arXiv cs.MA· Tianzhu Hu, Hang Yu, Zeyuan Zhao, Zeyu Pan, Huan Luo, Chun Gan, Mingzhe Cao, Liubin Wang, Mingming Li, Jiao Dai, Jizhong Han, Songlin Hu·· 4 天前AI 评分23
AdCoEvo：一种协同进化的多智能体广告投放系统
AdCoEvo: A Co-Evolving Multi-Agent Advertising Delivery System
AI 导读
研究者提出 AdCoEvo 多智能体广告投放系统，通过角色专业化连接不同时间尺度与运营粒度的投放决策，并依据实际投放结果更新模型参数，实现规划、调整与诊断的协同进化。实验显示，相较基于规则的运营基线，AdCoEvo 使广告主价值提升 14.02%、单次获客成本降低 7.07%；相较参数固定的多智能体系统，广告主价值提升 3.96%、单次获客成本降低 1.24%。
正文 · 原文
Authors:Tianzhu Hu, Hang Yu, Zeyuan Zhao, Zeyu Pan, Huan Luo, Chun Gan, Mingzhe Cao, Liubin Wang, Mingming Li, Jiao Dai, Jizhong Han, Songlin Hu
Abstract:Digital advertising is an important means for platforms to reach potential users and drive business growth. Advertising delivery is the core link between marketing objectives and actual returns. Existing methods have achieved partial automation in individual tasks such as bidding and budget allocation, but they still rely on human operators for overall coordination, making it difficult to support large-scale operations. They also often fail to achieve optimal performance, and individual experience remains difficult to turn into reusable operational capabilities. In addition, as advertising markets change, advertising delivery systems need timely strategy updates to remain effective in dynamic markets. To automate the entire advertising operations process and improve adaptability to market changes, we propose AdCoEvo, a co-evolving multi-agent advertising delivery system. Specifically, the system connects delivery decisions across different timescales and operational granularities through role specialization. Diagnostic results guide account planning and strategy adjustment in the next period. The multi-agent system updates its model parameters based on actual delivery outcomes and continually improves delivery strategies through mutual feedback among planning, adjustment, and diagnosis, thereby achieving co-evolution. Experiments show that, relative to the rule-based operational baseline (Rule), AdCoEvo increases advertiser value by 14.02% while reducing cost per acquisition by 7.07%. Compared with a multi-agent system with fixed parameters, AdCoEvo increases advertiser value by 3.96% and reduces cost per acquisition by 1.24%, indicating that co-evolution can yield greater delivery returns.
| Subjects: | Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.19454 [cs.AI] |
| (or arXiv:2610.19454v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19454 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tianzhu Hu [view email]
[v1]
Wed, 14 Oct 2026 02:40:56 UTC (10,538 KB)
来源：arXiv cs.MA · arxiv.org