GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910247301414912 |
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| author | Chen, Sixiang Xing, Zhaohu Ye, Tian Geng, Xinyu Lin, Yunlong Lai, Jianyu He, Xuanhua Zhai, Fuxiang Gao, Jialin Zhu, Lei |
| author_facet | Chen, Sixiang Xing, Zhaohu Ye, Tian Geng, Xinyu Lin, Yunlong Lai, Jianyu He, Xuanhua Zhai, Fuxiang Gao, Jialin Zhu, Lei |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21605 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation Chen, Sixiang Xing, Zhaohu Ye, Tian Geng, Xinyu Lin, Yunlong Lai, Jianyu He, Xuanhua Zhai, Fuxiang Gao, Jialin Zhu, Lei Computer Vision and Pattern Recognition 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/ |
| title | GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.21605 |