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arxiv: 2604.20133 · v2 · submitted 2026-04-22 · 💻 cs.AI

Recognition: unknown

EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation

Aimin Zhang , Jiajing Guo , Fuwei Jia , Chen Lv , Boyu Wang , Fangzheng Li
Authors on Pith no claims yet

Pith reviewed 2026-05-10 00:52 UTC · model grok-4.3

classification 💻 cs.AI
keywords EvoAgentLLM agentsskill learningmulti-agent delegationevolvable agentsuser feedback loopforeign trade scenariosLLM-as-Judge evaluation
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The pith

EvoAgent lets LLMs evolve structured skills through feedback loops and delegate tasks to sub-agents, raising performance 28 percent in foreign trade work.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces EvoAgent as a framework that turns LLMs into evolvable agents by representing skills as multi-file units with triggering rules and evolutionary metadata. These skills are generated and refined in a closed loop driven by user feedback, while a three-stage matching process and three-layer memory enable dynamic task breakdown and long-term retention. Experiments in real foreign trade scenarios show that adding this architecture to GPT5.2 lifts average scores by about 28 percent across professionalism, accuracy, and utility metrics judged by another LLM. The authors also report that gains depend on how well the base model meshes with the agent structure rather than on model size alone.

Core claim

EvoAgent models skills as structured multi-file capability units equipped with triggering mechanisms and evolutionary metadata, then runs a user-feedback closed loop for continuous skill creation and optimization; a three-stage skill matching strategy together with a three-layer memory architecture supports dynamic decomposition of complex problems, and when this system is added to GPT5.2 the resulting agent records an approximately 28 percent rise in overall score under five-dimensional LLM-as-Judge evaluation on foreign trade tasks.

What carries the argument

The structured skill learning loop with user-feedback-driven evolution of multi-file skill units, supported by hierarchical sub-agent delegation and three-layer memory for capability accumulation.

If this is right

  • Agent performance is shown to depend on the synergy between the underlying model and the agent architecture, not solely on the model's intrinsic capabilities.
  • Continuous user-feedback loops enable ongoing skill generation and optimization beyond one-time prompt engineering.
  • Hierarchical delegation combined with three-stage matching allows dynamic decomposition of complex, multi-step tasks.
  • Three-layer memory supports long-term accumulation of capabilities across repeated interactions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same skill-evolution pattern could be tested in other professional domains such as legal document review or medical diagnosis to check whether the 28 percent lift generalizes.
  • If architecture synergy matters more than raw model scale, development effort may shift toward designing evolvable agent shells rather than training ever-larger base models.
  • Persistent user-feedback loops could allow individual agents to develop personalized skill sets over time, creating a form of long-term specialization not available in static systems.

Load-bearing premise

The observed score gains are produced by the skill-learning loop and delegation architecture rather than by prompt choices, model version details, or biases in the LLM-as-Judge evaluation.

What would settle it

Run the same foreign trade tasks with identical base prompts but with the skill-learning loop and delegation mechanism turned off, then compare the five-dimensional LLM-as-Judge scores to the original 28 percent improvement.

Figures

Figures reproduced from arXiv: 2604.20133 by Aimin Zhang, Boyu Wang, Chen Lv, Fangzheng Li, Fuwei Jia, Jiajing Guo.

Figure 1
Figure 1. Figure 1: EvoAgent System Architecture routing and task distribution; the runtime layer executes core logic; the tool and session layer provides execution resources; and the persistence layer manages data storage. The architectural highlights include a three-layer delegation routing mechanism and a shared runtime design, enabling efficient context management and tool invocation. The system employs the ReAct loop to … view at source ↗
Figure 2
Figure 2. Figure 2: Performance Comparison Before and After EvoAgent Integration. Blue bars represent GPT5.2 performance [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Relative Performance of Models After EvoAgent Integration (vs GPT5.2) [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

4 major / 2 minor

Summary. The manuscript presents EvoAgent, an evolvable agent framework for LLMs that structures skills as multi-file units with triggering mechanisms and evolutionary metadata. It includes a feedback-driven skill generation and optimization loop, a three-stage skill matching strategy, a three-layer memory architecture, and hierarchical multi-agent delegation. In experiments on real-world foreign trade scenarios, integrating EvoAgent with GPT5.2 is reported to yield an approximately 28% increase in overall average scores under a five-dimensional LLM-as-Judge protocol, enhancing professionalism, accuracy, and practical utility. Additional experiments explore model transferability.

