1.9k citations · 2.9k across the 46 of their papers we have counts for
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NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints
Changyao Tian, Hao Li, Gen Luo +11
Compositional training has been the de-facto paradigm in existing Multimodal Large Language Models (MLLMs), where pre-trained vision encoders are connected with pre-trained LLMs th…
Docopilot: Improving Multimodal Models for Document-Level Understanding
Yuchen Duan, Zhe Chen, Yusong Hu +9
Despite significant progress in multimodal large language models (MLLMs), their performance on complex, multi-page document comprehension remains inadequate, largely due to the lac…
Visual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces
Gen Luo, Ganlin Yang, Ziyang Gong +15
The remarkable progress of Multimodal Large Language Models (MLLMs) has attracted increasing attention to extend them to physical entities like legged robot. This typically require…
VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models
Weiye Xu, Jiahao Wang, Weiyun Wang +10
Visual reasoning is a core component of human intelligence and a critical capability for advanced multimodal models. Yet current reasoning evaluations of multimodal large language…
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Jinguo Zhu, Weiyun Wang, Zhe Chen +48
We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model…
VisualPRM: An Effective Process Reward Model for Multimodal Reasoning
Weiyun Wang, Zhangwei Gao, Lianjie Chen +12
We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM) with 8B parameters, which improves the reasoning abilities of existing Multimodal Large Language Models (M…
Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding
Zhaokai Wang, Xizhou Zhu, Xue Yang +8
Image pyramids are widely adopted in top-performing methods to obtain multi-scale features for precise visual perception and understanding. However, current image pyramids use the…