一位对理想很有感情人士向TOP2表示，他认为理想很多人挺傲的，对友商的进步优秀之处认识不充 分。给李想本人反馈XX需求时，李想喜欢用用户不需要来回应。这位对理想很有感情人士内心想法 是，根据其接触到的大量用户反馈，其实很多用户是需要的。 本文将分享一个李想认为用户不需要，但后面真改正了的成功实践。 A是L系列车主兼员工，其在高速行驶时，即使和家人同行，也有很强的希望尽量多用电需求，需求 底层源自两点，1.省钱快感。2.纯电更平顺，NVH更好。 故其在家人同行高速场景下，也会脑中计算，采用什么模式，可以尽可能高速多用电。 再加上也有其他人反馈类似需求，A希望理想OTA可以升级功能，实现更加自动化的，面向L系列车 主的高速充电规划，且理念是尽可能多用电，更方便充电，并且根据实际里程，给予新的增程器工作 算法，在尽可能多用电前提下，还尽可能少用油（理想原先的模式里，高速油电混合与纯油模式耗油 量几乎没区别）。 这个思路上报上去时，李想认为这是一个伪需求，他认为大多数理想用户高速就是多用油的，故评审 不通过。 A后面想办法，调后台数据（这个过程并不算很容易，不是直接一导就实现的，还需要挺多步骤与协 调的），发现理想实 ...

来源 ：北北自律机 25年09月02日星期二 理想超充 5 新增。 超充建成数：3190→3195座 基于2025年底4000+座目标 还剩805座 今年新增数进度值：64.36%→64.58% 今年剩余120天 今年时间进度值：67.12% 需每日 6.71 座，达到年底目标值 【附】5 座新增建成 湖北省 神农架林区 神农架5A景区 白玉兰森林酒店 为城市/景区乐园4C站，规格：4C × 6 湖北省 武汉市 武汉四新体育公园 为城市4C站，规格：4C × 6 江苏省 南通市 南通海安星湖001商业广场 为城市4C站，规格：4C × 6 山西省 长治市 襄垣停车区(二广高速广州方向) 为高速服务区5C站，规格：2C × 3 5C × 1 陕西省 榆林市 榆林金刚寺 为城市4C站，规格：4C × 6 加微信，进群深度交流理想实际经营情况与长期基本面。不是车友群。 ———————————————————— ...

来源 ：北北自律机 25年09月01日星期一 理想超充 16 新增。 超充建成数：3174→3190座 基于2025年底4000+座目标 还剩810座 今年新增数进度值：63.66%→64.36% 今年剩余121天 今年时间进度值：66.85% 需每日 6.69 座，达到年底目标值 安徽省 合肥市 合肥金泉小区北门 为城市4C站，规格：4C × 6 北京市 朝阳区 北京望京万象汇 为城市5C站，规格：4C × 6 5C × 2 广东省 揭阳市 金和服务区(汕湛高速汕头方向) 为高速服务区5C站，规格：5C × 4 广东省 揭阳市 金和服务区(汕湛高速湛江方向) 为高速服务区5C站，规格：5C × 4 广东省 深圳市 深圳先科大院 为城市5C站，规格：5C × 4 广东省 湛江市 湛江恒福时代中心 为城市4C站，规格：4C × 6 广西壮族自治区 南宁市 南宁吾悦广场 为城市4C站，规格：4C × 4 海南省 海口市 海口日月广场南侧停车场 为城市5C站，规格：5C × 8 河北省 邢台市 ———————————————————— 【附】16 座新增建成 邢台宁晋唐朝酒店 为城市4C站，规格：4C × 4 江苏 ...