Significance. If validated, the results would suggest that agent architectures with evolvable skills and delegation can substantially enhance LLM performance on complex tasks beyond what base models achieve alone. This could have implications for developing more autonomous and adaptive AI systems in professional domains. The closed-loop learning aspect is particularly noteworthy as a step toward self-improving agents.

major comments (4)
  1. [§4] The performance claim of a ~28% score increase is not supported by ablation studies that isolate the effects of the skill learning loop, three-stage matching, or delegation mechanism (see abstract and §4).
  2. [§4] There are no baseline comparisons to a prompt-engineered GPT5.2 without the EvoAgent framework, which is necessary to attribute the gains specifically to the proposed architecture rather than prompt differences.
  3. [Evaluation in §4] The LLM-as-Judge protocol lacks reported correlation with human judgments or inter-annotator agreement metrics, and judge prompts are undisclosed, undermining the reliability of the five-dimensional scores.
  4. [§4] No error bars, standard deviations, or statistical tests are mentioned for the score improvements, and the number of trials or scenarios is not specified.
minor comments (2)
  1. [Abstract] Clarify what 'GPT5.2' refers to, as it may be a specific variant or typo for a known model.
  2. [Throughout] Ensure all components like the three-layer memory are clearly defined with diagrams or pseudocode for reproducibility.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We agree that the experimental section requires strengthening to better support the performance claims and evaluation methodology. We address each major comment below and will incorporate the necessary revisions.

read point-by-point responses

  1. Referee: [§4] The performance claim of a ~28% score increase is not supported by ablation studies that isolate the effects of the skill learning loop, three-stage matching, or delegation mechanism (see abstract and §4).

    Authors: We acknowledge that the current manuscript presents overall system performance without component-wise ablations. In the revised version, we will add ablation studies that isolate the contributions of the skill learning loop, three-stage matching strategy, and hierarchical delegation mechanism by systematically disabling each component and reporting the resulting performance deltas. revision: yes

  2. Referee: [§4] There are no baseline comparisons to a prompt-engineered GPT5.2 without the EvoAgent framework, which is necessary to attribute the gains specifically to the proposed architecture rather than prompt differences.

    Authors: The reported gains are measured against the unmodified base GPT5.2. We agree that a prompt-engineered baseline without the full EvoAgent architecture is needed to isolate architectural effects. We will add this comparison in the revised experiments, matching prompt styles as closely as possible while excluding the structured skill system and delegation. revision: yes

  3. Referee: [Evaluation in §4] The LLM-as-Judge protocol lacks reported correlation with human judgments or inter-annotator agreement metrics, and judge prompts are undisclosed, undermining the reliability of the five-dimensional scores.

    Authors: We will disclose the complete judge prompts in an appendix of the revised manuscript. For human correlation and inter-annotator agreement, we will perform a small-scale human evaluation on a representative subset of scenarios to compute agreement metrics and report correlation with the LLM-as-Judge scores. This addresses the core reliability concern while remaining feasible within revision scope. revision: partial

  4. Referee: [§4] No error bars, standard deviations, or statistical tests are mentioned for the score improvements, and the number of trials or scenarios is not specified.

