# GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation

arXiv: https://arxiv.org/abs/2605.21605 · HTML: https://mlsanity.com/paper/2605.21605/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 71 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 30 of 40)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Qwen-Image Technical Report](https://mlsanity.com/paper/2508.02324/) (2025)
- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [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)
- [PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://mlsanity.com/paper/2310.00426/) (2023)
- [Emu3: Next-Token Prediction is All You Need](https://mlsanity.com/paper/2409.18869/) (2024)
- [Show-o: One Single Transformer to Unify Multimodal Understanding and Generation](https://mlsanity.com/paper/2408.12528/) (2024)
- [Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models](https://mlsanity.com/paper/2601.18734/) (2026)
- [BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset](https://mlsanity.com/paper/2505.09568/) (2025)
- [Reinforcement Learning via Self-Distillation](https://mlsanity.com/paper/2601.20802/) (2026)
- [Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer](https://mlsanity.com/paper/2511.22699/) (2025)
- [WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation](https://mlsanity.com/paper/2503.07265/) (2025)
- [On-Policy Context Distillation for Language Models](https://mlsanity.com/paper/2602.12275/) (2026)
- [HunyuanImage 3.0 Technical Report](https://mlsanity.com/paper/2509.23951/) (2025)
- [LongCat-Image Technical Report](https://mlsanity.com/paper/2512.07584/) (2025)
- [Lumina-Image 2.0: A Unified and Efficient Image Generative Framework](https://mlsanity.com/paper/2503.21758/) (2025)
- [Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents](https://mlsanity.com/paper/2604.10674/) (2026)
- [Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback](https://mlsanity.com/paper/2510.16888/) (2025)
- [HDPO: Hybrid Distillation Policy Optimization via Privileged Self-Distillation](https://mlsanity.com/paper/2603.23871/) (2026)
- [PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework](https://mlsanity.com/paper/2506.10741/) (2025)
- [Gen-Searcher: Reinforcing Agentic Search for Image Generation](https://mlsanity.com/paper/2603.28767/) (2026)
- [On a few pitfalls in KL divergence gradient estimation for RL](https://mlsanity.com/paper/2506.09477/) (2025)

## Built on by (most cited first, 19 of 19)

- [RS-Gen: A Multi-Stage Agentic Framework for Reasoning and Search-Augmented Image Generation](https://mlsanity.com/paper/2606.23221/) (2026)
- [GenRouter: Unified Workflow Routing for Agentic Image Generation](https://mlsanity.com/paper/2608.16721/) (2026)
- [ToolArtist: Tool-Using Unified Multimodal Models for Agentic Image Generation](https://mlsanity.com/paper/2608.04436/) (2026)
- [MemoGen: Can Past Experience Improve Future Text-to-Image Generation?](https://mlsanity.com/paper/2606.03243/) (2026)
- [DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment](https://mlsanity.com/paper/2607.07820/) (2026)
- [OmniHarness: Harnessing Generalizable Visual Generation via Symbolic Policy Learning](https://mlsanity.com/paper/2609.16057/) (2026)
- [JarvisHub: An Open Harness for Canvas-Native Multimodal Creative Agents](https://mlsanity.com/paper/2607.23588/) (2026)
- [WeAgent-MMGenEdit: A Full-Stack Recipe for Multimodal Agentic Image Generation and Editing](https://mlsanity.com/paper/2609.05171/) (2026)
- [Agentic Visual Generation: From Generative Models to Agentic Control](https://mlsanity.com/paper/2609.06758/) (2026)
- [SCOPE: Score-Isolated Agentic Optimization for Video World Models](https://mlsanity.com/paper/2608.15043/) (2026)
- [AgenticGen: Reward-Guided Agentic Video Generation for Advertising](https://mlsanity.com/paper/2609.09187/) (2026)
- [SkillPE: Creativity-Oriented Cinematic Skill Evolution for Text-to-Video Prompt Engineering](https://mlsanity.com/paper/2609.34335/) (2026)
- [AutoRef: Harness Optimization for Agentic Multi-Reference Image Generation](https://mlsanity.com/paper/2609.35530/) (2026)
- [InfoAgent: Traceable Generation and Repair of Evidence-Grounded Infographics](https://mlsanity.com/paper/2609.39380/) (2026)
- [EvoGen-Harness: Learning Where and How to Evolve Image-Generation Harnesses](https://mlsanity.com/paper/2610.00383/) (2026)
- [Internalizing Agent Experience into Diffusion Model Weights via On-Policy Context Distillation](https://mlsanity.com/paper/2610.07250/) (2026)
- [MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing](https://mlsanity.com/paper/2610.12963/) (2026)
- [Stride: Scale-Aware Multi-Agent Evolutionary Refinement for Infographic Generation](https://mlsanity.com/paper/2610.13502/) (2026)
- [RSIgen: Reciprocal Self-Improvement for Agentic Visual Generation](https://mlsanity.com/paper/2610.18688/) (2026)

## Abstract (arXiv)

Open-ended image generation is no longer a simple prompt-to-image problem. High-quality generation often requires an agent to combine a model's internal generative ability with external resources. As requests become more diverse and demanding, we aim to develop a general image-generation agent that can self-evolve through trajectories and use tools more effectively across varied generation challenges. To this end, we propose GenEvolve, a self-evolving framework based on Tool-Orchestrated Visual Experience Distillation. In GenEvolve, each generation attempt is modeled as a tool-orchestrated trajectory, where the agent gathers evidence, selects references, invokes generation skills, and composes them into a prompt-reference program. Unlike existing agentic generation methods that mainly rely on image-level scalar rewards, GenEvolve compares multiple trajectories for the same request and abstracts best-worst differences into structured visual experience, provided only to a privileged teacher branch. Inspired by on-policy self-distillation, Visual Experience Distillation provides dense token-level supervision, helping the student internalize better search, knowledge activation, reference selection, and prompt construction. We further construct GenEvolve-Data and GenEvolve-Bench. Experiments on public benchmarks and GenEvolve-Bench show substantial gains over strong baselines, achieving state-of-the-art performance among current image-generation frameworks. Our website is as follows: https://ephemeral182.github.io/GenEvolve/