On July 19, during the Scientific Intelligence Open Forum at the 2026 World Artificial Intelligence Conference, the Shanghai Institute for Scientific Intelligence unveiled a foundational super brain - Shenzhen Scientific Multimodal Foundation Model, which supports multidisciplinary scientific intelligence research. The name is derived from the "Divine Pearl Iron" in Journey to the West. The team hopes that this open model can carry out diverse scientific tasks in a relatively concise form and allow more researchers to participate in testing, using, and co-building it. It is the "Great Sage" system-level intelligent research agent, launched in early March of this year, with its super brain.

Technically, Shenzhen is based on multiple disciplines such as material science, life science, and earth science, and has achieved a stage-wise unified representation of cross-domain scientific knowledge and multimodal integrated understanding. The model has about 11 billion parameters and supports scientific understanding and multi-type result generation for six types of scientific data: DNA, RNA, proteins, small molecules, Earth systems, and medical images, all within a single model. Its shared backbone uses Qwen3-VL-8B, and each of the six types of scientific data has a dedicated data processing path. Before connecting to the shared model, it preserves the sequence order of sequences, molecular connection structures, meteorological field spatial distribution, and local details of medical images. More practically, Shenzhen can directly generate RNA sequences, computer-readable molecular representations (SMILES), global meteorological fields, and medical image segmentation results, effectively integrating both understanding and generation capabilities into one framework.

Open source and openness are key characteristics of this model. The Shanghai Institute for Scientific Intelligence has released model weights, inference code, example scripts, and usage documentation. Researchers can download the model or call APIs through the Xinghe Qizhi Scientific Intelligence Open Platform, and use them together with over 1,500 scientific models and tools available on the platform; they can also download weights on Hugging Face and get code on GitHub, jointly building an open scientific intelligence community.

This release took place on the stage of the Scientific Intelligence Open Forum at WAIC2026. The forum was guided by the Office of the World Artificial Intelligence Conference Organizing Committee, and hosted by Fudan University and the Shanghai Institute for Scientific Intelligence. It focused on the scientific closed-loop discovery in the AI-native era, gathering many renowned scientists and young representatives from industry, academia, and research, deeply discussing how artificial intelligence could restructure the entire process of scientific discovery from the methodology level, aiming to create a new ecological environment of open and collaborative scientific intelligence. Four distinguished scientists delivered keynote speeches, opening up different dimensions to understand scientific intelligence.

Professor Arie Y. K. Wachter, winner of the Nobel Prize in Chemistry and professor at the Chinese University of Hong Kong, Shenzhen, emphasized that AI must be built on reliable physical mechanisms. Professor Gilles Brassard, winner of the Turing Award and professor at the University of Montreal, expressed high expectations for the potential of quantum computing in fields like new material discovery, and advised young researchers to follow their interests rather than industry trends, as current research findings might only truly take effect after a decade.

Director of the Zhejiang Lab and academician of the Chinese Academy of Engineering, Wang Jian, shared his views on scientific foundation models and science as a whole. He pointed out that scientific intelligence lies not in paper texts but in data, yet today's foundation models are still based on text. In the future, scientific data should become the native inhabitants of scientific intelligence, meaning that data from various disciplines should be tokenized and included in a common representation space. He believes AI is becoming as fundamental as mathematics, and the research paradigm will shift from STEM to STE+MAP, that is, the integration of mathematics, AI, and public infrastructure, making science a whole again.

Professor Jianqing Fan, member of the U.S. National Academy of Sciences and professor at Princeton University, spoke on the topic of intelligent science and society, summarizing AI as a dynamic cycle of statistical learning and optimization decision-making, and explained how AI transforms data into social insights with examples such as socioeconomic measurement, financial risk control, and large model applications. He emphasized that AI serves socioeconomic development, while promoting intelligent agents and scientific innovation, it must continuously respond to issues such as changes in employment structure, educational transformation, ethical security, and whether humans can maintain effective control.

In the summit dialogue session, participants reached a consensus: a major key to original innovation in the AI-native era lies in whether people, data, models, experiments, and academic judgment can form a new collaborative discovery mechanism. This requires not only systematic upgrades in research infrastructure, but also a fundamental transformation in the thinking patterns and capability structures of a generation of researchers.

Qi Yuan, a specially appointed professor at Fudan University and director of the Shanghai Institute for Scientific Intelligence, outlined the ultimate vision of this transformation. He stated that AI should move from predicting the next token to discovering unknown laws, and that the discovery of unknown scientific laws by AI will mark the beginning of super intelligence. The core of this process lies in compressing high-dimensional space to discover concise principles and achieving an efficient scientific verification loop, which will promote the integration of digital and physical worlds and the emergence of new model architectures. He reminded us that real research involves a long chain process including literature, hypotheses, data, models, simulations, experiments, and feedback, and the field of scientific intelligence is currently focusing on developing the systemic capabilities to organize complex research processes.

When Shenzhen, with its 11 billion parameters, brings six types of scientific data into one mind, the form of scientific discovery may be quietly rewritten.