EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918465784250368 |
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| author | Zhang, Aimin Guo, Jiajing Jia, Fuwei Lv, Chen Wang, Boyu Li, Fangzheng |
| author_facet | Zhang, Aimin Guo, Jiajing Jia, Fuwei Lv, Chen Wang, Boyu Li, Fangzheng |
| contents | This paper proposes EvoAgent - an evolvable large language model (LLM) agent framework that integrates structured skill learning with a hierarchical sub-agent delegation mechanism. EvoAgent models skills as multi-file structured capability units equipped with triggering mechanisms and evolutionary metadata, and enables continuous skill generation and optimization through a user-feedback-driven closed-loop process. In addition, by incorporating 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 based on real-world foreign trade scenarios demonstrate that, after integrating EvoAgent, GPT5.2 achieves significant improvements in professionalism, accuracy, and practical utility. Under a five-dimensional LLM-as-Judge evaluation protocol, the overall average score increases by approximately 28%. Further model transfer experiments indicate that the performance of an agent system depends not only on the intrinsic capabilities of the underlying model, but also on the degree of synergy between the model and the agent architecture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20133 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation Zhang, Aimin Guo, Jiajing Jia, Fuwei Lv, Chen Wang, Boyu Li, Fangzheng Artificial Intelligence This paper proposes EvoAgent - an evolvable large language model (LLM) agent framework that integrates structured skill learning with a hierarchical sub-agent delegation mechanism. EvoAgent models skills as multi-file structured capability units equipped with triggering mechanisms and evolutionary metadata, and enables continuous skill generation and optimization through a user-feedback-driven closed-loop process. In addition, by incorporating 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 based on real-world foreign trade scenarios demonstrate that, after integrating EvoAgent, GPT5.2 achieves significant improvements in professionalism, accuracy, and practical utility. Under a five-dimensional LLM-as-Judge evaluation protocol, the overall average score increases by approximately 28%. Further model transfer experiments indicate that the performance of an agent system depends not only on the intrinsic capabilities of the underlying model, but also on the degree of synergy between the model and the agent architecture. |
| title | EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.20133 |