EvoAgent: LLM Agent Framework with Skill Learning and Multi-Agent Delegation
A recent study published on arXiv introduces EvoAgent, a framework for an evolvable large language model (LLM) agent that combines structured skill learning with a hierarchical delegation system for sub-agents. This framework conceptualizes skills as multi-file structured capability units featuring triggering mechanisms and evolutionary metadata, facilitating ongoing skill development and refinement through a user-feedback-driven closed-loop system. It employs a three-stage skill matching approach and a three-layer memory structure to enable dynamic task decomposition for intricate challenges and long-term skill enhancement. Experimental findings from real-world foreign trade cases indicate that integrating EvoAgent leads to notable enhancements in professionalism, accuracy, and practical application for GPT5.2, with a significant rise in the average score under a five-dimensional LLM-as-Judge evaluation protocol.
Key facts
- EvoAgent is an evolvable LLM agent framework proposed in a paper on arXiv.
- It integrates structured skill learning with a hierarchical sub-agent delegation mechanism.
- Skills are modeled as multi-file structured capability units with triggering mechanisms and evolutionary metadata.
- The framework enables continuous skill generation and optimization through user-feedback-driven closed-loop process.
- It incorporates a three-stage skill matching strategy and a three-layer memory architecture.
- The framework supports dynamic task decomposition for complex problems and long-term capability accumulation.
- Experimental results are based on real-world foreign trade scenarios.
- After integrating EvoAgent, GPT5.2 achieves significant improvements in professionalism, accuracy, and practical utility.
- Evaluation uses a five-dimensional LLM-as-Judge protocol.
- The overall average score increases by approximately an unspecified amount.
Entities
Institutions
- arXiv