ERSkill: Self-Evolving Skill-Guided Memory Retrieval for LLM Agents
A recent research article presents ERSkill, a framework focused on retrieval that aims to improve the memory retrieval functions of Large Language Model (LLM) agents. This framework views retrieval mechanisms as adaptable elements, overcoming the challenges posed by static methods that find it difficult to handle diverse memory queries. ERSkill organizes interaction histories into a well-structured memory repository and defines retrieval actions as executable skills made up of basic components. During inference, a trained router dynamically aligns each query with the most suitable skill, generating customized evidence for responses. An experience trie is utilized to effectively log retrieval paths explored, facilitating ongoing enhancement through the co-evolution of the router and skill set during training. The paper can be found on arXiv with the identifier 2608.12720.
Key facts
- ERSkill is a retrieval-centric framework for self-evolving, skill-guided memory access in LLM agents.
- It addresses the problem of static retrieval mechanisms that limit performance on heterogeneous memory queries.
- The framework compiles interaction histories into a structured memory store.
- Retrieval behaviors are represented as executable skills composed of fundamental primitives.
- A trained router dynamically matches each query to the optimal skill at inference time.
- The framework co-evolves the skill set and the router during training.
- An experience trie is used to efficiently record explored retrieval paths.
- The paper is available on arXiv with identifier 2608.12720.
Entities
Institutions
- arXiv