Core Viewpoint - The article discusses the innovative PhysGM framework, which transforms 4D generation from an optimization problem into an inference problem, allowing for rapid and efficient generation of 4D simulations from a single image [1][2]. Group 1: Advantages of PhysGM - PhysGM significantly improves speed, generating results in under 30 seconds compared to previous methods that could take hours [3][9]. - The framework simplifies the process by eliminating the need for pre-processing and iterative scene optimization [3][9]. - It enhances physical realism and visual quality in the generated simulations [3][9]. - PhysGM does not rely on large language models, making it more accessible and scalable [3][9]. Group 2: Potential Limitations - There may be limitations in generalization, particularly for non-rigid objects, and the current model predicts only a single aggregate physical property vector [4]. - The performance of the model is constrained by the underlying models used for 3D reconstruction, which may lead to loss of geometric details or inconsistencies in texture [4][6]. Group 3: Training Strategy - The training consists of two phases: supervised pre-training to establish physical priors and DPO-based fine-tuning to align the model with real-world simulations [7][8]. - The first phase involves creating a dataset of over 24,000 3D assets, using a dual-head U-Net architecture to predict geometric and physical parameters [7]. - The second phase utilizes Direct Preference Optimization (DPO) to refine the model based on the quality of generated simulations compared to real reference videos [8]. Group 4: Comparison with Other Methods - PhysGM outperforms several existing methods across multiple dimensions, including the need for pre-processing, automation of parameter computation, generalizability, reliance on large language models, and inference time [9].

Core Viewpoint - The company is progressing towards its goal of establishing over 4000 supercharging stations by the end of 2025, with a current completion rate of 63.66% for this year [1][2]. Group 1: Supercharging Station Development - The total number of supercharging stations has increased from 3161 to 3174, with 13 new stations added recently [1]. - The company needs to complete an average of 6.77 stations per day to meet the year-end target, with 122 days remaining in the year [1]. - The current progress towards the annual target is at 63.66%, while the time progress is at 66.58% [1]. Group 2: New Station Locations - New supercharging stations have been established in various cities, including: - Copper City, Anhui: 4C × 6 configuration [1]. - Chaoyang District, Beijing: 5C × 8 configuration [1]. - Shenzhen, Guangdong: 4C × 6 and 4C × 8 configurations [1]. - Nanjing, Jiangsu: Multiple 4C × 6 configurations [1][2]. - Other locations include Wuxi, Qingdao, Xi'an, and Wenzhou, with various configurations [2].

Core Viewpoints - Li Xiang's personal interest in AI currently outweighs the focus on the incremental details of automotive hardware products [1][4] - Discussing the short-term market, Li Xiang's preference for AI over hardware may pose a potential risk to short-term sales, as many consumers prefer hardware-defined products [1] - The foundational anchor for both short-term and long-term commercial value is the product's utility, supported by varying levels of emotional value; in the AI era, models are products [1] - Within a three-month timeframe, AI-related product utility is unlikely to reach early mainstream adoption, remaining in the early adopter phase, with low emotional value among the general public [1] Detailed Analysis - The head of the first product line, Lao Tang, actively shares the product development process online, while the heads of the second and third product lines, Zhang Xiao and Li Xinyang, are less inclined to do so [3] - The MEGA Home was developed based on user feedback regarding accessibility for the elderly, with differing opinions between Li Xiang and Lao Tang on design solutions [3] - Li Xiang has been the primary decision-maker for many product details in the Li ONE, while there is speculation that the i8 may shift to a configuration with fewer options, likely influenced by Li Xiang [3] - There is no evidence from public information that Li Xiang has strongly insisted on hardware dimension enhancements for the new product lines [3] - Li Xiang's strong insistence on running VLA on dual Orin chips led to significant technical challenges being overcome, showcasing his first-principles thinking [5] - All vehicles equipped with the Thor chip are expected to be able to switch to Li Auto's own autonomous driving chip in the future, although it is uncertain if the Orin chip will also be replaceable [5]

Core Viewpoint - The company is addressing misinformation regarding the pricing and sales of the Li Auto i6, emphasizing that accurate details will be provided during the official launch event [1][2]. Group 1 - The head of the third product line at Li Auto, Li Xinyang, has stated that many reports about the i6's price and sales figures are inaccurate [1]. - The company is committed to ensuring that the price of the i6 will reflect its value, aiming for customer satisfaction [1]. - Further information regarding the i6 will be disclosed during the upcoming launch event, and the company appreciates the public's patience and understanding [1]. Group 2 - The company encourages deeper discussions about its actual operational status and long-term fundamentals through a WeChat group, indicating a focus on community engagement [2].

