原文来自微博用户肉鸡Frank 原文链接： https://weibo.com/2235289970/Q3TWbjjOS 25年9月10日： 下面详细说下为什么我说公开道路vl 能力上车后撤梯子板上钉钉。 -VLApreview3 根手指≈ADS3.3 -VLApreview4 根手指≈ADS3.3.2 ADS3.0 到现在迭代花了小一年，这玩意只花了不到一个月。而且测试全世界模型。 1，action 部分夸张迭代速度 熟悉我的都知道，ADS 所有版本我都开过 下面是凭印象对比 vla 和ADS 历史版本 -VLApreview≈ADS3.0 （i8 上的全量，上个月媒体试的版本） 真的特别像去年刚开始的 ADS，安心够，但怂急刹多，特别墨迹。当时我还各种嘲笑 ADS 博弈不如 400 万 clips，丝滑也不如。可惜理想从 1 月份就放弃端到端，从 1 月份到现在，ADS 都是吊打理想 -VLApreview1根手指≈ADS3.1 -VLApreview2 根手指≈ADS3.2 25年8月18日肉鸡Frank表示" 3.1 秀的是速度，重点是【模型即智能体】有了 3.1 这个速度基础， 10 月份 的【长程 ...

Group 1 - The core viewpoint of the article highlights the progress of the company's supercharging station construction, with a current total of 3203 stations built, moving towards a target of over 4000 stations by the end of 2025 [1] - The completion rate for new supercharging stations this year has increased from 64.85% to 64.94%, indicating a steady advancement towards the annual goal [1] - There are 797 stations remaining to be built to meet the 2025 target, with an average of 6.99 stations needed to be completed daily over the remaining 114 days of the year [1] Group 2 - Two new supercharging stations have been established in Zhejiang Province and Yunnan Province, each with a configuration of 4C × 6 [1]

Core Viewpoint - The article discusses the delivery guidance and performance of Li Auto for the third quarter of 2025, highlighting the challenges in meeting delivery targets and the implications for future performance [1][5][6]. Delivery Guidance and Performance - Li Auto's delivery guidance for Q3 2025 is set between 90,000 and 95,000 units, with July and August deliveries recorded at 30,731 and 28,529 units respectively [1]. - As of early September, Li Auto has delivered 6,100 units, indicating that the remaining weekly average deliveries need to be between 7,499 and 9,021 units to meet the lower and upper guidance limits respectively [1]. - Cumulative deliveries from January to August total 263,198 units, suggesting that if September meets the lower guidance, total deliveries for the first nine months would reach 293,938 units [1]. Historical Context and Challenges - Historically, Li Auto has only exceeded weekly deliveries of 13,000 units six times, with the highest two-week total being 28,020 units [4]. - The article notes that achieving a monthly average of 5.8 to 6.0 thousand units is essential for meeting the delivery guidance, which Li Auto may not be able to sustain in June 2025 [5]. - If Li Auto does not update its delivery guidance, it would mark the third instance in history where the company fails to meet quarterly delivery targets [5]. Recent Performance Metrics - For the weeks of May 26 to June 15, 2025, Li Auto's weekly deliveries were recorded at 12,020, 8,270, and 7,874 units respectively [2]. - A high estimate for the delivery volume from June 1 to June 15 suggests that the company would need to maintain a weekly average of 14,160 units for the remaining days to meet the June delivery guidance [3]. Conclusion - The analysis indicates that Li Auto faces significant challenges in meeting its delivery targets for Q3 2025, with historical performance metrics suggesting that achieving the necessary delivery volumes may be difficult [1][5][6].

