六、关于本次发行上市及"全流通"：（1）请说明全额行使超额配售权后的预计募集资金量；（2） 请说明本次拟参与"全流通"股东所持股份是否存在被质押、冻结或其他权利瑕疵的情形。 三、关于业务经营:（1）请说明你公司及下属公司经营范围包含"增值电信业务；市场调查；测绘 服务；利用自有媒体发布广告；广告制作；广告发布；广告设计、代理"的具体情况，是否实际开展相 关业务及具体运营情况，是否取得必要的资质许可，是否与第三方开展合作以及合作方名称（合作方是 否持有资质）、合作方式，是否涉及《外商投资准入特别管理措施（负面清单）（2024年版）》限制或 禁止领域，本次发行上市及"全流通"后是否持续符合外商投资准入要求；（2）请说明你公司子公司斑 智云图《试点增值电信业务经营许可证》的办理进展及拟从事相关业务的具体内容；（3）请以通俗易 懂的语言详述业务模式及涉及大语言模型的具体情况，并说明是否完成相关大模型备案。 四、关于规范运作:（1）请说明你公司及下属公司是否涉及开发、运营网站、小程序、APP、公众 号等产品，是否涉及向第三方提供信息内容，提供信息内容的类型以及信息内容安全保护措施；同时说 明收集及储存的用户信息规模，数 ...

Core Insights - The article discusses the evolution and potential of AI applications in the Chinese e-commerce sector, particularly focusing on Alibaba's Taobao platform and its integration of AI technologies to enhance user experience and operational efficiency [2][3][8]. Group 1: AI Application in E-commerce - The prediction of a super AI application with over 100 million DAU in China was overly optimistic, as the largest current application, Doubao, only reached 47 million DAU [2]. - The article emphasizes that the fundamental needs of users in e-commerce remain unchanged, with AI providing new methods to meet these needs [8]. - Taobao's AI initiatives focus on three main areas: improving underlying technology for better data processing, providing AI tools for merchants to enhance efficiency, and creating innovative AI-driven shopping experiences for users [3][15]. Group 2: User Experience and AI Integration - Taobao aims to integrate AI seamlessly into the user journey, allowing users to solve problems without needing to understand the underlying AI technology [7][10]. - The AI products developed by Taobao are designed to address specific user needs, such as AI fitting rooms and personalized recommendations, enhancing the overall shopping experience [9][18]. - The article highlights the importance of understanding user intent and improving product data quality to enhance search and recommendation systems [12][16]. Group 3: Operational Efficiency and Merchant Support - Taobao's AI initiatives have led to significant operational improvements, such as automating the generation of images and providing AI customer service, saving substantial costs for merchants [18]. - The platform's focus is on helping merchants reduce operational costs while improving the quality of product data, which in turn benefits the overall ecosystem [17]. - The integration of AI into various operational aspects aims to enhance efficiency and drive sales growth for merchants, ultimately benefiting the platform itself [15][17].

新浪财经ESG评级中心提供包括资讯、报告、培训、咨询等在内的14项ESG服务，助力上市 公司传播ESG理念，提升ESG可持续发展表现。点击查看【 ESG评级中心服务手册】 2025可持续全球领导者大会于10月16日-18日在上海市黄浦区世博园区召开。上海交通大学上海高级金 融学院副院长、金融学教授朱宁对话科技预言家、《2049》作者、《连线》杂志创始主编凯文·凯利， 共同探讨AI技术在中国语境下的落地与实践。 以下为对话实录： 朱宁：首先，感谢凯文·凯利先生您分享的非常棒的观点以及对未来的想象。 凯文·凯利：我觉得它使得人的能力更加强大。我成长的时候有一个恐惧，计算器会终结人类做算术的 过程和历史，即取代人类的价值，但有一点很明确，计算器可以让算术变得更加快。讲到最后，尤其用 于教育的AI，它会加速学习能力的上升以及学习速度的增加，同时可以扩充学生知识学习的范围以及 能力的提升。 朱宁：过去十年中，因为很多人都提到了AI，您觉得AI是不是成长速度或范围已经超过您的想象了？ 还是说它增长得没有您想象得那么好。 凯文·凯利：感谢您的邀请，我非常荣幸来到这里，我非常喜欢来到中国上海，我非常喜欢上海的变 化。 朱宁： ...

