Generalizable Self-Evolving Memory for Automatic Prompt Optimization
This addresses the need for more efficient and generalizable prompt optimization methods for users of large language models, though it is incremental as it builds on existing prompt optimization approaches.
The paper tackles the problem of limited generalization and lack of reusable knowledge in automatic prompt optimization for LLMs by proposing MemAPO, a memory-driven framework that accumulates and evolves prompting experience, resulting in consistent performance improvements and reduced optimization costs across diverse benchmarks.
Automatic prompt optimization is a promising approach for adapting large language models (LLMs) to downstream tasks, yet existing methods typically search for a specific prompt specialized to a fixed task. This paradigm limits generalization across heterogeneous queries and prevents models from accumulating reusable prompting knowledge over time. In this paper, we propose MemAPO, a memory-driven framework that reconceptualizes prompt optimization as generalizable and self-evolving experience accumulation. MemAPO maintains a dual-memory mechanism that distills successful reasoning trajectories into reusable strategy templates while organizing incorrect generations into structured error patterns that capture recurrent failure modes. Given a new prompt, the framework retrieves both relevant strategies and failure patterns to compose prompts that promote effective reasoning while discouraging known mistakes. Through iterative self-reflection and memory editing, MemAPO continuously updates its memory, enabling prompt optimization to improve over time rather than restarting from scratch for each task. Experiments on diverse benchmarks show that MemAPO consistently outperforms representative prompt optimization baselines while substantially reducing optimization cost.