视频： 建议读者观看原视频，这块的表现力的确视频大于文字 文字压缩版： 理想i8基于第一性原理，旨在解决大型纯电SUV普遍存在的怪圈：为追求空间而增大车身 -> 重量和 风阻增加 -> 续航下降 -> 不得不增加电池 -> 成本和车重再次增加，形成恶性循环。 理想i8通过从底层架构出发，极致压榨机械占用空间，将每一寸节省都还给乘客，实现"空间真正围 绕人服务"。 独特设计与技术亮点 1. 原生纯电平台与极致的机械结构压缩 基础架构：采用全新的原生纯电平台，布局极其简洁、规整，为乘员舱留出最大化的平整空间，没有 传统燃油车或油改电车型因发动机、变速箱、传动轴等造成的空间侵占和地板凸起。 短车头设计：以上设计最终实现了标志性的短前悬和子弹头式车头，同时通过优化的安全结构（三条 力传递路径）保证了碰撞安全。 后舱： 完全自研后电驱系统：为同时满足性能和空间需求，理想自研了从碳化硅芯片到整体结构的后电驱总 成。 原创三明治布局：将电机、减速器、控制器在水平线上并排布置，而非行业常见的垂直堆叠，极大地 释放了垂直空间，为第三排带来了舒展的坐高和头部空间。 分离式空气弹簧：将后桥的空气弹簧拆分布置，避免侵占横向宽度，保证 ...

25年8月30日理想发布 OmniReason:A Temporal-Guided Vision-Language-Action Framework for Autonomous Driving 最大的创新点是通过一个包含大数据集和新模型的闭环，将人类的先验驾驶知识和时序因果链条，通 过知识蒸馏的方式注入到模型中，让自动驾驶的行为不仅安全可靠，而且可解释、更像人。 三大核心贡献: 发布两个时空VLA数据集：OmniReason-nuScenes和OmniReason-Bench2Drive。 这两个大规模数据集富含密集的时空标注和自然语言形式的因果解释。与DRAMA、DriveLM等现有 数据集相比，OmniReason在多视角图像、时序数据、因果推理支持以及天气和道路类型的多样性上 实现了更全面的覆盖。 香港中文大学广州的Jun Ma为通讯作者，香港中文大学的Pei Liu与理想的Qingtian Ning为共同一作。 理想OmniReason是一个提升自动驾驶系统智能性与可靠性的VLA框架。传统方法主要聚焦于静态场 景的理解，类似看图说话，忽略了真实驾驶中至关重要的时间维度和决策背后的因果逻辑，导致其在 ...

2025年9月7日马斯克X表示： 今天刚和特斯拉AI5芯片设计团队进行了一次很棒的设计评审!这将会是一款史诗级的芯片。而接下来 的AI6有望成为迄今为止最好的AI芯片。 从两种芯片架构切换到一种，意味着我们所有的硅片人才都将专注于打造一款令人难以置信的芯片。 现在回想起来，这简直是理所当然。 我认为AI5可能是所有推理芯片中，对于参数数量低于约2500亿的模型来说最好的。迄今为止硅片成 本最低，性能功耗比最高。 AI6 将会更进一步。 我们的芯片团队大约有一半在湾区，一半在奥斯汀，另外还有许多工程师分散在世界各地。 关于250B，有两种可能，一种是直接就可以在本地推理250B，一种是类似DeepSeek V3/R1,671B总参 数，激活37B参数。 如果是类似的比例，那么AI5本地推理是处理的13.79B左右参数量。 理想目前本地处理的是4B左右参数量。 GPT-1/2/3的参数量分别为0.117B/1.5B/175B。 总得来说，自动驾驶前进方向真的越来越明晰，跑更大参数量模型，具备或超越人类思考能力，解决 时延，这事就解决了。 郑小康25年8月11日： 特斯拉已解散 Dojo 超算研发团队，负责人 Pe ...

Core Insights - The company has achieved a total of 3,201 supercharging stations as of September 7, 2025, with a goal of exceeding 4,000 stations by the end of the year [1] - The progress towards the annual target shows an increase from 64.80% to 64.85%, indicating a steady pace in station construction [1] - To meet the year-end target, the company needs to complete an average of 6.95 stations per day over the remaining 115 days of the year [1] Summary by Sections - **Supercharging Station Construction** - The total number of supercharging stations has increased from 3,195 to 3,201 in a short span, reflecting ongoing expansion efforts [1] - Six new stations have been established across various provinces, including Hunan, Guangdong, Guizhou, Shandong, Yunnan, and Zhejiang, with different specifications for each [1] - **Progress Metrics** - The current progress towards the annual target is at 64.85%, with a time progress value of 68.49%, indicating that the company is slightly behind schedule [1] - The company has 799 stations left to build to reach its goal, emphasizing the need for accelerated construction in the coming months [1]

