Closing the loop: agentic evaluation for image editing foundation models

Introduces EdiVal-Agent, a framework for agentic evaluation of image editing foundation models. EdiVal-Agent decomposes evaluation into object-level tasks, maintains state across editing turns using an evolving object pool, and coordinates specialized AI models and visual tools to verify instruction following, content consistency, and visual quality. It demonstrates 81.3% agreement with human judgments for instruction following, outperforming existing automated metrics. The framework reveals that strong single-turn performance does not guarantee strong multi-turn performance and provides fine-grained insights into model failures.