| license: mit | |
| configs: | |
| - config_name: ranking_panel | |
| data_dir: rank | |
| default: true | |
| - config_name: missing_panel | |
| data_dir: missing_panel | |
| # ComicVQA: A Benchmark for Visual Reasoning in Multimodal LLMs | |
| Check out our paper [here](https://aclanthology.org/2026.findings-acl.1268/) | |
| **ComicVQA** is a specialized diagnostic benchmark designed to assess the visual reasoning capabilities of Multimodal Large Language Models (MLLMs) in the comics domain. ComicVQA focuses of 2 distinct reasoning tasks: | |
| 1. **Missing Panel Prediction**: Predict the missing panel given a comic sequence. | |
| 2. **Panel Sorting**: Choose which of the 4 comic sequences is the correct arrangement. | |
| These tasks leverage the sequential nature of comics to expose vulnerabilities in how models synthesize visual and textual information over a temporal sequence. | |
| Do refer to our [Github](https://github.com/esther-gan/ComicVQA) respository for the handling of data and evaluation codes. | |
| --- | |
| ## 🖋️ Citation | |
| Cite us! | |
| ```bibtex | |
| @inproceedings{gan-etal-2026-comicvqa, | |
| title = "{C}omic{VQA}: A Benchmark for Visual Reasoning in Multimodal {LLM}s", | |
| author = "Gan, Esther and | |
| Brown, Hannah and | |
| Herel, David and | |
| Kawaguchi, Kenji and | |
| Kan, Min-Yen and | |
| Shieh, Michael Qizhe", | |
| editor = "Liakata, Maria and | |
| Moreira, Viviane P. and | |
| Zhang, Jiajun and | |
| Jurgens, David", | |
| booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026", | |
| month = jul, | |
| year = "2026", | |
| address = "San Diego, California, United States", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2026.findings-acl.1268/", | |
| pages = "25347--25370", | |
| ISBN = "979-8-89176-395-1", | |
| abstract = "We introduce Comic Visual Question Answering (\textbf{ComicVQA}), a comics-based benchmark for evaluating MLLMs on visual reasoning. ComicVQA comprises of (i) \textbf{Missing Panel Prediction}, testing fine-grained visual grounding and (ii) \textbf{Panel Sorting}, which evaluates sequential narrative understanding. Proprietary models achieve up to 62.6{\%} on Missing Panel Prediction and 46.4{\%} on Panel Sorting, whereas open-source models reach only 47.7{\%} and 26.9{\%}, respectively. In contrast, human annotators achieve over 83{\%} accuracy on both tasks, revealing a large gap between current models and human-level multimodal understanding in comics. Through controlled ordering ablations and a detailed error taxonomy, we show that current MLLMs rely primarily on coarse temporal cues and struggle with fine-grained visual reasoning. These findings demonstrate ComicVQA as a diagnostic benchmark for advancing multimodal visual reasoning in comics." | |
| } | |
| ``` |