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arXiv · 2509.12248

Humor in Pixels: Benchmarking Large Multimodal Models Understanding of Online Comics

Yuriel Ryan·Rui Yang Tan·Kenny Tsu Wei Choo·Roy Ka-Wei Lee
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Abstract

Understanding humor is a core aspect of social intelligence, yet it remains a significant challenge for Large Multimodal Models (LMMs). We introduce PixelHumor, a benchmark dataset of 2,800 annotated multi-panel comics designed to evaluate LMMs' ability to interpret multimodal humor and recognize narrative sequences. Experiments with state-of-the-art LMMs reveal substantial gaps: for instance, top models achieve only 61% accuracy in panel sequencing, far below human performance. This underscores critical limitations in current models' integration of visual and textual cues for coherent narrative and humor understanding. By providing a rigorous framework for evaluating multimodal contextual and narrative reasoning, PixelHumor aims to drive the development of LMMs that better engage in natural, socially aware interactions.

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Yuriel Ryan, Rui Yang Tan, Kenny Tsu Wei Choo, Roy Ka-Wei Lee. 2025-09-12. Humor in Pixels: Benchmarking Large Multimodal Models Understanding of Online Comics. https://arxiv.org/abs/2509.12248

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