@inproceedings{min-etal-2026-progressive,
    title = "Progressive Re-ranking for Multimodal Retrieval-Augmented Generation via Curriculum Learning",
    author = "Min, Zhu  and
      Hao, Yanchao  and
      Liu, Jian  and
      He, Shizhu  and
      Chen, Xi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.2045/",
    pages = "41135--41147",
    ISBN = "979-8-89176-395-1",
    abstract = "Retrieval-augmented generation (RAG) can enhance large language models (LLMs) by providing external knowledge and helping reduce hallucinations. In multimodal RAG, however, retrieval remains challenging because a single retriever may fail to capture fine-grained multimodal semantics, and visually or semantically similar entities may still contain misleading information for answer generation. We propose a progressive multimodal re-ranking framework with curriculum learning to improve CLIP-based visual coarse-grained retrieval. Our framework progressively refines retrieval results through two stages: fine-grained section-level re-ranking and multimodal section reassessment. To better align re-ranking with multimodal queries, we introduce a curriculum-learning strategy that trains the model with hard negatives that are visually or semantically similar but contain misleading information. Experiments on InfoSeek and Enc-VQA show that our method achieves state-of-the-art answer accuracy and competitive retrieval performance."
}