    Authors: We will update §4 to explicitly state the number of scenarios and trials. We will also report standard deviations, add error bars to figures, and include statistical significance tests (e.g., paired t-tests) for the observed score improvements. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework proposal with reported experimental outcomes

full rationale

The paper introduces EvoAgent as a structured agent framework with skill learning, hierarchical delegation, three-stage matching, and memory architecture, then reports empirical score gains (~28% on LLM-as-Judge) from integrating it with GPT5.2 in foreign-trade scenarios. No mathematical derivations, equations, fitted parameters, or predictions appear that reduce to the framework's own definitions by construction. The performance numbers are presented as direct experimental measurements rather than quantities defined in terms of the architecture itself, and no self-citation chains or ansatzes are invoked to justify core claims. The derivation chain is therefore self-contained as a descriptive proposal plus external validation data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 3 invented entities

The framework introduces several new constructs whose independent grounding is not supplied in the abstract.

invented entities (3)
  • multi-file structured skill units with triggering mechanisms and evolutionary metadata no independent evidence
    purpose: To represent and evolve agent capabilities as reusable, optimizable modules
    Defined in the abstract as the core representation for skill learning; no external validation or prior reference given.
  • three-stage skill matching strategy no independent evidence
    purpose: To enable dynamic task decomposition and skill selection
    Introduced as part of the framework without derivation or comparison to existing matching methods.
  • three-layer memory architecture no independent evidence
    purpose: To support long-term capability accumulation
    Presented as a novel component without details or external benchmarks.

pith-pipeline@v0.9.0 · 5477 in / 1393 out tokens · 29552 ms · 2026-05-10T00:52:11.890684+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

25 extracted references · 10 canonical work pages · 10 internal anchors

  1. [1]

    React: Synergizing reasoning and acting in language models

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. InThe eleventh international conference on learning representations, 2022

    2022

  2. [2]

    Toolformer: Language models can teach themselves to use tools

    Timo Schick, Jane Dwivedi-Yu, Roberto Dess`ı, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. Advances in neural information processing systems, 36:68539–68551, 2023

    2023

  3. [3]

    Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents

    Hanrong Zhang, Jingyuan Huang, Kai Mei, Yifei Yao, Zhenting Wang, Chenlu Zhan, Hongwei Wang, and Yongfeng Zhang. Agent security bench (asb): Formalizing and benchmarking attacks and defenses in llm-based agents.arXiv preprint arXiv:2410.02644, 2024

    work page internal anchor Pith review arXiv 2024

  4. [4]

    CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification

    Hanrong Zhang, Shicheng Fan, Henry Peng Zou, Yankai Chen, Zhenting Wang, Jiayu Zhou, Chengze Li, Wei- Chieh Huang, Yifei Yao, Kening Zheng, et al. Evoskills: Self-evolving agent skills via co-evolutionary verification. arXiv preprint arXiv:2604.01687, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  5. [5]

    Agent skills overview.https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview, 2025

    Anthropic. Agent skills overview.https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview, 2025

    2025

  6. [7]

    SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

    Xiangyi Li, Wenbo Chen, Yimin Liu, Shenghan Zheng, Xiaokun Chen, Yifeng He, Yubo Li, Bingran You, Haotian Shen, Jiankai Sun, et al. Skillsbench: Benchmarking how well agent skills work across diverse tasks.arXiv preprint arXiv:2602.12670, 2026

    work page internal anchor Pith review arXiv 2026

  7. [8]

    Multi-agent embodied ai: Advances and future directions.Science China Information Sciences, 69(5):151202, 2026

    Zhaohan Feng, Ruiqi Xue, Lei Yuan, Yang Yu, Ning Ding, Meiqin Liu, Bingzhao Gao, Jian Sun, Xinhu Zheng, and Gang Wang. Multi-agent embodied ai: Advances and future directions.Science China Information Sciences, 69(5):151202, 2026

    2026

  8. [9]

    Multi-Agent Collaboration Mechanisms: A Survey of LLMs

    Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, Quoc-Viet Pham, Barry O’Sullivan, and Hoang D Nguyen. Multi-agent collaboration mechanisms: A survey of llms.arXiv preprint arXiv:2501.06322, 2025

    work page internal anchor Pith review arXiv 2025

  9. [10]

    Harness engineering, 2026

    Mitchell Hashimoto. Harness engineering, 2026. Concept proposal on harness-based LLM system design

