完整早报音频，请点击标题下方小耳机收听 【星巴克作价40亿美元将其中国业务的60%出售给博裕资本，双方成立合资公司】 星巴克咖啡公司 宣布与博裕投资达成战略合作，双方将成立合资企业，共同运营星巴克在中国市场的零售业务。根据 协议，博裕将持有合资企业至多60%股权，星巴克保留40%股权，并将继续作为星巴克品牌与知识 产权的所有者和授权方，向新成立的合资企业进行授权。基于约40亿美元（不计现金与债务）的企业 价值，博裕将获得其相应权益。 据悉，新成立的合资企业将继续以上海为总部，管理并运营目前遍布 中国市场的8000家星巴克门店。秉持共同的发展愿景，双方将致力于未来将星巴克在中国的门店规 模逐步拓展至20000家。 （每日经济） 【奥尔特曼否认 OpenAI 明年上市】 在一档播客采访中， OpenAI CEO 奥尔特曼否认了有关于 OpenAI 计划明年上市的报道。他称上市没有具体的日期，董事会也没有就此做出任何决定。"我只是 觉得事情最终会朝着那个方向发展。"他还透露 OpenAI 的年收入远远超传闻中的 130 亿美元。 （ 每经网） 【威马汽车发文称好事将近】 11 月 3 日，威马汽车通过官微发文称，" ...

Core Insights - The announcement of the new e-commerce platform, "Autohome Mall," marks a significant strategic move for Autohome following its acquisition by Haier's subsidiary, Kataychi, which became the controlling shareholder just over two months ago [1][2] - Autohome aims to transform from a content platform to a comprehensive service provider, integrating content, tools, and transaction services to create a closed-loop ecosystem for car-related services [1][4] Company Developments - Autohome has officially launched its e-commerce platform, attracting 15 mainstream automotive brands to join, indicating a strong commitment to building a new digital car-buying ecosystem [1][5] - The integration of Kataychi's resources and Autohome's O2O (Online to Offline) capabilities is expected to enhance the overall service offerings and operational efficiency [2][3] Industry Trends - The automotive market is undergoing a transformation, shifting from traditional offline sales to a model that deeply integrates online and offline experiences, driven by changing consumer behaviors and the rise of e-commerce [4][5] - Autohome's new strategy is positioned as a response to the evolving retail landscape in the automotive sector, aiming to optimize user experience and provide a reliable one-stop shopping experience for consumers [5][6] Strategic Vision - The leadership of Kataychi sees Autohome as a key hub in Haier's automotive ecosystem, focusing on user needs and exploring the concept of "home-car interconnectivity" [2][3] - Autohome's management emphasizes the importance of collaboration with partners like Kataychi and Ping An Insurance to expand into emerging business areas and enhance service delivery [5][6]

自今年8月，海尔战略入股汽车之家，资方持续深化汽车产业生态布局，大力支持汽车之家发展新零售 业务。汽车之家商城将正式上线，也代表着汽车之家从"内容平台"正式升级为"内容生态+工具服务+交 易平台"一站式汽车生活服务提供商，实现"看车-买车-用车-换车"的生态闭环。 据介绍，作为集团"新零售"战略下的核心落地项目，汽车之家商城旨在优化用户体验，通过一站式、可 信赖的购车体验，实现"让好车 更好买"的核心价值。商城全面整合汽车上下游优势资源，打通"看、 选、买、用、换"的线上线下闭环。商城车源均由品牌官方提供。同时，从交付订金、购车消费到后期 质保，都将由平台全面保障。解决了传统购车信息不透明、价格不透明、交易流程不透明痛点。 汽车之家商城首批品牌也于今日正式入驻。涵盖零跑、比亚迪、阿维塔在内的15家主流汽车品牌、新势 力品牌及优质二手车服务商，携手汽车之家探索可持续发展的"新零售"格局。汽车之家新零售业务总经 理于乐表示："从流量场到交易场，汽车之家商城在重塑电商的产业半径。我们以创新模式粘合汽车产 业链各方，共同推动汽车零售模式的深度变革，助力汽车产业高质量、数字化蓬勃发展。" 新浪科技讯 11月3日晚间消息 ...

