Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu, Fuxing Leng, Guang Shi, Haobin Chen, Haoqi Fan, Jian Wang, Jianyu Jiang, Jiawei Wang, Jingji Chen, Jingjia Huang, Kang Lei, Liping Yuan, Lishu Luo, Pengfei Liu, Qinghao Ye, Rui Qian, Shen Yan, Shixiong Zhao, Shuai Peng, Shuangye Li
2025-05-13
Summary
This paper talks about Seed1.5-VL, a new AI model that can understand both pictures and text at the same time, making it really good at solving problems that need both types of information, like visual puzzles.
What's the problem?
The problem is that many AI models are only good at either understanding language or images, but not both together. This makes it hard for them to solve tasks that require combining what they see with what they read or hear.
What's the solution?
The researchers built Seed1.5-VL by combining a vision encoder, which processes images, with a powerful language model that uses a mixture-of-experts (MoE) approach. This combination allows the model to perform extremely well on tests that require understanding both images and text, setting new records on several benchmarks.
Why it matters?
This matters because having an AI that can reason about both pictures and words opens up new possibilities for things like smarter virtual assistants, better educational tools, and more advanced technology in fields like medicine or robotics.
Abstract
Seed1.5-VL, a vision-language foundation model combining a vision encoder and a large MoE LLM, achieves state-of-the-art performance across various benchmarks and excels in multimodal reasoning tasks such as visual puzzles.