Core Viewpoint - The article emphasizes the need for collaboration in the autonomous driving industry, inviting professionals to participate in training, course development, and research support to drive industry progress [2]. Group 1: Collaboration and Opportunities - The company is seeking partnerships with professionals in the autonomous driving field to enhance training and job assistance initiatives [2]. - High compensation and abundant industry resources will be provided to collaborators [3]. - The main focus areas for collaboration include roles such as autonomous driving product managers, 4D annotation/data loop, world models, VLA, autonomous driving large models, reinforcement learning, and end-to-end systems [4]. Group 2: Training and Development - The training collaboration is aimed at both B-end (enterprises, universities, research institutes) and C-end (students, job seekers) audiences [5]. - The company is also interested in course development and original article creation as part of its training initiatives [5]. Group 3: Contact Information - Interested parties can reach out via WeChat for further consultation [6].

Core Viewpoint - DeepSeek has introduced a new model, DeepSeek-OCR, which significantly reduces the number of tokens required to store and process information by utilizing images as memory carriers instead of relying solely on text tokens [3][6][12]. Group 1: Model Capabilities - DeepSeek-OCR can store nearly the same amount of information using only one-tenth of the tokens compared to traditional models [40][41]. - In tests, DeepSeek-OCR achieved superior performance, using only 100 visual tokens to surpass the 256 tokens required by GOT-OCR 2.0, and less than 800 visual tokens to outperform MinerU 2.0, which typically requires over 6000 tokens [13][14]. - The model supports various resolutions and compression modes, allowing it to adapt to different document complexities, such as using only 64 visual tokens for simple documents [18][21]. Group 2: Data Collection and Utilization - DeepSeek-OCR can capture previously uncollected data from two-dimensional information, such as graphs and images in academic papers, which traditional models could not interpret [32][33]. - The model can generate over 200,000 pages of training data in a day on an A100 GPU, indicating its efficiency in data collection [35]. Group 3: Resource Efficiency - By using images for memory, DeepSeek-OCR reduces the computational load, allowing for a significant decrease in token usage without sacrificing performance [40][41]. - The model can maintain 96.5% accuracy while using only one-tenth of the original token count, demonstrating its effectiveness in resource management [41][42]. Group 4: Open Source and Community Contributions - The development of DeepSeek-OCR is a collaborative effort, utilizing various open-source resources, including Huawei's Wukong dataset and Meta's SAM for image feature extraction [51][53]. - The integration of multiple open-source models has enabled DeepSeek to create an AI capable of "thinking in images," showcasing the power of community-driven innovation [53].

Core Insights - The article discusses the need to redefine the evaluation of world models in embodied intelligence, emphasizing that visual quality does not equate to task effectiveness [5][26]. - The introduction of the "World-in-World" platform aims to assess world models through closed-loop interactions, focusing on their practical utility rather than just visual fidelity [6][26]. Evaluation of World Models - Current evaluation systems prioritize visual clarity and scene rationality, neglecting whether these models can assist agents in decision-making for real tasks [5][6]. - The platform introduces a closed-loop system that integrates observation, decision-making, execution, and re-observation, ensuring fair and practical assessments [6][7]. Model Compatibility and Decision-Making - A unified action API is established to standardize input across different world models, allowing them to process the same tasks effectively [7]. - The decision-making process is structured into three phases: proposal generation, simulation of outcomes, and selection of the optimal action based on task goals [8][13]. Experimental Findings - Experiments with 12 mainstream world models revealed that visual realism does not guarantee task success; instead, action alignment is crucial [18][20]. - Fine-tuning smaller models with task-specific data proved more effective than simply using larger pre-trained models, highlighting a cost-effective optimization strategy [21][23]. - Increasing computational effort for simulations significantly improved task success rates, suggesting that more extensive predictive modeling leads to better decision-making [23]. Limitations and Future Directions - While models excel in perception and navigation, they struggle with physical manipulation tasks due to a lack of physical modeling considerations [25]. - The article concludes that future developments should focus on enhancing controllability, utilizing task data for fine-tuning, and incorporating physical modeling to improve the practical application of world models in robotics [26].

