We introduce Comic Visual Question Answering (ComicVQA), a comics-based benchmark for evaluating MLLMs on visual reasoning. ComicVQA comprises of (i) Missing Panel Prediction, testing fine-grained visual grounding and (ii) 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.

With NLP research being rapidly productionized into real-world applications, it is important to be aware of and think through the consequences of our work. Such ethical considerations are important in both senior and junior roles of researchers and students (e.g. privacy, consent, fairness, among others). This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues and review common considerations that recur in NLP research. The methodology is interactive and participatory, including discussion and group work related to case studies that involve bias and broader issues (e.g., environmental impact). Participants will gain practical experience on when to flag a paper for ethics review and how to write an ethical consideration section to be shared with the broader community. Most importantly, the participants will be co-creating the tutorial outcomes and extending tutorial materials to share as public outcomes.