@inproceedings{li-etal-2026-vista,
    title = "Vista-{LLM}: Decoupled Query-Guided Visual Token Pruning for Efficient Long-Video Large Language Models",
    author = "Li, Zhenyu  and
      Li, Zuchao  and
      Wang, Ping  and
      Zhang, Lefei  and
      Ai, Haojun",
    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.601/",
    pages = "13171--13187",
    ISBN = "979-8-89176-390-6",
    abstract = "Long-video understanding is bottlenecked by the high cost of processing massive visual tokens. Current reduction strategies often rely on static allocation or inefficient in-network selection that disrupts optimized attention kernels. In this paper, we introduce Vista-LLM, a decoupled framework for query-guided visual token pruning. By filtering redundancy prior to inference with minimal overhead, Vista-LLM ensures full compatibility with Flash Attention. Our method employs a coarse-to-fine pipeline: (1) Query-Guided Dynamic Budgeting for adaptive temporal allocation; (2) a lightweight Semantic Scout for fine-grained, query-specific selection; and (3) Structure-Aware Compensation to preserve global context. Extensive experiments on benchmarks like Video-MME and MLVU demonstrate a significantly improved Pareto frontier. Notably, on LLaVA-OneVision, Vista-LLM reduces visual tokens by 90{\%} and accelerates inference while retaining over 98{\%} of baseline performance on average, effectively filtering visual noise."
}