1 citations · 1 across the 9 of their papers we have counts for
10 papers
Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning
Kai Chen, Jifeng Ding, Ning Ding +44
We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve comp…
Intern-S2-Preview: Scientific Agentic Foundation Model
Lei Bai, Jiaqi Cao, Chiyu Chen +121
Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sus…
Scalable Visual Pretraining for Language Intelligence
Yiming Zhang, Zhonghan Zhao, Wenwei Zhang +14
The rapid progress of large foundation models has been driven predominantly by pretraining on large-scale text corpora. However, many forms of knowledge are conveyed through visual…
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, Yuhang Cao +173
In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that…
Semi-off-Policy Reinforcement Learning for Vision-Language Slow-Thinking Reasoning
Junhao Shen, Haiteng Zhao, Yuzhe Gu +7
Enhancing large vision-language models (LVLMs) with visual slow-thinking reasoning is crucial for solving complex multimodal tasks. However, since LVLMs are mainly trained with vis…
RIG: Synergizing Reasoning and Imagination in End-to-End Generalist Policy
Zhonghan Zhao, Wenwei Zhang, Haian Huang +4
Reasoning before action and imagining potential outcomes (i.e., world models) are essential for embodied agents operating in complex open-world environments. Yet, prior work either…
Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning
Chengqi Lyu, Songyang Gao, Yuzhe Gu +14
Reasoning abilities, especially those for solving complex math problems, are crucial components of general intelligence. Recent advances by proprietary companies, such as o-series…
InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling
Yi Wang, Xinhao Li, Ziang Yan +13
This paper aims to improve the performance of video multimodal large language models (MLLM) via long and rich context (LRC) modeling. As a result, we develop a new version of Inter…
VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling
Xinhao Li, Yi Wang, Jiashuo Yu +10
Long-context video modeling is critical for multimodal large language models (MLLMs), enabling them to process movies, online video streams, and so on. Despite its advances, handli…