CogAgent: A Visual Language Model for GUI Agents

TL;DR

CogAgent is a large visual language model designed for understanding and navigating GUIs, achieving state-of-the-art results on multiple benchmarks and surpassing HTML-based methods in GUI tasks using only screenshots.

Contribution

Introduces CogAgent, a visual language model with high-resolution input capability, setting new benchmarks in GUI understanding and navigation tasks.

Findings

Achieves state-of-the-art on 9 VQA benchmarks.

Outperforms HTML-based methods in GUI navigation tasks.

Supports recognition of tiny page elements and text.

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*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 https://github.com/THUDM/CogVLM, with a new version of CogAgent-9B-20241220 available at https://github.com/THUDM/CogAgent.