CORE-Flow: Counterfactual Observation and Retrieval-Guided Exploration for Efficient Agentic Workflow Evolution

Automated workflow design enables adaptive solving of complex tasks through fine-grained composition of capability modules and multi-round iterative optimization. However, efficiently searching the large space of workflow structures, operators, and prompts remains difficult. Existing methods typically rely on an optimizer model to diagnose the deficiencies of candidate workflows, thereby improving search efficiency. However, such diagnosis introduces uncertainty into the update direction due to the stochasticity of model outputs, while overlooking reusable historical evidence. Therefore, we propose CORE-Flow, an experience-driven framework for efficient workflow evolution through evidence reuse. CORE-Flow uses counterfactual observations of workflow edits to construct reusable optimization experience and retrieval-guided exploration to focus subsequent search on historically supported directions. Across six benchmarks spanning mathematical reasoning, code generation, and question answering, CORE-Flow achieves the best average score of 89.41, outperforming JUDGEFLOW and AFLOW by 2.92 and 7.89 points, respectively. The cost analysis tracked on the MATH dataset further demonstrates strong potential to reduce both token consumption and wall-clock execution time.