S2: In-Depth Review of the Multimodal Foundation Model Open-Sourced by Shanghai AI Lab

1. What is S2

S2 is a multimodal foundation model open-sourced by the Shanghai Artificial Intelligence Lab in December 2025, featuring up to 397B parameters. It is positioned as a "science-savvy" foundation model. Unlike traditional models, S2 introduces a revolutionary Memory Decoder architecture, enabling dynamic expansion of domain knowledge through pluggable specialized memory modules. It continuously integrates cutting-edge knowledge from fields such as biology and materials science without retraining the main model. The model maintains top-tier open-source performance in general capabilities, while its scientific evaluation results significantly outperform existing open and closed-source flagship models. Its mathematical reasoning ability reaches the level of Gemini 3.1 Pro, and it also possesses the capability to execute long-term research tasks and enable autonomous collaboration among agents.

Image source: Official article
Image source: official article

Technical positioning and domain: S2 belongs to the field of multimodal foundation models and is positioned as a general AI infrastructure tailored for scientific research scenarios. While maintaining general capabilities such as knowledge and code generation, it emphasizes enhanced understanding and generation abilities in specialized domains like biology and materials science, serving research automation and complex reasoning tasks.

Development background: The model was developed by the Shanghai Artificial Intelligence Lab, which has accumulated substantial technical expertise in the S2 series of models. The motivation for its development stems from the current issue that large models excel in general tasks but lack deep scientific knowledge. The team aims to address the challenge of continuous expansion of domain-specific knowledge through architectural innovation rather than simple data accumulation.

Core value: S2 addresses two core issues: the high cost and complexity of expanding domain-specific knowledge, which the Memory Decoder architecture enables with low-cost, real-time integration; and the rigid demand for long-range reasoning capabilities in scientific research tasks, where the model can perform rigorous mathematical reasoning and scientific analysis for several hours continuously.

Technical features: The model employs a pluggable memory mechanism with the Memory Decoder, deeply optimized in synergy with Ascend computing power. It also explores a latent space pre-training path based on the JEPA paradigm. Its unique memory module design allows it to significantly outperform competitors in specialized scientific evaluations, while maintaining the completeness and deployability of its general capabilities.

2. Key Features

• Pluggable Professional Memory Expansion: Based on the Memory Decoder architecture, domain-specific expertise is learned and stored in independent memory modules. When integrated, there is no need to modify the main model's parameters. During inference, the model dynamically coordinates contributions from the main model and professional memory modules according to the current context, deeply integrating domain knowledge into the complete reasoning chain and significantly reducing the cost of domain expansion.

• Scientific Knowledge Q&A and Reasoning: Provides professional-level understanding and generation capabilities in fields such as biology and materials science, covering analysis of DNA, RNA, proteins, and interactions between biological molecules. Performance on scientific benchmarks like Biology-Instructions and Mol-Instructions significantly outperforms other open and closed-source flagship models.

• Long-range Rigorous Mathematical Reasoning: Supports reasoning and verification on competition mathematics and advanced mathematics benchmarks such as IMO-Proof and AdvancedMathBench for several hours. Mathematical reasoning capabilities reach the level of Gemini 3.1 Pro, with some metrics surpassing it, enabling the model to tackle complex theoretical problems at the level of the GRE Subject Test in Mathematics.

• End-to-end Execution of Research Tasks: Capable of continuously analyzing, reasoning, and optimizing around real-world research problems such as protein binder design and crystal structure construction. The model not only provides answers but can also autonomously complete experimental plan design and iterative evaluation of candidate solutions, forming a complete research loop.

• Intelligent Agent Autonomous Collaboration: With a single sentence, users can issue complex instructions, and the model can autonomously complete information retrieval, task decomposition, multi-tool collaboration, and plan orchestration. For example, it can fully autonomously generate a 3D CAD model of the Long March 7 rocket, demonstrating its ability to transform natural language into complete engineering deliverables.

• Deep Optimization for Ascend Compute Power (NCP-ArchPreview): Designed for next-generation architectures, the model introduces a Next Concept Prediction (NCP) task in the form of a JEPA-style pre-training in latent space. Instead of predicting only the next Token, the model predicts discrete semantic concepts across Tokens through a dedicated Concept Module in the latent space, which then feeds back to guide the underlying autoregressive generation process.

