Core Viewpoint - The article discusses the "Action Plan" aimed at accelerating the integration of artificial intelligence (AI) into scientific research in Beijing, with a focus on developing high-quality scientific instruments and fostering innovation in various fields [2][4][7]. Group 1: Development Goals - By 2027, the plan aims to leverage Beijing's AI innovation advantages to establish new pathways for scientific research, focusing on foundational scientific issues and enhancing the application of AI across multiple fields [8]. - The initiative includes the construction of at least 10 high-quality scientific databases to support AI applications in various research areas, targeting over 10 million users [8]. Group 2: Key Tasks - The plan emphasizes critical technology breakthroughs, including foundational theoretical research in AI, the development of a universal scientific foundational model, and new generation scientific computing simulation software [9][10][11]. - Infrastructure development is a priority, with goals to create an open scientific data platform and a collaborative computing power platform to enhance research efficiency [12][13]. Group 3: Application Acceleration - The plan promotes the use of AI in basic scientific research, medical health innovation, and new materials development, aiming to enhance research efficiency and discovery processes [15][16][17]. - Specific focus is placed on empowering high-end scientific instruments through AI, encouraging collaboration between universities, research institutions, and enterprises to develop advanced scientific equipment [18]. Group 4: Innovation Ecosystem - The initiative aims to build a public service innovation platform for scientific intelligence, attract and cultivate innovative talent, and establish a multi-channel investment and financing service system [20][21][22]. - It also seeks to create a competitive scientific intelligence industry cluster and promote international cooperation and open-source ecosystems [23][24][25].

Industry Investment Rating - No relevant information provided Core Viewpoints - On July 11, 2025, the domestic and foreign venture capital markets disclosed a total of 29 investment and financing events, including 15 domestic enterprises and 14 foreign enterprises, with a total financing amount of approximately 9.239 billion yuan [1] Summary by Category Fundraising Events - Tianjin Jingkai Science and Technology Innovation Mother Fund expanded to 380 million yuan, aiming to build a state - owned capital market - oriented industrial fund system and a "Angel + Venture Capital + Industry + Merger" fund group [1] - China Chengtong and the Jiangsu Provincial People's Government signed a framework cooperation agreement to establish the 10 - billion - yuan Chengtong Science and Technology Innovation (Jiangsu) Fund, and 2 sub - funds of Chengtong Science and Technology Innovation (Beijing) Fund also signed relevant framework agreements, aiming to support early - mid - stage technology projects and form a "mother fund + direct investment fund" synergy [2][3] Large - scale Financing - Inmo Technology completed over 150 million yuan in Series B+ financing, and the funds will be used for R & D, AI core capacity building, and business expansion [4] - Shijia Technology completed nearly 100 million yuan in Series A financing, and the funds will be used for the construction of a smart manufacturing center and key technology R & D [5] - BILT completed a $250 million Series C financing, and the funds will be used for business expansion in multiple fields [6] Policy Focus - Beijing released the first local policy for scientific intelligence, aiming to build a scientific foundation large - model, multiple databases, and form an international - competitive industrial cluster by 2027 [7] - The 2025 national basic medical insurance drug catalog adjustment started, and a commercial health insurance innovative drug catalog was added for the first time [8] - The Shanghai Stock Exchange launched a pre - review mechanism for IPOs on the Science and Technology Innovation Board, allowing eligible technology - based enterprises to apply for pre - review [10]

Group 1 - The core objective of Shandong's "Robot Industry High-Quality Development Action Plan (2025-2027)" is to exceed a manufacturing scale of 50 billion yuan by 2027 and cultivate over three leading enterprises with an output value exceeding 2 billion yuan each [1] - The plan includes 18 measures to support the robot industry across all dimensions and cycles, aiming to establish Shandong as a national growth pole for robot research, manufacturing, and application [1][2] - Shandong's robot industry is positioned as the second tier in China's robot industry, indicating significant potential for expansion [2] Group 2 - The regional layout of Shandong's robot industry is evolving, focusing on four major development hubs: Jinan, Qingdao, Zibo, and Jining, each with specific development focuses [2][3] - Jinan aims to create a leading domestic robot testing and verification platform, while Qingdao will integrate various robot applications across multiple sectors [2] - By the end of 2024, Shandong's robot industry is projected to achieve over 26 billion yuan in revenue, with a target to reach 50 billion yuan in three years [3]

