IconEdit: Benchmarking and Improving Progressive SVG Editing with Change-AND-Preserve Reasoning

Progressive SVG icon editing requires models to execute each requested change while preserving visual and structural properties established by previous edits. Yet existing SVG editing benchmarks primarily evaluate isolated edits rather than sequential editing, where each generated output becomes the input to the next instruction. This distinction matters because an incomplete or unintended modification can propagate through subsequent edits, producing cumulative drift or degrading previously established content. We formulate progressive SVG editing as a trajectory-level change-and-preserve problem and introduce IconEditBench, an underlying corpus of 2.45 million SVG icons paired with edit instructions, together with a controlled benchmark of 4,000 source SVGs undergoing five sequential edits each. We further introduce twelve target-free metrics for measuring edit completion, preservation, drift, locality, validity, and trajectory-level reliability. We propose AgentIconEdit, a verifier-guided multi-trajectory framework that verifies and refines candidate edits before propagating them to subsequent states. Across four SOTA closed-source models, AgentIconEdit preserves perfect SVG validity while improving mean Edit Coverage by 4.8%, Spatial Placement by 31.6%, reducing geometric and topological drift by 37.5% and 35.3%, respectively. A 25-participant human study preferred AgentIconEdit over baseline in 80.8% of 25k pairwise comparisons. Together, these contributions provide a benchmark, evaluation metrics and a framework for studying and improving reliable progressive SVG editing. Code and dataset will be made publicly available.