最近和很多准备校招的小伙伴接触，发现大家在学校学习的东西和工作的差距越来越大。有不少工作多年的小 伙伴表示也在看机会，感知转大模型、世界模型，传统规控想转具身。但却不知道业内实际在做什么，导致秋 招的时候没有什么优势。。。 博主一直在鼓励大家坚持、多多交流，但归根结底个人的力量是有限的。我们希望共建一个大的社群和大家一 起成长，真正能够帮助到一些有需要的小伙伴，成为一个汇集全行业人才的综合型平台，真正做一个链接学校 和公司的桥梁。所以我们也开始正式运营求职与行业相关的社群。社群内部主要讨论相关产业、公司、产品研 发、求职与跳槽相关内容。如果您想结交更多同行业的朋友，第一时间了解产业。欢迎加入我们！ 微信扫码添加小助理邀请进群，备注自驾+昵称+求职； ...

Core Viewpoint - WuXi AppTec is entering a growth phase, with significant revenue and profit increases expected in the first half of 2025, driven by its unique "integrated, end-to-end" CRDMO business model [4][5][21]. Financial Performance - WuXi AppTec anticipates a revenue of approximately RMB 20.799 billion for the first half of 2025, representing a year-on-year growth of about 20.64%, with core business revenue expected to grow by approximately 24.24% [4]. - The adjusted net profit is projected to be around RMB 6.315 billion, reflecting a year-on-year increase of approximately 44.43% [4]. - The company expects to achieve a net profit of approximately RMB 8.561 billion, which is a year-on-year increase of about 101.92%, largely due to the sale of equity in an associate company [4][11]. Market Reaction - Following the positive earnings forecast, WuXi AppTec's stock surged over 10% in the Hong Kong market, indicating strong investor confidence in the company's recovery and growth potential [5][20]. Business Model and Growth Drivers - The company's success is attributed to its focus on the "integrated, end-to-end" CRDMO model, which allows for a steady flow of early-stage projects converting into downstream projects [14][15]. - WuXi AppTec's order backlog exceeded RMB 40 billion for the first time, with a significant increase in orders expected to drive future revenue growth [8][15]. Regional and Sectoral Insights - The overseas market remains a key revenue driver for WuXi AppTec, with faster recovery in biotech financing compared to domestic markets [16]. - The company is expanding its capabilities in new molecular businesses, particularly in peptides and oligonucleotides, which are expected to be significant growth drivers in the coming years [16][19]. Capacity Expansion - WuXi AppTec is actively expanding its production capacity, with plans to increase its peptide solid-phase synthesis reactor volume significantly by the end of 2025 [18][19]. - The company is also investing heavily in global D&M capacity construction, with capital expenditures projected to reach RMB 7-8 billion in 2025 [19]. Future Outlook - With the global biopharmaceutical investment climate improving and the domestic innovative drug market remaining strong, WuXi AppTec is well-positioned for continued growth [21].

Core Viewpoint - The article discusses the differences between VLA (Vision-Language-Action) and end-to-end models in the context of autonomous driving, emphasizing the importance of large models and their applications in the industry [2]. Group 1: Job Descriptions and Requirements - Positions related to large model development, including VLA and end-to-end roles, are highlighted, with a focus on skills in fine-tuning, lightweight models, and deployment [2]. - The job of an end-to-end/VLA engineer involves developing and implementing driving systems, optimizing model structures, and constructing high-quality training datasets [6]. - The VLA/VLM algorithm position requires a master's degree in computer science or AI, with 3-5 years of experience in autonomous driving or AI algorithms, and proficiency in VLA/VLM architectures [8][10]. Group 2: Technical Skills and Experience - Candidates are expected to have experience with multimodal large language models, fine-tuning existing models for specific business scenarios, and familiarity with Transformer and multimodal technologies [5]. - Experience in computer vision, trajectory prediction, and decision planning is essential, along with a strong foundation in mainstream technologies and frameworks like PyTorch [9]. - The article emphasizes the need for candidates to have published papers in top conferences or achieved notable results in international competitions [9][11].

