基于冻结主干双回路进化的多智能体潜空间策略研究

赵袁赫; 王昱; 李昊忱; 杨士铎; 郭柯楠; 高晨; 白小高; 管孜然; 王继业

doi:10.11959/j.issn.1000-0801.DXKX260121

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基于冻结主干双回路进化的多智能体潜空间策略研究

专题：智能体通信与网络技术 | 更新时间：2026-07-21

- 基于冻结主干双回路进化的多智能体潜空间策略研究
- Research on multi-agent latent space strategies based on frozen main branch dual-loop evolution
- 电信科学 2026年42卷第6期页码：78-91
- 作者机构：
  
  1.华北电力大学控制与计算机工程学院，北京 102206
  2.交通运输部天津水运工程科学研究院，天津 300072
- 作者简介：
  
  赵袁赫（2002- ），男，华北电力大学控制与计算机工程学院博士生，主要研究方向为电力信息安全、人工智能安全、多智能体协同。
  王昱（1998- ），男，华北电力大学控制与计算机工程学院博士生，主要研究方向为电力信息安全、人工智能安全、多智能体协同。
  李昊忱（1996- ），男，交通运输部天津水运工程科学研究院工程师，主要研究方向为计量测试。
  杨士铎（1998- ），男，华北电力大学控制与计算机工程学院博士生，主要研究方向为电力信息安全、人工智能安全、多智能体协同。
  郭柯楠（2000- ），男，华北电力大学控制与计算机工程学院硕士生，主要研究方向为电力信息安全、人工智能安全、多智能体协同。
  高晨（2003- ），女，华北电力大学控制与计算机工程学院硕士生，主要研究方向为电力信息安全、人工智能安全、多智能体协同。
  白小高（2003- ），男，华北电力大学控制与计算机工程学院硕士生，主要研究方向为电力信息安全、人工智能安全、多智能体协同。
  管孜然（2002- ），女，华北电力大学控制与计算机工程学院硕士生，主要研究方向为电力信息安全、人工智能安全、多智能体协同。
  王继业（1965- ），男，博士，华北电力大学控制与计算机工程学院教授、博士生导师、正高级工程师，主要研究方向为电力信息安全、人工智能安全、多智能体协同。
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260121
  中图分类号： TP181;TN915;TP39
- 收稿：2026-02-25，
  
  修回：2026-05-12，
  
  录用：2026-06-08，
  
  网络首发：2026-07-21，
  
  纸质出版：2026-06-20
- 稿件说明：
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赵袁赫,王昱,李昊忱等.基于冻结主干双回路进化的多智能体潜空间策略研究[J].电信科学,2026,42(06):78-91.

Zhao Yuanhe,Wang Yu,Li Haochen,et al.Research on multi-agent latent space strategies based on frozen main branch dual-loop evolution[J].Telecommunications Science,2026,42(06):78-91.
赵袁赫,王昱,李昊忱等.基于冻结主干双回路进化的多智能体潜空间策略研究[J].电信科学,2026,42(06):78-91. DOI： 10.11959/j.issn.1000-0801.DXKX260121.

Zhao Yuanhe,Wang Yu,Li Haochen,et al.Research on multi-agent latent space strategies based on frozen main branch dual-loop evolution[J].Telecommunications Science,2026,42(06):78-91. DOI： 10.11959/j.issn.1000-0801.DXKX260121.

摘要

针对大语言模型多智能体面临的“稳定性—可塑性困境”及微调引发的灾难性遗忘风险，提出一种基于冻结主干的双回路进化框架。该框架将通用推理与策略偏好解耦：行为回路利用离散价值迭代优化即时动作；语义回路引入预测误差调制的语义反思机制，将自然语言反馈转化为高维语义梯度，驱动外部潜向量在连续语义空间中演化。在GridWorld博弈环境实验中，该方法在完全冻结参数前提下实现了85.6%的任务成功率，路径加权成功率达到最高，且具备极低的推理延迟（2.1 s/step）和Token消耗（3.2 k/session）。实验还观察到基于隐式因果推理的社会协作行为，验证了该范式在低资源消耗与高可解释性方面的显著潜力。

Abstract

To address the “stability-plasticity dilemma” faced by multi-agent systems based on large language models and the risk of catastrophic forgetting caused by fine-tuning

a frozen main branch dual-loop evolution framework was proposed. General reasoning was decoupled from policy preferences by this framework. Discrete value iteration was utilized to optimize immediate actions by the behavioral loop. A prediction error-modulated semantic reflection mechanism was introduced by the semantic loop that transformed natural language feedback into high-dimensional semantic gradients

driving the evolution of external latent vectors in a continuous semantic space. In experiments conducted in the GridWorld game environment

this method achieves a 85.6% task success rate under fully frozen parameters

with the highest path-weighted success rate

while exhibiting extremely low inference latency (2.1 s/step) and token consumption (3.2 k/session). The experiments also observe socially cooperative behavior based on implicit causal reasoning

validating the significant potential of this paradigm in terms of low resource consumption and high interpretability.

关键词

Keywords

references

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