2025年11月25日（周二）报纸版的人民日报共有5版有广告。 | 01版:变闻 | 02版:要闻 | 03版:变闻 | 04版:要闻 | | --- | --- | --- | --- | | 05版:评论 | 06版:蛋闻 | 07版:文化 | 08版:广告 | | 09版:理论 | 10版:综合 | 11版: 固际 | 12版:广告 | | 13版:特别报道 | 14版:特别报道 | 15版:特别报道 | 16版:广告 | | 17版:国际 | 18版:新农村 | 19版:广告 | 20版:广告 | 作为对比2025年11月24日（周一）有1版有广告。 | 01版:变闻 | 02版:要闻 | 03版:变网 | 04版:要闻 | | --- | --- | --- | --- | | 05版:评论 | 06版:要闻 | 07版:读者来信 | 08版:广告 | | 09版:理论 | 10版:社会 | 11版:生态 | 12版:国际 | | 13版:特别报道 | 14版:特别报道 | 15版:特别报道 | 16版:新青年 | | 17版:国际 | 18版:财经 | 19版:绿色 | 20版:副刊 | | 01 ...

Core Insights - The Chinese automotive industry has achieved significant advancements over the past decade, particularly in the field of new energy vehicles (NEVs), which have become a vital component for high-quality development [1] - Li Auto, established in 2015, has emerged as a leading mid-to-high-end NEV brand in China, achieving annual sales of 500,000 vehicles and over 100 billion yuan in revenue for two consecutive years [1][2] - The company is committed to innovation and has developed a robust supply chain ecosystem, known as "Li Chain," which integrates nearly a thousand partners to enhance collaborative growth [4][5] Industry Development - The NEV sector in China is projected to exceed 12 million units in annual production and sales by 2024, with Li Auto reaching its milestone of 1 million vehicles produced in just 58 months [2] - Li Auto is actively contributing to the development of Changzhou as "China's New Energy Capital," aiming for the local NEV industry to surpass 850 billion yuan in scale by 2024 [2] Technological Innovation - During the 14th Five-Year Plan period, Li Auto has focused on becoming a global leader in artificial intelligence (AI) and plans to invest over 6 billion yuan in AI development this year [3] - The company has launched the VLA driver model and "Li Xiang Classmate" AI system, marking its entry into a new phase of AI-driven development [3] Supply Chain and Ecosystem - Li Auto has established a supply chain system characterized by "excellent growth, intelligent innovation, and green health," with procurement amounts growing from billions to trillions in just three years [4][5] - The "Li Chain" ecosystem promotes localized industrial collaboration, with 80% of suppliers located in the Yangtze River Delta region, and 50% concentrated in Jiangsu [5] Talent Development - Li Auto has partnered with over a hundred universities to cultivate more than 5,000 high-end industry talents through initiatives like the "Li Xiang+" talent program [6] Future Outlook - The company aims to leverage technological innovation to drive high-quality industry development and enhance user experiences in green and smart mobility [8]

Core Insights - Li Auto received the "Outstanding Contribution Award" at the 2025 World Internet Conference for its innovations in artificial intelligence and future mobility technologies [1][3] - The award highlights Li Auto's self-developed advanced driver assistance technology, which was selected from over 400 global technological achievements [1][3] Group 1: Award Recognition - The World Internet Conference aims to recognize individuals and companies that have made significant contributions to global internet development [3] - Li Auto was distinguished as a "Growth Potential" award recipient, standing out among nearly 200 applicants, indicating high recognition in the integration of AI technology and smart vehicles [3] Group 2: Technological Advancements - Li Auto has established a comprehensive capability from academic research to practical application, focusing on converting cutting-edge theories into user-perceptible technologies and products [4] - The company has made significant breakthroughs in AI products, particularly in advanced driver assistance and smart cockpit technologies, supported by a robust R&D system and substantial investment [4] - In 2024, Li Auto underwent two technological architecture transformations for its driver assistance system, transitioning from rule-based algorithms to an AI era centered on imitation learning and reinforcement learning [4] Group 3: AI Innovations - Li Auto launched the world's first VLA driver model in August, which possesses five core capabilities: spatial understanding, thinking, communication and memory, behavior, and iteration, providing a "personal driver" experience [4] - The MindGPT multimodal cognitive model, developed by Li Auto, is the first self-developed model by an automotive company to be registered under China's "Interim Measures for the Management of Generative Artificial Intelligence Services" [6] - The Li Auto assistant has evolved from a smart voice assistant to an intelligent agent, capable of tool usage, complex task completion, and memory understanding, enhancing user convenience [6] Group 4: Future Directions - Li Auto aims to continue its commitment to open collaboration and transform the values advocated by the World Internet Conference into practical pathways for the automotive industry, promoting high-quality industry development [6]

