Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning
  (v1)
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.
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Jing Bi
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jing bi
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Susan Liang
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Xiaofei Zhou
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Pinxin Liu
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Junjia Guo
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Yunlong Tang
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Yolo Y. Tang
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Luchuan Song
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Chao Huang
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Guangyu Sun
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Guangyu Sun
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Jinxi He
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Jiarui Wu
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Shu Yang
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Daoan Zhang
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Chen Chen
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Lianggong Bruce Wen
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Zhang Liu
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Jiebo Luo
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Chenliang Xu
Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning
  (v1)
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.
This paper provides an overview of reasoning techniques in large language models (LLMs) handling both textual and multimodal inputs, detailing challenges, opportunities, and optimization strategies.
large language models multimodal reasoning interpretative strategies post-training optimization test-time inference