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. With multimodal large language models (MLLMs), it is an open question whether agents can effectively benefit from visual representations of repositories. This paper presents the first systematic empirical study of visual repository representations for LLM-based agents on repository-level issue resolution. We evaluate four recent multimodal models. Our results show that a strictly vision-only setup degrades accuracy and increases token cost, because agents lack sufficient symbolic detail and compensate with repeated visual queries. In contrast, integrating visual graphs of 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 during fault localization and when the agent autonomously controls exploration depth. These findings point to a practical hybrid text-and-vision design for next-generation coding agents.

翻译：由大型语言模型驱动的编码代理在软件工程任务中表现出色。然而，大多数代理几乎完全将仓库视为文本来处理，这与人类开发者利用文件夹层次结构和依赖关系等视觉结构在大型代码库中定位自身的方式不同。借助多模态大型语言模型（MLLMs），代理能否有效从仓库的视觉表示中获益仍是一个开放性问题。本文首次对基于LLM的代理在仓库级问题解决中采用视觉仓库表示进行了系统的实证研究。我们评估了四种最新的多模态模型。结果表明，纯视觉设置会降低准确率并增加令牌成本，因为代理缺乏足够的符号细节，需通过重复的视觉查询来补偿。相比之下，将仓库结构可视化图谱作为补充模态与标准文本界面结合使用时，能够帮助代理更高效地理解结构：输入令牌消耗最多减少26%，同时问题解决准确率得以维持或提升。可视化在故障定位以及代理自主控制探索深度时最为有效。这些发现为下一代编码代理提出了一种实用的文本与视觉混合设计方案。