Core Insights - The first local policy for scientific intelligence in China, titled "Beijing's Action Plan for Accelerating AI Empowerment in Scientific Research for High-Quality Development (2025-2027)," has been released [1] Summary by Categories Policy Objectives - By 2027, Beijing aims to establish a scientific foundational model and create no less than 10 high-quality scientific databases [1] - The plan intends to serve at least 10 million users and promote deep applications of scientific intelligence in no less than 5 fields [1] Expected Outcomes - The initiative is expected to generate over 8 benchmark application cases [1]

Core Viewpoint - The article discusses the innovative framework DrSR (Dual Reasoning Symbolic Regression) developed by researchers at the Institute of Automation, Chinese Academy of Sciences, which enables large models to analyze data, reflect on failures, and optimize models like scientists do [2][14][56]. Group 1: Framework and Mechanism - DrSR employs a dual-path reasoning mechanism that integrates "data insights" and "experience summaries" to guide large models in scientific equation discovery [16][28]. - The framework consists of three virtual scientists: a data scientist, a theoretical scientist, and an experimental scientist, each contributing to a collaborative mechanism for efficient scientific equation discovery [3][7]. Group 2: Performance and Results - In various interdisciplinary modeling tasks, DrSR has demonstrated superior generalization capabilities, outperforming existing methods in accuracy and efficiency [4][30]. - Experimental results show that DrSR achieved an accuracy of 99.94% in nonlinear damping oscillation system modeling, significantly surpassing all baseline methods [31]. Group 3: Learning and Adaptation - DrSR's process is a closed loop: data analysis → prompt guidance → equation generation → evaluation and scoring → experience summarization, allowing the model to accumulate knowledge and refine its approach [28]. - The framework's experience-driven strategy helps avoid common failure structures, resulting in a higher proportion of valid equations generated compared to other methods [37]. Group 4: Robustness and Generalization - DrSR exhibits strong robustness against noise and out-of-distribution (OOD) data, maintaining low normalized mean square error (NMSE) across various tasks [40][41]. - The model's performance remains stable under different Gaussian noise levels, showcasing its generalization advantages [41]. Group 5: Future Directions - DrSR is integrated into the ScienceOne platform, providing efficient and interpretable scientific modeling services, with plans to enhance its reasoning capabilities and cross-task generalization [57]. - Future improvements will focus on expanding DrSR's capabilities to multi-modal scientific modeling scenarios and incorporating continuous learning mechanisms [61].

Group 1 - The event "IQ Talk" focused on the development strategies, key technologies, industrial applications, and talent cultivation in the field of scientific intelligence, highlighting the transformative impact of AI on scientific research [1][2] - Experts believe that "AI for Science" signifies a paradigm shift in scientific research, enabling a new form of interdisciplinary collaboration and innovation [1] - The need for new talent with cross-disciplinary thinking, engineering practice, and social insight was emphasized to navigate the scientific cognitive revolution [1] Group 2 - AI-driven technologies are expected to overcome traditional R&D challenges such as long cycles and high costs, facilitating new scientific discoveries [2] - AI tools are being developed to address real scientific problems, including algorithms for predicting biological interactions and driving drug molecule design [2] - Demonstrations of AI platforms that integrate literature review, computation, experimentation, and multidisciplinary collaboration were showcased, indicating a shift from trial-and-error to computation-driven research paradigms [2]

