VisionCoder: Empowering Multi-Agent Auto-Programming for Image Processing with Hybrid LLMs

• URL: http://arxiv.org/abs/2410.19245v1
• Date: Fri, 25 Oct 2024 01:52:15 GMT
• Title: VisionCoder: Empowering Multi-Agent Auto-Programming for Image Processing with Hybrid LLMs
• Authors: Zixiao Zhao, Jing Sun, Zhiyuan Wei, Cheng-Hao Cai, Zhe Hou, Jin Song Dong,
• Abstract summary: This paper presents a multi-agent framework that collaboratively completes auto-programming tasks. Each agent plays a distinct role in the software development cycle, collectively forming a virtual organisation. By establishing a tree-structured thought distribution and development mechanism across project, module, and function levels, this framework offers a cost-effective and efficient solution.
• Score: 8.380216582290025
• License:
• Abstract: In the field of automated programming, large language models (LLMs) have demonstrated foundational generative capabilities when given detailed task descriptions. However, their current functionalities are primarily limited to function-level development, restricting their effectiveness in complex project environments and specific application scenarios, such as complicated image-processing tasks. This paper presents a multi-agent framework that utilises a hybrid set of LLMs, including GPT-4o and locally deployed open-source models, which collaboratively complete auto-programming tasks. Each agent plays a distinct role in the software development cycle, collectively forming a virtual organisation that works together to produce software products. By establishing a tree-structured thought distribution and development mechanism across project, module, and function levels, this framework offers a cost-effective and efficient solution for code generation. We evaluated our approach using benchmark datasets, and the experimental results demonstrate that VisionCoder significantly outperforms existing methods in image processing auto-programming tasks.

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