title: Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning
    (v1)
summary: 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.
paper_authors._id: 67f55cf13a8855ea6818e7b1
paper_authors.name: Jing Bi
paper_authors.status: claimed_verified
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paper_authors.fullname: jing bi
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paper_authors.user: jing-bi
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paper_authors.name: Susan Liang
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paper_authors.name: Xiaofei Zhou
paper_authors._id: 67f55cf13a8855ea6818e7b4
paper_authors.name: Pinxin Liu
paper_authors._id: 67f55cf13a8855ea6818e7b5
paper_authors.name: Junjia Guo
paper_authors._id: 67f55cf13a8855ea6818e7b6
paper_authors.name: Yunlong Tang
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paper_authors.fullname: Yolo Y. Tang
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paper_authors.user: yunlong10
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paper_authors.name: Luchuan Song
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paper_authors.name: Chao Huang
paper_authors._id: 67f55cf13a8855ea6818e7b9
paper_authors.name: Guangyu Sun
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paper_authors.fullname: Guangyu Sun
paper_authors.type: user
paper_authors.user: imguangyu
paper_authors._id: 67f55cf13a8855ea6818e7ba
paper_authors.name: Jinxi He
paper_authors._id: 67f55cf13a8855ea6818e7bb
paper_authors.name: Jiarui Wu
paper_authors._id: 67f55cf13a8855ea6818e7bc
paper_authors.name: Shu Yang
paper_authors._id: 67f55cf13a8855ea6818e7bd
paper_authors.name: Daoan Zhang
paper_authors._id: 67f55cf13a8855ea6818e7be
paper_authors.name: Chen Chen
paper_authors._id: 67f55cf13a8855ea6818e7bf
paper_authors.name: Lianggong Bruce Wen
paper_authors._id: 67f55cf13a8855ea6818e7c0
paper_authors.name: Zhang Liu
paper_authors._id: 67f55cf13a8855ea6818e7c1
paper_authors.name: Jiebo Luo
paper_authors._id: 67f55cf13a8855ea6818e7c2
paper_authors.name: Chenliang Xu
paper_title: Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning
    (v1)
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: large language models multimodal reasoning interpretative strategies post-training optimization test-time inference