@inproceedings{chen-etal-2026-mpr,
    title = "{MPR}-{GUI}: Benchmarking and Enhancing Multilingual Perception and Reasoning in {GUI} Agents",
    author = "Chen, Ruihan  and
      Li, Qiming  and
      Feng, Xiaocheng  and
      Zhong, Weihong  and
      Yang, Xiaoliang  and
      Gu, Yuxuan  and
      Zhou, Zekun  and
      Lu, Yunfei  and
      Ren, Haoyu  and
      Chen, Kun  and
      Tu, Dandan  and
      Qin, Bing",
    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.1375/",
    pages = "29798--29832",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Vision{--}Language Models (LVLMs) have shown strong potential as multilingual Graphical User Interface (GUI) agents, as evidenced by existing GUI benchmarks. However, these benchmarks exhibit two primary limitations: (1) although Perception and Reasoning (P R) capabilities are fundamental for GUI agents, current benchmarks lack fine-grained diagnostics to identify which specific capabilities lead to task failures, hindering targeted improvements; (2) existing benchmarks fail to provide a strictly aligned cross-lingual evaluation environment, introducing confounding factors that prevent isolating the language impact on GUI agent performance. To address these issues, we propose the Multilingual P R GUI Benchmark (MPR-GUI-Bench), featuring strictly aligned environments across six languages and eight fine-grained P R tasks. Our benchmark reveals consistent P R gaps between English and non-English settings, particularly on reasoning-intensive tasks. To leverage the superior English P R capabilities for bridging cross-lingual gaps, we identify layers sensitive to language and propose GUI-XLI, a GUI Cross-Lingual Intervention method that aligns non-English hidden states with their English counterparts at these layers during inference. Experiments show that GUI-XLI effectively reduces the cross-lingual gaps, with an average gain of 6.5{\%} in non-English settings."
}