Paper page - EditAgent: Rethinking Agentic Image Editing

Source: https://huggingface.co/papers/2610.39664

Abstract

EditAgent broadens agentic image editing by routing steps to diverse editors and using verification records for listwise judging, improving reliability and efficiency.

Agentic image editorsdecompose complex requests into steps, but when a step fails, they mostly retry with the same narrow set of tools and a judge that scores each output by eye directly. We argue that such agents are bounded by two spaces: theaction spaceof what the agent can do, and theobservation spaceof what its judge can see. On our expert-annotated benchmark, a general-purpose editor yields no usable result in over 40% of the cases, and no judge is reliable across edit types. We present EditAgent, which expands both spaces. It routes each step to general editors and specialized methods, and attaches to each candidate a verification record. With these records, off-the-shelfVLM judgesoutperform finetunedreward models. Candidates are generated in parallel and judged in one listwise call: experts prefer this to sequential refinement in 78.5% of decided comparisons, at no more than half the judge calls. EditAgent improves twoopen-weight editorson three benchmarks and serves as a data engine for training stronger editors.

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