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GenEvolve, from MeiGen-AI, is a self-evolving framework for image generation agents. The core reframe: each generation attempt isn’t a one-shot prompt, it’s a tool-orchestrated trajectory — the agent gathers evidence, selects reference images, invokes generation skills, and composes them into a prompt-reference program.

## Tool-orchestrated visual experience distillation

Instead of calling a diffusion model once, the agent runs a multi-step process and learns from the outcome. Successful trajectories get distilled into reusable “visual experience” — so the agent gets better at combining its model’s internal generative ability with external resources over time. It self-evolves through accumulated trajectories rather than needing a retrain.

## Why it matters

Image generation has plateaued on raw model quality; the frontier is orchestration — how you prompt, which references you pull, how you compose multi-step edits. GenEvolve’s bet is that an agent learning from its own best attempts beats a static model called naively. It’s the same “agent learns from trajectories” idea applied to the visual domain — and it points where image tooling is heading: less “better model,” more “smarter agent around the model.”