Intern-S1: A Multimodal Foundation Model for Scientific Research

This article introduces Intern-S1, a multimodal foundation model designed specifically for scientific research, released by the InternLM team of the Shanghai Artificial Intelligence Laboratory. It aims to address the problem that general large language models struggle to deeply understand professional content in scientific fields (especially multimodal information such as charts, formulas, and experimental images), marking the evolution of the AI for Science field from general models to deep customization for professional domains, and exploring new possibilities of AI empowering scientific discovery.

Artificial intelligence has become a powerful assistant for scientists, but general large language models struggle to deeply understand professional content in scientific fields, especially scenarios involving multimodal information such as charts, formulas, and experimental images. Scientific research is inherently multimodal, and traditional models can only process text, which makes them inadequate when dealing with these contents—this provides the context for the emergence of professional scientific multimodal models.

The core advantage of Intern-S1 lies in its native multimodal design, which can simultaneously understand multiple types of information such as text, images, charts, and formulas and establish connections between them. Unlike general models, it is optimized for scientific scenarios. Its training data covers high-quality literature, textbooks, etc., across various disciplines, learning scientific writing styles, terminology usage, and chart conventions, resulting in significantly improved performance in tasks like scientific Q&A and chart understanding.

Intern-S1 has a wide range of application scenarios: In literature review, it can quickly read a large number of papers, extract key findings, identify trends, and generate structured reports; In experimental design, it can suggest reasonable plans, predict results, and identify risks; In data analysis, it can understand experimental images, recognize feature patterns, suggest statistical methods, and translate them into technical implementations using natural language.

Achieving scientific multimodal understanding faces three major challenges: modal alignment, scientific symbol understanding, and long document processing. Intern-S1 uses an advanced cross-modal attention mechanism to establish semantic connections between text and vision; it may use LaTeX formula encoding or graph neural network molecular representation to parse scientific symbols; and it uses efficient attention mechanisms or hierarchical strategies to handle long documents.

Intern-S1 continues the open-source philosophy of the InternLM team, allowing researchers worldwide to use it, promoting the democratization of scientific AI, and enabling the community to fine-tune it or integrate it into tools. Compared with general models like GPT-4V, its advantage lies in scientific professionalism—it can understand the scientific meaning of charts rather than just surface recognition, establishing a deeper level of scientific understanding.

Intern-S1 has limitations: its subject coverage needs to be strengthened, it is difficult to access the latest research progress, and its original scientific reasoning ability is insufficient. Future directions include expanding subject coverage, integrating retrieval-augmented technology, developing interactive scientific research assistants, and establishing scientific reasoning benchmarks.

Intern-S1 heralds a transformation in scientific research paradigms: information retrieval shifts from manual to intelligent recommendation, knowledge integration from manual to AI-assisted, and experimental design from experience-based to data-driven. Human-machine collaboration will become the mainstream, with AI enhancing scientists' capabilities. It is a microcosm of the deep integration of AI and science, pushing AI for Science into a new golden age and expanding the boundaries of science.