AIC CTU@AVerImaTeC: dual-retriever RAG for image-text fact checking
This work provides an accessible and cost-effective system for image-text fact checking, though it is incremental as it builds on previous RAG methods.
The authors tackled the AVerImaTeC shared task for image-text fact checking by combining a retrieval-augmented generation pipeline with a reverse image search module, achieving 3rd place with competitive performance at an average cost of $0.013 per fact-check using GPT5.1.
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.