Perception, Reason, Think, and Plan: A Survey on Large Multimodal
  Reasoning Models
Authors: name: Yunxin Li, name: Zhenyu Liu, name: Zitao Li, name: Xuanyu Zhang, name: Zhenran Xu, name: Xinyu Chen, name: Haoyuan Shi, name: Shenyuan Jiang, name: Xintong Wang, name: Jifang Wang, name: Shouzheng Huang, name: Xinping Zhao, name: Borui Jiang, name: Lanqing Hong, name: Longyue Wang, name: Zhuotao Tian, name: Baoxing Huai, name: Wenhan Luo, name: Weihua Luo, name: Zheng Zhang, name: Baotian Hu, name: Min Zhang
Organization: Lychee Team

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.