title: Perception, Reason, Think, and Plan: A Survey on Large Multimodal
    Reasoning Models
summary: Reasoning lies at the heart of intelligence, shaping the ability to make
  decisions, draw conclusions, and generalize across domains. In artificial
  intelligence, as systems increasingly operate in open, uncertain, and
  multimodal environments, reasoning becomes essential for enabling robust and
  adaptive behavior. Large Multimodal Reasoning Models (LMRMs) have emerged as a
  promising paradigm, integrating modalities such as text, images, audio, and
  video to support complex reasoning capabilities and aiming to achieve
  comprehensive perception, precise understanding, and deep reasoning. As
  research advances, multimodal reasoning has rapidly evolved from modular,
  perception-driven pipelines to unified, language-centric frameworks that offer
  more coherent cross-modal understanding. While instruction tuning and
  reinforcement learning have improved model reasoning, significant challenges
  remain in omni-modal generalization, reasoning depth, and agentic behavior. To
  address these issues, we present a comprehensive and structured survey of
  multimodal reasoning research, organized around a four-stage developmental
  roadmap that reflects the field's shifting design philosophies and emerging
  capabilities. First, we review early efforts based on task-specific modules,
  where reasoning was implicitly embedded across stages of representation,
  alignment, and fusion. Next, we examine recent approaches that unify reasoning
  into multimodal LLMs, with advances such as Multimodal Chain-of-Thought (MCoT)
  and multimodal reinforcement learning enabling richer and more structured
  reasoning chains. Finally, drawing on empirical insights from challenging
  benchmarks and experimental cases of OpenAI O3 and O4-mini, we discuss the
  conceptual direction of native large multimodal reasoning models (N-LMRMs),
  which aim to support scalable, agentic, and adaptive reasoning and planning in
  complex, real-world environments.
paper_authors._id: 681dbb9988ca86d430f1d0d2
paper_authors.name: Yunxin Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T09:05:42.761Z
paper_authors._id: 62fdb01bc1588e1d4c6c1a7c
paper_authors.avatarUrl: /avatars/bd03085995b1c34e0ac8a845cf2c4e83.svg
paper_authors.fullname: Yunxin Li
paper_authors.type: user
paper_authors.user: YunxinLi
paper_authors._id: 681dbb9988ca86d430f1d0d3
paper_authors.name: Zhenyu Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T10:08:42.493Z
paper_authors._id: 64380ae1819f3ab20d17431b
paper_authors.avatarUrl: /avatars/a36b073c1c783102ddb455204fd816bd.svg
paper_authors.fullname: ZhenyuLiu
paper_authors.type: user
paper_authors.user: foggyforest
paper_authors._id: 681dbb9988ca86d430f1d0d4
paper_authors.name: Zitao Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T08:33:26.702Z
paper_authors._id: 67ecc6a08647cfa1775a9fda
paper_authors.avatarUrl: /avatars/bb15abd7a3d2c51380b0b1f819ef76e2.svg
paper_authors.fullname: Zitao Li
paper_authors.type: user
paper_authors.user: TerenceL-TL
paper_authors._id: 681dbb9988ca86d430f1d0d5
paper_authors.name: Xuanyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T10:36:03.450Z
paper_authors._id: 676fcc8b98612a870ddb848a
paper_authors.avatarUrl: /avatars/7da1b77da1d18758b08d49436c5971c1.svg
paper_authors.fullname: xuanyu
paper_authors.type: user
paper_authors.user: xyidealist
paper_authors._id: 681dbb9988ca86d430f1d0d6
paper_authors.name: Zhenran Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T10:08:40.339Z
paper_authors._id: 639c379cdb7c5f35004066cb
paper_authors.avatarUrl: /avatars/3e435506ee85aa7d2d0ec2174a07462f.svg
paper_authors.fullname: Zhenran Xu
paper_authors.type: user
paper_authors.user: imryanxu
paper_authors._id: 681dbb9988ca86d430f1d0d7
paper_authors.name: Xinyu Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T13:54:43.127Z
paper_authors._id: 64d9da538767727dff1e8f19
paper_authors.avatarUrl: /avatars/aaad38795007c6cbcb94c7eed1706e51.svg
paper_authors.fullname: Yu
paper_authors.type: user