Core Viewpoint - The breakthrough in embodied intelligence will not occur in laboratories but in practical applications, indicating a shift from academic research to entrepreneurial ventures in the field [1][5]. Company Overview - Qianxun Intelligent was founded by Gao Yang, a chief scientist and assistant professor at Tsinghua University, and Han Fengtao, a veteran in the domestic robotics industry, to explore the potential of embodied intelligence [2][3]. - The company recently demonstrated its new Moz1 robot, capable of performing intricate tasks such as organizing office supplies [4][3]. Industry Trends - The development of embodied intelligence is currently at a critical scaling moment, similar to the advancements seen with large models like GPT-4, but it may take an additional four to five years for significant breakthroughs [2][29]. - There is a notable difference in the development of embodied intelligence between China and the U.S., with China having advantages in hardware manufacturing and faster repair times for robots [6][7]. Research and Development - Gao Yang transitioned from autonomous driving to robotics, believing that robotics offers more versatility and challenges compared to specialized applications like self-driving cars [10][12]. - The field of embodied intelligence is experiencing a convergence of ideas, with many previously explored paths being deemed unfeasible, leading to a more focused research agenda [12][13]. Technological Framework - Gao Yang defines the stages of embodied intelligence, with the industry currently approaching Level 2, where robots can perform a limited range of tasks in office settings [17][18]. - The preferred approach in the industry is end-to-end systems, particularly the vision-language-action (VLA) model, which integrates visual, linguistic, and action components into a unified framework [19][20]. Data and Training - The training of VLA models involves extensive data collection from the internet, followed by fine-tuning with real-world operation data and reinforcement learning to enhance performance [23][24]. - The scaling law observed in the field indicates that increasing data volume significantly improves model performance, with a ratio of 10-fold data increase leading to substantial performance gains [27][28]. Market Dynamics - The demand for humanoid robots stems from the need to operate in environments designed for humans, although non-humanoid designs may also be effective depending on the application [33][34]. - The industry is moving towards a model where both the "brain" (AI) and the "body" (robotic hardware) are developed in tandem, similar to the automotive industry, allowing for specialization in various components [39][41].

Industry Rating - The industry investment rating is maintained at "Outperform" [1] Core Insights - The report emphasizes the evolution of Apple from iPhone 1 to iPhone 4, highlighting how it reshaped the smartphone standard and established a composite profit system through hardware, services, and ecosystem integration [3][10] - The report discusses Li Auto's transition from range-extended vehicles to pure electric and AI integration, indicating a gradual enhancement of its competitive moat [4][34] Summary by Sections Apple Review - The iPhone 1 launched in 2007 revolutionized smartphones by changing interaction logic, functionality, design, and software ecosystem [10] - By 2024, iPhone products accounted for 51.45% of Apple's revenue, while services contributed 24.59% with a high gross margin of 73.9%, indicating a significant shift towards service profitability [3][16] Li Auto - Li Auto's product definition capabilities have redefined the home SUV market, with the Li ONE and the popular L series establishing a strong brand identity [4][23] - The company has successfully addressed key issues in the electric vehicle market, such as range anxiety and charging concerns, through its range-extended technology [4][33] - Li Auto is advancing towards L4 autonomous driving capabilities, with significant developments in AI and intelligent driving systems [34][38]

