The in-depth analysis of DS-STAR highlights the critical roles of its individual components through ablation studies. The Data File Analyzer is crucial for high performance; its absence resulted in a significant drop in accuracy to 26.98% on challenging tasks in the DABStep benchmark. This stresses the importance of rich data context for effective planning. Similarly, the Router agent proves vital, as excluding it led to a sequential addition of steps and decreased performance on both easy and hard tasks, underscoring that correcting plans is more beneficial than simply adding new steps. Additionally, DS-STAR’s generalizability was tested using GPT-5, yielding promising results and demonstrating adaptability across models. While DS-STAR excelled on easier tasks with GPT-5, the Gemini-2.5-Pro version showed superior performance on harder tasks, reinforcing its versatility and effectiveness in various contexts. This analysis affirms the importance of each component in enhancing planning accuracy and overall functionality.
