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.
Figures & tables
Figure 1: The SAGE (Synergistic Architecture for Generalizable Experts, including three layers, Foundation, Fusion, and Evolution) framework used in Intern-S1-Pro development, illustrating the core capabilities and the integrated learning process that enables synergistic improvements across domains.
Figure 2: Left: Illustration of the expert expansion process from Intern-S1 to Intern-S1-Pro. The grouped routing strategy ensures well-trained Top-1/Top-2 experts are distributed across groups to maintain training stability. Right: The illustration of Straight-Through Estimator for enabling updating all router embeddings in every pass.
Figure 3: Training with Grouped Router can achieve absolute load balancing across devices for MoE models with a Top-k configuration of k=8 under the EP8 training strategy.
Figure 4: FoPE models each dimension as a Fourier series of different frequency components, thereby separating information more effectively and mitigating spectral damage. Inadequately trained frequency components are also clipped as their harmful influence on spectral.
Figure 5: Architecture and subsampling mechanism of the time series module.
Figure 6: The comparison of natural caption (often occurs in scientific literature) and the desired dense caption for training VLM. The key is the text should explicitly refer the visual elements.
Figure 7: The workflow of caption pipeline used in data preparation, illustrating how high-quality aligned scientific multimodal data is produced in a efficient way.
Figure 8: Comparison between FP8 mixed-precision RL training and BF16 training on a 30B MoE model. The curves show the average accuracy across multiple validation benchmarks during RL optimization. FP8 training closely matches the BF16 baseline throughout training.
Thinking
Non-Thinking
max tokens
65536
32768
temperature
0.8
0
top_p
0.95
1.0
top_k
50
1
Table 1: Intern-S1-Pro Evaluation Configurations.
SciTS Task ID
ASU01
ASU03
BIU01
BIU03
EAU01
MEU01
NEU06
PHU01
PHU04
Text LLM
GPT-4.1-mini
67.2
15.6
0.2
12.7
67.0
44.0
16.1
24.0
52.7
Gemini2.5-Flash
64.1
16.3
1.5
12.4
67.6
60.9
5.8
20.7
64.8
DeepSeek-V3
1.1
12.3
0.0
5.8
40.2
59.3
13.6
28.9
50.7
VL LLM
GPT-5-mini
65.7
18.9
0.8
17.9
67.6
30.4
13.3
21.4
47.8
Gemini2.5-Flash
61.6
15.2
0.9
8.3
72.5
64.1
11.6
22.7
59.0
Intern-S1-Pro
98.0
75.9
20.8
88.3
99.5
65.6
71.3
36.8
93.2
Table 3: Results on a subset of the SciTS benchmark. F1 scores are reported.
Dataset
Biology-Instruction
Intern-S1-Pro
DNA-cpd
44.54
54.60
DNA-emp
8.10
14.02
DNA-pd
58.18
82.65
DNA-tf-h
24.45
54.11
DNA-tf-m
39.91
60.80
Multi_sequence-antibody_antigen
10.26
44.76
Table 4: Comparison between specialized model (Biology-Instruction [ 14 ] ) and Intern-S1-Pro on biological tasks.