internlm/Intern-S2 · Hugging Face
Intern-S2-397B is a new multimodal foundation model designed for scientific intelligence and long-horizon agents. It scales across pre-training, reinforcement-learning task coverage, and interactive agent environments. By integrating a novel vision-language pre-training paradigm with large-scale multi-task and long-horizon agent reinforcement learning, Intern-S2-397B significantly enhances general reasoning, scientific problem-solving, and agentic capabilities. It also improves generalization and raises the capability ceiling for long-horizon tasks in both general and scientific domains.
时间与来源
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发布当时偏移：UTC+02026年9月13日 10:19 UTC
收录当时偏移：UTC+02026年9月13日 15:01 UTC
- 发布
- 2026年9月13日 10:19
- 收录
- 2026年9月13日 15:01
- 来源类型
- 开发者社区
- 档位
- 社区
- 信源状态
- 正常
档位是按信源手工设定的编辑判断，不是逐条打分。
We introduce Intern-S2-397B, our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre-training, reinforcement-learning task coverage, and interactive agent environments. By combining a new vision-language pre-training paradigm with large-scale multi-task reinforcement learning and long-horizon agent reinforcement learning, Intern-S2-397B delivers a step change in general reasoning, scientific problem solving, and agentic capabilities.
- New Pre-training Paradigm. Via visual pretraining, Intern-S2-397B learns directly from raw pages of scientific literature, jointly modeling symbolic semantics and visual relationships in a shared representation space without intermediate parsing. This preserves text-visual correspondence, strengthens spatial and visual reasoning, and improves data efficiency.
- Scientific Modality Reasoning and Generation. By scaling diverse scientific reinforcement-learning tasks across more than 20 domains and training them jointly, Intern-S2-397B achieves leading general-reasoning performance among open-source models and strong results in specialized scientific tasks such as biomolecular interaction design and material structure generation.
- General & Scientific Long-Horizon Agents. By connecting multiple agent frameworks to large-scale sandboxed environments for black-box agentic reinforcement learning, Intern-S2-397B improves generalization and raises the capability ceiling for long-horizon tasks in both general and scientific domains.