@article{takenaka-yanaka-2026-seeing,
    title = "Seeing the Other Side: Diagnostic Tasks for Viewpoint Reasoning in Vision{--}Language Models",
    author = "Takenaka, Makoto  and
      Yanaka, Hitomi",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.737/",
    pages = "9386--9395",
    abstract = "Humans can integrate multiple visual perspectives and infer how an object appears from unseen sides. This study investigates whether Large Vision Language Models (LVLMs) exhibit a comparable ability for reference-grounded spatial reasoning. We propose two diagnostic tasks: Opposite-Side Reasoning, which determines whether two images show the same object from opposite viewpoints, and Viewpoint Identification, which predicts the viewpoint of a target image using a reference image and its label. An additional condition, Viewpoint Identification (no-ref), removes reference information to reveal cases solvable without it, distinguishing genuine reasoning from bias-driven shortcuts. Our evaluation shows that both open and proprietary LVLMs fall far short of human performance. Even state-of-the-art proprietary LVLMs with relatively high accuracy retain many correct answers when reference information is removed, suggesting that their success often relies on linguistic or dataset-driven priors rather than genuine reference-based reasoning. These findings indicate that current LVLMs have not yet achieved consistent, reference-grounded spatial reasoning. Our datasets in this work will be released on the Hugging Face Hub to support future research on multimodal viewpoint reasoning and spatial understanding."
}