Thinking with Comics: Enhancing Multimodal Reasoning through Structured Visual Storytelling
This work addresses the problem of inefficient and limited visual reasoning in AI systems, offering a domain-specific improvement for multimodal tasks.
The paper tackled the limitations of static images and videos in multimodal reasoning by proposing Thinking with Comics, a paradigm using comics as an intermediate visual representation, which outperformed Thinking with Images on multi-step temporal and causal reasoning tasks while being more efficient than Thinking with Video.
Chain-of-Thought reasoning has driven large language models to extend from thinking with text to thinking with images and videos. However, different modalities still have clear limitations: static images struggle to represent temporal structure, while videos introduce substantial redundancy and computational cost. In this work, we propose Thinking with Comics, a visual reasoning paradigm that uses comics as a high information-density medium positioned between images and videos. Comics preserve temporal structure, embedded text, and narrative coherence while requiring significantly lower reasoning cost. We systematically study two reasoning paths based on comics and evaluate them on a range of reasoning tasks and long-context understanding tasks. Experimental results show that Thinking with Comics outperforms Thinking with Images on multi-step temporal and causal reasoning tasks, while remaining substantially more efficient than Thinking with Video. Further analysis indicates that different comic narrative structures and styles consistently affect performance across tasks, suggesting that comics serve as an effective intermediate visual representation for improving multimodal reasoning.