Core Viewpoint - The article discusses the transition of AI into its "second half," emphasizing the need for new evaluation and configuration methods for AI to surpass human intelligence, particularly in the context of autonomous driving technology [1][5]. Group 1: AI Paradigm Shift - AI is moving from reliance on human-generated data to experience-based learning, as highlighted by Rich Sutton's paper "The Era of Experience" [1]. - OpenAI's former researcher, Yao Shunyu, asserts that AI must develop new evaluation methods to tackle real-world tasks effectively [1]. Group 2: Advancements in Autonomous Driving - At the ICCV 2025 conference, Li Auto's expert, Zhan Kun, presented a talk on evolving from data closed-loop to training closed-loop in autonomous driving [2][4]. - Li Auto introduced a systematic approach to integrate world models with reinforcement learning into mass-produced autonomous driving systems, marking a significant technological milestone [5]. Group 3: Li Auto's Technological Innovations - Li Auto's advanced driver assistance technology, LiAuto AD Max, is based on the Vision Language Action (VLA) model, showcasing a shift from rule-based algorithms to end-to-end solutions [7]. - The company has achieved significant improvements in its driver assistance capabilities, with a notable increase in the Human Takeover Mileage (MPI) over the past year [9]. Group 4: Challenges and Solutions in Data Utilization - Li Auto identified that the basic end-to-end learning approach faced diminishing returns as the training data expanded to 10 million clips, particularly due to sparse data in critical driving scenarios [11]. - The company aims to transition from a single data closed-loop to a more comprehensive training closed-loop, which includes data collection and iterative training through environmental feedback [12][14]. Group 5: World Model and Synthetic Data - Li Auto is developing a VLA vehicle model with prior knowledge and driving capabilities, supported by a cloud-based world model training environment that incorporates real, synthetic, and exploratory data [14]. - The ability to generate synthetic data has improved the training data distribution, enhancing the stability and generalization of Li Auto's driver assistance system [24]. Group 6: Research Contributions and Future Directions - Since 2021, Li Auto's research team has produced numerous papers, expanding their focus from perception tasks to advanced topics like VLM/VLA and world models [28]. - The company is addressing challenges in interactive intelligent agents and reinforcement learning engines, which are critical for the future of autonomous driving [35][38]. Group 7: Commitment to AI Development - Li Auto has committed nearly half of its R&D budget to AI, establishing multiple teams focused on various AI applications, including driver assistance and smart industrial solutions [43]. - The company has made significant strides in AI technology, with rapid iterations of its strategic AI products, including the VLA driver model launched with the Li Auto i8 [43].

Core Insights - The article discusses the five stages of artificial intelligence (AI) as defined by OpenAI, emphasizing the importance of each stage in the development and application of AI technologies [17][18]. Group 1: Stages of AI Development - The first stage is Chatbots, which serve as a foundational model that compresses human knowledge, akin to a person completing their education [19][4]. - The second stage is Reasoners, which utilize supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) to perform continuous reasoning tasks, similar to advanced academic training [20][21]. - The third stage is Agents, where AI begins to perform tasks autonomously, requiring a high level of professionalism and reliability, comparable to a person in a specialized job [22][23]. - The fourth stage is Innovators, focusing on the ability to generate and solve problems through real-world training and feedback, which is essential for enhancing the capabilities of AI [25][26]. - The fifth stage is Organizations, which manage multiple agents and innovations to prevent chaos, similar to how businesses manage human resources [27][28]. Group 2: Computational Needs - The demand for reasoning computational power is expected to increase by 100 times in the next five years, while training computational needs may expand by 10 times [10][29]. - The article highlights the necessity for both edge computing and cloud-based processing to support the various stages of AI development [28][29]. Group 3: Ideal Automotive Applications - The company is developing its own reasoning models (MindVLA/MindGPT) and agents (Driver Agent/Ideal Classmate Agent) to enhance its autonomous driving capabilities [31][33]. - By 2026, the company plans to equip its autonomous vehicles with self-developed advanced edge chips for deeper integration with AI [12][33]. Group 4: Training and Skill Development - Effective training for AI involves enhancing three key abilities: information processing, problem formulation and solving, and resource allocation [39][40][41]. - The article emphasizes that successful AI applications require extensive training, akin to the 10,000 hours of practice needed for mastery in a profession [36][42].