3. How to Use

InternLM-S2 provides multiple usage options, allowing users to choose between online experience, API calling, or local deployment based on their specific needs.

1. Online Experience: Users can directly access the official InternLM-PuYue dialogue platform at https://chat.intern-ai.org.cn/ to interact with InternLM-S2. This platform requires no installation or deployment and is ideal for quickly verifying the model's capabilities and performance, making it the most convenient way to experience the model.

2. API Calling: All users are granted a free API quota that is sufficient for daily research and development needs. For higher API quotas, users can apply at https://internlm.intern-ai.org.cn/api/strategy. The API method is suitable for integrating the model's capabilities into your own applications or research workflows.

3. Model Acquisition: The model weights can be downloaded from the HuggingFace platform at https://huggingface.co/internlm/Intern-S2-397B. Before downloading, ensure that your local storage space is sufficient (the 397B parameter model requires significant disk space), and apply for the necessary access permissions in advance.

4. Local Deployment: Refer to the deployment documentation in the GitHub repository at https://github.com/InternLM/Intern-S1 for local deployment. The deployment environment requires a high-performance GPU cluster. The Ascend computing environment has been deeply optimized and is recommended for use. The deployment process includes four stages: environment configuration, dependency installation, weight loading, and inference service startup.

5. Best Practices: For first-time users, it is recommended to start with the online experience entry to become familiar with the model's capability boundaries before deciding whether to proceed with API or local deployment. Research teams can combine the specialized memory module with custom domain data to build a dedicated research assistance system. It is worth noting that the GitHub repository name differs from the model version; when deploying, always refer to the latest documentation in the repository.

4. Pros and Cons Analysis

5. Comparative Analysis with Similar Tools

Selection Recommendations: For research institutions and enterprises requiring deep scientific analytical capabilities, Shusheng-S2 is currently the preferred open-source solution. It significantly outperforms other open-source models in specialized evaluations such as biology and materials science, and its free API quota and plug-and-play memory architecture greatly reduce the cost of domain customization, making it particularly suitable for knowledge-intensive scenarios like life sciences and new material development.

For users requiring the most comprehensive general capabilities and a mature product ecosystem, Gemini 3.1 Pro offers powerful cloud-based inference services through Google's closed-source infrastructure and continuous iteration advantages, making it suitable for lightweight applications with less stringent data privacy requirements. DeepSeek-V3.2 excels in developer community engagement and coding capabilities, making it ideal as a general-purpose programming assistant, though users should be mindful of the high resource consumption associated with its 671B MoE architecture. Qwen2.5-Max, as a closed-source service, demonstrates mature performance in both Chinese and English bilingual scenarios, making it suitable for API users who require optimized Chinese experience.

6. Editor's Summary

Shusheng-S2 represents an important technical exploration in the field of scientific vertical domains for open-source foundational large models in China. From an architectural innovation perspective, the Memory Decoder mechanism breaks away from the traditional approach of relying on full retraining or large-scale fine-tuning for knowledge expansion in large models. This "main model + pluggable memory module" design philosophy aligns closely with modular thinking in cognitive science, providing an engineering-feasible technical path for the efficient integration of multi-domain professional knowledge. The model's performance data in scientific evaluations demonstrate that it is not simply a result of stacking training data, but rather a substantial improvement in professional capabilities achieved through targeted architectural design.

In terms of practical value, Shusheng-S2 has real-world significance in advancing scientific research automation. Its long-range reasoning capabilities and agent collaboration abilities bring the concept of an "AI scientist" from theoretical validation to practical usability. The model's performance on real scientific problems such as protein binder design and crystal structure construction validates the feasibility of AI-assisted scientific research, and in the future, it may potentially reshape the workflow paradigm of scientific research.

In terms of target users and deployment, this model is more suitable for research institutions and large enterprise research institutes that have computational resources and professional technical teams. Individual developers can explore application scenarios using the free API quota. The model's deep compatibility with the Ascend computing ecosystem also provides strong empirical support for the self-reliance and controllability of domestic AI infrastructure.

The future development potential mainly lies in three directions: the continuous expansion of the Memory Decoder architecture to cover more scientific disciplines, the application validation of implicit space pre-training (NCP) technology in next-generation models, and the gradual improvement of the open-source ecosystem.

