EvoEdit: A Self-Evolving Agent Framework for Complex, Multi-Step Dependent Image Editing

With the rapid advancement of generative AI, image editing models have become increasingly powerful and gained significant attention. Despite this progress, existing models and agent systems often remain fragile and struggle to execute complex, especially chain-dependent, image editing tasks. While this vulnerability is partly due to poor planning and a reliance on step-by-step verification, a more fundamental limitation is their inability to learn from accumulated interaction history, causing them to persistently repeat past mistakes. To address these critical bottlenecks, we present EvoEdit, a novel intelligent agent framework designed to solve complex chain-dependent image editing problems, built on the ComfyUI platform. EvoEdit introduces three core innovations: Multi-Path Exploration (MPE), which concurrently explores diverse execution trajectories to identify a high-quality plan; a Recursive Verification mechanism, which backtracks through the execution history to pinpoint the earliest fault responsible for chain failures; and a Dual-Dimensional Self-Evolving mechanism, which dynamically synthesizes macroscopic planning rules and refines microscopic step executions by learning from accumulated experience. Together, these components improve the stability, physical plausibility, and visual harmony of complex interdependent editing workflows. Experimental results demonstrate that EvoEdit significantly outperforms Nano Banana, a strong closed-source image editing model, achieving a 20.68% improvement in average score and a 31.55% increase specifically on three-chain dependent tasks.