@inproceedings{lu-etal-2026-mllms,
title = "Do {MLLM}s Really Understand Space? A Mathematical Spatial Reasoning Evaluation",
author = "Lu, Shuo and
Cheng, Jianjie and
Xu, Yinuo and
Yu, Yongcan and
Sheng, Lijun and
Wang, Peijie and
Jiang, Siru and
Hu, Yongguan and
He, Lingxiao and
Wang, Meng and
Xie, Qianlong and
Wang, Xingxing and
He, Ran and
Liang, Jian",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1292/",
pages = "27327--27343",
ISBN = "979-8-89176-462-0",
abstract = "Multimodal large language models (MLLMs) have achieved strong performance on perception-oriented tasks, yet their ability to perform mathematical spatial reasoning, defined as the capacity to parse and manipulate two- and three-dimensional relations, remains unclear. While humans easily master textbook-style spatial problems, current MLLMs exhibit substantially higher error rates, exposing a fundamental weakness in spatial comprehension. To investigate this gap, we present MathSpatial, the first large-scale and systematic dataset resource dedicated to mathematical spatial reasoning in MLLMs. MathSpatial provides two complementary subsets: MathSpatial-Bench (2,000 problems) for evaluating spatial reasoning isolated from perceptual noise, and MathSpatial-Corpus (8,000 problems) equipped with structured reasoning traces for training. Benchmarking 20+ MLLMs reveals a severe deficit: non-reasoning models largely languish below 60{\%} accuracy, and even the strongest reasoning models fall {\textasciitilde}16 percentage points behind human performance (96.3{\%}). Crucially, we demonstrate that fine-tuning on MathSpatial-Corpus yields consistent performance gains across model families. MathSpatial provides a vital resource for diagnosing and advancing spatial reasoning in MLLMs. The dataset and related resources are available at https://github.com/shuolucs/MathSpatial."
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<abstract>Multimodal large language models (MLLMs) have achieved strong performance on perception-oriented tasks, yet their ability to perform mathematical spatial reasoning, defined as the capacity to parse and manipulate two- and three-dimensional relations, remains unclear. While humans easily master textbook-style spatial problems, current MLLMs exhibit substantially higher error rates, exposing a fundamental weakness in spatial comprehension. To investigate this gap, we present MathSpatial, the first large-scale and systematic dataset resource dedicated to mathematical spatial reasoning in MLLMs. MathSpatial provides two complementary subsets: MathSpatial-Bench (2,000 problems) for evaluating spatial reasoning isolated from perceptual noise, and MathSpatial-Corpus (8,000 problems) equipped with structured reasoning traces for training. Benchmarking 20+ MLLMs reveals a severe deficit: non-reasoning models largely languish below 60% accuracy, and even the strongest reasoning models fall ~16 percentage points behind human performance (96.3%). Crucially, we demonstrate that fine-tuning on MathSpatial-Corpus yields consistent performance gains across model families. MathSpatial provides a vital resource for diagnosing and advancing spatial reasoning in MLLMs. The dataset and related resources are available at https://github.com/shuolucs/MathSpatial.</abstract>
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%0 Conference Proceedings
%T Do MLLMs Really Understand Space? A Mathematical Spatial Reasoning Evaluation
%A Lu, Shuo
%A Cheng, Jianjie
%A Xu, Yinuo
%A Yu, Yongcan
%A Sheng, Lijun
%A Wang, Peijie
%A Jiang, Siru
%A Hu, Yongguan
%A He, Lingxiao
%A Wang, Meng
%A Xie, Qianlong
%A Wang, Xingxing
%A He, Ran
%A Liang, Jian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lu-etal-2026-mllms
%X Multimodal large language models (MLLMs) have achieved strong performance on perception-oriented tasks, yet their ability to perform mathematical spatial reasoning, defined as the capacity to parse and manipulate two- and three-dimensional relations, remains unclear. While humans easily master textbook-style spatial problems, current MLLMs exhibit substantially higher error rates, exposing a fundamental weakness in spatial comprehension. To investigate this gap, we present MathSpatial, the first large-scale and systematic dataset resource dedicated to mathematical spatial reasoning in MLLMs. MathSpatial provides two complementary subsets: MathSpatial-Bench (2,000 problems) for evaluating spatial reasoning isolated from perceptual noise, and MathSpatial-Corpus (8,000 problems) equipped with structured reasoning traces for training. Benchmarking 20+ MLLMs reveals a severe deficit: non-reasoning models largely languish below 60% accuracy, and even the strongest reasoning models fall ~16 percentage points behind human performance (96.3%). Crucially, we demonstrate that fine-tuning on MathSpatial-Corpus yields consistent performance gains across model families. MathSpatial provides a vital resource for diagnosing and advancing spatial reasoning in MLLMs. The dataset and related resources are available at https://github.com/shuolucs/MathSpatial.
%U https://aclanthology.org/2026.findings-emnlp.1292/
%P 27327-27343
Markdown (Informal)
[Do MLLMs Really Understand Space? A Mathematical Spatial Reasoning Evaluation](https://aclanthology.org/2026.findings-emnlp.1292/) (Lu et al., Findings 2026)
ACL
- Shuo Lu, Jianjie Cheng, Yinuo Xu, Yongcan Yu, Lijun Sheng, Peijie Wang, Siru Jiang, Yongguan Hu, Lingxiao He, Meng Wang, Qianlong Xie, Xingxing Wang, Ran He, and Jian Liang. 2026. Do MLLMs Really Understand Space? A Mathematical Spatial Reasoning Evaluation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 27327–27343, Budapest, Hungary. Association for Computational Linguistics.