在中国共产党成立104周年即将到来之际，6月30日，省委常委、市委书记吴桂英走访慰问老党员、困难党 员，向全市广大党员致以节日问候，并为基层党支部党员讲党课。 在雨花区跳马镇三仙岭村党群服务中心，吴桂英与村党总支、村委会成员和基层党员开展座谈，并讲授党 课。 在雨花区跳马镇三仙岭村党群服务中心，吴桂英听取基层深入贯彻中央八项规定精神学习教育、党建引领基 层治理有关情况，与村党总支、村委会成员和基层党员就发挥农村基层党组织战斗堡垒作用和党员先锋模范作 用、抢抓长株潭一体化机遇加快兴绿富民等开展座谈，并讲授党课。 讲党课现场。 6月30日，省委常委、市委书记吴桂英来到党员裴翠英家中慰问。 吴桂英慰问党员钟福盛及家人。 来到老党员裴翠英家中，吴桂英与老人促膝交谈，对其积极向上的生活态度表达敬意，希望她保重好身体， 继续发光发热，要求属地做好关怀帮扶工作，让老党员老有所养、老有所医、老有所乐。在生活困难党员钟福盛 家中，吴桂英关切询问其身体和生活情况，鼓励他坚定信心，保重身体，乐观面对生活，叮嘱有关方面用心用情 排忧解难，让困难党员真切感受到组织温暖。 吴桂英指出，以习近平同志为核心的党中央高度重视加强农村基层党组织 ...

招商局狮子山人工智能实验室 投稿 量子位 | 公众号 QbitAI 大模型可以不再依赖人类调教，真正"自学成才"啦？ 新研究仅通过 RLVR （可验证奖励的强化学习），成功让模型自主进化出 通用的探索、验证与记忆能力 ，让模型学会"自学"！ 当前主流的LLM Agent依然高度依赖于提示词工程、复杂的系统编排、甚至静态规则表，这使得它们在面对复杂任务时难以实现真正的智能 行为演化。 而来自招商局狮子山人工智能实验室的研究团队认为，RLVR范式是智能体（Agent）通往更高通用性和自主性的重要突破口。 于是，他们从两个关键层面出发构建了端到端Agent训练pipeline—— L0系统 ： 智能体架构层面 提出了结构化智能体框架——NB-Agent，在经典"代码即行动" （Code-as-Action） 架构基础上进行扩展，使智能体能够操作记忆/上下 文，从而获得类人类的记忆存储、信息总结与自我反思能力。 学习范式层面 探索了一个核心问题：是否可以仅通过RLVR范式，引导智能体从零开始，学会如何规划、搜索、验证与记忆，最终解决复杂的多轮推理 任务？ L0系统的框架、模型及训练集已 全部开源 ，详细可见文末链接。 ...

Group 1 - The core viewpoint emphasizes the importance of implementing the spirit of the Central Eight Regulations and enhancing the governance capabilities at the grassroots level to better serve the community [1] - Liu Xiaotao conducted an on-site inspection of the comprehensive governance center in Jinting Town, focusing on the optimization of service facilities and the effectiveness of community services [2] - Liu Xiaotao expressed gratitude to retired village medical worker Huang Shouxiang for his contributions to grassroots healthcare and encouraged him to inspire younger party members to serve the community [2] Group 2 - During a meeting in Shigong Village, Liu Xiaotao engaged with grassroots representatives and discussed various suggestions for improving local governance, including the introduction of smart monitoring in key fire areas and enhancing employment attractiveness in Xishan Island [3] - Liu Xiaotao responded to the suggestions made by grassroots representatives, urging relevant departments to thoroughly study and implement these proposals [3] - The city leadership expressed appreciation for the efforts of grassroots workers in contributing to rural revitalization and encouraged them to continue their dedication to community service [3]

Group 1 - The article emphasizes the importance of improving party conduct and governance at the grassroots level, highlighting various initiatives taken by local party organizations to address community issues and enhance service delivery [16][23]. - Jiangsu's Siyang County has initiated a comprehensive investigation into water supply issues affecting rural areas, leading to the identification and resolution of problems in 60% of the first batch of 12 problematic communities by mid-June [17][18]. - In Guangzhou's Nansha District, local authorities have established clear guidelines for government-business interactions to foster a healthy relationship while maintaining integrity, which includes defining safety zones and red lines for engagement [19][20]. Group 2 - Chongqing's Changshou District has adopted a community-driven approach to address local issues, successfully resolving over 30,000 community concerns this year by engaging residents and businesses in collaborative problem-solving [21][22]. - Ningxia's Yinchuan City has made significant strides in reducing bureaucratic formalities, allowing community workers to focus on practical issues rather than excessive paperwork, thus enhancing their ability to serve the public effectively [23][24].