Core Viewpoint - The article discusses the advancements and innovations in the VLA (Vision Language Architecture) and its comparison with DeepSeek's MoE (Mixture of Experts), highlighting the unique approaches and improvements in model architecture and training processes. Group 1: VLA and MoE Comparison - Both VLA and MoE have been previously proposed concepts but are now being fully realized in new domains with significant innovations and positive outcomes [2] - DeepSeek's MoE has improved upon traditional models by increasing the number of specialized experts and enhancing parameter utilization through Fine-Grained Expert Segmentation and Shared Expert Isolation [2] Group 2: Key Technical Challenges for VLA - The VLA needs to address six critical technical points, including the design and training processes, 3D spatial understanding, and real-time inference capabilities [4] - The design of the VLA base model requires a focus on sparsity to expand parameter capacity without significantly increasing inference load [6] Group 3: Model Training and Efficiency - The training process incorporates a significant amount of 3D data and driving-related information while reducing the proportion of historical data [7] - The model is designed to learn human thought processes, utilizing both fast and slow reasoning methods to balance parameter scale and real-time performance [8] Group 4: Diffusion and Trajectory Generation - Diffusion techniques are employed to decode action tokens into driving trajectories, enhancing the model's ability to predict complex traffic scenarios [9] - The use of an ODE sampler accelerates the diffusion generation process, allowing for stable trajectory generation in just 2-3 steps [11] Group 5: Reinforcement Learning and Model Training - The system aims to surpass human driving capabilities through reinforcement learning, addressing previous limitations related to training environments and information transfer [12] - The model has achieved end-to-end trainability, enhancing its ability to generate realistic 3D environments for training [12] Group 6: Positioning Against Competitors - The company is no longer seen as merely following Tesla in the autonomous driving space, especially since the introduction of V12, which marks a shift in its approach [13] - The VLM (Vision Language Model) consists of fast and slow systems, with the fast system being comparable to Tesla's capabilities, while the slow system represents a unique approach due to resource constraints [14] Group 7: Evolution of VLM to VLA - The development of VLM is viewed as a natural evolution towards VLA, indicating that the company is not just imitating competitors but innovating based on its own insights [15]

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The report highlights the evolution of advanced driver assistance systems (ADAS) in China, focusing on the expansion of operational design domains (ODD), technological equity, safety concerns, and supportive policies [4][21][23] - It emphasizes the need for algorithm, data, and computing power upgrades to address safety shortcomings in high-level ADAS technologies [23][66] - The report discusses the transition from modular to end-to-end architectures in vehicle algorithms, aiming for human-like driving capabilities [66][68] Summary by Sections 1. Market Background - The expansion of high-level ADAS ODD is noted, with a focus on technological inclusivity and addressing accident anxiety through safety redundancies [4][21] - Policy support is highlighted as crucial for rational promotion of ADAS technologies [4][21] 2. Technology Insights - The report decodes the underlying logic of data, algorithms, and computing power in high-level ADAS [4][28] - It discusses the computing power landscape, noting the shift towards higher TOPS (trillions of operations per second) capabilities in vehicle and cloud computing [42][44] - Data challenges, including collection and positioning technologies, are identified as critical areas for development [4][28] 3. Competitive Analysis - The competitive landscape is analyzed, detailing the tiered structure of companies and their development strategies [29][30] - The report outlines various collaboration models among automotive manufacturers and technology providers, emphasizing the balance between self-research and external sourcing [83] 4. Trend Insights - The report notes the commercialization progress of passenger vehicle L3 systems, indicating a growing market for advanced ADAS [31][32] - It highlights the importance of continuous upgrades and iterations in ADAS functionalities to meet evolving consumer expectations and safety standards [82][83]

文 | 李安琪 编辑 | 李勤 36氪汽车从多个渠道获悉，前一汽南京研究院CTO陈光已经入职小米汽车，出任辅助驾驶感知负责人， 向小米辅助驾驶负责人叶航军汇报。小米此前的辅助驾驶感知负责人蔡锐，已经转到了机器人相关部 门。 加入小米汽车前，陈光是一汽南京研究院CTO，负责自动驾驶业务整体技术研发和构架设计，带领团队 开发了一汽红旗第三代L4级全无人Robotaxi（自动驾驶出租车）。一汽南京研究院是一汽集团旗下子公 司，但近期传出了团队解散的传闻。 陈光博士毕业于美国密苏里大学电子计算机系，拥有多年人工智能和计算机视觉研发经验，在人工智能 顶级会议CVPR等发表过多篇论文。陈光还曾在百度Apollo美国研发中心就职多年，曾担任感知系统技 术负责人。 作为2024年才正式发布首款车的造车新势力，小米本身需要投入更多资源，才能赶上辅助驾驶行业平均 水平。 小米也在不断吸纳高端技术人才。无论是去年加入的前图森CTO王乃岩，还是雷军亲自挖来的Wayve原 主任科学家陈龙，以及最新加入的陈光，小米辅助驾驶高端人才密度已然不低。 此前据36氪汽车了解，小米辅助驾驶团队规模已经达到1200人。 然而，辅助驾驶量产的工程化挑 ...