Core Viewpoint - The "Science Intelligence White Paper 2025" emphasizes the integration of AI in scientific instruments, transforming them from mere data collection tools to intelligent research partners, enhancing efficiency and capabilities across various scientific fields [1][4][5]. Group 1: Integration of AI in Scientific Instruments - The white paper highlights the deep integration of scientific intelligence (AI for Science, AI4S) with scientific instruments, showcasing how AI empowers traditional instruments to become intelligent research partners [5]. - AI-driven automation in experiments has shown significant improvements, such as a 30% increase in stability and a 50% boost in data collection efficiency in nuclear fusion research [5][6]. - In drug development, automated laboratories utilizing AI algorithms can complete thousands of compound screenings within 48 hours, achieving speeds 10 times faster than traditional methods [6]. Group 2: Trends in Scientific Instrument Intelligence - The design philosophy of scientific instruments is undergoing three major transformations: - Human-machine interaction revolution, with 90% of new instruments featuring smart interfaces, including gesture control [11]. - Integrated data management and analysis, allowing instruments to generate visual reports directly, such as automatic mutation annotation in gene sequencers [13]. - Sustainable design, with 60% of instruments expected to use bio-based resins or recycled metals [15]. Group 3: Global Competitive Landscape and China's Breakthroughs - China is leading in specific fields such as Earth and environmental sciences, with advancements in AI meteorological models and remote sensing instruments [17]. - The domestic production rate of online detection instruments in intelligent manufacturing has surpassed 60% [18]. - However, there are challenges, including a reliance on imports for high-end analytical instruments, with over 80% of such instruments being imported [19]. Group 4: Future Outlook for Scientific Instruments - The future of scientific instruments is projected to focus on three main directions: - Intelligent upgrades, with AI deeply embedded in instrument control and data analysis processes [23]. - Development of specialized instruments for extreme environments, such as deep-sea and space applications [24]. - Establishing an open ecosystem through global laboratory alliances to share material databases [25].

Group 1 - Shanghai's first multimodal large model in the transportation sector has been launched, expected to improve intersection traffic efficiency by approximately 15% [1] - Red Hat has announced the launch of the open-source project llm-d, aimed at meeting the large-scale inference needs of generative AI, in collaboration with contributors like NVIDIA and Google Cloud [1] - Fudan University has released "AI Big Class 2.0" and the 2.0 version of its intelligent computing platform, emphasizing scientific intelligence [1] Group 2 - OpenAI plans to release its first AI hardware in 2026, driven by ChatGPT, aiming to integrate AI more deeply into users' daily lives [1]

Group 1 - Fudan University officially launched the CFFF Intelligent Computing Platform 2.0, which has undergone comprehensive upgrades in scientific model openness, scientific data security sharing, and software-hardware collaborative optimization compared to CFFF 1.0 [1][3] - The CFFF platform is the largest cloud-based research intelligence platform in domestic universities, set to officially go live on June 27, 2023, and covers the entire "teaching-learning-research" chain in artificial intelligence infrastructure [1][3] - The platform features 47 specialized academic models across various disciplines and provides over 40,000 datasets totaling 11PB for scientific model development and secure sharing [1][3] Group 2 - Fudan University aims to continuously promote AI course development and establish CFFF 2.0 as a foundational research facility in the field of scientific intelligence, facilitating a reform in teaching and learning [3] - The university's president emphasized the platform's role in accelerating the discovery of new scientific principles and technological breakthroughs through high-quality scientific resources and research tools [3][4] - A global alliance for scientific intelligence universities was proposed to foster collaboration, resource sharing, and ecological development in the field of scientific intelligence [4][6] Group 3 - Fudan University released the "AI Big Class" 2.0 white paper, focusing on collaborative innovation between teachers and students, and established an AI education and teaching innovation center to support the Shanghai International Science and Technology Innovation Center [6][7] - A strategic partnership with Springer Nature was formed to launch a comprehensive academic journal titled "Science and AI," aimed at addressing complex challenges through artificial intelligence and scientific principles [7]

Group 1 - Fudan University and Shanghai Institute of Science and Intelligent Research jointly released the "Science Intelligence White Paper 2025" in collaboration with Springer Nature's Nature Research Intelligence [1]