Core Viewpoint - The article discusses how Li Auto is transforming its cockpit experience through the development of its self-developed AI model "Mind GPT," positioning itself as a leader in the intelligent cockpit space amidst increasing competition in the automotive industry [4][5][6]. Group 1: Development of AI Capabilities - Li Auto has shifted from relying on third-party suppliers for its voice assistant to developing its own AI capabilities, marking a significant transformation in its cockpit technology [6][8]. - The company aims to enhance user interaction through the "Li Xiang Classmate" app, which integrates the Mind GPT model into its vehicle systems, allowing for more natural and efficient user engagement [4][23]. - The internal team was formed to regain data ownership and establish a self-sufficient AI development environment, which has led to significant improvements in user experience and interaction [14][15]. Group 2: Strategic Vision and Implementation - Li Auto's CEO, Li Xiang, emphasizes the necessity of developing large models to compete effectively in the AI space, stating that without them, the company cannot be considered an AI company [5][19]. - The company has set ambitious goals, including becoming a leading AI enterprise by 2030, which reflects a shift in its identity from a traditional car manufacturer to a technology-driven company [19][28]. - The introduction of the Mind GPT model is part of a broader strategy to integrate AI into various aspects of the user experience, including travel assistance, entertainment, and education [23][24]. Group 3: Future Directions and Innovations - Li Auto is focusing on the development of a foundational model that will support its AI initiatives, with plans to open-source its operating system to enhance collaboration and reduce costs [31][32]. - The company envisions vehicles evolving from mere transportation tools to AI-driven "space robots," indicating a significant shift in the automotive landscape [32][33]. - The establishment of an AI committee aims to oversee technological advancements and investment decisions, ensuring that the company remains at the forefront of AI innovation in the automotive sector [27][30].

Core Viewpoint - The article discusses the challenges and opportunities in the field of embodied intelligence and robotics, emphasizing the importance of collaboration among industry players to address technical difficulties and accelerate progress [3][25][48]. Group 1: Industry Challenges - The current state of robotics is characterized by diverse technical routes, leading to a lack of significant progress despite the apparent excitement in the field [4][25]. - Many robotics and chip manufacturers overlook the critical role of chips in robotics, which is essential for enhancing performance and reliability [16][18]. - The industry faces difficulties in deploying large-scale computing power in robots due to space constraints, battery capacity, and heat dissipation issues [20][21]. Group 2: Technological Developments - The goal of companies like Yushu Technology is to develop universal AI for robots that can perform various tasks in unfamiliar environments, akin to a "ChatGPT moment" for robotics [11][12]. - The development stages for achieving advanced robotic capabilities include fixed action demonstrations, real-time action generation, task execution in unfamiliar settings, and achieving high success rates in delicate operations [12]. - The future of embodied intelligence in robotics may involve using mobile phone chips, which could provide significant potential for innovation [24]. Group 3: Collaboration and Open Source - The article highlights the importance of open-sourcing models to foster collaboration and accelerate advancements in the field, similar to OpenAI's approach with earlier GPT models [28][29]. - Companies are encouraged to maintain an open attitude towards various models and collaborate with third parties to enhance development [30][31]. Group 4: AI and Agent Systems - The article discusses the role of agent systems in AI, emphasizing the need for end-cloud collaboration to improve user experience and privacy [35][36]. - The demand for end-side models is increasing, as they are crucial for understanding user needs and facilitating communication with cloud models [39][40]. - The industry lacks a unified standard for AI applications across different devices, leading to high development costs and fragmentation [48][50]. Group 5: Future Directions - The future of AI in robotics and other sectors will likely involve creating a cross-terminal operating system that integrates various services and enhances user experience [50][51]. - Collaboration among industry players is essential for building the necessary infrastructure and supporting innovation in smart devices [51].

