From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models

• URL: http://arxiv.org/abs/2511.12861v3
• Date: Fri, 21 Nov 2025 06:39:29 GMT
• Title: From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models
• Authors: Wenxin Zhu, Andong Chen, Yuchen Song, Kehai Chen, Conghui Zhu, Ziyan Chen, Tiejun Zhao,
• Abstract summary: Chain-of-Thought (CoT) reasoning has demonstrated significant efficacy in language models by enhancing reasoning transparency and output interpretability.<n>This paper provides a systematic review centered on "Multimodal Chain-of-Thought" (MCoT)
• Score: 36.54062692717823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the remarkable success of Multimodal Large Language Models (MLLMs) in perception tasks, enhancing their complex reasoning capabilities has emerged as a critical research focus. Existing models still suffer from challenges such as opaque reasoning paths and insufficient generalization ability. Chain-of-Thought (CoT) reasoning, which has demonstrated significant efficacy in language models by enhancing reasoning transparency and output interpretability, holds promise for improving model reasoning capabilities when extended to the multimodal domain. This paper provides a systematic review centered on "Multimodal Chain-of-Thought" (MCoT). First, it analyzes the background and theoretical motivations for its inception from the perspectives of technical evolution and task demands. Then, it introduces mainstream MCoT methods from three aspects: CoT paradigms, the post-training stage, and the inference stage, while also analyzing their underlying mechanisms. Furthermore, the paper summarizes existing evaluation benchmarks and metrics, and discusses the application scenarios of MCoT. Finally, it analyzes the challenges currently facing MCoT and provides an outlook on its future research directions.

Related papers

• CTRLS: Chain-of-Thought Reasoning via Latent State-Transition [57.51370433303236]
  Chain-of-thought (CoT) reasoning enables large language models to break down complex problems into interpretable intermediate steps.<n>We introduce groundingS, a framework that formulates CoT reasoning as a Markov decision process (MDP) with latent state transitions.<n>We show improvements in reasoning accuracy, diversity, and exploration efficiency across benchmark reasoning tasks.
  arXiv  Detail & Related papers  (2025-07-10T21:32:18Z)
• Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models [79.52467430114805]
  Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains.<n>In artificial intelligence, as systems increasingly operate in open, uncertain, and multimodal environments, reasoning becomes essential for enabling robust and adaptive behavior.<n>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.
  arXiv  Detail & Related papers  (2025-05-08T03:35:23Z)
• Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1) [66.51642638034822]
  Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks.<n>Recent advances in large language models (LLMs) have greatly enhanced their reasoning abilities in arithmetic, commonsense, and symbolic domains.<n>This paper offers a concise yet insightful overview of reasoning techniques in both textual and multimodal LLMs.
  arXiv  Detail & Related papers  (2025-04-04T04:04:56Z)
• Mind with Eyes: from Language Reasoning to Multimodal Reasoning [19.719640188412463]
  Language models have recently advanced into the realm of reasoning, yet it is through multimodal reasoning that we can fully unlock the potential to achieve more comprehensive, human-like cognitive capabilities.<n>This survey provides a systematic overview of the recent multimodal reasoning approaches, categorizing them into two levels: language-centric multimodal reasoning and collaborative multimodal reasoning.
  arXiv  Detail & Related papers  (2025-03-23T13:40:44Z)
• Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey [124.23247710880008]
  multimodal CoT (MCoT) reasoning has recently garnered significant research attention.<n>Existing MCoT studies design various methodologies to address the challenges of image, video, speech, audio, 3D, and structured data.<n>We present the first systematic survey of MCoT reasoning, elucidating the relevant foundational concepts and definitions.
  arXiv  Detail & Related papers  (2025-03-16T18:39:13Z)
• A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models [74.48084001058672]
  The rise of foundation models has transformed machine learning research.<n> multimodal foundation models (MMFMs) pose unique interpretability challenges beyond unimodal frameworks.<n>This survey explores two key aspects: (1) the adaptation of LLM interpretability methods to multimodal models and (2) understanding the mechanistic differences between unimodal language models and crossmodal systems.
  arXiv  Detail & Related papers  (2025-02-22T20:55:26Z)
• Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
  We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-04-24T17:59:48Z)
• DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models [28.712359821231182]
  Large language models (LLMs) have made remarkable strides in such multi-step reasoning on the language modality solely by leveraging the chain of thought (CoT) to mimic human thinking. The transfer of these advancements to multimodal contexts introduces heightened challenges, including but not limited to the impractical need for labor-intensive annotation. This study proposes a novel DDCoT prompting that maintains a critical attitude through negative-space prompting and incorporates multimodality into reasoning.
  arXiv  Detail & Related papers  (2023-10-25T08:03:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.