Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
This paper reviews how AI models that can understand and reason with different types of information, like text and images, have developed over time. It highlights the importance of these models in making intelligent decisions in complex situations.
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- 1 Reasoning is crucial for intelligent behavior in AI.
- 2 Large Multimodal Reasoning Models combine various data types for better understanding.
- 3 The field has evolved from simple, task-specific models to more integrated systems.
- 4 Challenges remain in making these models generalize well across different situations.
Introduction
The introduction discusses the importance of reasoning in both philosophy and AI, emphasizing its role in adaptive behavior and decision-making in multimodal environments. It introduces Large Multimodal Reasoning Models (LMRMs) as a promising approach to integrate multiple data modalities for complex reasoning tasks.
Evolving Paradigms of Multimodal Reasoning and Discussion
This section outlines the historical evolution of multimodal reasoning, identifying four key stages that reflect changes in model design and capabilities. It highlights the shift from modular systems to more unified approaches that enhance reasoning depth and cross-modal understanding.
Stage 1 Perception Driven Modular Reasoning -Developing Task-Specific Reasoning Modules
In the early stages, multimodal reasoning was characterized by task-specific models that utilized modular architectures. These models faced challenges due to limited data and less sophisticated learning methods, leading to a focus on modular reasoning networks and pretrained Vision-Language Models (VLMs).
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Modular Reasoning Networks
This section describes initial approaches that used CNNs and LSTMs for multimodal reasoning, detailing various architectures that modularized reasoning based on perceptual cues, such as Neural Module Networks and Hierarchical Co-Attention.
Vision-Language Models-based Modular Reasoning
Vision-Language Models (VLMs) are discussed as a significant advancement in multimodal reasoning, utilizing large-scale image-text pairs and Transformer architectures to unify representation, perception, fusion, and inference across modalities.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 2 :: Figure 2: The roadmap of large multimodal reasoning models. The models highlighted in the box are representative models transitioning from Stage 3 towards Stage 4, as indicated by the directional arrow.
- Figure 4 :: Figure 4: Taxonomy and representative methods of structural reasoning in multimodal chain-of-thought.
- Figure 5 :: Figure 5: Timeline (top) and core components (bottom) of recent multimodal O1-like and R1-like models. The top part illustrates the chronological emergence of representative models. The bottom part summarizes key components including structured reasoning paradigms, reinforcement learning algorithms (e.g., DPO and GRPO), and the design of rule-based reward models.
- Figure 6 :: Figure 6: Case study of OpenAI o3’s long multimodal chain-of-thought, reaching the correct answer after 8 minutes and 13 seconds of reasoning. The question is from Chinese Civil Service Examination.
- Figure 7 :: Figure 7: Case study of OpenAI o3: Find locations, solve a puzzle and create multimedia contents.
Frequently Asked Questions
This paper reviews how AI models that can understand and reason with different types of information, like text and images, have developed over time. It highlights the importance of these models in making intelligent decisions in complex situations.
The introduction discusses the importance of reasoning in both philosophy and AI, emphasizing its role in adaptive behavior and decision-making in multimodal environments. It introduces Large Multimodal Reasoning Models (LMRMs) as a.
This section outlines the historical evolution of multimodal reasoning, identifying four key stages that reflect changes in model design and capabilities. It highlights the shift from modular systems to more unified approaches.
Yes. PDFDigest can turn this paper into a structured explanation, key takeaways, visual summaries, and a narrated video when available.