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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2605.02200 |
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| _version_ | 1866913085416013824 |
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| author | Ji, Deyi Lu, Junyu Liu, Xuanyi Liu, Liqun Zhang, Hailong Shu, Peng Yu, Huan Jiang, Jie Chen, Tianru Zhu, Lanyun |
| author_facet | Ji, Deyi Lu, Junyu Liu, Xuanyi Liu, Liqun Zhang, Hailong Shu, Peng Yu, Huan Jiang, Jie Chen, Tianru Zhu, Lanyun |
| contents | Online advertising governance faces significant challenges due to the non-stationary nature of regulatory policies, where emerging mandates (e.g., restrictions on education or aesthetic anxiety) create severe label inconsistencies and reasoning ambiguities in historical datasets. In this paper, we propose ARGUS, a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring. ARGUS addresses the sparsity of new policy data by employing a three-stage framework: (1) Policy Seeding for initial perception; (2) Adversarial Label Rectification, which utilizes a ``Prosecutor-Defender-Umpire'' architecture to resolve conflicts between stale labels and new mandates; and (3) Latent Knowledge Discovery, which employs a tripartite dialectical discussion to unearth sophisticated, ``gray-area'' violations. By leveraging RAG-enhanced policy knowledge and Chain-of-Thought synthesis as dynamic rewards for reinforcement learning, ARGUS synchronizes its reasoning pathways with evolving regulations. Extensive experiments on both industrial and public datasets demonstrate that ARGUS significantly outperforms traditional fine-tuning baselines, achieving superior policy-adaptive learning with minimal gold data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02200 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring Ji, Deyi Lu, Junyu Liu, Xuanyi Liu, Liqun Zhang, Hailong Shu, Peng Yu, Huan Jiang, Jie Chen, Tianru Zhu, Lanyun Computation and Language Online advertising governance faces significant challenges due to the non-stationary nature of regulatory policies, where emerging mandates (e.g., restrictions on education or aesthetic anxiety) create severe label inconsistencies and reasoning ambiguities in historical datasets. In this paper, we propose ARGUS, a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring. ARGUS addresses the sparsity of new policy data by employing a three-stage framework: (1) Policy Seeding for initial perception; (2) Adversarial Label Rectification, which utilizes a ``Prosecutor-Defender-Umpire'' architecture to resolve conflicts between stale labels and new mandates; and (3) Latent Knowledge Discovery, which employs a tripartite dialectical discussion to unearth sophisticated, ``gray-area'' violations. By leveraging RAG-enhanced policy knowledge and Chain-of-Thought synthesis as dynamic rewards for reinforcement learning, ARGUS synchronizes its reasoning pathways with evolving regulations. Extensive experiments on both industrial and public datasets demonstrate that ARGUS significantly outperforms traditional fine-tuning baselines, achieving superior policy-adaptive learning with minimal gold data. |
| title | ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.02200 |