Core Viewpoint - Several banks are integrating party building with business operations, focusing on five key areas: technology finance, green finance, inclusive finance, pension finance, and digital finance, to support high-quality economic development [1] Group 1: Service to the Real Economy - Banks are emphasizing financial services to the real economy as their fundamental purpose, leveraging monetary policy tools to ensure the implementation of major decisions from the central government [1][2] - Industrial and Commercial Bank of China (ICBC) is enhancing support for small and micro enterprises through digital transformation and innovative financing products, improving accessibility and convenience for customers [1] - Agricultural Bank of China is focusing on green development and ecological protection, increasing financial support for energy transition and environmental sustainability [2] - China Construction Bank is providing a comprehensive technology finance service system to support innovation and technology enterprises with tailored financial products [2] - Bank of China is enhancing services for foreign trade and investment, contributing to the internationalization of the Renminbi [2] Group 2: Risk Management - Banks are prioritizing risk management amidst market volatility, emphasizing the importance of internal controls and operational risk prevention [3] - ICBC is adopting a proactive approach to risk management, ensuring overall risk control through compliance and internal oversight [3] - Regional banks like Baode Rural Commercial Bank are focusing on precise risk prevention and enhancing compliance management to address potential issues early [3] Group 3: Compliance and Ethical Standards - Banks are conducting educational initiatives to reinforce compliance with central regulations, emphasizing integrity and ethical conduct among employees [4][5] - China Bank's leadership is advocating for a culture of accountability and discipline, aiming to resist corruption and maintain high ethical standards [4] - Shanghai Pudong Development Bank is integrating compliance education into daily operations to ensure effective implementation and high-quality development [5]

Core Points - The article emphasizes the importance of continuous learning within the Chinese Communist Party (CCP) as a key to its success and adaptability in changing times [4][5][6] - It highlights the historical context of learning in the CCP, tracing back to Mao Zedong's call for a "learning competition" and the establishment of a daily learning system for cadres [5][6] - The article discusses the current emphasis on enhancing the "learning ability" of party members as a response to complex global challenges and the need for knowledge in various fields [7][8] Group 1 - The Central Political Bureau has conducted over 100 collective learning sessions since the 18th National Congress, covering a wide range of topics from economic reforms to advanced technologies [4] - The learning system established by the CCP serves as a strong foundation for enhancing its governance capabilities [4][6] - Xi Jinping's call for a "learning revolution" reflects the necessity for party members to adapt to modern demands and continuously improve their knowledge and skills [5][7] Group 2 - The 19th National Congress report identifies "learning ability" as the foremost skill to be enhanced within the party [7] - The article stresses that the tasks of learning have become more challenging in the current era, necessitating a proactive approach to knowledge acquisition [7][8] - The commitment to learning is framed as essential for the CCP's future success and its ability to navigate complex political and social landscapes [8]

Core Insights - The article discusses the integration of AI, particularly the DeepSeek model, into the automotive finance sector, highlighting the importance of data accumulation, scenario understanding, and algorithm innovation as key to building competitive barriers in the industry [1][6]. Industry Trends - The fusion of AI with automotive finance is advancing, with a focus on leveraging data and innovative algorithms to address compliance and transparency challenges [1][4]. - Hong Kong is positioned to become a critical node in cross-border data governance and standard-setting due to its status as an international financial center [1][6]. Data Security and Compliance - The company adheres to strict compliance requirements in data usage, employing federated machine learning to allow collaborative AI model training without sharing raw data, and implementing data anonymization for internal use [4][5]. - The challenges in applying AI in vertical fields include ensuring high-quality outcomes while maintaining transparency in decision-making and adhering to stringent data security regulations [5]. Opportunities and Challenges - In the next 3 to 5 years, opportunities in automotive finance will arise from AI empowering the industry and serving companies with international expansion strategies, with Hong Kong playing a significant role in unified data governance [6][7]. AI Model Development - The DeepSeek model is noted for its lower costs and strong algorithm capabilities, achieved through innovations in algorithm engineering and structure [7]. - The company has developed the YiXin-Distill-Qwen-72B inference model, the first open-source large-scale inference model in the automotive finance sector, which performs comparably to DeepSeek-r1 [7][8]. AI Innovation and Application - The company aims to automate complex decision-making processes in automotive finance, significantly enhancing industry efficiency through AI-driven solutions [8]. - The company possesses three core advantages: a vast repository of automotive data assets, extensive experience in AI training and inference, and a comprehensive talent pool, computational power, and high-quality data [8].

Core Viewpoint - The competition aims to advance research in spatial intelligence and embodied intelligence, focusing on visual perception as a key supporting technology for applications in autonomous driving, smart cities, and robotics [2][4]. Group 1: Competition Purpose and Significance - Visual perception is crucial for achieving spatial and embodied intelligence, with significant applications in various fields [2]. - The competition seeks to promote high-efficiency and high-quality research in spatial and embodied intelligence technologies [4]. - It aims to explore innovations in cutting-edge methods such as reinforcement learning, computer vision, and graphics [4]. Group 2: Competition Organization - The competition is organized by a team of experts from institutions like Beijing University of Science and Technology, Tsinghua University, and the Chinese Academy of Sciences [5]. - The competition is sponsored by Beijing Jiuzhang Yunjing Technology Co., Ltd., which also provides technical support [5]. Group 3: Competition Data and Resources - Participants will have access to real and simulated datasets, including multi-view drone aerial images and specific simulation environments for tasks [11]. - The sponsor will provide free computing resources, including H800 GPU power, for validating and testing submitted algorithms [12][13]. Group 4: Task Settings - The competition consists of two tracks: Spatial Intelligence and Embodied Intelligence, each with specific tasks and evaluation methods [17]. - Spatial Intelligence requires building a 3D reconstruction model based on multi-view aerial images, while Embodied Intelligence involves completing tasks in dynamic occlusion scenarios [17]. Group 5: Evaluation Methods - Evaluation for Spatial Intelligence includes rendering quality and geometric accuracy, with scores based on PSNR and F1-Score metrics [19][20]. - For Embodied Intelligence, evaluation focuses on task completion and execution efficiency, with metrics such as success rate and average pose error [23][21]. Group 6: Submission and Awards - Results must be submitted in a specified format, and top-ranking teams will have their results reproduced for evaluation [24]. - Awards for each track include cash prizes and computing vouchers, with a total of 12 awards distributed among the top teams [25].

