1.9k citations · 3.6k across the 73 of their papers we have counts for
78 papers
MiroFlow: Towards High-Performance and Robust Open-Source Agent Framework for General Deep Research Tasks
Shiqian Su, Sen Xing, Xuan Dong +13
Despite the remarkable progress of large language models (LLMs), the capabilities of standalone LLMs have begun to plateau when tackling real-world, complex tasks that require inte…
MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling
MiroMind Team, Song Bai, Lidong Bing +52
We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike previous agents that only scale…
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…
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Weiyun Wang, Zhangwei Gao, Lixin Gu +72
We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL…
MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI Agents
Xuehui Wang, Zhenyu Wu, JingJing Xie +25
We introduce MMBench-GUI, a hierarchical benchmark for evaluating GUI automation agents across Windows, macOS, Linux, iOS, Android, and Web platforms. It comprises four levels: GUI…
Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models
Gen Luo, Wenhan Dou, Wenhao Li +9
This paper focuses on monolithic Multimodal Large Language Models (MLLMs), which integrate visual encoding and language decoding into a single model. Existing structures and pre-tr…
UniFork: Exploring Modality Alignment for Unified Multimodal Understanding and Generation
Teng Li, Quanfeng Lu, Lirui Zhao +5
Unified image understanding and generation has emerged as a promising paradigm in multimodal artificial intelligence. Despite recent progress, the optimal architectural design for…
CoMemo: LVLMs Need Image Context with Image Memory
Shi Liu, Weijie Su, Xizhou Zhu +2
Recent advancements in Large Vision-Language Models built upon Large Language Models have established aligning visual features with LLM representations as the dominant paradigm. Ho…
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…
ZeroGUI: Automating Online GUI Learning at Zero Human Cost
Chenyu Yang, Shiqian Su, Shi Liu +11
The rapid advancement of large Vision-Language Models (VLMs) has propelled the development of pure-vision-based GUI Agents, capable of perceiving and operating Graphical User Inter…
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…
Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy
Zhi Hou, Tianyi Zhang, Yuwen Xiong +8
While recent vision-language-action models trained on diverse robot datasets exhibit promising generalization capabilities with limited in-domain data, their reliance on compact ac…
LangBridge: Interpreting Image as a Combination of Language Embeddings
Jiaqi Liao, Yuwei Niu, Fanqing Meng +9
Recent years have witnessed remarkable advances in Large Vision-Language Models (LVLMs), which have achieved human-level performance across various complex vision-language tasks. F…
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…
V2PE: Improving Multimodal Long-Context Capability of Vision-Language Models with Variable Visual Position Encoding
Junqi Ge, Ziyi Chen, Jintao Lin +4
Vision-Language Models (VLMs) have shown promising capabilities in handling various multimodal tasks, yet they struggle in long-context scenarios, particularly in tasks involving v…
PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models
Chenyu Yang, Xuan Dong, Xizhou Zhu +7
Large Vision-Language Models (VLMs) have been extended to understand both images and videos. Visual token compression is leveraged to reduce the considerable token length of visual…
SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding
Hao Li, Changyao Tian, Jie Shao +8
The remarkable success of Large Language Models (LLMs) has extended to the multimodal domain, achieving outstanding performance in image understanding and generation. Recent effort…
HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding
Chenxin Tao, Shiqian Su, Xizhou Zhu +8
The rapid advance of Large Language Models (LLMs) has catalyzed the development of Vision-Language Models (VLMs). Monolithic VLMs, which avoid modality-specific encoders, offer a p…
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
Zhe Chen, Weiyun Wang, Yue Cao +39
We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing signif…
Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance
Zhangwei Gao, Zhe Chen, Erfei Cui +12
Multimodal large language models (MLLMs) have demonstrated impressive performance in vision-language tasks across a broad spectrum of domains. However, the large model scale and as…
Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization
Weiyun Wang, Zhe Chen, Wenhai Wang +8
Existing open-source multimodal large language models (MLLMs) generally follow a training process involving pre-training and supervised fine-tuning. However, these models suffer fr…
Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training
Gen Luo, Xue Yang, Wenhan Dou +5
In this paper, we focus on monolithic Multimodal Large Language Models (MLLMs) that integrate visual encoding and language decoding into a single LLM. In particular, we identify th…