Group 1 - Changan Automobile and EHang have established a new technology company in Chongqing, focusing on smart unmanned aerial vehicles and AI development, indicating a positive outlook on the future technology market [1][2] - The new company aims to enhance Changan's competitiveness in smart transportation and robotics, potentially attracting capital market interest in drone and related technology applications [1][2] Group 2 - Li Auto and Sunwoda have formed a joint venture, Zhejiang Li Auto Battery Co., Ltd., which will focus on battery manufacturing and emerging energy technology, enhancing Li Auto's self-sufficiency in electric vehicle core components [2] - This collaboration is expected to drive technological upgrades and cost optimization across the battery supply chain, boosting investor confidence in the electric vehicle sector [2] Group 3 - Stellantis has partnered with NVIDIA, Uber, and Foxconn to explore the development and deployment of L4 autonomous vehicles for ride-hailing services, marking a new phase in automotive industry collaboration [3] - The partnership leverages NVIDIA's computing power, Foxconn's manufacturing capabilities, and Uber's data, potentially reshaping the valuation of the smart driving supply chain and benefiting sensor and high-precision mapping sectors [3] Group 4 - Autohome has responded to criticisms regarding its extreme range testing of new vehicles, emphasizing the professionalism and objectivity of its testing methods, which may enhance its credibility in the industry [4] - The incident highlights the growing importance of transparency in testing standards and procedures, potentially influencing automakers' strategies regarding range commitments [4]

Core Viewpoint - The article discusses the winter range testing results of popular electric SUVs, specifically the Xiaomi YU7, Li Auto i6, and Tesla Model Y, highlighting the methodologies and outcomes of the tests conducted by Autohome [1][3]. Group 1: Testing Methodology - The testing method involved using the most energy-efficient driving mode without affecting daily driving, with a focus on maximum energy recovery and a controlled environment [1]. - Data calculation for range achievement was defined as the ratio of measured standard range plus safety reserve range to the official CLTC range, expressed as a percentage [1]. - The testing scenario took place on Beijing's Fifth Ring Road under typical commuting conditions, with temperatures ranging from 3 to 13 degrees Celsius [1]. Group 2: Test Results - Xiaomi YU7 achieved a measured standard range of 687 km and a safety reserve of 11 km, resulting in a final range achievement rate of 83.59% [3]. - Li Auto i6 recorded a standard range of 573.9 km and a safety reserve of 16.6 km, leading to a range achievement rate of 82.01% [3]. - Tesla Model Y had a standard range of 469.8 km and a safety reserve of 16.1 km, with a final range achievement rate of 81.94% [3]. Group 3: Public Response and Criticism - Xiaomi's founder, Lei Jun, shared the winter testing data, emphasizing that the Xiaomi YU7 had the highest range and achievement rate [3]. - Automotive reviewer Chen Zhen expressed skepticism about the winter testing conditions, arguing that testing at 10 degrees Celsius may not accurately represent winter conditions [3].

Core Viewpoint - The board of directors of Autohome Inc. (02518.HK) will hold a meeting on November 6, 2025, to approve the unaudited financial results for the three months ending September 30, 2025, and to discuss the declaration and payment of cash dividends, if any [1] Group 1 - The board meeting is scheduled for November 6, 2025, at Hong Kong time [1] - The financial results will cover the period ending September 30, 2025 [1] - The results will be announced after the Hong Kong Stock Exchange trading hours and before the opening of the U.S. stock market [1]

本公司將於2025年11月6日（星期四）晚上八時（香港時間）舉行業績電話會議，討論該業 績並回答問題。出席會議前，請使用以下提供的註冊鏈接註冊。註冊後，各與會者將收 到一組用以參與電話會議的與會者撥入號碼及個人識別碼。 香港交易及結算所有限公司及香港聯合交易所有限公司對本公告的內容概不負責，對其準確性或完整性亦不發表 任何聲明，並明確表示概不會就本公告全部或任何部分內容而產生或因倚賴該等內容而引致的任何損失承擔任何 責任。 Autohome Inc. 汽車之家* （於開曼群島註冊成立的有限公司） （股份代號：2518） 董事會會議日期及 2025 年第三季度 業績公告刊發日期 Autohome Inc.（「本公司」）董 事 會（「董事會」）將 於2025年11月6日（星 期 四）（香 港 時 間） 舉行董事會會議，並將於會上批准本公司及其附屬公司截至2025年9月30日止三個月 的 未 經 審 計 財 務 業 績（「業 績」）及 公 告 以 及 宣 派 及 派 付 現 金 分 紅（如 有）等 議 案。本 公 司將於2025年11月6日（星期四）（香港時間）香港聯合交易所有限公司交易時段後及美 國股市開市前於香 ...

