DelvePO: Direction-Guided Self-Evolving Framework for Flexible Prompt Optimization

Prompt Optimization has emerged as a crucial approach due to its capabilities in steering Large Language Models to solve various tasks. However, current works mainly rely on the random rewriting ability of LLMs, and the optimization process generally focus on specific influencing factors, which makes it easy to fall into local optimum. Besides, the performance of the optimized prompt is often unstable, which limits its transferability in different tasks. To address the above challenges, we propose DelvePO (Direction-Guided Self-Evolving Framework for Flexible Prompt Optimization), a task-agnostic framework to optimize prompts in self-evolve manner. In our framework, we decouple prompts into different components that can be used to explore the impact that different factors may have on various tasks. On this basis, we introduce working memory, through which LLMs can alleviate the deficiencies caused by their own uncertainties and further obtain key insights to guide the generation of new prompts. Extensive experiments conducted on different tasks covering various domains for both open-and closed-source LLMs, including DeepSeek-R1-Distill-Llama-8B, Qwen2.5-7B-Instruct and GPT -4o-mini. Experimental results show that DelvePO consistently outperforms previous SOT A methods under identical experimental settings, demonstrating its effectiveness and transferability across different tasks. The rapid advancement of Large Language Models (LLMs) (DeepSeek-AI, 2025; Li et al., 2025) has revolutionized various real-world applications (Shao et al., 2024; Zheng et al., 2025) . Prompt, a method that steers LLMs to produce desired results without modifying parameters, has garnered significant interest among non-AI experts from different domains (Wan et al., 2024; Guo et al., 2025; Fernando et al., 2024). Consequently, the rapid growth in users has increased demand for prompt engineering methods. Previous efforts primarily focused on manually designing specialized prompts (Brown et al., 2020; Kojima et al., 2022; Wei et al., 2023).