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 ...