Core Viewpoint - Autohome Inc. will report its financial results for Q3 2025 on November 6, 2025, before U.S. markets open, indicating the company's ongoing commitment to transparency and communication with investors [1]. Group 1: Financial Reporting - The financial results for the third quarter ended September 30, 2025, will be announced before the U.S. markets open on November 6, 2025 [1]. - An earnings conference call is scheduled for 7:00 AM U.S. Eastern Time on the same day, allowing stakeholders to engage directly with the management team [2]. Group 2: Conference Call Details - Participants must register in advance to join the earnings conference call, receiving dial-in numbers and a personal PIN upon registration [2]. - A live and archived webcast of the conference call will be available on Autohome's investor relations website, ensuring accessibility for all interested parties [3]. Group 3: Company Overview - Autohome Inc. is recognized as the leading online platform for automobile consumers in China, focusing on reducing decision-making and transaction costs through advanced technology [4]. - The company offers a variety of content types, including user-generated and AI-generated content, and provides extensive automobile listing information, covering the entire car purchase and ownership cycle [4]. - Autohome's services extend to dealer subscriptions and advertising, enabling dealers to market their inventory effectively and generate sales leads [4]. - The company operates "Autohome Mall," a full-service online transaction platform, and offers additional services such as auto financing, insurance, used car transactions, and aftermarket services [4].

Core Insights - The article emphasizes the importance of Post-Training as a transformative approach in AI, moving beyond simple model optimization to creating specialized intelligent engines tailored to specific business needs [1][4] - The evolution of Post-Training technology is highlighted, showcasing a shift from Supervised Fine-Tuning (SFT) to Reinforcement Learning (RL) methodologies, which better align with complex business requirements [2][4] Summary by Sections Post-Training Evolution - The initial approach in the industry was SFT, which allowed models to learn specific domain knowledge and dialogue styles [2] - However, SFT was insufficient for teaching models complex value judgments and strategic choices, which are critical in real business scenarios [3] - The focus has shifted to RL, evolving from human-dependent methods (RLHF) to automated systems (RLVR) and the innovative use of Natural Language Rewards [4][5] Implementation Pathway - The article outlines a four-step pathway for enterprises to implement Post-Training effectively, addressing challenges such as data quality, high labeling costs, and defining reward signals [5][8] - Successful case studies from companies like Zhihu, AutoHome, and Weibo illustrate practical applications of these steps, showcasing improvements in data quality and model performance [7][8] Step 1: Data Preparation - High-quality data is identified as the cornerstone of successful Post-Training, with companies spending 60-70% of their time on data preparation [10] - Zhihu and AutoHome have developed methods to enhance data quality through pre-labeling and structured data utilization, respectively [11][13] Step 2: Model Selection - Choosing the right base model is crucial, with many companies opting for the Tongyi Qianwen series due to its performance and support for Post-Training [14][16] - The model's architecture and open-source ecosystem facilitate easier implementation of Post-Training techniques [15][18] Step 3: Reward Mechanism Design - The design of a reward mechanism is essential for aligning model outputs with business objectives, transitioning from human feedback to automated verification systems [24][25] - Companies like Yingmi Fund are exploring ways to integrate expert decision-making frameworks into their models to enhance performance [26] Step 4: Evaluation System - A robust evaluation system is necessary to measure the effectiveness of Post-Training, with Yingmi Fund developing benchmarks to assess model performance in real-world scenarios [27][28] - Successful implementations have led to significant improvements in model accuracy and business outcomes, as seen in the case of Baifeng Cloud and Quark [30][32] Conclusion - The article concludes that the true competitive advantage in AI lies in how companies leverage their unique data and business insights through Post-Training to create proprietary intelligent engines [32]

EF001 免責聲明 | 香港交易及結算所有限公司及香港聯合交易所有限公司對本公告的內容概不負責，對其準確性或完整性亦不發表任何聲明，並明確表示，概不對因 | | | --- | --- | | 公告全部或任何部份內容而產生或因倚賴該等內容而引致的任何損失承擔任何責任。 | | | 股票發行人現金股息公告 | | | 發行人名稱 | Autohome Inc. | | 股份代號 | 02518 | | 多櫃檯股份代號及貨幣 | 不適用 | | 相關股份代號及名稱 | 不適用 | | 公告標題 | 宣派及派付現金分紅 | | 公告日期 | 2025年9月30日 | | 公告狀態 | 新公告 | | 股息信息 | | | 股息類型 | 其他 | | 普通分紅 | | | 股息性質 | 普通股息 | | 財政年末 | 不適用 | | 宣派股息的報告期末 | 不適用 | | 宣派股息 | 每 股 0.1475 USD | | 股東批准日期 | 不適用 | | 香港過戶登記處相關信息 | | | 派息金額及公司預設派發貨幣 | 每 股 0.1475 USD | | 匯率 | 1 USD : 1USD | | 除淨日 | ...