@inproceedings{wang-etal-2025-metok,
    title = "{MET}ok: Multi-Stage Event-based Token Compression for Efficient Long Video Understanding",
    author = "Wang, Mengyue  and
      Chen, Shuo  and
      Kersting, Kristian  and
      Tresp, Volker  and
      Ma, Yunpu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.954/",
    pages = "18881--18895",
    ISBN = "979-8-89176-332-6",
    abstract = "Recent advances in Video Large Language Models (VLLMs) have significantly enhanced their ability to understand video content. Nonetheless, processing long videos remains challenging due to high computational demands and the redundancy present in the visual data. In this work, we propose $\textbf{METok}$, a training-free, $\textbf{M}$ulti-stage $\textbf{E}$vent-based $\textbf{Tok}$en compression framework designed to accelerate VLLMs' inference while preserving accuracy. METok progressively eliminates redundant visual tokens across three critical stages: (1) event-aware compression during vision encoding, (2) hierarchical token pruning in the prefilling stage based on semantic alignment and event importance, and (3) a decoding-stage KV Cache optimization that further reduces memory consumption. Our experiments on diverse video benchmarks demonstrate that METok achieves an optimal trade-off between efficiency and accuracy by dynamically selecting informative visual tokens. For instance, equipping LongVA-7B with METok realizes an 80.6{\%} FLOPs reduction and 93.5{\%} KV Cache memory savings, all while maintaining comparable or even superior accuracy."
}