AttriOpt: Attribution-Based Credit Assignment for Evolutionary API-Calling Optimization

Large language models (LLMs) are increasingly used to solve complex tasks by composing sequences of calls to external APIs, forming executable API workflows. However, optimizing such API workflows remains challenging because existing methods typically rely on a single evaluation signal of each workflow, providing little information about which API calls actually contribute to performance. This coarse feedback limits the effectiveness of subsequent search, especially when workflow evaluation is expensive. To address this problem, we propose AttriOpt, a statistical attribution framework for evolutionary optimization of LLM-generated API workflows. AttriOpt exploits accumulated evaluations across workflows to estimate fine-grained attribution scores for API identities, parameter configurations, and interaction patterns. These attribution signals are then incorporated into evolutionary search to effectively guide the API calling process. Experiments on mixed-integer linear programming, traveling salesman problem, and logic synthesis show that AttriOpt consistently outperforms existing API-calling methods and LLM-driven evolutionary approaches. Further ablation and attribution analyses demonstrate that these attribution signals provide more informative guidance than relying solely on a single evaluation signal for each workflow.