Core Viewpoint - 2023 marks the year of end-to-end production, with 2024 expected to be a significant year for end-to-end production in the automotive industry, as leading new forces and manufacturers have already achieved end-to-end production [1][3]. Group 1: End-to-End Production Development - The automotive industry is witnessing rapid development in end-to-end methods, particularly the one-stage approach exemplified by UniAD, which directly models vehicle trajectories from sensor inputs [1][3]. - There are two main paradigms in the industry: one-stage and two-stage methods, with the one-stage approach gaining traction and leading to various derivatives based on perception, world models, diffusion models, and VLA [3][5]. Group 2: Course Overview - A course titled "End-to-End and VLA Autonomous Driving" has been launched, focusing on cutting-edge algorithms in both one-stage and two-stage end-to-end methods, aimed at bridging academic and industrial advancements [5][15]. - The course is structured into several chapters, covering the history and evolution of end-to-end methods, background knowledge on VLA, and detailed discussions on both one-stage and two-stage approaches [9][10][12]. Group 3: Key Technologies - The course emphasizes critical technologies such as BEV perception, visual language models (VLM), diffusion models, and reinforcement learning, which are essential for mastering the latest advancements in autonomous driving [5][11][19]. - The second chapter of the course is highlighted as containing the most frequently asked technical keywords for job interviews in the next two years [10]. Group 4: Practical Applications - The course includes practical assignments, such as RLHF fine-tuning, allowing participants to apply their knowledge in real-world scenarios and understand how to build and experiment with pre-trained and reinforcement learning modules [13][19]. - The curriculum also covers various subfields of one-stage end-to-end methods, including those based on perception, world models, diffusion models, and VLA, providing a comprehensive understanding of the current landscape in autonomous driving technology [14][19].

Core Insights - Li Auto has successfully entered the domestic smart driving tier one since the mass production of its end-to-end + VLM dual system last year, maintaining a leading position in both academic work and mass production solutions [3][4] - The company is transitioning from a new energy vehicle brand to an AI enterprise, driven by advancements in embodied intelligence and large models [3] - The VLA driver model, featuring innovative architecture, enhances capabilities in spatial understanding, reasoning, communication, memory, and behavior [3][4] VLA & VLM - ReflectDrive introduces discrete diffusion for reflective vision-language-action models in autonomous driving, aiming for scalable and efficient trajectory generation [8][13] - OmniReason establishes a temporal-guided framework for VLA, emphasizing causal reasoning in diverse driving scenarios [11][16] - LightVLA presents a differentiable token pruning framework to enhance efficiency in VLA models, achieving significant reductions in computational load while improving success rates [14][17] - DriveAgent-R1 focuses on human-like driving decisions, introducing a hybrid thinking architecture that adapts to complex environments [19] End-to-End Trajectory Generation - World4Drive is an open-source VLA dataset covering diverse driving scenarios across 148 cities in China, ensuring high-quality and representative data [21][25] - TransDiffuser enhances trajectory generation through a novel end-to-end framework that integrates multimodal driving intentions without relying on perception annotations [23][26] World Models - RLGF proposes a reinforcement learning framework for generating driving videos, addressing geometric distortion issues in autonomous driving [29][34] - GeoDrive innovatively incorporates 3D point cloud rendering into the generation paradigm, improving spatial consistency and controllability [40] Other Innovations - TokenFLEX introduces a unified training framework for dynamic visual token inference, enhancing model robustness across varying token counts [50] - RuscaRL addresses exploration bottlenecks in reinforcement learning, promoting independent learning through structured external support [56]

Core Viewpoint - The article emphasizes the need for collaboration in the autonomous driving industry, inviting professionals to participate in training, course development, and research support to drive industry progress [2]. Group 1: Collaboration and Opportunities - The company is seeking partnerships with professionals in the autonomous driving field to enhance training and job guidance services [2]. - High compensation and abundant industry resources will be provided to collaborators [3]. - The main focus areas for collaboration include roles such as autonomous driving product managers, 4D annotation/data loop, world models, VLA, autonomous driving large models, reinforcement learning, and end-to-end systems [4]. Group 2: Training and Development - The positions are primarily aimed at B2B training for enterprises, universities, and research institutions, as well as C2C training for students and job seekers [5]. - The company encourages interested individuals to reach out for further consultation via WeChat [6].

Core Insights - Yoshua Bengio has become the first scholar globally to surpass one million citations on Google Scholar, marking a significant milestone in AI academic influence [3][5][6] - Geoffrey Hinton follows closely with approximately 970,000 citations, positioning him as the second-highest cited scholar [5][6] - The citation growth of AI papers has surged, reflecting the current AI era's prominence [19][30] Citation Rankings - Yoshua Bengio ranks first globally in total citations, with a significant increase in citations post-2018 when he received the Turing Award [6][9][38] - Geoffrey Hinton ranks second, with a notable citation count of 972,944, showcasing his enduring impact in the field [5][8] - Yann LeCun, another Turing Award winner, has over 430,000 citations, but remains lower than both Bengio and Hinton [13][18] AI Research Growth - The total number of AI papers has nearly tripled from approximately 88,000 in 2010 to over 240,000 in 2022, indicating a massive increase in research output [30] - By 2023, AI papers constituted 41.8% of all computer science papers, up from 21.6% in 2013, highlighting AI's growing dominance in the field [31][32] - The foundational works of AI pioneers have become standard references in subsequent research, contributing to their citation growth [22][33] Key Contributions - The introduction of AlexNet in 2012 is considered a pivotal moment that significantly advanced deep learning methodologies [20] - The development of the Transformer model in 2017 and subsequent innovations like BERT have further accelerated research and citations in AI [24][27] - The increasing number of AI-related submissions to top conferences reflects the field's rapid evolution and the growing interest in AI research [36]

