# When Does Visual Generation Help Visual Understanding in Unified Multimodal Models?

arXiv: https://arxiv.org/abs/2608.22174 · HTML: https://mlsanity.com/paper/2608.22174/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 23 Aug 2026.

## Builds on (most cited first, 19 of 19)

- [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)
- [OmniGen2: Towards Instruction-Aligned Multimodal Generation](https://mlsanity.com/paper/2506.18871/) (2025)
- [Show-o2: Improved Native Unified Multimodal Models](https://mlsanity.com/paper/2506.15564/) (2025)
- [Imagine while Reasoning in Space: Multimodal Visualization-of-Thought](https://mlsanity.com/paper/2501.07542/) (2025)
- [ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding](https://mlsanity.com/paper/2501.05452/) (2025)
- [LongCat-Next: Lexicalizing Modalities as Discrete Tokens](https://mlsanity.com/paper/2603.27538/) (2026)
- [The LLM Already Knows: Estimating LLM-Perceived Question Difficulty via Hidden Representations](https://mlsanity.com/paper/2509.12886/) (2025)
- [Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark](https://mlsanity.com/paper/2510.13759/) (2025)
- [MME-Unify: A Comprehensive Benchmark for Unified Multimodal Understanding and Generation Models](https://mlsanity.com/paper/2504.03641/) (2025)
- [Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better](https://mlsanity.com/paper/2506.09040/) (2025)
- [ViC-Bench: Benchmarking Visual-Interleaved Chain-of-Thought Capability in MLLMs with Free-Style Intermediate State Representations](https://mlsanity.com/paper/2505.14404/) (2025)
- [CrossWordBench: Evaluating the Reasoning Capabilities of LLMs and LVLMs with Controllable Puzzle Generation](https://mlsanity.com/paper/2504.00043/) (2025)
- [MammothModa2: A Unified AR-Diffusion Framework for Multimodal Understanding and Generation](https://mlsanity.com/paper/2511.18262/) (2025)
- [MentisOculi: Revealing the Limits of Reasoning with Mental Imagery](https://mlsanity.com/paper/2602.02465/) (2026)
- [UniG2U-Bench: Do Unified Models Advance Multimodal Understanding?](https://mlsanity.com/paper/2603.03241/) (2026)
- [Anchoring Values in Temporal and Group Dimensions for Flow Matching Model Alignment](https://mlsanity.com/paper/2512.12387/) (2025)
- [STAR: STacked AutoRegressive Scheme for Unified Multimodal Learning](https://mlsanity.com/paper/2512.13752/) (2025)
- [See Different, Think Better: Visual Variations Mitigating Hallucinations in LVLMs](https://mlsanity.com/paper/2507.22003/) (2025)

## Built on by (most cited first, 2 of 2)

- [When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks](https://mlsanity.com/paper/2610.12823/) (2026)
- [MaskSense: Confronting the Visual Exploration Trap in Masked Image Generation](https://mlsanity.com/paper/2610.17009/) (2026)

## Abstract (arXiv)

Unified multimodal models (UMMs) can perform both understanding and generation, raising a central question: can visual generation improve understanding? Existing evaluations provide mixed evidence, but confound task difficulty, reasoning paradigms, and the closed-loop interaction between generation and understanding. We introduce VGAU-Diag, a fine-grained evaluation framework for vision generation-assisted understanding. It stratifies samples by difficulty, enables unified evaluation of multiple reasoning paradigms, and uses Oracle-Assisted Reference Protocols. Our analysis shows that generated visual aids help on easier instances but become unreliable as reasoning complexity increases. Oracle-assisted diagnosis further reveals that the main bottleneck often lies on the visual-understanding side rather than the visual-generation side, as current UMMs struggle to leverage even faithful visual aids. We also show that effective visual generation should target visual-understanding bottlenecks rather than add more reasoning steps, and identify a three-stage transition from task-irrelevant noise, to misleading plausible guidance, and finally to useful assistance. These findings would be useful to guide the development of better UMMs.