Thinking with Comics: Efficient Multimodal Reasoning through Sequential Visual Narratives

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 and dynamic processes, while videos introduce substantial redundancy and computational cost. In this work, we propose Thinking with Comics (TwC), 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. To systematically evaluate reasoning over sequential visual narratives, we introduce GutterBench, a benchmark that measures whether models can represent, update, and apply intermediate states across comic panels rather than merely predict final answers. We further evaluate TwC across diverse reasoning tasks and visual understanding scenarios. 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 reveals that comics provide an effective intermediate representation for modeling cross-panel state transitions, offering a promising direction for multimodal reasoning.