Core Insights - Bosch announced an investment of over €2.5 billion in artificial intelligence by 2027, predicting that sales of software, sensor technology, high-performance computing units, and vehicle communication components will double by 2035, potentially exceeding €10 billion in sales [1][3] - The company aims to leverage AI in advanced driver assistance and autonomous driving, combining AI with deep industrial knowledge to enhance vehicle safety and reduce product development cycles [1][3] Investment and Sales Projections - Bosch's investment in AI is part of a broader strategy to capitalize on the growing market for autonomous driving technologies [1] - The company forecasts that by 2035, the sales of relevant components will surpass €10 billion, driven by advancements in AI and sensor technologies [1][3] Technological Advancements - Bosch has deployed AI in cameras and radar systems to enhance object recognition and environmental perception, allowing vehicles to make informed driving decisions [1] - The integration of generative AI models enables Bosch to simulate various driving conditions, enhancing the training of AI systems with over 200 petabytes of global traffic scene data [2] Collaborative Efforts and Global Strategy - Bosch is collaborating with innovative players in AI technology to apply new advancements directly to products, particularly in the context of autonomous driving [3] - The company has established a successful partnership with Chery in China, creating an AI computing cluster and utilizing local data for model training through federated learning [3] Market Trends and Consumer Influence - The trend towards advanced driver assistance systems is driven by consumer demand, with Bosch believing that these technologies will be crucial for attracting buyers in the Chinese market [3] - Bosch anticipates that the expansion of autonomous driving technology will lead to long-term commercial success, with significant growth expected in various global markets [3]

Core Insights - The article discusses the evolution of Unmanned Aerial Vehicles (UAVs) into Agentic UAVs, which are characterized by autonomous reasoning, multimodal perception, and reflective control, marking a significant shift from traditional automation platforms [5][6][11]. Research Background - The motivation for this research stems from the rapid development of UAVs from remote-controlled platforms to complex autonomous agents, driven by advancements in artificial intelligence (AI) [6][7]. - The increasing demand for autonomy, adaptability, and interpretability in UAV operations across various sectors such as agriculture, logistics, environmental monitoring, and public safety is highlighted [6][7]. Definition and Architecture of Agentic UAVs - Agentic UAVs are defined as a new class of autonomous aerial systems with cognitive capabilities, situational adaptability, and goal-directed behavior, contrasting with traditional UAVs that operate based on predefined instructions [11][12]. - The architecture of Agentic UAVs consists of four core layers: perception, cognition, control, and communication, enabling autonomous sensing, reasoning, action, and interaction [12][13]. Enabling Technologies - Key technologies enabling the development of Agentic UAVs include: - **Perception Layer**: Utilizes a suite of sensors (RGB cameras, LiDAR, thermal sensors) for real-time semantic understanding of the environment [13][14]. - **Cognition Layer**: Acts as the decision-making core, employing techniques like reinforcement learning and probabilistic modeling for adaptive control strategies [13][14]. - **Control Layer**: Converts planned actions into specific flight trajectories and commands [13][14]. - **Communication Layer**: Facilitates data exchange and task coordination among UAVs and other systems [13][14]. Applications of Agentic UAVs - **Precision Agriculture**: Agentic UAVs are transforming precision agriculture by autonomously identifying crop health issues and optimizing pesticide application through real-time data analysis [17][18]. - **Disaster Response and Search and Rescue**: These UAVs excel in dynamic environments, providing real-time adaptability and autonomous task reconfiguration during disaster scenarios [20][21]. - **Environmental Monitoring**: Agentic UAVs serve as intelligent, mobile environmental sentinels, capable of monitoring rapidly changing ecosystems with high spatial and temporal resolution [22][23]. - **Urban Infrastructure Inspection**: They offer a transformative approach to infrastructure inspections, enabling real-time damage detection and adaptive task planning [24]. - **Logistics and Smart Delivery**: Agentic UAVs are emerging as intelligent aerial couriers, capable of executing complex delivery tasks with minimal supervision [25][26]. Challenges and Limitations - Despite the transformative potential of Agentic UAVs, their widespread application faces challenges related to technical constraints, regulatory hurdles, and cognitive dimensions [43].