Agentic image editors decompose 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 th
EditAgent broadens agentic image editing by routing steps to diverse editors and using verification records for listwise judging, improving reliability and efficiency.
Agentic image editors decompose 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: the action space of what the agent can do, and the observation space of 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-shelf VLM…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
EditAgent: Rethinking Agentic Image Editing was first seen on Hugging Face · Daily papers on Oct 30, 2026. Since then it has appeared in 37 checks on 4 different days. Its best position was #24 of 30, reached on Oct 30, 09:28 UTC. It dropped off the list after Nov 2, 07:28 UTC.
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#24 on Hugging Face · Daily papers, on Oct 30, 09:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 30, 2026 and has been on the list on 4 days (37 checks) since.