Learning to Continually Learn via Meta-learning Agentic Memory Designs

This paper details the development of **ALMA**, a meta-learning framework designed to generate and refine **agentic memory structures** for AI systems. This system utilizes a **Meta Agent** to synthesize specialized memory layers, such as **strategy libraries**, **spatial experience graphs**, and **reflex rules**, which help agents navigate complex environments like TextWorld and MiniHack. To ensure operational security, the framework employs **isolated sandbox environments** and human oversight to prevent unintended behaviors or harmful code execution. The research demonstrates that these **autonomous memory designs** significantly improve task success rates while maintaining lower computational costs compared to traditional retrieval methods. Additionally, the sources include extensive **technical documentation** and utility functions for parsing environmental data, managing databases, and distilling past experiences into actionable logic. These components work together to enable agents to **continually learn** and adapt their strategies based on historical performance and environmental feedback.