In this paper, we present our 3rd place system in the AVerImaTeC shared task, which combines our last year's retrieval-augmented generation (RAG) pipeline with a reverse image search (RIS) module. Despite its simplicity, our system delivers competitive performance with a single multimodal LLM call per fact-check at just $0.013 on average using GPT5.1 via OpenAI Batch API. Our system is also easy to reproduce and tweak, consisting of only three decoupled modules - a textual retrieval module based on similarity search, an image retrieval module based on API-accessed RIS, and a generation module using GPT5.1 - which is why we suggest it as an accesible starting point for further experimentation. We publish its code and prompts, as well as our vector stores and insights into the scheme's running costs and directions for further improvement.

翻译：本文介绍了我们在AVerImaTeC共享任务中获得第三名的系统，该系统将我们去年提出的检索增强生成（RAG）流程与反向图像搜索（RIS）模块相结合。尽管架构简洁，我们的系统在每次事实核查仅需调用一次多模态大语言模型（通过OpenAI Batch API使用GPT5.1，平均成本仅0.013美元）的情况下，仍展现出具有竞争力的性能。本系统易于复现和调整，仅包含三个解耦模块——基于相似性搜索的文本检索模块、基于API调用的反向图像搜索（RIS）的图像检索模块，以及使用GPT5.1的生成模块——因此我们建议将其作为进一步实验的易用起点。我们公开了系统代码、提示词、向量数据库，并对方案运行成本及改进方向提供了详细说明。