Core Viewpoint - The focus of academia and industry is shifting towards VLA (Visual Language Action), which provides human-like reasoning capabilities for more reliable and safer autonomous driving [1][4]. Summary by Sections Overview of Autonomous Driving VLA - Autonomous driving VLA can be categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA [1]. - Traditional perception methods like BEV (Bird's Eye View) and lane detection are becoming mature, leading to decreased attention from both academia and industry [4]. Key Content of Autonomous Driving VLA - Core components of autonomous driving VLA include visual perception, large language models, action modeling, large model deployment, and dataset creation [7]. - Cutting-edge algorithms such as Chain-of-Thought (CoT), Mixture of Experts (MoE), Retrieval-Augmented Generation (RAG), and reinforcement learning are at the forefront of this field [7]. Course Structure - The course titled "Autonomous Driving VLA and Large Model Practical Course" includes detailed explanations of cutting-edge algorithms in the three subfields of autonomous driving VLA, along with practical assignments [8]. Chapter Summaries 1. **Introduction to VLA Algorithms** - This chapter provides a comprehensive overview of VLA algorithms, their concepts, and development history, along with open-source benchmarks and evaluation metrics [14]. 2. **Algorithm Fundamentals of VLA** - Focuses on foundational knowledge of Vision, Language, and Action modules, and includes a section on deploying and using popular large models [15]. 3. **VLM as an Autonomous Driving Interpreter** - Discusses the role of VLM (Visual Language Model) in scene understanding and covers classic and recent algorithms like DriveGPT4 and TS-VLM [16]. 4. **Modular & Integrated VLA** - Explores the evolution of language models from passive descriptions to active planning components, emphasizing the direct mapping from perception to control [17]. 5. **Reasoning-Enhanced VLA** - Focuses on the trend of integrating reasoning modules into autonomous driving models, highlighting the parallel output of control signals and natural language explanations [18]. 6. **Capstone Project** - Involves practical tasks starting from network construction, allowing participants to customize datasets and fine-tune models, emphasizing hands-on experience [21]. Learning Outcomes - The course aims to advance the understanding of autonomous driving VLA in both academic and industrial contexts, equipping participants with the ability to apply VLA concepts in real-world projects [23]. Course Schedule - The course is set to begin on October 20, with a duration of approximately two and a half months, featuring offline video lectures and online Q&A sessions [24]. Prerequisites - Participants are expected to have a foundational knowledge of autonomous driving, familiarity with transformer models, reinforcement learning, and basic mathematical concepts [25].

二、请说明你公司是否存在应办理国有股东标识但尚未完成的情况，并请律师对你公司是否存在国有股 东出具明确结论性意见。 三、关于业务经营：(1)请说明你公司及下属公司经营范围包含"增值电信业务;市场调查;测绘服务;利用 自有媒体发布广告;广告制作;广告发布;广告设计、代理"的具体情况，是否实际开展相关业务及具体运 营情况，是否取得必要的资质许可，是否与第三方开展合作以及合作方名称(合作方是否持有资质)、合 作方式，是否涉及《外商投资准入特别管理措施(负面清单)(2024年版)》限制或禁止领域，本次发行上 市及"全流通"后是否持续符合外商投资准入要求;(2)请说明你公司子公司斑智云图《试点增值电信业务 经营许可证》的办理进展及拟从事相关业务的具体内容;(3)请以通俗易懂的语言详述业务模式及涉及大 语言模型的具体情况，并说明是否完成相关大模型备案。 智通财经APP获悉，10月18日，中国证监会公布境外发行上市备案补充材料要求(2025年10月12日至 2025年10月17日)，其中提到，要求斑马智行补充说明公司股权变动、业务经营等事项。据港交所8月20 日披露，斑马智行向港交所主板提交上市申请书，德意志银行、中金公司 ...

