Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale

arXiv cs.LG / 3/27/2026

📰 NewsDeveloper Stack & InfrastructureSignals & Early TrendsModels & Research

Key Points

  • Intern-S1-Pro is presented as a one-trillion-parameter scientific multimodal foundation model, claimed to be the first of its kind at that scale.
  • The model is said to improve both general reasoning and image-text understanding, while also adding advanced agent capabilities.
  • Its scientific competence is claimed to span 100+ specialized tasks across chemistry, materials, life sciences, and earth sciences.
  • The article attributes the ability to train at trillion-parameter scale to XTuner and LMDeploy, emphasizing efficient RL training and strict precision consistency between training and inference.
  • It positions Intern-S1-Pro as a “specializable generalist,” claiming top-tier open-source general performance and stronger results than proprietary models on specialized scientific tasks.

Abstract

We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancement across both general and scientific domains. Beyond stronger reasoning and image-text understanding capabilities, its intelligence is augmented with advanced agent capabilities. Simultaneously, its scientific expertise has been vastly expanded to master over 100 specialized tasks across critical science fields, including chemistry, materials, life sciences, and earth sciences. Achieving this massive scale is made possible by the robust infrastructure support of XTuner and LMDeploy, which facilitates highly efficient Reinforcement Learning (RL) training at the 1-trillion parameter level while ensuring strict precision consistency between training and inference. By seamlessly integrating these advancements, Intern-S1-Pro further fortifies the fusion of general and specialized intelligence, working as a Specializable Generalist, demonstrating its position in the top tier of open-source models for general capabilities, while outperforming proprietary models in the depth of specialized scientific tasks.