# Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark

arXiv: https://arxiv.org/abs/2510.13759 · HTML: https://mlsanity.com/paper/2510.13759/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [Emerging Properties in Unified Multimodal Pretraining](https://mlsanity.com/paper/2505.14683/) (2025)
- [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://mlsanity.com/paper/2501.17811/) (2025)
- [VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness](https://mlsanity.com/paper/2503.21755/) (2025)
- [Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers](https://mlsanity.com/paper/2506.23918/) (2025)
- [Envisioning Beyond the Pixels: Benchmarking Reasoning-Informed Visual Editing](https://mlsanity.com/paper/2504.02826/) (2025)
- [ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation](https://mlsanity.com/paper/2506.18095/) (2025)
- [VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2411.13503/) (2024)
- [Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models](https://mlsanity.com/paper/2412.09645/) (2024)
- [Skywork UniPic 2.0: Building Kontext Model with Online RL for Unified Multimodal Model](https://mlsanity.com/paper/2509.04548/) (2025)
- [OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation](https://mlsanity.com/paper/2411.18499/) (2024)
- [MME-Unify: A Comprehensive Benchmark for Unified Multimodal Understanding and Generation Models](https://mlsanity.com/paper/2504.03641/) (2025)
- [Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment](https://mlsanity.com/paper/2411.17188/) (2024)
- [The Telephone Game: Evaluating Semantic Drift in Unified Models](https://mlsanity.com/paper/2509.04438/) (2025)

## Built on by (most cited first, 29 of 29)

- [SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture](https://mlsanity.com/paper/2605.12500/) (2026)
- [MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents](https://mlsanity.com/paper/2601.12346/) (2026)
- [Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models](https://mlsanity.com/paper/2601.19834/) (2026)
- [Demystifying Video Reasoning](https://mlsanity.com/paper/2603.16870/) (2026)
- [Quantifying the Gap between Understanding and Generation within Unified Multimodal Models](https://mlsanity.com/paper/2602.02140/) (2026)
- [Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward](https://mlsanity.com/paper/2511.20561/) (2025)
- [UEval: A Benchmark for Unified Multimodal Generation](https://mlsanity.com/paper/2601.22155/) (2026)
- [Seeing Is Believing? A Benchmark for Multimodal Large Language Models on Visual Illusions and Anomalies](https://mlsanity.com/paper/2602.01816/) (2026)
- [SenseNova-U1.5: Towards Native Unified Visual Intelligence](https://mlsanity.com/paper/2609.11929/) (2026)
- [VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning](https://mlsanity.com/paper/2608.26105/) (2026)
- [Unveiling Fine-Grained Visual Traces: Evaluating Multimodal Interleaved Reasoning Chains in Multimodal STEM Tasks](https://mlsanity.com/paper/2604.19697/) (2026)
- [Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights](https://mlsanity.com/paper/2512.01816/) (2025)
- [TorchUMM: A Unified Multimodal Model Codebase for Evaluation, Analysis, and Post-training](https://mlsanity.com/paper/2604.10784/) (2026)
- [Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models](https://mlsanity.com/paper/2608.09666/) (2026)
- [When Does Visual Generation Help Visual Understanding in Unified Multimodal Models?](https://mlsanity.com/paper/2608.22174/) (2026)
- [AIA: Rethinking Architecture Decoupling Strategy In Unified Multimodal Model](https://mlsanity.com/paper/2511.22663/) (2025)
- [EchoGen: Cycle-Consistent Learning for Unified Layout-Image Generation and Understanding](https://mlsanity.com/paper/2603.18001/) (2026)
- [Pseudo-Unification: Entropy Probing Reveals Divergent Information Patterns in Unified Multimodal Models](https://mlsanity.com/paper/2604.10949/) (2026)
- [LatentUMM: Dual Latent Alignment for Unified Multimodal Models](https://mlsanity.com/paper/2605.17766/) (2026)
- [DeltaV: Thinking with Visual State Updates in Unified Large Multimodal Models](https://mlsanity.com/paper/2607.08434/) (2026)
- [Thinking in Pictures: A Systematic Benchmark for Reasoning-driven Image Generation](https://mlsanity.com/paper/2609.02864/) (2026)
- [The CompMath-MCQ Dataset: Are LLMs Ready for Higher-Level Math?](https://mlsanity.com/paper/2603.03334/) (2026)
- [SketchVLM: Vision language models can annotate images to explain thoughts and guide users](https://mlsanity.com/paper/2604.22875/) (2026)
- [The Cartesian Shortcut: Re-evaluate Vision Reasoning in Polar Coordinate Space](https://mlsanity.com/paper/2605.09883/) (2026)
- [InterSketch: An Interleaved Reasoning Model with Self-correcting Visual Sketch and Stepwise Reward](https://mlsanity.com/paper/2605.26520/) (2026)
- [IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation](https://mlsanity.com/paper/2606.09169/) (2026)
- [UniVR: Thinking in Visual Space for Unified Visual Reasoning](https://mlsanity.com/paper/2607.12800/) (2026)
- [V-Rubrics: Visual Faithfulness via Rubric-Based Reinforcement Learning](https://mlsanity.com/paper/2608.25580/) (2026)
- [What Comes Next? Omni-StoryBench for Evaluating Story-Grounded Omnimodal Generation](https://mlsanity.com/paper/2609.37317/) (2026)

## Abstract (arXiv)

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.