Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark
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.
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Kai Zou
claimed_verified
2025-10-16T06:35:13.604Z
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Kai Zou
jackyhate
user
jackyhate
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Ziqi Huang
claimed_verified
2025-11-18T14:10:10.931Z
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Ziqi Huang
Ziqi
user
Ziqi
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Yuhao Dong
claimed_verified
2025-11-17T10:32:40.341Z
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Yuhao Dong
THUdyh
user
THUdyh
68f05f6336f8b025381e19c6
Shulin Tian
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Dian Zheng
claimed_verified
2025-12-03T09:19:56.506Z
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Dian Zheng
zhengli1013
user
zhengli1013
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Hongbo Liu
68f05f6336f8b025381e19c9
Jingwen He
68f05f6336f8b025381e19ca
Bin Liu
68f05f6336f8b025381e19cb
Yu Qiao
68f05f6336f8b025381e19cc
Ziwei Liu
Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark
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.
Uni-MMMU presents a comprehensive benchmark for evaluating bidirectional synergy between visual understanding and generation in unified multimodal models across multiple reasoning domains.
unified multimodal models visual understanding visual generation bidirectional synergy reasoning-centric domains multimodal reasoning cross-modal dependencies