What is Intern-S1-Pro
Intern-S1-Pro is a trillion-parameter scientific multimodal large model open-sourced by Shanghai AI Laboratory. It adopts a MoE architecture (1T total parameters, 22B activated) and is built on the "general-specialized integration" SAGE technology. Through Fourier position encoding and a reconstructed temporal encoder, the model is endowed with "physical intuition," enabling unified understanding from microscopic life signals to macroscopic cosmic fluctuations. It performs excellently in Olympiad-level mathematical and physical reasoning, five major scientific disciplines (chemistry, materials, life, earth, and physics), and real-world scientific research scenarios. It is the scientific multimodal model with the largest parameter scale in the global open-source community, driving AI4S from a "tool revolution" toward a new paradigm of "scientific discovery."
Main Features of Intern-S1-Pro
- Scientific Reasoning: The model possesses gold-medal-level mathematical and logical reasoning abilities, performing excellently in International Mathematical Olympiad and International Physics Olympiad evaluations.
- Multimodal Understanding: The model can accurately parse complex scientific visual content such as molecular structure diagrams, experimental charts, and remote sensing images.
- Time-Series Signal Analysis: It uniformly processes heterogeneous time-series data ranging from several to million-level samples, covering multiple fields such as astronomy, geography, physiological signals, and bioacoustics.
- Interdisciplinary Research: It builds a full-spectrum capability matrix spanning the five core disciplines of chemistry, materials, life, earth, and physics, supporting more than 100 specialized subtasks such as retrosynthesis and protein sequence generation.
- Agent Capabilities: It supports the leap from static task planning to dynamic environment interaction, demonstrating world-class autonomous planning and execution capabilities in complex scientific research workflows.
- General Capabilities: It ranks firmly in the first tier of open-source models in dimensions such as image-text cross-modal understanding, high-quality text generation, complex instruction following, and tool calling.
Technical Principles of Intern-S1-Pro
- SAGE "General-Specialized Integration" Architecture: Through the design of shared foundational representation layers and differentiated expert layers, the model can mutually enhance itself during training, maintaining broad general cognitive abilities and deeply specialized scientific reasoning abilities, achieving the goal of a "deeply specialized general model."
- Mixture-of-Experts Architecture (MoE): Intern-S1-Pro adopts a MoE architecture with a total of 1 trillion parameters and 512 experts, activating only 8 experts per forward pass (about 22 billion activated parameters). Through an innovative routed dense estimation mechanism, it improves training stability, avoids the expert collapse problem common in traditional MoE, and introduces a grouped routing strategy to achieve load balancing across massive compute chips, efficiently scheduling compute resources like an intelligent transportation system.
- Physical Perception Layer Innovation: The research team introduced Fourier position encoding (FoPE) to give the model a unique "physical intuition"—able to capture the relative distances between text tokens like observing particles, and grasp the overall frequency patterns of scientific signals like analyzing waves; at the same time, it reconstructed an adaptive temporal encoder so that it automatically adjusts according to data density, achieving for the first time unified modeling of heterogeneous time-series signals spanning up to six orders of magnitude in sampling scale.
- Deep Adaptation to Domestic Compute: From the very beginning of its architectural design, the model established a joint research and development path with the Ascend computing ecosystem, achieving full-stack deep adaptation from the lowest-level operator optimization and compiler adaptation to the upper-level training framework XTuner V1 and inference engine LMDeploy, overcoming core technical challenges such as precision alignment in large-scale training and the stability of ultra-long sequence reinforcement learning, and building an autonomous and controllable integrated "computing power-algorithm" foundation.
Intern-S1-Pro Project Address
- Project Official Website：https://chat.intern-ai.org.cn/
- GitHub Repository：https://github.com/InternLM/Intern-S1
- HuggingFace Model Library：https://huggingface.co/internlm/Intern-S1-Pro
Application Scenarios of Intern-S1-Pro
- Basic Scientific Research: Intern-S1-Pro can assist with theoretical research in mathematics and physics, chemical materials design and synthesis pathway planning, and protein prediction and drug development in the life sciences.
- Earth and Environmental Science: The model supports environmental science research such as remote sensing image analysis, climate monitoring, geological exploration, and disaster risk prediction.
- Engineering and Technology Development: The model can interpret engineering drawings, analyze experimental data, generate technical documents, and integrate with external software to realize automated research and development processes.
- Scientific Research Agent Collaboration: The model can build autonomous agents to perform literature retrieval, experimental design, result analysis, and iterative optimization, forming a closed-loop scientific research process.
- Science Education and Popularization: It provides students and researchers with personalized academic tutoring, problem-solving guidance, and research method training, lowering the threshold for science learning.