Core Insights - The automotive industry is transitioning towards more advanced autonomous driving technologies, moving beyond the simplistic "end-to-end" models that have been prevalent [2][3][25] - Companies are exploring new architectures and models, such as VLA and world models, to address the limitations of current systems and enhance safety and reliability in autonomous driving [4][14][25] Group 1: Industry Trends - Major players like Huawei, Li Auto, and Xpeng are developing unique software architectures to improve autonomous driving capabilities, indicating a shift towards more complex systems [4][5][14] - The introduction of new terminologies and models reflects a diversification in approaches to autonomous driving, with no clear standard emerging [4][25] - The industry is witnessing a split in technological pathways, with some companies focusing on L3 capabilities while others remain at L2, leading to a potential widening of the technology gap [25][26] Group 2: Data Challenges - The demand for high-quality data is critical for training large models in the new phase of autonomous driving, but companies face challenges in acquiring and annotating sufficient real-world data [15][22] - Companies are increasingly turning to simulation and AI-generated data to overcome data scarcity, with some suggesting that simulated data may become more important than real-world data in the future [22][23] Group 3: Competitive Landscape - The competition is intensifying as companies with self-developed capabilities advance towards more complex technologies, while others may rely on suppliers, leading to a concentration of orders among a few capable suppliers [26][27] - The shift towards L3 capabilities will require companies to focus not only on technology but also on operational aspects, as the responsibility for safety and maintenance will shift from users to manufacturers [25][26]

Core Viewpoint - The article discusses Li Auto's strategic focus on artificial intelligence (AI) and its evolution from a vehicle-centric AI assistant to a multi-platform intelligent application, emphasizing the importance of AI in future competitiveness [4][5][6]. Group 1: Strategic Meetings and AI Prioritization - Li Auto holds biannual closed-door strategy meetings to discuss future directions, with significant participation from top executives and industry leaders [3]. - Following a strategic meeting, Li Auto adjusted its AI-related business priorities, emphasizing the strategic importance of intelligent driving over other AI applications [4][5]. - The company aims to become a global leader in AI by 2030, with a clear focus on enhancing its AI capabilities and applications [5][6]. Group 2: Development of AI Capabilities - Li Auto has transitioned its AI assistant, "Li Xiang," from a vehicle-only application to a multi-platform tool, including mobile and web applications [7]. - The company has invested in self-developed algorithms, achieving a full switch to in-house technology for its AI functionalities by March 2023 [7][8]. - The introduction of the multi-modal cognitive model, Mind GPT 1.0, marks a significant advancement in Li Auto's AI capabilities [7]. Group 3: Intelligent Driving and Technological Advancements - Li Auto's intelligent driving system, AD Max, was launched to address product shortcomings and enhance competitive positioning in the market [10][11]. - The company has initiated a large-scale recruitment drive for its intelligent driving team, reflecting its commitment to advancing this technology [10]. - The shift towards an "end-to-end" model for intelligent driving aims to streamline processes and improve system performance through better data utilization [10][11]. Group 4: Organizational Changes and AI Integration - Li Auto established an AI Technical Committee to integrate AI capabilities across various business lines, enhancing collaboration and execution [15][16]. - The committee includes leaders from key departments, ensuring that AI is a core focus in strategic decision-making [16][17]. - The company aims to develop a foundational model that serves as a core capability for all AI projects, positioning itself as a leader in the automotive AI landscape [17].