@inproceedings{zou-etal-2026-uni,
title = "Uni-{MMMU}: A Massive Multi-discipline Multimodal Unified Benchmark",
author = "Zou, Kai and
Huang, Ziqi and
Dong, Yuhao and
Tian, Shulin and
Zheng, Dian and
Liu, Hongbo and
He, Jingwen and
Liu, Bin and
Qiao, Yu and
Liu, Ziwei",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.40/",
pages = "908--924",
ISBN = "979-8-89176-390-6",
abstract = "Unified multimodal models aim to jointly enable visual understanding and generation, yet current benchmarks rarely examine their true integration. Existing evaluations either treat the two abilities in isolation or overlook tasks that inherently couple them. To address this gap, we present \textbf{Uni-MMMU}, a comprehensive and discipline-aware benchmark that systematically unfolds the bidirectional synergy between generation and understanding across eight reasoning-centric domains, including science, coding, mathematics, and puzzles. Each task is \textit{bidirectionally coupled}, demanding models to (i) leverage conceptual understanding to guide precise visual synthesis, or (ii) utilize generation as a cognitive scaffold for analytical reasoning. Uni-MMMU incorporates verifiable intermediate reasoning steps, unique ground truths, and a reproducible scoring protocol for both textual and visual outputs. Through extensive evaluation of state-of-the-art unified, generation-only, and understanding-only models, we reveal substantial performance disparities and cross-modal dependencies, offering new insights into \textit{when and how} these abilities reinforce one another, and establishing a reliable foundation for advancing unified models."
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%0 Conference Proceedings
%T Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark
%A Zou, Kai
%A Huang, Ziqi
%A Dong, Yuhao
%A Tian, Shulin
%A Zheng, Dian
%A Liu, Hongbo
%A He, Jingwen
%A Liu, Bin
%A Qiao, Yu
%A Liu, Ziwei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zou-etal-2026-uni
%X Unified multimodal models aim to jointly enable visual understanding and generation, yet current benchmarks rarely examine their true integration. Existing evaluations either treat the two abilities in isolation or overlook tasks that inherently couple them. To address this gap, we present Uni-MMMU, a comprehensive and discipline-aware benchmark that systematically unfolds the bidirectional synergy between generation and understanding across eight reasoning-centric domains, including science, coding, mathematics, and puzzles. Each task is bidirectionally coupled, demanding models to (i) leverage conceptual understanding to guide precise visual synthesis, or (ii) utilize generation as a cognitive scaffold for analytical reasoning. Uni-MMMU incorporates verifiable intermediate reasoning steps, unique ground truths, and a reproducible scoring protocol for both textual and visual outputs. Through extensive evaluation of state-of-the-art unified, generation-only, and understanding-only models, we reveal substantial performance disparities and cross-modal dependencies, offering new insights into when and how these abilities reinforce one another, and establishing a reliable foundation for advancing unified models.
%U https://aclanthology.org/2026.acl-long.40/
%P 908-924
Markdown (Informal)
[Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark](https://aclanthology.org/2026.acl-long.40/) (Zou et al., ACL 2026)
ACL
- Kai Zou, Ziqi Huang, Yuhao Dong, Shulin Tian, Dian Zheng, Hongbo Liu, Jingwen He, Bin Liu, Yu Qiao, and Ziwei Liu. 2026. Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 908–924, San Diego, California, United States. Association for Computational Linguistics.