Title: DelvePO: Direction-Guided Self-Evolving Framework for Flexible Prompt Optimization
Primary area: foundation or frontier models, including LLMs
Keywords: Prompt Optimization, Prompt Engineering, Evolutionary Algorithm, Large Language Models

Abstract: 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 $\textbf{DelvePO}$ 
($\textbf{D}$irection-Guid$\textbf{e}$d Se$\textbf{l}$f-E$\textbf{v}$olving
Framework for Fl$\textbf{e}$xible $\textbf{P}$rompt $\textbf{O}$ptimization), 
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 SOTA methods 
under identical experimental settings, demonstrating 
its effectiveness and transferability across different tasks.

TL;DR: This paper presents a task-agnostic, component-customizable and flexible framework to optimize the prompts in a self-evolve manner. To the best of our knowledge, this is the first work that introduces memory mechanism to PO.