Core Insights - The article discusses the transition from AI for Science (AI4S) to AGI for Science (AGI4S), emphasizing the need for a specialized generalist model to enhance scientific discovery and reasoning capabilities [1][2][71]. Group 1: Current State of AI in Science - AI for Science, exemplified by AlphaFold, has achieved significant milestones in specific fields like protein folding and weather prediction, but reliance on existing deep learning models may limit the exploration of new knowledge and hinder innovation [1][71]. - A systematic evaluation involving 100 scientists from 10 different scientific fields revealed that cutting-edge models scored 50 out of 100 in general scientific reasoning tasks, but dropped to scores between 15 and 30 in specialized reasoning tasks [1][71]. Group 2: The Need for AGI4S - The transition from AI4S 1.0 to AGI4S 2.0 is necessary to integrate general reasoning with specialized capabilities, addressing the limitations of current models in scientific discovery [2][71]. - The concept of "Specialized Generalist" is proposed as a feasible path to achieve AGI, which combines deep specialization with general capabilities [2][90]. Group 3: Technological Framework - SAGE - The "SAGE" architecture is introduced as a synergistic framework for developing generalizable experts, consisting of three layers: foundational, collaborative, and evolutionary [3][18]. - The foundational layer focuses on decoupling knowledge and reasoning capabilities, while the collaborative layer employs reinforcement learning to balance intuitive and logical reasoning [27][28]. - The evolutionary layer aims to enable self-evolution of models through continuous interaction and feedback, addressing the challenges of adapting to complex tasks [55][56]. Group 4: Innovations in Reinforcement Learning - The article highlights the development of the PRIME algorithm, which provides dense rewards for reinforcement learning without the need for extensive manual labeling, significantly improving model performance [38][39]. - FlowRL is introduced to enhance the diversity of reasoning paths in models, allowing them to explore multiple solutions rather than converging on a single answer [47][50]. Group 5: Applications and Case Studies - The Intern-S1 model is designed to be a deep specialized generalist for scientific applications, demonstrating superior performance in various scientific domains compared to existing models [77][79]. - The Intern-Discovery platform integrates the Intern-S1 model with extensive data and tools, facilitating a closed-loop system for hypothesis generation and experimental validation [80][84]. Group 6: Future Directions - The article calls for collaboration among researchers to fill the gaps in the current framework and advance the development of AGI4S, emphasizing the potential for AI to revolutionize scientific research [89][90].

在周伯文看来，ANI在2016年已趋于成熟，而通往AGI的必经之路并非直接跃迁，而是必须率先实现具 备跨领域泛化能力的ABI。这一跨越需要技术范式的根本性变革，最少包括从有监督学习转向自监督学 习、从人类分割任务级联式系统转向端到端架构、从判别式工具进化为生成式助手。ChatGPT的问世第 一次验证了人工智能系统同时达成这三方面变革，实质上宣告了ABI阶段的到来。这一历史性突破验证 了规模法则（Scaling Law）的有效性，通过扩大Transformer架构并将"下一个词预测"作为优化目标，人 类首次实现了对世界知识的压缩。 "科学发现是AI的下一个前沿阵地，大规模深度推理将赋能科学发现，科学发现亦将反哺推理能力的进 化。"上海人工智能实验室主任、首席科学家周伯文日前在第四十届人工智能协会年会（AAAI 2026） 发布特邀报告。周伯文表示，当前我们已身处通用人工智能的前夕，但仍缺失通专融合的智能，亟需推 动科学智能从1.0向2.0迭代演进，即从AI4S迈向AGI4S。 AGI必须打破通专二元对立 人工智能的发展历程并非线性堆叠，而是呈现出明显的阶段性跃迁。回顾AI发展的历史坐标，有助于 厘清当前所处的位 ...

Core Views - Beijing University Shenzhen Graduate School (PKUSZ) aims to integrate its research and teaching with the resources of the Greater Bay Area, focusing on academic innovation and societal service [2] - The establishment of the School of AI for Science (PKUSAI4S) in 2025 represents a strategic initiative to merge artificial intelligence with fundamental sciences, fostering interdisciplinary research and innovation [3][4] - The School of Intelligent Engineering (PKUSECE) emphasizes the integration of education, academic research, and industry, leveraging the advantages of the Greater Bay Area [7] Recruitment Areas - PKUSZ is recruiting in three core areas: - Integrated Circuit Science and Engineering, focusing on analog circuit design, micro-nano electronic devices, or electronic design automation for tenured or tenure-track assistant professors [8] - Computer Science and Technology, with emphasis on video and audio processing, XR/3D media processing, computer vision, computer graphics, haptic information processing, artificial intelligence, and robotics for tenure-track assistant professors [8] - Communication and Information Systems, concentrating on wireless communication, intelligent communication, the Internet of Things, and RF devices for tenured or tenure-track assistant professors [8] Academic Vision - The vision of PKUSZ is to accelerate scientific discovery and define the future of industries, promoting a collaborative and innovative academic environment [4] - The educational philosophy emphasizes fundamental principles, wisdom-driven approaches, and cross-disciplinary integration to shape the future [5]

