most citedInternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

8 citations · 11 across the 16 of their papers we have counts for

collaborators

27 papers

cs.CV2025

MetaCaptioner: Towards Generalist Visual Captioning with Open-source Suites

Zhenxin Lei, Zhangwei Gao, Changyao Tian +12

Generalist visual captioning goes beyond a simple appearance description task, but requires integrating a series of visual cues into a caption and handling various visual domains.…

cs.CV2025

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…

cs.CV2025

ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution

Long Cui, Weiyun Wang, Jie Shao +6

Existing Multimodal Large Language Models (MLLMs) suffer from increased inference costs due to the additional vision tokens introduced by image inputs. In this work, we propose Vis…

cs.CL2025

Sequential Diffusion Language Models

Yangzhou Liu, Yue Cao, Hao Li +13

Diffusion language models (DLMs) have strong theoretical efficiency but are limited by fixed-length decoding and incompatibility with key-value (KV) caches. Block diffusion mitigat…

cs.CV20251 cited

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…

cs.CV2025

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…