Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909923441377280 |
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| author | Bi, Jing Liang, Susan Zhou, Xiaofei Liu, Pinxin Guo, Junjia Tang, Yunlong Song, Luchuan Huang, Chao Vosoughi, Ali Sun, Guangyu He, Jinxi Wu, Jiarui Yang, Shu Zhang, Daoan Chen, Chen Wen, Lianggong Bruce Liu, Zhang Luo, Jiebo Xu, Chenliang |
| author_facet | Bi, Jing Liang, Susan Zhou, Xiaofei Liu, Pinxin Guo, Junjia Tang, Yunlong Song, Luchuan Huang, Chao Vosoughi, Ali Sun, Guangyu He, Jinxi Wu, Jiarui Yang, Shu Zhang, Daoan Chen, Chen Wen, Lianggong Bruce Liu, Zhang Luo, Jiebo Xu, Chenliang |
| contents | Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks. Recent advances in large language models (LLMs) have greatly enhanced their reasoning abilities in arithmetic, commonsense, and symbolic domains. However, effectively extending these capabilities into multimodal contexts-where models must integrate both visual and textual inputs-continues to be a significant challenge. Multimodal reasoning introduces complexities, such as handling conflicting information across modalities, which require models to adopt advanced interpretative strategies. Addressing these challenges involves not only sophisticated algorithms but also robust methodologies for evaluating reasoning accuracy and coherence. This paper offers a concise yet insightful overview of reasoning techniques in both textual and multimodal LLMs. Through a thorough and up-to-date comparison, we clearly formulate core reasoning challenges and opportunities, highlighting practical methods for post-training optimization and test-time inference. Our work provides valuable insights and guidance, bridging theoretical frameworks and practical implementations, and sets clear directions for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03151 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1) Bi, Jing Liang, Susan Zhou, Xiaofei Liu, Pinxin Guo, Junjia Tang, Yunlong Song, Luchuan Huang, Chao Vosoughi, Ali Sun, Guangyu He, Jinxi Wu, Jiarui Yang, Shu Zhang, Daoan Chen, Chen Wen, Lianggong Bruce Liu, Zhang Luo, Jiebo Xu, Chenliang Computation and Language Machine Learning Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks. Recent advances in large language models (LLMs) have greatly enhanced their reasoning abilities in arithmetic, commonsense, and symbolic domains. However, effectively extending these capabilities into multimodal contexts-where models must integrate both visual and textual inputs-continues to be a significant challenge. Multimodal reasoning introduces complexities, such as handling conflicting information across modalities, which require models to adopt advanced interpretative strategies. Addressing these challenges involves not only sophisticated algorithms but also robust methodologies for evaluating reasoning accuracy and coherence. This paper offers a concise yet insightful overview of reasoning techniques in both textual and multimodal LLMs. Through a thorough and up-to-date comparison, we clearly formulate core reasoning challenges and opportunities, highlighting practical methods for post-training optimization and test-time inference. Our work provides valuable insights and guidance, bridging theoretical frameworks and practical implementations, and sets clear directions for future research. |
| title | Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1) |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2504.03151 |