@inproceedings{zhang-etal-2026-editing,
    title = "Editing the Moving World: Model Editing for Video {LLM}s",
    author = "Zhang, Qian  and
      Li, Xinye  and
      Wu, Xiaokai  and
      Xu, Junhao  and
      Qin, Zhanyue  and
      Liu, Qingbin  and
      Cai, Junxian  and
      Chen, Xi  and
      Zhang, Bolin  and
      Tu, Zhiying  and
      Chu, Dianhui  and
      Yu, Xiaoyan  and
      Sui, Dianbo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.274/",
    pages = "6072--6091",
    ISBN = "979-8-89176-390-6",
    abstract = "Model Editing, also known as knowledge editing, is receiving increasing attention in the field of Large Language Models (LLMs). However, existing model editing approaches predominantly focus on knowledge-level or static visual domains, overlooking dynamic semantics. This paper exploratively applies six representative model editing methods (FT, IKE, MEND, SERAC, MEMIT and AlphaEdit) to Video Large Language Models (Vid-LLMs) and introduces the first benchmark specifically designed for Vid-LLMs editing{---}VMEB (Vid-LLMs Model Editing Benchmark){---}systematically extending model editing research from static modalities to dynamic video scenarios. We position this work as a forward-looking benchmark and a foundational diagnostic study: in the video paradigm, our evaluation dimensions encompass traditional metrics including Reliability, Locality, and Generality, while also introducing a video-specific metric: Robustness. Based on experimental results, we analyze the strengths and limitations of existing model editing approaches, and identify new challenges and research directions for the future development of the model editing field within the context of multimodal and video paradigms. Our benchmark is available at https://github.com/Sakabamrisa/VMEB."
}