This work demonstrates how to build AI agents for science by combining multimodal pre-training with agentic reinforcement learning and memory-augmented architectures, achieving strong performance on scientific reasoning and forecasting tasks without requiring task-specific model modifications.
Intern-S2-Preview is a large multimodal AI system designed to tackle scientific discovery tasks by reasoning over diverse data types, using scientific tools, and working across long-horizon problems.