Core Viewpoint - The discussion revolves around the future of autonomous driving and the role of AI in enhancing human capabilities, with a focus on the timeline for achieving Level 4 (L4) autonomous driving by 2027, as well as the implications of AI on work and personal relationships [2][28]. Group 1: Autonomous Driving and AI - The optimistic timeline for achieving L4 autonomous driving is set at three years, with a more cautious estimate of five years, driven by advancements in AI capabilities and addressing latency issues [2][28]. - The current limitations in AI are attributed to insufficient computational power at the edge, likened to insect-level capabilities compared to human brain functions [28][30]. - The core value of cars is identified as a tool for transportation, a space for shelter, and a companion for exploration, which can be enhanced through AI and autonomous driving technologies [21][22]. Group 2: Human Relationships and Personal Growth - The importance of expressing needs in personal relationships is emphasized, suggesting that recognizing and articulating these needs can strengthen connections with loved ones [3][4][38]. - The role of children in personal growth is discussed, highlighting that children can help parents grow rather than the other way around, fostering a supportive environment [5][38]. - The necessity of hobbies and passions is identified as crucial for maintaining energy and motivation in life, paralleling the need for a continuous energy source in driving [39][40]. Group 3: AI's Impact on Work and Society - Historical trends indicate that AI will not lead to mass unemployment, as new forms of content creation and consumption emerge, replacing traditional media formats [18][19]. - The potential for AI to reduce work hours and enhance creativity is discussed, suggesting that effective use of AI could allow for a four-day workweek, freeing up time for personal development [26][27]. - The conversation highlights the need for individuals to actively choose how to utilize their time and energy in the face of technological advancements, advocating for a proactive approach to personal choices [32][33].

Core Viewpoint - The article discusses the advancements in Li Auto's self-developed chip architecture, particularly focusing on the VLA architecture and its implications for autonomous driving capabilities [1][2]. Group 1: Chip Development and Architecture - Li Auto's self-developed chip is designed with a data flow architecture that emphasizes hardware-software co-design, making it suitable for running large neural networks efficiently [5][9]. - The chip is expected to achieve 2x performance compared to leading chips when running large language models like GPT and 3x for vision models like CNN [5][8]. - The development timeline from project initiation to vehicle deployment is approximately three years, indicating a rapid pace compared to similar projects [5][8]. Group 2: Challenges and Innovations - Achieving real-time inference on the vehicle's chip is a significant challenge, with efforts focused on optimizing performance through various engineering techniques [3][4]. - Li Auto is implementing innovative parallel decoding methods to enhance the efficiency of action token inference, which is crucial for autonomous driving [4]. - The integration of CPU, GPU, and NPU in the Thor chip aims to improve versatility and performance in processing large amounts of data, which is essential for autonomous driving applications [3][6]. Group 3: Future Outlook - The company expresses strong confidence in its innovative architecture and full-stack development capabilities, which are expected to become key differentiators in the future [7][10]. - The relationship between increased computing power and improved performance in advanced driver-assistance systems (ADAS) is highlighted, suggesting a predictable enhancement in capabilities as technology evolves [6][9].

Core Viewpoint - The article discusses the significance of language in shaping human cognition and understanding, particularly in the context of the VLA (Vision, Language, Action) architecture used in autonomous driving technology [1][2]. Group 1: Language and Cognition - The concept "language is the world" emphasizes that language fundamentally shapes and limits human understanding and expression of the world [1]. - Human cognitive abilities, such as reasoning and understanding, are primarily learned through language, distinguishing humans from animals [1]. - Different languages provide unique cognitive frameworks, leading to variations in thought processes among speakers of different languages [1]. Group 2: VLA Architecture - In the VLA framework, 'V' represents perception, 'A' represents action, and 'L' represents language capabilities, which are crucial for understanding and decision-making [2]. - The 'L' component does not merely involve explicit language output but relies on implicit logical reasoning derived from data learned through human language [2]. - The current auxiliary driving tasks are relatively simple, making the advantages of the VLA architecture less apparent compared to other end-to-end solutions [2]. - The VLA architecture is expected to demonstrate significant advantages in more complex Level 3 and Level 4 autonomous driving tasks, where it can outperform other systems [2].