Core Insights - The 2025 Weibo V Influence Conference highlighted significant changes in Weibo's video consumption strategy, shifting from a focus on playback volume to watch time, aiming to enhance user experience and content quality [1][2] User Engagement Metrics - As of June this year, Weibo reported 588 million monthly active users (MAU) and 261 million daily active users (DAU), with an average of 105 million posts, 63 million comments, and 197 million likes per day [1] - The user demographic is shifting, with 22.9% of MAU aged 31-40 and 70.7% of active users under 30 years old, while 31.5% of users are from tier 4 cities and below [1] Creator Ecosystem - By September, Weibo's "Golden Orange V" creators reached 125,000, with 18,000 "Golden V" authors and a significant 57% year-on-year increase in "Orange V" authors to 107,000 [1] Video Content Strategy - Weibo has revamped its video distribution policy to prioritize watch time over playback volume, focusing on incentivizing mid-to-long videos (over 1 minute) to improve content quality and consumption duration [2] - The production of "high-quality videos" (over 30 seconds with a quality score above 3) increased by 51% year-on-year in Q3, with overall video watch time growing by 12% [2] Recommendation Algorithm - Weibo has integrated large language models into its recommendation algorithms, enhancing content understanding and user experience by balancing user interests and historical preferences [3] - The recommendation system operates on three main engines: interest, social, and trending topics, facilitating user engagement and content discovery [3][4] Social Recommendation Importance - Social recommendations are emphasized, leveraging social relationships to provide more personalized content, including indirect and complex relationships among users [4]

Group 1 - Suno, an AI music generation company, is negotiating to raise over $100 million at a valuation exceeding $2 billion, which has quadrupled from its previous valuation. The company has an annual recurring revenue exceeding $100 million and is currently resolving legal disputes with major record labels [2] - As of June 2025, the user base for generative artificial intelligence in China is projected to reach 515 million, an increase of 266 million users from December 2024, effectively doubling the user base in six months, with a penetration rate of 36.5% [2] - Apple CEO Tim Cook announced that Apple Intelligence is working to enter the Chinese market, emphasizing the transformative impact of artificial intelligence on people's lives [2] - Zhang Fan, former COO of Zhiyun AI, has launched a startup focused on developing large language models for specific tasks, having completed an angel round of financing with no significant premium in valuation [2]

Core Insights - The focus of academia and industry is shifting towards VLA (Vision-Language-Action) for enhancing autonomous driving capabilities, providing human-like reasoning in vehicle decision-making processes [1][4] - Traditional methods in perception and lane detection are becoming mature, leading to a decline in interest, while VLA is seen as a critical area for development by major players in the autonomous driving sector [4] Summary by Sections Introduction to VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, which are essential for improving the reliability and safety of autonomous driving [1][4] Course Overview - A comprehensive learning roadmap for VLA has been designed, covering principles to practical applications, with a focus on core areas such as visual perception, large language models, action modeling, and dataset creation [6] Course Content - The course includes detailed explanations of cutting-edge algorithms like CoT, MoE, RAG, and reinforcement learning, aimed at deepening understanding of autonomous driving perception systems [6] Course Structure - The course is structured into six chapters, each focusing on different aspects of VLA, including algorithm introduction, foundational algorithms, VLM as an interpreter, modular and integrated VLA, reasoning-enhanced VLA, and a final project [12][20] Chapter Highlights - Chapter 1 provides an overview of VLA algorithms and their development history, along with benchmarks and evaluation metrics [13] - Chapter 2 delves into foundational algorithms related to Vision, Language, and Action, and discusses the deployment of large models [14] - Chapter 3 focuses on VLM's role as an interpreter in autonomous driving, covering classic and recent algorithms [15] - Chapter 4 discusses modular and integrated VLA, emphasizing the evolution of language models in planning and control [16] - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action output [17] - Chapter 6 involves a hands-on project where participants will build and fine-tune their own VLA models [20] Learning Outcomes - The course aims to provide a deep understanding of current advancements in VLA, covering three main subfields: VLM as an interpreter, modular & integrated VLA, and reasoning-enhanced VLA [24] - Participants will gain insights into key AI technologies such as visual perception, multimodal large models, and reinforcement learning, enabling them to apply their knowledge in practical projects [24]

