@inproceedings{ma-etal-2026-thinklinker,
    title = "{T}hink{L}inker: From Low-Rank Interaction to Knowledge-Aware Verification for Multimodal Entity Linking",
    author = "Ma, Yingyao  and
      Zhou, Yuanyuan  and
      Zhang, Congyu  and
      Yuan, Yi  and
      Wu, Jiasong  and
      Senhadji, Lotfi  and
      Shu, Huazhong",
    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.1248/",
    pages = "24926--24942",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent advances in Multimodal Entity Linking (MEL) exploit textual and visual information to disambiguate mentions and align them with entities in a knowledge base. Existing methods typically design separate and complex network modules for each type of interaction among multi-granular and multimodal features, while lacking explicit modeling of the joint dependencies among these features. Moreover, most approaches rely on unidirectional retrieval-based matching and lack knowledge-driven verification, leading to unreliable disambiguation in weak-context scenarios. To address these challenges, we propose a novel two-stage MEL framework termed ThinkLinker. First, we introduce a low-rank fusion mechanism to model the joint dependencies among multi-granular and multimodal features, enabling comprehensive and explicit interactions while learning task-relevant discriminative information for candidate ranking in a lower-dimensional space. Subsequently, we develop a bidirectional retrieval-verification paradigm, where the ranked candidate entities guide an LLM-based multi-turn, dialogue-style verification process to generate mention-specific contextual augmentation. The augmented context is then adaptively fused with the original representation to further refine the linking model. Experimental results on public benchmark datasets demonstrate that the proposed ThinkLinker outperforms all state-of-the-art baselines. The code is publicly available at https://github.com/zhouyuanyu/ThinkLinker."
}