@inproceedings{chung-etal-2026-multivationbench,
title = "{M}ultivation{B}ench: A Benchmark for Multimodal Sequential Motivation Reasoning",
author = "Chung, Kawai and
Chan, Chunkit and
Yim, Yauwai and
Liu, Yuxuan and
Shi, Haochen and
Wang, Weiqi and
Zong, Qing and
Zheng, Tianshi and
Yixuan, FU and
Chung, Wong Kai and
Liang, Hao and
Gao, Yifan and
Yang, Xi and
Hsiao, Janet Hui-wen and
Song, Yangqiu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.107/",
pages = "2194--2228",
ISBN = "979-8-89176-462-0",
abstract = "Multimodal Large Language Models have sparked significant interest due to their potential for social intelligence; however, their ability to perform sequential motivation reasoning remains insufficiently studied. Existing evaluations predominantly examine static text or isolated visual snapshots, which do not reflect the cumulative nature of real-world behavioral drivers. To address this gap, we introduce MulTivationBench, a benchmark designed to rigorously evaluate multimodal motivation reasoning within story-driven visual narratives. The benchmark builds upon established psychological frameworks{---}Maslow{'}s hierarchy and Reiss{'}s basic desires{---}and requires models to integrate accumulated multimodal context to infer evolving motivations. Results indicate that MulTivationBench presents a significant challenge: all tested models struggle to maintain consistent motivation reasoning across sequential contexts, revealing a critical disconnect between static recognition capabilities and the dynamic reasoning essential for human-like social understanding."
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%0 Conference Proceedings
%T MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning
%A Chung, Kawai
%A Chan, Chunkit
%A Yim, Yauwai
%A Liu, Yuxuan
%A Shi, Haochen
%A Wang, Weiqi
%A Zong, Qing
%A Zheng, Tianshi
%A Yixuan, F. U.
%A Chung, Wong Kai
%A Liang, Hao
%A Gao, Yifan
%A Yang, Xi
%A Hsiao, Janet Hui-wen
%A Song, Yangqiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chung-etal-2026-multivationbench
%X Multimodal Large Language Models have sparked significant interest due to their potential for social intelligence; however, their ability to perform sequential motivation reasoning remains insufficiently studied. Existing evaluations predominantly examine static text or isolated visual snapshots, which do not reflect the cumulative nature of real-world behavioral drivers. To address this gap, we introduce MulTivationBench, a benchmark designed to rigorously evaluate multimodal motivation reasoning within story-driven visual narratives. The benchmark builds upon established psychological frameworks—Maslow’s hierarchy and Reiss’s basic desires—and requires models to integrate accumulated multimodal context to infer evolving motivations. Results indicate that MulTivationBench presents a significant challenge: all tested models struggle to maintain consistent motivation reasoning across sequential contexts, revealing a critical disconnect between static recognition capabilities and the dynamic reasoning essential for human-like social understanding.
%U https://aclanthology.org/2026.findings-emnlp.107/
%P 2194-2228
Markdown (Informal)
[MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning](https://aclanthology.org/2026.findings-emnlp.107/) (Chung et al., Findings 2026)
ACL
- Kawai Chung, Chunkit Chan, Yauwai Yim, Yuxuan Liu, Haochen Shi, Weiqi Wang, Qing Zong, Tianshi Zheng, FU Yixuan, Wong Kai Chung, Hao Liang, Yifan Gao, Xi Yang, Janet Hui-wen Hsiao, and Yangqiu Song. 2026. MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 2194–2228, Budapest, Hungary. Association for Computational Linguistics.