@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs
%A Kancheti, Sai Srinivas
%A Kanade, Aditya Sanjiv
%A Balasubramanian, Vineeth N.
%A Ganu, Tanuja
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-391-3
%F kancheti-etal-2026-chain
%X 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.
%U https://aclanthology.org/2026.acl-short.71/
%P 862-876
Markdown (Informal)
[Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs](https://aclanthology.org/2026.acl-short.71/) (Kancheti et al., ACL 2026)
ACL