Summary of Key Points from the Conference Call Industry and Company Overview - The conference focused on the advancements and future trends in the **Artificial Intelligence (AI)** industry, particularly through the lens of **ZhiYuan Research Institute**. The discussions highlighted the transition of AI into commercial applications and the evolution of AI technologies. Core Insights and Arguments 1. **AI Development Trends**: AI is accelerating towards commercial applications, with AI agents evolving towards specialization and unified protocols. Machine intelligence is shifting from superficial imitation to understanding and modeling the laws of the physical world, entering a new paradigm of "state space prediction" which enables forecasting future trends [1][2][3]. 2. **Technological Achievements**: Significant progress has been made in areas such as world models, scaling laws, and AI agents. Large models have shown rapid advancements in language and visual understanding, with AI for Science becoming an essential tool in research [1][4]. 3. **Multimodal World Models**: The development of multimodal world models is progressing through pre-training with multimodal data, learning real-world dynamics. This evolution from Next Token Prediction to Next Day Prediction signifies a leap in capabilities [1][14]. 4. **Growth in the AI for Science Sector**: The transition from traditional methods to AI-driven approaches in scientific research is evident, with AI for Science becoming integral to research workflows. The U.S. "Genesis Project" aims to integrate resources across the entire scientific process [1][18][19]. 5. **Challenges in the AI Industry**: The AI industry faces challenges such as data quality, the maturity of multi-agent systems, and high costs. A potential disillusionment phase is anticipated in early 2026, but a rebound is expected later in the year [22][46]. 6. **Synthetic Data Utilization**: The reliance on high-quality data is diminishing, leading to a rise in synthetic data and reinforcement learning. The synthetic data market is projected to surpass real data by 2030, indicating a shift in data sourcing strategies [23][35]. 7. **AI Super Applications**: The emergence of AI super applications is being driven by direct productization of AI technologies, with expectations for new dominant players in the market. These applications are expected to integrate multiple industry APIs to enhance functionality [21][42]. 8. **Future of AI Agents**: Multi-agent systems are anticipated to become mainstream in enterprise applications, with protocols like MCPASA potentially revolutionizing interactions between agents [20][26]. Other Important but Overlooked Content 1. **AI's Societal Impact**: The development of AI is reshaping scientific innovation, transitioning from traditional research methods to AI-driven approaches, which could help address systemic risks that humanity faces [6]. 2. **Community Support for Researchers**: The ZhiYuan community is actively supporting researchers by providing access to a vast array of AI papers and facilitating collaboration through various initiatives [8]. 3. **Safety and Security in AI**: The increase in AI applications has led to a rise in reported safety incidents, emphasizing the need for robust safety measures and research into AI behavior [62]. 4. **Future AI Research Directions**: The focus is shifting towards solving specific problems rather than merely accumulating knowledge, with expectations for AI to enhance research efficiency significantly [40][56]. This summary encapsulates the key points discussed during the conference, highlighting the advancements, challenges, and future directions of the AI industry as presented by ZhiYuan Research Institute.

Core Insights - The 2025 Xili Lake Forum focuses on "AI for Science," aiming to explore how artificial intelligence can revolutionize scientific research and drive industrial innovation [1][3][8] Group 1: Forum Overview - The forum features 28 specialized sessions covering various topics such as industry forums, achievement transformation, basic research, talent forums, and popular science [3] - The theme "Accelerating Scientific Discovery, Defining the Future of Industry" emphasizes the role of AI in reshaping scientific research paradigms and fostering new scientific discoveries [3][10] Group 2: Key Announcements - Three major announcements were made during the opening ceremony, including the establishment of the International Intellectual Property Academy in collaboration with Peking University [10] - The launch of the "Boya AI4S Top Talent Program" for integrated undergraduate, master's, and doctoral education was also highlighted [10] - The signing ceremony for the delivery of the first phase of the Shenzhen University Town International Campus took place, aiming to enhance collaboration in industry, academia, and research [10][13] Group 3: AI Applications and Collaborations - The forum aims to build bridges between scientific breakthroughs, educational reforms, and industrial upgrades, positioning AI as a core engine for high-quality social development [3][8] - The "AI for X" dialogue session focused on practical applications of AI across various industries, discussing how AI can reshape future technology and industrial ecosystems [3][5] Group 4: Future Initiatives - The forum will continue throughout November, promoting an open and integrated innovation ecosystem, and will feature various activities including the "Open Day" and sports events among the X9 Alliance universities [15]

Core Insights - The third World Scientific Intelligence Competition was held in Shanghai, featuring 30 teams competing for awards in five major categories, with a total of 5 first prizes, 10 second prizes, and 15 third prizes awarded [1][3] - The competition aimed to select global talent in the field of AI for Science, with no restrictions on nationality or region, and attracted nearly 16,000 participants from around 30 countries and regions [1][4] Group 1: Competition Overview - The event was co-hosted by the Shanghai Institute of Scientific Intelligence and Fudan University, with support from various institutions including Alibaba Cloud and Shanghai Fosun Pharmaceutical [1] - The competition focused on high-value industrial scenarios, with real-world data sets provided for the challenges, such as aviation safety and renewable energy power forecasting [4][5] - A new "Physical AI track" was launched to address core technological challenges in space intelligence and reasoning models, promoting the application of AI technology [4] Group 2: Open Collaboration and Platform Development - The competition emphasized open-source principles, providing access to real data from industrial scenarios and offering computational resources and toolchain support for participants [5] - Outstanding models from the competition will be deployed on the newly launched Xinghe Qizhi Scientific Intelligence Open Platform, which aims to facilitate collaboration among scientists, AI researchers, and engineers [5] - The platform currently hosts over 200 scientific models across 12 disciplines and has accumulated more than 12PB of scientific data, attracting over 120 research teams [5] Group 3: Youth Engagement - The competition introduced a middle school category, attracting 331 teams from 146 schools in Shanghai, with an average participant age of around 14 years [7] - This initiative aims to enhance the youth training system and showcase the innovative potential of young participants in the field of scientific intelligence [7] Group 4: Future Directions - The organizing committee plans to continue leveraging the competition platform to launch more cutting-edge events focused on scientific intelligence, fostering a sustainable ecosystem for innovation and talent development [10]