7 papers
MOSS-VL Technical Report
Pengyu Wang, Chenkun Tan, Shaojun Zhou +29
We present MOSS-VL, an open vision-language model family that treats real-time interaction -- perceiving while it speaks -- as a first-class capability. It is co-designed across th…
MOSS-Video-Preview: Toward Real-Time Video Understanding via Cross-Attention
Pengyu Wang, Chenkun Tan, Shaojun Zhou +18
Video understanding is shifting from the offline paradigm -- taking a fully recorded video as input and producing a single answer after it ends -- toward real-time interaction, in…
Sparser Block-Sparse Attention via Token Permutation
Xinghao Wang, Pengyu Wang, Dong Zhang +7
Scaling the context length of large language models (LLMs) offers significant benefits but is computationally expensive. This expense stems primarily from the self-attention mechan…
UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets
Pengyu Wang, Shaojun Zhou, Chenkun Tan +7
Unified vision large language models (VLLMs) have recently achieved impressive advancements in both multimodal understanding and generation, powering applications such as visual qu…
Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLM
Chenkun Tan, Pengyu Wang, Shaojun Zhou +6
Multimodal large language models (MLLMs) have gained significant attention due to their impressive ability to integrate vision and language modalities. Recent advancements in MLLMs…
LongSafety: Enhance Safety for Long-Context LLMs
Mianqiu Huang, Xiaoran Liu, Shaojun Zhou +11
Recent advancements in model architectures and length extrapolation techniques have significantly extended the context length of large language models (LLMs), paving the way for th…