MuSLR: Multimodal Symbolic Logical Reasoning Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2509.25851
Multimodal symbolic logical reasoning, which aims to deduce new facts from multimodal input via formal logic, is critical in high-stakes applications such as autonomous driving and medical diagnosis, as its rigorous, deterministic reasoning helps prevent serious consequences. To evaluate such capabilities of current state-of-the-art vision language models (VLMs), we introduce the first benchmark MuSLR for multimodal symbolic logical reasoning grounded in formal logical rules. MuSLR comprises 1,093 instances across 7 domains, including 35 atomic symbolic logic and 976 logical combinations, with reasoning depths ranging from 2 to 9. We evaluate 7 state-of-the-art VLMs on MuSLR and find that they all struggle with multimodal symbolic reasoning, with the best model, GPT-4.1, achieving only 46.8%. Thus, we propose LogiCAM, a modular framework that applies formal logical rules to multimodal inputs, boosting GPT-4.1's Chain-of-Thought performance by 14.13%, and delivering even larger gains on complex logics such as first-order logic. We also conduct a comprehensive error analysis, showing that around 70% of failures stem from logical misalignment between modalities, offering key insights to guide future improvements. All data and code are publicly available at https://llm-symbol.github.io/MuSLR.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2509.25851
- https://arxiv.org/pdf/2509.25851
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4415338263
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4415338263Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2509.25851Digital Object Identifier
- Title
-
MuSLR: Multimodal Symbolic Logical ReasoningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-30Full publication date if available
- Authors
-
Jianyu Xu, Fei, Hao, Yuhui Zhang, Liangming Pan, Qijun Huang, Qian Liu, Preslav Nakov, Min‐Yen Kan, William Yang Wang, Mong Li Lee, Wynne HsuList of authors in order
- Landing page
-
https://arxiv.org/abs/2509.25851Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2509.25851Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2509.25851Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
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