Core Insights - The article emphasizes that AI agents are not standalone products but rather catalysts for business processes, urging a shift in perspective on their role in technology [1][12]. Group 1: Understanding AI Agents - AI agents are defined as large language model (LLM) agents, which can be simplified to LLM + tools + memory, highlighting their foundational components [1]. - The development of approximately 300 AI agents has provided insights into effective methods and future directions in the field [1][2]. Group 2: Frameworks and Development - The importance of focusing on core application processes rather than being limited by specific frameworks is highlighted, with various frameworks like crewai, dspy, and langgraph being utilized [3]. - A solid software engineering foundation is deemed essential for effectively utilizing AI agents, as the role often involves API calls and prompt engineering rather than advanced AI/ML skills [4]. Group 3: Context and Tools - The quality of an AI agent is significantly influenced by the context provided, including prompts, tools, and memory, rather than solely relying on the capabilities of the language model [5]. - Tools are essential for AI agents to function effectively; without them, agents become ineffective [6][7]. Group 4: Design and Evaluation - Simplicity in design is crucial for effective AI agents, with successful agents often having clear prompts, defined tools, and specific responsibilities [8]. - The importance of establishing testing and feedback loops is emphasized as a means to differentiate between toy projects and reliable production systems [9]. Group 5: Future Directions and Cultural Aspects - DSPy is identified as a promising framework for developing AI agents, with its features being user-friendly and intuitive [10]. - The collaboration with startups has underscored that the human element, including a culture of experimentation and clear vision, is more critical than technology alone [11]. - The development of AI agents is still in its early stages, with potential for integration into various products to enhance workflows and user experiences [12].

Core Viewpoint - The article emphasizes the advancements in autonomous driving technology by Li Auto, focusing on innovative solutions to enhance safety, efficiency, and sustainability in transportation [1]. Group 1: Autonomous Driving Technologies - The company is developing a large language model (LLM) to interpret complex driving scenarios, enabling smarter and quicker responses from autonomous vehicles [2]. - A world model project aims to simulate real driving environments for testing and improving autonomous driving algorithms under various conditions [3]. - The 3D geometric scene (3DGS) understanding project focuses on creating detailed 3D maps of urban environments to enhance the perception systems of autonomous vehicles for better navigation and decision-making [4]. - The company is pioneering an end-to-end neural network model that simplifies the entire processing flow from perception to execution in autonomous driving systems [5]. Group 2: Research and Development Projects - DriveVLM is a dual-system architecture combining end-to-end and vision-language models for autonomous driving [7]. - TOP3Cap is a dataset that describes autonomous driving street scenes in natural language, containing 850 outdoor scenes, over 64,300 objects, and 2.3 million textual descriptions [7]. - StreetGaussians presents an efficient method for creating realistic, dynamic urban street models for autonomous driving scenarios [8]. - DiVE is a model based on the Diffusion Transformer architecture that generates videos consistent in time and multiple perspectives, matching given bird's-eye view layouts [8]. - GaussianAD utilizes sparse and comprehensive 3D Gaussian functions to represent and convey scene information, addressing the trade-off between information completeness and computational efficiency [8]. - 3DRealCar is a large-scale real-world 3D car dataset containing 2,500 cars scanned in 3D, with an average of 200 dense RGB-D views per car [8]. - DriveDreamer4D employs a video generation model as a data machine to create video data of vehicles executing complex maneuvers, supplementing real data [8]. - DrivingSphere combines 4D world modeling and video generation technologies to create a generative closed-loop simulation framework [8]. - StreetCrafter is a video diffusion model designed for street scene synthesis, utilizing precise laser radar data for pixel-level control [8]. - GeoDrive generates highly realistic, temporally consistent driving scene videos using 3D geometric information [10]. - LightVLA is the first adaptive visual token pruning framework that enhances the success rate and operational efficiency of robot VLA models [10].