Core Insights - Tesla has shared insights into its architecture, emphasizing the use of a large model and extensive data, which allows for a fixed computation time and high-frequency actions in its Full Self-Driving (FSD) system [5][6]. Group 1: Reasons for End-to-End Approach - The complexity of human driving behavior makes it difficult to define a single evaluation function, leading to challenges in rule-based optimization [8]. - The interface definition between perception, prediction, and planning is problematic, resulting in information loss [8]. - An end-to-end approach is better suited for scalability and addressing long-tail problems [8]. - Fixed computation time based on neural networks reduces latency compared to traditional methods [8]. - Philosophically, reliance on computational power and data is preferred over human experience [8]. Group 2: Challenges of End-to-End Systems - The three main challenges faced by end-to-end systems include evaluation, the curse of dimensionality, and ensuring interpretability and safety [19][20]. - The curse of dimensionality leads to insufficient supervisory signals when transitioning from high-dimensional to low-dimensional spaces [21]. - Ensuring interpretability and safety is crucial, as the model must genuinely understand driving behavior rather than just fitting shortcuts [23]. Group 3: Evaluation Challenges - High-quality datasets cannot solely describe performance through loss metrics, indicating a need for more comprehensive evaluation methods [39]. - Open-loop evaluations cannot replace closed-loop assessments, highlighting the necessity for real-world testing [39]. - Driving behavior is multimodal, requiring evaluation metrics that encompass various driving actions [39]. - One proposed method involves predicting the consequences of actions, potentially using a critic to assess model performance [39]. - Balancing the evaluation dataset is essential for accurate assessments [39]. Group 4: World Model Simulator - Tesla introduced a world model simulator that generates subsequent videos based on real scenarios, indicating a high barrier to entry for this technology [41]. - The simulator allows for replaying previous issues to assess improvements, akin to two-stage simulations [44]. - This technology can also be applied to humanoid robots, enabling reinforcement training and simulation [46].

每年，AI 领域有数以万计的论文涌现，但大多数研究者都会遇到同一个问题： 如何让我的工作脱颖而出？ 一份精美的项目主页，往往是论文"出圈"的第一步。它不仅是成果展示的窗口，更是吸引合作、获得引用的重要渠道。然而，从论文PDF 到交互式网页，这其中 充满了重复、琐碎和低效的工作：筛选模版，从论文中挑选文字、复制粘贴、贴图排版、写 HTML, CSS……足以让科研人头秃。 论文链接: https://arxiv.org/abs/2510.19600 项目主页: https://mqleet.github.io/AutoPage_ProjectPage (刷新页面可以看到不同风格的项目主页！) 代码: https://github.com/AutoLab-SAI-SJTU/AutoPage (求Star⭐️) Huggingface Space: https://huggingface.co/spaces/Mqleet/AutoPage 自动解析章节结构与图表信息 智能生成叙事文本与模块化内容块 自动调整图像大小和排版 一键渲染出支持动态交互的网页结构 这些页面不仅忠实呈现了论文的核心思想，还能根据用户指令快速微调 ...

Core Viewpoint - The article emphasizes the importance of targeted guidance and mentorship for students aiming to publish high-quality research papers in top conferences like CVPR and ICRA, highlighting the need for strategic focus in the final stages of the submission process [2][3]. Group 1: Submission Insights - The current submission volume for CVPR 2026 has exceeded 2000, indicating a competitive landscape similar to ICLR [1]. - Historical trends show that successful submissions often focus on specific breakthroughs and verifiable improvements rather than broad themes, aligning closely with the main topics of the conference [1]. - The anticipated main theme for CVPR 2026 is likely to revolve around "world models," suggesting a strategic direction for potential submissions [1]. Group 2: Mentorship and Guidance - The organization offers specialized mentorship programs aimed at helping students navigate the complexities of research paper writing and submission, particularly for those in the fields of autonomous driving and AI [2][3]. - With over 300 dedicated instructors from top global universities, the organization provides a wealth of academic resources and expertise to assist students in producing high-quality research [3]. - The mentorship program includes personalized guidance through the entire research process, from topic selection to submission, ensuring that students are well-prepared for the rigorous demands of top-tier conferences [11]. Group 3: Student Support and Outcomes - The organization addresses common challenges faced by students, such as lack of guidance, fragmented knowledge, and difficulties in understanding the research process [5]. - Students are encouraged to develop a systematic understanding of both classic and cutting-edge algorithms, enhancing their practical skills and research capabilities [5]. - Successful participants in the program may receive recommendations from prestigious institutions and direct job placements in leading tech companies, emphasizing the program's potential impact on students' academic and professional trajectories [16].