paper_authors.user: Ghaser
paper_authors._id: 681dbb9988ca86d430f1d0d8
paper_authors.name: Haoyuan Shi
paper_authors._id: 681dbb9988ca86d430f1d0d9
paper_authors.name: Shenyuan Jiang
paper_authors._id: 681dbb9988ca86d430f1d0da
paper_authors.name: Xintong Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-12T06:50:56.441Z
paper_authors._id: 674879885d97f2d66a14006a
paper_authors.avatarUrl: /avatars/cdeff44a898560cb460223f30922b081.svg
paper_authors.fullname: Xintong Wang
paper_authors.type: user
paper_authors.user: shanewang
paper_authors._id: 681dbb9988ca86d430f1d0db
paper_authors.name: Jifang Wang
paper_authors._id: 681dbb9988ca86d430f1d0dc
paper_authors.name: Shouzheng Huang
paper_authors._id: 681dbb9988ca86d430f1d0dd
paper_authors.name: Xinping Zhao
paper_authors._id: 681dbb9988ca86d430f1d0de
paper_authors.name: Borui Jiang
paper_authors._id: 681dbb9988ca86d430f1d0df
paper_authors.name: Lanqing Hong
paper_authors._id: 681dbb9988ca86d430f1d0e0
paper_authors.name: Longyue Wang
paper_authors._id: 681dbb9988ca86d430f1d0e1
paper_authors.name: Zhuotao Tian
paper_authors._id: 681dbb9988ca86d430f1d0e2
paper_authors.name: Baoxing Huai
paper_authors._id: 681dbb9988ca86d430f1d0e3
paper_authors.name: Wenhan Luo
paper_authors._id: 681dbb9988ca86d430f1d0e4
paper_authors.name: Weihua Luo
paper_authors._id: 681dbb9988ca86d430f1d0e5
paper_authors.name: Zheng Zhang
paper_authors._id: 681dbb9988ca86d430f1d0e6
paper_authors.name: Baotian Hu
paper_authors._id: 681dbb9988ca86d430f1d0e7
paper_authors.name: Min Zhang
paper_title: Perception, Reason, Think, and Plan: A Survey on Large Multimodal
    Reasoning Models
paper_summary: Reasoning lies at the heart of intelligence, shaping the ability to make
  decisions, draw conclusions, and generalize across domains. In artificial
  intelligence, as systems increasingly operate in open, uncertain, and
  multimodal environments, reasoning becomes essential for enabling robust and
  adaptive behavior. Large Multimodal Reasoning Models (LMRMs) have emerged as a
  promising paradigm, integrating modalities such as text, images, audio, and
  video to support complex reasoning capabilities and aiming to achieve
  comprehensive perception, precise understanding, and deep reasoning. As
  research advances, multimodal reasoning has rapidly evolved from modular,
  perception-driven pipelines to unified, language-centric frameworks that offer
  more coherent cross-modal understanding. While instruction tuning and
  reinforcement learning have improved model reasoning, significant challenges
  remain in omni-modal generalization, reasoning depth, and agentic behavior. To
  address these issues, we present a comprehensive and structured survey of
  multimodal reasoning research, organized around a four-stage developmental
  roadmap that reflects the field's shifting design philosophies and emerging
  capabilities. First, we review early efforts based on task-specific modules,
  where reasoning was implicitly embedded across stages of representation,
  alignment, and fusion. Next, we examine recent approaches that unify reasoning
  into multimodal LLMs, with advances such as Multimodal Chain-of-Thought (MCoT)
  and multimodal reinforcement learning enabling richer and more structured
  reasoning chains. Finally, drawing on empirical insights from challenging
  benchmarks and experimental cases of OpenAI O3 and O4-mini, we discuss the
  conceptual direction of native large multimodal reasoning models (N-LMRMs),
  which aim to support scalable, agentic, and adaptive reasoning and planning in
  complex, real-world environments.
paper_ai_summary: This survey outlines the development of multimodal reasoning models, progressing from task-specific modules to unified frameworks, and discusses emerging capabilities and challenges in integrating reasoning across diverse data types.
paper_ai_keywords: Multimodal Reasoning Models LMRMs multimodal LLMs Multimodal Chain-of-Thought multimodal reinforcement learning OpenAI O3 OpenAI O4-mini native large multimodal reasoning models N-LMRMs