Group 1 - The core viewpoint of the article highlights the progress of the company's supercharging station construction, with a current total of 3161 stations and a target of over 4000 by the end of 2025, indicating a remaining need for 839 stations to meet this goal [1] - The completion rate for new supercharging stations this year has increased from 62.87% to 63.09%, with 123 days left in the year, suggesting a need for an average of 6.82 new stations to be built daily to achieve the year-end target [1] - Five new supercharging stations have been established in various locations, including Hainan, Henan, Jiangsu, Shanxi, and Zhejiang, all categorized as urban 4C stations with varying specifications [1]

Core Viewpoint - The company emphasizes the importance of integrated hardware and software solutions in the development of intelligent vehicles, highlighting the need for a cohesive approach to chip delivery and functionality [1][2]. Group 1: Product Development and Strategy - The company is focused on matching its own chips with larger-scale models and longer cognitive chains, ensuring that chip delivery is accompanied by functional capabilities [1]. - The company aims to create a high-quality aesthetic for its vehicles, as visual appeal is considered a critical factor in consumer choice, serving as a potential deal-breaker [2]. - The company believes that the development of autonomous driving technology has progressed significantly, moving from a lagging position to being in the first tier of the industry, with expectations of further advancements in the coming year [3]. Group 2: Market Perception and Communication - The company acknowledges that new technological advancements may not be easily understood by all consumers, drawing parallels to the initial reception of Apple's M1 chip, which was not immediately grasped by the general public [2]. - The company is committed to conducting research and development before product commercialization, ensuring that technological innovations are well-founded and effectively communicated to the market [2].

Core Viewpoint - The article discusses Li Xiang's belief in achieving Level 4 (L4) autonomous driving by 2027, based on three main points: the clear direction of enhancing AI capabilities, the perspective of pessimistic optimists like Li Xiang and Elon Musk, and the importance of presenting a vision to the capital market [2]. Group 1: AI Development and Autonomous Driving - The main trajectory of AI development since 2012 is "compression is intelligence," which emphasizes the ability to encode and predict vast amounts of seemingly chaotic data with shorter model descriptions [3]. - The three main lines to achieve this trajectory are foundation models, scaling laws, and emergent abilities [3]. - The concept of "compression is intelligence" indicates that a model's ability to predict future content reflects its understanding of the underlying structure, patterns, and causal relationships in the data [3]. - Current large language models (LLMs) have strong capabilities in understanding complex semantics, which can assist in solving the high cognitive demands of autonomous driving [4][5]. Group 2: Technical Aspects of Autonomous Driving - The scaling laws suggest that model performance improves with increased computational resources, data volume, and model parameters, although this is an empirical observation without mathematical proof [4]. - For the company, computational resources can be acquired through funding, while data volume relies on simulation data for reinforcement learning, necessitating the development of proprietary autonomous driving chips to meet latency requirements [5]. - The direction for enhancing vehicle capabilities is clear, akin to the significant advancements seen from GPT-1 to GPT-3.5 [6]. Group 3: Future Considerations and Innovations - While achieving L4 by 2027 may not be guaranteed, the specific architecture may evolve, and the company aims to enhance the vehicle's understanding of the physical world rather than merely addressing engineering problems [7]. - The company is capable of quickly assimilating core ideas from rapid developments in the AI sector, as evidenced by its adaptation of concepts from other models [7]. - The article highlights the importance of selective learning in reinforcement learning, where only verified solutions are used as learning signals, ensuring the quality of the training data [8][9]. Group 4: Research and Development Initiatives - The company collaborates with local scientific committees to fund research initiatives, aiming to engage with academic professionals to acquire the latest research findings [11].