    2026

  10. [11]

    Harness engineering: leveraging codex in an agent-first world, February 2026

    OpenAI. Harness engineering: leveraging codex in an agent-first world, February 2026. Accessed: 2026-02-11

    2026

  11. [12]

    Harness engineering, February 2026

    Birgitta B ¨ockeler and Martin Fowler. Harness engineering, February 2026. Accessed: 2026-02-17

    2026

  12. [13]

    Agentic harness engineering: Llms as the new operating system, 2026

    Paul Iusztin. Agentic harness engineering: Llms as the new operating system, 2026. Engineering blog post. 12 EvoAgent

    2026

  13. [14]

    Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward

    Renjun Xu and Yang Yan. Agent skills for large language models: Architecture, acquisition, security, and the path forward.arXiv preprint arXiv:2602.12430, 2026

    work page internal anchor Pith review arXiv 2026

  14. [15]

    Tarisai Mugambiwa and Belinda Ndlovu. Multi-agent retrieval augmented generation for clinical decision support: A systematic review and integrative conceptual framework.Journal of Applied Informatics and Computing, 10(1):171–183, 2026

    2026

  15. [16]

    Mobile-agent-rag: Driving smart multi- agent coordination with contextual knowledge empowerment for long-horizon mobile automation

    Yuxiang Zhou, Jichang Li, Yanhao Zhang, Haonan Lu, and Guanbin Li. Mobile-agent-rag: Driving smart multi- agent coordination with contextual knowledge empowerment for long-horizon mobile automation. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 29939–29947, 2026

    2026

  16. [17]

    Hermes agent.https://hermes-agent.org/, 2026

    Nous Research. Hermes agent.https://hermes-agent.org/, 2026

    2026

  17. [18]

    CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

    Guohao Li et al. Camel: Communicative agents for ”mind” exploration of large language model society.arXiv preprint arXiv:2303.17760, 2023

    work page internal anchor Pith review arXiv 2023

  18. [19]

    AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

    Qingyun Wu et al. Autogen: Enabling next-gen llm applications via multi-agent conversation.arXiv preprint arXiv:2308.08155, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  19. [20]

    Reflexion: Language Agents with Verbal Reinforcement Learning

    Noah Shinn et al. Reflexion: Language agents with verbal reinforcement learning.arXiv preprint arXiv:2303.11366, 2023

    work page internal anchor Pith review arXiv 2023

  20. [21]

    Voyager: An Open-Ended Embodied Agent with Large Language Models

    Guanzhi Wang et al. V oyager: An open-ended embodied agent with large language models.arXiv preprint arXiv:2305.16291, 2023

    work page internal anchor Pith review arXiv 2023

  21. [22]

    Generative agents: Interactive simulacra of human behavior

    Joon Sung Park et al. Generative agents: Interactive simulacra of human behavior. InUIST, 2023

    2023

  22. [23]

    A survey of human-in-the- loop for machine learning.Future Generation Computer Systems, 135:364–381, 2022

    Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, and Liang He. A survey of human-in-the- loop for machine learning.Future Generation Computer Systems, 135:364–381, 2022

    2022

  23. [24]

    Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

    Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, and et al. Judging llm-as-a-judge with mt-bench and chatbot arena. arXiv preprint arXiv:2306.05685, 2023

    work page internal anchor Pith review arXiv 2023

  24. [25]

    Gpt-4.1 technical report.https://openai.com/research, 2025

    OpenAI. Gpt-4.1 technical report.https://openai.com/research, 2025

    2025

  25. [26]

    keyword

    Qwen Team. Qwen3.5 technical report.https://huggingface.co/Qwen, 2026. Appendices Appendix A: EvoAgent Self-Evolution Process Pseudocode 1A lg or it hm : EvoAgent Skill Ev ol ut io n 2% Note : This p s e u d o c o d e reflects i m p l e m e n t a t i o n using OpenAI Agents SDK . 3 4Input : 5u - User input 6S_skills - Skills Base 7u _p ro fi le - USER . m...

    2026