LLM Agents Can See Code Repositories
Summary
This paper presents the first systematic empirical study of using visual repository representations to enhance LLM-based coding agents, showing that integrating visual graphs as a supplementary modality reduces token consumption by up to 26% while maintaining or improving issue-resolution accuracy.
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Paper page - LLM Agents Can See Code Repositories
Source: https://huggingface.co/papers/2606.14061
Abstract
Visual repository representations enhance LLM-based coding agents by improving structural understanding and reducing token consumption during issue resolution.
Coding agents powered by large language models have demonstrated strong performance on software engineering tasks. Yet most agents consume repositories almost entirely as text, which differs from how human developers use visual structure such as folder hierarchies and dependency relationships to orient themselves in large codebases. Withmultimodal large language models(MLLMs), it is an open question whether agents can effectively benefit fromvisual representationsof repositories. This paper presents the first systematic empirical study of visual repository representations for LLM-based agents onrepository-level issue resolution. We evaluate four recent multimodal models. Our results show that a strictlyvision-only setupdegrades accuracy and increases token cost, because agents lack sufficientsymbolic detailand compensate with repeated visual queries. In contrast, integratingvisual graphsof repository structure as a supplementary modality alongside standard text interfaces helps agents understand structure more efficiently: input token consumption decreases by up to 26% while issue-resolution accuracy is maintained or improved. Visualization is most useful duringfault localizationand when the agent autonomously controlsexploration depth. These findings point to a practical hybrid text-and-vision design for next-generation coding agents.
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