Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark
Authors: name: Kai Zou; name: Ziqi Huang; name: Yuhao Dong; name: Shulin Tian; name: Dian Zheng; name: Hongbo Liu; name: Jingwen He; name: Bin Liu; name: Yu Qiao; name: Ziwei Liu
Published: 2025-10-15 17:10:35

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 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.