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CogAgent: A Visual Language Model for GUI Agents

Wenyi Hong · Weihan Wang · Qingsong Lv · Jiazheng Xu · Wenmeng Yu · Junhui Ji · Yan Wang · Zihan Wang · Yuxiao Dong · Ming Ding · Jie Tang

Arch 4A-E Poster #448
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Thu 20 Jun 10:30 a.m. PDT — noon PDT

Abstract: People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e.g., computer or smartphone screens. Large language models (LLMs) such as ChatGPT can assist people in tasks like writing emails, but struggle to understand and interact with GUIs, thus limiting their potential to increase automation levels. In this paper, we introduce CogAgent, an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation. By utilizing both low-resolution and high-resolution image encoders, CogAgent supports input at a resolution of $1120\times 1120$, enabling it to recognize tiny page elements and text. As a generalist visual language model, CogAgent achieves the state of the art on five text-rich and four general VQA benchmarks, including VQAv2, OK-VQA, Text-VQA, ST-VQA, ChartQA, infoVQA, DocVQA, MM-Vet, and POPE. CogAgent, using only screenshots as input, outperforms LLM-based methods that consume extracted HTML text on both PC and Android GUI navigation tasks---Mind2Web and AITW, advancing the state of the art. The model and codes are available at

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