Core Viewpoint - Alibaba Group's CEO emphasizes that AI will be the next generation operating system, with aspirations towards Artificial Superintelligence (ASI) [1] Group 1: AI in Automotive Industry - The automotive industry is increasingly integrating AI agents into smart cockpits, with companies like Li Auto, BYD, and NIO leading the charge [1][2] - Li Auto's AI agent can perform tasks such as ordering food through voice commands, showcasing the practical applications of AI in vehicles [1] - The goal is to create a closed-loop system for users that connects "people, cars, and life" through the vehicle's AI capabilities [2] Group 2: Functionality of AI Agents - AI agents in vehicles primarily assist with navigation, food ordering, and ride-hailing, but require external ecosystem connectivity to function effectively [3] - Li Auto's AI agent operates on two frameworks: CUA (Cockpit Using Agent) and MCP/A2A (Multi-Channel Processing/Agent-to-Agent), allowing for task execution through third-party applications [3][5] - CUA is the prevalent technical route, enabling multi-modal understanding and task execution via apps like Alipay [3] Group 3: Challenges and Solutions - CUA faces limitations, particularly in complex tasks where accuracy is low, with only about 30% success in multi-step tasks [4] - Li Auto proposes a solution by breaking down tasks into manageable steps, improving prediction accuracy for user actions [4] Group 4: Future Developments - The vision for Li Auto's AI agent includes "full memory" and "environmental perception," allowing the agent to remember user actions and recognize environmental cues [7] - Full memory encompasses user interactions and relationships, enabling the AI to proactively assist rather than just react [7] - Environmental perception is crucial for the AI to interact with real-world data, enhancing its ability to perform tasks effectively [8]

Core Viewpoint - Alibaba's CEO emphasizes that AI will be the next generation operating system, with a focus on advancing towards Artificial Superintelligence (ASI) [1] Group 1: AI in Automotive Industry - Several automotive companies, including Li Auto, BYD, and NIO, have introduced AI agents into their smart cockpits, allowing drivers to perform tasks like ordering food through voice commands [1] - Li Auto's AI agent aims to create a closed loop for users to interact with their vehicle and daily life, enhancing user experience beyond simple tasks [2] Group 2: Functionality of AI Agents - The initial applications of automotive AI agents include navigation, food ordering, and ride-hailing, primarily assisting daily consumer activities [4] - Li Auto's AI agent operates on two frameworks: CUA (Cockpit Using Agent) and MCP/A2A (Multi-Channel Processing/Agent-to-Agent), with CUA being the more common approach among automakers [4][7] - CUA involves multi-modal large model understanding tasks and executing them through apps, while MCP/A2A allows tasks to be delegated to third-party agents for efficiency [7] Group 3: Challenges and Solutions - CUA faces challenges in accurately completing complex tasks, with a reported success rate of only about 30% for multi-step tasks [5] - Li Auto proposes a solution by breaking down tasks into manageable steps, predicting user actions to improve accuracy [5] Group 4: Future Developments - Li Auto's Agent 2.0 framework emphasizes "full information memory" and "environmental perception," aiming to enhance user interaction and task execution [9] - Full information memory includes tracking all user actions and interactions, while environmental perception allows the AI to recognize and respond to real-world stimuli [9][10] - The integration of real-world data is crucial for AI to achieve higher levels of understanding and functionality, as highlighted by Alibaba's CEO [10]

Core Insights - Alibaba's CEO, Wu Yongming, asserts that AI will become the next generation operating system, with a focus on advancing towards Artificial Superintelligence (ASI) [1] - The market reacted positively to these statements, with Alibaba's stock rising over 6%, reaching its highest point since October 2021 [1] Group 1: AI Integration in Automotive Industry - Several automotive companies, including Li Auto, BYD, and NIO, have introduced AI agents into their smart cockpits, enabling features like voice-activated food ordering while driving [2] - The initial applications of these AI agents are relatively simple, focusing on navigation, food ordering, and ride-hailing, but the ultimate goal is to create a seamless "human-vehicle-life" interaction [2][3] - Li Auto's AI agent, "Li Xiang," aims to enhance its capabilities with environmental awareness and comprehensive memory, allowing for more complex interactions [2] Group 2: Technical Frameworks for AI Agents - Li Auto employs two frameworks for its AI agent: CUA (Cockpit Using Agent) and MCP/A2A (Multi-Channel Processing/Agent-to-Agent) [2][3] - CUA involves multi-modal large model understanding tasks and executing them through apps, while MCP/A2A allows the AI agent to delegate tasks to third-party agents for efficiency [3][4] - The accuracy of current AI agents in completing complex tasks is around 30%, indicating a need for improved predictive capabilities [3] Group 3: Future Developments in AI Capabilities - Li Auto's vision for its AI agent includes "full information memory," which encompasses user actions, environmental interactions, and semantic memory regarding relationships [5] - The AI agent is expected to not only remember user behaviors but also proactively assist by mimicking past actions, enhancing user experience [5] - Environmental perception is crucial for the AI agent, enabling it to recognize real-world cues and complete tasks autonomously [5][6] Group 4: Industry Perspectives on AI - Wu Yongming emphasizes that for AI to surpass human capabilities, it must continuously interact with the physical world to gather comprehensive data [6] - The advancement of autonomous driving technology is cited as an example of how AI learns from raw data to improve performance [6]