7. Application Scenarios

• Life Sciences and Pharmaceutical R&D: Research teams leverage the biological expertise of Bookworm-S2 for analyzing interactions among DNA, RNA, proteins, and biomolecules. For example, around the immunotherapy target IL-7Rα, the model can continuously design and optimize candidate molecules for protein binding, significantly shortening the exploration cycle in early-stage drug discovery.

• Materials and Crystal Structure Design: Materials scientists use the model to automatically complete space group matching and systematic construction of lattice parameters and atomic coordinates based on chemical formulas. This process shifts new material exploration from the traditional "empirical trial-and-error" paradigm to a "model-driven" approach, enhancing the efficiency and accuracy of discovering new materials.

• Mathematics and Theoretical Scientific Research: Researchers tackle complex problems at the level of competition mathematics, advanced mathematics, and doctoral qualifying exams by utilizing the model's ability to perform continuous reasoning and verification over several hours, producing rigorous solutions. This scenario is particularly suitable for theoretical scientific research that requires repeated derivation and validation.

• Engineering Design and Agent Automation: Engineers issue complex instructions in natural language, and the model autonomously performs information retrieval, task decomposition, and multi-tool collaboration orchestration, ultimately generating complete engineering outcomes such as the 3D CAD model of the Long March 7 rocket. This achieves full-process automation from design intent to engineering deliverables.

• Science Education and Knowledge Popularization: Universities and scientific training institutions can use Bookworm-S2 as an interactive teaching tool, providing students with in-depth explanations of scientific concepts, guidance on experimental design, and assistance with academic paper writing, addressing the issue of insufficient specialized faculty.

8. FAQ

Q: What is the relationship between Intern-S2 and Intern-S1?
A: Intern-S2 is the latest version in the Intern series of models, with significant improvements in parameter scale, scientific and professional capabilities, and long-range reasoning abilities. S2 introduces the Memory Decoder pluggable memory architecture and enhances scientific evaluation performance. Since the official GitHub repository still uses the Intern-S1 naming convention, please refer to the latest instructions from the repository and HuggingFace model card for deployment.

Q: How to deploy Intern-S2 when there is insufficient memory?
A: Intern-S2 has 397B parameters and requires high hardware resources. It is recommended to deploy using a multi-node GPU cluster, employing model parallelism and pipeline parallelism strategies to distribute the memory pressure. The Ascend computing environment has been deeply optimized and is recommended for priority use. For regular development environments, you can first experience the model via online platforms or APIs without the need for local deployment.

Q: What is the difference between the Memory Decoder memory module and fine-tuning?
A: The Memory Decoder uses an independent memory module to learn domain knowledge, and no modifications to the main model's parameters are required when integrating it. During inference, it dynamically coordinates the output of the main model and the memory module based on the context. Compared to traditional fine-tuning (which requires updating all or part of the model parameters), this method avoids the problem of catastrophic forgetting and allows for lower cost and faster integration of new domain knowledge. Fine-tuning is more suitable for overall adjustments to specific task behaviors, while the Memory Decoder is more oriented toward continuous knowledge expansion.

Q: What is the quota for the free API of Intern-S2?
A: According to the official documentation, all users can use the free open API quota, which is suitable for daily experience and lightweight research usage. The exact quota amount is not clearly specified in the public release. Users can directly access the platform to experience the actual available quota. For higher usage volumes, you can submit an application, and upon approval, you will receive an increased quota.

Q: Does the model support commercial use?
A: Intern-S2 is released under an open-source license, with weights available for download and deployment. Users can apply the model to commercial applications. However, the specific type and details of the open-source license should be reviewed in the official license file within the HuggingFace model repository to confirm the exact limitations and compliance requirements for commercial use.

Q: Can Intern-S2 run offline?
A: Yes. After downloading the model weights, users can deploy and configure the model locally or in a private environment for offline use, without needing to connect to external API services. This is particularly important for data-sensitive research projects and internal enterprise applications. When deploying, you need to prepare a GPU server that meets the memory and computing power requirements.

9. Project Links

• InternLM Chat Official Dialogue Platform: https://chat.intern-ai.org.cn/ (Online Experience Entry)
• API Quota Application Page: https://internlm.intern-ai.org.cn/api/strategy
• GitHub Repository: https://github.com/InternLM/Intern-S1 (Deployment Documentation)
• HuggingFace Model Library: https://huggingface.co/internlm/Intern-S2-397B