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：基于大型语言模型的编程智能体在软件工程任务中展现出强大性能。然而，大多数智能体几乎完全将仓库视为文本形式处理，这与人类开发者通过文件夹层次结构和依赖关系等视觉结构来定位大型代码库的方式有所不同。借助多模态大型语言模型（MLLMs），目前尚不清楚智能体能否有效利用仓库的视觉表示。本文首次对基于LLM的智能体在仓库级问题解决中视觉仓库表示展开系统性实证研究。我们评估了四种最新多模态模型。研究结果表明，纯视觉模式会降低准确率并增加令牌成本，因为智能体缺乏足够的符号细节，需通过重复视觉查询来弥补。相反，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，能帮助智能体更高效地理解结构：输入令牌消耗最高减少26%，同时问题解决准确率得以保持或提升。可视化在故障定位阶段以及智能体自主控制探索深度时最具实用价值。这些发现为下一代编程智能体指向了文本与视觉混合的实用设计方案。