Yucheng Mao

When an image-editing agent fails, does it really need another try? Introducing our new paper: EditAgent: Rethinking Agentic Image Editing. We argue that editing agents are bounded by two spaces: 🔧 Action space: what the agent can do. 🔍 Observation space: what its judge can see. Three findings: 1️⃣ Expanding the action space matters. Across 600 expert-annotated candidate sets, FLUX.2-klein-9B produced no acceptable candidate in 42.7%. Specialized methods rescued 175 of those 256 failures. 2️⃣ Expand what the judge can observe. EditAgent enriches the observation space with parallel candidates for comparison, task-specific verification evidence, and earlier images and requests as historical context—supporting better candidate selection and detecting when a new edit undoes an earlier one. 3️⃣ Explore diverse alternatives through parallel search. Different specialists, generalists, and repeated samples offer different ways to fulfill a request. Parallel search lets the judge compare these alternatives, while generating each candidate from the same source avoids carrying errors from one attempt into the next. Even with a single editor and the same four-generation budget, experts preferred parallel search over sequential refinement in 78.5% of non-tied comparisons—with at most half as many judge calls. EditAgent expands both spaces and connects them through parallel candidate generation and comparative judging. It improves two editing backbones on three benchmarks, and its accepted sessions provide training data that strengthens standalone editors. Our takeaway: building reliable editing agents requires expanding both what they can do and what their judges can observe.