时刻保持专注。此前在11月21日2025广州车展 开幕日，小米汽车端到端辅助驾驶"Xiaomi HAD增强版"正式发布，其在1000万Clips版本基础上引入"强 化学习"与"世界模型"，AEB防碰撞辅助升级，新增紧急转向辅助。 ...

IT之家 11 月 23 日消息，小米创办人、董事长兼 CEO 雷军今日发文，总结了小米端到端辅助驾驶 HAD 增强版的升级点。 纵向加减速更舒适，旁车加塞时提前预判减速，及时跟车提速行车更舒适安全。 横向变道更丝滑，变道并线、借道绕行时，更丝滑、不犹豫。 路况理解更充分，多车道的复杂大路口，提前看懂导航信息，优化走对路、选对道能力。 雷军也再次提醒：辅助驾驶不是自动驾驶，驾驶时仍需时刻保持专注。 据IT之家此前报道，在 11 月 21 日 2025 广州车展开幕日当天，小米汽车端到端辅助驾驶"Xiaomi HAD 增强版"正式发布，其在 1000 万 Clips 版本的基础上 引入了「强化学习」与「世界模型」，同时 AEB 防碰撞辅助升级，并新增紧急转向辅助。 车道保持辅助 - 预警 车道保持辅助 - 纠偏 紧急车道保持 盲区监测预警 车门开启预警 变道辅助预警 其他安全能力 超速告警 红绿灯提醒 自适应防眩目矩阵3 辅助驾驶不是自动驾驶，驾驶仍需时刻保持 侧向安全能力 ...

广州车展首日，雷军没有出现在小米汽车的发布会上。 作者 | 周智宇 编辑 | 张晓玲 站在台前的，是小米汽车副总裁李肖爽。除了技术迭新，李肖爽演讲的核心只有一个：安全。屏幕上， 是不加小字注释的三行大字——安全是前提，安全是基础，安全是一切。 营销味突然淡去，将安全牌打上牌桌。今年深陷舆论漩涡的小米，正在发生一些微妙的变化。 广州车展上，李肖爽没有谈论那些锦上添花的功能，重点展示的Xiaomi HAD增强版以及安全辅助功 能。 在智能驾驶的上半场，行业普遍采用"规则驱动"或基础的"数据驱动"。这就像是让司机死记硬背交规， 或者通过模仿记忆来开车。但这种模式很快遇到了天花板：面对极端稀缺的场景，由于缺少训练样本， 模型无法有效学习。 小米此次打出的"世界模型"，本质上是构建了一个高保真的虚拟仿真引擎。在这个虚拟世界里，系统不 再依赖现实中必须发生的事故来学习，而是可以通过"日夜兼程"的模拟演练，在海量生成的场景中试错 ——"走对了加分、走错了扣分"。 这种从"模仿"到"认知"的范式转移，目的是试图通过算法的泛化能力，去覆盖那些长尾的风险场景，从 而降低系统性的行车风险。 此次小米HAD增强版AEB也全面升级，A ...