@inproceedings{kancheti-etal-2026-chain,
    title = "Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal {LLM}s",
    author = "Kancheti, Sai Srinivas  and
      Kanade, Aditya Sanjiv  and
      Balasubramanian, Vineeth N.  and
      Ganu, Tanuja",
    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 2: Short 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-short.71/",
    pages = "862--876",
    ISBN = "979-8-89176-391-3",
    abstract = "Multimodal Reasoning Models (MRMs) leveraging Chain-of-Though (CoT) based thinking have revolutionized mathematical and logical problem-solving. However, we show that this paradigm struggles with generalized spatial intelligence. We perform a comprehensive evaluation of sixteen models across thirteen spatial benchmarks and identify a critical gap: CoT prompting consistently degrades performance in visual spatial reasoning. Furthermore, through a novel No-Image++ ablation, we demonstrate that MRMs and CoT prompted MLMs suffer from severe shortcut learning, and hallucinate visual details from textual priors even when the image is absent. These findings challenge the efficacy of text-only CoT for spatial tasks and underscore the need for vision-centric reasoning paradigms."
}