OZONE: Object-Oriented Optimization of Agentic Workflow for Scientific Survey Generation

We introduce OZONE, an object-oriented agentic workflow optimization framework for scientific survey generation. Survey writing comprises tasks that share common requirements but differ in their objectives, operator compositions, and evaluation criteria. OZONE represents the search space with predefined abstract classes and task-specific subclasses that inherit common contracts. The optimizer constructs and extends operator implementations under these contracts, assigns execution units to subclasses based on their operators and outputs, and connects the units into a directed acyclic graph. We design local rubrics for intermediate outputs and a global rubric for completed surveys, as improvements to individual tasks do not always improve overall survey quality. Local diagnosis guides changes to unit implementations and their connections, while the global score provides a basis for comparing candidate workflows. At inference time, OZONE supports runtime editing guided by a rubric proxy and refinement through review-based revision. We introduce CROFTBench, a benchmark of recent CS survey topics that extends evaluation to figures and tables. With GPT-4o-mini and inference refinement, OZONE achieves 72.2 on SurveyBench, 46.02 on Bloom-Eval, and 43.34 on CROFTBench.