# Dreaming in Flow: Generative Grounding Feedback for Self-Evolving Unified Multimodal Models

arXiv: https://arxiv.org/abs/2609.08282 · HTML: https://mlsanity.com/paper/2609.08282/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 36 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 27 of 27)

- [GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment](https://mlsanity.com/paper/2310.11513/) (2023)
- [Chameleon: Mixed-Modal Early-Fusion Foundation Models](https://mlsanity.com/paper/2405.09818/) (2024)
- [Emerging Properties in Unified Multimodal Pretraining](https://mlsanity.com/paper/2505.14683/) (2025)
- [NExT-GPT: Any-to-Any Multimodal LLM](https://mlsanity.com/paper/2309.05519/) (2023)
- [Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model](https://mlsanity.com/paper/2408.11039/) (2024)
- [ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment](https://mlsanity.com/paper/2403.05135/) (2024)
- [Evaluating Text-to-Visual Generation with Image-to-Text Generation](https://mlsanity.com/paper/2404.01291/) (2024)
- [Generative Multimodal Models are In-Context Learners](https://mlsanity.com/paper/2312.13286/) (2023)
- [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://mlsanity.com/paper/2303.11897/) (2023)
- [BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset](https://mlsanity.com/paper/2505.09568/) (2025)
- [SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation](https://mlsanity.com/paper/2404.14396/) (2024)
- [Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action](https://mlsanity.com/paper/2312.17172/) (2023)
- [Show-o2: Improved Native Unified Multimodal Models](https://mlsanity.com/paper/2506.15564/) (2025)
- [MetaMorph: Multimodal Understanding and Generation via Instruction Tuning](https://mlsanity.com/paper/2412.14164/) (2024)
- [ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance](https://mlsanity.com/paper/2412.06673/) (2024)
- [Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback](https://mlsanity.com/paper/2307.04749/) (2023)
- [UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision](https://mlsanity.com/paper/2601.03193/) (2026)
- [SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models](https://mlsanity.com/paper/2510.12784/) (2025)
- [Unified Multimodal Models as Auto-Encoders](https://mlsanity.com/paper/2509.09666/) (2025)
- [Understanding-in-Generation: Reinforcing Generative Capability of Unified Model via Infusing Understanding into Generation](https://mlsanity.com/paper/2509.18639/) (2025)
- [Turning Internal Gap into Self-Improvement: Promoting the Generation-Understanding Unification in MLLMs](https://mlsanity.com/paper/2507.16663/) (2025)
- [Endogenous Reprompting: Self-Evolving Cognitive Alignment for Unified Multimodal Models](https://mlsanity.com/paper/2601.20305/) (2026)
- [Ask, Solve, Generate: Self-Evolving Unified Multimodal Understanding and Generation via Self-Consistency Rewards](https://mlsanity.com/paper/2606.27376/) (2026)
- [Learning to Generate via Understanding: Understanding-Driven Intrinsic Rewarding for Unified Multimodal Models](https://mlsanity.com/paper/2603.06043/) (2026)
- [Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models](https://mlsanity.com/paper/2602.15772/) (2026)
- [Steering Visual Generation in Unified Multimodal Models with Understanding Supervision](https://mlsanity.com/paper/2605.05781/) (2026)
- [Generation as Auxiliary Supervision: Enhancing Visual Understanding at Zero Inference Overhead via Decoupled Embedding Prediction](https://mlsanity.com/paper/2608.12209/) (2026)

## Built on by (most cited first, 3 of 3)

- [UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement](https://mlsanity.com/paper/2609.38721/) (2026)
- [Recursive Self-Improvement in Unified Multimodal Models](https://mlsanity.com/paper/2610.03002/) (2026)
- [RSIgen: Reciprocal Self-Improvement for Agentic Visual Generation](https://mlsanity.com/paper/2610.18688/) (2026)

## Abstract (arXiv)

Unified multimodal models integrate visual understanding and generation within a single network, yet the two capabilities are commonly optimized as separate tasks. We introduce Generative Grounding Feedback(GGF), a self-evolving post-training framework that uses only text prompts and the model's own visual experience. Given a prompt, the model first generates a visual ``dream.'' Flow-level feedback compares text-, image-, and repair-conditioned predictions at the same noisy latent state, transferring image-grounded generation directions to the prompt condition. Dream replay grounding replays this dream through captioning and re-imagination, training claim-level evidence to remain consistent across the replay while separating unrelated visual experiences. Jointly optimized, these two directions let generation provide visual grounding for understanding and understanding refine subsequent generation without paired image--text supervision. Experiments across unified models with different understanding--generation integration designs show consistent improvements in text-to-image generation together with modest gains in visual understanding.