@inproceedings{hong-etal-2026-moprune,
    title = "{M}o{P}rune: Scene-Guided Motion-Aware Token Pruning for Efficient Video Large Language Models",
    author = "Hong, Wenhao  and
      Wang, Ziyang  and
      Zhang, Yixin  and
      Wang, Zilei",
    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.344/",
    pages = "6929--6941",
    ISBN = "979-8-89176-395-1",
    abstract = "Video Large Language Models (VideoLLMs) struggle with the heavy computational cost of long or high-resolution videos due to massive visual token counts and the quadratic complexity of attention. Prior pruning approaches mainly rely on token importance or similarity, while largely overlooking video dynamics and the fact that different scenes exhibit different redundancy patterns. We introduce MoPrune, a training-free, scene-guided and motion-centric token pruning framework for accelerating VideoLLMs. MoPrune first segments videos into semantically coherent scenes to preserve temporal and motion consistency. Within each scene, it determines frame retention rates from intra-scene frame uniqueness. Finally, at the token level, MoPrune retains visually distinctive tokens and motion-salient tokens via a unified score, preserving both informative static details and dynamic regions. Extensive experiments across multiple VideoLLMs and public benchmarks demonstrate MoPrune{'}s superior efficiency{--}performance trade-offs. On LLaVA-OneVision, retaining 25{\%} of visual tokens matches or slightly improves the dense baseline, and retaining 15{\%} tokens preserves 99{\%} of the original performance. MoPrune is fully compatible with hardware-efficient techniques such as Flash Attention."
}