@inproceedings{liao-etal-2026-using,
    title = "Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods",
    author = "Liao, Chenfei  and
      Wang, Wensong  and
      Wen, Zichen  and
      Zheng, Xu  and
      Wang, Yiyu  and
      He, Haocong  and
      Lyu, Yuanhuiyi  and
      Jiang, Lutao  and
      Zou, Xin  and
      Fu, Yuqian  and
      Ren, Bin  and
      Zhang, Linfeng  and
      Hu, Xuming",
    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.195/",
    pages = "4236--4253",
    ISBN = "979-8-89176-390-6",
    abstract = "Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectiveness of these methods is commonly evaluated by measuring the accuracy drop on existing MLLM benchmarks before and after compression. However, these benchmarks are originally designed to assess general perception and reasoning abilities, rather than the specific challenges posed by visual token compression, leading to a fundamental task mismatch. In this work, we uncover a counterintuitive yet consistent phenomenon: simple image downsampling outperforms many advanced visual token compression methods across multiple widely used benchmarks. Through a comprehensive empirical study spanning eight popular benchmarks and multiple state-of-the-art compression techniques, we show that (i) current benchmarks contain substantial noise (task-irrelevant samples) for evaluating visual token compression, and (ii) downsampling can act as an effective data filter that distinguishes between simple and difficult samples with respect to compression sensitivity. Motivated by these findings, we propose VTC-Bench, an evaluation framework that explicitly leverages downsampling as a discriminator to denoise existing benchmarks, enabling a fairer and more meaningful additional assessment of visual token compression methods."
}